From b7ae30b00a1fa26a683214bb63c105d258f26cc2 Mon Sep 17 00:00:00 2001 From: Jonas Keller Date: Tue, 25 Nov 2025 15:51:23 +0100 Subject: [PATCH 1/5] project setup --- .devcontainer/Dockerfile | 10 +- .devcontainer/devcontainer.json | 38 +- .gitignore | 4 +- README.md | 4 +- ecir24/README.md | 84 +- ecir24/baselines/.devcontainer.json | 16 +- .../workflows/upload-docker-submission.yml | 124 +- ecir24/baselines/README.md | 66 +- ecir24/baselines/dev-container/Dockerfile | 28 +- .../baselines/dev-container/Dockerfile.beir | 28 +- .../baselines/document-processing/.gitignore | 2 +- .../baselines/document-processing/Dockerfile | 24 +- .../baselines/document-processing/README.md | 52 +- .../baseline_document_processing.py | 64 +- ecir24/baselines/query-processing/.gitignore | 2 +- ecir24/baselines/query-processing/Dockerfile | 28 +- ecir24/baselines/query-processing/README.md | 50 +- .../baseline_query_processing.py | 64 +- ecir24/baselines/re-ranking/Dockerfile | 28 +- ecir24/baselines/re-ranking/README.md | 50 +- .../re-ranking/baseline_re_ranking.py | 60 +- .../baselines/retrieval/baseline_retrieval.py | 46 +- ecir24/post-hoc-notebooks/README.md | 40 +- .../keyphrase-extraction.ipynb | 924 +- .../query-segmentation.ipynb | 1584 ++-- ecir24/post-hoc-notebooks/requirements.txt | 6 +- ecir24/tutorials/README.md | 44 +- ecir25/README.md | 246 +- ecir25/baselines/pairwise-autoqrels.ipynb | 974 +-- ecir25/baselines/pairwise-naive.ipynb | 634 +- ecir25/baselines/pairwise-retrieval.ipynb | 662 +- ecir25/baselines/pairwise_snorkel.ipynb | 2392 +++--- ecir25/baselines/pointwise-autoqrels.ipynb | 936 +- ecir25/baselines/pointwise-naive.ipynb | 602 +- ecir25/baselines/pointwise-retrieval.ipynb | 694 +- ecir25/baselines/pointwise_snorkel.ipynb | 3254 +++---- ecir25/data/create-pairwise-data.py | 256 +- ecir25/data/create-pairwise-test-data.py | 254 +- ecir25/evaluation.ipynb | 572 +- ecir25/evaluation.jsonl | 66 +- ecir25/wows-eval/Makefile | 16 +- ecir25/wows-eval/build-runs/build-runs.py | 164 +- ecir25/wows-eval/build-runs/verify-runs.py | 88 +- ecir25/wows-eval/setup.cfg | 106 +- ecir25/wows-eval/setup.py | 4 +- .../tests/wows_eval_pairwise_test.py | 358 +- ecir25/wows-eval/tests/wows_eval_test.py | 400 +- ecir25/wows-eval/wows_eval/__init__.py | 274 +- ecir25/wows-eval/wows_eval/correlation.py | 184 +- ecir25/wows-eval/wows_eval/wows_2025.py | 64 +- ecir26/README.md | 150 +- ecir26/chatnoir-baseline/.devcontainer.json | 34 +- ecir26/chatnoir-baseline/.gitignore | 2 +- ecir26/chatnoir-baseline/Dockerfile | 20 +- ecir26/chatnoir-baseline/README.md | 76 +- ecir26/chatnoir-baseline/requirements.txt | 12 +- ecir26/chatnoir-baseline/run-chatnoir.py | 74 +- .../collection-jena-kassel/config.json | 12 +- .../doccano-label-configs.json | 36 +- .../topic-mapping.jsonl | 48 +- .../collection-jena-kassel/topics.xml | 410 +- .../collection-radboud/README.md | 98 +- .../collection-radboud/config.json | 12 +- .../doccano-label-configs.json | 84 +- .../extract-validation-dataset.py | 92 +- .../collection-radboud/index.py | 18 +- .../collection-radboud/manual.csv | 556 +- .../collection-radboud/search.py | 62 +- .../collection-radboud/topic-mapping.jsonl | 156 +- .../ir_datasets_wows/ir_datasets_owi.py | 254 +- .../subsampled-corpora/README.md | 48 +- .../radboud-validation/README.md | 64 +- .../radboud-validation/config.json | 16 +- .../radboud-validation/dataset-metadata.json | 5942 ++++++------- .../radboud-validation/dataset-metadata.yml | 5854 ++++++------- .../subsampled-corpora/spot-check/README.md | 64 +- .../subsampled-corpora/spot-check/config.json | 16 +- .../spot-check/dataset-metadata.json | 7578 ++++++++--------- .../spot-check/dataset-metadata.yml | 7490 ++++++++-------- .../subsampled-corpora/spot-check/qrels.txt | 226 +- .../spot-check/queries.jsonl | 6 +- .../subsampled-corpora/spot-check/queries.xml | 6 +- .../subsampled-corpora/spot-check/topics.xml | 34 +- ecir26/pyterrier-baseline/.devcontainer.json | 34 +- ecir26/pyterrier-baseline/Dockerfile | 30 +- ecir26/pyterrier-baseline/README.md | 124 +- ecir26/pyterrier-baseline/index.py | 68 +- ecir26/pyterrier-baseline/requirements.txt | 10 +- ecir26/pyterrier-baseline/retrieve.py | 62 +- ecir26/submission-skeleton/ir-metadata.yml | 90 +- .../template-new-approach/.devcontainer.json | 34 +- ecir26/template-new-approach/.gitignore | 4 +- ecir26/template-new-approach/Dockerfile | 14 +- ecir26/template-new-approach/Dockerfile.dev | 24 +- ecir26/template-new-approach/README.md | 128 +- ecir26/template-new-approach/analysis.ipynb | 488 +- ecir26/template-new-approach/requirements.txt | 10 +- ecir26/template-new-approach/retrieve.py | 134 +- irixys23/search-engine-prototyping/README.md | 84 +- irixys23/webpage-classification/README.md | 146 +- .../baselines/sklearn-baseline/Dockerfile | 28 +- .../baselines/sklearn-baseline/README.md | 38 +- .../sklearn-baseline/requirements.txt | 8 +- .../sklearn-baseline/sklearn_baseline.py | 86 +- .../sklearn_baseline_train.py | 110 +- .../baselines/snorkel-baseline/Dockerfile | 30 +- .../baselines/snorkel-baseline/README.md | 38 +- .../snorkel-baseline/requirements.txt | 6 +- .../snorkel-baseline/snorkel_baseline.py | 126 +- .../snorkel_baseline_train.py | 550 +- .../snorkel_tutorial.ipynb | 1486 ++-- .../baselines/trivial-baseline/Dockerfile | 8 +- .../baselines/trivial-baseline/README.md | 50 +- .../baselines/trivial-baseline/baseline.py | 62 +- .../clueweb-test-data/README.md | 26 +- .../step-01-cw09-sample-documents.sh | 8 +- .../step-01-cw12-sample-documents.sh | 8 +- .../step-02-create-dataset.py | 92 +- .../evaluation/Dockerfile | 10 +- .../evaluation/README.md | 26 +- .../evaluation/evaluation.py | 148 +- .../example-data/input/inputs.jsonl | 20 +- .../example-data/truth/truths.jsonl | 20 +- ks-ORAKEL/.gitkeep | 0 ows-webinar25/README.md | 28 +- .../search-components-hands-on-notebook.ipynb | 1874 ++-- 126 files changed, 26295 insertions(+), 26295 deletions(-) create mode 100644 ks-ORAKEL/.gitkeep diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 38aa91a..8a9a963 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,6 +1,6 @@ -FROM ubuntu:noble - -RUN apt-get update \ - && apt-get install -y curl python3 python3-pip git - +FROM ubuntu:noble + +RUN apt-get update \ + && apt-get install -y curl python3 python3-pip git + RUN pip3 install git+https://github.com/tira-io/tira.git@pyterrier-artifacts#egg=tira&subdirectory=python-client \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 98cbd71..696f986 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,20 +1,20 @@ -{ - "build": {"dockerfile": "Dockerfile", "context": ".."}, - "customizations": { - "vscode": { - "extensions": [ - "ms-python.python", - "ms-python.vscode-pylance", - "ms-toolsai.jupyter", - "ms-toolsai.datawrangler", - "ms-azuretools.vscode-docker" - ] - }, - "codespaces": { - "openFiles":[ - "README.md" - ] - } - }, - "runArgs": ["--network=host"] +{ + "build": {"dockerfile": "Dockerfile", "context": ".."}, + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "ms-toolsai.jupyter", + "ms-toolsai.datawrangler", + "ms-azuretools.vscode-docker" + ] + }, + "codespaces": { + "openFiles":[ + "README.md" + ] + } + }, + "runArgs": ["--network=host"] } \ No newline at end of file diff --git a/.gitignore b/.gitignore index 96417da..643741d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,2 @@ -__pycache__ -wows_eval.egg-info +__pycache__ +wows_eval.egg-info diff --git a/README.md b/README.md index 0c928a0..10b1b23 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,2 @@ -# wows-code -Code used for evaluation and baselines of the shared tasks and data challenges hosted as part of the Workshops on Open Web Search (WOWS). +# wows-code +Code used for evaluation and baselines of the shared tasks and data challenges hosted as part of the Workshops on Open Web Search (WOWS). diff --git a/ecir24/README.md b/ecir24/README.md index a192fbd..1654d36 100644 --- a/ecir24/README.md +++ b/ecir24/README.md @@ -1,42 +1,42 @@ -# Code and Tutorials for the [1st International Workshop on Open Web Search](https://opensearchfoundation.org/en/events-osf/wows2024) #wows2024 - -This repository contains all code, tutorials, and baselines for the [1st International Workshop on Open Web Search](https://opensearchfoundation.org/en/events-osf/wows2024) hosted at the [46th European Conference on Information Retrieval](https://www.ecir2024.org/) in Glasgow (24th-28th March, 2024). - - -## Tutorials - -We included all components into a [dashboard of resources](https://tira-io.github.io/teaching-ir-with-shared-tasks/) that can be easily re-used within teaching. - -- [Re-using pre-built indices](https://github.com/tira-io/teaching-ir-with-shared-tasks/blob/main/tutorials/tutorial-reuse-pyterrier-indices.ipynb) - -## Resources - -- [PyTerrier indices of public datasets](https://zenodo.org/records/10743990) - -|Team | Component | Link to Tutorial Notebok| -|------------------|-----------|-------------------------| -|sean | | | -|naverlabseurope | | | -|fschlatt | | | -|tu-dresden-01 | | | -|tu-dresden-02 | | | -|tu-dresden-03 | | https://github.com/tira-io/teaching-ir-with-shared-tasks/blob/main/tutorials/tutorial-query-expansion-with-llms.ipynb | -|tu-dresden-04 | | | -|qspell | | | -|marcelgohsen | | | - -## Links - - -## Resources - -Important links/resources: -- [The workshop page](https://opensearchfoundation.org/en/events-osf/wows2024) -- [Software submissions](https://www.tira.io/task-overview/workshop-on-open-web-search/) -- [Pre-registration](https://www.tira.io/t/pre-registration) to promote new collaborations and brainstorming for the [ECIR Collab-a-thon](https://www.ecir2024.org/collab-a-thon/) -- [Tutorial on how to submit](https://github.com/OpenWebSearch/wows-code/tree/main/ecir24/tutorials) -- [Baselines for software submissions](https://github.com/OpenWebSearch/wows-code/tree/main/ecir24/baselines) -- [Jupyter notebooks showcasing already submitted components in declarative PyTerrier pipelines](https://github.com/OpenWebSearch/wows-code/tree/main/ecir24/post-hoc-notebooks) - -If you have any questions or problems, please do not hesitate to contact us via [the forum](https://www.tira.io/t/the-forum-for-the-1st-international-workshop-on-open-web-search-wows2024) or via mail. - +# Code and Tutorials for the [1st International Workshop on Open Web Search](https://opensearchfoundation.org/en/events-osf/wows2024) #wows2024 + +This repository contains all code, tutorials, and baselines for the [1st International Workshop on Open Web Search](https://opensearchfoundation.org/en/events-osf/wows2024) hosted at the [46th European Conference on Information Retrieval](https://www.ecir2024.org/) in Glasgow (24th-28th March, 2024). + + +## Tutorials + +We included all components into a [dashboard of resources](https://tira-io.github.io/teaching-ir-with-shared-tasks/) that can be easily re-used within teaching. + +- [Re-using pre-built indices](https://github.com/tira-io/teaching-ir-with-shared-tasks/blob/main/tutorials/tutorial-reuse-pyterrier-indices.ipynb) + +## Resources + +- [PyTerrier indices of public datasets](https://zenodo.org/records/10743990) + +|Team | Component | Link to Tutorial Notebok| +|------------------|-----------|-------------------------| +|sean | | | +|naverlabseurope | | | +|fschlatt | | | +|tu-dresden-01 | | | +|tu-dresden-02 | | | +|tu-dresden-03 | | https://github.com/tira-io/teaching-ir-with-shared-tasks/blob/main/tutorials/tutorial-query-expansion-with-llms.ipynb | +|tu-dresden-04 | | | +|qspell | | | +|marcelgohsen | | | + +## Links + + +## Resources + +Important links/resources: +- [The workshop page](https://opensearchfoundation.org/en/events-osf/wows2024) +- [Software submissions](https://www.tira.io/task-overview/workshop-on-open-web-search/) +- [Pre-registration](https://www.tira.io/t/pre-registration) to promote new collaborations and brainstorming for the [ECIR Collab-a-thon](https://www.ecir2024.org/collab-a-thon/) +- [Tutorial on how to submit](https://github.com/OpenWebSearch/wows-code/tree/main/ecir24/tutorials) +- [Baselines for software submissions](https://github.com/OpenWebSearch/wows-code/tree/main/ecir24/baselines) +- [Jupyter notebooks showcasing already submitted components in declarative PyTerrier pipelines](https://github.com/OpenWebSearch/wows-code/tree/main/ecir24/post-hoc-notebooks) + +If you have any questions or problems, please do not hesitate to contact us via [the forum](https://www.tira.io/t/the-forum-for-the-1st-international-workshop-on-open-web-search-wows2024) or via mail. + diff --git a/ecir24/baselines/.devcontainer.json b/ecir24/baselines/.devcontainer.json index abf99d3..8480fee 100644 --- a/ecir24/baselines/.devcontainer.json +++ b/ecir24/baselines/.devcontainer.json @@ -1,8 +1,8 @@ -{ - "image": "mam10eks/tira-for-school:0.0.1", - "customizations": { - "vscode": { - "extensions": ["ms-python.python", "ms-python.vscode-pylance", "ms-toolsai.jupyter"] - } - } -} +{ + "image": "mam10eks/tira-for-school:0.0.1", + "customizations": { + "vscode": { + "extensions": ["ms-python.python", "ms-python.vscode-pylance", "ms-toolsai.jupyter"] + } + } +} diff --git a/ecir24/baselines/.github/workflows/upload-docker-submission.yml b/ecir24/baselines/.github/workflows/upload-docker-submission.yml index 7fc6b4f..179b6a8 100644 --- a/ecir24/baselines/.github/workflows/upload-docker-submission.yml +++ b/ecir24/baselines/.github/workflows/upload-docker-submission.yml @@ -1,62 +1,62 @@ -name: Upload Docker Software to TIRA -on: - workflow_dispatch: - inputs: - dockerpath: - description: The path to the directory where the Docker image should be build. Should contain a Dockerfile that specifies everything. - required: true - default: query-processing - -jobs: - docker-build: - runs-on: ubuntu-latest - timeout-minutes: 45 - steps: - - - name: Checkout - uses: actions/checkout@v3 - - - name: Set up QEMU - uses: docker/setup-qemu-action@v2 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v2 - - - name: Login to Docker Hub - uses: docker/login-action@v2 - with: - registry: registry.webis.de - username: ${{ secrets.TIRA_DOCKER_REGISTRY_USER }} - password: ${{ secrets.TIRA_DOCKER_REGISTRY_TOKEN }} - - - name: Set up Dependencies - uses: actions/setup-python@v4 - with: - python-version: 3.8 - - - name: Install required python packages - run: | - python -m pip install --upgrade pip - pip install tira==0.0.100 - - - name: Set environment variables - env: - TIRA_TASK_ID: TIRA_TASK_ID_FOR_AUTOMATIC_REPLACEMENT - TIRA_VM_ID: TIRA_USER_FOR_AUTOMATIC_REPLACEMENT - run: | - tira-run --export-submission-environment \ - GITHUB_SHA=${{ github.sha }} \ - TIRA_VM_ID=${{ env.TIRA_VM_ID }} \ - TIRA_TASK_ID=${{ env.TIRA_TASK_ID }} \ - TIRA_DOCKER_REGISTRY_TOKEN=${{ secrets.TIRA_DOCKER_REGISTRY_TOKEN }} \ - TIRA_DOCKER_REGISTRY_USER=${{ secrets.TIRA_DOCKER_REGISTRY_USER }} \ - TIRA_CLIENT_TOKEN=${{ secrets.TIRA_CLIENT_TOKEN }} \ - TIRA_CLIENT_USER=${{ secrets.TIRA_CLIENT_USER }} \ - TIRA_CODE_REPOSITORY_ID=${{ secrets.TIRA_CODE_REPOSITORY_ID }} \ - TIRA_DOCKER_FILE=${{ inputs.dockerfile }}/Dockerfile \ - TIRA_DOCKER_PATH=${{ inputs.dockerpath }} - >> $GITHUB_ENV - - name: Build, test, and upload image - run: | - docker build -t ${{ env.IMAGE_TAG }} -f ${{ github.workspace }}/${{ inputs.dockerpath }} - tira-run --input-dataset workshop-on-open-web-search/document-processing-20231027-training --image ${{ env.IMAGE_TAG }} --push true +name: Upload Docker Software to TIRA +on: + workflow_dispatch: + inputs: + dockerpath: + description: The path to the directory where the Docker image should be build. Should contain a Dockerfile that specifies everything. + required: true + default: query-processing + +jobs: + docker-build: + runs-on: ubuntu-latest + timeout-minutes: 45 + steps: + - + name: Checkout + uses: actions/checkout@v3 + - + name: Set up QEMU + uses: docker/setup-qemu-action@v2 + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Login to Docker Hub + uses: docker/login-action@v2 + with: + registry: registry.webis.de + username: ${{ secrets.TIRA_DOCKER_REGISTRY_USER }} + password: ${{ secrets.TIRA_DOCKER_REGISTRY_TOKEN }} + - + name: Set up Dependencies + uses: actions/setup-python@v4 + with: + python-version: 3.8 + - + name: Install required python packages + run: | + python -m pip install --upgrade pip + pip install tira==0.0.100 + - + name: Set environment variables + env: + TIRA_TASK_ID: TIRA_TASK_ID_FOR_AUTOMATIC_REPLACEMENT + TIRA_VM_ID: TIRA_USER_FOR_AUTOMATIC_REPLACEMENT + run: | + tira-run --export-submission-environment \ + GITHUB_SHA=${{ github.sha }} \ + TIRA_VM_ID=${{ env.TIRA_VM_ID }} \ + TIRA_TASK_ID=${{ env.TIRA_TASK_ID }} \ + TIRA_DOCKER_REGISTRY_TOKEN=${{ secrets.TIRA_DOCKER_REGISTRY_TOKEN }} \ + TIRA_DOCKER_REGISTRY_USER=${{ secrets.TIRA_DOCKER_REGISTRY_USER }} \ + TIRA_CLIENT_TOKEN=${{ secrets.TIRA_CLIENT_TOKEN }} \ + TIRA_CLIENT_USER=${{ secrets.TIRA_CLIENT_USER }} \ + TIRA_CODE_REPOSITORY_ID=${{ secrets.TIRA_CODE_REPOSITORY_ID }} \ + TIRA_DOCKER_FILE=${{ inputs.dockerfile }}/Dockerfile \ + TIRA_DOCKER_PATH=${{ inputs.dockerpath }} + >> $GITHUB_ENV + - name: Build, test, and upload image + run: | + docker build -t ${{ env.IMAGE_TAG }} -f ${{ github.workspace }}/${{ inputs.dockerpath }} + tira-run --input-dataset workshop-on-open-web-search/document-processing-20231027-training --image ${{ env.IMAGE_TAG }} --push true diff --git a/ecir24/baselines/README.md b/ecir24/baselines/README.md index 4717aa2..224c98a 100644 --- a/ecir24/baselines/README.md +++ b/ecir24/baselines/README.md @@ -1,33 +1,33 @@ -# Submissions of TIRA_USER_FOR_AUTOMATIC_REPLACEMENT - -This repository contains baseline submissions (document-processing, query-processing, re-ranking, and retrieval) together with a Github action and a development container configuration as starting point for submissions for the [](). - -We recommend that you work either in Github Codespaces or using [dev containers with Docker](https://code.visualstudio.com/docs/devcontainers/containers). Github Codespaces are an easy option to start in a few minutes (free tier of 130 compute hours per month), whereas dev container with Docker might be interesting if you want to put a bit more focus on technical/deployment details. - - -## Developing in Github Codespaces - -- Open this repository in Github Codespaces (i.e., click on "Code" -> "Codespaces" -> "Create ..."). -- Please do not forget to commit often - - -## Developing in Dev Containers - -A dev container (please find a suitable installation instruction [here](https://code.visualstudio.com/docs/devcontainers/containers)) allows you to directly work in the prepared Docker container so that you do not have to install the dependencies (which can sometimes be a bit tricky). - -To develop with dev containers, please: - -- Install [VS Code](https://code.visualstudio.com/download) and [Docker](https://docs.docker.com/engine/install/) on your machine -- Clone this repository: `git clone ...` -- Open the directory `jupyter-notebook-submissions` with VS Code (it should ask you to open the repository in a dev container) - -If you do not want to use VS Code, you can start and develop in a jupyter notebook via (please execute the command within the `jupyter-notebook-submissions` directory): - -``` -docker run --rm -it -p 8888:8888 --entrypoint jupyter -w /workspace -v ${PWD}:/workspace webis/ir-lab-wise-2023:0.0.1 notebook --allow-root --ip 0.0.0.0 -``` - -## Submitting Your Software - -Run the github action to submit your software. - +# Submissions of TIRA_USER_FOR_AUTOMATIC_REPLACEMENT + +This repository contains baseline submissions (document-processing, query-processing, re-ranking, and retrieval) together with a Github action and a development container configuration as starting point for submissions for the [](). + +We recommend that you work either in Github Codespaces or using [dev containers with Docker](https://code.visualstudio.com/docs/devcontainers/containers). Github Codespaces are an easy option to start in a few minutes (free tier of 130 compute hours per month), whereas dev container with Docker might be interesting if you want to put a bit more focus on technical/deployment details. + + +## Developing in Github Codespaces + +- Open this repository in Github Codespaces (i.e., click on "Code" -> "Codespaces" -> "Create ..."). +- Please do not forget to commit often + + +## Developing in Dev Containers + +A dev container (please find a suitable installation instruction [here](https://code.visualstudio.com/docs/devcontainers/containers)) allows you to directly work in the prepared Docker container so that you do not have to install the dependencies (which can sometimes be a bit tricky). + +To develop with dev containers, please: + +- Install [VS Code](https://code.visualstudio.com/download) and [Docker](https://docs.docker.com/engine/install/) on your machine +- Clone this repository: `git clone ...` +- Open the directory `jupyter-notebook-submissions` with VS Code (it should ask you to open the repository in a dev container) + +If you do not want to use VS Code, you can start and develop in a jupyter notebook via (please execute the command within the `jupyter-notebook-submissions` directory): + +``` +docker run --rm -it -p 8888:8888 --entrypoint jupyter -w /workspace -v ${PWD}:/workspace webis/ir-lab-wise-2023:0.0.1 notebook --allow-root --ip 0.0.0.0 +``` + +## Submitting Your Software + +Run the github action to submit your software. + diff --git a/ecir24/baselines/dev-container/Dockerfile b/ecir24/baselines/dev-container/Dockerfile index 9618ee4..3c6514d 100644 --- a/ecir24/baselines/dev-container/Dockerfile +++ b/ecir24/baselines/dev-container/Dockerfile @@ -1,14 +1,14 @@ -#docker build -t webis/wows-2024-baseline:0.0.6 . -FROM pytorch/pytorch:latest - -ENV PYTERRIER_VERSION='5.8' -ENV PYTERRIER_HELPER_VERSION='0.0.8' - -RUN apt-get update \ - && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y git openjdk-11-jdk wget \ - && rm -rf /root/.cache - -RUN apt-get install -y gcc \ - && pip3 install tira ir_datasets python-terrier \ - && rm -rf /root/.cache - +#docker build -t webis/wows-2024-baseline:0.0.6 . +FROM pytorch/pytorch:latest + +ENV PYTERRIER_VERSION='5.8' +ENV PYTERRIER_HELPER_VERSION='0.0.8' + +RUN apt-get update \ + && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y git openjdk-11-jdk wget \ + && rm -rf /root/.cache + +RUN apt-get install -y gcc \ + && pip3 install tira ir_datasets python-terrier \ + && rm -rf /root/.cache + diff --git a/ecir24/baselines/dev-container/Dockerfile.beir b/ecir24/baselines/dev-container/Dockerfile.beir index d802d36..a0de479 100644 --- a/ecir24/baselines/dev-container/Dockerfile.beir +++ b/ecir24/baselines/dev-container/Dockerfile.beir @@ -1,14 +1,14 @@ -#docker build -t webis/wows-2024-baseline:0.0.1-beir -f Dockerfile.beir . -FROM pytorch/pytorch:latest - -RUN apt-get update \ - && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y git openjdk-11-jdk build-essential \ - && rm -rf /root/.cache - -RUN pip3 install --no-dependencies ir_datasets \ - && pip3 install tira pandas \ - && rm -rf /root/.cache - -# OMITTED: Move this to the real container -# RUN pip3 install beir pyserini - +#docker build -t webis/wows-2024-baseline:0.0.1-beir -f Dockerfile.beir . +FROM pytorch/pytorch:latest + +RUN apt-get update \ + && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y git openjdk-11-jdk build-essential \ + && rm -rf /root/.cache + +RUN pip3 install --no-dependencies ir_datasets \ + && pip3 install tira pandas \ + && rm -rf /root/.cache + +# OMITTED: Move this to the real container +# RUN pip3 install beir pyserini + diff --git a/ecir24/baselines/document-processing/.gitignore b/ecir24/baselines/document-processing/.gitignore index dd4eca8..32e6d72 100644 --- a/ecir24/baselines/document-processing/.gitignore +++ b/ecir24/baselines/document-processing/.gitignore @@ -1 +1 @@ -documents.jsonl.gz +documents.jsonl.gz diff --git a/ecir24/baselines/document-processing/Dockerfile b/ecir24/baselines/document-processing/Dockerfile index ceddcb5..08d5cbb 100644 --- a/ecir24/baselines/document-processing/Dockerfile +++ b/ecir24/baselines/document-processing/Dockerfile @@ -1,12 +1,12 @@ -FROM ubuntu:22.04 - -# Install dependencies -RUN apt-get update \ - && apt-get upgrade -y \ - && apt-get install -y jq libffi-dev python3 python3-pip python-is-python3 openjdk-11-jdk wget \ - && pip3 install jupyter ir_datasets tira>=0.0.72 \ - && rm -Rf /var/cache/apt \ - && rm -Rf /root/.cache/pip - -ADD baseline_document_processing.py /code/baseline_document_processing.py - +FROM ubuntu:22.04 + +# Install dependencies +RUN apt-get update \ + && apt-get upgrade -y \ + && apt-get install -y jq libffi-dev python3 python3-pip python-is-python3 openjdk-11-jdk wget \ + && pip3 install jupyter ir_datasets tira>=0.0.72 \ + && rm -Rf /var/cache/apt \ + && rm -Rf /root/.cache/pip + +ADD baseline_document_processing.py /code/baseline_document_processing.py + diff --git a/ecir24/baselines/document-processing/README.md b/ecir24/baselines/document-processing/README.md index 989a1e5..6f205bb 100644 --- a/ecir24/baselines/document-processing/README.md +++ b/ecir24/baselines/document-processing/README.md @@ -1,26 +1,26 @@ -# Baseline Submission for a Document Processor in the 1st International Workshop on Open Web Search #wows2024 - -This is a baseline document processor that does some dummy processing by classifying each document as a spam document. - -You can run it directly via (please install `tira` via `pip3 install tira`, Docker, and Python >= 3.7 on your machine): - -``` -tira-run \ - --input-dataset workshop-on-open-web-search/document-processing-20231027-training \ - --image mam10eks/wows-baselines:doc-processor-0.0.1 \ - --command 'python3 /code/baseline_document_processing.py' -``` - -## Development - -You can build the Docker image via: - -``` -docker build -t mam10eks/wows-baselines:doc-processor-0.0.1 . -``` - -To publish the image to dockerhub, run: - -``` -docker push mam10eks/wows-baselines:doc-processor-0.0.1 -``` +# Baseline Submission for a Document Processor in the 1st International Workshop on Open Web Search #wows2024 + +This is a baseline document processor that does some dummy processing by classifying each document as a spam document. + +You can run it directly via (please install `tira` via `pip3 install tira`, Docker, and Python >= 3.7 on your machine): + +``` +tira-run \ + --input-dataset workshop-on-open-web-search/document-processing-20231027-training \ + --image mam10eks/wows-baselines:doc-processor-0.0.1 \ + --command 'python3 /code/baseline_document_processing.py' +``` + +## Development + +You can build the Docker image via: + +``` +docker build -t mam10eks/wows-baselines:doc-processor-0.0.1 . +``` + +To publish the image to dockerhub, run: + +``` +docker push mam10eks/wows-baselines:doc-processor-0.0.1 +``` diff --git a/ecir24/baselines/document-processing/baseline_document_processing.py b/ecir24/baselines/document-processing/baseline_document_processing.py index db6729f..241995d 100755 --- a/ecir24/baselines/document-processing/baseline_document_processing.py +++ b/ecir24/baselines/document-processing/baseline_document_processing.py @@ -1,32 +1,32 @@ -#!/usr/bin/env python3 -# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox -from tira.third_party_integrations import ir_datasets, get_output_directory -from pathlib import Path -import pandas as pd - - -def process_document(document): - # Dummy processing of documents: classify each document as spam - return {'docno': document.doc_id, 'label': 'spam'} - - -def process_documents(document_iter): - return pd.DataFrame([process_document(i) for i in document_iter]) - - -if __name__ == '__main__': - # In the TIRA sandbox, this is the injected ir_dataset, injected via the environment variable TIRA_INPUT_DIRECTORY - dataset = ir_datasets.load('workshop-on-open-web-search/document-processing-20231027-training') - - # The expected output directory, injected via the environment variable TIRA_OUTPUT_DIRECTORY - output_dir = get_output_directory('.') - - # Document processors persist their results in a file documents.jsonl.gz in the output directory. - output_file = Path(output_dir) / 'documents.jsonl.gz' - - # You can pass as many additional arguments to your program, e.g., via argparse, to modify the behaviour - - # process the documents, store results at expected location. - processed_documents = process_documents(dataset.docs_iter()) - processed_documents.to_json(output_file, lines=True, orient='records') - +#!/usr/bin/env python3 +# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox +from tira.third_party_integrations import ir_datasets, get_output_directory +from pathlib import Path +import pandas as pd + + +def process_document(document): + # Dummy processing of documents: classify each document as spam + return {'docno': document.doc_id, 'label': 'spam'} + + +def process_documents(document_iter): + return pd.DataFrame([process_document(i) for i in document_iter]) + + +if __name__ == '__main__': + # In the TIRA sandbox, this is the injected ir_dataset, injected via the environment variable TIRA_INPUT_DIRECTORY + dataset = ir_datasets.load('workshop-on-open-web-search/document-processing-20231027-training') + + # The expected output directory, injected via the environment variable TIRA_OUTPUT_DIRECTORY + output_dir = get_output_directory('.') + + # Document processors persist their results in a file documents.jsonl.gz in the output directory. + output_file = Path(output_dir) / 'documents.jsonl.gz' + + # You can pass as many additional arguments to your program, e.g., via argparse, to modify the behaviour + + # process the documents, store results at expected location. + processed_documents = process_documents(dataset.docs_iter()) + processed_documents.to_json(output_file, lines=True, orient='records') + diff --git a/ecir24/baselines/query-processing/.gitignore b/ecir24/baselines/query-processing/.gitignore index 2de8c2c..0447b73 100644 --- a/ecir24/baselines/query-processing/.gitignore +++ b/ecir24/baselines/query-processing/.gitignore @@ -1 +1 @@ -queries.jsonl +queries.jsonl diff --git a/ecir24/baselines/query-processing/Dockerfile b/ecir24/baselines/query-processing/Dockerfile index 84b2b37..acaa868 100644 --- a/ecir24/baselines/query-processing/Dockerfile +++ b/ecir24/baselines/query-processing/Dockerfile @@ -1,14 +1,14 @@ -FROM ubuntu:22.04 - -# Install dependencies -RUN apt-get update \ - && apt-get upgrade -y \ - && apt-get install -y python3 python3-pip python-is-python3 openjdk-11-jdk wget \ - && pip3 install jupyter ir_datasets tira>=0.0.89 \ - && rm -Rf /var/cache/apt \ - && rm -Rf /root/.cache/pip - -ADD baseline_query_processing.py /code/baseline_query_processing.py - -ENTRYPOINT [ "python3", "/code/baseline_query_processing.py" ] - +FROM ubuntu:22.04 + +# Install dependencies +RUN apt-get update \ + && apt-get upgrade -y \ + && apt-get install -y python3 python3-pip python-is-python3 openjdk-11-jdk wget \ + && pip3 install jupyter ir_datasets tira>=0.0.89 \ + && rm -Rf /var/cache/apt \ + && rm -Rf /root/.cache/pip + +ADD baseline_query_processing.py /code/baseline_query_processing.py + +ENTRYPOINT [ "python3", "/code/baseline_query_processing.py" ] + diff --git a/ecir24/baselines/query-processing/README.md b/ecir24/baselines/query-processing/README.md index 5b07abc..4e4f0e1 100644 --- a/ecir24/baselines/query-processing/README.md +++ b/ecir24/baselines/query-processing/README.md @@ -1,25 +1,25 @@ -# Baseline Submission for a Query Processor in the 1st International Workshop on Open Web Search #wows2024 - -This is a baseline query processor that appends the query id to each query. - -You can run it directly via (please install `tira` via `pip3 install tira`, Docker, and Python >= 3.7 on your machine): - -``` -tira-run \ - --input-dataset workshop-on-open-web-search/query-processing-20231027-training \ - --image mam10eks/wows-baselines:query-processor-0.0.1 -``` - -## Development - -You can build the Docker image via: - -``` -docker build -t mam10eks/wows-baselines:query-processor-0.0.1 . -``` - -To publish the image to dockerhub, run: - -``` -docker push mam10eks/wows-baselines:query-processor-0.0.1 -``` +# Baseline Submission for a Query Processor in the 1st International Workshop on Open Web Search #wows2024 + +This is a baseline query processor that appends the query id to each query. + +You can run it directly via (please install `tira` via `pip3 install tira`, Docker, and Python >= 3.7 on your machine): + +``` +tira-run \ + --input-dataset workshop-on-open-web-search/query-processing-20231027-training \ + --image mam10eks/wows-baselines:query-processor-0.0.1 +``` + +## Development + +You can build the Docker image via: + +``` +docker build -t mam10eks/wows-baselines:query-processor-0.0.1 . +``` + +To publish the image to dockerhub, run: + +``` +docker push mam10eks/wows-baselines:query-processor-0.0.1 +``` diff --git a/ecir24/baselines/query-processing/baseline_query_processing.py b/ecir24/baselines/query-processing/baseline_query_processing.py index c29254d..2cfc98b 100755 --- a/ecir24/baselines/query-processing/baseline_query_processing.py +++ b/ecir24/baselines/query-processing/baseline_query_processing.py @@ -1,32 +1,32 @@ -#!/usr/bin/env python3 -# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox -from tira.third_party_integrations import ir_datasets, get_output_directory -from pathlib import Path -import pandas as pd - - -def process_query(query): - # Dummy processing of queries: Append the query id to each query. - return {'qid': query.query_id, 'query': query.default_text() + ' ' + str(query.query_id)} - - -def process_queries(queries_iter): - return pd.DataFrame([process_query(i) for i in queries_iter]) - - -if __name__ == '__main__': - # In the TIRA sandbox, this is the injected ir_dataset, injected via the environment variable TIRA_INPUT_DIRECTORY - dataset = ir_datasets.load('workshop-on-open-web-search/query-processing-20231027-training') - - # The expected output directory, injected via the environment variable TIRA_OUTPUT_DIRECTORY - output_dir = get_output_directory('.') - - # Query processors persist their results in a file queries.jsonl in the output directory. - output_file = Path(output_dir) / 'queries.jsonl' - - # You can pass as many additional arguments to your program, e.g., via argparse, to modify the behaviour - - # process the queries, store results at expected location. - processed_queries = process_queries(dataset.queries_iter()) - processed_queries.to_json(output_file, lines=True, orient='records') - +#!/usr/bin/env python3 +# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox +from tira.third_party_integrations import ir_datasets, get_output_directory +from pathlib import Path +import pandas as pd + + +def process_query(query): + # Dummy processing of queries: Append the query id to each query. + return {'qid': query.query_id, 'query': query.default_text() + ' ' + str(query.query_id)} + + +def process_queries(queries_iter): + return pd.DataFrame([process_query(i) for i in queries_iter]) + + +if __name__ == '__main__': + # In the TIRA sandbox, this is the injected ir_dataset, injected via the environment variable TIRA_INPUT_DIRECTORY + dataset = ir_datasets.load('workshop-on-open-web-search/query-processing-20231027-training') + + # The expected output directory, injected via the environment variable TIRA_OUTPUT_DIRECTORY + output_dir = get_output_directory('.') + + # Query processors persist their results in a file queries.jsonl in the output directory. + output_file = Path(output_dir) / 'queries.jsonl' + + # You can pass as many additional arguments to your program, e.g., via argparse, to modify the behaviour + + # process the queries, store results at expected location. + processed_queries = process_queries(dataset.queries_iter()) + processed_queries.to_json(output_file, lines=True, orient='records') + diff --git a/ecir24/baselines/re-ranking/Dockerfile b/ecir24/baselines/re-ranking/Dockerfile index 3522939..df3edc6 100644 --- a/ecir24/baselines/re-ranking/Dockerfile +++ b/ecir24/baselines/re-ranking/Dockerfile @@ -1,14 +1,14 @@ -FROM ubuntu:22.04 - -# Install dependencies -RUN apt-get update \ - && apt-get upgrade -y \ - && apt-get install -y python3 python3-pip python-is-python3 openjdk-11-jdk wget \ - && pip3 install jupyter ir_datasets tira>=0.0.89 \ - && rm -Rf /var/cache/apt \ - && rm -Rf /root/.cache/pip - -ADD baseline_re_ranking.py /code/baseline_re_ranking.py - -ENTRYPOINT [ "python3", "/code/baseline_re_ranking.py" ] - +FROM ubuntu:22.04 + +# Install dependencies +RUN apt-get update \ + && apt-get upgrade -y \ + && apt-get install -y python3 python3-pip python-is-python3 openjdk-11-jdk wget \ + && pip3 install jupyter ir_datasets tira>=0.0.89 \ + && rm -Rf /var/cache/apt \ + && rm -Rf /root/.cache/pip + +ADD baseline_re_ranking.py /code/baseline_re_ranking.py + +ENTRYPOINT [ "python3", "/code/baseline_re_ranking.py" ] + diff --git a/ecir24/baselines/re-ranking/README.md b/ecir24/baselines/re-ranking/README.md index 330dd13..6a007ea 100644 --- a/ecir24/baselines/re-ranking/README.md +++ b/ecir24/baselines/re-ranking/README.md @@ -1,25 +1,25 @@ -# Baseline Submission for a Re-Ranker in the 1st International Workshop on Open Web Search #wows2024 - -This is a baseline re-ranker that increases the score of each document by 1. - -You can run it directly via (please install `tira` via `pip3 install tira`, Docker, and Python >= 3.7 on your machine): - -``` -tira-run \ - --input-dataset workshop-on-open-web-search/re-ranking-20231027-training \ - --image mam10eks/wows-baselines:re-ranker-0.0.1 -``` - -## Development - -You can build the Docker image via: - -``` -docker build -t mam10eks/wows-baselines:re-ranker-0.0.1 . -``` - -To publish the image to dockerhub, run: - -``` -docker push mam10eks/wows-baselines:re-ranker-0.0.1 -``` +# Baseline Submission for a Re-Ranker in the 1st International Workshop on Open Web Search #wows2024 + +This is a baseline re-ranker that increases the score of each document by 1. + +You can run it directly via (please install `tira` via `pip3 install tira`, Docker, and Python >= 3.7 on your machine): + +``` +tira-run \ + --input-dataset workshop-on-open-web-search/re-ranking-20231027-training \ + --image mam10eks/wows-baselines:re-ranker-0.0.1 +``` + +## Development + +You can build the Docker image via: + +``` +docker build -t mam10eks/wows-baselines:re-ranker-0.0.1 . +``` + +To publish the image to dockerhub, run: + +``` +docker push mam10eks/wows-baselines:re-ranker-0.0.1 +``` diff --git a/ecir24/baselines/re-ranking/baseline_re_ranking.py b/ecir24/baselines/re-ranking/baseline_re_ranking.py index 38d25a1..6bfe2e9 100755 --- a/ecir24/baselines/re-ranking/baseline_re_ranking.py +++ b/ecir24/baselines/re-ranking/baseline_re_ranking.py @@ -1,30 +1,30 @@ -#!/usr/bin/env python3 -# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox -from tira.third_party_integrations import load_rerank_data, persist_and_normalize_run -from pathlib import Path -import pandas as pd - -def score_query_document_pair(query, document_text, score_of_previous_stage): - # Our baseline re-ranker just emits 1 + the score of the previous ranker. - return 1 + score_of_previous_stage - -if __name__ == '__main__': - # In the TIRA sandbox, this is the injected re-ranking dataset, injected via the environment variable TIRA_INPUT_DIRECTORY - re_rank_dataset = load_rerank_data(default='workshop-on-open-web-search/re-ranking-20231027-training') - - # Alternatively, you could use the scored docs of ir_datasets, e.g.: - # from tira.third_party_integrations import ir_dataset - # dataset = ir_datasets.load(default='workshop-on-open-web-search/document-processing-20231027-training') - - run = [] - - for _, i in re_rank_dataset.iterrows(): - run += [{'qid': i['qid'], 'docno': i['docno'], 'score': score_query_document_pair(i['query'], i['text'], i['score'])}] - - run = pd.DataFrame(run) - - # Re-rankers are expected to produce a TREC-style run.txt file in the output directory. - persist_and_normalize_run(run, 'my-system-name', default_output='./run.txt') - - # You can pass additional arguments to your program, e.g., via argparse, to modify the behaviour of your program. - +#!/usr/bin/env python3 +# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox +from tira.third_party_integrations import load_rerank_data, persist_and_normalize_run +from pathlib import Path +import pandas as pd + +def score_query_document_pair(query, document_text, score_of_previous_stage): + # Our baseline re-ranker just emits 1 + the score of the previous ranker. + return 1 + score_of_previous_stage + +if __name__ == '__main__': + # In the TIRA sandbox, this is the injected re-ranking dataset, injected via the environment variable TIRA_INPUT_DIRECTORY + re_rank_dataset = load_rerank_data(default='workshop-on-open-web-search/re-ranking-20231027-training') + + # Alternatively, you could use the scored docs of ir_datasets, e.g.: + # from tira.third_party_integrations import ir_dataset + # dataset = ir_datasets.load(default='workshop-on-open-web-search/document-processing-20231027-training') + + run = [] + + for _, i in re_rank_dataset.iterrows(): + run += [{'qid': i['qid'], 'docno': i['docno'], 'score': score_query_document_pair(i['query'], i['text'], i['score'])}] + + run = pd.DataFrame(run) + + # Re-rankers are expected to produce a TREC-style run.txt file in the output directory. + persist_and_normalize_run(run, 'my-system-name', default_output='./run.txt') + + # You can pass additional arguments to your program, e.g., via argparse, to modify the behaviour of your program. + diff --git a/ecir24/baselines/retrieval/baseline_retrieval.py b/ecir24/baselines/retrieval/baseline_retrieval.py index 7d6f4d7..539f2aa 100755 --- a/ecir24/baselines/retrieval/baseline_retrieval.py +++ b/ecir24/baselines/retrieval/baseline_retrieval.py @@ -1,23 +1,23 @@ -#!/usr/bin/env python3 -# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox -from tira.third_party_integrations import ir_datasets, get_output_directory -from pathlib import Path -import pandas as pd - -if __name__ == '__main__': - # In the TIRA sandbox, this is the injected ir_dataset, injected via the environment variable TIRA_INPUT_DIRECTORY - dataset = ir_datasets.load('workshop-on-open-web-search/document-processing-20231027-training') - - # The expected output directory, injected via the environment variable TIRA_OUTPUT_DIRECTORY - output_dir = get_output_directory('.') - - # Retrieval approaches are expected to produce a TREC-style run.txt file in the output directory. - output_file = Path(output_dir) / 'run.txt' - - # You can pass as many additional arguments to your program, e.g., via argparse, to modify the behaviour of your program. - - # Step 1: build an Index. For a real scenario, you should pass the index from the previous stage of the pipeline. - - # Step 2: produce a ranking - - # Step 3: persist the ranking +#!/usr/bin/env python3 +# Load a patched ir_datasets that loads the injected data inside the TIRA sandbox +from tira.third_party_integrations import ir_datasets, get_output_directory +from pathlib import Path +import pandas as pd + +if __name__ == '__main__': + # In the TIRA sandbox, this is the injected ir_dataset, injected via the environment variable TIRA_INPUT_DIRECTORY + dataset = ir_datasets.load('workshop-on-open-web-search/document-processing-20231027-training') + + # The expected output directory, injected via the environment variable TIRA_OUTPUT_DIRECTORY + output_dir = get_output_directory('.') + + # Retrieval approaches are expected to produce a TREC-style run.txt file in the output directory. + output_file = Path(output_dir) / 'run.txt' + + # You can pass as many additional arguments to your program, e.g., via argparse, to modify the behaviour of your program. + + # Step 1: build an Index. For a real scenario, you should pass the index from the previous stage of the pipeline. + + # Step 2: produce a ranking + + # Step 3: persist the ranking diff --git a/ecir24/post-hoc-notebooks/README.md b/ecir24/post-hoc-notebooks/README.md index bbf9bfd..834188e 100644 --- a/ecir24/post-hoc-notebooks/README.md +++ b/ecir24/post-hoc-notebooks/README.md @@ -1,20 +1,20 @@ -# Post-Hoc Usage of Components submitted to #wows2024 - -This directory contains jupyter notebooks showcasing how already submitted components to the wows workshop can be re-used in declarative PyTerrier pipelines. - -We will maintain the list of available components together with their hello world notebooks. - -## Document Processors - -### Keyphrase Extraction - -- Description: Reduce the length of a document drastically by extracting only the top keyphrases. -- Tutorial notebook: [keyphrase-extraction.ipynb](keyphrase-extraction.ipynb) - -## Query Processors - -### Query Segmentation - -- Description: Detect query terms that belong together, e.g., segment the query `hubble telescope achievements` into `['hubble telescope', 'achievements']`. -- Tutorial notebook: [query-segmentation.ipynb](query-segmentation.ipynb) - +# Post-Hoc Usage of Components submitted to #wows2024 + +This directory contains jupyter notebooks showcasing how already submitted components to the wows workshop can be re-used in declarative PyTerrier pipelines. + +We will maintain the list of available components together with their hello world notebooks. + +## Document Processors + +### Keyphrase Extraction + +- Description: Reduce the length of a document drastically by extracting only the top keyphrases. +- Tutorial notebook: [keyphrase-extraction.ipynb](keyphrase-extraction.ipynb) + +## Query Processors + +### Query Segmentation + +- Description: Detect query terms that belong together, e.g., segment the query `hubble telescope achievements` into `['hubble telescope', 'achievements']`. +- Tutorial notebook: [query-segmentation.ipynb](query-segmentation.ipynb) + diff --git a/ecir24/post-hoc-notebooks/keyphrase-extraction.ipynb b/ecir24/post-hoc-notebooks/keyphrase-extraction.ipynb index 092d464..95e08d1 100644 --- a/ecir24/post-hoc-notebooks/keyphrase-extraction.ipynb +++ b/ecir24/post-hoc-notebooks/keyphrase-extraction.ipynb @@ -1,462 +1,462 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Keyphrase Extraction in PyTerrier from TIREx executions" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", - "\n", - "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n" - ] - } - ], - "source": [ - "from tira.local_client import Client\n", - "import pyterrier as pt\n", - "import pandas as pd\n", - "\n", - "if not pt.started():\n", - " pt.init()\n", - "\n", - "tira = Client()\n", - "dataset = \"trec-robust-2004\"\n", - "pt_dataset = pt.get_dataset(dataset)\n", - "\n", - "bce_5_keyphrase_extractor = tira.pt.transform_documents('ir-benchmarks/webis-keyphrase-extraction/BCExtractorFO-5', dataset)\n", - "bm25 = tira.pt.from_retriever_submission(\"ir-benchmarks/tira-ir-starter/PyTerrierBM25\", dataset)\n", - "bm25_score = pt.text.scorer(takes=\"docs\", body_attr=\"text\", wmodel=\"BM25\")\n", - "bm25_retrieval = bm25 >> bm25_score" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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qidquery
0301international organized crime
1302poliomyelitis and post polio
2303hubble telescope achievements
3304endangered species mammals
4305most dangerous vehicles
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" - ], - "text/plain": [ - " qid query\n", - "0 301 international organized crime\n", - "1 302 poliomyelitis and post polio\n", - "2 303 hubble telescope achievements\n", - "3 304 endangered species mammals\n", - "4 305 most dangerous vehicles" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dev_topics = pt_dataset.get_topics()\n", - "dev_topics = dev_topics[dev_topics['qid'].astype(int) <= 305]\n", - "dev_topics" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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qidquerytextkeyphrases
0301international organized crimeText of Presidential Edict on Organized Crime ...[aforementioned crimes, federation additional ...
1301international organized crimeSeminar on Criminology Held 1990-1993 Crime Fi...[real state, further increase, public control,...
2301international organized crimeFreeh Visit To Focus on Organized Crime BFN\\n[...[organized postcommunist crime, organized crim...
3301international organized crimeGovernment Ties to Organized Crime Examined CS...[characteristic organized crime, organized cri...
4301international organized crimeNorth Caucasus Anticrime Chief Views Current T...[territorial administrations, malbakhova crime...
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" - ], - "text/plain": [ - " qid query \\\n", - "0 301 international organized crime \n", - "1 301 international organized crime \n", - "2 301 international organized crime \n", - "3 301 international organized crime \n", - "4 301 international organized crime \n", - "\n", - " text \\\n", - "0 Text of Presidential Edict on Organized Crime ... \n", - "1 Seminar on Criminology Held 1990-1993 Crime Fi... \n", - "2 Freeh Visit To Focus on Organized Crime BFN\\n[... \n", - "3 Government Ties to Organized Crime Examined CS... \n", - "4 North Caucasus Anticrime Chief Views Current T... \n", - "\n", - " keyphrases \n", - "0 [aforementioned crimes, federation additional ... \n", - "1 [real state, further increase, public control,... \n", - "2 [organized postcommunist crime, organized crim... \n", - "3 [characteristic organized crime, organized cri... \n", - "4 [territorial administrations, malbakhova crime... " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(bm25 >> bce_5_keyphrase_extractor)(dev_topics).head(5)[['qid', 'query', 'text', 'keyphrases']]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def append_keyphrases(df):\n", - " df['text'] = df['text'] + df['keyphrases'].apply(lambda i: ' '.join(i))\n", - " return df\n", - "\n", - "append_keyphrases = pt.apply.generic(append_keyphrases)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10:10:43.640 [main] WARN org.terrier.querying.ApplyTermPipeline - The index has no termpipelines configuration, and no control configuration is found. Defaulting to global termpipelines configuration of 'Stopwords,PorterStemmer'. Set a termpipelines control to remove this warning.\n" - ] - }, - { - "data": { - "text/html": [ - "
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qidquerytextscorerank
0301international organized crimeText of Presidential Edict on Organized Crime ...8.2798190
1301international organized crimeSeminar on Criminology Held 1990-1993 Crime Fi...8.0359285
2301international organized crimeFreeh Visit To Focus on Organized Crime BFN\\n[...8.0398954
3301international organized crimeGovernment Ties to Organized Crime Examined CS...7.9287208
4301international organized crimeNorth Caucasus Anticrime Chief Views Current T...8.0650752
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" - ], - "text/plain": [ - " qid query \\\n", - "0 301 international organized crime \n", - "1 301 international organized crime \n", - "2 301 international organized crime \n", - "3 301 international organized crime \n", - "4 301 international organized crime \n", - "\n", - " text score rank \n", - "0 Text of Presidential Edict on Organized Crime ... 8.279819 0 \n", - "1 Seminar on Criminology Held 1990-1993 Crime Fi... 8.035928 5 \n", - "2 Freeh Visit To Focus on Organized Crime BFN\\n[... 8.039895 4 \n", - "3 Government Ties to Organized Crime Examined CS... 7.928720 8 \n", - "4 North Caucasus Anticrime Chief Views Current T... 8.065075 2 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bm_25_bce_5 = (bm25 >> bce_5_keyphrase_extractor >> append_keyphrases >> bm25_score)\n", - "\n", - "bm_25_bce_5(dev_topics).head(5)[['qid', 'query', 'text', 'score', 'rank']]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10:13:13.000 [main] WARN org.terrier.querying.ApplyTermPipeline - The index has no termpipelines configuration, and no control configuration is found. Defaulting to global termpipelines configuration of 'Stopwords,PorterStemmer'. Set a termpipelines control to remove this warning.\n", - "10:15:09.554 [main] WARN org.terrier.querying.ApplyTermPipeline - The index has no termpipelines configuration, and no control configuration is found. Defaulting to global termpipelines configuration of 'Stopwords,PorterStemmer'. Set a termpipelines control to remove this warning.\n" - ] - }, - { - "data": { - "text/html": [ - "
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namerecip_rankP_3P_5P_10ndcg_cut_10ndcg_cut_5
0BM250.6548090.4966530.4658630.4092370.4212580.444258
1BM25 >> BCE 50.6577570.5060240.4714860.4104420.4240800.449429
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" - ], - "text/plain": [ - " name recip_rank P_3 P_5 P_10 ndcg_cut_10 \\\n", - "0 BM25 0.654809 0.496653 0.465863 0.409237 0.421258 \n", - "1 BM25 >> BCE 5 0.657757 0.506024 0.471486 0.410442 0.424080 \n", - "\n", - " ndcg_cut_5 \n", - "0 0.444258 \n", - "1 0.449429 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pt.Experiment([bm25_retrieval, bm_25_bce_5], pt_dataset.get_topics(), pt_dataset.get_qrels(), eval_metrics=[\"recip_rank\", \"P_3\", \"P_5\", \"P_10\", \"ndcg_cut_10\", \"ndcg_cut_5\"], names=['BM25', 'BM25 >> BCE 5'])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.13" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Keyphrase Extraction in PyTerrier from TIREx executions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n" + ] + } + ], + "source": [ + "from tira.local_client import Client\n", + "import pyterrier as pt\n", + "import pandas as pd\n", + "\n", + "if not pt.started():\n", + " pt.init()\n", + "\n", + "tira = Client()\n", + "dataset = \"trec-robust-2004\"\n", + "pt_dataset = pt.get_dataset(dataset)\n", + "\n", + "bce_5_keyphrase_extractor = tira.pt.transform_documents('ir-benchmarks/webis-keyphrase-extraction/BCExtractorFO-5', dataset)\n", + "bm25 = tira.pt.from_retriever_submission(\"ir-benchmarks/tira-ir-starter/PyTerrierBM25\", dataset)\n", + "bm25_score = pt.text.scorer(takes=\"docs\", body_attr=\"text\", wmodel=\"BM25\")\n", + "bm25_retrieval = bm25 >> bm25_score" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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qidquery
0301international organized crime
1302poliomyelitis and post polio
2303hubble telescope achievements
3304endangered species mammals
4305most dangerous vehicles
\n", + "
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qidquerytextkeyphrases
0301international organized crimeText of Presidential Edict on Organized Crime ...[aforementioned crimes, federation additional ...
1301international organized crimeSeminar on Criminology Held 1990-1993 Crime Fi...[real state, further increase, public control,...
2301international organized crimeFreeh Visit To Focus on Organized Crime BFN\\n[...[organized postcommunist crime, organized crim...
3301international organized crimeGovernment Ties to Organized Crime Examined CS...[characteristic organized crime, organized cri...
4301international organized crimeNorth Caucasus Anticrime Chief Views Current T...[territorial administrations, malbakhova crime...
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qidquerytextscorerank
0301international organized crimeText of Presidential Edict on Organized Crime ...8.2798190
1301international organized crimeSeminar on Criminology Held 1990-1993 Crime Fi...8.0359285
2301international organized crimeFreeh Visit To Focus on Organized Crime BFN\\n[...8.0398954
3301international organized crimeGovernment Ties to Organized Crime Examined CS...7.9287208
4301international organized crimeNorth Caucasus Anticrime Chief Views Current T...8.0650752
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" + ], + "text/plain": [ + " qid query \\\n", + "0 301 international organized crime \n", + "1 301 international organized crime \n", + "2 301 international organized crime \n", + "3 301 international organized crime \n", + "4 301 international organized crime \n", + "\n", + " text score rank \n", + "0 Text of Presidential Edict on Organized Crime ... 8.279819 0 \n", + "1 Seminar on Criminology Held 1990-1993 Crime Fi... 8.035928 5 \n", + "2 Freeh Visit To Focus on Organized Crime BFN\\n[... 8.039895 4 \n", + "3 Government Ties to Organized Crime Examined CS... 7.928720 8 \n", + "4 North Caucasus Anticrime Chief Views Current T... 8.065075 2 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm_25_bce_5 = (bm25 >> bce_5_keyphrase_extractor >> append_keyphrases >> bm25_score)\n", + "\n", + "bm_25_bce_5(dev_topics).head(5)[['qid', 'query', 'text', 'score', 'rank']]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10:13:13.000 [main] WARN org.terrier.querying.ApplyTermPipeline - The index has no termpipelines configuration, and no control configuration is found. Defaulting to global termpipelines configuration of 'Stopwords,PorterStemmer'. Set a termpipelines control to remove this warning.\n", + "10:15:09.554 [main] WARN org.terrier.querying.ApplyTermPipeline - The index has no termpipelines configuration, and no control configuration is found. Defaulting to global termpipelines configuration of 'Stopwords,PorterStemmer'. Set a termpipelines control to remove this warning.\n" + ] + }, + { + "data": { + "text/html": [ + "
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namerecip_rankP_3P_5P_10ndcg_cut_10ndcg_cut_5
0BM250.6548090.4966530.4658630.4092370.4212580.444258
1BM25 >> BCE 50.6577570.5060240.4714860.4104420.4240800.449429
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" + ], + "text/plain": [ + " name recip_rank P_3 P_5 P_10 ndcg_cut_10 \\\n", + "0 BM25 0.654809 0.496653 0.465863 0.409237 0.421258 \n", + "1 BM25 >> BCE 5 0.657757 0.506024 0.471486 0.410442 0.424080 \n", + "\n", + " ndcg_cut_5 \n", + "0 0.444258 \n", + "1 0.449429 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.Experiment([bm25_retrieval, bm_25_bce_5], pt_dataset.get_topics(), pt_dataset.get_qrels(), eval_metrics=[\"recip_rank\", \"P_3\", \"P_5\", \"P_10\", \"ndcg_cut_10\", \"ndcg_cut_5\"], names=['BM25', 'BM25 >> BCE 5'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ecir24/post-hoc-notebooks/query-segmentation.ipynb b/ecir24/post-hoc-notebooks/query-segmentation.ipynb index ecf3954..af2a9d1 100644 --- a/ecir24/post-hoc-notebooks/query-segmentation.ipynb +++ b/ecir24/post-hoc-notebooks/query-segmentation.ipynb @@ -1,792 +1,792 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Query Segmentation in PyTerrier from TIREx executions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the environment" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", - "\n", - "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n" - ] - } - ], - "source": [ - "from tira.local_client import Client\n", - "import pyterrier as pt\n", - "import pandas as pd\n", - "\n", - "tira = Client()\n", - "\n", - "if not pt.started():\n", - " pt.init()\n", - "\n", - "dataset = \"trec-robust-2004\"\n", - "pt_dataset = pt.get_dataset(dataset)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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qidquery
2303hubble telescope achievements
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" - ], - "text/plain": [ - " qid query\n", - "2 303 hubble telescope achievements" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Some Dev topics\n", - "dev_topics = pt_dataset.get_topics()\n", - "dev_topics = dev_topics[dev_topics['qid'].astype(int) == 303]\n", - "dev_topics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare Retrieval Components" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "bm25 = tira.pt.from_retriever_submission(\"ir-benchmarks/tira-ir-starter/PyTerrierBM25\", dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "bm25_score = pt.text.scorer(takes=\"docs\", body_attr=\"text\", wmodel=\"BM25\")\n", - "bm25_retrieval = bm25 >> bm25_score" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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qiddocnolabel
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qiddocnolabel
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" - ], - "text/plain": [ - " qid docno label\n", - "2519 303 FT921-7107 1\n", - "2595 303 FT924-286 1\n", - "2627 303 FT931-6554 1\n", - "2731 303 FT941-15661 1\n", - "2739 303 FT941-17652 1\n", - "2818 303 FT944-128 1\n", - "2918 303 LA051290-0079 1\n", - "3109 303 LA110590-0076 1\n", - "3119 303 LA112190-0043 1\n", - "3145 303 LA122990-0029 1" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dev_qrels[dev_qrels['label'] > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "11:53:50.468 [main] WARN org.terrier.querying.ApplyTermPipeline - The index has no termpipelines configuration, and no control configuration is found. Defaulting to global termpipelines configuration of 'Stopwords,PorterStemmer'. Set a termpipelines control to remove this warning.\n" - ] - }, - { - "data": { - "text/html": [ - "
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namerecip_rankP_3P_5P_10ndcg_cut_10ndcg_cut_5
0BM250.0526320.00.00.00.00.0
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" - ], - "text/plain": [ - " name recip_rank P_3 P_5 P_10 ndcg_cut_10 ndcg_cut_5\n", - "0 BM25 0.052632 0.0 0.0 0.0 0.0 0.0" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pt.Experiment([bm25_retrieval], dev_topics, dev_qrels, eval_metrics=[\"recip_rank\", \"P_3\", \"P_5\", \"P_10\", \"ndcg_cut_10\", \"ndcg_cut_5\"], names=['BM25'])" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12:56:09.343 [main] WARN org.terrier.querying.ApplyTermPipeline - The index has no termpipelines configuration, and no control configuration is found. Defaulting to global termpipelines configuration of 'Stopwords,PorterStemmer'. Set a termpipelines control to remove this warning.\n" - ] - }, - { - "data": { - "text/html": [ - "
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namerecip_rankP_3P_5P_10ndcg_cut_10ndcg_cut_5
0BM251.00.3333330.40.20.314880.485229
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" - ], - "text/plain": [ - " name recip_rank P_3 P_5 P_10 ndcg_cut_10 ndcg_cut_5\n", - "0 BM25 1.0 0.333333 0.4 0.2 0.31488 0.485229" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "phrase_search = (bm25_retrieval >> pt.apply.doc_score(lambda i: i['text'].lower().count(i['query']) ))\n", - "\n", - "dev_topics = pd.DataFrame([{'qid': '303', 'query': 'hubble telescope'}])\n", - "\n", - "pt.Experiment([phrase_search], dev_topics, dev_qrels, eval_metrics=[\"recip_rank\", \"P_3\", \"P_5\", \"P_10\", \"ndcg_cut_10\", \"ndcg_cut_5\"], names=['BM25'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "pt.Experiment([phrase_search], dev_topics, dev_qrels, eval_metrics=[\"recip_rank\", \"P_3\", \"P_5\", \"P_10\", \"ndcg_cut_10\", \"ndcg_cut_5\"], names=['BM25'])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "wt_query_segmentation = tira.pt.transform_queries('ir-benchmarks/webis-query-segmentation/wt-snp-baseline', dataset)\n", - "hyp_a_query_segmentation = tira.pt.transform_queries('ir-benchmarks/webis-query-segmentation/hyb-a', dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup Retrieval Pipelines" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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querysegmentation
0international organized crime[international organized crime]
1poliomyelitis and post polio[poliomyelitis, and, post-polio]
2hubble telescope achievements[hubble telescope achievements]
3endangered species mammals[endangered species, mammals]
4most dangerous vehicles[most, dangerous vehicles]
5african civilian deaths[african, civilian deaths]
6new hydroelectric projects[new hydroelectric projects]
7implant dentistry[implant, dentistry]
8rap and crime[rap, and, crime]
9radio waves and brain cancer[radio waves, and, brain cancer]
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" - ], - "text/plain": [ - " query segmentation\n", - "0 international organized crime [international organized crime]\n", - "1 poliomyelitis and post polio [poliomyelitis, and, post-polio]\n", - "2 hubble telescope achievements [hubble telescope achievements]\n", - "3 endangered species mammals [endangered species, mammals]\n", - "4 most dangerous vehicles [most, dangerous vehicles]\n", - "5 african civilian deaths [african, civilian deaths]\n", - "6 new hydroelectric projects [new hydroelectric projects]\n", - "7 implant dentistry [implant, dentistry]\n", - "8 rap and crime [rap, and, crime]\n", - "9 radio waves and brain cancer [radio waves, and, brain cancer]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wt_query_segmentation(dev_topics)[['query', 'segmentation']]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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querysegmentation
0international organized crime[international, organized crime]
1poliomyelitis and post polio[poliomyelitis, and, post-polio]
2hubble telescope achievements[hubble telescope, achievements]
3endangered species mammals[endangered species, mammals]
4most dangerous vehicles[most, dangerous, vehicles]
5african civilian deaths[african, civilian deaths]
6new hydroelectric projects[new, hydroelectric projects]
7implant dentistry[implant, dentistry]
8rap and crime[rap, and, crime]
9radio waves and brain cancer[radio waves, and, brain cancer]
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" - ], - "text/plain": [ - " query segmentation\n", - "0 international organized crime [international, organized crime]\n", - "1 poliomyelitis and post polio [poliomyelitis, and, post-polio]\n", - "2 hubble telescope achievements [hubble telescope, achievements]\n", - "3 endangered species mammals [endangered species, mammals]\n", - "4 most dangerous vehicles [most, dangerous, vehicles]\n", - "5 african civilian deaths [african, civilian deaths]\n", - "6 new hydroelectric projects [new, hydroelectric projects]\n", - "7 implant dentistry [implant, dentistry]\n", - "8 rap and crime [rap, and, crime]\n", - "9 radio waves and brain cancer [radio waves, and, brain cancer]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hyp_a_query_segmentation(dev_topics)[['query', 'segmentation']]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.13" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Query Segmentation in PyTerrier from TIREx executions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the environment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n" + ] + } + ], + "source": [ + "from tira.local_client import Client\n", + "import pyterrier as pt\n", + "import pandas as pd\n", + "\n", + "tira = Client()\n", + "\n", + "if not pt.started():\n", + " pt.init()\n", + "\n", + "dataset = \"trec-robust-2004\"\n", + "pt_dataset = pt.get_dataset(dataset)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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qidquery
2303hubble telescope achievements
\n", + "
" + ], + "text/plain": [ + " qid query\n", + "2 303 hubble telescope achievements" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Some Dev topics\n", + "dev_topics = pt_dataset.get_topics()\n", + "dev_topics = dev_topics[dev_topics['qid'].astype(int) == 303]\n", + "dev_topics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare Retrieval Components" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "bm25 = tira.pt.from_retriever_submission(\"ir-benchmarks/tira-ir-starter/PyTerrierBM25\", dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "bm25_score = pt.text.scorer(takes=\"docs\", body_attr=\"text\", wmodel=\"BM25\")\n", + "bm25_retrieval = bm25 >> bm25_score" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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qiddocnolabel
2386303FBIS3-162170
2387303FBIS3-190930
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2390303FBIS3-235610
............
3142303LA122589-00680
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Adjust `YOUR-COMMAND` where `$inputDataset` points to the input and `$outputDir` is the directory where the output file(s) should be stored. The `tira-run` command automatically checks that the output of the software is valid. - -Step 5: Click on PUSH NEW DOCKER IMAGE to get your personalized instructions to push the image to TIRA. Click REFRESH IMAGES after it was pushed to be then able to select if from the Docker Image dropdown. Put the same command you used in 4. into the command text field. Click NEXT and copy the two commands to check your approach another time. +# HowTo: Software Submissions to the 1st International Workshop on Open Web Search + +To start your submission, you should have prepared a Docker image of your approach. +To get you started, please look at our [baseline submissions](../baselines) that you can use as inspiration. +If you have no experience with Docker, please do not hesitate to contact us, we are happy to help you to dockerize your software. + +## Step-by-Step Guide + +Step 1: Open the task’s [submission page](https://www.tira.io/task-overview/workshop-on-open-web-search/) + +Step 2: Click on "SUBMIT", registering your team if necessary. + +![Screenshot_20231028_123222](https://github.com/OpenWebSearch/wows-code/assets/10050886/44aece55-c14d-4b02-ba40-0ab095717b52) + +Step 3: Click on "DOCKER SUBMISSION" and "CREATE NEW SOFTWARE". + +![Screenshot_20231028_123359](https://github.com/OpenWebSearch/wows-code/assets/10050886/11ad7f7e-7e55-4384-b2c3-2740205fc9c4) + +Step 4: Follow the instructions, installing `tira` and executing the Docker submission with `tira-run` to test that everything works as expected. +The `tira-run` command will download the data automatically. Adjust `YOUR-COMMAND` where `$inputDataset` points to the input and `$outputDir` is the directory where the output file(s) should be stored. The `tira-run` command automatically checks that the output of the software is valid. + +Step 5: Click on PUSH NEW DOCKER IMAGE to get your personalized instructions to push the image to TIRA. Click REFRESH IMAGES after it was pushed to be then able to select if from the Docker Image dropdown. Put the same command you used in 4. into the command text field. Click NEXT and copy the two commands to check your approach another time. diff --git a/ecir25/README.md b/ecir25/README.md index 1af3109..102914e 100644 --- a/ecir25/README.md +++ b/ecir25/README.md @@ -1,123 +1,123 @@ -# Code and Tutorials for the [2nd International Workshop on Open Web Search](https://opensearchfoundation.org/en/events-osf/wows2025) #wows2025 - -This repository contains all code, tutorials, and baselines for the WOWS-EVAL shared task at [WOWS25](https://opensearchfoundation.org/en/events-osf/wows2025/)@[ECIR'25](https://ecir2025.eu/) - -## Goal - -With the WOWS-EVAL shared task, we aim to collaboratively and cooperatively enrich the Open Web Index (OWI) with relevance judgments transferred from existing TREC-like test collections. For this, we develop and collect automated relevance assessors (similar to the [LLMJudge shared task](https://github.com/llm4eval/LLMJudge)) that use a TREC-style information need, optionally a document already known as relevant to the information need, and document as input and output a probability that the document is relevant to the query. - -## Data Format - -We aim to collect pointwise and pairwise relevance assessors. An overview of all datasets for the shared task is available at [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval). Please use the smoke test datasets to ensure that your software works as expected before processing the larger test datasets. - -### Pointwise Relevance Assessments - -Given a query and a document, predict the probability that the document is relevant to the query. The data comes in jsonl format where each input line has the following fields: - -- `id`: The identifier for the query-document pair. -- `query`: The query for which the relevance should be predicted. -- `unknown`: The text of the document for which the relevance to the `query` should be predicted. - -You can directly load the dataset(s) into a [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) via the tira client (install via `pip3 install tira`) for simplified processing. For instance, execute the following command to load the pointwise smoke test dataset into a DataFrame (see [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers): - -``` -from tira.rest_api_client import Client -tira = Client() -input_data = tira.pd.inputs('wows-eval/pointwise-smoke-test-20250128-training') -``` - -The dataset looks like this: - -![example of pointwise data](figures/pointwise-data-example.png) - -The task of an pointwise relevance assessor is to produce an output in jsonl with two fields per line: -- `id`: The identifier for the query-document pair. -- `probability_relevant`: The probability (between 0 for non-relevant and 1 for relevant) that the document is relevant to the query. - -### Pairwise Relevance Assessments - -Given a query, a known relevant document, and an document with an unknown relevance to a query, predict the probability that the unknown document is relevant to the query given the known relevant document. - -You can directly load the dataset(s) into a [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) via the tira client (install via `pip3 install tira`) for simplified processing. For instance, execute the following command to load the pairwise smoke test dataset into a DataFrame (see [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers): - -``` -from tira.rest_api_client import Client -tira = Client() -input_data = tira.pd.inputs('wows-eval/pairwise-smoke-test-20250210-training') -``` - -The dataset looks like this: - -![example of pointwise data](figures/pairwise-data-example.png) - - -## Step-by-Step Submission Guide - -We use [TIRA](https://www.tira.io) with run submissions and encourage to include [ir-metadata](https://www.ir-metadata.org/) documentation for all submitted runs. All our baselines use ir-metadata and monitor the consumed resources to create a run (GPU, RAM, CPU, etc.). - -### Step 1: Install the wows-eval Script to Evaluate and Submit Your Solutions - -We have created a python script `wows-eval` that you can use on the command line and in python to evaluate your relevance assessor. You can pass `--upload` on the command line respectively `upload=True` to directly upload your submission to TIRA and print an ownership ID to stdout that you can use to claim ownership for your submitted run. - -Install `wows-eval` via: - -``` -pip3 install wows-eval -``` - -The easiest option is to use wows-eval from python (assuming that your pipeline uses python). Assuming that you have stored your predictions for the smoke test dataset (see [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers) into a variable `predictions`, you can evaluate and upload your run via like this (complete examples in the baseline notebooks): - -![example of wows-eval in python](figures/wows-eval-python.png) - -If you do not use python, you can use the `wows-eval` that pip3 installs into your python binaries. The `wows-eval --help` command shows an overview of the usage: - -``` -wows-eval --help -Usage: wows-eval [OPTIONS] PREDICTIONS TRUTHS - - Evaluate the predictions of your relevance assessor passed in a file - PREDICTIONS against the TRUTHS (either a file or a string with the TIRA - dataset ID). wows-eval calculates ranking correlations between the predicted - probabilities that documents are relevant against the ground truth ranking - when ordering documents by their ground truth relevance labels. - -Options: - --upload Upload predictions to TIRA. - --help Show this message and exit. -``` - -Assumed that you have stored your predictions for the smoke test dataset into a file predictions.jsonl, evaluating and uploading your run would look like: - -![example of wows-eval in cli](figures/wows-eval-cli.png) - -### Step 2: Implement your Relevance Assessors - -Now that we know the basics, we can start to implement our own point and pairwise relevance assessors. We have a set of baselines available that you can use as starting points: - -- [baselines/pointwise-autoqrels.ipynb](baselines/pointwise-autoqrels.ipynb): A pointwise [autoqrels](https://github.com/seanmacavaney/autoqrels) baseline where you could easily modify the prompt and the underlying language model. -- [baselines/pointwise-naive.ipynb](baselines/pointwise-naive.ipynb): A naive pointwise baseline that always predicts that a document is relevant to a query with a probability of 50%. -- [baselines/pairwise-autoqrels.ipynb](baselines/pairwise-autoqrels.ipynb): A pairwise [autoqrels](https://github.com/seanmacavaney/autoqrels) baseline where you could easily modify the prompt and the underlying language model. -- [baselines/pairwise-naive.ipynb](baselines/pairwise-naive.ipynb): A naive pairwise baseline that always predicts that a document is relevant to a query with a probability of 50%. - - -### Step 3: Register to TIRA and to the WOWS-EVAL task - -Please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on "Register". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list). - -### Step 4: - -To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed from the `wows-eval` command. In the form, you can overwrite the name and description of your system: - -![example of claim ownership form](figures/claim-run-in-tira.png) - -## Resources - -Important links/resources: -- [The workshop page](https://opensearchfoundation.org/en/events-osf/wows2025) -- [Software submissions](https://www.tira.io/task-overview/workshop-on-open-web-search/) -- [Baseline submissions](https://github.com/OpenWebSearch/wows-code/tree/main/ecir25/baselines) - -# Contact - -If you have any questions or problems, please do not hesitate to contact us via [the forum](https://www.tira.io/t/the-forum-for-the-2nd-international-workshop-on-open-web-search-wows2025) or via mail. - +# Code and Tutorials for the [2nd International Workshop on Open Web Search](https://opensearchfoundation.org/en/events-osf/wows2025) #wows2025 + +This repository contains all code, tutorials, and baselines for the WOWS-EVAL shared task at [WOWS25](https://opensearchfoundation.org/en/events-osf/wows2025/)@[ECIR'25](https://ecir2025.eu/) + +## Goal + +With the WOWS-EVAL shared task, we aim to collaboratively and cooperatively enrich the Open Web Index (OWI) with relevance judgments transferred from existing TREC-like test collections. For this, we develop and collect automated relevance assessors (similar to the [LLMJudge shared task](https://github.com/llm4eval/LLMJudge)) that use a TREC-style information need, optionally a document already known as relevant to the information need, and document as input and output a probability that the document is relevant to the query. + +## Data Format + +We aim to collect pointwise and pairwise relevance assessors. An overview of all datasets for the shared task is available at [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval). Please use the smoke test datasets to ensure that your software works as expected before processing the larger test datasets. + +### Pointwise Relevance Assessments + +Given a query and a document, predict the probability that the document is relevant to the query. The data comes in jsonl format where each input line has the following fields: + +- `id`: The identifier for the query-document pair. +- `query`: The query for which the relevance should be predicted. +- `unknown`: The text of the document for which the relevance to the `query` should be predicted. + +You can directly load the dataset(s) into a [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) via the tira client (install via `pip3 install tira`) for simplified processing. For instance, execute the following command to load the pointwise smoke test dataset into a DataFrame (see [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers): + +``` +from tira.rest_api_client import Client +tira = Client() +input_data = tira.pd.inputs('wows-eval/pointwise-smoke-test-20250128-training') +``` + +The dataset looks like this: + +![example of pointwise data](figures/pointwise-data-example.png) + +The task of an pointwise relevance assessor is to produce an output in jsonl with two fields per line: +- `id`: The identifier for the query-document pair. +- `probability_relevant`: The probability (between 0 for non-relevant and 1 for relevant) that the document is relevant to the query. + +### Pairwise Relevance Assessments + +Given a query, a known relevant document, and an document with an unknown relevance to a query, predict the probability that the unknown document is relevant to the query given the known relevant document. + +You can directly load the dataset(s) into a [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) via the tira client (install via `pip3 install tira`) for simplified processing. For instance, execute the following command to load the pairwise smoke test dataset into a DataFrame (see [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers): + +``` +from tira.rest_api_client import Client +tira = Client() +input_data = tira.pd.inputs('wows-eval/pairwise-smoke-test-20250210-training') +``` + +The dataset looks like this: + +![example of pointwise data](figures/pairwise-data-example.png) + + +## Step-by-Step Submission Guide + +We use [TIRA](https://www.tira.io) with run submissions and encourage to include [ir-metadata](https://www.ir-metadata.org/) documentation for all submitted runs. All our baselines use ir-metadata and monitor the consumed resources to create a run (GPU, RAM, CPU, etc.). + +### Step 1: Install the wows-eval Script to Evaluate and Submit Your Solutions + +We have created a python script `wows-eval` that you can use on the command line and in python to evaluate your relevance assessor. You can pass `--upload` on the command line respectively `upload=True` to directly upload your submission to TIRA and print an ownership ID to stdout that you can use to claim ownership for your submitted run. + +Install `wows-eval` via: + +``` +pip3 install wows-eval +``` + +The easiest option is to use wows-eval from python (assuming that your pipeline uses python). Assuming that you have stored your predictions for the smoke test dataset (see [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers) into a variable `predictions`, you can evaluate and upload your run via like this (complete examples in the baseline notebooks): + +![example of wows-eval in python](figures/wows-eval-python.png) + +If you do not use python, you can use the `wows-eval` that pip3 installs into your python binaries. The `wows-eval --help` command shows an overview of the usage: + +``` +wows-eval --help +Usage: wows-eval [OPTIONS] PREDICTIONS TRUTHS + + Evaluate the predictions of your relevance assessor passed in a file + PREDICTIONS against the TRUTHS (either a file or a string with the TIRA + dataset ID). wows-eval calculates ranking correlations between the predicted + probabilities that documents are relevant against the ground truth ranking + when ordering documents by their ground truth relevance labels. + +Options: + --upload Upload predictions to TIRA. + --help Show this message and exit. +``` + +Assumed that you have stored your predictions for the smoke test dataset into a file predictions.jsonl, evaluating and uploading your run would look like: + +![example of wows-eval in cli](figures/wows-eval-cli.png) + +### Step 2: Implement your Relevance Assessors + +Now that we know the basics, we can start to implement our own point and pairwise relevance assessors. We have a set of baselines available that you can use as starting points: + +- [baselines/pointwise-autoqrels.ipynb](baselines/pointwise-autoqrels.ipynb): A pointwise [autoqrels](https://github.com/seanmacavaney/autoqrels) baseline where you could easily modify the prompt and the underlying language model. +- [baselines/pointwise-naive.ipynb](baselines/pointwise-naive.ipynb): A naive pointwise baseline that always predicts that a document is relevant to a query with a probability of 50%. +- [baselines/pairwise-autoqrels.ipynb](baselines/pairwise-autoqrels.ipynb): A pairwise [autoqrels](https://github.com/seanmacavaney/autoqrels) baseline where you could easily modify the prompt and the underlying language model. +- [baselines/pairwise-naive.ipynb](baselines/pairwise-naive.ipynb): A naive pairwise baseline that always predicts that a document is relevant to a query with a probability of 50%. + + +### Step 3: Register to TIRA and to the WOWS-EVAL task + +Please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on "Register". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list). + +### Step 4: + +To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed from the `wows-eval` command. In the form, you can overwrite the name and description of your system: + +![example of claim ownership form](figures/claim-run-in-tira.png) + +## Resources + +Important links/resources: +- [The workshop page](https://opensearchfoundation.org/en/events-osf/wows2025) +- [Software submissions](https://www.tira.io/task-overview/workshop-on-open-web-search/) +- [Baseline submissions](https://github.com/OpenWebSearch/wows-code/tree/main/ecir25/baselines) + +# Contact + +If you have any questions or problems, please do not hesitate to contact us via [the forum](https://www.tira.io/t/the-forum-for-the-2nd-international-workshop-on-open-web-search-wows2025) or via mail. + diff --git a/ecir25/baselines/pairwise-autoqrels.ipynb b/ecir25/baselines/pairwise-autoqrels.ipynb index 1d607ba..4524584 100644 --- a/ecir25/baselines/pairwise-autoqrels.ipynb +++ b/ecir25/baselines/pairwise-autoqrels.ipynb @@ -1,487 +1,487 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WOWS-Eval Autoqrels Pairwise Baseline\n", - "\n", - "This is a pairwise autoqrels baseline to WOWS-EVAL that uses autoqrels to predict the probability that a document is relevant to a query given a already known relevant document." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install wows-eval 'git+https://github.com/mam10eks/autoqrels.git'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Load the Data\n", - "\n", - "Pairwise models have a query, a known relevant document, and an document with an unknown relevance to a query as input and predict the probability that the unknown document is relevant to the query given the known relevant document into a field `probability_relevant`.\n", - "\n", - "In the following, we will process the pwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "from tira.rest_api_client import Client\n", - "from wows_eval import evaluate as wows_evaluate\n", - "from autoqrels.oneshot import DuoPrompt\n", - "import pandas as pd\n", - "\n", - "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", - "from tirex_tracker import tracking, ExportFormat\n", - "\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "DATASET_ID = 'wows-eval/pairwise-smoke-test-20250210-training'\n", - "# DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", - "\n", - "tira = Client()\n", - "input_data = tira.pd.inputs(DATASET_ID)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Look at the data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idqueryrelevantunknown
03d080873-98a1-4388-af86-fe2c8b47ebcawho sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano.
1468a9e92-467f-47c9-810b-fe6fa9dca634who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners.
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" - ], - "text/plain": [ - " id query \\\n", - "0 3d080873-98a1-4388-af86-fe2c8b47ebca who sings monk theme song \n", - "1 468a9e92-467f-47c9-810b-fe6fa9dca634 who sings monk theme song \n", - "\n", - " relevant \\\n", - "0 exists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman. \n", - "1 exists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman. \n", - "\n", - " unknown \n", - "0 Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano. \n", - "1 One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners. " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_data.head(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Implement the AutoQrels approach\n", - "\n", - "Here, we use a prompted language model (you can modify the prompt and the backbone model) to predict the relevance of the document to the query and store the probability in the field `probability_relevant`. We wrap all computations into a `tira_measure.Environment` to measure the resources consumed for our computations to later include the used resources into the ir-metadata of our run." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "BACKBONE_MODEL = \"flan-t5-small\"\n", - "\n", - "PROMPT = \"\"\"Determine if passage B is as relevant as passage A\n", - "for the given query.\n", - "'Passage A: \"...{{ rel_doc_text | replace(\"\\\\\"\", \"\\'\") }}...\"\n", - "'Passage B: \"...{{ unk_doc_text | replace(\"\\\\\"\", \"\\'\") }}...\"\n", - "'Query: \"{{ query_text }}\" '\n", - "\"Is passage B as relevant as passage A? \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "autoqrels_assessor = DuoPrompt(\n", - " backbone=f'google/{BACKBONE_MODEL}',\n", - " prompt=PROMPT,\n", - " dataset=None\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Run the predictions and look at the outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", - "\n", - "===== Processor information =====\n", - "Linux arch_perfmon flag : yes\n", - "Hybrid processor : yes\n", - "IBRS and IBPB supported : yes\n", - "STIBP supported : yes\n", - "Spec arch caps supported : yes\n", - "Max CPUID level : 32\n", - "CPU model number : 154\n", - "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", - "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", - "Can not access CPUs Model Specific Registers (MSRs).\n", - "execute 'modprobe msr' as root user, then execute pcm as root user.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Give read access to /sys/class/powercap/intel-rapl/intel-rapl:1/energy_uj\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/6 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idqueryrelevantunknownprobability_relevant
03d080873-98a1-4388-af86-fe2c8b47ebcawho sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano.0.616158
1468a9e92-467f-47c9-810b-fe6fa9dca634who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners.0.713638
2846a69d0-0c0e-4d86-baf2-c3e8d31fdc86who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Singing elegant, melancholic songs in a glamorously tattered voice, Leonard Cohen emerged from Montreal in the 1960s, an artist well into his thirties before he even made his first album. After a few records, he was royalty, on equal footing with Joni Mitchell, Randy Newman, and other top-notch singer-songwriters.0.684873
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" - ], - "text/plain": [ - " system tau_ap kendall spearman pearson\n", - "0 auto-qrels-pairwise-flan-t5-small -0.303889 -0.152381 -0.178571 -0.178571" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wows_evaluate(\n", - " predictions,\n", - " DATASET_ID,\n", - " tracking_results=tracked,\n", - " upload=True,\n", - " system_name=f'auto-qrels-pairwise-{BACKBONE_MODEL}',\n", - " system_description=\"We use autoqrels [1] with a custom in-context learning prompt for pairwise relevance judgments.\\n\\n[1] - https://github.com/seanmacavaney/autoqrels\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 7: Register to TIRA and to the WOWS-EVAL task\n", - "\n", - "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", - "\n", - "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", - "\n", - "\n", - "![example of claim ownership form](../figures/claim-run-in-tira.png)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WOWS-Eval Autoqrels Pairwise Baseline\n", + "\n", + "This is a pairwise autoqrels baseline to WOWS-EVAL that uses autoqrels to predict the probability that a document is relevant to a query given a already known relevant document." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install wows-eval 'git+https://github.com/mam10eks/autoqrels.git'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load the Data\n", + "\n", + "Pairwise models have a query, a known relevant document, and an document with an unknown relevance to a query as input and predict the probability that the unknown document is relevant to the query given the known relevant document into a field `probability_relevant`.\n", + "\n", + "In the following, we will process the pwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "from autoqrels.oneshot import DuoPrompt\n", + "import pandas as pd\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "DATASET_ID = 'wows-eval/pairwise-smoke-test-20250210-training'\n", + "# DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", + "\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idqueryrelevantunknown
03d080873-98a1-4388-af86-fe2c8b47ebcawho sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano.
1468a9e92-467f-47c9-810b-fe6fa9dca634who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners.
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" + ], + "text/plain": [ + " id query \\\n", + "0 3d080873-98a1-4388-af86-fe2c8b47ebca who sings monk theme song \n", + "1 468a9e92-467f-47c9-810b-fe6fa9dca634 who sings monk theme song \n", + "\n", + " relevant \\\n", + "0 exists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman. \n", + "1 exists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman. \n", + "\n", + " unknown \n", + "0 Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano. \n", + "1 One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners. " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_data.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Implement the AutoQrels approach\n", + "\n", + "Here, we use a prompted language model (you can modify the prompt and the backbone model) to predict the relevance of the document to the query and store the probability in the field `probability_relevant`. We wrap all computations into a `tira_measure.Environment` to measure the resources consumed for our computations to later include the used resources into the ir-metadata of our run." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "BACKBONE_MODEL = \"flan-t5-small\"\n", + "\n", + "PROMPT = \"\"\"Determine if passage B is as relevant as passage A\n", + "for the given query.\n", + "'Passage A: \"...{{ rel_doc_text | replace(\"\\\\\"\", \"\\'\") }}...\"\n", + "'Passage B: \"...{{ unk_doc_text | replace(\"\\\\\"\", \"\\'\") }}...\"\n", + "'Query: \"{{ query_text }}\" '\n", + "\"Is passage B as relevant as passage A? \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "autoqrels_assessor = DuoPrompt(\n", + " backbone=f'google/{BACKBONE_MODEL}',\n", + " prompt=PROMPT,\n", + " dataset=None\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the predictions and look at the outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", + "\n", + "===== Processor information =====\n", + "Linux arch_perfmon flag : yes\n", + "Hybrid processor : yes\n", + "IBRS and IBPB supported : yes\n", + "STIBP supported : yes\n", + "Spec arch caps supported : yes\n", + "Max CPUID level : 32\n", + "CPU model number : 154\n", + "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", + "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", + "Can not access CPUs Model Specific Registers (MSRs).\n", + "execute 'modprobe msr' as root user, then execute pcm as root user.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Give read access to /sys/class/powercap/intel-rapl/intel-rapl:1/energy_uj\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/6 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idqueryrelevantunknownprobability_relevant
03d080873-98a1-4388-af86-fe2c8b47ebcawho sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano.0.616158
1468a9e92-467f-47c9-810b-fe6fa9dca634who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners.0.713638
2846a69d0-0c0e-4d86-baf2-c3e8d31fdc86who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Singing elegant, melancholic songs in a glamorously tattered voice, Leonard Cohen emerged from Montreal in the 1960s, an artist well into his thirties before he even made his first album. After a few records, he was royalty, on equal footing with Joni Mitchell, Randy Newman, and other top-notch singer-songwriters.0.684873
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" + ], + "text/plain": [ + " system tau_ap kendall spearman pearson\n", + "0 auto-qrels-pairwise-flan-t5-small -0.303889 -0.152381 -0.178571 -0.178571" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wows_evaluate(\n", + " predictions,\n", + " DATASET_ID,\n", + " tracking_results=tracked,\n", + " upload=True,\n", + " system_name=f'auto-qrels-pairwise-{BACKBONE_MODEL}',\n", + " system_description=\"We use autoqrels [1] with a custom in-context learning prompt for pairwise relevance judgments.\\n\\n[1] - https://github.com/seanmacavaney/autoqrels\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7: Register to TIRA and to the WOWS-EVAL task\n", + "\n", + "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", + "\n", + "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", + "\n", + "\n", + "![example of claim ownership form](../figures/claim-run-in-tira.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ecir25/baselines/pairwise-naive.ipynb b/ecir25/baselines/pairwise-naive.ipynb index 57b6ba8..777d7ae 100644 --- a/ecir25/baselines/pairwise-naive.ipynb +++ b/ecir25/baselines/pairwise-naive.ipynb @@ -1,317 +1,317 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WOWS-Eval Naive Pairwise Baseline\n", - "\n", - "This is a naive baseline to WOWS-EVAL that always predicts that a document is relevant to a query with a probability of 50%." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Install Dependencies\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install 'wows-eval>=0.0.6'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Load the Data\n", - "\n", - "Pairwise models have a query, a known relevant document, and an document with an unknown relevance to a query as input and predict the probability that the unknown document is relevant to the query given the known relevant document into a field `probability_relevant`. For this naive baseline, we always predict a probability of 0.5.\n", - "\n", - "In the following, we will process the pwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from tira.rest_api_client import Client\n", - "from wows_eval import evaluate as wows_evaluate\n", - "import pandas as pd\n", - "\n", - "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", - "from tirex_tracker import tracking, ExportFormat\n", - "\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "DATASET_ID = 'wows-eval/pairwise-smoke-test-20250210-training'\n", - "# DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", - "\n", - "tira = Client()\n", - "input_data = tira.pd.inputs(DATASET_ID)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Look at the data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idqueryrelevantunknown
03d080873-98a1-4388-af86-fe2c8b47ebcawho sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano.
1468a9e92-467f-47c9-810b-fe6fa9dca634who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners.
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" - ], - "text/plain": [ - " system tau_ap kendall spearman pearson\n", - "0 naive-pairwise 0.34 0.361905 0.45 0.45" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wows_evaluate(\n", - " predictions,\n", - " DATASET_ID,\n", - " tracking_results=tracked,\n", - " upload=True,\n", - " system_name='naive-pairwise',\n", - " system_description='A naive approach that predicts that each document is relevant with a probabilty of 50%.'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 6: Register to TIRA and to the WOWS-EVAL task\n", - "\n", - "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", - "\n", - "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", - "\n", - "\n", - "![example of claim ownership form](../figures/claim-run-in-tira.png)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WOWS-Eval Naive Pairwise Baseline\n", + "\n", + "This is a naive baseline to WOWS-EVAL that always predicts that a document is relevant to a query with a probability of 50%." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Dependencies\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install 'wows-eval>=0.0.6'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load the Data\n", + "\n", + "Pairwise models have a query, a known relevant document, and an document with an unknown relevance to a query as input and predict the probability that the unknown document is relevant to the query given the known relevant document into a field `probability_relevant`. For this naive baseline, we always predict a probability of 0.5.\n", + "\n", + "In the following, we will process the pwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "import pandas as pd\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "DATASET_ID = 'wows-eval/pairwise-smoke-test-20250210-training'\n", + "# DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", + "\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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03d080873-98a1-4388-af86-fe2c8b47ebcawho sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.Randy Newman (album) Randy Newman is the debut recording by Randy Newman, released in 1968. Unlike his later albums which featured Newman and his piano backed by guitar, bass guitar and drums, Randy Newman was highly orchestral and aimed to blend the orchestra with Newman's voice and piano.
1468a9e92-467f-47c9-810b-fe6fa9dca634who sings monk theme songexists and is an alternate of . The Monk theme song is It's a Jungle Out There by Randy Newman. The Monk theme song is It's a Jungle Out There by Randy Newman.One of Monk's most important contributions to jazz was his use of space and simplicity in his performances and in his compositions. Monk's unconventional use of harmony and rhythm has had a lasting influence on jazz as well. Finally, Monk's compositions are very well-known, both by performers and by listeners.
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" + ], + "text/plain": [ + " system tau_ap kendall spearman pearson\n", + "0 naive-pairwise 0.34 0.361905 0.45 0.45" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wows_evaluate(\n", + " predictions,\n", + " DATASET_ID,\n", + " tracking_results=tracked,\n", + " upload=True,\n", + " system_name='naive-pairwise',\n", + " system_description='A naive approach that predicts that each document is relevant with a probabilty of 50%.'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Register to TIRA and to the WOWS-EVAL task\n", + "\n", + "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", + "\n", + "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", + "\n", + "\n", + "![example of claim ownership form](../figures/claim-run-in-tira.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ecir25/baselines/pairwise-retrieval.ipynb b/ecir25/baselines/pairwise-retrieval.ipynb index 691bcf5..7c22f21 100644 --- a/ecir25/baselines/pairwise-retrieval.ipynb +++ b/ecir25/baselines/pairwise-retrieval.ipynb @@ -1,331 +1,331 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WOWS-Eval Pairwise Retrieval Baseline\n", - "\n", - "This is a retrieval baseline to WOWS-EVAL that uses a PyTerrier retrieval model to assign the probability that an unknown document is relevant by ranking all unknown documents for all known relevant documents. We then use the min-max normalized rank that an unknown document has for the a known relevant document as the probability that the unknown document is relevant." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install 'wows-eval>=0.0.6' python-terrier==0.10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Load the Data\n", - "\n", - "Pairwise models have a query, a known relevant document, and an document with an unknown relevance to a query as input and predict the probability that the unknown document is relevant to the query given the known relevant document into a field `probability_relevant`. For this naive baseline, we always predict a probability of 0.5.\n", - "\n", - "In the following, we will process the pwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PyTerrier 0.10.0 has loaded Terrier 5.11 (built by craig.macdonald on 2025-01-13 21:29) and terrier-helper 0.0.8\n", - "\n" - ] - } - ], - "source": [ - "import pyterrier as pt\n", - "from tqdm import tqdm\n", - "if not pt.started():\n", - " pt.init()\n", - "\n", - "from tira.rest_api_client import Client\n", - "from wows_eval import evaluate as wows_evaluate\n", - "import pandas as pd\n", - "from jnius import autoclass\n", - "import numpy as np\n", - "\n", - "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", - "from tirex_tracker import tracking, ExportFormat\n", - "\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "DATASET_ID = 'wows-eval/pairwise-smoke-test-20250210-training'\n", - "#DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", - "\n", - "tira = Client()\n", - "input_data = tira.pd.inputs(DATASET_ID)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Implement the Approach\n", - "\n", - "We wrap all computations into a [tirex_tracker.tracking](https://github.com/tira-io/tirex-tracker/) environment to measure the resources consumed for our computations and also a snapshot of our code in the [ir-metadata format](https://www.ir-metadata.org/)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class QueryByRelevantDocument:\n", - " def __init__(self):\n", - " self.results = {}\n", - " def process(self, query, retrieval_system, rel, unk):\n", - " if query in self.results:\n", - " raise ValueError('This query was already processed: ' + query)\n", - " \n", - " ret = {}\n", - " tokeniser = autoclass(\"org.terrier.indexing.tokenisation.Tokeniser\").getTokeniser()\n", - " for doc in rel.values():\n", - " doc_text = \" \".join(tokeniser.getTokens(doc))\n", - " run = retrieval_system.search(doc_text)\n", - " last_rank = -1\n", - " scores = {}\n", - " for _, i in run.iterrows():\n", - " assert last_rank < i['rank']\n", - " last_rank = i['rank']\n", - " if i['docno'] in unk:\n", - " scores[unk[i['docno']]] = i['rank']\n", - "\n", - " max_score = max(scores.values())\n", - " min_score = min(scores.values())\n", - " ret[doc] = {k: ((v-min_score)/(max_score-min_score)) for k, v in scores.items()}\n", - " \n", - " self.results[query] = ret\n", - "\n", - "\n", - "class QueryByUnknownDocument:\n", - " def __init__(self):\n", - " self.results = {}\n", - " def process(self, query, retrieval_system, rel, unk):\n", - " if query in self.results:\n", - " raise ValueError('This query was already processed: ' + query)\n", - " \n", - " ret = {}\n", - " tokeniser = autoclass(\"org.terrier.indexing.tokenisation.Tokeniser\").getTokeniser()\n", - " for doc in unk.values():\n", - " doc_text = \" \".join(tokeniser.getTokens(doc))\n", - " run = retrieval_system.search(doc_text)\n", - " last_rank = -1\n", - " dcg = 0\n", - " for _, i in run.iterrows():\n", - " assert last_rank < i['rank']\n", - " last_rank = i['rank']\n", - " if i['rank'] >= 20:\n", - " break\n", - " if i['docno'] in rel:\n", - " # https://github.com/joaopalotti/trectools/blob/master/trectools/trec_eval.py#L499C28-L499C56\n", - " dcg += 1. / np.log2(i['rank']+1)\n", - " \n", - " ret[doc] = dcg\n", - " max_score = max(ret.values())\n", - " min_score = min(ret.values())\n", - " ret = {k: ((v-min_score)/(max_score-min_score)) for k, v in ret.items()}\n", - " ret = {r: ret for r in rel.values()}\n", - "\n", - " self.results[query] = ret\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", - "\n", - "===== Processor information =====\n", - "Linux arch_perfmon flag : yes\n", - "Hybrid processor : yes\n", - "IBRS and IBPB supported : yes\n", - "STIBP supported : yes\n", - "Spec arch caps supported : yes\n", - "Max CPUID level : 32\n", - "CPU model number : 154\n", - "ERROR: Can not open /sys/module/msr/parameters/allow_writes file.\n", - "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", - "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", - "Can not access CPUs Model Specific Registers (MSRs).\n", - "execute 'modprobe msr' as root user, then execute pcm as root user.\n", - "100%|██████████| 2/2 [00:00<00:00, 2.38it/s]\n" - ] - } - ], - "source": [ - "WMODEL = \"BM25\"\n", - "system_name = f'query-by-relevant-doc-{WMODEL}'\n", - "#system_name = 'query-by-unknown-doc-{WMODEL}'\n", - "\n", - "!rm -Rf tmp\n", - "with tracking(export_file_path='tmp/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", - " queries = set(input_data['query'].unique())\n", - "\n", - " def known_relevant_documents(query):\n", - " docs = set(input_data[input_data['query'] == query]['relevant'].unique())\n", - " return {f'{i[0]}-rel': i[1] for i in zip(range(len(docs)), docs)}\n", - "\n", - " def unknown_documents(query):\n", - " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", - " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", - "\n", - " if system_name.startswith('query-by-relevant-doc'):\n", - " processor = QueryByRelevantDocument()\n", - " elif system_name.startswith('query-by-unknown-doc'):\n", - " processor = QueryByUnknownDocument()\n", - " else:\n", - " raise ValueError('foo')\n", - "\n", - " for query in tqdm(queries):\n", - " rel = known_relevant_documents(query)\n", - " unk = unknown_documents(query)\n", - "\n", - " docs = [{'docno': k, 'text': v} for k, v in rel.items()]+[{'docno': k, 'text': v} for k, v in unk.items()]\n", - " indexer = pt.IterDictIndexer(\"/tmp/index\", overwrite=True, meta={'docno': 100, 'text': 20480})\n", - " index_ref = indexer.index(docs)\n", - " bm25 = pt.BatchRetrieve(index_ref, wmodel=WMODEL)\n", - " processor.process(query, bm25, rel, unk)\n", - "\n", - " predictions = []\n", - " for _, i in input_data.iterrows():\n", - " res = processor.results[i['query']]\n", - " res = res[i['relevant']]\n", - " predictions.append({\n", - " 'id': i['id'],\n", - " 'probability_relevant': res.get(i['unknown'], -1)\n", - " })\n", - " predictions = pd.DataFrame(predictions)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Evaluate and Submit Your Run\n", - "\n", - "We use the `wows_evaluate` method imported above to evaluate our predictions and to upload them, to TIRA.\n", - "\n", - "The `wows_evaluate` method has optional parameters that you can pass to describe your system and to include the resource measurements used during your computations in the ir-metadata format into your submission. You can remove those attributes or modify them for your submission accordingly. Call `help(wows_evaluate)` to see a full description." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/59f8a4d8-5fc5-4349-88eb-694db23eb457\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " system tau_ap kendall spearman pearson\n", - "0 query-by-relevant-doc-BM25 0.526667 0.485714 0.621429 0.621429" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wows_evaluate(\n", - " predictions,\n", - " DATASET_ID,\n", - " tracking_results=tracked,\n", - " upload=True,\n", - " system_name=system_name,\n", - " system_description=f'We use the PyTerrier retrieval model {WMODEL} to assign the probability that an unknown document is relevant by ranking all unknown documents for all known relevant documents. We then use the min-max normalized rank that an unknown document has for the a known relevant document as the probability that the unknown document is relevant.'\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WOWS-Eval Pairwise Retrieval Baseline\n", + "\n", + "This is a retrieval baseline to WOWS-EVAL that uses a PyTerrier retrieval model to assign the probability that an unknown document is relevant by ranking all unknown documents for all known relevant documents. We then use the min-max normalized rank that an unknown document has for the a known relevant document as the probability that the unknown document is relevant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install 'wows-eval>=0.0.6' python-terrier==0.10.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load the Data\n", + "\n", + "Pairwise models have a query, a known relevant document, and an document with an unknown relevance to a query as input and predict the probability that the unknown document is relevant to the query given the known relevant document into a field `probability_relevant`. For this naive baseline, we always predict a probability of 0.5.\n", + "\n", + "In the following, we will process the pwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PyTerrier 0.10.0 has loaded Terrier 5.11 (built by craig.macdonald on 2025-01-13 21:29) and terrier-helper 0.0.8\n", + "\n" + ] + } + ], + "source": [ + "import pyterrier as pt\n", + "from tqdm import tqdm\n", + "if not pt.started():\n", + " pt.init()\n", + "\n", + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "import pandas as pd\n", + "from jnius import autoclass\n", + "import numpy as np\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "DATASET_ID = 'wows-eval/pairwise-smoke-test-20250210-training'\n", + "#DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", + "\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Implement the Approach\n", + "\n", + "We wrap all computations into a [tirex_tracker.tracking](https://github.com/tira-io/tirex-tracker/) environment to measure the resources consumed for our computations and also a snapshot of our code in the [ir-metadata format](https://www.ir-metadata.org/)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class QueryByRelevantDocument:\n", + " def __init__(self):\n", + " self.results = {}\n", + " def process(self, query, retrieval_system, rel, unk):\n", + " if query in self.results:\n", + " raise ValueError('This query was already processed: ' + query)\n", + " \n", + " ret = {}\n", + " tokeniser = autoclass(\"org.terrier.indexing.tokenisation.Tokeniser\").getTokeniser()\n", + " for doc in rel.values():\n", + " doc_text = \" \".join(tokeniser.getTokens(doc))\n", + " run = retrieval_system.search(doc_text)\n", + " last_rank = -1\n", + " scores = {}\n", + " for _, i in run.iterrows():\n", + " assert last_rank < i['rank']\n", + " last_rank = i['rank']\n", + " if i['docno'] in unk:\n", + " scores[unk[i['docno']]] = i['rank']\n", + "\n", + " max_score = max(scores.values())\n", + " min_score = min(scores.values())\n", + " ret[doc] = {k: ((v-min_score)/(max_score-min_score)) for k, v in scores.items()}\n", + " \n", + " self.results[query] = ret\n", + "\n", + "\n", + "class QueryByUnknownDocument:\n", + " def __init__(self):\n", + " self.results = {}\n", + " def process(self, query, retrieval_system, rel, unk):\n", + " if query in self.results:\n", + " raise ValueError('This query was already processed: ' + query)\n", + " \n", + " ret = {}\n", + " tokeniser = autoclass(\"org.terrier.indexing.tokenisation.Tokeniser\").getTokeniser()\n", + " for doc in unk.values():\n", + " doc_text = \" \".join(tokeniser.getTokens(doc))\n", + " run = retrieval_system.search(doc_text)\n", + " last_rank = -1\n", + " dcg = 0\n", + " for _, i in run.iterrows():\n", + " assert last_rank < i['rank']\n", + " last_rank = i['rank']\n", + " if i['rank'] >= 20:\n", + " break\n", + " if i['docno'] in rel:\n", + " # https://github.com/joaopalotti/trectools/blob/master/trectools/trec_eval.py#L499C28-L499C56\n", + " dcg += 1. / np.log2(i['rank']+1)\n", + " \n", + " ret[doc] = dcg\n", + " max_score = max(ret.values())\n", + " min_score = min(ret.values())\n", + " ret = {k: ((v-min_score)/(max_score-min_score)) for k, v in ret.items()}\n", + " ret = {r: ret for r in rel.values()}\n", + "\n", + " self.results[query] = ret\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", + "\n", + "===== Processor information =====\n", + "Linux arch_perfmon flag : yes\n", + "Hybrid processor : yes\n", + "IBRS and IBPB supported : yes\n", + "STIBP supported : yes\n", + "Spec arch caps supported : yes\n", + "Max CPUID level : 32\n", + "CPU model number : 154\n", + "ERROR: Can not open /sys/module/msr/parameters/allow_writes file.\n", + "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", + "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", + "Can not access CPUs Model Specific Registers (MSRs).\n", + "execute 'modprobe msr' as root user, then execute pcm as root user.\n", + "100%|██████████| 2/2 [00:00<00:00, 2.38it/s]\n" + ] + } + ], + "source": [ + "WMODEL = \"BM25\"\n", + "system_name = f'query-by-relevant-doc-{WMODEL}'\n", + "#system_name = 'query-by-unknown-doc-{WMODEL}'\n", + "\n", + "!rm -Rf tmp\n", + "with tracking(export_file_path='tmp/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", + " queries = set(input_data['query'].unique())\n", + "\n", + " def known_relevant_documents(query):\n", + " docs = set(input_data[input_data['query'] == query]['relevant'].unique())\n", + " return {f'{i[0]}-rel': i[1] for i in zip(range(len(docs)), docs)}\n", + "\n", + " def unknown_documents(query):\n", + " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", + " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", + "\n", + " if system_name.startswith('query-by-relevant-doc'):\n", + " processor = QueryByRelevantDocument()\n", + " elif system_name.startswith('query-by-unknown-doc'):\n", + " processor = QueryByUnknownDocument()\n", + " else:\n", + " raise ValueError('foo')\n", + "\n", + " for query in tqdm(queries):\n", + " rel = known_relevant_documents(query)\n", + " unk = unknown_documents(query)\n", + "\n", + " docs = [{'docno': k, 'text': v} for k, v in rel.items()]+[{'docno': k, 'text': v} for k, v in unk.items()]\n", + " indexer = pt.IterDictIndexer(\"/tmp/index\", overwrite=True, meta={'docno': 100, 'text': 20480})\n", + " index_ref = indexer.index(docs)\n", + " bm25 = pt.BatchRetrieve(index_ref, wmodel=WMODEL)\n", + " processor.process(query, bm25, rel, unk)\n", + "\n", + " predictions = []\n", + " for _, i in input_data.iterrows():\n", + " res = processor.results[i['query']]\n", + " res = res[i['relevant']]\n", + " predictions.append({\n", + " 'id': i['id'],\n", + " 'probability_relevant': res.get(i['unknown'], -1)\n", + " })\n", + " predictions = pd.DataFrame(predictions)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Evaluate and Submit Your Run\n", + "\n", + "We use the `wows_evaluate` method imported above to evaluate our predictions and to upload them, to TIRA.\n", + "\n", + "The `wows_evaluate` method has optional parameters that you can pass to describe your system and to include the resource measurements used during your computations in the ir-metadata format into your submission. You can remove those attributes or modify them for your submission accordingly. Call `help(wows_evaluate)` to see a full description." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/59f8a4d8-5fc5-4349-88eb-694db23eb457\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " system tau_ap kendall spearman pearson\n", + "0 query-by-relevant-doc-BM25 0.526667 0.485714 0.621429 0.621429" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wows_evaluate(\n", + " predictions,\n", + " DATASET_ID,\n", + " tracking_results=tracked,\n", + " upload=True,\n", + " system_name=system_name,\n", + " system_description=f'We use the PyTerrier retrieval model {WMODEL} to assign the probability that an unknown document is relevant by ranking all unknown documents for all known relevant documents. We then use the min-max normalized rank that an unknown document has for the a known relevant document as the probability that the unknown document is relevant.'\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ecir25/baselines/pairwise_snorkel.ipynb b/ecir25/baselines/pairwise_snorkel.ipynb index 25b841b..e173070 100644 --- a/ecir25/baselines/pairwise_snorkel.ipynb +++ b/ecir25/baselines/pairwise_snorkel.ipynb @@ -1,1197 +1,1197 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "Step 1: install dependencies\n", - "\n", - "> Add blockquote\n", - "\n" - ], - "metadata": { - "id": "nrNuNdOkWxU2" - } - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jqYpJ_Gqn0v3", - "outputId": "af2950db-9969-4371-cefc-1a908de14af2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting tira\n", - " Using cached tira-0.0.157-py3-none-any.whl.metadata (4.8 kB)\n", - "Requirement already satisfied: snorkel in /usr/local/lib/python3.11/dist-packages (0.10.0)\n", - "Collecting wows-eval\n", - " Using cached wows_eval-0.0.6-py3-none-any.whl.metadata (1.3 kB)\n", - "Requirement already satisfied: textdistance in /usr/local/lib/python3.11/dist-packages (4.6.3)\n", - "Requirement already satisfied: rank-bm25 in /usr/local/lib/python3.11/dist-packages (0.2.2)\n", - 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}, - "id": "nF6rUuWHtImS", - "outputId": "344e88b2-117c-4caa-b9ee-56bbac7b0c07" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Found existing installation: tira 0.0.157\n", - "Uninstalling tira-0.0.157:\n", - " Successfully uninstalled tira-0.0.157\n", - "Found existing installation: wows-eval 0.0.6\n", - "Uninstalling wows-eval-0.0.6:\n", - " Successfully uninstalled wows-eval-0.0.6\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip3 install wows-eval>=0.0.6" - ], - "metadata": { - "id": "qfh3GolMtcj3" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "!pip3 install python-terrier==0.10.0" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6Fo04IIKtxVA", - "outputId": "758a5ff8-a271-4bb7-95d3-740532680c01" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: python-terrier==0.10.0 in /usr/local/lib/python3.11/dist-packages (0.10.0)\n", - 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] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip3 uninstall numpy -y\n", - "!pip3 install numpy --upgrade --force-reinstall" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9vVpwA4Xt2ni", - "outputId": "704adba1-c74e-4819-eafd-666630f9e5fd" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Found existing installation: numpy 1.26.4\n", - "Uninstalling numpy-1.26.4:\n", - " Successfully uninstalled numpy-1.26.4\n", - "Collecting numpy\n", - " Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\n", - "Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB)\n", - "Installing collected packages: numpy\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "tira 0.0.157 requires numpy==1.*, but you have numpy 2.2.4 which is incompatible.\n", - "tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.2.4 which is incompatible.\n", - "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.2.4 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed numpy-2.2.4\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip3 install numpy==1.26.4 --force-reinstall\n", - "import os\n", - "os.kill(os.getpid(), 9)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vzk4NnBduZHU", - "outputId": "8edabf54-9ef7-404d-cd9f-093f1032d069" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting numpy==1.26.4\n", - " Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", - "Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n", - "Installing collected packages: numpy\n", - " Attempting uninstall: numpy\n", - " Found existing installation: numpy 2.2.4\n", - " Uninstalling numpy-2.2.4:\n", - " Successfully uninstalled numpy-2.2.4\n", - "Successfully installed numpy-1.26.4\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pyterrier as pt\n", - "from tqdm import tqdm\n", - "if not pt.started():\n", - " pt.init()\n", - "\n", - "from tira.rest_api_client import Client\n", - "from wows_eval import evaluate as wows_evaluate\n", - "import pandas as pd\n", - "from jnius import autoclass\n", - "import numpy as np\n", - "\n", - "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", - "from tirex_tracker import tracking, ExportFormat\n", - "\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "# Dataset IDs visible at https://archive.tira.io/datasets?query=wows-eval\n", - "DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", - "\n", - "#, 'wows-eval/pairwise-smoke-test-20250210-training'\n", - "tira = Client()\n", - "input_data = tira.pd.inputs(DATASET_ID)" - ], - "metadata": { - "id": "KrRf64t2n6ke", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "791a7ee1-3267-4515-cb06-b911b47b2b72" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "PyTerrier 0.10.0 has loaded Terrier 5.11 (built by craig.macdonald on 2025-01-13 21:29) and terrier-helper 0.0.8\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip3 install rapidfuzz" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Xsbs5EDi7t0y", - "outputId": "1f177a70-b55d-4a8d-fa2a-9ff25dce3af4" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: rapidfuzz in /usr/local/lib/python3.11/dist-packages (3.13.0)\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Step 2: load all the libraries" - ], - "metadata": { - "id": "CB_34eKz68Pn" - } - }, - { - "cell_type": "code", - "source": [ - "from snorkel.labeling import labeling_function, PandasLFApplier\n", - "from snorkel.labeling.model.label_model import LabelModel\n", - "from rank_bm25 import BM25Okapi\n", - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from sentence_transformers import SentenceTransformer\n", - "from sklearn.metrics.pairwise import cosine_similarity\n", - "from wows_eval import evaluate as wows_evaluate\n", - "from rapidfuzz import fuzz\n", - "import numpy as np\n", - "import textdistance\n", - "import pandas as pd\n", - "vectorizer = TfidfVectorizer()\n", - "import re" - ], - "metadata": { - "id": "5D-oLP_54FtB" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import nltk\n", - "from nltk.corpus import stopwords, wordnet\n", - "from nltk.stem import WordNetLemmatizer\n", - "# Download stopwords if not already present\n", - "nltk.download(\"stopwords\")\n", - "stop_words = set(stopwords.words(\"english\"))\n", - "nltk.download(\"wordnet\")\n", - "lemmatizer = WordNetLemmatizer()" - ], - "metadata": { - "id": "qBVEpFOn4LNN", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "23d98486-7f44-49a5-ade5-944e4ad9d5c6" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n", - "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", - "[nltk_data] Package wordnet is already up-to-date!\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Step 3: define global variables" - ], - "metadata": { - "id": "NpyWVji-7C1f" - } - }, - { - "cell_type": "code", - "source": [ - "# Global Variables\n", - "min_bm25 = None\n", - "max_bm25 = None\n", - "bm25 = None\n" - ], - "metadata": { - "id": "w7M09OVo4CQb" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Global Min/Max Values (These should be computed dynamically)\n", - "GLOBAL_MIN = -1\n", - "GLOBAL_MAX = 1\n" - ], - "metadata": { - "id": "guLY8AnOcG6X" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Step 4: define helping functions" - ], - "metadata": { - "id": "2fbjNSaCQgHw" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "def tokenize(text):\n", - " \"\"\"Tokenizes text into lowercase words without punctuation.\"\"\"\n", - " return re.findall(r'\\w+', text.lower())\n", - "\n", - "def tokenize_and_lemmatize(text):\n", - " \"\"\"Tokenizes text into lowercase words without punctuation and lemmatizes words.\"\"\"\n", - " tokens = re.findall(r'\\w+', text.lower()) # Tokenization\n", - " return {lemmatizer.lemmatize(word) for word in tokens} - stop_words\n", - "\n", - "def get_ngrams(text, n=2):\n", - " \"\"\"Generate n-grams from text\"\"\"\n", - " tokens = list(tokenize_and_lemmatize(text))\n", - " return set(zip(*[tokens[i:] for i in range(n)])) if len(tokens) >= n else set()" - ], - "metadata": { - "id": "yFpLW_LO4iop" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Step 5: Create Snorkel labeling functions" - ], - "metadata": { - "id": "HaFNdAUBQvVH" - } - }, - { - "cell_type": "code", - "source": [ - "from snorkel.labeling import labeling_function\n", - "from rank_bm25 import BM25Okapi\n", - "\n", - "@labeling_function()\n", - "def bm25_prob_diff(input_data):\n", - " query = input_data[\"query\"]\n", - " relevant_doc = input_data[\"relevant\"]\n", - " unknown_doc = input_data[\"unknown\"]\n", - "\n", - " # Tokenize query and documents\n", - " tokenized_query = tokenize(query)\n", - " tokenized_relevant = tokenize(relevant_doc)\n", - " tokenized_unknown = tokenize(unknown_doc)\n", - "\n", - " # Compute BM25 scores separately for \"relevant\" and \"unknown\"\n", - " bm25_relevant_model = BM25Okapi([tokenized_relevant]) # BM25 model with relevant doc\n", - " bm25_unknown_model = BM25Okapi([tokenized_unknown]) # BM25 model with unknown doc\n", - "\n", - " bm25_relevant = bm25_relevant_model.get_scores(tokenized_query)[0] # Score for relevant\n", - " bm25_unknown = bm25_unknown_model.get_scores(tokenized_query)[0] # Score for unknown\n", - "\n", - " # Compute difference without normalization\n", - " diff = bm25_relevant - bm25_unknown\n", - " return diff # Return raw difference\n", - "\n", - "\n", - "\n", - "@labeling_function()\n", - "def jaccard_similarity_prob_diff(input_data):\n", - " query = input_data[\"query\"]\n", - " relevant_doc = input_data[\"relevant\"]\n", - " unknown_doc = input_data[\"unknown\"]\n", - "\n", - " # Concatenate Query + Document\n", - " query_rel = f\"{query} {relevant_doc}\"\n", - " query_unk = f\"{query} {unknown_doc}\"\n", - "\n", - " # Compute Jaccard Similarity\n", - " def jaccard_sim(text1, text2):\n", - " set1, set2 = set(text1.split()), set(text2.split())\n", - " intersection = len(set1 & set2)\n", - " union = len(set1 | set2)\n", - " return intersection / union if union != 0 else 0\n", - "\n", - " jaccard_relevant = jaccard_sim(query, query_rel)\n", - " jaccard_unknown = jaccard_sim(query, query_unk)\n", - "\n", - " diff = jaccard_relevant - jaccard_unknown\n", - " return diff # Return raw difference\n", - "\n", - "\n", - "@labeling_function()\n", - "def word_level_levenshtein_prob_diff(input_data):\n", - " query = input_data[\"query\"]\n", - " relevant_doc = input_data[\"relevant\"]\n", - " unknown_doc = input_data[\"unknown\"]\n", - "\n", - " # Compute Levenshtein similarity\n", - " lev_relevant = fuzz.ratio(query, relevant_doc) / 100\n", - " lev_unknown = fuzz.ratio(query, unknown_doc) / 100\n", - "\n", - " # Compute difference\n", - " diff = lev_relevant - lev_unknown\n", - " return diff # Return raw difference\n", - "\n", - "\n", - "@labeling_function()\n", - "def tfidf_cosine_similarity_prob_diff(input_data):\n", - " query = input_data[\"query\"]\n", - " relevant_doc = input_data[\"relevant\"]\n", - " unknown_doc = input_data[\"unknown\"]\n", - "\n", - " # Concatenate Query + Document\n", - " query_rel = f\"{query} {relevant_doc}\"\n", - " query_unk = f\"{query} {unknown_doc}\"\n", - "\n", - " # Compute TF-IDF Cosine Similarity\n", - " docs = [query, query_rel, query_unk]\n", - " tfidf_matrix = vectorizer.fit_transform(docs)\n", - "\n", - " tfidf_relevant = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])[0][0]\n", - " tfidf_unknown = cosine_similarity(tfidf_matrix[0], tfidf_matrix[2])[0][0]\n", - "\n", - " diff = tfidf_relevant - tfidf_unknown\n", - " return diff # Return raw difference\n", - "\n", - "bert_model = SentenceTransformer('all-MiniLM-L6-v2')\n", - "\n", - "\n", - "@labeling_function()\n", - "def bert_cosine_similarity_prob_diff(input_data):\n", - " query = input_data[\"query\"]\n", - " relevant_doc = input_data[\"relevant\"]\n", - " unknown_doc = input_data[\"unknown\"]\n", - "\n", - " # Concatenate Query + Document\n", - " query_rel = f\"{query} [SEP] {relevant_doc}\"\n", - " query_unk = f\"{query} [SEP] {unknown_doc}\"\n", - "\n", - " # Compute BERT Embeddings\n", - " embeddings = bert_model.encode([query, query_rel, query_unk])\n", - "\n", - " bert_relevant = np.dot(embeddings[0], embeddings[1]) / (np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1]))\n", - " bert_unknown = np.dot(embeddings[0], embeddings[2]) / (np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[2]))\n", - "\n", - " diff = bert_relevant - bert_unknown\n", - " return diff # Return raw difference\n", - "\n" - ], - "metadata": { - "id": "VhMWSVtm4yDh", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4574c801-1059-4670-d966-c515815fae6a" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Step 6: train Snorkel" - ], - "metadata": { - "id": "lZW9HxQpXCF3" - } - }, - { - "cell_type": "code", - "source": [ - "lfs = [bm25_prob_diff, word_level_levenshtein_prob_diff,tfidf_cosine_similarity_prob_diff, bert_cosine_similarity_prob_diff, jaccard_similarity_prob_diff]\n", - "\n", - "applier = PandasLFApplier(lfs=lfs)\n", - "L_train = applier.apply(input_data)\n", - "\n", - "label_model = LabelModel(cardinality=4, verbose=True)\n", - "label_model.fit(L_train, n_epochs=100, log_freq=10)\n", - "\n", - "train_prob = label_model.predict_proba(L_train)\n", - "train_prob_relevant = train_prob[:, 1]\n", - "\n", - "input_data[\"probability_relevant\"] = train_prob_relevant\n" - ], - "metadata": { - "id": "CDDrCtAO5C-S", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "c54f7d82-68bd-4ea6-9c81-7abe67a3e5d0" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 15080/15080 [1:43:34<00:00, 2.43it/s]\n", - "100%|██████████| 100/100 [00:00<00:00, 423.04epoch/s]\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "WMODEL = \"Snorkel\"\n", - "\n", - "!rm -Rf run\n", - "\n", - "\n", - "with tracking(export_file_path='run/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", - " queries = set(input_data['query'].unique())\n", - "\n", - " def unknown_documents(query):\n", - " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", - " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", - "\n", - " results = {}\n", - "\n", - " for query in tqdm(queries):\n", - " unk = unknown_documents(query)\n", - "\n", - " # Build index\n", - " docs = [{'docno': k, 'text': v} for k, v in unk.items()]\n", - 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We rank all unknown documents that are to-be judged against the query. The probability that a document is relevant is then the min-max normalized rank of an unknown document in the ranking of all unknown documents to the query.'\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sgLKVDdEwg3P", - "outputId": "3da712d2-c272-4a6f-c984-0b89caa758e8" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/c1baecf2-84dd-46ae-bda8-995cf4dc3724\n", - "No truth data is available yet. The evaluation is possible after the deadline when the truth data was published.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "id": "P5jmP8SopXEd" - } - } - ] +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "Step 1: install dependencies\n", + "\n", + "> Add blockquote\n", + "\n" + ], + "metadata": { + "id": "nrNuNdOkWxU2" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jqYpJ_Gqn0v3", + "outputId": "af2950db-9969-4371-cefc-1a908de14af2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting tira\n", + " Using cached tira-0.0.157-py3-none-any.whl.metadata (4.8 kB)\n", + "Requirement 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tira-0.0.157 wows-eval-0.0.6\n" + ] + } + ], + "source": [ + "!pip3 install tira snorkel wows-eval textdistance rank-bm25" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 uninstall -y tira wows-eval" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nF6rUuWHtImS", + "outputId": "344e88b2-117c-4caa-b9ee-56bbac7b0c07" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found existing installation: tira 0.0.157\n", + "Uninstalling tira-0.0.157:\n", + " Successfully uninstalled tira-0.0.157\n", + "Found existing installation: wows-eval 0.0.6\n", + "Uninstalling wows-eval-0.0.6:\n", + " Successfully uninstalled wows-eval-0.0.6\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 install wows-eval>=0.0.6" + ], + "metadata": { + "id": "qfh3GolMtcj3" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip3 install python-terrier==0.10.0" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6Fo04IIKtxVA", + "outputId": "758a5ff8-a271-4bb7-95d3-740532680c01" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: python-terrier==0.10.0 in /usr/local/lib/python3.11/dist-packages (0.10.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (1.26.4)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (2.2.2)\n", + "Requirement already satisfied: wget in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (3.2)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (4.67.1)\n", + "Requirement already satisfied: pyjnius>=1.4.2 in /usr/local/lib/python3.11/dist-packages (from 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], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9vVpwA4Xt2ni", + "outputId": "704adba1-c74e-4819-eafd-666630f9e5fd" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found existing installation: numpy 1.26.4\n", + "Uninstalling numpy-1.26.4:\n", + " Successfully uninstalled numpy-1.26.4\n", + "Collecting numpy\n", + " Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\n", + "Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB)\n", + "Installing collected packages: numpy\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tira 0.0.157 requires numpy==1.*, but you have numpy 2.2.4 which is incompatible.\n", + "tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.2.4 which is incompatible.\n", + "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.2.4 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-2.2.4\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 install numpy==1.26.4 --force-reinstall\n", + "import os\n", + "os.kill(os.getpid(), 9)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vzk4NnBduZHU", + "outputId": "8edabf54-9ef7-404d-cd9f-093f1032d069" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting numpy==1.26.4\n", + " Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", + "Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n", + "Installing collected packages: numpy\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 2.2.4\n", + " Uninstalling numpy-2.2.4:\n", + " Successfully uninstalled numpy-2.2.4\n", + "Successfully installed numpy-1.26.4\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pyterrier as pt\n", + "from tqdm import tqdm\n", + "if not pt.started():\n", + " pt.init()\n", + "\n", + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "import pandas as pd\n", + "from jnius import autoclass\n", + "import numpy as np\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "# Dataset IDs visible at https://archive.tira.io/datasets?query=wows-eval\n", + "DATASET_ID = 'wows-eval/pairwise-20250309-test'\n", + "\n", + "#, 'wows-eval/pairwise-smoke-test-20250210-training'\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ], + "metadata": { + "id": "KrRf64t2n6ke", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "791a7ee1-3267-4515-cb06-b911b47b2b72" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "PyTerrier 0.10.0 has loaded Terrier 5.11 (built by craig.macdonald on 2025-01-13 21:29) and terrier-helper 0.0.8\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 install rapidfuzz" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xsbs5EDi7t0y", + "outputId": "1f177a70-b55d-4a8d-fa2a-9ff25dce3af4" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: rapidfuzz in /usr/local/lib/python3.11/dist-packages (3.13.0)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Step 2: load all the libraries" + ], + "metadata": { + "id": "CB_34eKz68Pn" + } + }, + { + "cell_type": "code", + "source": [ + "from snorkel.labeling import labeling_function, PandasLFApplier\n", + "from snorkel.labeling.model.label_model import LabelModel\n", + "from rank_bm25 import BM25Okapi\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sentence_transformers import SentenceTransformer\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "from wows_eval import evaluate as wows_evaluate\n", + "from rapidfuzz import fuzz\n", + "import numpy as np\n", + "import textdistance\n", + "import pandas as pd\n", + "vectorizer = TfidfVectorizer()\n", + "import re" + ], + "metadata": { + "id": "5D-oLP_54FtB" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import nltk\n", + "from nltk.corpus import stopwords, wordnet\n", + "from nltk.stem import WordNetLemmatizer\n", + "# Download stopwords if not already present\n", + "nltk.download(\"stopwords\")\n", + "stop_words = set(stopwords.words(\"english\"))\n", + "nltk.download(\"wordnet\")\n", + "lemmatizer = WordNetLemmatizer()" + ], + "metadata": { + "id": "qBVEpFOn4LNN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "23d98486-7f44-49a5-ade5-944e4ad9d5c6" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Step 3: define global variables" + ], + "metadata": { + "id": "NpyWVji-7C1f" + } + }, + { + "cell_type": "code", + "source": [ + "# Global Variables\n", + "min_bm25 = None\n", + "max_bm25 = None\n", + "bm25 = None\n" + ], + "metadata": { + "id": "w7M09OVo4CQb" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Global Min/Max Values (These should be computed dynamically)\n", + "GLOBAL_MIN = -1\n", + "GLOBAL_MAX = 1\n" + ], + "metadata": { + "id": "guLY8AnOcG6X" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Step 4: define helping functions" + ], + "metadata": { + "id": "2fbjNSaCQgHw" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "def tokenize(text):\n", + " \"\"\"Tokenizes text into lowercase words without punctuation.\"\"\"\n", + " return re.findall(r'\\w+', text.lower())\n", + "\n", + "def tokenize_and_lemmatize(text):\n", + " \"\"\"Tokenizes text into lowercase words without punctuation and lemmatizes words.\"\"\"\n", + " tokens = re.findall(r'\\w+', text.lower()) # Tokenization\n", + " return {lemmatizer.lemmatize(word) for word in tokens} - stop_words\n", + "\n", + "def get_ngrams(text, n=2):\n", + " \"\"\"Generate n-grams from text\"\"\"\n", + " tokens = list(tokenize_and_lemmatize(text))\n", + " return set(zip(*[tokens[i:] for i in range(n)])) if len(tokens) >= n else set()" + ], + "metadata": { + "id": "yFpLW_LO4iop" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Step 5: Create Snorkel labeling functions" + ], + "metadata": { + "id": "HaFNdAUBQvVH" + } + }, + { + "cell_type": "code", + "source": [ + "from snorkel.labeling import labeling_function\n", + "from rank_bm25 import BM25Okapi\n", + "\n", + "@labeling_function()\n", + "def bm25_prob_diff(input_data):\n", + " query = input_data[\"query\"]\n", + " relevant_doc = input_data[\"relevant\"]\n", + " unknown_doc = input_data[\"unknown\"]\n", + "\n", + " # Tokenize query and documents\n", + " tokenized_query = tokenize(query)\n", + " tokenized_relevant = tokenize(relevant_doc)\n", + " tokenized_unknown = tokenize(unknown_doc)\n", + "\n", + " # Compute BM25 scores separately for \"relevant\" and \"unknown\"\n", + " bm25_relevant_model = BM25Okapi([tokenized_relevant]) # BM25 model with relevant doc\n", + " bm25_unknown_model = BM25Okapi([tokenized_unknown]) # BM25 model with unknown doc\n", + "\n", + " bm25_relevant = bm25_relevant_model.get_scores(tokenized_query)[0] # Score for relevant\n", + " bm25_unknown = bm25_unknown_model.get_scores(tokenized_query)[0] # Score for unknown\n", + "\n", + " # Compute difference without normalization\n", + " diff = bm25_relevant - bm25_unknown\n", + " return diff # Return raw difference\n", + "\n", + "\n", + "\n", + "@labeling_function()\n", + "def jaccard_similarity_prob_diff(input_data):\n", + " query = input_data[\"query\"]\n", + " relevant_doc = input_data[\"relevant\"]\n", + " unknown_doc = input_data[\"unknown\"]\n", + "\n", + " # Concatenate Query + Document\n", + " query_rel = f\"{query} {relevant_doc}\"\n", + " query_unk = f\"{query} {unknown_doc}\"\n", + "\n", + " # Compute Jaccard Similarity\n", + " def jaccard_sim(text1, text2):\n", + " set1, set2 = set(text1.split()), set(text2.split())\n", + " intersection = len(set1 & set2)\n", + " union = len(set1 | set2)\n", + " return intersection / union if union != 0 else 0\n", + "\n", + " jaccard_relevant = jaccard_sim(query, query_rel)\n", + " jaccard_unknown = jaccard_sim(query, query_unk)\n", + "\n", + " diff = jaccard_relevant - jaccard_unknown\n", + " return diff # Return raw difference\n", + "\n", + "\n", + "@labeling_function()\n", + "def word_level_levenshtein_prob_diff(input_data):\n", + " query = input_data[\"query\"]\n", + " relevant_doc = input_data[\"relevant\"]\n", + " unknown_doc = input_data[\"unknown\"]\n", + "\n", + " # Compute Levenshtein similarity\n", + " lev_relevant = fuzz.ratio(query, relevant_doc) / 100\n", + " lev_unknown = fuzz.ratio(query, unknown_doc) / 100\n", + "\n", + " # Compute difference\n", + " diff = lev_relevant - lev_unknown\n", + " return diff # Return raw difference\n", + "\n", + "\n", + "@labeling_function()\n", + "def tfidf_cosine_similarity_prob_diff(input_data):\n", + " query = input_data[\"query\"]\n", + " relevant_doc = input_data[\"relevant\"]\n", + " unknown_doc = input_data[\"unknown\"]\n", + "\n", + " # Concatenate Query + Document\n", + " query_rel = f\"{query} {relevant_doc}\"\n", + " query_unk = f\"{query} {unknown_doc}\"\n", + "\n", + " # Compute TF-IDF Cosine Similarity\n", + " docs = [query, query_rel, query_unk]\n", + " tfidf_matrix = vectorizer.fit_transform(docs)\n", + "\n", + " tfidf_relevant = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])[0][0]\n", + " tfidf_unknown = cosine_similarity(tfidf_matrix[0], tfidf_matrix[2])[0][0]\n", + "\n", + " diff = tfidf_relevant - tfidf_unknown\n", + " return diff # Return raw difference\n", + "\n", + "bert_model = SentenceTransformer('all-MiniLM-L6-v2')\n", + "\n", + "\n", + "@labeling_function()\n", + "def bert_cosine_similarity_prob_diff(input_data):\n", + " query = input_data[\"query\"]\n", + " relevant_doc = input_data[\"relevant\"]\n", + " unknown_doc = input_data[\"unknown\"]\n", + "\n", + " # Concatenate Query + Document\n", + " query_rel = f\"{query} [SEP] {relevant_doc}\"\n", + " query_unk = f\"{query} [SEP] {unknown_doc}\"\n", + "\n", + " # Compute BERT Embeddings\n", + " embeddings = bert_model.encode([query, query_rel, query_unk])\n", + "\n", + " bert_relevant = np.dot(embeddings[0], embeddings[1]) / (np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1]))\n", + " bert_unknown = np.dot(embeddings[0], embeddings[2]) / (np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[2]))\n", + "\n", + " diff = bert_relevant - bert_unknown\n", + " return diff # Return raw difference\n", + "\n" + ], + "metadata": { + "id": "VhMWSVtm4yDh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4574c801-1059-4670-d966-c515815fae6a" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Step 6: train Snorkel" + ], + "metadata": { + "id": "lZW9HxQpXCF3" + } + }, + { + "cell_type": "code", + "source": [ + "lfs = [bm25_prob_diff, word_level_levenshtein_prob_diff,tfidf_cosine_similarity_prob_diff, bert_cosine_similarity_prob_diff, jaccard_similarity_prob_diff]\n", + "\n", + "applier = PandasLFApplier(lfs=lfs)\n", + "L_train = applier.apply(input_data)\n", + "\n", + "label_model = LabelModel(cardinality=4, verbose=True)\n", + "label_model.fit(L_train, n_epochs=100, log_freq=10)\n", + "\n", + "train_prob = label_model.predict_proba(L_train)\n", + "train_prob_relevant = train_prob[:, 1]\n", + "\n", + "input_data[\"probability_relevant\"] = train_prob_relevant\n" + ], + "metadata": { + "id": "CDDrCtAO5C-S", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c54f7d82-68bd-4ea6-9c81-7abe67a3e5d0" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 15080/15080 [1:43:34<00:00, 2.43it/s]\n", + "100%|██████████| 100/100 [00:00<00:00, 423.04epoch/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "WMODEL = \"Snorkel\"\n", + "\n", + "!rm -Rf run\n", + "\n", + "\n", + "with tracking(export_file_path='run/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", + " queries = set(input_data['query'].unique())\n", + "\n", + " def unknown_documents(query):\n", + " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", + " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", + "\n", + " results = {}\n", + "\n", + " for query in tqdm(queries):\n", + " unk = unknown_documents(query)\n", + "\n", + " # Build index\n", + " docs = [{'docno': k, 'text': v} for k, v in unk.items()]\n", + " indexer = pt.IterDictIndexer(\"/tmp/index\", overwrite=True, meta={'docno': 100, 'text': 20480})\n", + " index_ref = indexer.index(docs)\n", + " retriever = pt.BatchRetrieve(index_ref, wmodel=WMODEL)\n", + " rels = input_data[input_data['query'] == query][['unknown', 'probability_relevant']]\n", + " rel_dict = dict(zip(rels['unknown'], rels['probability_relevant']))\n", + "\n", + " results[query] = rel_dict\n", + "\n", + " predictions = []\n", + " for _, i in input_data.iterrows():\n", + " res = results[i['query']]\n", + " predictions.append({\n", + " 'id': i['id'],\n", + " 'probability_relevant': res.get(i['unknown'], -1)\n", + " })\n", + " predictions = pd.DataFrame(predictions)\n", + "\n", + "predictions" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 442 + }, + "id": "GLSUuWymwbOS", + "outputId": "7825db38-4692-49c3-f8ec-f065cf1055bf" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 13/13 [00:14<00:00, 1.11s/it]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id probability_relevant\n", + "0 ce293ca6-f54c-4499-a7c8-8546029af919 0.183267\n", + "1 e035e319-db74-4d31-aa01-13bf60c595ae 0.183267\n", + "2 2e58ffb0-3815-4033-a363-f0d9f3dab89f 0.183267\n", + "3 43463fe4-63de-4ff3-ab26-43d002c18c72 0.183267\n", + "4 141436c9-2d5a-4fdc-abd5-bb8de9952592 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032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.
1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
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" - ], - "text/plain": [ - " id query \\\n", - "0 32d23068-7440-4891-9958-42325f98a604 who sings monk theme song \n", - "1 cde83146-ac3e-4bc5-a959-f2006ac7b8de who sings monk theme song \n", - "\n", - " unknown \n", - "0 This is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk. \n", - "1 Walker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself. " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_data.head(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Implement the AutoQrels approach\n", - "\n", - "Here, we use a prompted language model (you can modify the prompt and the backbone model) to predict the relevance of the document to the query and store the probability in the field `probability_relevant`. We wrap all computations into a `tira_measure.Environment` to measure the resources consumed for our computations to later include the used resources into the ir-metadata of our run." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "BACKBONE_MODEL = \"flan-t5-small\"\n", - "\n", - "PROMPT = \"\"\"Instruction: Indicate if the passage answers the question.\n", - "###\n", - "Example 1:\n", - "Question: At about what age do adults normally begin to lose bone mass?\n", - "Passage: For most people, bone mass peaks during the third decade of life. By this age, men typically have accumulated more bone mass than women. After this point, the amount of bone in the skeleton typically begins to decline slowly as removal of old bone exceeds formation of new bone.\n", - "Answer: Perfectly relevant\n", - "###\n", - "Example 2:\n", - "Question: when and where did the battle of manassas take place\n", - "Passage: Summary of the Battle of Bull Run. The conflict took place close to Manassas Junction, Virginia. Around 35,000 Union soldiers marched from Washing D.C. towards Bull Run (a small river) where a 20,000 troop Confederate force was stationed.\n", - "Answer: Irrelevant\n", - "###\n", - "Example 3:\n", - "Question: which kind of continental boundary is formed where two plates move horizontally past one another?\n", - "Passage: One plate slides horizontally past another. The best-known example is the earthquake-prone San Andreas Fault Zone of California, which marks the boundary between the Pacific and North America Plates. See: Teaching About Transform Plate Boundaries.\n", - "Answer: Highly relevant\n", - "###\n", - "Example 4:\n", - "Question: what foods should you stay away from if you have asthma\n", - "Passage: Get early and regular prenatal care. The first 8 weeks of your pregnancy are important to your baby's development. Early and regular prenatal care can boost your chances of having a safe pregnancy and a healthy baby. Prenatal care includes screenings, regular exams, pregnancy and childbirth education, and counseling and support.\n", - "Answer: Irrelevant\n", - "###\n", - "Example 5:\n", - "Question: what is lbm in body composition\n", - "Passage: They also measured the participants’ body fat and “lean body mass” – a measure. Trusted Source. of muscle mass, obtained by subtracting the body fat weight from the total body weight.\n", - "Answer: Relevant\n", - "###\n", - "Example 6:\n", - "Question: {{ query_text }}\n", - "Passage: {{ unk_doc_text }}\n", - "Answer:\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "autoqrels_assessor = GradedMonoPrompt(\n", - " backbone=f'google/{BACKBONE_MODEL}',\n", - " prompt=PROMPT,\n", - " dataset=None\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Run the predictions and look at the outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", - "\n", - "===== Processor information =====\n", - "Linux arch_perfmon flag : yes\n", - "Hybrid processor : yes\n", - "IBRS and IBPB supported : yes\n", - "STIBP supported : yes\n", - "Spec arch caps supported : yes\n", - "Max CPUID level : 32\n", - "CPU model number : 154\n", - "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", - "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", - "Can not access CPUs Model Specific Registers (MSRs).\n", - "execute 'modprobe msr' as root user, then execute pcm as root user.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Give read access to /sys/class/powercap/intel-rapl/intel-rapl:1/energy_uj\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.0.226887
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" - ], - "text/plain": [ - " system tau_ap kendall spearman pearson\n", - "0 auto-qrels-pointwise-flan-t5-small 0.323889 0.380952 0.564286 0.564286" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wows_evaluate(\n", - " predictions,\n", - " DATASET_ID,\n", - " tracking_results=tracked,\n", - " upload=True,\n", - " system_name=f'auto-qrels-pointwise-{BACKBONE_MODEL}',\n", - " system_description=\"We use autoqrels [1] with a custom in-context learning prompt for pointwise relevance judgments.\\n\\n[1] - https://github.com/seanmacavaney/autoqrels\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 7: Register to TIRA and to the WOWS-EVAL task\n", - "\n", - "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", - "\n", - "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", - "\n", - "\n", - "![example of claim ownership form](../figures/claim-run-in-tira.png)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WOWS-Eval Autoqrels Pointwise Baseline\n", + "\n", + "This is a pointwise autoqrels baseline to WOWS-EVAL that uses autoqrels to predict the probability that a document is relevant to a query." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install 'wows-eval>=0.0.6' 'git+https://github.com/mam10eks/autoqrels.git'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load the Data\n", + "\n", + "Pointwise models have a query and a document as input and output the probability that the document is relevant into a field `probability_relevant`.\n", + "\n", + "In the following, we will process the pointwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "from autoqrels.zeroshot import GradedMonoPrompt\n", + "import pandas as pd\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "DATASET_ID = 'wows-eval/pointwise-smoke-test-20250128-training'\n", + "#DATASET_ID = 'wows-eval/pointwise-20250309-test'\n", + "\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idqueryunknown
032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.
1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
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" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_data.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Implement the AutoQrels approach\n", + "\n", + "Here, we use a prompted language model (you can modify the prompt and the backbone model) to predict the relevance of the document to the query and store the probability in the field `probability_relevant`. We wrap all computations into a `tira_measure.Environment` to measure the resources consumed for our computations to later include the used resources into the ir-metadata of our run." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "BACKBONE_MODEL = \"flan-t5-small\"\n", + "\n", + "PROMPT = \"\"\"Instruction: Indicate if the passage answers the question.\n", + "###\n", + "Example 1:\n", + "Question: At about what age do adults normally begin to lose bone mass?\n", + "Passage: For most people, bone mass peaks during the third decade of life. By this age, men typically have accumulated more bone mass than women. After this point, the amount of bone in the skeleton typically begins to decline slowly as removal of old bone exceeds formation of new bone.\n", + "Answer: Perfectly relevant\n", + "###\n", + "Example 2:\n", + "Question: when and where did the battle of manassas take place\n", + "Passage: Summary of the Battle of Bull Run. The conflict took place close to Manassas Junction, Virginia. Around 35,000 Union soldiers marched from Washing D.C. towards Bull Run (a small river) where a 20,000 troop Confederate force was stationed.\n", + "Answer: Irrelevant\n", + "###\n", + "Example 3:\n", + "Question: which kind of continental boundary is formed where two plates move horizontally past one another?\n", + "Passage: One plate slides horizontally past another. The best-known example is the earthquake-prone San Andreas Fault Zone of California, which marks the boundary between the Pacific and North America Plates. See: Teaching About Transform Plate Boundaries.\n", + "Answer: Highly relevant\n", + "###\n", + "Example 4:\n", + "Question: what foods should you stay away from if you have asthma\n", + "Passage: Get early and regular prenatal care. The first 8 weeks of your pregnancy are important to your baby's development. Early and regular prenatal care can boost your chances of having a safe pregnancy and a healthy baby. Prenatal care includes screenings, regular exams, pregnancy and childbirth education, and counseling and support.\n", + "Answer: Irrelevant\n", + "###\n", + "Example 5:\n", + "Question: what is lbm in body composition\n", + "Passage: They also measured the participants’ body fat and “lean body mass” – a measure. Trusted Source. of muscle mass, obtained by subtracting the body fat weight from the total body weight.\n", + "Answer: Relevant\n", + "###\n", + "Example 6:\n", + "Question: {{ query_text }}\n", + "Passage: {{ unk_doc_text }}\n", + "Answer:\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "autoqrels_assessor = GradedMonoPrompt(\n", + " backbone=f'google/{BACKBONE_MODEL}',\n", + " prompt=PROMPT,\n", + " dataset=None\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the predictions and look at the outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", + "\n", + "===== Processor information =====\n", + "Linux arch_perfmon flag : yes\n", + "Hybrid processor : yes\n", + "IBRS and IBPB supported : yes\n", + "STIBP supported : yes\n", + "Spec arch caps supported : yes\n", + "Max CPUID level : 32\n", + "CPU model number : 154\n", + "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", + "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", + "Can not access CPUs Model Specific Registers (MSRs).\n", + "execute 'modprobe msr' as root user, then execute pcm as root user.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Give read access to /sys/class/powercap/intel-rapl/intel-rapl:1/energy_uj\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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systemtau_apkendallspearmanpearson
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" + ], + "text/plain": [ + " system tau_ap kendall spearman pearson\n", + "0 auto-qrels-pointwise-flan-t5-small 0.323889 0.380952 0.564286 0.564286" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wows_evaluate(\n", + " predictions,\n", + " DATASET_ID,\n", + " tracking_results=tracked,\n", + " upload=True,\n", + " system_name=f'auto-qrels-pointwise-{BACKBONE_MODEL}',\n", + " system_description=\"We use autoqrels [1] with a custom in-context learning prompt for pointwise relevance judgments.\\n\\n[1] - https://github.com/seanmacavaney/autoqrels\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7: Register to TIRA and to the WOWS-EVAL task\n", + "\n", + "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", + "\n", + "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", + "\n", + "\n", + "![example of claim ownership form](../figures/claim-run-in-tira.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ecir25/baselines/pointwise-naive.ipynb b/ecir25/baselines/pointwise-naive.ipynb index 6bad137..5df5d21 100644 --- a/ecir25/baselines/pointwise-naive.ipynb +++ b/ecir25/baselines/pointwise-naive.ipynb @@ -1,301 +1,301 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WOWS-Eval Naive Pointwise Baseline\n", - "\n", - "This is a naive baseline to WOWS-EVAL that always predicts that a document is relevant to a query with a probability of 50%." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Install Dependencies\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install 'wows-eval>=0.0.6'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Load the Data\n", - "\n", - "Pointwise models have a query and a document as input and output the probability that the document is relevant into a field `probability_relevant`. For this naive baseline, we always predict a probability of 0.5.\n", - "\n", - "In the following, we will process the pointwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from tira.rest_api_client import Client\n", - "from wows_eval import evaluate as wows_evaluate\n", - "import pandas as pd\n", - "\n", - "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", - "from tirex_tracker import tracking, ExportFormat\n", - "\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "DATASET_ID = 'wows-eval/pointwise-smoke-test-20250128-training'\n", - "#DATASET_ID = 'wows-eval/pointwise-20250309-test'\n", - "\n", - "tira = Client()\n", - "input_data = tira.pd.inputs(DATASET_ID)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Look at the data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.
1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
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" - ], - "text/plain": [ - " id query \\\n", - "0 32d23068-7440-4891-9958-42325f98a604 who sings monk theme song \n", - "1 cde83146-ac3e-4bc5-a959-f2006ac7b8de who sings monk theme song \n", - "\n", - " unknown \n", - "0 This is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk. \n", - "1 Walker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself. " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_data.head(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Implement the Naive Baseline\n", - "\n", - "Here, we just fill the expected field `probability_relevant` with 0.5. We wrap all computations into a tira_measure.Environment to measure the resources consumed for our computations to later include the used resources into the ir-metadata of our run." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", - "\n", - "===== Processor information =====\n", - "Linux arch_perfmon flag : yes\n", - "Hybrid processor : yes\n", - "IBRS and IBPB supported : yes\n", - "STIBP supported : yes\n", - "Spec arch caps supported : yes\n", - "Max CPUID level : 32\n", - "CPU model number : 154\n", - "ERROR: Can not open /sys/module/msr/parameters/allow_writes file.\n", - "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", - "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", - "Can not access CPUs Model Specific Registers (MSRs).\n", - "execute 'modprobe msr' as root user, then execute pcm as root user.\n" - ] - } - ], - "source": [ - "!rm -Rf runs\n", - "with tracking(export_format = ExportFormat.IR_METADATA, export_file_path=\"runs/ir_metadata.yml\") as tracked:\n", - " # now we do the \"computation\"\n", - " predictions = input_data.copy()\n", - " predictions['probability_relevant'] = 0.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Evaluate and Submit Your Run\n", - "\n", - "We use the `wows_evaluate` method imported above to evaluate our predictions and to upload them, to TIRA.\n", - "\n", - "The `wows_evaluate` method has optional parameters that you can pass to describe your system and to include the resource measurements used during your computations in the ir-metadata format into your submission. You can remove those attributes or modify them for your submission accordingly. Call `help(wows_evaluate)` to see a full description." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/bada828b-355d-4f16-81c3-de32c4b7eb81\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " system tau_ap kendall spearman pearson\n", - "0 naive-pointwise 0.34 0.361905 0.45 0.45" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wows_evaluate(\n", - " predictions,\n", - " DATASET_ID,\n", - " tracking_results=tracked,\n", - " upload=True,\n", - " system_name='naive-pointwise',\n", - " system_description='A naive approach that predicts that each document is relevant with a probabilty of 50%.'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 6: Register to TIRA and to the WOWS-EVAL task\n", - "\n", - "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", - "\n", - "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", - "\n", - "\n", - "![example of claim ownership form](../figures/claim-run-in-tira.png)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WOWS-Eval Naive Pointwise Baseline\n", + "\n", + "This is a naive baseline to WOWS-EVAL that always predicts that a document is relevant to a query with a probability of 50%." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Dependencies\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install 'wows-eval>=0.0.6'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load the Data\n", + "\n", + "Pointwise models have a query and a document as input and output the probability that the document is relevant into a field `probability_relevant`. For this naive baseline, we always predict a probability of 0.5.\n", + "\n", + "In the following, we will process the pointwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "import pandas as pd\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "DATASET_ID = 'wows-eval/pointwise-smoke-test-20250128-training'\n", + "#DATASET_ID = 'wows-eval/pointwise-20250309-test'\n", + "\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idqueryunknown
032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.
1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
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" + ], + "text/plain": [ + " system tau_ap kendall spearman pearson\n", + "0 naive-pointwise 0.34 0.361905 0.45 0.45" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wows_evaluate(\n", + " predictions,\n", + " DATASET_ID,\n", + " tracking_results=tracked,\n", + " upload=True,\n", + " system_name='naive-pointwise',\n", + " system_description='A naive approach that predicts that each document is relevant with a probabilty of 50%.'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Register to TIRA and to the WOWS-EVAL task\n", + "\n", + "To finalize your submission to WOWS-EVAL, you must now claim your submission via the URL printed above. For this, please register at [tira.io](https://www.tira.io) and navigate to the [WOWS-EVAL](https://www.tira.io/task-overview/wows-eval/) task and click on \"Register\". You can choose your team name from a list of [fictional](https://en.wikipedia.org/wiki/Category:Fictional_librarians) and [real](https://en.wikipedia.org/wiki/List_of_librarians) librarians ([please drop a message](#contact) if your favourite team name is not in the list).\n", + "\n", + "For instance, after clicking on the ownership link above, you can claim ownership via a form in TIRA (where you can potentially overwrite the name and description of your system):\n", + "\n", + "\n", + "![example of claim ownership form](../figures/claim-run-in-tira.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ecir25/baselines/pointwise-retrieval.ipynb b/ecir25/baselines/pointwise-retrieval.ipynb index 69c2788..f15d1da 100644 --- a/ecir25/baselines/pointwise-retrieval.ipynb +++ b/ecir25/baselines/pointwise-retrieval.ipynb @@ -1,347 +1,347 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WOWS-Eval Retrieva Baseline\n", - "\n", - "This is a retrieval baseline to WOWS-EVAL that uses a retrieval model to assign the probability that an unknown document is relevant. The query is used as query which retrieves against all unknown documents that are to-be judged. The probability that a document is relevant is then the min-max normalized rank of an unknown document in the ranking of all unknown documents to the query." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Install Dependencies\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install 'wows-eval>=0.0.6' python-terrier==0.10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Load the Data\n", - "\n", - "Pointwise models have a query and a document as input and output the probability that the document is relevant into a field `probability_relevant`. For this retrieval baseline, we just take a PyTerrier retrieval model and use the min-max normalized retrieval rank as probability.\n", - "\n", - "In the following, we will process the pointwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PyTerrier 0.10.0 has loaded Terrier 5.11 (built by craig.macdonald on 2025-01-13 21:29) and terrier-helper 0.0.8\n", - "\n" - ] - } - ], - "source": [ - "import pyterrier as pt\n", - "from tqdm import tqdm\n", - "if not pt.started():\n", - " pt.init()\n", - "\n", - "from tira.rest_api_client import Client\n", - "from wows_eval import evaluate as wows_evaluate\n", - "import pandas as pd\n", - "from jnius import autoclass\n", - "import numpy as np\n", - "\n", - "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", - "from tirex_tracker import tracking, ExportFormat\n", - "\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "# Dataset IDs visible at https://archive.tira.io/datasets?query=wows-eval\n", - "DATASET_ID = 'wows-eval/pointwise-smoke-test-20250128-training'\n", - "#DATASET_ID = 'wows-eval/pointwise-20250309-test'\n", - "\n", - "tira = Client()\n", - "input_data = tira.pd.inputs(DATASET_ID)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Look at the data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
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" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_data.head(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Implement the Approach\n", - "\n", - "We wrap all computations into a [tirex_tracker.tracking](https://github.com/tira-io/tirex-tracker/) environment to measure the resources consumed for our computations and also a snapshot of our code in the [ir-metadata format](https://www.ir-metadata.org/)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", - "\n", - "===== Processor information =====\n", - "Linux arch_perfmon flag : yes\n", - "Hybrid processor : yes\n", - "IBRS and IBPB supported : yes\n", - "STIBP supported : yes\n", - "Spec arch caps supported : yes\n", - "Max CPUID level : 32\n", - "CPU model number : 154\n", - "ERROR: Can not open /sys/module/msr/parameters/allow_writes file.\n", - "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", - "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", - "Can not access CPUs Model Specific Registers (MSRs).\n", - "execute 'modprobe msr' as root user, then execute pcm as root user.\n", - "100%|██████████| 2/2 [00:00<00:00, 2.51it/s]\n" - ] - } - ], - "source": [ - "WMODEL = \"BM25\"\n", - "system_name = f'pointwise-retrieval-{WMODEL}'.lower()\n", - "\n", - "def retrieve_and_normalize(query, retrieval_system, unk): \n", - " ret = {}\n", - " tokeniser = autoclass(\"org.terrier.indexing.tokenisation.Tokeniser\").getTokeniser()\n", - " query_text = \" \".join(tokeniser.getTokens(query))\n", - " run = retrieval_system.search(query_text)\n", - " last_rank = -1\n", - " scores = {}\n", - " for _, i in run.iterrows():\n", - " assert last_rank < i['rank']\n", - " last_rank = i['rank']\n", - " if i['docno'] in unk:\n", - " scores[unk[i['docno']]] = i['rank']\n", - "\n", - " max_score = max(scores.values())\n", - " min_score = min(scores.values())\n", - "\n", - " return {k: ((v-min_score)/(max_score-min_score)) for k, v in scores.items()}\n", - "\n", - "!rm -Rf run\n", - "with tracking(export_file_path='run/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", - " queries = set(input_data['query'].unique())\n", - "\n", - " def unknown_documents(query):\n", - " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", - " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", - "\n", - " results = {}\n", - " for query in tqdm(queries):\n", - " unk = unknown_documents(query)\n", - "\n", - " docs = [{'docno': k, 'text': v} for k, v in unk.items()]\n", - " indexer = pt.IterDictIndexer(\"/tmp/index\", overwrite=True, meta={'docno': 100, 'text': 20480})\n", - " index_ref = indexer.index(docs)\n", - " retriever = pt.BatchRetrieve(index_ref, wmodel=WMODEL)\n", - " results[query] = retrieve_and_normalize(query, retriever, unk)\n", - "\n", - " predictions = []\n", - " for _, i in input_data.iterrows():\n", - " res = results[i['query']]\n", - " predictions.append({\n", - " 'id': i['id'],\n", - " 'probability_relevant': res.get(i['unknown'], -1)\n", - " })\n", - " predictions = pd.DataFrame(predictions)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Evaluate and Submit Your Run\n", - "\n", - "We use the `wows_evaluate` method imported above to evaluate our predictions and to upload them, to TIRA.\n", - "\n", - "The `wows_evaluate` method has optional parameters that you can pass to describe your system and to include the resource measurements used during your computations in the ir-metadata format into your submission. You can remove those attributes or modify them for your submission accordingly. Call `help(wows_evaluate)` to see a full description." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/18f7f8aa-192f-4d08-a03b-0866d540ccae\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " system tau_ap kendall spearman pearson\n", - "0 pointwise-retrieval-bm25 0.040556 0.085714 0.1 0.1" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wows_evaluate(\n", - " predictions,\n", - " DATASET_ID,\n", - " tracking_results=tracked,\n", - " upload=True,\n", - " system_name=system_name,\n", - " system_description=f'We use the PyTerrier retrieval model {WMODEL} to assign the probability that an unknown document is relevant. We rank all unknown documents that are to-be judged against the query. The probability that a document is relevant is then the min-max normalized rank of an unknown document in the ranking of all unknown documents to the query.'\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WOWS-Eval Retrieva Baseline\n", + "\n", + "This is a retrieval baseline to WOWS-EVAL that uses a retrieval model to assign the probability that an unknown document is relevant. The query is used as query which retrieves against all unknown documents that are to-be judged. The probability that a document is relevant is then the min-max normalized rank of an unknown document in the ranking of all unknown documents to the query." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Dependencies\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install 'wows-eval>=0.0.6' python-terrier==0.10.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load the Data\n", + "\n", + "Pointwise models have a query and a document as input and output the probability that the document is relevant into a field `probability_relevant`. For this retrieval baseline, we just take a PyTerrier retrieval model and use the min-max normalized retrieval rank as probability.\n", + "\n", + "In the following, we will process the pointwise smoke test dataset. Please modify the variable `DATASET_ID` to submit for other datasets. See [tira.io/datasets?query=wows-eval](https://archive.tira.io/datasets?query=wows-eval) for an complete overview of dataset identifiers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PyTerrier 0.10.0 has loaded Terrier 5.11 (built by craig.macdonald on 2025-01-13 21:29) and terrier-helper 0.0.8\n", + "\n" + ] + } + ], + "source": [ + "import pyterrier as pt\n", + "from tqdm import tqdm\n", + "if not pt.started():\n", + " pt.init()\n", + "\n", + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "import pandas as pd\n", + "from jnius import autoclass\n", + "import numpy as np\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "# Dataset IDs visible at https://archive.tira.io/datasets?query=wows-eval\n", + "DATASET_ID = 'wows-eval/pointwise-smoke-test-20250128-training'\n", + "#DATASET_ID = 'wows-eval/pointwise-20250309-test'\n", + "\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idqueryunknown
032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.
1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
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" + ], + "text/plain": [ + " id query \\\n", + "0 32d23068-7440-4891-9958-42325f98a604 who sings monk theme song \n", + "1 cde83146-ac3e-4bc5-a959-f2006ac7b8de who sings monk theme song \n", + "\n", + " unknown \n", + "0 This is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk. \n", + "1 Walker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself. " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_data.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Implement the Approach\n", + "\n", + "We wrap all computations into a [tirex_tracker.tracking](https://github.com/tira-io/tirex-tracker/) environment to measure the resources consumed for our computations and also a snapshot of our code in the [ir-metadata format](https://www.ir-metadata.org/)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PCM Info: setrlimit for file limit 1000000 failed with error Operation not permitted\n", + "\n", + "===== Processor information =====\n", + "Linux arch_perfmon flag : yes\n", + "Hybrid processor : yes\n", + "IBRS and IBPB supported : yes\n", + "STIBP supported : yes\n", + "Spec arch caps supported : yes\n", + "Max CPUID level : 32\n", + "CPU model number : 154\n", + "ERROR: Can not open /sys/module/msr/parameters/allow_writes file.\n", + "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", + "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", + "Can not access CPUs Model Specific Registers (MSRs).\n", + "execute 'modprobe msr' as root user, then execute pcm as root user.\n", + "100%|██████████| 2/2 [00:00<00:00, 2.51it/s]\n" + ] + } + ], + "source": [ + "WMODEL = \"BM25\"\n", + "system_name = f'pointwise-retrieval-{WMODEL}'.lower()\n", + "\n", + "def retrieve_and_normalize(query, retrieval_system, unk): \n", + " ret = {}\n", + " tokeniser = autoclass(\"org.terrier.indexing.tokenisation.Tokeniser\").getTokeniser()\n", + " query_text = \" \".join(tokeniser.getTokens(query))\n", + " run = retrieval_system.search(query_text)\n", + " last_rank = -1\n", + " scores = {}\n", + " for _, i in run.iterrows():\n", + " assert last_rank < i['rank']\n", + " last_rank = i['rank']\n", + " if i['docno'] in unk:\n", + " scores[unk[i['docno']]] = i['rank']\n", + "\n", + " max_score = max(scores.values())\n", + " min_score = min(scores.values())\n", + "\n", + " return {k: ((v-min_score)/(max_score-min_score)) for k, v in scores.items()}\n", + "\n", + "!rm -Rf run\n", + "with tracking(export_file_path='run/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", + " queries = set(input_data['query'].unique())\n", + "\n", + " def unknown_documents(query):\n", + " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", + " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", + "\n", + " results = {}\n", + " for query in tqdm(queries):\n", + " unk = unknown_documents(query)\n", + "\n", + " docs = [{'docno': k, 'text': v} for k, v in unk.items()]\n", + " indexer = pt.IterDictIndexer(\"/tmp/index\", overwrite=True, meta={'docno': 100, 'text': 20480})\n", + " index_ref = indexer.index(docs)\n", + " retriever = pt.BatchRetrieve(index_ref, wmodel=WMODEL)\n", + " results[query] = retrieve_and_normalize(query, retriever, unk)\n", + "\n", + " predictions = []\n", + " for _, i in input_data.iterrows():\n", + " res = results[i['query']]\n", + " predictions.append({\n", + " 'id': i['id'],\n", + " 'probability_relevant': res.get(i['unknown'], -1)\n", + " })\n", + " predictions = pd.DataFrame(predictions)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Evaluate and Submit Your Run\n", + "\n", + "We use the `wows_evaluate` method imported above to evaluate our predictions and to upload them, to TIRA.\n", + "\n", + "The `wows_evaluate` method has optional parameters that you can pass to describe your system and to include the resource measurements used during your computations in the ir-metadata format into your submission. You can remove those attributes or modify them for your submission accordingly. Call `help(wows_evaluate)` to see a full description." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/18f7f8aa-192f-4d08-a03b-0866d540ccae\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " system tau_ap kendall spearman pearson\n", + "0 pointwise-retrieval-bm25 0.040556 0.085714 0.1 0.1" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wows_evaluate(\n", + " predictions,\n", + " DATASET_ID,\n", + " tracking_results=tracked,\n", + " upload=True,\n", + " system_name=system_name,\n", + " system_description=f'We use the PyTerrier retrieval model {WMODEL} to assign the probability that an unknown document is relevant. We rank all unknown documents that are to-be judged against the query. The probability that a document is relevant is then the min-max normalized rank of an unknown document in the ranking of all unknown documents to the query.'\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ecir25/baselines/pointwise_snorkel.ipynb b/ecir25/baselines/pointwise_snorkel.ipynb index 27a12bc..222b056 100644 --- a/ecir25/baselines/pointwise_snorkel.ipynb +++ b/ecir25/baselines/pointwise_snorkel.ipynb @@ -1,1628 +1,1628 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "Step 1: install dependencies" - ], - "metadata": { - "id": "nrNuNdOkWxU2" - } - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "jqYpJ_Gqn0v3", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7ddb4f2d-3811-4036-90db-25bd47219a67" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: tira in /usr/local/lib/python3.11/dist-packages (0.0.156)\n", - "Requirement already satisfied: snorkel in /usr/local/lib/python3.11/dist-packages (0.10.0)\n", - "Requirement already satisfied: wows-eval in /usr/local/lib/python3.11/dist-packages (0.0.6)\n", - "Requirement already satisfied: textdistance in /usr/local/lib/python3.11/dist-packages (4.6.3)\n", - "Requirement already satisfied: rank-bm25 in /usr/local/lib/python3.11/dist-packages (0.2.2)\n", - "Requirement already satisfied: requests==2.*,>=2.26 in /usr/local/lib/python3.11/dist-packages (from tira) (2.32.3)\n", - 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"Uninstalling tira-0.0.156:\n", - " Successfully uninstalled tira-0.0.156\n", - "Found existing installation: wows-eval 0.0.6\n", - "Uninstalling wows-eval-0.0.6:\n", - " Successfully uninstalled wows-eval-0.0.6\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip3 install wows-eval>=0.0.6" - ], - "metadata": { - "id": "wvDfHommgJYR" - }, - "execution_count": 17, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "!pip3 install python-terrier==0.10.0" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yU3XtFHxgcnh", - "outputId": "bc29e453-3224-456e-9377-8d77be571b41" - }, - "execution_count": 18, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: python-terrier==0.10.0 in /usr/local/lib/python3.11/dist-packages (0.10.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (1.26.4)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (2.2.2)\n", - 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"colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CGSLY40DiLaV", - "outputId": "180f3871-8310-4697-b2b8-10a46a79e6db" - }, - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Found existing installation: numpy 1.26.4\n", - "Uninstalling numpy-1.26.4:\n", - " Successfully uninstalled numpy-1.26.4\n", - "Collecting numpy\n", - " Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\n", - "Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB)\n", - "Installing collected packages: numpy\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "tira 0.0.156 requires numpy==1.*, but you have numpy 2.2.4 which is incompatible.\n", - "tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.2.4 which is incompatible.\n", - "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.2.4 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed numpy-2.2.4\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip3 install numpy==1.26.4 --force-reinstall\n", - "import os\n", - "os.kill(os.getpid(), 9)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "v2PPsFOoi7EV", - "outputId": "aaf8afdd-1420-433b-98f8-47167983d2a6" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting numpy==1.26.4\n", - " Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", - "Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n", - "Installing collected packages: numpy\n", - " Attempting uninstall: numpy\n", - " Found existing installation: numpy 1.26.4\n", - " Uninstalling numpy-1.26.4:\n", - " Successfully uninstalled numpy-1.26.4\n", - "Successfully installed numpy-1.26.4\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pyterrier as pt\n", - "from tqdm import tqdm\n", - "if not pt.started():\n", - " pt.init()\n", - "\n", - "from tira.rest_api_client import Client\n", - "from wows_eval import evaluate as wows_evaluate\n", - "import pandas as pd\n", - "from jnius import autoclass\n", - "import numpy as np\n", - "\n", - "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", - "from tirex_tracker import tracking, ExportFormat\n", - "\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "# Dataset IDs visible at https://archive.tira.io/datasets?query=wows-eval\n", - "DATASET_ID = 'wows-eval/pointwise-20250309-test'\n", - "tira = Client()\n", - "input_data = tira.pd.inputs(DATASET_ID)" - ], - "metadata": { - "id": "KrRf64t2n6ke" - }, - "execution_count": 30, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "input_data" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 719 - }, - "id": "OWQSca5EnvUb", - "outputId": "562837cb-6f6a-4c83-c58f-63a2974f5817" - }, - "execution_count": 31, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id query \\\n", - "0 e1786a4d-2b3d-4fcf-ad84-443acf3696e2 journalist risks \n", - "1 8e64b35c-60be-4292-9542-c4639a749336 journalist risks \n", - "2 a8ff3f18-9b80-40b9-b529-799ed3367980 journalist risks \n", - "3 80d821ed-94b3-4fb6-9159-a71089c869a2 journalist risks \n", - "4 709154d7-13c6-492c-b977-07396510909c journalist risks \n", - "... ... ... \n", - "1013 527a986a-c2d9-46b6-8a14-3f305beaf7fd Lyme disease \n", - "1014 4c8dee20-f7cb-44b3-ae69-8ea00a7e2ede Lyme disease \n", - "1015 4a773373-3f0b-4821-a78e-86ce9bc429da Lyme disease \n", - "1016 369ce954-c156-485c-8cdc-37e0137287d2 Lyme disease \n", - "1017 931bbd92-c9c7-48af-b91f-9cfb7c2abdcb Lyme disease \n", - "\n", - " unknown \n", - "0 IPI Death Watch: 45 journalists killed in 2021 - International Press Institute\\nNewsroom\\nIPI Death Watch: 45 journalists killed in 2021\\nViolence against journalists remains global challenge\\nIPI Contributor Anne ter Rele\\nDec 25, 2016\\nSHARE\\n In 2021, a total of 45 journalists were killed in connection with their work, IPI research revealed today. The sombre tally reflects the continued risks of doing journalism and reaffirms journalist safety as a global challenge. IPI calls on authorities to end impunity for these crimes and to ensure the protection of journalists, who must be able to do their work freely and safely.\\n Today, the IPI global network published its yearly Death Watch. IPI’s research shows that since the beginning of 2021, a total of 45 journalists were killed in connection with their work, or lost their lives on assignment. Of these 45 journalists, 40 were male and five were female. A total of 28 were targeted due to their work, while three were killed while covering conflict, two lost their lives covering civil unrest, and one journalist was killed while on assignment. \n", - "1 Two journalists were killed in the MENA region, both in Yemen, while nine journalists were killed in Sub-Saharan Africa, most of whom in the Democratic Republic of Congo (3), followed by Burkina Faso and Somalia (both 2).\\n Like last year, more journalists were killed in Mexico (7) in 2021 than any other country in the world. All seven cases were targeted killings. According to IPI’s analysis, journalists researching local politics and organized crime, including drug trafficking, are especially at risk. Most of the targeted journalists were examining these or related topics.\\n Another explanation for the high number of killed journalists is the level of impunity. According to ARTICLE 19 Mexico, in only one of these seven cases have suspects have been arrested. The continued high number of killings confirms Mexico’s status as one of the deadliest countries for journalists to work. Despite this tragic status quo, the government there has decided to stop funds allocated for upholding the Law for the Protection of Human Rights Defenders and Journalists (LPPDHP).\\n After Mexico, Afghanistan and India are the two next-deadliest countries, with six killings each. In Afghanistan, most killings occurred in relation to the violent conflict as a result of the Taliban’s takeover of the country this summer. \n", - "2 Importantly, journalists were also murdered in countries with relatively high levels of press freedom, which arguably shows the global nature of the risks of doing journalism. For example, high-risk crime journalist Peter R. De Vries was shot on an Amsterdam city street in broad daylight on July 6, 2021, despite the fact that the Netherlands is considered one of the countries with the highest degree of press freedom in the world. The De Vries case is currently classified as Under Investigation on IPI’s Death Watch. In Greece, crime reporter Giorgos Karaivaz was shot outside his home in Athens. As of December 2021, no suspects have been publicly identified and no arrests have been made, while public information about the status of the investigation remains scarce.\\nImpunity\\nIPI’s analysis shows an alarmingly insufficient response from authorities to the killings, leading to high levels of impunity for crimes against journalists. In only six of the 28 cases have local police reportedly arrested suspects. \n", - "3 Hire a Journalist, Reporter, Correspondent in Ukraine - paydesk\\nGlobal Directory - All Countries - Journalists in Ukraine\\nJournalists in Ukraine\\n\\nStefan Sigaard Weichert\\nHi. I am a freelance journalist living in Kyiv, Ukraine, where I report about politics, war and social stories from Eastern Europe. I am a former Erasmus Mundus student from Swansea University in the UK, where I...\\nKyiv, Ukraine\\nDanish German English Russian\\nLive Phoner for Radio Audio package Interview News Gathering Feature Stories Content Writing Risk Analysis Research Investigative Journalism Interview\\nBusiness War Reporter Social Politics Risk Analysis Climate Change Current Affairs Natural Disasters Investigative Reporting\\nAvailable\\nBook\\nJoti Heir\\nJoti Heir is a Canadian journalist experienced in reporting for live radio and television. She has produced and reported for CTV, CBC and Astral Media. Additionally, she has written for a variety of publications.\\n Kyiv, Ukraine\\nEnglish Punjabi Turkish\\nLive On-camera Online Live Phoner for Radio News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview\\nAvailable\\nBook\\nAnna Chernikova\\nAnna Chernikova is a journalist based in Kyiv, Ukraine.\\n \n", - "4 Covered Euromaidan in Kyiv (also known as the Ukrainian revolution of Dignity), Russian invasion of...\\nKyiv, Ukraine\\nEnglish Polish Russian Ukrainian\\nPhotography Fixing\\nNatural Disasters Breaking News Social Travel Politics Cultural Sports War Reporter\\n Available\\nBook\\nJack Losh\\nJack Losh is a journalist, producer/director and photographer with a focus on conflict, conservation and humanitarian issues. With assignments spanning Europe, Africa and the Middle East, he has been shortlisted for...\\nCrimea\\nEnglish French Russian\\nLive Stand-up Live On-camera Online Live Phoner for Radio Video Package Interview Documentary Films News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview Photography Fixing Live Phoner for TV Video Rushes Journalism\\nMedia Training Travel Natural Disasters Science & Environment War Reporter Current Affairs Risk Analysis Investigative Reporting Breaking News Research Climate Change\\nAvailable\\nBook\\nSergey Myasoedov\\nSergey Myasoedov is a video journalist based in Kyiv, Ukraine.\\n Kyiv, Ukraine\\nEnglish Russian Ukrainian\\nVideo Package Vox Pop News Gathering Research Photography Fixing Video Rushes Interview\\nResearch Social Politics Medical Cultural Investigative Reporting Climate Change Sports War Reporter Current Affairs Natural Disasters Breaking News\\nAvailable\\nBook\\nOxana Bekheshti\\nOxana Bekheshti is a journalist based in Kyiv, Ukraine.\\n \n", - "... ... \n", - "1013 Lyme disease is medically described in three phases (stages) as\\nearly localized disease with skin inflammation and rash;\\nearly disseminated disease with heart and nervous system involvement, including palsies and meningitis; and\\nlate disease featuring motor and sensory nerve damage and brain inflammation, as well as arthritis.\\n In the early phase of the illness, within days to weeks of the tick bite, the skin around the bite develops an expanding ring of unraised redness. There may be an outer ring of brighter redness with or without a central area of clearing, leading to a \"bulls-eye\" appearance. This classic initial rash is called \"erythema migrans\" (formerly called erythema chronicum migrans). Patients often can't recall the tick bite (the ticks can be as small as the periods in this paragraph). Also, they may not have the identifying rash to signal the doctor. The rash may or may not itch. More than one in four patients never even develop a rash. The redness of the skin is often accompanied by generalized fatigue, muscle and joint pain and stiffness, swollen lymph nodes (\"swollen glands\"), headache, and less often fever, resembling symptoms of a viral infection.\\n The redness resolves, without treatment, in about a month. \n", - "1014 She opens up about it in her new book, “Believe Me.”\\nSuzanne Somer’s interviews her granddaughter Violet about her struggle with Lyme and her successful treatment at Sponaugle Wellness Institute\\nLyme disease is a tick-borne infection caused by a bacteria called Borrelia burgdorferi. Lyme disease is most often transmitted by black-legged tick bites and in recent studies has also been found in mosquitos. The Lyme literate medical team at Sponaugle Wellness Institute have also concluded it can be sexually transmitted or passed on to an unborn child.\\n Black-legged ticks, also known as deer ticks, are most prevalent in the Northeast United States but have been found in all 50 states and Canada. These ticks live in wooded and grassy areas. Cases of Lyme disease are on the rise in recent years showing reported cases nearly doubling since 2014. The CDC estimates cases in the United States at more than 300,000 people infected annually while acknowledging that Lyme disease can be difficult to diagnose.\\n Borrelia burgdorferi, the bacteria that causes Lyme disease, is a corkscrew or helix-shaped organism called a spirochete. These spirochetes are known for being serious pathogens with Lyme disease’s cellular make-up being close to that of syphilis. \n", - "1015 Symptoms of Lyme Disease for those infected often include fever, headache, fatigue, chills, swollen lymph nodes, muscle or joint pain, and other flu-like symptoms. As discussed above, many suffer notice a bulls-eye like rash called “erythema migrans” which is a sign of the bacteria multiplying in the bloodstream. If the Lyme disease is left untreated, Lyme patients may experience a wide range of physical and neurological symptoms including, loss of memory or concentration, depression, speech problems, pain and swelling, cough, facial palsy or muscle loss and more.\\n Lyme disease can be broken down into 3 stages.\\n Stage 1 (Detection)\\nOften called the “early localized” stage of lyme disease, symptoms start a few days to a few weeks after contraction. The symptoms of stage 1 lyme disease mimic those of the flu making them difficult to link to a bite, especially if the sufferer does not remember being bitten.\\n \n", - "1016 When multiplying, many patients experience little or no symptoms, leading them to believe the bacteria is no longer within their system. When symptoms reappear, they often mimic that of other illnesses like arthritis, multiple sclerosis, chronic fatigue syndrome, fibromyalgia, depression, insomnia and other autoimmune disorders. It is important to mention that patients who have received treatment for lyme disease may still be at risk. Post-treatment lyme disease (PTLD) may affect up to 50% of people who receive treatment at stage 1. Stage 3 symptoms are severe and debilitating and can include:\\nExtreme Fatigue\\nChronic Pain and Soreness\\nDepression, Anxiety, Stress\\nCognitive Impairment\\nMigraines and Headaches\\nArthritis\\nInsomnia\\nBell’s Palsy\\nLoss of Vision\\nHearing Impairment\\nIrregular Heartbeat\\nLyme disease is most often diagnosed and confirmed through blood tests or testing of the cerebrospinal fluid. During a blood test, blood is usually drawn from a vein in the arm and tested for the antibodies a person’s body produces in response to the invading Borrelia burgdorferi. Patients experiencing neurological symptoms may be recommended a spinal tap to test the cerebrospinal fluid. In a spinal tap, fluid is drawn a lumbar puncture between two vertebrae in the lower spine.\\n These tests reveal if there are antibodies in the blood or cerebrospinal fluid. \n", - "1017 Lyme Disease\\nPublic Health\\nLyme Disease\\nLyme Disease\\nIf you have Lyme disease, there’s no doubt you have already done your own research and read a lot of information about the disease. As scientists and physicians, we are concerned about your health. That’s why we want you to have the most up-to-date information about Lyme disease.\\n Clinical Practice Guidelines\\nIDSA, AAN and ACR release guidelines for prevention, diagnosis and treatment of Lyme disease \n", - "\n", - "[1018 rows x 3 columns]" - ], - "text/html": [ - "\n", - "
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0e1786a4d-2b3d-4fcf-ad84-443acf3696e2journalist risksIPI Death Watch: 45 journalists killed in 2021 - International Press Institute\\nNewsroom\\nIPI Death Watch: 45 journalists killed in 2021\\nViolence against journalists remains global challenge\\nIPI Contributor Anne ter Rele\\nDec 25, 2016\\nSHARE\\n In 2021, a total of 45 journalists were killed in connection with their work, IPI research revealed today. The sombre tally reflects the continued risks of doing journalism and reaffirms journalist safety as a global challenge. IPI calls on authorities to end impunity for these crimes and to ensure the protection of journalists, who must be able to do their work freely and safely.\\n Today, the IPI global network published its yearly Death Watch. IPI’s research shows that since the beginning of 2021, a total of 45 journalists were killed in connection with their work, or lost their lives on assignment. Of these 45 journalists, 40 were male and five were female. A total of 28 were targeted due to their work, while three were killed while covering conflict, two lost their lives covering civil unrest, and one journalist was killed while on assignment.
18e64b35c-60be-4292-9542-c4639a749336journalist risksTwo journalists were killed in the MENA region, both in Yemen, while nine journalists were killed in Sub-Saharan Africa, most of whom in the Democratic Republic of Congo (3), followed by Burkina Faso and Somalia (both 2).\\n Like last year, more journalists were killed in Mexico (7) in 2021 than any other country in the world. All seven cases were targeted killings. According to IPI’s analysis, journalists researching local politics and organized crime, including drug trafficking, are especially at risk. Most of the targeted journalists were examining these or related topics.\\n Another explanation for the high number of killed journalists is the level of impunity. According to ARTICLE 19 Mexico, in only one of these seven cases have suspects have been arrested. The continued high number of killings confirms Mexico’s status as one of the deadliest countries for journalists to work. Despite this tragic status quo, the government there has decided to stop funds allocated for upholding the Law for the Protection of Human Rights Defenders and Journalists (LPPDHP).\\n After Mexico, Afghanistan and India are the two next-deadliest countries, with six killings each. In Afghanistan, most killings occurred in relation to the violent conflict as a result of the Taliban’s takeover of the country this summer.
2a8ff3f18-9b80-40b9-b529-799ed3367980journalist risksImportantly, journalists were also murdered in countries with relatively high levels of press freedom, which arguably shows the global nature of the risks of doing journalism. For example, high-risk crime journalist Peter R. De Vries was shot on an Amsterdam city street in broad daylight on July 6, 2021, despite the fact that the Netherlands is considered one of the countries with the highest degree of press freedom in the world. The De Vries case is currently classified as Under Investigation on IPI’s Death Watch. In Greece, crime reporter Giorgos Karaivaz was shot outside his home in Athens. As of December 2021, no suspects have been publicly identified and no arrests have been made, while public information about the status of the investigation remains scarce.\\nImpunity\\nIPI’s analysis shows an alarmingly insufficient response from authorities to the killings, leading to high levels of impunity for crimes against journalists. In only six of the 28 cases have local police reportedly arrested suspects.
380d821ed-94b3-4fb6-9159-a71089c869a2journalist risksHire a Journalist, Reporter, Correspondent in Ukraine - paydesk\\nGlobal Directory - All Countries - Journalists in Ukraine\\nJournalists in Ukraine\\n\\nStefan Sigaard Weichert\\nHi. I am a freelance journalist living in Kyiv, Ukraine, where I report about politics, war and social stories from Eastern Europe. I am a former Erasmus Mundus student from Swansea University in the UK, where I...\\nKyiv, Ukraine\\nDanish German English Russian\\nLive Phoner for Radio Audio package Interview News Gathering Feature Stories Content Writing Risk Analysis Research Investigative Journalism Interview\\nBusiness War Reporter Social Politics Risk Analysis Climate Change Current Affairs Natural Disasters Investigative Reporting\\nAvailable\\nBook\\nJoti Heir\\nJoti Heir is a Canadian journalist experienced in reporting for live radio and television. She has produced and reported for CTV, CBC and Astral Media. Additionally, she has written for a variety of publications.\\n Kyiv, Ukraine\\nEnglish Punjabi Turkish\\nLive On-camera Online Live Phoner for Radio News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview\\nAvailable\\nBook\\nAnna Chernikova\\nAnna Chernikova is a journalist based in Kyiv, Ukraine.\\n
4709154d7-13c6-492c-b977-07396510909cjournalist risksCovered Euromaidan in Kyiv (also known as the Ukrainian revolution of Dignity), Russian invasion of...\\nKyiv, Ukraine\\nEnglish Polish Russian Ukrainian\\nPhotography Fixing\\nNatural Disasters Breaking News Social Travel Politics Cultural Sports War Reporter\\n Available\\nBook\\nJack Losh\\nJack Losh is a journalist, producer/director and photographer with a focus on conflict, conservation and humanitarian issues. With assignments spanning Europe, Africa and the Middle East, he has been shortlisted for...\\nCrimea\\nEnglish French Russian\\nLive Stand-up Live On-camera Online Live Phoner for Radio Video Package Interview Documentary Films News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview Photography Fixing Live Phoner for TV Video Rushes Journalism\\nMedia Training Travel Natural Disasters Science & Environment War Reporter Current Affairs Risk Analysis Investigative Reporting Breaking News Research Climate Change\\nAvailable\\nBook\\nSergey Myasoedov\\nSergey Myasoedov is a video journalist based in Kyiv, Ukraine.\\n Kyiv, Ukraine\\nEnglish Russian Ukrainian\\nVideo Package Vox Pop News Gathering Research Photography Fixing Video Rushes Interview\\nResearch Social Politics Medical Cultural Investigative Reporting Climate Change Sports War Reporter Current Affairs Natural Disasters Breaking News\\nAvailable\\nBook\\nOxana Bekheshti\\nOxana Bekheshti is a journalist based in Kyiv, Ukraine.\\n
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1013527a986a-c2d9-46b6-8a14-3f305beaf7fdLyme diseaseLyme disease is medically described in three phases (stages) as\\nearly localized disease with skin inflammation and rash;\\nearly disseminated disease with heart and nervous system involvement, including palsies and meningitis; and\\nlate disease featuring motor and sensory nerve damage and brain inflammation, as well as arthritis.\\n In the early phase of the illness, within days to weeks of the tick bite, the skin around the bite develops an expanding ring of unraised redness. There may be an outer ring of brighter redness with or without a central area of clearing, leading to a \"bulls-eye\" appearance. This classic initial rash is called \"erythema migrans\" (formerly called erythema chronicum migrans). Patients often can't recall the tick bite (the ticks can be as small as the periods in this paragraph). Also, they may not have the identifying rash to signal the doctor. The rash may or may not itch. More than one in four patients never even develop a rash. The redness of the skin is often accompanied by generalized fatigue, muscle and joint pain and stiffness, swollen lymph nodes (\"swollen glands\"), headache, and less often fever, resembling symptoms of a viral infection.\\n The redness resolves, without treatment, in about a month.
10144c8dee20-f7cb-44b3-ae69-8ea00a7e2edeLyme diseaseShe opens up about it in her new book, “Believe Me.”\\nSuzanne Somer’s interviews her granddaughter Violet about her struggle with Lyme and her successful treatment at Sponaugle Wellness Institute\\nLyme disease is a tick-borne infection caused by a bacteria called Borrelia burgdorferi. Lyme disease is most often transmitted by black-legged tick bites and in recent studies has also been found in mosquitos. The Lyme literate medical team at Sponaugle Wellness Institute have also concluded it can be sexually transmitted or passed on to an unborn child.\\n Black-legged ticks, also known as deer ticks, are most prevalent in the Northeast United States but have been found in all 50 states and Canada. These ticks live in wooded and grassy areas. Cases of Lyme disease are on the rise in recent years showing reported cases nearly doubling since 2014. The CDC estimates cases in the United States at more than 300,000 people infected annually while acknowledging that Lyme disease can be difficult to diagnose.\\n Borrelia burgdorferi, the bacteria that causes Lyme disease, is a corkscrew or helix-shaped organism called a spirochete. These spirochetes are known for being serious pathogens with Lyme disease’s cellular make-up being close to that of syphilis.
10154a773373-3f0b-4821-a78e-86ce9bc429daLyme diseaseSymptoms of Lyme Disease for those infected often include fever, headache, fatigue, chills, swollen lymph nodes, muscle or joint pain, and other flu-like symptoms. As discussed above, many suffer notice a bulls-eye like rash called “erythema migrans” which is a sign of the bacteria multiplying in the bloodstream. If the Lyme disease is left untreated, Lyme patients may experience a wide range of physical and neurological symptoms including, loss of memory or concentration, depression, speech problems, pain and swelling, cough, facial palsy or muscle loss and more.\\n Lyme disease can be broken down into 3 stages.\\n Stage 1 (Detection)\\nOften called the “early localized” stage of lyme disease, symptoms start a few days to a few weeks after contraction. The symptoms of stage 1 lyme disease mimic those of the flu making them difficult to link to a bite, especially if the sufferer does not remember being bitten.\\n
1016369ce954-c156-485c-8cdc-37e0137287d2Lyme diseaseWhen multiplying, many patients experience little or no symptoms, leading them to believe the bacteria is no longer within their system. When symptoms reappear, they often mimic that of other illnesses like arthritis, multiple sclerosis, chronic fatigue syndrome, fibromyalgia, depression, insomnia and other autoimmune disorders. It is important to mention that patients who have received treatment for lyme disease may still be at risk. Post-treatment lyme disease (PTLD) may affect up to 50% of people who receive treatment at stage 1. Stage 3 symptoms are severe and debilitating and can include:\\nExtreme Fatigue\\nChronic Pain and Soreness\\nDepression, Anxiety, Stress\\nCognitive Impairment\\nMigraines and Headaches\\nArthritis\\nInsomnia\\nBell’s Palsy\\nLoss of Vision\\nHearing Impairment\\nIrregular Heartbeat\\nLyme disease is most often diagnosed and confirmed through blood tests or testing of the cerebrospinal fluid. During a blood test, blood is usually drawn from a vein in the arm and tested for the antibodies a person’s body produces in response to the invading Borrelia burgdorferi. Patients experiencing neurological symptoms may be recommended a spinal tap to test the cerebrospinal fluid. In a spinal tap, fluid is drawn a lumbar puncture between two vertebrae in the lower spine.\\n These tests reveal if there are antibodies in the blood or cerebrospinal fluid.
1017931bbd92-c9c7-48af-b91f-9cfb7c2abdcbLyme diseaseLyme Disease\\nPublic Health\\nLyme Disease\\nLyme Disease\\nIf you have Lyme disease, there’s no doubt you have already done your own research and read a lot of information about the disease. As scientists and physicians, we are concerned about your health. That’s why we want you to have the most up-to-date information about Lyme disease.\\n Clinical Practice Guidelines\\nIDSA, AAN and ACR release guidelines for prevention, diagnosis and treatment of Lyme disease
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But it easily could have gone the other way had there been no video or audio.\\n The officer had stopped a woman for a traffic infraction and she was on her cell phone as well. \",\n \" Financial Advisers Australia (FAA) / AFS Licensees Search\\nThe Sydney Morning Herald\\nHome\\nFinancial Advisers Australia (FAA)\\nFinancial Advisers Australia (FAA)\\nAustralian Financial Services Licensees are required to keep the details of their financial advisers up to date on ASIC's Financial Advisers Register. Information contained in the register is made available to the public to search via ASIC's Moneysmart website.\\n \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } - }, - "metadata": {}, - "execution_count": 31 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Step 2: load all the libraries" - ], - "metadata": { - "id": "CB_34eKz68Pn" - } - }, - { - "cell_type": "code", - "source": [ - "from snorkel.labeling import labeling_function, PandasLFApplier\n", - "from snorkel.labeling.model.label_model import LabelModel\n", - "from rank_bm25 import BM25Okapi\n", - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from sentence_transformers import SentenceTransformer\n", - "from sklearn.metrics.pairwise import cosine_similarity\n", - "import textdistance\n", - "import pandas as pd\n", - "vectorizer = TfidfVectorizer()\n", - "import re" - ], - "metadata": { - "id": "5D-oLP_54FtB" - }, - "execution_count": 32, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import nltk\n", - "from nltk.corpus import stopwords, wordnet\n", - "from nltk.stem import WordNetLemmatizer\n", - "# Download stopwords if not already present\n", - "nltk.download(\"stopwords\")\n", - "stop_words = set(stopwords.words(\"english\"))\n", - "nltk.download(\"wordnet\")\n", - "lemmatizer = WordNetLemmatizer()" - ], - "metadata": { - "id": "qBVEpFOn4LNN", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7fe994b3-3f68-43fc-bfdc-794121e11125" - }, - "execution_count": 33, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n", - "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", - "[nltk_data] Package wordnet is already up-to-date!\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Step 3: define global variables" - ], - "metadata": { - "id": "NpyWVji-7C1f" - } - }, - { - "cell_type": "code", - "source": [ - "# Global Variables\n", - "min_bm25 = None\n", - "max_bm25 = None\n", - "bm25 = None\n" - ], - "metadata": { - "id": "w7M09OVo4CQb" - }, - "execution_count": 34, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Step 4: do a min-max normalisation for BM25, functions that tokenise and precompute n-grams" - ], - "metadata": { - "id": "JDphg1r9jQBE" - } - }, - { - "cell_type": "code", - "source": [ - "def tokenize(text):\n", - " \"\"\"Tokenizes text into lowercase words without punctuation.\"\"\"\n", - " return re.findall(r'\\w+', text.lower())\n", - "\n", - "def normalize_bm25(bm25_score):\n", - " \"\"\"Normalizes BM25 using min-max scaling.\"\"\"\n", - " if min_bm25 == max_bm25:\n", - " return 0.0\n", - " return (bm25_score - min_bm25) / (max_bm25 - min_bm25)\n", - "\n", - "\n", - "def tokenize_and_lemmatize(text):\n", - " \"\"\"Tokenizes text into lowercase words without punctuation and lemmatizes words.\"\"\"\n", - " tokens = re.findall(r'\\w+', text.lower()) # Tokenization\n", - " return {lemmatizer.lemmatize(word) for word in tokens} - stop_words\n", - "\n", - "def get_ngrams(text, n=2):\n", - " \"\"\"Generate n-grams from text\"\"\"\n", - " tokens = list(tokenize_and_lemmatize(text))\n", - " return set(zip(*[tokens[i:] for i in range(n)])) if len(tokens) >= n else set()\n", - "\n", - "def make_bm25_score_prob(bm25_model, doc_lookup):\n", - " def f(x):\n", - " query_tokens = tokenize(x.query)\n", - " doc_idx = doc_lookup.get(x.name) # x.name is the row index\n", - " if doc_idx is None:\n", - " return -1 # Abstain\n", - " score = bm25_model.get_scores(query_tokens)[doc_idx]\n", - " return normalize_bm25(score)\n", - " return labeling_function()(f)" - ], - "metadata": { - "id": "yFpLW_LO4iop" - }, - "execution_count": 35, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "tokenized_docs = [tokenize(doc) for doc in input_data['unknown']]\n", - "bm25_model = BM25Okapi(tokenized_docs)\n", - "doc_lookup = dict(zip(input_data.index, range(len(input_data))))\n" - ], - "metadata": { - "id": "kMRT2wwNrW-6" - }, - "execution_count": 36, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Step 5: Create Snorkel labelling functions" - ], - "metadata": { - "id": "Y1zKhUPTW52w" - } - }, - { - "cell_type": "code", - "source": [ - "bm25_score_prob = make_bm25_score_prob(bm25_model, doc_lookup)\n", - "\n", - "\n", - "@labeling_function()\n", - "def boolean_match_prob(x):\n", - " \"\"\"Returns 1 if at least one query term appears in the document.\"\"\"\n", - " return 1.0 if set(tokenize(x.query)) & set(tokenize(x.unknown)) else 0.0\n", - "\n", - "\n", - "@labeling_function()\n", - "def word_level_levenshtein_prob(x):\n", - " \"\"\"Computes a boosted word-level Levenshtein similarity\"\"\"\n", - " query_words = tokenize_and_lemmatize(x.query)\n", - " doc_words = tokenize_and_lemmatize(x.unknown)\n", - "\n", - " if not query_words or not doc_words:\n", - " return 0.0 # Avoid division by zero\n", - "\n", - " matched_scores = []\n", - " for q in query_words:\n", - " similarities = [textdistance.levenshtein.normalized_similarity(q, d) for d in doc_words]\n", - " best_match = max(similarities) if similarities else 0.0\n", - " matched_scores.append(best_match)\n", - "\n", - " # Compute final score\n", - " avg_similarity = sum(matched_scores) / len(matched_scores) if matched_scores else 0.0\n", - " boosted_score = avg_similarity * (1.5 if avg_similarity > 0.8 else 1.0)\n", - "\n", - " return min(boosted_score, 1.0) #Scores in range [0,1]\n", - "\n", - "@labeling_function()\n", - "def relaxed_bigram_match_prob(x):\n", - " \"\"\"Computes the proportion of query bigrams where at least one word appears in the document.\"\"\"\n", - " query_bigrams = get_ngrams(x.query, n=2)\n", - " doc_bigrams = get_ngrams(x.unknown, n=2)\n", - "\n", - " if not query_bigrams:\n", - " return 0.0 # Avoid division by zero\n", - "\n", - " matched_bigrams = set()\n", - " unmatched_bigrams = set()\n", - "\n", - " for q_bigram in query_bigrams:\n", - " # If either word in the bigram appears in the document, count it as a partial match\n", - " if any(word in x.unknown for word in q_bigram):\n", - " matched_bigrams.add(q_bigram)\n", - " else:\n", - " unmatched_bigrams.add(q_bigram)\n", - " return len(matched_bigrams) / len(query_bigrams) # Fraction of bigrams with at least one match\n", - "\n", - "\n", - "@labeling_function()\n", - "def percent_matching_query_words(x):\n", - " \"\"\"Computes the percentage of query words that appear in the document.\"\"\"\n", - "\n", - " if not x.query or not x.unknown:\n", - " return 0.0 # Handle missing data\n", - "\n", - " # Tokenization: Split words and remove punctuation\n", - " query_words = set(re.split(r'\\W+', x.query.lower())) - stop_words\n", - " doc_words = set(re.split(r'\\W+', x.unknown.lower())) - stop_words\n", - "\n", - " if not query_words: # Avoid division by zero\n", - " return 0.0\n", - "\n", - " # Compute match percentage\n", - " matching_words = query_words.intersection(doc_words)\n", - " match_percent = len(matching_words) / len(query_words) # Fraction of matched query words\n", - "\n", - " return match_percent # Already in [0,1] range\n", - "\n", - "\n", - "# Load a strong BERT model for semantic similarity\n", - "bert_model = SentenceTransformer(\"all-mpnet-base-v2\") # Better than MiniLM for ranking tasks\n", - "\n", - "@labeling_function()\n", - "def bert_cosine_similarity(x):\n", - " \"\"\"Computes boosted BERT cosine similarity as a probability of relevance.\"\"\"\n", - "\n", - " if not x.query or not x.unknown:\n", - " return 0.0 # Handle missing data\n", - "\n", - " # Encode query and document\n", - " query_vec = bert_model.encode(x.query, convert_to_numpy=True)\n", - " doc_vec = bert_model.encode(x.unknown, convert_to_numpy=True)\n", - "\n", - " # Handle cases where vectors are zero (to avoid division by zero)\n", - " if np.linalg.norm(query_vec) == 0 or np.linalg.norm(doc_vec) == 0:\n", - " return 0.0\n", - "\n", - " # Compute cosine similarity\n", - " cosine_sim = np.dot(query_vec, doc_vec) / (np.linalg.norm(query_vec) * np.linalg.norm(doc_vec) + 1e-9)\n", - "\n", - " # Normalize cosine similarity to [0,1]\n", - " probability = (cosine_sim + 1) / 2\n", - "\n", - " # Apply Boosting\n", - " if probability > 0.8:\n", - " probability *= 1.5 # Boost high similarities\n", - " elif probability > 0.5:\n", - " probability *= 1.2 # Moderate boost for medium similarities\n", - "\n", - " return min(probability, 1.0) # Keep score within [0,1]\n", - "\n", - "\n", - "\n", - "@labeling_function()\n", - "def query_term_coverage(x):\n", - " \"\"\"Computes a relaxed query term coverage using lemmatization and synonym matching.\"\"\"\n", - "\n", - " if not x.query or not x.unknown:\n", - " return 0.0 # Handle missing data\n", - "\n", - " # Tokenization: Extract words using regex\n", - " query_tokens = [lemmatizer.lemmatize(word) for word in re.findall(r'\\b\\w+\\b', x.query.lower())]\n", - " doc_tokens = [lemmatizer.lemmatize(word) for word in re.findall(r'\\b\\w+\\b', x.unknown.lower())]\n", - "\n", - " # Create word sets\n", - " query_words = set(query_tokens) - stop_words # Remove stopwords\n", - " doc_words = set(doc_tokens)\n", - "\n", - " if not query_words: # Avoid division by zero\n", - " return 0.0\n", - "\n", - " # === Relaxed Matching: Check for Synonyms ===\n", - " def word_matches(word):\n", - " if word in doc_words:\n", - " return True # Exact match\n", - " synonyms = {lemma.name() for syn in wordnet.synsets(word) for lemma in syn.lemmas()} # Get synonyms\n", - " return bool(synonyms.intersection(doc_words)) # Check if any synonym is in the doc\n", - "\n", - " covered_terms = sum(1 for word in query_words if word_matches(word))\n", - " coverage = covered_terms / len(query_words) # Fraction of query terms covered by document\n", - " if coverage > 0.7:\n", - " coverage *= 1.8 # Strong boost\n", - " elif coverage > 0.5:\n", - " coverage *= 1.5 # Moderate boost\n", - " elif coverage > 0.2:\n", - " coverage *= 1.2 # Mild boost\n", - "\n", - " coverage = min(coverage, 1.0) # Keep within [0,1]\n", - " return coverage\n", - "\n", - "\n", - "# Global TF-IDF vectorizer with n-grams for better lexical matching\n", - "tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,2), min_df=1)\n", - "\n", - "@labeling_function()\n", - "def tfidf_cosine_similarity(x):\n", - " \"\"\"Computes a boosted TF-IDF cosine similarity for document relevance probability.\"\"\"\n", - "\n", - " if not x.query or not x.unknown:\n", - " return 0.0 # Handle missing data\n", - "\n", - " # Create TF-IDF matrix\n", - " corpus = [x.query, x.unknown]\n", - " tfidf_matrix = tfidf_vectorizer.fit_transform(corpus)\n", - "\n", - " # Ensure matrix is valid\n", - " if tfidf_matrix.shape[0] < 2 or tfidf_matrix.nnz == 0:\n", - " return 0.0 # Avoid invalid similarity computation\n", - "\n", - " # Compute cosine similarity\n", - " tfidf_sim = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])[0, 0]\n", - "\n", - " tfidf_prob = max(tfidf_sim, 0.0)\n", - "\n", - " if tfidf_prob > 0.7:\n", - " tfidf_prob *= 1.8 # Strong boost\n", - " elif tfidf_prob > 0.5:\n", - " tfidf_prob *= 1.5 # Moderate boost\n", - " elif tfidf_prob > 0.2:\n", - " tfidf_prob *= 1.2 # Mild boost\n", - "\n", - " return min(tfidf_prob, 1.0) # Keep within [0,1]\n", - "\n", - "\n" - ], - "metadata": { - "id": "VhMWSVtm4yDh" - }, - "execution_count": 37, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "6. Train Snorkel" - ], - "metadata": { - "id": "fXEgFJZ_mtwo" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "lfs = [bm25_score_prob, boolean_match_prob, word_level_levenshtein_prob, tfidf_cosine_similarity, bert_cosine_similarity]\n", - "\n", - "applier = PandasLFApplier(lfs=lfs)\n", - "L_train = applier.apply(input_data)\n", - "\n", - "label_model = LabelModel(cardinality=2, verbose=True)\n", - "label_model.fit(L_train, n_epochs=100, log_freq=10)\n", - "train_prob = label_model.predict_proba(L_train)\n", - "train_prob_relevant = train_prob[:, 1]\n", - "\n", - "input_data['probability_relevant'] = train_prob_relevant" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8piLiWnPpZ1G", - "outputId": "2051dc3e-df06-4f87-b24a-06ae93d3e1e8" - }, - "execution_count": 38, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 1018/1018 [18:35<00:00, 1.10s/it]\n", - "100%|██████████| 100/100 [00:00<00:00, 781.09epoch/s]\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "WMODEL = \"Snorkel\"\n", - "\n", - "!rm -Rf run\n", - "\n", - "\n", - "with tracking(export_file_path='run/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", - " queries = set(input_data['query'].unique())\n", - "\n", - " def unknown_documents(query):\n", - " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", - " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", - "\n", - " results = {}\n", - "\n", - " for query in tqdm(queries):\n", - " unk = unknown_documents(query)\n", - "\n", - " # Build index\n", - " docs = [{'docno': k, 'text': v} for k, v in unk.items()]\n", - " indexer = pt.IterDictIndexer(\"/tmp/index\", overwrite=True, meta={'docno': 100, 'text': 20480})\n", - " index_ref = indexer.index(docs)\n", - " retriever = pt.BatchRetrieve(index_ref, wmodel=WMODEL)\n", - " rels = input_data[input_data['query'] == query][['unknown', 'probability_relevant']]\n", - " rel_dict = dict(zip(rels['unknown'], rels['probability_relevant']))\n", - "\n", - " results[query] = rel_dict\n", - "\n", - " predictions = []\n", - " for _, i in input_data.iterrows():\n", - " res = results[i['query']]\n", - " predictions.append({\n", - " 'id': i['id'],\n", - " 'probability_relevant': res.get(i['unknown'], -1)\n", - " })\n", - " predictions = pd.DataFrame(predictions)\n", - "\n", - "predictions" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 442 - }, - "id": "eyZfnmDHjvz0", - "outputId": "03ff4f86-6ba8-435f-da3a-c4e3d2cd77e7" - }, - "execution_count": 39, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 13/13 [00:11<00:00, 1.17it/s]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id probability_relevant\n", - "0 e1786a4d-2b3d-4fcf-ad84-443acf3696e2 0.782072\n", - "1 8e64b35c-60be-4292-9542-c4639a749336 0.000311\n", - "2 a8ff3f18-9b80-40b9-b529-799ed3367980 0.318625\n", - "3 80d821ed-94b3-4fb6-9159-a71089c869a2 0.318625\n", - "4 709154d7-13c6-492c-b977-07396510909c 0.318625\n", - "... ... ...\n", - "1013 527a986a-c2d9-46b6-8a14-3f305beaf7fd 0.782072\n", - "1014 4c8dee20-f7cb-44b3-ae69-8ea00a7e2ede 0.782072\n", - "1015 4a773373-3f0b-4821-a78e-86ce9bc429da 0.782072\n", - "1016 369ce954-c156-485c-8cdc-37e0137287d2 0.782072\n", - "1017 931bbd92-c9c7-48af-b91f-9cfb7c2abdcb 0.782072\n", - "\n", - "[1018 rows x 2 columns]" - ], - "text/html": [ - "\n", - "
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We rank all unknown documents that are to-be judged against the query. The probability that a document is relevant is then the min-max normalized rank of an unknown document in the ranking of all unknown documents to the query.'\n", - ")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O_insImNmdzT", - "outputId": "a9a47e90-dbdb-4466-b18a-0f65411d3e11" - }, - "execution_count": 40, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/3ecb6039-9455-4636-86a5-8645c806559d\n", - "No truth data is available yet. The evaluation is possible after the deadline when the truth data was published.\n" - ] - } - ] - } - ] +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "Step 1: install dependencies" + ], + "metadata": { + "id": "nrNuNdOkWxU2" + } + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "jqYpJ_Gqn0v3", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7ddb4f2d-3811-4036-90db-25bd47219a67" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: tira in /usr/local/lib/python3.11/dist-packages (0.0.156)\n", + "Requirement already satisfied: snorkel in /usr/local/lib/python3.11/dist-packages (0.10.0)\n", + "Requirement already satisfied: wows-eval in 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"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (11.6.1.9)\n", + "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (12.3.1.170)\n", + "Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (0.6.2)\n", + "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (2.21.5)\n", + "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (12.4.127)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (12.4.127)\n", + "Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (3.2.0)\n", + "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.2.0->snorkel) (1.13.1)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.2.0->snorkel) (1.3.0)\n", + "Requirement already satisfied: sarge>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from trectools->wows-eval) (0.1.7.post1)\n", + "Requirement already satisfied: lxml>=3.6.0 in /usr/local/lib/python3.11/dist-packages (from trectools->wows-eval) (5.3.1)\n", + "Requirement already satisfied: bs4>=0.0.0.1 in /usr/local/lib/python3.11/dist-packages (from trectools->wows-eval) (0.0.2)\n", + "Requirement already satisfied: matplotlib>=1.5 in /usr/local/lib/python3.11/dist-packages (from trectools->wows-eval) (3.10.0)\n", + "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from bs4>=0.0.0.1->trectools->wows-eval) (4.13.3)\n", + 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(from matplotlib>=1.5->trectools->wows-eval) (11.1.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=1.5->trectools->wows-eval) (3.2.3)\n", + "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.11/dist-packages (from ruamel.yaml~=0.18.0->tirex-tracker>=0.2.10->wows-eval) (0.2.12)\n", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard>=2.13.0->snorkel) (3.0.2)\n", + "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (0.19.2)\n", + "Requirement already satisfied: decorator in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (4.4.2)\n", + "Requirement already satisfied: pickleshare in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (0.7.5)\n", + "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (5.7.1)\n", + "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (3.0.50)\n", + "Requirement already satisfied: pygments in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (2.18.0)\n", + "Requirement already satisfied: backcall in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (0.2.0)\n", + "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (0.1.7)\n", + "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.11/dist-packages (from ipython->tirex-tracker>=0.2.10->wows-eval) (4.9.0)\n", + "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /usr/local/lib/python3.11/dist-packages (from jedi>=0.16->ipython->tirex-tracker>=0.2.10->wows-eval) (0.8.4)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.11/dist-packages (from pexpect>4.3->ipython->tirex-tracker>=0.2.10->wows-eval) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /usr/local/lib/python3.11/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->tirex-tracker>=0.2.10->wows-eval) (0.2.13)\n", + "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4->bs4>=0.0.0.1->trectools->wows-eval) (2.6)\n" + ] + } + ], + "source": [ + "!pip3 install tira snorkel wows-eval textdistance rank-bm25" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 uninstall -y tira wows-eval" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GpSjn_xkgAut", + "outputId": "af9a9da3-3d82-48af-915b-b43f4e2e95e8" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found existing installation: tira 0.0.156\n", + "Uninstalling tira-0.0.156:\n", + " Successfully uninstalled tira-0.0.156\n", + "Found existing installation: wows-eval 0.0.6\n", + "Uninstalling wows-eval-0.0.6:\n", + " Successfully uninstalled wows-eval-0.0.6\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 install wows-eval>=0.0.6" + ], + "metadata": { + "id": "wvDfHommgJYR" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip3 install python-terrier==0.10.0" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yU3XtFHxgcnh", + "outputId": "bc29e453-3224-456e-9377-8d77be571b41" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: python-terrier==0.10.0 in /usr/local/lib/python3.11/dist-packages (0.10.0)\n", + 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python-terrier==0.10.0) (1.2.18)\n", + "Requirement already satisfied: chest in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (0.2.3)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (1.14.1)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (2.32.3)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (1.4.2)\n", + "Requirement already satisfied: nptyping==1.4.4 in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (1.4.4)\n", + "Requirement already satisfied: more-itertools in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (10.6.0)\n", + "Requirement already satisfied: ir-datasets>=0.3.2 in /usr/local/lib/python3.11/dist-packages (from python-terrier==0.10.0) (0.5.10)\n", + "Requirement already satisfied: jinja2 in 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/usr/local/lib/python3.11/dist-packages (from beautifulsoup4>=4.4.1->ir-datasets>=0.3.2->python-terrier==0.10.0) (4.13.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->python-terrier==0.10.0) (1.17.0)\n", + "Requirement already satisfied: cbor>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from trec-car-tools>=2.5.4->ir-datasets>=0.3.2->python-terrier==0.10.0) (1.0.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 uninstall numpy -y\n", + "!pip3 install numpy --upgrade --force-reinstall\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CGSLY40DiLaV", + "outputId": "180f3871-8310-4697-b2b8-10a46a79e6db" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found existing installation: numpy 1.26.4\n", + "Uninstalling numpy-1.26.4:\n", + " Successfully uninstalled numpy-1.26.4\n", + "Collecting numpy\n", + " Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\n", + "Using cached numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB)\n", + "Installing collected packages: numpy\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tira 0.0.156 requires numpy==1.*, but you have numpy 2.2.4 which is incompatible.\n", + "tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.2.4 which is incompatible.\n", + "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.2.4 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-2.2.4\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip3 install numpy==1.26.4 --force-reinstall\n", + "import os\n", + "os.kill(os.getpid(), 9)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "v2PPsFOoi7EV", + "outputId": "aaf8afdd-1420-433b-98f8-47167983d2a6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting numpy==1.26.4\n", + " Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", + "Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n", + "Installing collected packages: numpy\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.26.4\n", + " Uninstalling numpy-1.26.4:\n", + " Successfully uninstalled numpy-1.26.4\n", + "Successfully installed numpy-1.26.4\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pyterrier as pt\n", + "from tqdm import tqdm\n", + "if not pt.started():\n", + " pt.init()\n", + "\n", + "from tira.rest_api_client import Client\n", + "from wows_eval import evaluate as wows_evaluate\n", + "import pandas as pd\n", + "from jnius import autoclass\n", + "import numpy as np\n", + "\n", + "# For measuring consumed resources (e.g., GPU, CPU, RAM, etc.)\n", + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "# Dataset IDs visible at https://archive.tira.io/datasets?query=wows-eval\n", + "DATASET_ID = 'wows-eval/pointwise-20250309-test'\n", + "tira = Client()\n", + "input_data = tira.pd.inputs(DATASET_ID)" + ], + "metadata": { + "id": "KrRf64t2n6ke" + }, + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "input_data" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 719 + }, + "id": "OWQSca5EnvUb", + "outputId": "562837cb-6f6a-4c83-c58f-63a2974f5817" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id query \\\n", + "0 e1786a4d-2b3d-4fcf-ad84-443acf3696e2 journalist risks \n", + "1 8e64b35c-60be-4292-9542-c4639a749336 journalist risks \n", + "2 a8ff3f18-9b80-40b9-b529-799ed3367980 journalist risks \n", + "3 80d821ed-94b3-4fb6-9159-a71089c869a2 journalist risks \n", + "4 709154d7-13c6-492c-b977-07396510909c journalist risks \n", + "... ... ... \n", + "1013 527a986a-c2d9-46b6-8a14-3f305beaf7fd Lyme disease \n", + "1014 4c8dee20-f7cb-44b3-ae69-8ea00a7e2ede Lyme disease \n", + "1015 4a773373-3f0b-4821-a78e-86ce9bc429da Lyme disease \n", + "1016 369ce954-c156-485c-8cdc-37e0137287d2 Lyme disease \n", + "1017 931bbd92-c9c7-48af-b91f-9cfb7c2abdcb Lyme disease \n", + "\n", + " unknown \n", + "0 IPI Death Watch: 45 journalists killed in 2021 - International Press Institute\\nNewsroom\\nIPI Death Watch: 45 journalists killed in 2021\\nViolence against journalists remains global challenge\\nIPI Contributor Anne ter Rele\\nDec 25, 2016\\nSHARE\\n In 2021, a total of 45 journalists were killed in connection with their work, IPI research revealed today. The sombre tally reflects the continued risks of doing journalism and reaffirms journalist safety as a global challenge. IPI calls on authorities to end impunity for these crimes and to ensure the protection of journalists, who must be able to do their work freely and safely.\\n Today, the IPI global network published its yearly Death Watch. IPI’s research shows that since the beginning of 2021, a total of 45 journalists were killed in connection with their work, or lost their lives on assignment. Of these 45 journalists, 40 were male and five were female. A total of 28 were targeted due to their work, while three were killed while covering conflict, two lost their lives covering civil unrest, and one journalist was killed while on assignment. \n", + "1 Two journalists were killed in the MENA region, both in Yemen, while nine journalists were killed in Sub-Saharan Africa, most of whom in the Democratic Republic of Congo (3), followed by Burkina Faso and Somalia (both 2).\\n Like last year, more journalists were killed in Mexico (7) in 2021 than any other country in the world. All seven cases were targeted killings. According to IPI’s analysis, journalists researching local politics and organized crime, including drug trafficking, are especially at risk. Most of the targeted journalists were examining these or related topics.\\n Another explanation for the high number of killed journalists is the level of impunity. According to ARTICLE 19 Mexico, in only one of these seven cases have suspects have been arrested. The continued high number of killings confirms Mexico’s status as one of the deadliest countries for journalists to work. Despite this tragic status quo, the government there has decided to stop funds allocated for upholding the Law for the Protection of Human Rights Defenders and Journalists (LPPDHP).\\n After Mexico, Afghanistan and India are the two next-deadliest countries, with six killings each. In Afghanistan, most killings occurred in relation to the violent conflict as a result of the Taliban’s takeover of the country this summer. \n", + "2 Importantly, journalists were also murdered in countries with relatively high levels of press freedom, which arguably shows the global nature of the risks of doing journalism. For example, high-risk crime journalist Peter R. De Vries was shot on an Amsterdam city street in broad daylight on July 6, 2021, despite the fact that the Netherlands is considered one of the countries with the highest degree of press freedom in the world. The De Vries case is currently classified as Under Investigation on IPI’s Death Watch. In Greece, crime reporter Giorgos Karaivaz was shot outside his home in Athens. As of December 2021, no suspects have been publicly identified and no arrests have been made, while public information about the status of the investigation remains scarce.\\nImpunity\\nIPI’s analysis shows an alarmingly insufficient response from authorities to the killings, leading to high levels of impunity for crimes against journalists. In only six of the 28 cases have local police reportedly arrested suspects. \n", + "3 Hire a Journalist, Reporter, Correspondent in Ukraine - paydesk\\nGlobal Directory - All Countries - Journalists in Ukraine\\nJournalists in Ukraine\\n\\nStefan Sigaard Weichert\\nHi. I am a freelance journalist living in Kyiv, Ukraine, where I report about politics, war and social stories from Eastern Europe. I am a former Erasmus Mundus student from Swansea University in the UK, where I...\\nKyiv, Ukraine\\nDanish German English Russian\\nLive Phoner for Radio Audio package Interview News Gathering Feature Stories Content Writing Risk Analysis Research Investigative Journalism Interview\\nBusiness War Reporter Social Politics Risk Analysis Climate Change Current Affairs Natural Disasters Investigative Reporting\\nAvailable\\nBook\\nJoti Heir\\nJoti Heir is a Canadian journalist experienced in reporting for live radio and television. She has produced and reported for CTV, CBC and Astral Media. Additionally, she has written for a variety of publications.\\n Kyiv, Ukraine\\nEnglish Punjabi Turkish\\nLive On-camera Online Live Phoner for Radio News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview\\nAvailable\\nBook\\nAnna Chernikova\\nAnna Chernikova is a journalist based in Kyiv, Ukraine.\\n \n", + "4 Covered Euromaidan in Kyiv (also known as the Ukrainian revolution of Dignity), Russian invasion of...\\nKyiv, Ukraine\\nEnglish Polish Russian Ukrainian\\nPhotography Fixing\\nNatural Disasters Breaking News Social Travel Politics Cultural Sports War Reporter\\n Available\\nBook\\nJack Losh\\nJack Losh is a journalist, producer/director and photographer with a focus on conflict, conservation and humanitarian issues. With assignments spanning Europe, Africa and the Middle East, he has been shortlisted for...\\nCrimea\\nEnglish French Russian\\nLive Stand-up Live On-camera Online Live Phoner for Radio Video Package Interview Documentary Films News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview Photography Fixing Live Phoner for TV Video Rushes Journalism\\nMedia Training Travel Natural Disasters Science & Environment War Reporter Current Affairs Risk Analysis Investigative Reporting Breaking News Research Climate Change\\nAvailable\\nBook\\nSergey Myasoedov\\nSergey Myasoedov is a video journalist based in Kyiv, Ukraine.\\n Kyiv, Ukraine\\nEnglish Russian Ukrainian\\nVideo Package Vox Pop News Gathering Research Photography Fixing Video Rushes Interview\\nResearch Social Politics Medical Cultural Investigative Reporting Climate Change Sports War Reporter Current Affairs Natural Disasters Breaking News\\nAvailable\\nBook\\nOxana Bekheshti\\nOxana Bekheshti is a journalist based in Kyiv, Ukraine.\\n \n", + "... ... \n", + "1013 Lyme disease is medically described in three phases (stages) as\\nearly localized disease with skin inflammation and rash;\\nearly disseminated disease with heart and nervous system involvement, including palsies and meningitis; and\\nlate disease featuring motor and sensory nerve damage and brain inflammation, as well as arthritis.\\n In the early phase of the illness, within days to weeks of the tick bite, the skin around the bite develops an expanding ring of unraised redness. There may be an outer ring of brighter redness with or without a central area of clearing, leading to a \"bulls-eye\" appearance. This classic initial rash is called \"erythema migrans\" (formerly called erythema chronicum migrans). Patients often can't recall the tick bite (the ticks can be as small as the periods in this paragraph). Also, they may not have the identifying rash to signal the doctor. The rash may or may not itch. More than one in four patients never even develop a rash. The redness of the skin is often accompanied by generalized fatigue, muscle and joint pain and stiffness, swollen lymph nodes (\"swollen glands\"), headache, and less often fever, resembling symptoms of a viral infection.\\n The redness resolves, without treatment, in about a month. \n", + "1014 She opens up about it in her new book, “Believe Me.”\\nSuzanne Somer’s interviews her granddaughter Violet about her struggle with Lyme and her successful treatment at Sponaugle Wellness Institute\\nLyme disease is a tick-borne infection caused by a bacteria called Borrelia burgdorferi. Lyme disease is most often transmitted by black-legged tick bites and in recent studies has also been found in mosquitos. The Lyme literate medical team at Sponaugle Wellness Institute have also concluded it can be sexually transmitted or passed on to an unborn child.\\n Black-legged ticks, also known as deer ticks, are most prevalent in the Northeast United States but have been found in all 50 states and Canada. These ticks live in wooded and grassy areas. Cases of Lyme disease are on the rise in recent years showing reported cases nearly doubling since 2014. The CDC estimates cases in the United States at more than 300,000 people infected annually while acknowledging that Lyme disease can be difficult to diagnose.\\n Borrelia burgdorferi, the bacteria that causes Lyme disease, is a corkscrew or helix-shaped organism called a spirochete. These spirochetes are known for being serious pathogens with Lyme disease’s cellular make-up being close to that of syphilis. \n", + "1015 Symptoms of Lyme Disease for those infected often include fever, headache, fatigue, chills, swollen lymph nodes, muscle or joint pain, and other flu-like symptoms. As discussed above, many suffer notice a bulls-eye like rash called “erythema migrans” which is a sign of the bacteria multiplying in the bloodstream. If the Lyme disease is left untreated, Lyme patients may experience a wide range of physical and neurological symptoms including, loss of memory or concentration, depression, speech problems, pain and swelling, cough, facial palsy or muscle loss and more.\\n Lyme disease can be broken down into 3 stages.\\n Stage 1 (Detection)\\nOften called the “early localized” stage of lyme disease, symptoms start a few days to a few weeks after contraction. The symptoms of stage 1 lyme disease mimic those of the flu making them difficult to link to a bite, especially if the sufferer does not remember being bitten.\\n \n", + "1016 When multiplying, many patients experience little or no symptoms, leading them to believe the bacteria is no longer within their system. When symptoms reappear, they often mimic that of other illnesses like arthritis, multiple sclerosis, chronic fatigue syndrome, fibromyalgia, depression, insomnia and other autoimmune disorders. It is important to mention that patients who have received treatment for lyme disease may still be at risk. Post-treatment lyme disease (PTLD) may affect up to 50% of people who receive treatment at stage 1. Stage 3 symptoms are severe and debilitating and can include:\\nExtreme Fatigue\\nChronic Pain and Soreness\\nDepression, Anxiety, Stress\\nCognitive Impairment\\nMigraines and Headaches\\nArthritis\\nInsomnia\\nBell’s Palsy\\nLoss of Vision\\nHearing Impairment\\nIrregular Heartbeat\\nLyme disease is most often diagnosed and confirmed through blood tests or testing of the cerebrospinal fluid. During a blood test, blood is usually drawn from a vein in the arm and tested for the antibodies a person’s body produces in response to the invading Borrelia burgdorferi. Patients experiencing neurological symptoms may be recommended a spinal tap to test the cerebrospinal fluid. 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0e1786a4d-2b3d-4fcf-ad84-443acf3696e2journalist risksIPI Death Watch: 45 journalists killed in 2021 - International Press Institute\\nNewsroom\\nIPI Death Watch: 45 journalists killed in 2021\\nViolence against journalists remains global challenge\\nIPI Contributor Anne ter Rele\\nDec 25, 2016\\nSHARE\\n In 2021, a total of 45 journalists were killed in connection with their work, IPI research revealed today. The sombre tally reflects the continued risks of doing journalism and reaffirms journalist safety as a global challenge. IPI calls on authorities to end impunity for these crimes and to ensure the protection of journalists, who must be able to do their work freely and safely.\\n Today, the IPI global network published its yearly Death Watch. IPI’s research shows that since the beginning of 2021, a total of 45 journalists were killed in connection with their work, or lost their lives on assignment. Of these 45 journalists, 40 were male and five were female. A total of 28 were targeted due to their work, while three were killed while covering conflict, two lost their lives covering civil unrest, and one journalist was killed while on assignment.
18e64b35c-60be-4292-9542-c4639a749336journalist risksTwo journalists were killed in the MENA region, both in Yemen, while nine journalists were killed in Sub-Saharan Africa, most of whom in the Democratic Republic of Congo (3), followed by Burkina Faso and Somalia (both 2).\\n Like last year, more journalists were killed in Mexico (7) in 2021 than any other country in the world. All seven cases were targeted killings. According to IPI’s analysis, journalists researching local politics and organized crime, including drug trafficking, are especially at risk. Most of the targeted journalists were examining these or related topics.\\n Another explanation for the high number of killed journalists is the level of impunity. According to ARTICLE 19 Mexico, in only one of these seven cases have suspects have been arrested. The continued high number of killings confirms Mexico’s status as one of the deadliest countries for journalists to work. Despite this tragic status quo, the government there has decided to stop funds allocated for upholding the Law for the Protection of Human Rights Defenders and Journalists (LPPDHP).\\n After Mexico, Afghanistan and India are the two next-deadliest countries, with six killings each. In Afghanistan, most killings occurred in relation to the violent conflict as a result of the Taliban’s takeover of the country this summer.
2a8ff3f18-9b80-40b9-b529-799ed3367980journalist risksImportantly, journalists were also murdered in countries with relatively high levels of press freedom, which arguably shows the global nature of the risks of doing journalism. For example, high-risk crime journalist Peter R. De Vries was shot on an Amsterdam city street in broad daylight on July 6, 2021, despite the fact that the Netherlands is considered one of the countries with the highest degree of press freedom in the world. The De Vries case is currently classified as Under Investigation on IPI’s Death Watch. In Greece, crime reporter Giorgos Karaivaz was shot outside his home in Athens. As of December 2021, no suspects have been publicly identified and no arrests have been made, while public information about the status of the investigation remains scarce.\\nImpunity\\nIPI’s analysis shows an alarmingly insufficient response from authorities to the killings, leading to high levels of impunity for crimes against journalists. In only six of the 28 cases have local police reportedly arrested suspects.
380d821ed-94b3-4fb6-9159-a71089c869a2journalist risksHire a Journalist, Reporter, Correspondent in Ukraine - paydesk\\nGlobal Directory - All Countries - Journalists in Ukraine\\nJournalists in Ukraine\\n\\nStefan Sigaard Weichert\\nHi. I am a freelance journalist living in Kyiv, Ukraine, where I report about politics, war and social stories from Eastern Europe. I am a former Erasmus Mundus student from Swansea University in the UK, where I...\\nKyiv, Ukraine\\nDanish German English Russian\\nLive Phoner for Radio Audio package Interview News Gathering Feature Stories Content Writing Risk Analysis Research Investigative Journalism Interview\\nBusiness War Reporter Social Politics Risk Analysis Climate Change Current Affairs Natural Disasters Investigative Reporting\\nAvailable\\nBook\\nJoti Heir\\nJoti Heir is a Canadian journalist experienced in reporting for live radio and television. She has produced and reported for CTV, CBC and Astral Media. Additionally, she has written for a variety of publications.\\n Kyiv, Ukraine\\nEnglish Punjabi Turkish\\nLive On-camera Online Live Phoner for Radio News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview\\nAvailable\\nBook\\nAnna Chernikova\\nAnna Chernikova is a journalist based in Kyiv, Ukraine.\\n
4709154d7-13c6-492c-b977-07396510909cjournalist risksCovered Euromaidan in Kyiv (also known as the Ukrainian revolution of Dignity), Russian invasion of...\\nKyiv, Ukraine\\nEnglish Polish Russian Ukrainian\\nPhotography Fixing\\nNatural Disasters Breaking News Social Travel Politics Cultural Sports War Reporter\\n Available\\nBook\\nJack Losh\\nJack Losh is a journalist, producer/director and photographer with a focus on conflict, conservation and humanitarian issues. With assignments spanning Europe, Africa and the Middle East, he has been shortlisted for...\\nCrimea\\nEnglish French Russian\\nLive Stand-up Live On-camera Online Live Phoner for Radio Video Package Interview Documentary Films News Gathering Feature Stories Content Writing Corporate Content Risk Analysis Research Investigative Journalism Interview Photography Fixing Live Phoner for TV Video Rushes Journalism\\nMedia Training Travel Natural Disasters Science & Environment War Reporter Current Affairs Risk Analysis Investigative Reporting Breaking News Research Climate Change\\nAvailable\\nBook\\nSergey Myasoedov\\nSergey Myasoedov is a video journalist based in Kyiv, Ukraine.\\n Kyiv, Ukraine\\nEnglish Russian Ukrainian\\nVideo Package Vox Pop News Gathering Research Photography Fixing Video Rushes Interview\\nResearch Social Politics Medical Cultural Investigative Reporting Climate Change Sports War Reporter Current Affairs Natural Disasters Breaking News\\nAvailable\\nBook\\nOxana Bekheshti\\nOxana Bekheshti is a journalist based in Kyiv, Ukraine.\\n
............
1013527a986a-c2d9-46b6-8a14-3f305beaf7fdLyme diseaseLyme disease is medically described in three phases (stages) as\\nearly localized disease with skin inflammation and rash;\\nearly disseminated disease with heart and nervous system involvement, including palsies and meningitis; and\\nlate disease featuring motor and sensory nerve damage and brain inflammation, as well as arthritis.\\n In the early phase of the illness, within days to weeks of the tick bite, the skin around the bite develops an expanding ring of unraised redness. There may be an outer ring of brighter redness with or without a central area of clearing, leading to a \"bulls-eye\" appearance. This classic initial rash is called \"erythema migrans\" (formerly called erythema chronicum migrans). Patients often can't recall the tick bite (the ticks can be as small as the periods in this paragraph). Also, they may not have the identifying rash to signal the doctor. The rash may or may not itch. More than one in four patients never even develop a rash. The redness of the skin is often accompanied by generalized fatigue, muscle and joint pain and stiffness, swollen lymph nodes (\"swollen glands\"), headache, and less often fever, resembling symptoms of a viral infection.\\n The redness resolves, without treatment, in about a month.
10144c8dee20-f7cb-44b3-ae69-8ea00a7e2edeLyme diseaseShe opens up about it in her new book, “Believe Me.”\\nSuzanne Somer’s interviews her granddaughter Violet about her struggle with Lyme and her successful treatment at Sponaugle Wellness Institute\\nLyme disease is a tick-borne infection caused by a bacteria called Borrelia burgdorferi. Lyme disease is most often transmitted by black-legged tick bites and in recent studies has also been found in mosquitos. The Lyme literate medical team at Sponaugle Wellness Institute have also concluded it can be sexually transmitted or passed on to an unborn child.\\n Black-legged ticks, also known as deer ticks, are most prevalent in the Northeast United States but have been found in all 50 states and Canada. These ticks live in wooded and grassy areas. Cases of Lyme disease are on the rise in recent years showing reported cases nearly doubling since 2014. The CDC estimates cases in the United States at more than 300,000 people infected annually while acknowledging that Lyme disease can be difficult to diagnose.\\n Borrelia burgdorferi, the bacteria that causes Lyme disease, is a corkscrew or helix-shaped organism called a spirochete. These spirochetes are known for being serious pathogens with Lyme disease’s cellular make-up being close to that of syphilis.
10154a773373-3f0b-4821-a78e-86ce9bc429daLyme diseaseSymptoms of Lyme Disease for those infected often include fever, headache, fatigue, chills, swollen lymph nodes, muscle or joint pain, and other flu-like symptoms. As discussed above, many suffer notice a bulls-eye like rash called “erythema migrans” which is a sign of the bacteria multiplying in the bloodstream. If the Lyme disease is left untreated, Lyme patients may experience a wide range of physical and neurological symptoms including, loss of memory or concentration, depression, speech problems, pain and swelling, cough, facial palsy or muscle loss and more.\\n Lyme disease can be broken down into 3 stages.\\n Stage 1 (Detection)\\nOften called the “early localized” stage of lyme disease, symptoms start a few days to a few weeks after contraction. The symptoms of stage 1 lyme disease mimic those of the flu making them difficult to link to a bite, especially if the sufferer does not remember being bitten.\\n
1016369ce954-c156-485c-8cdc-37e0137287d2Lyme diseaseWhen multiplying, many patients experience little or no symptoms, leading them to believe the bacteria is no longer within their system. When symptoms reappear, they often mimic that of other illnesses like arthritis, multiple sclerosis, chronic fatigue syndrome, fibromyalgia, depression, insomnia and other autoimmune disorders. It is important to mention that patients who have received treatment for lyme disease may still be at risk. Post-treatment lyme disease (PTLD) may affect up to 50% of people who receive treatment at stage 1. Stage 3 symptoms are severe and debilitating and can include:\\nExtreme Fatigue\\nChronic Pain and Soreness\\nDepression, Anxiety, Stress\\nCognitive Impairment\\nMigraines and Headaches\\nArthritis\\nInsomnia\\nBell’s Palsy\\nLoss of Vision\\nHearing Impairment\\nIrregular Heartbeat\\nLyme disease is most often diagnosed and confirmed through blood tests or testing of the cerebrospinal fluid. During a blood test, blood is usually drawn from a vein in the arm and tested for the antibodies a person’s body produces in response to the invading Borrelia burgdorferi. Patients experiencing neurological symptoms may be recommended a spinal tap to test the cerebrospinal fluid. In a spinal tap, fluid is drawn a lumbar puncture between two vertebrae in the lower spine.\\n These tests reveal if there are antibodies in the blood or cerebrospinal fluid.
1017931bbd92-c9c7-48af-b91f-9cfb7c2abdcbLyme diseaseLyme Disease\\nPublic Health\\nLyme Disease\\nLyme Disease\\nIf you have Lyme disease, there’s no doubt you have already done your own research and read a lot of information about the disease. As scientists and physicians, we are concerned about your health. That’s why we want you to have the most up-to-date information about Lyme disease.\\n Clinical Practice Guidelines\\nIDSA, AAN and ACR release guidelines for prevention, diagnosis and treatment of Lyme disease
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "input_data", + "summary": "{\n \"name\": \"input_data\",\n \"rows\": 1018,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1018,\n \"samples\": [\n \"5157c731-3a48-43e4-adb5-cd2d2d098d0e\",\n \"8202d553-201a-47b0-8a85-ba63c52b8b8b\",\n \"df5e80a5-a4cf-4b6e-912d-45836b08c1bf\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"query\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 13,\n \"samples\": [\n \"art, stolen, forged\",\n \"Are social networking sites good for our society?\",\n \"journalist risks\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"unknown\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \" OLYMPUS Power, LLC\\nEdom Hills Wind Farm \\u00bb\\nKeystone Generating Station \\u00bb\\nOlympus Power, LLC is a leading power plant investment and management firm with assets located throughout the United States.\\nLOCATIONS\\nPROJECT GALLERY \",\n \"1.2K views\\nView upvotes\\nRelated Answer\\nBrian Jones\\n, 30 years law enforcement experience\\nAnswered Jan 14, 2022 \\u00b7 Author has 4.7K answers and 2.8M answer views\\nHow effective are body cameras on the police?\\nToo effective for some: tfey show the police running around all day doing the right things\\n124 views\\nView upvotes\\nRelated Answer\\nQuora User\\n, Retired Police officer\\nAnswered 5 years ago \\u00b7 Author has 1.2K answers and 7.5M answer views\\nWhy are body cameras effective with police?\\nBecause they can accurately show an observer who wasn't there exactly what words were spoken, the tone of voice used by all involved as well as any behavior by officer or civilian.\\n People usually think of them being most beneficial to civilians, but there is one incident that sticks out in my mind in which the benefit was definitely the officers\\u2019. But it easily could have gone the other way had there been no video or audio.\\n The officer had stopped a woman for a traffic infraction and she was on her cell phone as well. \",\n \" Financial Advisers Australia (FAA) / AFS Licensees Search\\nThe Sydney Morning Herald\\nHome\\nFinancial Advisers Australia (FAA)\\nFinancial Advisers Australia (FAA)\\nAustralian Financial Services Licensees are required to keep the details of their financial advisers up to date on ASIC's Financial Advisers Register. Information contained in the register is made available to the public to search via ASIC's Moneysmart website.\\n \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 31 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Step 2: load all the libraries" + ], + "metadata": { + "id": "CB_34eKz68Pn" + } + }, + { + "cell_type": "code", + "source": [ + "from snorkel.labeling import labeling_function, PandasLFApplier\n", + "from snorkel.labeling.model.label_model import LabelModel\n", + "from rank_bm25 import BM25Okapi\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sentence_transformers import SentenceTransformer\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "import textdistance\n", + "import pandas as pd\n", + "vectorizer = TfidfVectorizer()\n", + "import re" + ], + "metadata": { + "id": "5D-oLP_54FtB" + }, + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import nltk\n", + "from nltk.corpus import stopwords, wordnet\n", + "from nltk.stem import WordNetLemmatizer\n", + "# Download stopwords if not already present\n", + "nltk.download(\"stopwords\")\n", + "stop_words = set(stopwords.words(\"english\"))\n", + "nltk.download(\"wordnet\")\n", + "lemmatizer = WordNetLemmatizer()" + ], + "metadata": { + "id": "qBVEpFOn4LNN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7fe994b3-3f68-43fc-bfdc-794121e11125" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Step 3: define global variables" + ], + "metadata": { + "id": "NpyWVji-7C1f" + } + }, + { + "cell_type": "code", + "source": [ + "# Global Variables\n", + "min_bm25 = None\n", + "max_bm25 = None\n", + "bm25 = None\n" + ], + "metadata": { + "id": "w7M09OVo4CQb" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Step 4: do a min-max normalisation for BM25, functions that tokenise and precompute n-grams" + ], + "metadata": { + "id": "JDphg1r9jQBE" + } + }, + { + "cell_type": "code", + "source": [ + "def tokenize(text):\n", + " \"\"\"Tokenizes text into lowercase words without punctuation.\"\"\"\n", + " return re.findall(r'\\w+', text.lower())\n", + "\n", + "def normalize_bm25(bm25_score):\n", + " \"\"\"Normalizes BM25 using min-max scaling.\"\"\"\n", + " if min_bm25 == max_bm25:\n", + " return 0.0\n", + " return (bm25_score - min_bm25) / (max_bm25 - min_bm25)\n", + "\n", + "\n", + "def tokenize_and_lemmatize(text):\n", + " \"\"\"Tokenizes text into lowercase words without punctuation and lemmatizes words.\"\"\"\n", + " tokens = re.findall(r'\\w+', text.lower()) # Tokenization\n", + " return {lemmatizer.lemmatize(word) for word in tokens} - stop_words\n", + "\n", + "def get_ngrams(text, n=2):\n", + " \"\"\"Generate n-grams from text\"\"\"\n", + " tokens = list(tokenize_and_lemmatize(text))\n", + " return set(zip(*[tokens[i:] for i in range(n)])) if len(tokens) >= n else set()\n", + "\n", + "def make_bm25_score_prob(bm25_model, doc_lookup):\n", + " def f(x):\n", + " query_tokens = tokenize(x.query)\n", + " doc_idx = doc_lookup.get(x.name) # x.name is the row index\n", + " if doc_idx is None:\n", + " return -1 # Abstain\n", + " score = bm25_model.get_scores(query_tokens)[doc_idx]\n", + " return normalize_bm25(score)\n", + " return labeling_function()(f)" + ], + "metadata": { + "id": "yFpLW_LO4iop" + }, + "execution_count": 35, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "tokenized_docs = [tokenize(doc) for doc in input_data['unknown']]\n", + "bm25_model = BM25Okapi(tokenized_docs)\n", + "doc_lookup = dict(zip(input_data.index, range(len(input_data))))\n" + ], + "metadata": { + "id": "kMRT2wwNrW-6" + }, + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Step 5: Create Snorkel labelling functions" + ], + "metadata": { + "id": "Y1zKhUPTW52w" + } + }, + { + "cell_type": "code", + "source": [ + "bm25_score_prob = make_bm25_score_prob(bm25_model, doc_lookup)\n", + "\n", + "\n", + "@labeling_function()\n", + "def boolean_match_prob(x):\n", + " \"\"\"Returns 1 if at least one query term appears in the document.\"\"\"\n", + " return 1.0 if set(tokenize(x.query)) & set(tokenize(x.unknown)) else 0.0\n", + "\n", + "\n", + "@labeling_function()\n", + "def word_level_levenshtein_prob(x):\n", + " \"\"\"Computes a boosted word-level Levenshtein similarity\"\"\"\n", + " query_words = tokenize_and_lemmatize(x.query)\n", + " doc_words = tokenize_and_lemmatize(x.unknown)\n", + "\n", + " if not query_words or not doc_words:\n", + " return 0.0 # Avoid division by zero\n", + "\n", + " matched_scores = []\n", + " for q in query_words:\n", + " similarities = [textdistance.levenshtein.normalized_similarity(q, d) for d in doc_words]\n", + " best_match = max(similarities) if similarities else 0.0\n", + " matched_scores.append(best_match)\n", + "\n", + " # Compute final score\n", + " avg_similarity = sum(matched_scores) / len(matched_scores) if matched_scores else 0.0\n", + " boosted_score = avg_similarity * (1.5 if avg_similarity > 0.8 else 1.0)\n", + "\n", + " return min(boosted_score, 1.0) #Scores in range [0,1]\n", + "\n", + "@labeling_function()\n", + "def relaxed_bigram_match_prob(x):\n", + " \"\"\"Computes the proportion of query bigrams where at least one word appears in the document.\"\"\"\n", + " query_bigrams = get_ngrams(x.query, n=2)\n", + " doc_bigrams = get_ngrams(x.unknown, n=2)\n", + "\n", + " if not query_bigrams:\n", + " return 0.0 # Avoid division by zero\n", + "\n", + " matched_bigrams = set()\n", + " unmatched_bigrams = set()\n", + "\n", + " for q_bigram in query_bigrams:\n", + " # If either word in the bigram appears in the document, count it as a partial match\n", + " if any(word in x.unknown for word in q_bigram):\n", + " matched_bigrams.add(q_bigram)\n", + " else:\n", + " unmatched_bigrams.add(q_bigram)\n", + " return len(matched_bigrams) / len(query_bigrams) # Fraction of bigrams with at least one match\n", + "\n", + "\n", + "@labeling_function()\n", + "def percent_matching_query_words(x):\n", + " \"\"\"Computes the percentage of query words that appear in the document.\"\"\"\n", + "\n", + " if not x.query or not x.unknown:\n", + " return 0.0 # Handle missing data\n", + "\n", + " # Tokenization: Split words and remove punctuation\n", + " query_words = set(re.split(r'\\W+', x.query.lower())) - stop_words\n", + " doc_words = set(re.split(r'\\W+', x.unknown.lower())) - stop_words\n", + "\n", + " if not query_words: # Avoid division by zero\n", + " return 0.0\n", + "\n", + " # Compute match percentage\n", + " matching_words = query_words.intersection(doc_words)\n", + " match_percent = len(matching_words) / len(query_words) # Fraction of matched query words\n", + "\n", + " return match_percent # Already in [0,1] range\n", + "\n", + "\n", + "# Load a strong BERT model for semantic similarity\n", + "bert_model = SentenceTransformer(\"all-mpnet-base-v2\") # Better than MiniLM for ranking tasks\n", + "\n", + "@labeling_function()\n", + "def bert_cosine_similarity(x):\n", + " \"\"\"Computes boosted BERT cosine similarity as a probability of relevance.\"\"\"\n", + "\n", + " if not x.query or not x.unknown:\n", + " return 0.0 # Handle missing data\n", + "\n", + " # Encode query and document\n", + " query_vec = bert_model.encode(x.query, convert_to_numpy=True)\n", + " doc_vec = bert_model.encode(x.unknown, convert_to_numpy=True)\n", + "\n", + " # Handle cases where vectors are zero (to avoid division by zero)\n", + " if np.linalg.norm(query_vec) == 0 or np.linalg.norm(doc_vec) == 0:\n", + " return 0.0\n", + "\n", + " # Compute cosine similarity\n", + " cosine_sim = np.dot(query_vec, doc_vec) / (np.linalg.norm(query_vec) * np.linalg.norm(doc_vec) + 1e-9)\n", + "\n", + " # Normalize cosine similarity to [0,1]\n", + " probability = (cosine_sim + 1) / 2\n", + "\n", + " # Apply Boosting\n", + " if probability > 0.8:\n", + " probability *= 1.5 # Boost high similarities\n", + " elif probability > 0.5:\n", + " probability *= 1.2 # Moderate boost for medium similarities\n", + "\n", + " return min(probability, 1.0) # Keep score within [0,1]\n", + "\n", + "\n", + "\n", + "@labeling_function()\n", + "def query_term_coverage(x):\n", + " \"\"\"Computes a relaxed query term coverage using lemmatization and synonym matching.\"\"\"\n", + "\n", + " if not x.query or not x.unknown:\n", + " return 0.0 # Handle missing data\n", + "\n", + " # Tokenization: Extract words using regex\n", + " query_tokens = [lemmatizer.lemmatize(word) for word in re.findall(r'\\b\\w+\\b', x.query.lower())]\n", + " doc_tokens = [lemmatizer.lemmatize(word) for word in re.findall(r'\\b\\w+\\b', x.unknown.lower())]\n", + "\n", + " # Create word sets\n", + " query_words = set(query_tokens) - stop_words # Remove stopwords\n", + " doc_words = set(doc_tokens)\n", + "\n", + " if not query_words: # Avoid division by zero\n", + " return 0.0\n", + "\n", + " # === Relaxed Matching: Check for Synonyms ===\n", + " def word_matches(word):\n", + " if word in doc_words:\n", + " return True # Exact match\n", + " synonyms = {lemma.name() for syn in wordnet.synsets(word) for lemma in syn.lemmas()} # Get synonyms\n", + " return bool(synonyms.intersection(doc_words)) # Check if any synonym is in the doc\n", + "\n", + " covered_terms = sum(1 for word in query_words if word_matches(word))\n", + " coverage = covered_terms / len(query_words) # Fraction of query terms covered by document\n", + " if coverage > 0.7:\n", + " coverage *= 1.8 # Strong boost\n", + " elif coverage > 0.5:\n", + " coverage *= 1.5 # Moderate boost\n", + " elif coverage > 0.2:\n", + " coverage *= 1.2 # Mild boost\n", + "\n", + " coverage = min(coverage, 1.0) # Keep within [0,1]\n", + " return coverage\n", + "\n", + "\n", + "# Global TF-IDF vectorizer with n-grams for better lexical matching\n", + "tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,2), min_df=1)\n", + "\n", + "@labeling_function()\n", + "def tfidf_cosine_similarity(x):\n", + " \"\"\"Computes a boosted TF-IDF cosine similarity for document relevance probability.\"\"\"\n", + "\n", + " if not x.query or not x.unknown:\n", + " return 0.0 # Handle missing data\n", + "\n", + " # Create TF-IDF matrix\n", + " corpus = [x.query, x.unknown]\n", + " tfidf_matrix = tfidf_vectorizer.fit_transform(corpus)\n", + "\n", + " # Ensure matrix is valid\n", + " if tfidf_matrix.shape[0] < 2 or tfidf_matrix.nnz == 0:\n", + " return 0.0 # Avoid invalid similarity computation\n", + "\n", + " # Compute cosine similarity\n", + " tfidf_sim = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])[0, 0]\n", + "\n", + " tfidf_prob = max(tfidf_sim, 0.0)\n", + "\n", + " if tfidf_prob > 0.7:\n", + " tfidf_prob *= 1.8 # Strong boost\n", + " elif tfidf_prob > 0.5:\n", + " tfidf_prob *= 1.5 # Moderate boost\n", + " elif tfidf_prob > 0.2:\n", + " tfidf_prob *= 1.2 # Mild boost\n", + "\n", + " return min(tfidf_prob, 1.0) # Keep within [0,1]\n", + "\n", + "\n" + ], + "metadata": { + "id": "VhMWSVtm4yDh" + }, + "execution_count": 37, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "6. Train Snorkel" + ], + "metadata": { + "id": "fXEgFJZ_mtwo" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "lfs = [bm25_score_prob, boolean_match_prob, word_level_levenshtein_prob, tfidf_cosine_similarity, bert_cosine_similarity]\n", + "\n", + "applier = PandasLFApplier(lfs=lfs)\n", + "L_train = applier.apply(input_data)\n", + "\n", + "label_model = LabelModel(cardinality=2, verbose=True)\n", + "label_model.fit(L_train, n_epochs=100, log_freq=10)\n", + "train_prob = label_model.predict_proba(L_train)\n", + "train_prob_relevant = train_prob[:, 1]\n", + "\n", + "input_data['probability_relevant'] = train_prob_relevant" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8piLiWnPpZ1G", + "outputId": "2051dc3e-df06-4f87-b24a-06ae93d3e1e8" + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 1018/1018 [18:35<00:00, 1.10s/it]\n", + "100%|██████████| 100/100 [00:00<00:00, 781.09epoch/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "WMODEL = \"Snorkel\"\n", + "\n", + "!rm -Rf run\n", + "\n", + "\n", + "with tracking(export_file_path='run/.metadata.yml', export_format=ExportFormat.IR_METADATA) as tracked:\n", + " queries = set(input_data['query'].unique())\n", + "\n", + " def unknown_documents(query):\n", + " docs = set(input_data[input_data['query'] == query]['unknown'].unique())\n", + " return {f'{i[0]}-unkn': i[1] for i in zip(range(len(docs)), docs)}\n", + "\n", + " results = {}\n", + "\n", + " for query in tqdm(queries):\n", + " unk = unknown_documents(query)\n", + "\n", + " # Build index\n", + " docs = [{'docno': k, 'text': v} for k, v in unk.items()]\n", + " indexer = pt.IterDictIndexer(\"/tmp/index\", overwrite=True, meta={'docno': 100, 'text': 20480})\n", + " index_ref = indexer.index(docs)\n", + " retriever = pt.BatchRetrieve(index_ref, wmodel=WMODEL)\n", + " rels = input_data[input_data['query'] == query][['unknown', 'probability_relevant']]\n", + " rel_dict = dict(zip(rels['unknown'], rels['probability_relevant']))\n", + "\n", + " results[query] = rel_dict\n", + "\n", + " predictions = []\n", + " for _, i in input_data.iterrows():\n", + " res = results[i['query']]\n", + " predictions.append({\n", + " 'id': i['id'],\n", + " 'probability_relevant': res.get(i['unknown'], -1)\n", + " })\n", + " predictions = pd.DataFrame(predictions)\n", + "\n", + "predictions" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 442 + }, + "id": "eyZfnmDHjvz0", + "outputId": "03ff4f86-6ba8-435f-da3a-c4e3d2cd77e7" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 13/13 [00:11<00:00, 1.17it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id probability_relevant\n", + "0 e1786a4d-2b3d-4fcf-ad84-443acf3696e2 0.782072\n", + "1 8e64b35c-60be-4292-9542-c4639a749336 0.000311\n", + "2 a8ff3f18-9b80-40b9-b529-799ed3367980 0.318625\n", + "3 80d821ed-94b3-4fb6-9159-a71089c869a2 0.318625\n", + "4 709154d7-13c6-492c-b977-07396510909c 0.318625\n", + "... ... ...\n", + "1013 527a986a-c2d9-46b6-8a14-3f305beaf7fd 0.782072\n", + "1014 4c8dee20-f7cb-44b3-ae69-8ea00a7e2ede 0.782072\n", + "1015 4a773373-3f0b-4821-a78e-86ce9bc429da 0.782072\n", + "1016 369ce954-c156-485c-8cdc-37e0137287d2 0.782072\n", + "1017 931bbd92-c9c7-48af-b91f-9cfb7c2abdcb 0.782072\n", + "\n", + "[1018 rows x 2 columns]" + ], + "text/html": [ + "\n", + "
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We rank all unknown documents that are to-be judged against the query. The probability that a document is relevant is then the min-max normalized rank of an unknown document in the ranking of all unknown documents to the query.'\n", + ")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O_insImNmdzT", + "outputId": "a9a47e90-dbdb-4466-b18a-0f65411d3e11" + }, + "execution_count": 40, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/3ecb6039-9455-4636-86a5-8645c806559d\n", + "No truth data is available yet. The evaluation is possible after the deadline when the truth data was published.\n" + ] + } + ] + } + ] } \ No newline at end of file diff --git a/ecir25/data/create-pairwise-data.py b/ecir25/data/create-pairwise-data.py index 8ed98d6..5493221 100755 --- a/ecir25/data/create-pairwise-data.py +++ b/ecir25/data/create-pairwise-data.py @@ -1,128 +1,128 @@ -#!/usr/bin/env python3 -from tira.third_party_integrations import ir_datasets -import uuid -import json - -dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training') - -def browse_dataset(dataset): - qid_to_rel = {} - - for qrel in dataset.qrels_iter(): - if qrel.query_id not in qid_to_rel: - qid_to_rel[qrel.query_id] = {} - - if qrel.relevance not in qid_to_rel[qrel.query_id]: - qid_to_rel[qrel.query_id][qrel.relevance] = 0 - - qid_to_rel[qrel.query_id][qrel.relevance] += 1 - - for query in dataset.queries_iter(): - print(query.query_id, '->', query.title, qid_to_rel[query.query_id]) - - - -def select_qrels(dataset, qid, target_relevance): - for qrel in dataset.qrels_iter(): - - if str(qrel.query_id) == str(qid) and qrel.relevance == target_relevance: - print(qrel) - -def calculate_pairwise_data(qid_to_split, dataset): - qid_to_inference_pairs = {} - qid_to_query = {} - qid_to_doc_to_rel = {} - docs_store = dataset.docs_store() - - for query in dataset.queries_iter(): - qid_to_query[query.query_id] = query.default_text() - - for qrel in dataset.qrels_iter(): - if str(qrel.query_id) not in qid_to_doc_to_rel: - qid_to_doc_to_rel[str(qrel.query_id)] = {} - - qid_to_doc_to_rel[str(qrel.query_id)][str(qrel.doc_id)] = qrel.relevance - - for qid in qid_to_split: - for training_doc in qid_to_split[qid]['training']: - for test_doc in qid_to_split[qid]['test']: - if test_doc in qid_to_split[qid]['training']: - raise ValueError('foo') - - if (training_doc, test_doc) in qid_to_inference_pairs: - raise ValueError('foo') - - qid_to_inference_pairs[(training_doc, test_doc)] = { - 'id': str(uuid.uuid4()), - 'query': qid_to_query[str(qid)], - 'query_id': qid, - 'relevant': docs_store.get(training_doc).default_text(), - 'relevant_doc_id': training_doc, - 'unknown': docs_store.get(test_doc).default_text(), - 'unknown_doc_id': test_doc, - 'qrel_unknown_doc': qid_to_doc_to_rel[str(qid)][str(test_doc)] - } - - return qid_to_inference_pairs - -def persist_data_pairwise(qid_to_split, dataset): - qid_to_inference_pairs = calculate_pairwise_data(qid_to_split, dataset) - - with open('smoke-test-dataset/pairwise/inputs/inputs.jsonl', 'w') as input_file, open('smoke-test-dataset/pairwise/labels/labels.jsonl', 'w') as labels_file: - for training_doc, test_doc in qid_to_inference_pairs.keys(): - i = qid_to_inference_pairs[(training_doc, test_doc)].copy() - t = qid_to_inference_pairs[(training_doc, test_doc)].copy() - - del i['query_id'] - del i['relevant_doc_id'] - del i['unknown_doc_id'] - del i['qrel_unknown_doc'] - - input_file.write(json.dumps(i) + '\n') - labels_file.write(json.dumps(t) + '\n') - -def persist_data_pointwise(qid_to_split, dataset): - qid_to_inference_pairs = calculate_pairwise_data(qid_to_split, dataset) - covered_ids = set() - with open('smoke-test-dataset/pointwise/inputs/inputs.jsonl', 'w') as input_file, open('smoke-test-dataset/pointwise/labels/labels.jsonl', 'w') as labels_file: - for training_doc, test_doc in qid_to_inference_pairs.keys(): - i = qid_to_inference_pairs[(training_doc, test_doc)].copy() - t = qid_to_inference_pairs[(training_doc, test_doc)].copy() - - if i['unknown_doc_id'] in covered_ids: - continue - - covered_ids.add(i['unknown_doc_id']) - - - del t['relevant_doc_id'] - del t['relevant'] - del i['query_id'] - del i['relevant_doc_id'] - del i['relevant'] - del i['unknown_doc_id'] - del i['qrel_unknown_doc'] - - input_file.write(json.dumps(i) + '\n') - labels_file.write(json.dumps(t) + '\n') - - -QID_TO_SPLITS = { - # who sings monk theme song {0: 100, 2: 7, 1: 30, 3: 3} - 1051399: { - 'training': set(['2530579', '3943228', '69813']), - 'test': set(['7865069', '4642930', '3376628', '5040048', '3108511', '2378828', '4426187']) - }, - - 833860: { - 'training': set(['5167510', '2206197', '115142']), - 'test': set(['1889863', '7831470', '45135', '6943600', '7103434', '2830558']), - } -} - -if __name__ == '__main__': - persist_data_pairwise(QID_TO_SPLITS, dataset) - persist_data_pointwise(QID_TO_SPLITS, dataset) - # print('foo') - # select_qrels(dataset, 833860, 0) - print('finished') +#!/usr/bin/env python3 +from tira.third_party_integrations import ir_datasets +import uuid +import json + +dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training') + +def browse_dataset(dataset): + qid_to_rel = {} + + for qrel in dataset.qrels_iter(): + if qrel.query_id not in qid_to_rel: + qid_to_rel[qrel.query_id] = {} + + if qrel.relevance not in qid_to_rel[qrel.query_id]: + qid_to_rel[qrel.query_id][qrel.relevance] = 0 + + qid_to_rel[qrel.query_id][qrel.relevance] += 1 + + for query in dataset.queries_iter(): + print(query.query_id, '->', query.title, qid_to_rel[query.query_id]) + + + +def select_qrels(dataset, qid, target_relevance): + for qrel in dataset.qrels_iter(): + + if str(qrel.query_id) == str(qid) and qrel.relevance == target_relevance: + print(qrel) + +def calculate_pairwise_data(qid_to_split, dataset): + qid_to_inference_pairs = {} + qid_to_query = {} + qid_to_doc_to_rel = {} + docs_store = dataset.docs_store() + + for query in dataset.queries_iter(): + qid_to_query[query.query_id] = query.default_text() + + for qrel in dataset.qrels_iter(): + if str(qrel.query_id) not in qid_to_doc_to_rel: + qid_to_doc_to_rel[str(qrel.query_id)] = {} + + qid_to_doc_to_rel[str(qrel.query_id)][str(qrel.doc_id)] = qrel.relevance + + for qid in qid_to_split: + for training_doc in qid_to_split[qid]['training']: + for test_doc in qid_to_split[qid]['test']: + if test_doc in qid_to_split[qid]['training']: + raise ValueError('foo') + + if (training_doc, test_doc) in qid_to_inference_pairs: + raise ValueError('foo') + + qid_to_inference_pairs[(training_doc, test_doc)] = { + 'id': str(uuid.uuid4()), + 'query': qid_to_query[str(qid)], + 'query_id': qid, + 'relevant': docs_store.get(training_doc).default_text(), + 'relevant_doc_id': training_doc, + 'unknown': docs_store.get(test_doc).default_text(), + 'unknown_doc_id': test_doc, + 'qrel_unknown_doc': qid_to_doc_to_rel[str(qid)][str(test_doc)] + } + + return qid_to_inference_pairs + +def persist_data_pairwise(qid_to_split, dataset): + qid_to_inference_pairs = calculate_pairwise_data(qid_to_split, dataset) + + with open('smoke-test-dataset/pairwise/inputs/inputs.jsonl', 'w') as input_file, open('smoke-test-dataset/pairwise/labels/labels.jsonl', 'w') as labels_file: + for training_doc, test_doc in qid_to_inference_pairs.keys(): + i = qid_to_inference_pairs[(training_doc, test_doc)].copy() + t = qid_to_inference_pairs[(training_doc, test_doc)].copy() + + del i['query_id'] + del i['relevant_doc_id'] + del i['unknown_doc_id'] + del i['qrel_unknown_doc'] + + input_file.write(json.dumps(i) + '\n') + labels_file.write(json.dumps(t) + '\n') + +def persist_data_pointwise(qid_to_split, dataset): + qid_to_inference_pairs = calculate_pairwise_data(qid_to_split, dataset) + covered_ids = set() + with open('smoke-test-dataset/pointwise/inputs/inputs.jsonl', 'w') as input_file, open('smoke-test-dataset/pointwise/labels/labels.jsonl', 'w') as labels_file: + for training_doc, test_doc in qid_to_inference_pairs.keys(): + i = qid_to_inference_pairs[(training_doc, test_doc)].copy() + t = qid_to_inference_pairs[(training_doc, test_doc)].copy() + + if i['unknown_doc_id'] in covered_ids: + continue + + covered_ids.add(i['unknown_doc_id']) + + + del t['relevant_doc_id'] + del t['relevant'] + del i['query_id'] + del i['relevant_doc_id'] + del i['relevant'] + del i['unknown_doc_id'] + del i['qrel_unknown_doc'] + + input_file.write(json.dumps(i) + '\n') + labels_file.write(json.dumps(t) + '\n') + + +QID_TO_SPLITS = { + # who sings monk theme song {0: 100, 2: 7, 1: 30, 3: 3} + 1051399: { + 'training': set(['2530579', '3943228', '69813']), + 'test': set(['7865069', '4642930', '3376628', '5040048', '3108511', '2378828', '4426187']) + }, + + 833860: { + 'training': set(['5167510', '2206197', '115142']), + 'test': set(['1889863', '7831470', '45135', '6943600', '7103434', '2830558']), + } +} + +if __name__ == '__main__': + persist_data_pairwise(QID_TO_SPLITS, dataset) + persist_data_pointwise(QID_TO_SPLITS, dataset) + # print('foo') + # select_qrels(dataset, 833860, 0) + print('finished') diff --git a/ecir25/data/create-pairwise-test-data.py b/ecir25/data/create-pairwise-test-data.py index 124c9fb..9986664 100755 --- a/ecir25/data/create-pairwise-test-data.py +++ b/ecir25/data/create-pairwise-test-data.py @@ -1,127 +1,127 @@ -#!/usr/bin/env python3 -from glob import glob -import json -from tqdm import tqdm -import gzip -import uuid -from pathlib import Path - -DATA_DIR = '/mnt/ceph/storage/data-in-progress/data-research/web-search/wows25/' - - -def parse_qrel(labels): - if labels == ['Not Relevant (0)']: - return 0 - elif labels == ['Relevant (1)']: - return 1 - elif labels == ['Highly Relevant (2)']: - return 2 - raise ValueError('foo') - -def load_qrels(): - ret = {} - for annotation_file in tqdm(glob(f'{DATA_DIR}/truth-data/*')): - with open(annotation_file, 'r') as f: - for l in f: - l = json.loads(l) - if l['query_id'] not in ret: - ret[l['query_id']] = {} - - label = parse_qrel(l['label']) - assert l['passage_id'] not in ret[l['query_id']], (l['passage_id']) - ret[l['query_id']][l['passage_id']] = label - return ret - -def load_pairwise_files(qrels): - ret = [] - - qrels_covered = {} - for pairwise_file in tqdm(glob(f'{DATA_DIR}/inputs/*')): - with gzip.open(pairwise_file, 'r') as f: - for l in f: - pair_id = str(uuid.uuid4()) - l = json.loads(l) - query_id = l['qid'] - if type(l['known_relevant_passage']) == str: - continue - query = l['query_text'] - relevant_doc_id = l['known_relevant_passage']['docno'] - relevant_doc = l['known_relevant_passage']['text'] - unknown_doc_id = l['passage_to_judge']['docno'] - unknown_doc = l['passage_to_judge']['text'] - - if unknown_doc_id not in qrels[query_id]: - continue - - if query_id not in qrels_covered: - qrels_covered[query_id] = set() - qrels_covered[query_id].add(unknown_doc_id) - ret.append({ - "id": pair_id, - "query": query, - "query_id": str(query_id), - "relevant": relevant_doc, - "relevant_doc_id": str(relevant_doc_id), - "unknown": unknown_doc, - "unknown_doc_id": str(unknown_doc_id), - "qrel_unknown_doc": qrels[query_id][unknown_doc_id], - "source_dataset_id": l['source_dataset_id'], - }) - - preds = 0 - for q in qrels.keys(): - for docid in qrels[q].keys(): - preds += 1 - if q not in qrels_covered or docid not in qrels_covered[q]: - raise ValueError('-->', q, docid) - print('Preds ' + str(preds) + ' -> ' + str(len(ret))) - return ret - -qrels = load_qrels() -pairwise_data = load_pairwise_files(qrels) - -Path(f'{DATA_DIR}/zenodo/test-pairwise/truths/').mkdir(parents=True, exist_ok=True) -Path(f'{DATA_DIR}/zenodo/test-pairwise/inputs/').mkdir(parents=True, exist_ok=True) -Path(f'{DATA_DIR}/zenodo/test-pointwise/truths/').mkdir(parents=True, exist_ok=True) -Path(f'{DATA_DIR}/zenodo/test-pointwise/inputs/').mkdir(parents=True, exist_ok=True) - -with open(f'{DATA_DIR}/zenodo/test-pairwise/truths/inputs.jsonl', 'w') as f: - for l in pairwise_data: - f.write(json.dumps(l) + '\n') - -with open(f'{DATA_DIR}/zenodo/test-pairwise/inputs/inputs.jsonl', 'w') as f: - for l in pairwise_data: - del l['source_dataset_id'] - del l['query_id'] - del l['relevant_doc_id'] - del l['unknown_doc_id'] - del l['qrel_unknown_doc'] - - f.write(json.dumps(l) + '\n') - -pointwise_data = [] -covered_ids = set() - -with open(f'{DATA_DIR}/zenodo/test-pairwise/truths/inputs.jsonl', 'r') as f: - for l in f: - l = json.loads(l) - if l['unknown_doc_id'] in covered_ids: - continue - - covered_ids.add(l['unknown_doc_id']) - l['id'] = str(uuid.uuid4()) - del l['relevant'] - del l['relevant_doc_id'] - pointwise_data.append(l) - -with open(f'{DATA_DIR}/zenodo/test-pointwise/truths/inputs.jsonl', 'w') as f: - for l in pointwise_data: - f.write(json.dumps(l) + '\n') - -with open(f'{DATA_DIR}/zenodo/test-pointwise/inputs/inputs.jsonl', 'w') as f: - for l in pointwise_data: - del l['source_dataset_id'] - del l['query_id'] - del l['unknown_doc_id'] - del l['qrel_unknown_doc'] - f.write(json.dumps(l) + '\n') +#!/usr/bin/env python3 +from glob import glob +import json +from tqdm import tqdm +import gzip +import uuid +from pathlib import Path + +DATA_DIR = '/mnt/ceph/storage/data-in-progress/data-research/web-search/wows25/' + + +def parse_qrel(labels): + if labels == ['Not Relevant (0)']: + return 0 + elif labels == ['Relevant (1)']: + return 1 + elif labels == ['Highly Relevant (2)']: + return 2 + raise ValueError('foo') + +def load_qrels(): + ret = {} + for annotation_file in tqdm(glob(f'{DATA_DIR}/truth-data/*')): + with open(annotation_file, 'r') as f: + for l in f: + l = json.loads(l) + if l['query_id'] not in ret: + ret[l['query_id']] = {} + + label = parse_qrel(l['label']) + assert l['passage_id'] not in ret[l['query_id']], (l['passage_id']) + ret[l['query_id']][l['passage_id']] = label + return ret + +def load_pairwise_files(qrels): + ret = [] + + qrels_covered = {} + for pairwise_file in tqdm(glob(f'{DATA_DIR}/inputs/*')): + with gzip.open(pairwise_file, 'r') as f: + for l in f: + pair_id = str(uuid.uuid4()) + l = json.loads(l) + query_id = l['qid'] + if type(l['known_relevant_passage']) == str: + continue + query = l['query_text'] + relevant_doc_id = l['known_relevant_passage']['docno'] + relevant_doc = l['known_relevant_passage']['text'] + unknown_doc_id = l['passage_to_judge']['docno'] + unknown_doc = l['passage_to_judge']['text'] + + if unknown_doc_id not in qrels[query_id]: + continue + + if query_id not in qrels_covered: + qrels_covered[query_id] = set() + qrels_covered[query_id].add(unknown_doc_id) + ret.append({ + "id": pair_id, + "query": query, + "query_id": str(query_id), + "relevant": relevant_doc, + "relevant_doc_id": str(relevant_doc_id), + "unknown": unknown_doc, + "unknown_doc_id": str(unknown_doc_id), + "qrel_unknown_doc": qrels[query_id][unknown_doc_id], + "source_dataset_id": l['source_dataset_id'], + }) + + preds = 0 + for q in qrels.keys(): + for docid in qrels[q].keys(): + preds += 1 + if q not in qrels_covered or docid not in qrels_covered[q]: + raise ValueError('-->', q, docid) + print('Preds ' + str(preds) + ' -> ' + str(len(ret))) + return ret + +qrels = load_qrels() +pairwise_data = load_pairwise_files(qrels) + +Path(f'{DATA_DIR}/zenodo/test-pairwise/truths/').mkdir(parents=True, exist_ok=True) +Path(f'{DATA_DIR}/zenodo/test-pairwise/inputs/').mkdir(parents=True, exist_ok=True) +Path(f'{DATA_DIR}/zenodo/test-pointwise/truths/').mkdir(parents=True, exist_ok=True) +Path(f'{DATA_DIR}/zenodo/test-pointwise/inputs/').mkdir(parents=True, exist_ok=True) + +with open(f'{DATA_DIR}/zenodo/test-pairwise/truths/inputs.jsonl', 'w') as f: + for l in pairwise_data: + f.write(json.dumps(l) + '\n') + +with open(f'{DATA_DIR}/zenodo/test-pairwise/inputs/inputs.jsonl', 'w') as f: + for l in pairwise_data: + del l['source_dataset_id'] + del l['query_id'] + del l['relevant_doc_id'] + del l['unknown_doc_id'] + del l['qrel_unknown_doc'] + + f.write(json.dumps(l) + '\n') + +pointwise_data = [] +covered_ids = set() + +with open(f'{DATA_DIR}/zenodo/test-pairwise/truths/inputs.jsonl', 'r') as f: + for l in f: + l = json.loads(l) + if l['unknown_doc_id'] in covered_ids: + continue + + covered_ids.add(l['unknown_doc_id']) + l['id'] = str(uuid.uuid4()) + del l['relevant'] + del l['relevant_doc_id'] + pointwise_data.append(l) + +with open(f'{DATA_DIR}/zenodo/test-pointwise/truths/inputs.jsonl', 'w') as f: + for l in pointwise_data: + f.write(json.dumps(l) + '\n') + +with open(f'{DATA_DIR}/zenodo/test-pointwise/inputs/inputs.jsonl', 'w') as f: + for l in pointwise_data: + del l['source_dataset_id'] + del l['query_id'] + del l['unknown_doc_id'] + del l['qrel_unknown_doc'] + f.write(json.dumps(l) + '\n') diff --git a/ecir25/evaluation.ipynb b/ecir25/evaluation.ipynb index 75b4d52..86b76dd 100644 --- a/ecir25/evaluation.ipynb +++ b/ecir25/evaluation.ipynb @@ -1,286 +1,286 @@ -{ - 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approachwows_spearman
0pw-prompt-01-flan-t5-large0.427081
1pw-prompt-02-flan-t5-large0.409818
2pw-prompt-01-flan-t5-large0.396166
3pw-prompt-02-flan-t5-large0.356522
4pw-prompt-02-flan-t5-base0.280503
5relevance-feedback-one0.275520
6pw-prompt-01-flan-t5-base0.266453
7relevance-feedback-all0.265796
8snorkel-pointwise0.229684
9pointwise-retrieval-bm250.151261
10auto-qrels-pairwise-f-t5-s0.138531
11pw-prompt-01-flan-t5-base0.134980
12pw-prompt-02-flan-t5-small0.123831
13pw-prompt-02-flan-t5-base0.084510
14pw-prompt-03-flan-t5-base0.077348
15pw-prompt-02-flan-t5-small0.072502
16pw-prompt-01-flan-t5-small0.067304
17pairwise-snorkel0.035513
18pointwise-retrieval-pl20.017817
19pw-prompt-01-flan-t5-small-0.001260
20pointwise-retrieval-dph-0.018225
21pw-prompt-03-flan-t5-large-0.018662
22pointwise-retrieval-h-lm-0.033709
23auto-qrels-pointwise-f-t5-s-0.044013
24query-by-relevant-doc-bm25-0.135498
25pointwise-retrieval-tf-0.136482
26pointwise-retrieval-tf-idf-0.152008
27pw-prompt-03-flan-t5-small-0.241717
28query-by-relevant-doc-tf-0.258090
29query-by-relevant-doc-pl2-0.268689
30query-by-relevant-doc-h-lm-0.331531
31query-by-relevant-doc-dph-0.355890
32query-by-relevant-doc-tf-idf-0.410646
\n", - "
" - ], - "text/plain": [ - " approach wows_spearman\n", - "0 pw-prompt-01-flan-t5-large 0.427081\n", - "1 pw-prompt-02-flan-t5-large 0.409818\n", - "2 pw-prompt-01-flan-t5-large 0.396166\n", - "3 pw-prompt-02-flan-t5-large 0.356522\n", - "4 pw-prompt-02-flan-t5-base 0.280503\n", - "5 relevance-feedback-one 0.275520\n", - "6 pw-prompt-01-flan-t5-base 0.266453\n", - "7 relevance-feedback-all 0.265796\n", - "8 snorkel-pointwise 0.229684\n", - "9 pointwise-retrieval-bm25 0.151261\n", - "10 auto-qrels-pairwise-f-t5-s 0.138531\n", - "11 pw-prompt-01-flan-t5-base 0.134980\n", - "12 pw-prompt-02-flan-t5-small 0.123831\n", - "13 pw-prompt-02-flan-t5-base 0.084510\n", - "14 pw-prompt-03-flan-t5-base 0.077348\n", - "15 pw-prompt-02-flan-t5-small 0.072502\n", - "16 pw-prompt-01-flan-t5-small 0.067304\n", - "17 pairwise-snorkel 0.035513\n", - "18 pointwise-retrieval-pl2 0.017817\n", - "19 pw-prompt-01-flan-t5-small -0.001260\n", - "20 pointwise-retrieval-dph -0.018225\n", - "21 pw-prompt-03-flan-t5-large -0.018662\n", - "22 pointwise-retrieval-h-lm -0.033709\n", - "23 auto-qrels-pointwise-f-t5-s -0.044013\n", - "24 query-by-relevant-doc-bm25 -0.135498\n", - "25 pointwise-retrieval-tf -0.136482\n", - "26 pointwise-retrieval-tf-idf -0.152008\n", - "27 pw-prompt-03-flan-t5-small -0.241717\n", - "28 query-by-relevant-doc-tf -0.258090\n", - "29 query-by-relevant-doc-pl2 -0.268689\n", - "30 query-by-relevant-doc-h-lm -0.331531\n", - "31 query-by-relevant-doc-dph -0.355890\n", - "32 query-by-relevant-doc-tf-idf -0.410646" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[[\"approach\", \"wows_spearman\"]]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "id": "fe3853ef", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_json(\"evaluation.jsonl\", lines=True)\n", + "df = df.sort_values('wows_spearman', ascending=False).reset_index()\n", + "del df[\"index\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f470b96c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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approachwows_spearman
0pw-prompt-01-flan-t5-large0.427081
1pw-prompt-02-flan-t5-large0.409818
2pw-prompt-01-flan-t5-large0.396166
3pw-prompt-02-flan-t5-large0.356522
4pw-prompt-02-flan-t5-base0.280503
5relevance-feedback-one0.275520
6pw-prompt-01-flan-t5-base0.266453
7relevance-feedback-all0.265796
8snorkel-pointwise0.229684
9pointwise-retrieval-bm250.151261
10auto-qrels-pairwise-f-t5-s0.138531
11pw-prompt-01-flan-t5-base0.134980
12pw-prompt-02-flan-t5-small0.123831
13pw-prompt-02-flan-t5-base0.084510
14pw-prompt-03-flan-t5-base0.077348
15pw-prompt-02-flan-t5-small0.072502
16pw-prompt-01-flan-t5-small0.067304
17pairwise-snorkel0.035513
18pointwise-retrieval-pl20.017817
19pw-prompt-01-flan-t5-small-0.001260
20pointwise-retrieval-dph-0.018225
21pw-prompt-03-flan-t5-large-0.018662
22pointwise-retrieval-h-lm-0.033709
23auto-qrels-pointwise-f-t5-s-0.044013
24query-by-relevant-doc-bm25-0.135498
25pointwise-retrieval-tf-0.136482
26pointwise-retrieval-tf-idf-0.152008
27pw-prompt-03-flan-t5-small-0.241717
28query-by-relevant-doc-tf-0.258090
29query-by-relevant-doc-pl2-0.268689
30query-by-relevant-doc-h-lm-0.331531
31query-by-relevant-doc-dph-0.355890
32query-by-relevant-doc-tf-idf-0.410646
\n", + "
" + ], + "text/plain": [ + " approach wows_spearman\n", + "0 pw-prompt-01-flan-t5-large 0.427081\n", + "1 pw-prompt-02-flan-t5-large 0.409818\n", + "2 pw-prompt-01-flan-t5-large 0.396166\n", + "3 pw-prompt-02-flan-t5-large 0.356522\n", + "4 pw-prompt-02-flan-t5-base 0.280503\n", + "5 relevance-feedback-one 0.275520\n", + "6 pw-prompt-01-flan-t5-base 0.266453\n", + "7 relevance-feedback-all 0.265796\n", + "8 snorkel-pointwise 0.229684\n", + "9 pointwise-retrieval-bm25 0.151261\n", + "10 auto-qrels-pairwise-f-t5-s 0.138531\n", + "11 pw-prompt-01-flan-t5-base 0.134980\n", + "12 pw-prompt-02-flan-t5-small 0.123831\n", + "13 pw-prompt-02-flan-t5-base 0.084510\n", + "14 pw-prompt-03-flan-t5-base 0.077348\n", + "15 pw-prompt-02-flan-t5-small 0.072502\n", + "16 pw-prompt-01-flan-t5-small 0.067304\n", + "17 pairwise-snorkel 0.035513\n", + "18 pointwise-retrieval-pl2 0.017817\n", + "19 pw-prompt-01-flan-t5-small -0.001260\n", + "20 pointwise-retrieval-dph -0.018225\n", + "21 pw-prompt-03-flan-t5-large -0.018662\n", + "22 pointwise-retrieval-h-lm -0.033709\n", + "23 auto-qrels-pointwise-f-t5-s -0.044013\n", + "24 query-by-relevant-doc-bm25 -0.135498\n", + "25 pointwise-retrieval-tf -0.136482\n", + "26 pointwise-retrieval-tf-idf -0.152008\n", + "27 pw-prompt-03-flan-t5-small -0.241717\n", + "28 query-by-relevant-doc-tf -0.258090\n", + "29 query-by-relevant-doc-pl2 -0.268689\n", + "30 query-by-relevant-doc-h-lm -0.331531\n", + "31 query-by-relevant-doc-dph -0.355890\n", + "32 query-by-relevant-doc-tf-idf -0.410646" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[[\"approach\", \"wows_spearman\"]]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ecir25/evaluation.jsonl b/ecir25/evaluation.jsonl index 63f11ca..8931b52 100644 --- a/ecir25/evaluation.jsonl +++ b/ecir25/evaluation.jsonl @@ -1,33 +1,33 @@ -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-small","wows_tau_ap":-0.0207343422,"wows_kendall":-0.0022917846,"wows_spearman":-0.0012596584,"wows_pearson":-0.0012596584} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-base","wows_tau_ap":0.0844840563,"wows_kendall":0.0985052138,"wows_spearman":0.1349802295,"wows_pearson":0.1349802295} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-large","wows_tau_ap":0.2130480274,"wows_kendall":0.2831335366,"wows_spearman":0.3961664239,"wows_pearson":0.3961664239} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-02-flan-t5-large","wows_tau_ap":0.1971099721,"wows_kendall":0.2505301997,"wows_spearman":0.3565220588,"wows_pearson":0.3565220588} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-02-flan-t5-base","wows_tau_ap":0.0506070917,"wows_kendall":0.0604154958,"wows_spearman":0.0845099546,"wows_pearson":0.0845099546} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-02-flan-t5-small","wows_tau_ap":0.0386072051,"wows_kendall":0.0612705854,"wows_spearman":0.0725023907,"wows_pearson":0.0725023907} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-03-flan-t5-small","wows_tau_ap":-0.1528344045,"wows_kendall":-0.1744836313,"wows_spearman":-0.2417170179,"wows_pearson":-0.2417170179} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-03-flan-t5-base","wows_tau_ap":0.0488583793,"wows_kendall":0.0622226164,"wows_spearman":0.0773476563,"wows_pearson":0.0773476563} -{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-03-flan-t5-large","wows_tau_ap":-0.0182896086,"wows_kendall":-0.0115079765,"wows_spearman":-0.0186615056,"wows_pearson":-0.0186615056} -{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-bm25","wows_tau_ap":0.0844479821,"wows_kendall":0.107949136,"wows_spearman":0.1512607584,"wows_pearson":0.1512607584} -{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-pl2","wows_tau_ap":0.0046357991,"wows_kendall":0.0125631219,"wows_spearman":0.0178166218,"wows_pearson":0.0178166218} -{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-dph","wows_tau_ap":-0.0234427955,"wows_kendall":-0.0139686679,"wows_spearman":-0.0182248009,"wows_pearson":-0.0182248009} -{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-tf-idf","wows_tau_ap":-0.1200655828,"wows_kendall":-0.1152696136,"wows_spearman":-0.1520076491,"wows_pearson":-0.1520076491} -{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-tf","wows_tau_ap":-0.0865174659,"wows_kendall":-0.0977257399,"wows_spearman":-0.136481784,"wows_pearson":-0.136481784} -{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-h-lm","wows_tau_ap":-0.0335518773,"wows_kendall":-0.0287973663,"wows_spearman":-0.0337090828,"wows_pearson":-0.0337090828} -{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"auto-qrels-pointwise-f-t5-s","wows_tau_ap":-0.0648822612,"wows_kendall":-0.0365255801,"wows_spearman":-0.0440127188,"wows_pearson":-0.0440127188} -{"dataset":"pointwise-20250309-test","team":"wows25-ada-adler","approach":"snorkel-pointwise","wows_tau_ap":0.169652726,"wows_kendall":0.1684274431,"wows_spearman":0.2296841708,"wows_pearson":0.2296841708} -{"dataset":"pairwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-small","wows_tau_ap":0.0248908812,"wows_kendall":0.0454594766,"wows_spearman":0.0673040221,"wows_pearson":0.0673040221} -{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-bm25","wows_tau_ap":-0.1256838918,"wows_kendall":-0.1011950577,"wows_spearman":-0.1354983548,"wows_pearson":-0.1354983548} -{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-pl2","wows_tau_ap":-0.1989991941,"wows_kendall":-0.2062058014,"wows_spearman":-0.2686889923,"wows_pearson":-0.2686889923} -{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-dph","wows_tau_ap":-0.2162329612,"wows_kendall":-0.2633348382,"wows_spearman":-0.3558899705,"wows_pearson":-0.3558899705} -{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-tf","wows_tau_ap":-0.1574624329,"wows_kendall":-0.1842506729,"wows_spearman":-0.2580897033,"wows_pearson":-0.2580897033} -{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-tf-idf","wows_tau_ap":-0.2405813655,"wows_kendall":-0.2989045922,"wows_spearman":-0.4106459561,"wows_pearson":-0.4106459561} -{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-h-lm","wows_tau_ap":-0.2221358608,"wows_kendall":-0.2425820353,"wows_spearman":-0.3315313773,"wows_pearson":-0.3315313773} -{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"auto-qrels-pairwise-f-t5-s","wows_tau_ap":0.0802843652,"wows_kendall":0.0926537248,"wows_spearman":0.1385306474,"wows_pearson":0.1385306474} -{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-01-flan-t5-base","wows_tau_ap":0.1637348671,"wows_kendall":0.1789383586,"wows_spearman":0.2664533902,"wows_pearson":0.2664533902} -{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-01-flan-t5-large","wows_tau_ap":0.2419641969,"wows_kendall":0.2968963318,"wows_spearman":0.4270814112,"wows_pearson":0.4270814112} -{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-02-flan-t5-large","wows_tau_ap":0.2362664566,"wows_kendall":0.2855011843,"wows_spearman":0.4098179639,"wows_pearson":0.4098179639} -{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-02-flan-t5-base","wows_tau_ap":0.2016007919,"wows_kendall":0.2080253253,"wows_spearman":0.2805026667,"wows_pearson":0.2805026667} -{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-02-flan-t5-small","wows_tau_ap":0.0445548605,"wows_kendall":0.0911896823,"wows_spearman":0.1238306512,"wows_pearson":0.1238306512} -{"dataset":"pairwise-20250309-test","team":"wows25-ada-adler","approach":"pairwise-snorkel","wows_tau_ap":0.0321392321,"wows_kendall":0.0244760427,"wows_spearman":0.0355126509,"wows_pearson":0.0355126509} -{"dataset":"pairwise-20250309-test","team":"wows25-ada-adler","approach":"relevance-feedback-all","wows_tau_ap":0.1835679066,"wows_kendall":0.19285148,"wows_spearman":0.2657961179,"wows_pearson":0.2657961179} -{"dataset":"pairwise-20250309-test","team":"wows25-ada-adler","approach":"relevance-feedback-one","wows_tau_ap":0.160905374,"wows_kendall":0.2001110197,"wows_spearman":0.2755199825,"wows_pearson":0.2755199825} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-small","wows_tau_ap":-0.0207343422,"wows_kendall":-0.0022917846,"wows_spearman":-0.0012596584,"wows_pearson":-0.0012596584} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-base","wows_tau_ap":0.0844840563,"wows_kendall":0.0985052138,"wows_spearman":0.1349802295,"wows_pearson":0.1349802295} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-large","wows_tau_ap":0.2130480274,"wows_kendall":0.2831335366,"wows_spearman":0.3961664239,"wows_pearson":0.3961664239} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-02-flan-t5-large","wows_tau_ap":0.1971099721,"wows_kendall":0.2505301997,"wows_spearman":0.3565220588,"wows_pearson":0.3565220588} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-02-flan-t5-base","wows_tau_ap":0.0506070917,"wows_kendall":0.0604154958,"wows_spearman":0.0845099546,"wows_pearson":0.0845099546} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-02-flan-t5-small","wows_tau_ap":0.0386072051,"wows_kendall":0.0612705854,"wows_spearman":0.0725023907,"wows_pearson":0.0725023907} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-03-flan-t5-small","wows_tau_ap":-0.1528344045,"wows_kendall":-0.1744836313,"wows_spearman":-0.2417170179,"wows_pearson":-0.2417170179} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-03-flan-t5-base","wows_tau_ap":0.0488583793,"wows_kendall":0.0622226164,"wows_spearman":0.0773476563,"wows_pearson":0.0773476563} +{"dataset":"pointwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-03-flan-t5-large","wows_tau_ap":-0.0182896086,"wows_kendall":-0.0115079765,"wows_spearman":-0.0186615056,"wows_pearson":-0.0186615056} +{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-bm25","wows_tau_ap":0.0844479821,"wows_kendall":0.107949136,"wows_spearman":0.1512607584,"wows_pearson":0.1512607584} +{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-pl2","wows_tau_ap":0.0046357991,"wows_kendall":0.0125631219,"wows_spearman":0.0178166218,"wows_pearson":0.0178166218} +{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-dph","wows_tau_ap":-0.0234427955,"wows_kendall":-0.0139686679,"wows_spearman":-0.0182248009,"wows_pearson":-0.0182248009} +{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-tf-idf","wows_tau_ap":-0.1200655828,"wows_kendall":-0.1152696136,"wows_spearman":-0.1520076491,"wows_pearson":-0.1520076491} +{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-tf","wows_tau_ap":-0.0865174659,"wows_kendall":-0.0977257399,"wows_spearman":-0.136481784,"wows_pearson":-0.136481784} +{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"pointwise-retrieval-h-lm","wows_tau_ap":-0.0335518773,"wows_kendall":-0.0287973663,"wows_spearman":-0.0337090828,"wows_pearson":-0.0337090828} +{"dataset":"pointwise-20250309-test","team":"wows25-karma","approach":"auto-qrels-pointwise-f-t5-s","wows_tau_ap":-0.0648822612,"wows_kendall":-0.0365255801,"wows_spearman":-0.0440127188,"wows_pearson":-0.0440127188} +{"dataset":"pointwise-20250309-test","team":"wows25-ada-adler","approach":"snorkel-pointwise","wows_tau_ap":0.169652726,"wows_kendall":0.1684274431,"wows_spearman":0.2296841708,"wows_pearson":0.2296841708} +{"dataset":"pairwise-20250309-test","team":"wows25-elizabeth-futas","approach":"pw-prompt-01-flan-t5-small","wows_tau_ap":0.0248908812,"wows_kendall":0.0454594766,"wows_spearman":0.0673040221,"wows_pearson":0.0673040221} +{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-bm25","wows_tau_ap":-0.1256838918,"wows_kendall":-0.1011950577,"wows_spearman":-0.1354983548,"wows_pearson":-0.1354983548} +{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-pl2","wows_tau_ap":-0.1989991941,"wows_kendall":-0.2062058014,"wows_spearman":-0.2686889923,"wows_pearson":-0.2686889923} +{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-dph","wows_tau_ap":-0.2162329612,"wows_kendall":-0.2633348382,"wows_spearman":-0.3558899705,"wows_pearson":-0.3558899705} +{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-tf","wows_tau_ap":-0.1574624329,"wows_kendall":-0.1842506729,"wows_spearman":-0.2580897033,"wows_pearson":-0.2580897033} +{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-tf-idf","wows_tau_ap":-0.2405813655,"wows_kendall":-0.2989045922,"wows_spearman":-0.4106459561,"wows_pearson":-0.4106459561} +{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"query-by-relevant-doc-h-lm","wows_tau_ap":-0.2221358608,"wows_kendall":-0.2425820353,"wows_spearman":-0.3315313773,"wows_pearson":-0.3315313773} +{"dataset":"pairwise-20250309-test","team":"wows25-karma","approach":"auto-qrels-pairwise-f-t5-s","wows_tau_ap":0.0802843652,"wows_kendall":0.0926537248,"wows_spearman":0.1385306474,"wows_pearson":0.1385306474} +{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-01-flan-t5-base","wows_tau_ap":0.1637348671,"wows_kendall":0.1789383586,"wows_spearman":0.2664533902,"wows_pearson":0.2664533902} +{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-01-flan-t5-large","wows_tau_ap":0.2419641969,"wows_kendall":0.2968963318,"wows_spearman":0.4270814112,"wows_pearson":0.4270814112} +{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-02-flan-t5-large","wows_tau_ap":0.2362664566,"wows_kendall":0.2855011843,"wows_spearman":0.4098179639,"wows_pearson":0.4098179639} +{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-02-flan-t5-base","wows_tau_ap":0.2016007919,"wows_kendall":0.2080253253,"wows_spearman":0.2805026667,"wows_pearson":0.2805026667} +{"dataset":"pairwise-20250309-test","team":"wows25-zoe-heriot","approach":"pw-prompt-02-flan-t5-small","wows_tau_ap":0.0445548605,"wows_kendall":0.0911896823,"wows_spearman":0.1238306512,"wows_pearson":0.1238306512} +{"dataset":"pairwise-20250309-test","team":"wows25-ada-adler","approach":"pairwise-snorkel","wows_tau_ap":0.0321392321,"wows_kendall":0.0244760427,"wows_spearman":0.0355126509,"wows_pearson":0.0355126509} +{"dataset":"pairwise-20250309-test","team":"wows25-ada-adler","approach":"relevance-feedback-all","wows_tau_ap":0.1835679066,"wows_kendall":0.19285148,"wows_spearman":0.2657961179,"wows_pearson":0.2657961179} +{"dataset":"pairwise-20250309-test","team":"wows25-ada-adler","approach":"relevance-feedback-one","wows_tau_ap":0.160905374,"wows_kendall":0.2001110197,"wows_spearman":0.2755199825,"wows_pearson":0.2755199825} diff --git a/ecir25/wows-eval/Makefile b/ecir25/wows-eval/Makefile index 3966c86..914958b 100644 --- a/ecir25/wows-eval/Makefile +++ b/ecir25/wows-eval/Makefile @@ -1,8 +1,8 @@ -build-pypi-package: run-tests - rm -Rf dist - python3 -m build --sdist . - python3 -m build --wheel . - twine upload dist/wows_eval-*-py3-none-any.whl dist/wows_eval-*.tar.gz - -run-tests: - pytest +build-pypi-package: run-tests + rm -Rf dist + python3 -m build --sdist . + python3 -m build --wheel . + twine upload dist/wows_eval-*-py3-none-any.whl dist/wows_eval-*.tar.gz + +run-tests: + pytest diff --git a/ecir25/wows-eval/build-runs/build-runs.py b/ecir25/wows-eval/build-runs/build-runs.py index f36707a..ece87c6 100755 --- a/ecir25/wows-eval/build-runs/build-runs.py +++ b/ecir25/wows-eval/build-runs/build-runs.py @@ -1,82 +1,82 @@ -#!/usr/bin/env python3 -from trectools import TrecQrel, TrecRun, TrecEval -from pathlib import Path -import pandas as pd -import json -from random import shuffle - -def load_qrels(file_name): - ret = TrecQrel() - df = [] - with open(file_name, 'r') as f: - for l in f: - l = json.loads(l) - df.append({"query": l["query_id"], "q0": "0", "docid": l["unknown_doc_id"], "rel": l["qrel_unknown_doc"]}) - - ret.qrel_data = pd.DataFrame(df) - return ret - -def trec_run(query, docs): - ret = TrecRun() - df = [] - for doc in docs: - df.append({"query": query, "q0": "0", "docid": doc, "rank": 1+len(df), "score": 1000-len(df), "system": "system"}) - ret.run_data = pd.DataFrame(df) - return ret - -def produce_rankings(qrels): - query = list(qrels["query"].unique())[0] - ret = [] - q = TrecQrel() - q.qrels_data = qrels - docs = list(qrels.sort_values("rel", ascending=False)["docid"]) - for i in range(0, 500): - shuffle(docs) - ret.append(trec_run(query, docs)) - - ret_parsed = [] - ret = [trec_run(query, qrels.sort_values("rel", ascending=False)["docid"])] + \ - ret + \ - [trec_run(query, qrels.sort_values("rel", ascending=True)["docid"])] - for run in ret: - te = TrecEval(run, q) - ret_parsed.append({"run": run, "ndcg": te.get_ndcg(depth=10)}) - return ret_parsed - -def process(directory): - qrels = load_qrels(directory / "labels.jsonl") - final_runs = [[] for i in range(20)] - - for query in qrels.qrel_data["query"].unique(): - out_dir = directory/ "synthetic-runs"/ query - out_dir.mkdir(exist_ok=True, parents=True) - qrels_for_query = qrels.qrel_data[qrels.qrel_data["query"] == query].copy() - runs = produce_rankings(qrels_for_query) - selected_runs = [] - lower_bound = 0.99 - while lower_bound >= 0: - upper_bound = lower_bound + 0.02 - for run in runs: - if run["ndcg"] >= lower_bound and run["ndcg"] <= upper_bound: - selected_runs.append(run) - break - - lower_bound -= 0.02 - - q = TrecQrel() - q.qrels_data = qrels_for_query - scores = [] - num = 0 - for run in selected_runs: - te = TrecEval(run["run"], q) - scores.append(te.get_ndcg(depth=10)) - run["run"].run_data.to_csv(out_dir / f"{num}.run.gz", sep=" ", header=False, index=False) - num += 1 - Path(query) - print("\t", len(scores), ":", scores) - - print("\n\n\n") - -if __name__ == '__main__': - process(Path('data')) - +#!/usr/bin/env python3 +from trectools import TrecQrel, TrecRun, TrecEval +from pathlib import Path +import pandas as pd +import json +from random import shuffle + +def load_qrels(file_name): + ret = TrecQrel() + df = [] + with open(file_name, 'r') as f: + for l in f: + l = json.loads(l) + df.append({"query": l["query_id"], "q0": "0", "docid": l["unknown_doc_id"], "rel": l["qrel_unknown_doc"]}) + + ret.qrel_data = pd.DataFrame(df) + return ret + +def trec_run(query, docs): + ret = TrecRun() + df = [] + for doc in docs: + df.append({"query": query, "q0": "0", "docid": doc, "rank": 1+len(df), "score": 1000-len(df), "system": "system"}) + ret.run_data = pd.DataFrame(df) + return ret + +def produce_rankings(qrels): + query = list(qrels["query"].unique())[0] + ret = [] + q = TrecQrel() + q.qrels_data = qrels + docs = list(qrels.sort_values("rel", ascending=False)["docid"]) + for i in range(0, 500): + shuffle(docs) + ret.append(trec_run(query, docs)) + + ret_parsed = [] + ret = [trec_run(query, qrels.sort_values("rel", ascending=False)["docid"])] + \ + ret + \ + [trec_run(query, qrels.sort_values("rel", ascending=True)["docid"])] + for run in ret: + te = TrecEval(run, q) + ret_parsed.append({"run": run, "ndcg": te.get_ndcg(depth=10)}) + return ret_parsed + +def process(directory): + qrels = load_qrels(directory / "labels.jsonl") + final_runs = [[] for i in range(20)] + + for query in qrels.qrel_data["query"].unique(): + out_dir = directory/ "synthetic-runs"/ query + out_dir.mkdir(exist_ok=True, parents=True) + qrels_for_query = qrels.qrel_data[qrels.qrel_data["query"] == query].copy() + runs = produce_rankings(qrels_for_query) + selected_runs = [] + lower_bound = 0.99 + while lower_bound >= 0: + upper_bound = lower_bound + 0.02 + for run in runs: + if run["ndcg"] >= lower_bound and run["ndcg"] <= upper_bound: + selected_runs.append(run) + break + + lower_bound -= 0.02 + + q = TrecQrel() + q.qrels_data = qrels_for_query + scores = [] + num = 0 + for run in selected_runs: + te = TrecEval(run["run"], q) + scores.append(te.get_ndcg(depth=10)) + run["run"].run_data.to_csv(out_dir / f"{num}.run.gz", sep=" ", header=False, index=False) + num += 1 + Path(query) + print("\t", len(scores), ":", scores) + + print("\n\n\n") + +if __name__ == '__main__': + process(Path('data')) + diff --git a/ecir25/wows-eval/build-runs/verify-runs.py b/ecir25/wows-eval/build-runs/verify-runs.py index 8a5fc9c..371796e 100755 --- a/ecir25/wows-eval/build-runs/verify-runs.py +++ b/ecir25/wows-eval/build-runs/verify-runs.py @@ -1,44 +1,44 @@ -#!/usr/bin/env python3 -from trectools import TrecQrel, TrecRun, TrecEval -from pathlib import Path -import pandas as pd -import json -from random import shuffle -import os - -def load_qrels(file_name): - ret = TrecQrel() - df = [] - with open(file_name, 'r') as f: - for l in f: - l = json.loads(l) - df.append({"query": l["query_id"], "q0": "0", "docid": l["unknown_doc_id"], "rel": l["qrel_unknown_doc"]}) - - ret.qrel_data = pd.DataFrame(df) - return ret - -def trec_runs(directory, query): - ret = [] - for f in os.listdir(directory / query): - ret.append(TrecRun(directory / query / f)) - return ret - -def process(directory, run_dir): - qrels = load_qrels(directory / "labels.jsonl") - final_runs = [[] for i in range(20)] - - for query in qrels.qrel_data["query"].unique(): - qrels_for_query = qrels.qrel_data[qrels.qrel_data["query"] == query].copy() - runs = trec_runs(run_dir, query) - - q = TrecQrel() - q.qrels_data = qrels_for_query - scores = [] - for run in runs: - te = TrecEval(run, q) - scores.append(te.get_ndcg(depth=10)) - print("\t", len(scores), ":", scores) - -if __name__ == '__main__': - process(Path('data'), Path('data') / 'synthetic-runs') - +#!/usr/bin/env python3 +from trectools import TrecQrel, TrecRun, TrecEval +from pathlib import Path +import pandas as pd +import json +from random import shuffle +import os + +def load_qrels(file_name): + ret = TrecQrel() + df = [] + with open(file_name, 'r') as f: + for l in f: + l = json.loads(l) + df.append({"query": l["query_id"], "q0": "0", "docid": l["unknown_doc_id"], "rel": l["qrel_unknown_doc"]}) + + ret.qrel_data = pd.DataFrame(df) + return ret + +def trec_runs(directory, query): + ret = [] + for f in os.listdir(directory / query): + ret.append(TrecRun(directory / query / f)) + return ret + +def process(directory, run_dir): + qrels = load_qrels(directory / "labels.jsonl") + final_runs = [[] for i in range(20)] + + for query in qrels.qrel_data["query"].unique(): + qrels_for_query = qrels.qrel_data[qrels.qrel_data["query"] == query].copy() + runs = trec_runs(run_dir, query) + + q = TrecQrel() + q.qrels_data = qrels_for_query + scores = [] + for run in runs: + te = TrecEval(run, q) + scores.append(te.get_ndcg(depth=10)) + print("\t", len(scores), ":", scores) + +if __name__ == '__main__': + process(Path('data'), Path('data') / 'synthetic-runs') + diff --git a/ecir25/wows-eval/setup.cfg b/ecir25/wows-eval/setup.cfg index b91ebfb..6f49f8b 100644 --- a/ecir25/wows-eval/setup.cfg +++ b/ecir25/wows-eval/setup.cfg @@ -1,53 +1,53 @@ -[metadata] -name = wows-eval -version = attr: wows_eval.__version__ -author = Maik Froebe -author_email = maik.froebe@uni-weimar.de -maintainer = Maik Froebe -description = Evaluation script for the wows-eval shared task. -long_description = file: README.md -long_description_content_type = text/markdown -license_file = LICENSE -url = https://github.com/OpenWebSearch/wows-code/tree/main/ecir25/wows-eval -project_urls = - Bug Tracker = https://github.com/OpenWebSearch/wows-code/issues -classifiers = - Programming Language :: Python :: 3 - Programming Language :: Python :: 3 :: Only - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Programming Language :: Python :: 3.11 - Programming Language :: Python :: 3.12 - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Intended Audience :: Science/Research - Topic :: Scientific/Engineering - -[options] -python_requires = >=3.7 -include_package_data = True -packages = find: -install_requires = - tira>=0.0.156 - tirex-tracker>=0.2.10 - pyyaml - pandas - packaging - trectools - click - tqdm - -[options.extras_require] -test = - pytest>=8.0,==8.* - -[options.entry_points] -console_scripts = - wows-eval = wows_eval:cli - -[flake8] -max-line-length = 120 -extend-ignore = E203 -max-complexity = 10 +[metadata] +name = wows-eval +version = attr: wows_eval.__version__ +author = Maik Froebe +author_email = maik.froebe@uni-weimar.de +maintainer = Maik Froebe +description = Evaluation script for the wows-eval shared task. +long_description = file: README.md +long_description_content_type = text/markdown +license_file = LICENSE +url = https://github.com/OpenWebSearch/wows-code/tree/main/ecir25/wows-eval +project_urls = + Bug Tracker = https://github.com/OpenWebSearch/wows-code/issues +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3 :: Only + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Programming Language :: Python :: 3.11 + Programming Language :: Python :: 3.12 + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Intended Audience :: Science/Research + Topic :: Scientific/Engineering + +[options] +python_requires = >=3.7 +include_package_data = True +packages = find: +install_requires = + tira>=0.0.156 + tirex-tracker>=0.2.10 + pyyaml + pandas + packaging + trectools + click + tqdm + +[options.extras_require] +test = + pytest>=8.0,==8.* + +[options.entry_points] +console_scripts = + wows-eval = wows_eval:cli + +[flake8] +max-line-length = 120 +extend-ignore = E203 +max-complexity = 10 diff --git a/ecir25/wows-eval/setup.py b/ecir25/wows-eval/setup.py index fc1f76c..e31fce4 100644 --- a/ecir25/wows-eval/setup.py +++ b/ecir25/wows-eval/setup.py @@ -1,3 +1,3 @@ -from setuptools import setup - +from setuptools import setup + setup() \ No newline at end of file diff --git a/ecir25/wows-eval/tests/wows_eval_pairwise_test.py b/ecir25/wows-eval/tests/wows_eval_pairwise_test.py index 4f1bd92..77aeead 100644 --- a/ecir25/wows-eval/tests/wows_eval_pairwise_test.py +++ b/ecir25/wows-eval/tests/wows_eval_pairwise_test.py @@ -1,180 +1,180 @@ -import unittest - -from wows_eval import evaluate -from tirex_tracker import tracking - -class TestEvaluationTest(unittest.TestCase): - def test_perfect_correlation_01(self): - expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} - - predictions = [ - {"id": "1", "probability_relevant": 2}, - {"id": "2", "probability_relevant": 2}, - {"id": "3", "probability_relevant": 3}, - {"id": "4", "probability_relevant": 3}, - {"id": "5", "probability_relevant": 1}, - {"id": "6", "probability_relevant": 1}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_perfect_correlation_02(self): - expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} - - predictions = [ - {"id": "1", "probability_relevant": 20}, - {"id": "2", "probability_relevant": 20}, - {"id": "3", "probability_relevant": 30}, - {"id": "4", "probability_relevant": 30}, - {"id": "5", "probability_relevant": 10}, - {"id": "6", "probability_relevant": 10}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_non_perfect_correlation_01(self): - expected = {'tau_ap': 0.5, 'kendall': 0.33333333333, 'pearson': 0.5, 'spearman': 0.5} - - predictions = [ - {"id": "1", "probability_relevant": 20}, - {"id": "2", "probability_relevant": 20}, - {"id": "3", "probability_relevant": 30}, - {"id": "4", "probability_relevant": 30}, - {"id": "5", "probability_relevant": 21}, - {"id": "6", "probability_relevant": 21}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - - def test_non_perfect_correlation_02(self): - expected = {'tau_ap': 0.5, 'kendall': 0.33333333333, 'pearson': 0.5, 'spearman': 0.5} - - predictions = [ - {"id": "1", "probability_relevant": 1}, - {"id": "2", "probability_relevant": 1}, - {"id": "3", "probability_relevant": 3}, - {"id": "4", "probability_relevant": 3}, - {"id": "5", "probability_relevant": 2}, - {"id": "6", "probability_relevant": 2}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_least_non_perfect_correlation_01(self): - expected = {'tau_ap': -0.5, 'kendall': -0.33333333333, 'pearson': -0.5, 'spearman': -0.5} - - predictions = [ - {"id": "1", "probability_relevant": 1}, - {"id": "2", "probability_relevant": 1}, - {"id": "3", "probability_relevant": 2}, - {"id": "4", "probability_relevant": 2}, - {"id": "5", "probability_relevant": 3}, - {"id": "6", "probability_relevant": 3}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_least_non_perfect_correlation_02(self): - expected = {'tau_ap': -0.5, 'kendall': -0.33333333333, 'pearson': -0.5, 'spearman': -0.5} - - predictions = [ - {"id": "1", "probability_relevant": 1}, - {"id": "2", "probability_relevant": 1}, - {"id": "3", "probability_relevant": 2}, - {"id": "4", "probability_relevant": 2}, - {"id": "5", "probability_relevant": 3}, - {"id": "6", "probability_relevant": 3}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - ] - - actual_df = evaluate(predictions, truth_data, 'system_name', 'description') - self.assertEqual(1, len(actual_df)) - actual = actual_df.iloc[0].to_dict() - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - - def test_least_non_perfect_correlation_03(self): - with tracking() as tracking_results: - expected = {'tau_ap': -0.5, 'kendall': -0.33333333333, 'pearson': -0.5, 'spearman': -0.5} - - predictions = [ - {"id": "1", "probability_relevant": 1}, - {"id": "2", "probability_relevant": 1}, - {"id": "3", "probability_relevant": 2}, - {"id": "4", "probability_relevant": 2}, - {"id": "5", "probability_relevant": 3}, - {"id": "6", "probability_relevant": 3}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, - {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, - ] - - actual_df = evaluate(predictions, truth_data, 'system_name', 'description', tracking_results=tracking_results) - self.assertEqual(1, len(actual_df)) - actual = actual_df.iloc[0].to_dict() - for k in expected: +import unittest + +from wows_eval import evaluate +from tirex_tracker import tracking + +class TestEvaluationTest(unittest.TestCase): + def test_perfect_correlation_01(self): + expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} + + predictions = [ + {"id": "1", "probability_relevant": 2}, + {"id": "2", "probability_relevant": 2}, + {"id": "3", "probability_relevant": 3}, + {"id": "4", "probability_relevant": 3}, + {"id": "5", "probability_relevant": 1}, + {"id": "6", "probability_relevant": 1}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_perfect_correlation_02(self): + expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} + + predictions = [ + {"id": "1", "probability_relevant": 20}, + {"id": "2", "probability_relevant": 20}, + {"id": "3", "probability_relevant": 30}, + {"id": "4", "probability_relevant": 30}, + {"id": "5", "probability_relevant": 10}, + {"id": "6", "probability_relevant": 10}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_non_perfect_correlation_01(self): + expected = {'tau_ap': 0.5, 'kendall': 0.33333333333, 'pearson': 0.5, 'spearman': 0.5} + + predictions = [ + {"id": "1", "probability_relevant": 20}, + {"id": "2", "probability_relevant": 20}, + {"id": "3", "probability_relevant": 30}, + {"id": "4", "probability_relevant": 30}, + {"id": "5", "probability_relevant": 21}, + {"id": "6", "probability_relevant": 21}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + + def test_non_perfect_correlation_02(self): + expected = {'tau_ap': 0.5, 'kendall': 0.33333333333, 'pearson': 0.5, 'spearman': 0.5} + + predictions = [ + {"id": "1", "probability_relevant": 1}, + {"id": "2", "probability_relevant": 1}, + {"id": "3", "probability_relevant": 3}, + {"id": "4", "probability_relevant": 3}, + {"id": "5", "probability_relevant": 2}, + {"id": "6", "probability_relevant": 2}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_least_non_perfect_correlation_01(self): + expected = {'tau_ap': -0.5, 'kendall': -0.33333333333, 'pearson': -0.5, 'spearman': -0.5} + + predictions = [ + {"id": "1", "probability_relevant": 1}, + {"id": "2", "probability_relevant": 1}, + {"id": "3", "probability_relevant": 2}, + {"id": "4", "probability_relevant": 2}, + {"id": "5", "probability_relevant": 3}, + {"id": "6", "probability_relevant": 3}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_least_non_perfect_correlation_02(self): + expected = {'tau_ap': -0.5, 'kendall': -0.33333333333, 'pearson': -0.5, 'spearman': -0.5} + + predictions = [ + {"id": "1", "probability_relevant": 1}, + {"id": "2", "probability_relevant": 1}, + {"id": "3", "probability_relevant": 2}, + {"id": "4", "probability_relevant": 2}, + {"id": "5", "probability_relevant": 3}, + {"id": "6", "probability_relevant": 3}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + ] + + actual_df = evaluate(predictions, truth_data, 'system_name', 'description') + self.assertEqual(1, len(actual_df)) + actual = actual_df.iloc[0].to_dict() + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + + def test_least_non_perfect_correlation_03(self): + with tracking() as tracking_results: + expected = {'tau_ap': -0.5, 'kendall': -0.33333333333, 'pearson': -0.5, 'spearman': -0.5} + + predictions = [ + {"id": "1", "probability_relevant": 1}, + {"id": "2", "probability_relevant": 1}, + {"id": "3", "probability_relevant": 2}, + {"id": "4", "probability_relevant": 2}, + {"id": "5", "probability_relevant": 3}, + {"id": "6", "probability_relevant": 3}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "3", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-2", "qrel_unknown_doc": 3}, + {"id": "5", "query_id": "qid-1", "relevant_doc_id": "a", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + {"id": "6", "query_id": "qid-1", "relevant_doc_id": "b", "unknown_doc_id": "d-3", "qrel_unknown_doc": 1}, + ] + + actual_df = evaluate(predictions, truth_data, 'system_name', 'description', tracking_results=tracking_results) + self.assertEqual(1, len(actual_df)) + actual = actual_df.iloc[0].to_dict() + for k in expected: self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) \ No newline at end of file diff --git a/ecir25/wows-eval/tests/wows_eval_test.py b/ecir25/wows-eval/tests/wows_eval_test.py index ad0d98a..c56e622 100644 --- a/ecir25/wows-eval/tests/wows_eval_test.py +++ b/ecir25/wows-eval/tests/wows_eval_test.py @@ -1,201 +1,201 @@ -import unittest - -from wows_eval import evaluate -import pandas as pd -from tirex_tracker import tracking - -class TestEvaluationTest(unittest.TestCase): - def test_perfect_correlation_01(self): - expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} - - predictions = [ - {"id": "1", "probability_relevant": 2}, - {"id": "2", "probability_relevant": 1}, - {"id": "3", "probability_relevant": 3}, - {"id": "4", "probability_relevant": 1}, - {"id": "5", "probability_relevant": 1}, - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_perfect_correlation_01_for_df(self): - expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} - - predictions = pd.DataFrame([ - {"id": "1", "probability_relevant": 2}, - {"id": "2", "probability_relevant": 1}, - {"id": "3", "probability_relevant": 3}, - {"id": "4", "probability_relevant": 1}, - {"id": "5", "probability_relevant": 1}, - ]) - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_perfect_correlation_02(self): - expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} - - predictions = [ - {"id": "1", "probability_relevant": 0.2}, - {"id": "2", "probability_relevant": 0.1}, - {"id": "3", "probability_relevant": 0.3}, - {"id": "4", "probability_relevant": 0.1}, - {"id": "5", "probability_relevant": 0.1}, - - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_perfect_correlation_03(self): - expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} - - predictions = [ - {"id": "1", "probability_relevant": 10.2}, - {"id": "2", "probability_relevant": 10.1}, - {"id": "3", "probability_relevant": 10.3}, - {"id": "4", "probability_relevant": 10.1}, - {"id": "5", "probability_relevant": 10.1}, - - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_perfect_inverse_correlation_01(self): - expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} - - predictions = [ - {"id": "1", "probability_relevant": -2}, - {"id": "2", "probability_relevant": -1}, - {"id": "3", "probability_relevant": -3}, - {"id": "4", "probability_relevant": -1}, - {"id": "5", "probability_relevant": -1}, - - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_perfect_inverse_correlation_02(self): - expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} - - predictions = [ - {"id": "1", "probability_relevant": -0.2}, - {"id": "2", "probability_relevant": -0.1}, - {"id": "3", "probability_relevant": -0.3}, - {"id": "4", "probability_relevant": -0.1}, - {"id": "5", "probability_relevant": -0.1}, - - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - - ] - - actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - def test_perfect_inverse_correlation_03(self): - expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} - - predictions = [ - {"id": "1", "probability_relevant": -0.2}, - {"id": "2", "probability_relevant": -0.1}, - {"id": "3", "probability_relevant": -0.3}, - {"id": "4", "probability_relevant": -0.1}, - {"id": "5", "probability_relevant": -0.1}, - - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - - ] - - actual_df = evaluate(predictions, truth_data, 'system_name', 'description') - self.assertEqual(1, len(actual_df)) - actual = actual_df.iloc[0].to_dict() - for k in expected: - self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) - - - def test_perfect_inverse_correlation_04(self): - with tracking() as tracking_results: - expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} - - predictions = [ - {"id": "1", "probability_relevant": -0.2}, - {"id": "2", "probability_relevant": -0.1}, - {"id": "3", "probability_relevant": -0.3}, - {"id": "4", "probability_relevant": -0.1}, - {"id": "5", "probability_relevant": -0.1}, - - ] - truth_data = [ - {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, - {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, - {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, - {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, - {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, - - ] - - actual_df = evaluate(predictions, truth_data, 'system_name', 'description', tracking_results=tracking_results) - self.assertEqual(1, len(actual_df)) - actual = actual_df.iloc[0].to_dict() - for k in expected: +import unittest + +from wows_eval import evaluate +import pandas as pd +from tirex_tracker import tracking + +class TestEvaluationTest(unittest.TestCase): + def test_perfect_correlation_01(self): + expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} + + predictions = [ + {"id": "1", "probability_relevant": 2}, + {"id": "2", "probability_relevant": 1}, + {"id": "3", "probability_relevant": 3}, + {"id": "4", "probability_relevant": 1}, + {"id": "5", "probability_relevant": 1}, + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_perfect_correlation_01_for_df(self): + expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} + + predictions = pd.DataFrame([ + {"id": "1", "probability_relevant": 2}, + {"id": "2", "probability_relevant": 1}, + {"id": "3", "probability_relevant": 3}, + {"id": "4", "probability_relevant": 1}, + {"id": "5", "probability_relevant": 1}, + ]) + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_perfect_correlation_02(self): + expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} + + predictions = [ + {"id": "1", "probability_relevant": 0.2}, + {"id": "2", "probability_relevant": 0.1}, + {"id": "3", "probability_relevant": 0.3}, + {"id": "4", "probability_relevant": 0.1}, + {"id": "5", "probability_relevant": 0.1}, + + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_perfect_correlation_03(self): + expected = {'tau_ap': 1.0, 'kendall': 1.0, 'pearson': 1.0, 'spearman': 1.0} + + predictions = [ + {"id": "1", "probability_relevant": 10.2}, + {"id": "2", "probability_relevant": 10.1}, + {"id": "3", "probability_relevant": 10.3}, + {"id": "4", "probability_relevant": 10.1}, + {"id": "5", "probability_relevant": 10.1}, + + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_perfect_inverse_correlation_01(self): + expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} + + predictions = [ + {"id": "1", "probability_relevant": -2}, + {"id": "2", "probability_relevant": -1}, + {"id": "3", "probability_relevant": -3}, + {"id": "4", "probability_relevant": -1}, + {"id": "5", "probability_relevant": -1}, + + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_perfect_inverse_correlation_02(self): + expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} + + predictions = [ + {"id": "1", "probability_relevant": -0.2}, + {"id": "2", "probability_relevant": -0.1}, + {"id": "3", "probability_relevant": -0.3}, + {"id": "4", "probability_relevant": -0.1}, + {"id": "5", "probability_relevant": -0.1}, + + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + + ] + + actual = evaluate(predictions, truth_data, 'system_name', 'description', return_df=False) + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + def test_perfect_inverse_correlation_03(self): + expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} + + predictions = [ + {"id": "1", "probability_relevant": -0.2}, + {"id": "2", "probability_relevant": -0.1}, + {"id": "3", "probability_relevant": -0.3}, + {"id": "4", "probability_relevant": -0.1}, + {"id": "5", "probability_relevant": -0.1}, + + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + + ] + + actual_df = evaluate(predictions, truth_data, 'system_name', 'description') + self.assertEqual(1, len(actual_df)) + actual = actual_df.iloc[0].to_dict() + for k in expected: + self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) + + + def test_perfect_inverse_correlation_04(self): + with tracking() as tracking_results: + expected = {'tau_ap': 0.0, 'kendall': -0.39999999, 'pearson': -0.6, 'spearman': -0.6} + + predictions = [ + {"id": "1", "probability_relevant": -0.2}, + {"id": "2", "probability_relevant": -0.1}, + {"id": "3", "probability_relevant": -0.3}, + {"id": "4", "probability_relevant": -0.1}, + {"id": "5", "probability_relevant": -0.1}, + + ] + truth_data = [ + {"id": "1", "query_id": "qid-1", "unknown_doc_id": "d-1", "qrel_unknown_doc": 2}, + {"id": "2", "query_id": "qid-1", "unknown_doc_id": "d-2", "qrel_unknown_doc": 1}, + {"id": "3", "query_id": "qid-1", "unknown_doc_id": "d-3", "qrel_unknown_doc": 3}, + {"id": "4", "query_id": "qid-1", "unknown_doc_id": "d-4", "qrel_unknown_doc": 1}, + {"id": "5", "query_id": "qid-1", "unknown_doc_id": "d-5", "qrel_unknown_doc": 1}, + + ] + + actual_df = evaluate(predictions, truth_data, 'system_name', 'description', tracking_results=tracking_results) + self.assertEqual(1, len(actual_df)) + actual = actual_df.iloc[0].to_dict() + for k in expected: self.assertAlmostEqual(expected[k], actual[k], delta=0.00001, msg=k) \ No newline at end of file diff --git a/ecir25/wows-eval/wows_eval/__init__.py b/ecir25/wows-eval/wows_eval/__init__.py index bc6cc9c..b7ef017 100644 --- a/ecir25/wows-eval/wows_eval/__init__.py +++ b/ecir25/wows-eval/wows_eval/__init__.py @@ -1,137 +1,137 @@ -import click -from pathlib import Path -import pandas as pd -from tira.check_format import JsonlFormat, _fmt -from tira.evaluators import WowsEvalEvaluator -from tira.rest_api_client import Client -from tira.third_party_integrations import upload_run_anonymous -from tirex_tracker import tracking -import json -from shutil import copytree, copyfile -import yaml - -__version__ = "0.0.6" - -import gzip - -import tempfile - -def evaluate(predictions, truths, system_name=None, system_description=None, upload=False, tracking_results=None, return_df=True): - """Evaluate the predictions (a dataframe or a list of dictionaries) against the truths. We calculate ranking correlations between the predicted probabilities that documents are relevant against the ground truth ranking when ordering documents by their ground truth relevance labels. - - Args: - predictions (a dataframe or a list of dictionaries): The predictions made by a relevance assessor. Each entry must contain a field "id" and a field "probability_relevant". - truths (a dataset identifier for the tira dataset or a list of dictionaries or a dataframe): The ground truth relevance labels for each query--document pair. - system_name (str, optional): The name of your system. Defaults to None. - system_description (str, optional): The description of your system. Defaults to None. - upload (bool, optional): Should your run be uploaded to TIRA. Defaults to False. - tracking_results (TrackingHandle, optional): A tirex-tracker trace of your experiment execution. Defaults to None. - return_df (bool, optional): return the evaluation results as DataFrame. Defaults to True. - - Raises: - ValueError: Raised if there is an error. - - Returns: - DataFrame/Dictionary: The correlation between the predicted probabilities that documents are relevant and the ground truth relevance labels. - """ - id_to_query_doc = {} - dataset_id = None - - def evaluator(): - return WowsEvalEvaluator('*.jsonl', None, '*.jsonl', None, ['wows_tau_ap', 'wows_kendall', 'wows_spearman', 'wows_pearson']) - - if isinstance(truths, str): - tira = Client() - dataset_id = truths - truths = tira.pd.truths(truths) - if 'qrel_unknown_doc' not in truths.columns: - truths = None - else: - truths = evaluator().normalize_data(truths) - - if not system_name: - system_name = 'no-system-name' - - if not system_description: - system_description = 'no-system-description' - - if tracking_results is not None and isinstance(tracking_results, dict): - raise ValueError('I expected that the tracking_results is a TrackingHandle from tirex-tracker. Got:' + str(tracking_results)) - - predictions = evaluator().normalize_data(predictions) - if dataset_id is not None and upload: - with tempfile.TemporaryDirectory() as f, tempfile.TemporaryDirectory() as meta: - f = Path(f) - meta = Path(meta) - with gzip.open(f / 'predictions.jsonl.gz', 'wt') as output_file: - for l in predictions: - for d in ['query', 'relevant', 'unknown']: - if d in l: - del l[d] - - output_file.write(json.dumps(l) + '\n') - - with tracking(system_name=system_name, system_description=system_description, export_file_path=meta / 'ir-metadata.yml') as tracking_results_code: - pass - if tracking_results is None: - tracking_results = tracking_results_code - - copytree(tracking_results_code._export_file_path.parent / ".tirex-tracker", f / ".tirex-tracker") - metadata = '' - with open(tracking_results._export_file_path, 'r') as tmp_f: - for l in tmp_f: - if l.startswith('ir_metadata.end') or l.startswith('ir_metadata.start'): - continue - metadata += l - - with open(f / 'ir-metadata.yml', 'w') as tmp_f: - tmp_f.write(metadata) - - upload_run_anonymous(f, dataset_id=dataset_id) - #upload_run_anonymous(f, dataset_id='task_1/foo-pointwise-20250130_0-training', tira_client=Client(base_url='https://127.0.0.1:8080/', verify=False)) - - if truths: - ret = evaluator()._eval(predictions, truths) - ret = {'system': system_name, 'tau_ap': ret['wows_tau_ap'], 'kendall': ret['wows_kendall'], 'spearman': ret['wows_spearman'], 'pearson': ret['wows_pearson']} - else: - print("No truth data is available yet. The evaluation is possible after the deadline when the truth data was published.") - return None - - if return_df: - return pd.DataFrame([ret]) - else: - return ret - - -@click.command(help="Evaluate the predictions of your relevance assessor passed in a file PREDICTIONS against the TRUTHS (either a file or a string with the TIRA dataset ID). wows-eval calculates ranking correlations between the predicted probabilities that documents are relevant against the ground truth ranking when ordering documents by their ground truth relevance labels.") -@click.argument('predictions', type=str) -@click.argument('truths', type=str) -@click.option('--upload', type=bool, is_flag=True, help="Upload predictions to TIRA.") -def cli(predictions: str, truths: str, upload: bool): - predictions_formatter = JsonlFormat(('id', 'probability_relevant')) - truths_formatter = JsonlFormat(('id', 'query_id', 'unknown_doc_id', 'qrel_unknown_doc')) - - result, msg = predictions_formatter.check_format(Path(predictions)) - - if result != _fmt.OK: - print('Could not load the predictions:\n\n' + msg) - return - - actual_predictions = predictions_formatter.all_lines(Path(predictions)) - actual_truths = None - - result, msg = truths_formatter.check_format(Path(truths)) - - if result == _fmt.OK: - actual_truths = truths_formatter.all_lines(Path(truths)) - elif isinstance(truths, str): - actual_truths = truths - else: - print('Could not load the truths:\n\n' + msg) - return - - print(evaluate(actual_predictions, actual_truths, return_df=False, upload=upload)) - - -if __name__ == '__main__': - cli() +import click +from pathlib import Path +import pandas as pd +from tira.check_format import JsonlFormat, _fmt +from tira.evaluators import WowsEvalEvaluator +from tira.rest_api_client import Client +from tira.third_party_integrations import upload_run_anonymous +from tirex_tracker import tracking +import json +from shutil import copytree, copyfile +import yaml + +__version__ = "0.0.6" + +import gzip + +import tempfile + +def evaluate(predictions, truths, system_name=None, system_description=None, upload=False, tracking_results=None, return_df=True): + """Evaluate the predictions (a dataframe or a list of dictionaries) against the truths. We calculate ranking correlations between the predicted probabilities that documents are relevant against the ground truth ranking when ordering documents by their ground truth relevance labels. + + Args: + predictions (a dataframe or a list of dictionaries): The predictions made by a relevance assessor. Each entry must contain a field "id" and a field "probability_relevant". + truths (a dataset identifier for the tira dataset or a list of dictionaries or a dataframe): The ground truth relevance labels for each query--document pair. + system_name (str, optional): The name of your system. Defaults to None. + system_description (str, optional): The description of your system. Defaults to None. + upload (bool, optional): Should your run be uploaded to TIRA. Defaults to False. + tracking_results (TrackingHandle, optional): A tirex-tracker trace of your experiment execution. Defaults to None. + return_df (bool, optional): return the evaluation results as DataFrame. Defaults to True. + + Raises: + ValueError: Raised if there is an error. + + Returns: + DataFrame/Dictionary: The correlation between the predicted probabilities that documents are relevant and the ground truth relevance labels. + """ + id_to_query_doc = {} + dataset_id = None + + def evaluator(): + return WowsEvalEvaluator('*.jsonl', None, '*.jsonl', None, ['wows_tau_ap', 'wows_kendall', 'wows_spearman', 'wows_pearson']) + + if isinstance(truths, str): + tira = Client() + dataset_id = truths + truths = tira.pd.truths(truths) + if 'qrel_unknown_doc' not in truths.columns: + truths = None + else: + truths = evaluator().normalize_data(truths) + + if not system_name: + system_name = 'no-system-name' + + if not system_description: + system_description = 'no-system-description' + + if tracking_results is not None and isinstance(tracking_results, dict): + raise ValueError('I expected that the tracking_results is a TrackingHandle from tirex-tracker. Got:' + str(tracking_results)) + + predictions = evaluator().normalize_data(predictions) + if dataset_id is not None and upload: + with tempfile.TemporaryDirectory() as f, tempfile.TemporaryDirectory() as meta: + f = Path(f) + meta = Path(meta) + with gzip.open(f / 'predictions.jsonl.gz', 'wt') as output_file: + for l in predictions: + for d in ['query', 'relevant', 'unknown']: + if d in l: + del l[d] + + output_file.write(json.dumps(l) + '\n') + + with tracking(system_name=system_name, system_description=system_description, export_file_path=meta / 'ir-metadata.yml') as tracking_results_code: + pass + if tracking_results is None: + tracking_results = tracking_results_code + + copytree(tracking_results_code._export_file_path.parent / ".tirex-tracker", f / ".tirex-tracker") + metadata = '' + with open(tracking_results._export_file_path, 'r') as tmp_f: + for l in tmp_f: + if l.startswith('ir_metadata.end') or l.startswith('ir_metadata.start'): + continue + metadata += l + + with open(f / 'ir-metadata.yml', 'w') as tmp_f: + tmp_f.write(metadata) + + upload_run_anonymous(f, dataset_id=dataset_id) + #upload_run_anonymous(f, dataset_id='task_1/foo-pointwise-20250130_0-training', tira_client=Client(base_url='https://127.0.0.1:8080/', verify=False)) + + if truths: + ret = evaluator()._eval(predictions, truths) + ret = {'system': system_name, 'tau_ap': ret['wows_tau_ap'], 'kendall': ret['wows_kendall'], 'spearman': ret['wows_spearman'], 'pearson': ret['wows_pearson']} + else: + print("No truth data is available yet. The evaluation is possible after the deadline when the truth data was published.") + return None + + if return_df: + return pd.DataFrame([ret]) + else: + return ret + + +@click.command(help="Evaluate the predictions of your relevance assessor passed in a file PREDICTIONS against the TRUTHS (either a file or a string with the TIRA dataset ID). wows-eval calculates ranking correlations between the predicted probabilities that documents are relevant against the ground truth ranking when ordering documents by their ground truth relevance labels.") +@click.argument('predictions', type=str) +@click.argument('truths', type=str) +@click.option('--upload', type=bool, is_flag=True, help="Upload predictions to TIRA.") +def cli(predictions: str, truths: str, upload: bool): + predictions_formatter = JsonlFormat(('id', 'probability_relevant')) + truths_formatter = JsonlFormat(('id', 'query_id', 'unknown_doc_id', 'qrel_unknown_doc')) + + result, msg = predictions_formatter.check_format(Path(predictions)) + + if result != _fmt.OK: + print('Could not load the predictions:\n\n' + msg) + return + + actual_predictions = predictions_formatter.all_lines(Path(predictions)) + actual_truths = None + + result, msg = truths_formatter.check_format(Path(truths)) + + if result == _fmt.OK: + actual_truths = truths_formatter.all_lines(Path(truths)) + elif isinstance(truths, str): + actual_truths = truths + else: + print('Could not load the truths:\n\n' + msg) + return + + print(evaluate(actual_predictions, actual_truths, return_df=False, upload=upload)) + + +if __name__ == '__main__': + cli() diff --git a/ecir25/wows-eval/wows_eval/correlation.py b/ecir25/wows-eval/wows_eval/correlation.py index 9ba3bca..3fc0abe 100644 --- a/ecir25/wows-eval/wows_eval/correlation.py +++ b/ecir25/wows-eval/wows_eval/correlation.py @@ -1,93 +1,93 @@ -from tira.evaluators import WowsEvalEvaluator - -from pathlib import Path -from typing import List -from trectools import TrecQrel, TrecRun, TrecEval -import pandas as pd -from glob import glob - -from statistics import mean - -class WowsCorrelationEvalEvaluator(WowsEvalEvaluator): - def throw_if_conf_invalid(self, config: dict) -> None: - if 'run_dirs' not in config: - raise ValueError('I need a run_dirs configuration') - self.run_dirs = config['run_dirs'] - - def to_qrels(self, query_id, sorted_data): - ret = TrecQrel() - qrels_data = [] - for (qrel, docno) in sorted_data: - qrels_data.append({"query": query_id, "q0": "0", "docid": docno, "rel": qrel}) - - ret.qrels_data = pd.DataFrame(qrels_data) - return ret - - - def _eval(self, run_data: List[dict], truth_data: List[dict]) -> dict: - id_to_query_doc = {} - pairwise = False - predictions = self.normalize_data(run_data) - truths = self.normalize_data(truth_data) - from trectools import misc - - for i in truths: - id_to_query_doc[i["id"]] = { - "query_id": i["query_id"], - "doc_id": i["unknown_doc_id"], - "qrel": int(i["qrel_unknown_doc"]), - } - if "relevant_doc_id" in i: - pairwise = True - id_to_query_doc[i["id"]]["relevant_doc_id"] = i["relevant_doc_id"] - - predictions = {i["id"]: i for i in predictions} - - if predictions.keys() != id_to_query_doc.keys(): - raise ValueError("fooo") - - if pairwise: - truths_rankings, predictions_rankings = self._WowsEvalEvaluator__pairwise_rankings(id_to_query_doc, predictions) - else: - truths_rankings, predictions_rankings = self._WowsEvalEvaluator__pointwise_rankings(id_to_query_doc, predictions) - - tau_ap = [] - kendall = [] - spearman = [] - pearson = [] - - for query_id in truths_rankings.keys(): - truth_ranking = self._WowsEvalEvaluator__sorted(truths_rankings[query_id]) - predicted_ranking = self._WowsEvalEvaluator__sorted(predictions_rankings[query_id]) - - truth_qrels = self.to_qrels(query_id, truth_ranking) - pred_qrels = self.to_qrels(query_id, predicted_ranking) - - truth_eval = [] - pred_eval = [] - for run_dir in self.run_dirs: - for run_name in glob(f'{run_dir}/{query_id}/*.run.gz'): - run_path = Path(run_name) - run = TrecRun(run_path) - - assert run_path.name not in [i['doc_id'] for i in truth_eval] - assert run_path.name not in [i['doc_id'] for i in pred_eval] - pred_eval.append({"doc_id": run_path.name, "score": TrecEval(run, pred_qrels).get_ndcg(depth=10)}) - truth_eval.append({"doc_id": run_path.name, "score": TrecEval(run, truth_qrels).get_ndcg(depth=10)}) - - assert len(pred_eval) > 10 - assert len(truth_eval) > 10 - pred_eval = self._WowsEvalEvaluator__sorted(pred_eval) - truth_eval = self._WowsEvalEvaluator__sorted(truth_eval) - tau_ap.append(misc.get_correlation(truth_eval, pred_eval, correlation="tauap")[0]) - kendall.append(misc.get_correlation(truth_eval, pred_eval, correlation="kendall")[0]) - spearman.append(misc.get_correlation(truth_eval, pred_eval, correlation="spearman")[0]) - pearson.append(misc.get_correlation(truth_eval, pred_eval, correlation="pearson")[0]) - - ret = { - "wows_tau_ap": mean(tau_ap), - "wows_kendall": mean(kendall), - "wows_spearman": mean(spearman), - "wows_pearson": mean(pearson), - } +from tira.evaluators import WowsEvalEvaluator + +from pathlib import Path +from typing import List +from trectools import TrecQrel, TrecRun, TrecEval +import pandas as pd +from glob import glob + +from statistics import mean + +class WowsCorrelationEvalEvaluator(WowsEvalEvaluator): + def throw_if_conf_invalid(self, config: dict) -> None: + if 'run_dirs' not in config: + raise ValueError('I need a run_dirs configuration') + self.run_dirs = config['run_dirs'] + + def to_qrels(self, query_id, sorted_data): + ret = TrecQrel() + qrels_data = [] + for (qrel, docno) in sorted_data: + qrels_data.append({"query": query_id, "q0": "0", "docid": docno, "rel": qrel}) + + ret.qrels_data = pd.DataFrame(qrels_data) + return ret + + + def _eval(self, run_data: List[dict], truth_data: List[dict]) -> dict: + id_to_query_doc = {} + pairwise = False + predictions = self.normalize_data(run_data) + truths = self.normalize_data(truth_data) + from trectools import misc + + for i in truths: + id_to_query_doc[i["id"]] = { + "query_id": i["query_id"], + "doc_id": i["unknown_doc_id"], + "qrel": int(i["qrel_unknown_doc"]), + } + if "relevant_doc_id" in i: + pairwise = True + id_to_query_doc[i["id"]]["relevant_doc_id"] = i["relevant_doc_id"] + + predictions = {i["id"]: i for i in predictions} + + if predictions.keys() != id_to_query_doc.keys(): + raise ValueError("fooo") + + if pairwise: + truths_rankings, predictions_rankings = self._WowsEvalEvaluator__pairwise_rankings(id_to_query_doc, predictions) + else: + truths_rankings, predictions_rankings = self._WowsEvalEvaluator__pointwise_rankings(id_to_query_doc, predictions) + + tau_ap = [] + kendall = [] + spearman = [] + pearson = [] + + for query_id in truths_rankings.keys(): + truth_ranking = self._WowsEvalEvaluator__sorted(truths_rankings[query_id]) + predicted_ranking = self._WowsEvalEvaluator__sorted(predictions_rankings[query_id]) + + truth_qrels = self.to_qrels(query_id, truth_ranking) + pred_qrels = self.to_qrels(query_id, predicted_ranking) + + truth_eval = [] + pred_eval = [] + for run_dir in self.run_dirs: + for run_name in glob(f'{run_dir}/{query_id}/*.run.gz'): + run_path = Path(run_name) + run = TrecRun(run_path) + + assert run_path.name not in [i['doc_id'] for i in truth_eval] + assert run_path.name not in [i['doc_id'] for i in pred_eval] + pred_eval.append({"doc_id": run_path.name, "score": TrecEval(run, pred_qrels).get_ndcg(depth=10)}) + truth_eval.append({"doc_id": run_path.name, "score": TrecEval(run, truth_qrels).get_ndcg(depth=10)}) + + assert len(pred_eval) > 10 + assert len(truth_eval) > 10 + pred_eval = self._WowsEvalEvaluator__sorted(pred_eval) + truth_eval = self._WowsEvalEvaluator__sorted(truth_eval) + tau_ap.append(misc.get_correlation(truth_eval, pred_eval, correlation="tauap")[0]) + kendall.append(misc.get_correlation(truth_eval, pred_eval, correlation="kendall")[0]) + spearman.append(misc.get_correlation(truth_eval, pred_eval, correlation="spearman")[0]) + pearson.append(misc.get_correlation(truth_eval, pred_eval, correlation="pearson")[0]) + + ret = { + "wows_tau_ap": mean(tau_ap), + "wows_kendall": mean(kendall), + "wows_spearman": mean(spearman), + "wows_pearson": mean(pearson), + } return {i: ret[i] for i in self._measures} \ No newline at end of file diff --git a/ecir25/wows-eval/wows_eval/wows_2025.py b/ecir25/wows-eval/wows_eval/wows_2025.py index 84a0583..703be4d 100644 --- a/ecir25/wows-eval/wows_eval/wows_2025.py +++ b/ecir25/wows-eval/wows_eval/wows_2025.py @@ -1,33 +1,33 @@ -#!/usr/bin/env python3 -from tira.rest_api_client import Client -from wows_eval.correlation import WowsCorrelationEvalEvaluator -from pathlib import Path -tira = Client() -import pandas as pd - -TASK_ID = 'wows-eval' -DATASETS = ["pointwise-20250309-test", "pairwise-20250309-test"] -DATA_DIR = Path("/home/maik/workspace/wows-code/ecir25/wows-eval/build-runs/data") - -def evaluator(): - ret = WowsCorrelationEvalEvaluator('*.jsonl', None, '*.jsonl', None, ['wows_tau_ap', 'wows_kendall', 'wows_spearman', 'wows_pearson']) - ret.throw_if_conf_invalid({"run_dirs": [DATA_DIR / "synthetic-runs"]}) - return ret - - -df = [] - -for dataset in DATASETS: - submissions = tira.submissions(TASK_ID, dataset) - for _, i in submissions.iterrows(): - i = i.to_dict() - if 'evaluator' in i['software'] or 'naive' in i['software']: - continue - print(i['team'], i['software']) - ev = {"dataset": dataset, "team": i['team'], "approach": i['software']} - - run_dir = tira.get_run_output(f"{TASK_ID}/{i['team']}/{i['software']}", dataset) - ev.update(evaluator().evaluate(Path(run_dir), DATA_DIR / "truths" / dataset)) - df.append(ev) - +#!/usr/bin/env python3 +from tira.rest_api_client import Client +from wows_eval.correlation import WowsCorrelationEvalEvaluator +from pathlib import Path +tira = Client() +import pandas as pd + +TASK_ID = 'wows-eval' +DATASETS = ["pointwise-20250309-test", "pairwise-20250309-test"] +DATA_DIR = Path("/home/maik/workspace/wows-code/ecir25/wows-eval/build-runs/data") + +def evaluator(): + ret = WowsCorrelationEvalEvaluator('*.jsonl', None, '*.jsonl', None, ['wows_tau_ap', 'wows_kendall', 'wows_spearman', 'wows_pearson']) + ret.throw_if_conf_invalid({"run_dirs": [DATA_DIR / "synthetic-runs"]}) + return ret + + +df = [] + +for dataset in DATASETS: + submissions = tira.submissions(TASK_ID, dataset) + for _, i in submissions.iterrows(): + i = i.to_dict() + if 'evaluator' in i['software'] or 'naive' in i['software']: + continue + print(i['team'], i['software']) + ev = {"dataset": dataset, "team": i['team'], "approach": i['software']} + + run_dir = tira.get_run_output(f"{TASK_ID}/{i['team']}/{i['software']}", dataset) + ev.update(evaluator().evaluate(Path(run_dir), DATA_DIR / "truths" / dataset)) + df.append(ev) + pd.DataFrame(df).to_json("evaluation.jsonl", lines=True, orient="records") \ No newline at end of file diff --git a/ecir26/README.md b/ecir26/README.md index f847fe0..240509c 100644 --- a/ecir26/README.md +++ b/ecir26/README.md @@ -1,75 +1,75 @@ -# Submission Instructions - -This directory contains example submissions. - -We use [TIRA.io](https://www.tira.io/task-overview/ir-lab-wise-2025) for submissions, you can either make run submissions (i.e., upload run files, e.g., via the command line) or optionally software submissions. We aim to collaboratively develop and prepare a set of [PyTerrier Artifacts](https://dl.acm.org/doi/abs/10.1145/3726302.3730147) for the datasets we collaboratively develop. An overview (currently in alpha development stage) of the available artifacts is available at [tira.io/tirex/pyterrier-artifacts-beta?query=ir-lab-wise-2025](https://www.tira.io/tirex/pyterrier-artifacts-beta?query=ir-lab-wise-2025). - - -## Available Datasets - -All datasets that we developed collaboratively are (while they are in progress, as soon as everything is finalized we will also mirror them to Zenodo properly) listed at [tira.io/datasets?query=ir-lab-wise-2025](https://www.tira.io/datasets?query=ir-lab-wise-2025). - -At the moment, the datasets are: - -- `spot-check-20251122-training`: A tiny spot check dataset so that you can fastly ensure your approach gives valid outputs. -- `radboud-validation-20251114-training`: A validation dataset created in the Radboud IR course. - -Additionally, two test datasets are currently under construction, one created in the Radboud IR course, and one created in the Jena/Kassel IR courses. - - -## Run Submissions - -Submissions are expected to have the following structure: - -``` -├── ir-metadata.yml -└── run.txt.gz -``` - -The `ir-metadata.yml` file describes your approach in the [ir-metadata format](https://www.ir-metadata.org/) and the `run.txt.gz` file is a standard TREC-style run file for the dataset. - -The directory [submission-skeleton](submission-skeleton) contains an example (please replace all `ENTER_VALUE_HERE` occurences in the ir-metadata.yml file of the skeleton with your actual data to make it valid). - - -You can verify and submit your submission via `tira-cli`. - -1. Install the tira client via: - -``` -pip3 install --upgrade tira -``` - -2. Verify your submission (i.e., the `--dry-run` flag): - -``` -tira-cli upload --dry-run --directory submission-skeleton -``` - -For valid submissions, the output should look like: - -Screenshot_20251123_192741 - - -3. Upload your submission (i.e., remove the `--dry-run` flag and login): - -``` -# First login via your token from TIRA.io submission page -tira-cli login --token YOUR-TOKEN-FROM-TIRA-IO -tira-cli upload --directory web-submission-skeleton -``` - -## Approaches - -We aim to maintain all work here in a mono-repository to have everything at one place. Therefore, we want to collect all approaches as directories in this repository. If you have a new approach, please add them via a pull request (i.e., fork the repository, and then, as soon as your approach is "finished enough", please create a pull request that adds your approach as a new directory, you do not need to add all your approaches, but the ones that you think could be interesting for others as well). - -If you want to add a new approach, please develop them in a [dev container](https://containers.dev/), as this makes it simpler for others to get your approach running. - -We have a set of baseline approaches that you can take as inspiration: - -- [pyterrier-baseline](pyterrier-baseline) provides a minimal baseline that loads PyTerrier indexes as Artifacts and should therefore be the fastest approach to get something to run. -- [template-new-approach](template-new-approach) does everything from scratch (i.e., without artifacts), so that you have full control over every potential aspect that you might want to modify. Please use this directory as starting point for new approaches. -- [chatnoir-baseline](chatnoir-baseline) does baseline retrieval with [ChatNoir](https://www.chatnoir.eu/). - - -We want to improve the re-usability of experimental artifacts with the [PyTerrier Artifacts](https://dl.acm.org/doi/abs/10.1145/3726302.3730147) paradigm, therefore, please have a look at the [corresponding tutorial](https://github.com/tira-io/teaching-ir-with-shared-tasks/blob/main/tutorials/tutorial-pyterrier-artifacts.ipynb) so that you get a feeling on how to use them. Ideally, if you have an idea of experimental artifacts that you think others could also use, please do not hesitate to make them available (e.g., as upload to tira makes them available). - +# Submission Instructions + +This directory contains example submissions. + +We use [TIRA.io](https://www.tira.io/task-overview/ir-lab-wise-2025) for submissions, you can either make run submissions (i.e., upload run files, e.g., via the command line) or optionally software submissions. We aim to collaboratively develop and prepare a set of [PyTerrier Artifacts](https://dl.acm.org/doi/abs/10.1145/3726302.3730147) for the datasets we collaboratively develop. An overview (currently in alpha development stage) of the available artifacts is available at [tira.io/tirex/pyterrier-artifacts-beta?query=ir-lab-wise-2025](https://www.tira.io/tirex/pyterrier-artifacts-beta?query=ir-lab-wise-2025). + + +## Available Datasets + +All datasets that we developed collaboratively are (while they are in progress, as soon as everything is finalized we will also mirror them to Zenodo properly) listed at [tira.io/datasets?query=ir-lab-wise-2025](https://www.tira.io/datasets?query=ir-lab-wise-2025). + +At the moment, the datasets are: + +- `spot-check-20251122-training`: A tiny spot check dataset so that you can fastly ensure your approach gives valid outputs. +- `radboud-validation-20251114-training`: A validation dataset created in the Radboud IR course. + +Additionally, two test datasets are currently under construction, one created in the Radboud IR course, and one created in the Jena/Kassel IR courses. + + +## Run Submissions + +Submissions are expected to have the following structure: + +``` +├── ir-metadata.yml +└── run.txt.gz +``` + +The `ir-metadata.yml` file describes your approach in the [ir-metadata format](https://www.ir-metadata.org/) and the `run.txt.gz` file is a standard TREC-style run file for the dataset. + +The directory [submission-skeleton](submission-skeleton) contains an example (please replace all `ENTER_VALUE_HERE` occurences in the ir-metadata.yml file of the skeleton with your actual data to make it valid). + + +You can verify and submit your submission via `tira-cli`. + +1. Install the tira client via: + +``` +pip3 install --upgrade tira +``` + +2. Verify your submission (i.e., the `--dry-run` flag): + +``` +tira-cli upload --dry-run --directory submission-skeleton +``` + +For valid submissions, the output should look like: + +Screenshot_20251123_192741 + + +3. Upload your submission (i.e., remove the `--dry-run` flag and login): + +``` +# First login via your token from TIRA.io submission page +tira-cli login --token YOUR-TOKEN-FROM-TIRA-IO +tira-cli upload --directory web-submission-skeleton +``` + +## Approaches + +We aim to maintain all work here in a mono-repository to have everything at one place. Therefore, we want to collect all approaches as directories in this repository. If you have a new approach, please add them via a pull request (i.e., fork the repository, and then, as soon as your approach is "finished enough", please create a pull request that adds your approach as a new directory, you do not need to add all your approaches, but the ones that you think could be interesting for others as well). + +If you want to add a new approach, please develop them in a [dev container](https://containers.dev/), as this makes it simpler for others to get your approach running. + +We have a set of baseline approaches that you can take as inspiration: + +- [pyterrier-baseline](pyterrier-baseline) provides a minimal baseline that loads PyTerrier indexes as Artifacts and should therefore be the fastest approach to get something to run. +- [template-new-approach](template-new-approach) does everything from scratch (i.e., without artifacts), so that you have full control over every potential aspect that you might want to modify. Please use this directory as starting point for new approaches. +- [chatnoir-baseline](chatnoir-baseline) does baseline retrieval with [ChatNoir](https://www.chatnoir.eu/). + + +We want to improve the re-usability of experimental artifacts with the [PyTerrier Artifacts](https://dl.acm.org/doi/abs/10.1145/3726302.3730147) paradigm, therefore, please have a look at the [corresponding tutorial](https://github.com/tira-io/teaching-ir-with-shared-tasks/blob/main/tutorials/tutorial-pyterrier-artifacts.ipynb) so that you get a feeling on how to use them. Ideally, if you have an idea of experimental artifacts that you think others could also use, please do not hesitate to make them available (e.g., as upload to tira makes them available). + diff --git a/ecir26/chatnoir-baseline/.devcontainer.json b/ecir26/chatnoir-baseline/.devcontainer.json index c4699ca..b4924d1 100644 --- a/ecir26/chatnoir-baseline/.devcontainer.json +++ b/ecir26/chatnoir-baseline/.devcontainer.json @@ -1,17 +1,17 @@ -{ - "dockerFile": "Dockerfile", - "customizations": { - "vscode": { - "extensions": [ - "ms-python.python", - "ms-python.vscode-pylance", - "ms-toolsai.jupyter", - "ms-toolsai.datawrangler", - "ms-azuretools.vscode-docker" - ] - } - }, - "runArgs": [ - "--network=host", "--privileged" - ] -} +{ + "dockerFile": "Dockerfile", + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "ms-toolsai.jupyter", + "ms-toolsai.datawrangler", + "ms-azuretools.vscode-docker" + ] + } + }, + "runArgs": [ + "--network=host", "--privileged" + ] +} diff --git a/ecir26/chatnoir-baseline/.gitignore b/ecir26/chatnoir-baseline/.gitignore index 344f079..73897b3 100644 --- a/ecir26/chatnoir-baseline/.gitignore +++ b/ecir26/chatnoir-baseline/.gitignore @@ -1 +1 @@ -runs +runs diff --git a/ecir26/chatnoir-baseline/Dockerfile b/ecir26/chatnoir-baseline/Dockerfile index e5e13e6..a9f51f8 100644 --- a/ecir26/chatnoir-baseline/Dockerfile +++ b/ecir26/chatnoir-baseline/Dockerfile @@ -1,10 +1,10 @@ -FROM ubuntu:jammy - - -RUN apt-get update \ - && apt-get install -y git openjdk-11-jdk python3 python3-pip - -ADD requirements.txt /tmp/requirements.txt - -RUN pip3 install -r /tmp/requirements.txt - +FROM ubuntu:jammy + + +RUN apt-get update \ + && apt-get install -y git openjdk-11-jdk python3 python3-pip + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt + diff --git a/ecir26/chatnoir-baseline/README.md b/ecir26/chatnoir-baseline/README.md index b4dbb7b..8eddab3 100644 --- a/ecir26/chatnoir-baseline/README.md +++ b/ecir26/chatnoir-baseline/README.md @@ -1,38 +1,38 @@ -# ChatNoir Baseline - -This is a [chatnoir-pyterrier](https://github.com/chatnoir-eu/chatnoir-pyterrier) baseline. This baseline submits builds runs via submitting the title respectively the description against [ChatNoir](https://www.chatnoir.eu/) using BM25 respectively the default ChatNoir retrieval model (which is a form of weighted BM25f optimized for retrieval on the [ClueWebs](https://ir-datasets.com/clueweb09.html)). - -## Overview - -Get an overview of all possibilities to execute this baseline via: - -``` -./run-chatnoir.py --help -``` - -This should yield an output like: - -``` -Usage: run-chatnoir.py [OPTIONS] - -Options: - --k INTEGER The retrieval depth. - --dataset [radboud-validation-20251114-training] - The dataset. [required] - --output PATH The output directory. - --query-field [title|description] - The query field. - --retrieval [bm25|default] The retrieval model to use. - --help Show this message and exit. -``` - -## Run All Variants - -We run retreival via: - -``` -./run-chatnoir.py --dataset DATASET-ID --k 100 --query-field title --retrieval bm25 -./run-chatnoir.py --dataset DATASET-ID --k 100 --query-field description --retrieval bm25 -./run-chatnoir.py --dataset DATASET-ID --k 10 --query-field title --retrieval default -./run-chatnoir.py --dataset DATASET-ID --k 10 --query-field description --retrieval default -``` +# ChatNoir Baseline + +This is a [chatnoir-pyterrier](https://github.com/chatnoir-eu/chatnoir-pyterrier) baseline. This baseline submits builds runs via submitting the title respectively the description against [ChatNoir](https://www.chatnoir.eu/) using BM25 respectively the default ChatNoir retrieval model (which is a form of weighted BM25f optimized for retrieval on the [ClueWebs](https://ir-datasets.com/clueweb09.html)). + +## Overview + +Get an overview of all possibilities to execute this baseline via: + +``` +./run-chatnoir.py --help +``` + +This should yield an output like: + +``` +Usage: run-chatnoir.py [OPTIONS] + +Options: + --k INTEGER The retrieval depth. + --dataset [radboud-validation-20251114-training] + The dataset. [required] + --output PATH The output directory. + --query-field [title|description] + The query field. + --retrieval [bm25|default] The retrieval model to use. + --help Show this message and exit. +``` + +## Run All Variants + +We run retreival via: + +``` +./run-chatnoir.py --dataset DATASET-ID --k 100 --query-field title --retrieval bm25 +./run-chatnoir.py --dataset DATASET-ID --k 100 --query-field description --retrieval bm25 +./run-chatnoir.py --dataset DATASET-ID --k 10 --query-field title --retrieval default +./run-chatnoir.py --dataset DATASET-ID --k 10 --query-field description --retrieval default +``` diff --git a/ecir26/chatnoir-baseline/requirements.txt b/ecir26/chatnoir-baseline/requirements.txt index 5cbcc22..9adaf36 100644 --- a/ecir26/chatnoir-baseline/requirements.txt +++ b/ecir26/chatnoir-baseline/requirements.txt @@ -1,6 +1,6 @@ -python-terrier==0.13.2 -tira -ir_datasets -tirex-tracker -click -chatnoir-pyterrier +python-terrier==0.13.2 +tira +ir_datasets +tirex-tracker +click +chatnoir-pyterrier diff --git a/ecir26/chatnoir-baseline/run-chatnoir.py b/ecir26/chatnoir-baseline/run-chatnoir.py index 4f50cb2..d1bc07e 100755 --- a/ecir26/chatnoir-baseline/run-chatnoir.py +++ b/ecir26/chatnoir-baseline/run-chatnoir.py @@ -1,37 +1,37 @@ -#!/usr/bin/env python3 -import click -import pyterrier as pt -from pathlib import Path -from tira.third_party_integrations import ensure_pyterrier_is_loaded -from chatnoir_pyterrier import ChatNoirRetrieve -from tirex_tracker import tracking, ExportFormat - - -def load_topics(irds, field): - ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False) - pt_dataset = pt.datasets.get_dataset(f"irds:ir-lab-wise-2025/{irds}") - return pt_dataset.get_topics(field) - - -@click.command() -@click.option("--k", type=int, required=False, default=100, help="The retrieval depth.") -@click.option("--dataset", type=click.Choice(["radboud-validation-20251114-training"]), required=True, help="The dataset.") -@click.option("--output", type=Path, required=False, default=Path("runs"), help="The output directory.") -@click.option("--query-field", type=click.Choice(["title", "description"]), required=False, default="title", help="The query field.") -@click.option("--retrieval", type=click.Choice(["bm25", "default"]), required=False, default="bm25", help="The retrieval model to use.") -def main(dataset, query_field, output, retrieval, k): - topics = load_topics(dataset, query_field) - tag = f"chatnoir-{query_field}-{retrieval}-{k}" - target_dir = output / dataset/ tag - target_dir.mkdir(parents=True, exist_ok=True) - description = f"This is a chatnoir-pyterrier baseline that retrieves the top-{k} results via the {query_field} field against ChatNoir using the {retrieval} model." - - with tracking(export_file_path=target_dir / "retrieval-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): - chatnoir = ChatNoirRetrieve(index="wows-owi/2025", search_method=retrieval, features=[], verbose=True, num_results=k, page_size=k) - run = chatnoir(topics) - - pt.io.write_results(run, target_dir / "run.txt.gz") - - -if __name__ == '__main__': - main() +#!/usr/bin/env python3 +import click +import pyterrier as pt +from pathlib import Path +from tira.third_party_integrations import ensure_pyterrier_is_loaded +from chatnoir_pyterrier import ChatNoirRetrieve +from tirex_tracker import tracking, ExportFormat + + +def load_topics(irds, field): + ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False) + pt_dataset = pt.datasets.get_dataset(f"irds:ir-lab-wise-2025/{irds}") + return pt_dataset.get_topics(field) + + +@click.command() +@click.option("--k", type=int, required=False, default=100, help="The retrieval depth.") +@click.option("--dataset", type=click.Choice(["radboud-validation-20251114-training"]), required=True, help="The dataset.") +@click.option("--output", type=Path, required=False, default=Path("runs"), help="The output directory.") +@click.option("--query-field", type=click.Choice(["title", "description"]), required=False, default="title", help="The query field.") +@click.option("--retrieval", type=click.Choice(["bm25", "default"]), required=False, default="bm25", help="The retrieval model to use.") +def main(dataset, query_field, output, retrieval, k): + topics = load_topics(dataset, query_field) + tag = f"chatnoir-{query_field}-{retrieval}-{k}" + target_dir = output / dataset/ tag + target_dir.mkdir(parents=True, exist_ok=True) + description = f"This is a chatnoir-pyterrier baseline that retrieves the top-{k} results via the {query_field} field against ChatNoir using the {retrieval} model." + + with tracking(export_file_path=target_dir / "retrieval-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): + chatnoir = ChatNoirRetrieve(index="wows-owi/2025", search_method=retrieval, features=[], verbose=True, num_results=k, page_size=k) + run = chatnoir(topics) + + pt.io.write_results(run, target_dir / "run.txt.gz") + + +if __name__ == '__main__': + main() diff --git a/ecir26/corpus-construction/collection-jena-kassel/config.json b/ecir26/corpus-construction/collection-jena-kassel/config.json index d07c138..9dd9f3a 100644 --- a/ecir26/corpus-construction/collection-jena-kassel/config.json +++ b/ecir26/corpus-construction/collection-jena-kassel/config.json @@ -1,6 +1,6 @@ -{ - "topics": "topics.xml", - "runs": "runs", - "team-mapping": "topic-mapping.jsonl", - "chatnoir-index": "wows-owi/2025" -} +{ + "topics": "topics.xml", + "runs": "runs", + "team-mapping": "topic-mapping.jsonl", + "chatnoir-index": "wows-owi/2025" +} diff --git a/ecir26/corpus-construction/collection-jena-kassel/doccano-label-configs.json b/ecir26/corpus-construction/collection-jena-kassel/doccano-label-configs.json index 6913602..ddff4ca 100644 --- a/ecir26/corpus-construction/collection-jena-kassel/doccano-label-configs.json +++ b/ecir26/corpus-construction/collection-jena-kassel/doccano-label-configs.json @@ -1,18 +1,18 @@ -[ - { - "id": 11340, - "text": "Not Relevant (0)", - "prefixKey": null, - "suffixKey": "0", - "backgroundColor": "#D33115", - "textColor": "#ffffff" - }, - { - "id": 11351, - "text": "Relevant (1)", - "prefixKey": null, - "suffixKey": "1", - "backgroundColor": "#194D33", - "textColor": "#ffffff" - } -] +[ + { + "id": 11340, + "text": "Not Relevant (0)", + "prefixKey": null, + "suffixKey": "0", + "backgroundColor": "#D33115", + "textColor": "#ffffff" + }, + { + "id": 11351, + "text": "Relevant (1)", + "prefixKey": null, + "suffixKey": "1", + "backgroundColor": "#194D33", + "textColor": "#ffffff" + } +] diff --git a/ecir26/corpus-construction/collection-jena-kassel/topic-mapping.jsonl b/ecir26/corpus-construction/collection-jena-kassel/topic-mapping.jsonl index 1231a2e..99a531d 100644 --- a/ecir26/corpus-construction/collection-jena-kassel/topic-mapping.jsonl +++ b/ecir26/corpus-construction/collection-jena-kassel/topic-mapping.jsonl @@ -1,24 +1,24 @@ -{"account": "ir-25-fsu-51", "topics": ["51"]} -{"account": "ir-25-uk-52", "topics": ["52"]} -{"account": "ir-25-fsu-53", "topics": ["53"]} -{"account": "ir-25-uk-54", "topics": ["54"]} -{"account": "ir-25-fsu-55", "topics": ["55"]} -{"account": "ir-25-fsu-56", "topics": ["56"]} -{"account": "ir-25-fsu-57", "topics": ["57"]} -{"account": "ir-25-fsu-58", "topics": ["58"]} -{"account": "ir-25-fsu-59", "topics": ["59"]} -{"account": "ir-25-fsu-60", "topics": ["60"]} -{"account": "ir-25-uk-61", "topics": ["61"]} -{"account": "ir-25-uk-62", "topics": ["62"]} -{"account": "ir-25-uk-63", "topics": ["63"]} -{"account": "ir-25-fsu-64", "topics": ["64"]} -{"account": "ir-25-uk-65", "topics": ["65"]} -{"account": "ir-25-fsu-66", "topics": ["66"]} -{"account": "ir-25-fsu-67", "topics": ["67"]} -{"account": "ir-25-fsu-68", "topics": ["68"]} -{"account": "ir-25-fsu-69", "topics": ["69"]} -{"account": "ir-25-fsu-70", "topics": ["70"]} -{"account": "ir-25-fsu-71", "topics": ["71"]} -{"account": "ir-25-fsu-72", "topics": ["72"]} -{"account": "ir-25-fsu-73", "topics": ["73"]} -{"account": "ir-25-fsu-74", "topics": ["74"]} +{"account": "ir-25-fsu-51", "topics": ["51"]} +{"account": "ir-25-uk-52", "topics": ["52"]} +{"account": "ir-25-fsu-53", "topics": ["53"]} +{"account": "ir-25-uk-54", "topics": ["54"]} +{"account": "ir-25-fsu-55", "topics": ["55"]} +{"account": "ir-25-fsu-56", "topics": ["56"]} +{"account": "ir-25-fsu-57", "topics": ["57"]} +{"account": "ir-25-fsu-58", "topics": ["58"]} +{"account": "ir-25-fsu-59", "topics": ["59"]} +{"account": "ir-25-fsu-60", "topics": ["60"]} +{"account": "ir-25-uk-61", "topics": ["61"]} +{"account": "ir-25-uk-62", "topics": ["62"]} +{"account": "ir-25-uk-63", "topics": ["63"]} +{"account": "ir-25-fsu-64", "topics": ["64"]} +{"account": "ir-25-uk-65", "topics": ["65"]} +{"account": "ir-25-fsu-66", "topics": ["66"]} +{"account": "ir-25-fsu-67", "topics": ["67"]} +{"account": "ir-25-fsu-68", "topics": ["68"]} +{"account": "ir-25-fsu-69", "topics": ["69"]} +{"account": "ir-25-fsu-70", "topics": ["70"]} +{"account": "ir-25-fsu-71", "topics": ["71"]} +{"account": "ir-25-fsu-72", "topics": ["72"]} +{"account": "ir-25-fsu-73", "topics": ["73"]} +{"account": "ir-25-fsu-74", "topics": ["74"]} diff --git a/ecir26/corpus-construction/collection-jena-kassel/topics.xml b/ecir26/corpus-construction/collection-jena-kassel/topics.xml index e3b0706..b4534c5 100644 --- a/ecir26/corpus-construction/collection-jena-kassel/topics.xml +++ b/ecir26/corpus-construction/collection-jena-kassel/topics.xml @@ -1,205 +1,205 @@ - - - - Alles was die Vorteile sind von Rust Web-Frameworks. Welche gibt es? Vergleichsseiten von gängigen Web-Frameworks. - a8c786474b56f0e30b9eeb61ba8766b82c30a02a3ff192c6b1de178523667eb2 - Relevant are comparison sites of common web frameworks. Not relevant are iron-related sites or websites about Rust frameworks that are not a web framework. - 0af76192c09d82e2701dea8ef9309352edd4c9b4e4ce9a76714751f2b5345764 - rust web frameworks - - - How are pets or animals used in therapy for humans and what are the benefits? - 8c96671bda4e546d3ad2330e5f3328807083b699c3921f11cc23c9dc82ff2d9d,a5d6e28526fd33467976248d541744553f0d963fc3c0e8f4987becc36e349bf6,4c305963d4256ee345f4aa37a612db0df1012b7954f2abdaadd087e5797c16f7 - Relevant documents must include details of how pet or animal-assisted therapy is or has been used. Relevant details include information about pet therapy programs, descriptions of the circumstances in which pet therapy is used, the benefits of this type of therapy, the degree of success of this therapy, and any laws or regulations governing it. -Documents on therapy for pets is irrelevant. - 615c3d227810554ba628b53c6a1cd557fa0b15e1b831ec854406dd3b034cfff8,1c2884f45042162aa26ec84007cfaa0d8dd4d1b9e0cb96afb64abe4ae6aade6d,67b193a009008d0c7c5dda6f5ce2f7c5d6d7a484b70d18a7b8240662cf9e7c90 - pet therapy - - - Which rare trees (species) exist and where they grow. What are the reasons for the tree being rare? - 8c2dbe4ad46c3f8015b086dbf22e6b355a187a28d080fd4a805e95dd6c539671,95201875e1ccbdcb752500ec2a8e8ab906bea15b0bc549de6a9e0c2b3242da05,39d3e3c11f92ff44dbfe0fb1b075406c81e5e5a8aea5f5db2bde8e7b4e9bb033 - Lists of rare trees, reasons why they are rare and where each of them grows are relevant. Such as information about rare tree species. Non-rare trees or lists of other rare things aren't relevant. No computer-science related trees, just biological ones. - eab888a646c500682e82548585fc6c685ae735f6832aa7e229dcafad85686b80,32cda6ceaa1a04cfaebfa613ad05575760b4ab7486362e1280e5a1e6fcfbbdc4 - rare trees - - - What is active learning in the context of machine learning? - f5ffc112a86a58a26baf6aa6f5c1de6c06b126bb25eaac815628ac9b8c76da4d,b4f9a3ab7be710b307854edb076fe7c379d8c541d6fb75cb43d7e417801a5cde,2395d49d109eaef94f052c4c07cbec1b47698c6e01bc0afa6743a9a4e2d55dcb - Relevant documents must define the general active learning problem. They should also detail its main components, algorithms, and typical use cases. Documents covering pedagogical learning methods (e.g., interactive lessons for children) are irrelevant. Commercial websites that primarily aim to sell a product or service are also irrelevant. - e07a58637b8482f4f52351523e74e8737c1a35d2f5af9208d712345a0b7989f0,9816e104cd81012da728fbe2a142e3532b94773470341ca73e58bb218bdc26a1 - Active learning ml - - - How artificial intelligence is applied in healthcare to improve diagnosis, treatment and patient care. The aim is to identify real-world example of AI improving medical practice. - 4fde93c7c224b5cdb6bd57a3c1884f425afa5f83943b47ea662b977239f803d8,18b10c89f7effc8dad1bd0555b613d5b224df27a2e8891822f34cc0127dfa54c - Relevant documents describe practical or research-based applications showing how AI is applied within healthcare. This includes direct medical applications such as medical image analysis, disease prediction or clinical decision support, as well as administrative tasks such as patient data management or hospital resource optimization. Non-relevant documents discuss AI in general or focus on other domains without direct connection to healthcare. - b237e8e465c6abd2fbbbcfcce326b26bc2168ab7a1f4f107a3ae3930ebc521e0,0e2879d3f64d99adefd7554e3c2c47aa9b7755d0eefd70d844eb565d439140c5 - Applications for AI in healthcare - - - What pc components or prebuilds are good with respect to gaming in 2025. Gaming should be at minimum 1440p, 60 fps. Budget should be interpreted as one of the cheapest ways to archive the gaming goal. - 793326eff66b190b8e9551d7235cc0cdc0c03d64265e7a67c0f02ac0ffdb379d,0fd25ce3f9f2474c6b75deb5d6127052a7dafb8c0ff30bfb526bfb14d3fdd09c - The relevant documents should describe what gaming will mean in 2025 and what is required to build a gaming PC. Relevant documents could include price/performance comparisons or pre-built options. Documents that include other aspects of gaming, such as monitors, laptops or peripherals, are not relevant. Documents that do not focus on budget are also not relevant. - 30997bc9f86f2e07f97fb5867a616cfe57bac5ff5e20c9691ce927a05a567208,636baec19a13476ef0e54734f13fef1d4ca1a6001e80d39871b8ef06d75ad0d2 - best budget gaming pc 2025 - - - What are the principal reasons for the concentration lack of the nowadays young generations? - - Relevant documents include reasons explaining the lack of concentration among younger generations. Nowadays, this issue mainly concerns those born after the 2000s. The focus of this topic is the easy access to the internet and how, for young people, it is easy to become accustomed to isolation due to immersive technology and larger causes such as pandemics and others. Relevant to this topic are statistics and arguments from experts such as psychologists or educators who deal directly with students. It isn't relevant topics concerning studies before the 2000s. - 179717f2f9d30f96ab76d8228565be62fefa67afec8d887d6b2abb50163d38b3,f887238158c7a508595977a6c81499de5bd527b569ddfbad93a4d09236adbe38 - Lack of concentration - - - What are the technical differences between the iPhone 11 and the iPhone 12? I would like to form an opinion, whether I should upgrade my phone or not, based on the differences between my phone (iPhone 11) and a newer model (iPhone 12). - 783e28298253328990f73fd5deee39e21d8643df881042cb8170fa57baf6379a,e83ee15fe96ddba6a1c1d6eec1087c37a502b61f47587e7d85f5a0ab45e7f581,2a95c773345b26812fc15b9962c79ed652a60a5517bfac3844b404df215fcb0b - Relevant documents must include information on both the iPhone 11 and the iPhone 12, comparing their technical differences. Relevant details include information on battery and screen size, camera details such as resolution and zoom options, processor type, weight, color, and connectivity. Relevant documents should list these differences for the base iPhone models. Information on the Pro or Pro Max models of the iPhone is not relevant. - 8b896738d679511217e7e5773a2a592ac9797bec02fb739fdab80466647bafe8 - iPhone 11 vs 12 - - - Why did the producers of the tv show Game of Thrones make less episodes in season 7, than in the others? - d15c8f74fa0483ff29bc1a08e4e236ed78e128e3571272a570c53d0e1f6d621b - Relevant are documents that give reasons for this change in production, irrelevant are documents that misinterpret GoT as some an abbreviation that means something other than Game of Thrones, or documents that are concerned with other sesons. - 7c21095cc31a799561fc233576b90b735a0edc5897c6be004c9e35ea9e57136c - GoT why is season 7 shorter - - - How does the total cost of ownership of battery-electric passenger cars compare -with that of gasoline and diesel cars in Europe, and what factors account for -the differences? - 1c67d7e92be317f8e05ef64b7ba6f02e424b753880750768b32c2d89464bf70f,4fd826d036d5fe150ad830195a9e0d7de26068af9e224ea7189c21e0cf2fe47f,d0112a245cac7ee8c92be421c8a62038c2d163d111a765b00f32ecf3ffc2b823 - Relevant: documents must include details of how total ownership costs are -calculated for electric and combustion cars in Europe and present comparative -results. Relevant details include purchase price and incentives, energy / fuel -costs, maintenance and repair, insurance, taxes and fees, depreciation and -resale value, assumed mileage and holding period, geographic scope, and -sensitivity analyses or scenarios (e.g., energy prices or incentives). -Irrelevant: documents that focus on non-passenger vehicles (e.g., buses, trucks, two-/three-wheelers), non-European markets without transferable assumptions, marketing/PR or news items lacking transparent TCO methodology and quantitative results, studies discussing only technology specs or list prices without ownership-cost modeling, and analyses that do not compare BEV and ICE passenger cars. - 6715f61abe094a5d15411dc2d1d1b9936f89cf4a3af4a414ab81267ae3cef1f3,18d0b4de0b44e70c04524ac99a88081e60648b7429a8d4b7fc0ae44449ea3b03,49d3ed5cb98384f98cea9dc9a96ed6c6d5bfe4b92fd8c67f8135bddc05086575 - Total cost of ownership for electric vs combustion cars in Europe - - - The process of training artificial intelligence or machine learning models - 09365b78687a5f8ba4d035e9ff434c4e0b9815297ad3ec39e7cb90e9dd60bc47,dcf579eeb30209945886f51e2bb6659893050cf0146163f0f1c687537692f479,973dc5578eba4e323be330ea3bc0804d2f599d3ba2801dbc7cfcb16f21f7f228 - Relevant documents should describe how AI or machine learning models are trained. This includes training methods (e.g., supervised, unsupervised, reinforcement learning), optimization algorithms, data preparation, use of frameworks, and challenges like bias or computational cost. -Documents focusing on human training with AI tools, are not relevant. - 27386093bb3a6e90c3cd59fedc6abfe9ad5d6e37f7e8c7d103e53e363c0e0a9e,ec5cac44bd942e17491dc0f7ef10439cda5e54be3f0f4c15eaae7f348f93953d,10392259634068055dcb3bd4009f65bd12fd3fab1eed3a2314d9939165addf1c - AI Training - - - How does veganism as a dietary choice affect dog's health? - fbb795de39fd86606f81caeab64bbb3c7809c23ad9337acbcb0ab550451c6f03,f061311da277fc160f59cb2b5508ca1ae6673ec059ab196525d124492fa98a54,d05418608805ec6ad18be86953e3bba647127467f8f0542d74b959bb1e982544 - Relevant sources include those that mention the effect of veganism as a dietary choice for dogs in the context of health and life expectancy. Sources that focus on vegan dog food in the context of sales and advertisement are irrelevant. Sources about hot dogs are also not relevant. - 45ac759c051a156178bc17f3feccaa74ca50bb43534b40721d3096f1690421b2,ceec8328a6f9c3b08392d15b10149363338e58267a6c1b62faa5c7fb1f490c56,dc651afd34da5c25106d84d61b8787e73c0d3e56077d3654bebc08770fb00dfc - vegan dog - - - What is the definition of dog whistle as a political term - 2e3930d1d50fbe9e0143be1e8ea35d0cae442e6f8623b79a555a79bd56dd7f9e,e4169ccee0473e19bbcc6dbdb778130f32de584b1b7ea2592a67898a3bc84d55,a41703cec0048dfc50bc337c8011963e87abad76735bd05ee9369d5eb587214e - Relevant documents include explanations of dog whistle as a political term and examples of its historical and current usage. Not relevant documents are those that refer to silent whistles, a type of whistle that emits sound in the ultrasonic range that used to train dogs. - -Sources that don't explain the definition of dog whistle but uses it in a political context are also irrelevant. - cff298614f3801cc2b832fcfe6faa3b7fab712677d0bb7355fb08e5a4ea7cbb0 - dog whistle - - - What is the syntax of the teleport command in Minecraft? - fe06e42fd22876ba196b9602d1b20683555599ce72f90e059445a0dbd325b5f9,cd4071fac6967d6589540fe288a71138b4091516ea12d10f975a263617d5ddfd - This question is specifically about the syntax of the teleport command in Minecraft. -Relevant documents are those that explain the different input options, mention when the command was added to Minecraft, and show examples of how to use it. -Irrelevant documents are anything not related to Minecraft. - 00fb793062de36fd9c0f50eb8a6dbb41f6727b33f347a757b272007f356358d1 - mc teleport command - - - How do dogs positively influence the mental health of humans? - 79f5b0d9bedb6089a6bf3b8975643f5808cc43fc8fb66b3b6c9c4cc64e102455,158df0f30972a54b8afd5a4fcc37e0c8c8e5f250b3c8e42579f49bf4688ca480,9f558fa8ae6cca67b95c4d3c1f1dc837a2af6720e8ca8fe89a5f649c5aac2044 - Relevant documents must specifically describe how interaction with dogs improves human mental health. Relevant details include evidence or studies showing reductions in stress, anxiety, loneliness, or depressive symptoms through dog ownership, daily interaction, or structured therapy sessions with dogs. -Documents that discuss the mental health benefits of animals or pets in general without focusing on dogs are irrelevant. Likewise, documents about the mental health of dogs themselves, general dog care, or training techniques are irrelevant. Documents about people's mental health in general are also irrelevant. - 571fb9c141a32f928f6c190b3bc095c957f25a28c3056d9e7a4c789887dcf74f,cff54f1891bebd02879b1dbcffd06a97233df9ccf7bb8cd77d4256898b7bbfe5,44a2cb5a96261f447d7110c7dcc98ad663b09ea4741744f80d33da280f4d1943 - dogs mental health - - - Search for dog breeds that posess white or very light fur. - 5dae365a378ed5a1670d852389748c515ba5d79933fe0e209dd81a52330cc799,00cdfcdf3d604320f0bb61fec1b7e453681d214ecf4a037cf6417a680c1ccaef,05c3095b7897da1808895ac4a4275752dd89c5f43403ed83f290164e0bf1fc41 - Relevant documents must include information of dog breeds with white or very light fur. The results should include pictures and distinguishing features of different matching dog breeds. Information on where to buy matching dogs is also suitable. Not relevant are results regarding other animal species (i.e. cats), fur coats and other clothing articles as well as branded products/companies that use the phrase "White Dog". - 4fe1c6a4f7ef7e3ee4cda36400b3169eab2d7b6c026539f4c4636d7752e28ba7,b847f90f699cb2b260cc6f37ca5960d8472e417886d69dae65a6d8ca608da9e6,b8cc2e46e584c94e581a14530e111d3cb7efc63c7cf155ad09a2d6e9019242de - white dogs - - - How is artificial intelligence affecting employment in various industries, and what are the potential benefits or mitigation strategies? - e1b74ac9c35188b40746bb4847e0aa86004c95e87cff4c204f3ab49c3914b303 - - Relevant documents must include details on how AI technologies, such as automation or machine learning, are causing job losses or transformations in specific sectors like manufacturing, healthcare, or creative fields. - -- Relevant details include: case studies of AI implementation leading to workforce changes, statistics on job displacement rates, discussions of reskilling programs or policies to address the impacts, economic impacts, and any regulations or ethical considerations governing AI's role in employment. - -- Documents are not relevant if they only discuss AI in general terms without connecting to employment effects (e.g., overviews of AI history or technical specs), or if they focus solely on AI's positive job creation without addressing displacement. - -- For misunderstandings: If a document interprets "AI job displacement" as voluntary career shifts due to tech adoption rather than involuntary losses, it is not relevant unless it ties back to forced changes; relevance requires evidence of negative or transformative impacts on workers. - 963faa92e0420b0bb6edcfd0bb02c01e650b76f2e3a31cc0380e0e3ebd20d150,53171f72e6d01786c6f1080ce6b0bfa01797bae725289d773e7617d8bfcca366,49dd9f0a915789a362c551206a1fce6ec2931476a59728733cb315d21d09cf23 - AI job displacement - - - Does looking at screens in the dark harm your eyes, and what evidence exists about potential short-term or long-term effects? - 774cabeec0ce1fe104187e8af184e84bffa699b5728f9b403927551062dbde48,676246ec3c5cfcecfcbf0b071ec3455f3c75144599bf191e011c8fb33d3f7772,001159a70ad7e1f52bda9ae4209b72b12f6a36ee8d04d3ba88c0d8858ea2f312 - Relevant documents must include information about how screen use in low-light or dark environments affects eye health. Relevant details include descriptions of eye strain, digital eye fatigue, or retinal effects, explanations of how light exposure or contrast in darkness influences vision, and any studies, expert opinions, or medical guidance related to these effects. Documents discussing preventive measures (e.g., screen filters, lighting conditions, or usage guidelines) are also relevant. - d0067eca4a7ca790a8f8d13512c8be8d2d297a4feaed3cddffae5e4177e2933f,68dc8eef5495e81f3c077b8d8c420c792e253b58a6086a69d4fa91cc991a4f87,8f9cf4c2022674b0207a49bfd31a0e111f736e47a5b73e0f008f07fcded521b8 - screen use in the dark and eye health - - - What is the "getting things done" method of David Allen about? - 9f9c7e9674275ecb46b34407ce60004864f6145939dc2245fdacdb7648361c78,418833400109c4076ab43a7d490ab64b0853aac4054408ae86ea0e2931ac778f - Any relevant documents should have info about the book “Getting Things Done” of David Allen or his methodology. Anything related to getting things done or productivity that is not connected to David Allen is not relevant. - 6ab1e3a0a829e007bfec7b709d15961bfe58fcc651207f2fdee237a66ee82564,496f2f54c125c405eba7215f731ca306208677170e7bb8707bad402f4cdfb329 - getting things done - - - The user is seeking information on how to maintain the health and well-being of senior dogs, focusing on the best nutritional practices and health care approaches for older dogs. The user is particularly interested in diet recommendations, including the types of food that are most beneficial for older dogs and how their nutritional needs change as they age. Additionally, the user is looking for information on common health issues that affect senior dogs and how these can be managed or prevented. Veterinary advice and research studies that provide guidelines on age-appropriate care are also important. - 927cc237a60eab035519c7f9627e650623a8c7a03f6489a2c8488bbe055191ec,444c8e7fe80ea2cae50cd9f68ae405e2598df47bd8dbe67daecb220b16113fe3 - Relevant Content: -- Veterinary advice and research studies on the health care needs of senior dogs, including preventative measures and management of common age-related conditions. -- Guidelines on dog nutrition, specifically for senior dogs, including the types of foods that provide the necessary nutrients for older dogs. -- Articles or books on senior dog care, focusing on dietary needs and physical exercise for old dogs. -- Case studies or expert opinions on managing the health and nutrition of senior dogs. - -Non-relevant Content: -- General dog care tips not specific to senior dogs (e.g., puppy care or basic training). -- Promotional content for dog food or supplements that does not include scientifically backed information or recommendations from veterinary experts. -- Information on specific dog breeds that can't be translated and applied to all senior dogs. -- Personal anecdotal stories or non-scientific advice about senior dogs, unless it includes professional or veterinary recommendations. - 10ce1eabe50a1fc88047822100133c6f2a5dd50193515c0aed7025de9f96dfc0,b2b357f1839434c8b837caedcdf6c654ceabb006a917695038253ad2b1ad7d5f,a09f5c244e70e89a13a3dac241a795c9c7cc3a7ba79e160a597c0ec690754583 - Best nutrition and health care for senior dogs - - - What are the works of J. R. R. Tolkien? Which books, poems etc. did he pen? - 4e4285c651c9f2bc9ce6af5b062249d3ff9a19ee9566017049b1b93169e8e387 - Relevant documents must include a list of J. R. R. Tolkiens works. Irrelevant are documents that focus on something else like his life for example. - 34c571ec36716900b644dcabc9de9f910a384273961321c9ba61f994d1782484 - J. R. R. Tolkien - - - Which companies produce great chocolate pudding? What are some chocolate pudding recipes? How can good chocolate pudding be made? - 426d3490a0b256965c0b4c89d9edc5be9af48e578245a88cab6399ef32e17692 - Relevant documents include recipes for chocolate pudding or recommendations for specific chocolate pudding producers. Relevant details for the recipes include the ingredients and instructions for making chocolate pudding. Relevant details for recommendations include information about what companies are recommended or in which stores the recommended chocolate pudding can be found. -Not relevant are other flavours of pudding or other food made with chocolate. - 9eb910a9ac7b9c6478201c9fca991e792cfa8a98107e9ad471223f1e35fee313 - Best chocolate pudding - - - How is energy from renewable sources such as solar and wind stored, and what are the main technologies and challenges involved? - 2548da6a768e38c9b3d472c9eae6de82c87038e7fe4a22c47b3a8d300ae59d96 - Relevant documents must describe or discuss how energy generated from renewable sources is stored for later use. This includes documents that explain or evaluate energy storage technologies, their applications, performance, costs, or policies influencing their use. Documents that analyze case studies of renewable energy systems integrating storage, compare storage methods, or discuss technological or economic barriers are also relevant. - -Documents not relevant include those focusing solely on fossil fuel energy storage, electrical grid management without mention of renewable sources, or general discussions of renewable energy generation that do not address storage mechanisms. Documents describing small-scale storage unrelated to renewable energy (e.g., consumer electronics batteries) or purely theoretical energy concepts without practical connection to renewable storage applications are also not relevant. - eb5e5d83c5742af735a9d6452805ee00d960c7bafa4afb59523d61410ba84a53 - renewable energy storage - - - What is Alcoholics Anonymous, how is the organisation structered and how does it help people with alcohol-related problems? - d1bd7ffa3d3d6cc65e3ff30c9fe8af47cdaa2852e7f22541134de50d442e209a - Relevant documents should give information about Alcoholics Anonymous' purpose and their concepts. Relevant details include information about how the meetings proceed, what the goals are, how high the success rate is and how to find a near Alcoholics Anonymous group. Information about other unrelated organisations, personal stories or alcohol use without connection to the Alcoholics Anonymous are irrelevant. - a0c931da4cc3a8dc4609c291f312f44dca86461b0b3467b48069e083769e03b1,b5861cf98ac8d31ce303e111b78530fdd70a811842d4075e070dfd6d12287d43 - Alcoholics Anonymous - - + + + + Alles was die Vorteile sind von Rust Web-Frameworks. Welche gibt es? Vergleichsseiten von gängigen Web-Frameworks. + a8c786474b56f0e30b9eeb61ba8766b82c30a02a3ff192c6b1de178523667eb2 + Relevant are comparison sites of common web frameworks. Not relevant are iron-related sites or websites about Rust frameworks that are not a web framework. + 0af76192c09d82e2701dea8ef9309352edd4c9b4e4ce9a76714751f2b5345764 + rust web frameworks + + + How are pets or animals used in therapy for humans and what are the benefits? + 8c96671bda4e546d3ad2330e5f3328807083b699c3921f11cc23c9dc82ff2d9d,a5d6e28526fd33467976248d541744553f0d963fc3c0e8f4987becc36e349bf6,4c305963d4256ee345f4aa37a612db0df1012b7954f2abdaadd087e5797c16f7 + Relevant documents must include details of how pet or animal-assisted therapy is or has been used. Relevant details include information about pet therapy programs, descriptions of the circumstances in which pet therapy is used, the benefits of this type of therapy, the degree of success of this therapy, and any laws or regulations governing it. +Documents on therapy for pets is irrelevant. + 615c3d227810554ba628b53c6a1cd557fa0b15e1b831ec854406dd3b034cfff8,1c2884f45042162aa26ec84007cfaa0d8dd4d1b9e0cb96afb64abe4ae6aade6d,67b193a009008d0c7c5dda6f5ce2f7c5d6d7a484b70d18a7b8240662cf9e7c90 + pet therapy + + + Which rare trees (species) exist and where they grow. What are the reasons for the tree being rare? + 8c2dbe4ad46c3f8015b086dbf22e6b355a187a28d080fd4a805e95dd6c539671,95201875e1ccbdcb752500ec2a8e8ab906bea15b0bc549de6a9e0c2b3242da05,39d3e3c11f92ff44dbfe0fb1b075406c81e5e5a8aea5f5db2bde8e7b4e9bb033 + Lists of rare trees, reasons why they are rare and where each of them grows are relevant. Such as information about rare tree species. Non-rare trees or lists of other rare things aren't relevant. No computer-science related trees, just biological ones. + eab888a646c500682e82548585fc6c685ae735f6832aa7e229dcafad85686b80,32cda6ceaa1a04cfaebfa613ad05575760b4ab7486362e1280e5a1e6fcfbbdc4 + rare trees + + + What is active learning in the context of machine learning? + f5ffc112a86a58a26baf6aa6f5c1de6c06b126bb25eaac815628ac9b8c76da4d,b4f9a3ab7be710b307854edb076fe7c379d8c541d6fb75cb43d7e417801a5cde,2395d49d109eaef94f052c4c07cbec1b47698c6e01bc0afa6743a9a4e2d55dcb + Relevant documents must define the general active learning problem. They should also detail its main components, algorithms, and typical use cases. Documents covering pedagogical learning methods (e.g., interactive lessons for children) are irrelevant. Commercial websites that primarily aim to sell a product or service are also irrelevant. + e07a58637b8482f4f52351523e74e8737c1a35d2f5af9208d712345a0b7989f0,9816e104cd81012da728fbe2a142e3532b94773470341ca73e58bb218bdc26a1 + Active learning ml + + + How artificial intelligence is applied in healthcare to improve diagnosis, treatment and patient care. The aim is to identify real-world example of AI improving medical practice. + 4fde93c7c224b5cdb6bd57a3c1884f425afa5f83943b47ea662b977239f803d8,18b10c89f7effc8dad1bd0555b613d5b224df27a2e8891822f34cc0127dfa54c + Relevant documents describe practical or research-based applications showing how AI is applied within healthcare. This includes direct medical applications such as medical image analysis, disease prediction or clinical decision support, as well as administrative tasks such as patient data management or hospital resource optimization. Non-relevant documents discuss AI in general or focus on other domains without direct connection to healthcare. + b237e8e465c6abd2fbbbcfcce326b26bc2168ab7a1f4f107a3ae3930ebc521e0,0e2879d3f64d99adefd7554e3c2c47aa9b7755d0eefd70d844eb565d439140c5 + Applications for AI in healthcare + + + What pc components or prebuilds are good with respect to gaming in 2025. Gaming should be at minimum 1440p, 60 fps. Budget should be interpreted as one of the cheapest ways to archive the gaming goal. + 793326eff66b190b8e9551d7235cc0cdc0c03d64265e7a67c0f02ac0ffdb379d,0fd25ce3f9f2474c6b75deb5d6127052a7dafb8c0ff30bfb526bfb14d3fdd09c + The relevant documents should describe what gaming will mean in 2025 and what is required to build a gaming PC. Relevant documents could include price/performance comparisons or pre-built options. Documents that include other aspects of gaming, such as monitors, laptops or peripherals, are not relevant. Documents that do not focus on budget are also not relevant. + 30997bc9f86f2e07f97fb5867a616cfe57bac5ff5e20c9691ce927a05a567208,636baec19a13476ef0e54734f13fef1d4ca1a6001e80d39871b8ef06d75ad0d2 + best budget gaming pc 2025 + + + What are the principal reasons for the concentration lack of the nowadays young generations? + + Relevant documents include reasons explaining the lack of concentration among younger generations. Nowadays, this issue mainly concerns those born after the 2000s. The focus of this topic is the easy access to the internet and how, for young people, it is easy to become accustomed to isolation due to immersive technology and larger causes such as pandemics and others. Relevant to this topic are statistics and arguments from experts such as psychologists or educators who deal directly with students. It isn't relevant topics concerning studies before the 2000s. + 179717f2f9d30f96ab76d8228565be62fefa67afec8d887d6b2abb50163d38b3,f887238158c7a508595977a6c81499de5bd527b569ddfbad93a4d09236adbe38 + Lack of concentration + + + What are the technical differences between the iPhone 11 and the iPhone 12? I would like to form an opinion, whether I should upgrade my phone or not, based on the differences between my phone (iPhone 11) and a newer model (iPhone 12). + 783e28298253328990f73fd5deee39e21d8643df881042cb8170fa57baf6379a,e83ee15fe96ddba6a1c1d6eec1087c37a502b61f47587e7d85f5a0ab45e7f581,2a95c773345b26812fc15b9962c79ed652a60a5517bfac3844b404df215fcb0b + Relevant documents must include information on both the iPhone 11 and the iPhone 12, comparing their technical differences. Relevant details include information on battery and screen size, camera details such as resolution and zoom options, processor type, weight, color, and connectivity. Relevant documents should list these differences for the base iPhone models. Information on the Pro or Pro Max models of the iPhone is not relevant. + 8b896738d679511217e7e5773a2a592ac9797bec02fb739fdab80466647bafe8 + iPhone 11 vs 12 + + + Why did the producers of the tv show Game of Thrones make less episodes in season 7, than in the others? + d15c8f74fa0483ff29bc1a08e4e236ed78e128e3571272a570c53d0e1f6d621b + Relevant are documents that give reasons for this change in production, irrelevant are documents that misinterpret GoT as some an abbreviation that means something other than Game of Thrones, or documents that are concerned with other sesons. + 7c21095cc31a799561fc233576b90b735a0edc5897c6be004c9e35ea9e57136c + GoT why is season 7 shorter + + + How does the total cost of ownership of battery-electric passenger cars compare +with that of gasoline and diesel cars in Europe, and what factors account for +the differences? + 1c67d7e92be317f8e05ef64b7ba6f02e424b753880750768b32c2d89464bf70f,4fd826d036d5fe150ad830195a9e0d7de26068af9e224ea7189c21e0cf2fe47f,d0112a245cac7ee8c92be421c8a62038c2d163d111a765b00f32ecf3ffc2b823 + Relevant: documents must include details of how total ownership costs are +calculated for electric and combustion cars in Europe and present comparative +results. Relevant details include purchase price and incentives, energy / fuel +costs, maintenance and repair, insurance, taxes and fees, depreciation and +resale value, assumed mileage and holding period, geographic scope, and +sensitivity analyses or scenarios (e.g., energy prices or incentives). +Irrelevant: documents that focus on non-passenger vehicles (e.g., buses, trucks, two-/three-wheelers), non-European markets without transferable assumptions, marketing/PR or news items lacking transparent TCO methodology and quantitative results, studies discussing only technology specs or list prices without ownership-cost modeling, and analyses that do not compare BEV and ICE passenger cars. + 6715f61abe094a5d15411dc2d1d1b9936f89cf4a3af4a414ab81267ae3cef1f3,18d0b4de0b44e70c04524ac99a88081e60648b7429a8d4b7fc0ae44449ea3b03,49d3ed5cb98384f98cea9dc9a96ed6c6d5bfe4b92fd8c67f8135bddc05086575 + Total cost of ownership for electric vs combustion cars in Europe + + + The process of training artificial intelligence or machine learning models + 09365b78687a5f8ba4d035e9ff434c4e0b9815297ad3ec39e7cb90e9dd60bc47,dcf579eeb30209945886f51e2bb6659893050cf0146163f0f1c687537692f479,973dc5578eba4e323be330ea3bc0804d2f599d3ba2801dbc7cfcb16f21f7f228 + Relevant documents should describe how AI or machine learning models are trained. This includes training methods (e.g., supervised, unsupervised, reinforcement learning), optimization algorithms, data preparation, use of frameworks, and challenges like bias or computational cost. +Documents focusing on human training with AI tools, are not relevant. + 27386093bb3a6e90c3cd59fedc6abfe9ad5d6e37f7e8c7d103e53e363c0e0a9e,ec5cac44bd942e17491dc0f7ef10439cda5e54be3f0f4c15eaae7f348f93953d,10392259634068055dcb3bd4009f65bd12fd3fab1eed3a2314d9939165addf1c + AI Training + + + How does veganism as a dietary choice affect dog's health? + fbb795de39fd86606f81caeab64bbb3c7809c23ad9337acbcb0ab550451c6f03,f061311da277fc160f59cb2b5508ca1ae6673ec059ab196525d124492fa98a54,d05418608805ec6ad18be86953e3bba647127467f8f0542d74b959bb1e982544 + Relevant sources include those that mention the effect of veganism as a dietary choice for dogs in the context of health and life expectancy. Sources that focus on vegan dog food in the context of sales and advertisement are irrelevant. Sources about hot dogs are also not relevant. + 45ac759c051a156178bc17f3feccaa74ca50bb43534b40721d3096f1690421b2,ceec8328a6f9c3b08392d15b10149363338e58267a6c1b62faa5c7fb1f490c56,dc651afd34da5c25106d84d61b8787e73c0d3e56077d3654bebc08770fb00dfc + vegan dog + + + What is the definition of dog whistle as a political term + 2e3930d1d50fbe9e0143be1e8ea35d0cae442e6f8623b79a555a79bd56dd7f9e,e4169ccee0473e19bbcc6dbdb778130f32de584b1b7ea2592a67898a3bc84d55,a41703cec0048dfc50bc337c8011963e87abad76735bd05ee9369d5eb587214e + Relevant documents include explanations of dog whistle as a political term and examples of its historical and current usage. Not relevant documents are those that refer to silent whistles, a type of whistle that emits sound in the ultrasonic range that used to train dogs. + +Sources that don't explain the definition of dog whistle but uses it in a political context are also irrelevant. + cff298614f3801cc2b832fcfe6faa3b7fab712677d0bb7355fb08e5a4ea7cbb0 + dog whistle + + + What is the syntax of the teleport command in Minecraft? + fe06e42fd22876ba196b9602d1b20683555599ce72f90e059445a0dbd325b5f9,cd4071fac6967d6589540fe288a71138b4091516ea12d10f975a263617d5ddfd + This question is specifically about the syntax of the teleport command in Minecraft. +Relevant documents are those that explain the different input options, mention when the command was added to Minecraft, and show examples of how to use it. +Irrelevant documents are anything not related to Minecraft. + 00fb793062de36fd9c0f50eb8a6dbb41f6727b33f347a757b272007f356358d1 + mc teleport command + + + How do dogs positively influence the mental health of humans? + 79f5b0d9bedb6089a6bf3b8975643f5808cc43fc8fb66b3b6c9c4cc64e102455,158df0f30972a54b8afd5a4fcc37e0c8c8e5f250b3c8e42579f49bf4688ca480,9f558fa8ae6cca67b95c4d3c1f1dc837a2af6720e8ca8fe89a5f649c5aac2044 + Relevant documents must specifically describe how interaction with dogs improves human mental health. Relevant details include evidence or studies showing reductions in stress, anxiety, loneliness, or depressive symptoms through dog ownership, daily interaction, or structured therapy sessions with dogs. +Documents that discuss the mental health benefits of animals or pets in general without focusing on dogs are irrelevant. Likewise, documents about the mental health of dogs themselves, general dog care, or training techniques are irrelevant. Documents about people's mental health in general are also irrelevant. + 571fb9c141a32f928f6c190b3bc095c957f25a28c3056d9e7a4c789887dcf74f,cff54f1891bebd02879b1dbcffd06a97233df9ccf7bb8cd77d4256898b7bbfe5,44a2cb5a96261f447d7110c7dcc98ad663b09ea4741744f80d33da280f4d1943 + dogs mental health + + + Search for dog breeds that posess white or very light fur. + 5dae365a378ed5a1670d852389748c515ba5d79933fe0e209dd81a52330cc799,00cdfcdf3d604320f0bb61fec1b7e453681d214ecf4a037cf6417a680c1ccaef,05c3095b7897da1808895ac4a4275752dd89c5f43403ed83f290164e0bf1fc41 + Relevant documents must include information of dog breeds with white or very light fur. The results should include pictures and distinguishing features of different matching dog breeds. Information on where to buy matching dogs is also suitable. Not relevant are results regarding other animal species (i.e. cats), fur coats and other clothing articles as well as branded products/companies that use the phrase "White Dog". + 4fe1c6a4f7ef7e3ee4cda36400b3169eab2d7b6c026539f4c4636d7752e28ba7,b847f90f699cb2b260cc6f37ca5960d8472e417886d69dae65a6d8ca608da9e6,b8cc2e46e584c94e581a14530e111d3cb7efc63c7cf155ad09a2d6e9019242de + white dogs + + + How is artificial intelligence affecting employment in various industries, and what are the potential benefits or mitigation strategies? + e1b74ac9c35188b40746bb4847e0aa86004c95e87cff4c204f3ab49c3914b303 + - Relevant documents must include details on how AI technologies, such as automation or machine learning, are causing job losses or transformations in specific sectors like manufacturing, healthcare, or creative fields. + +- Relevant details include: case studies of AI implementation leading to workforce changes, statistics on job displacement rates, discussions of reskilling programs or policies to address the impacts, economic impacts, and any regulations or ethical considerations governing AI's role in employment. + +- Documents are not relevant if they only discuss AI in general terms without connecting to employment effects (e.g., overviews of AI history or technical specs), or if they focus solely on AI's positive job creation without addressing displacement. + +- For misunderstandings: If a document interprets "AI job displacement" as voluntary career shifts due to tech adoption rather than involuntary losses, it is not relevant unless it ties back to forced changes; relevance requires evidence of negative or transformative impacts on workers. + 963faa92e0420b0bb6edcfd0bb02c01e650b76f2e3a31cc0380e0e3ebd20d150,53171f72e6d01786c6f1080ce6b0bfa01797bae725289d773e7617d8bfcca366,49dd9f0a915789a362c551206a1fce6ec2931476a59728733cb315d21d09cf23 + AI job displacement + + + Does looking at screens in the dark harm your eyes, and what evidence exists about potential short-term or long-term effects? + 774cabeec0ce1fe104187e8af184e84bffa699b5728f9b403927551062dbde48,676246ec3c5cfcecfcbf0b071ec3455f3c75144599bf191e011c8fb33d3f7772,001159a70ad7e1f52bda9ae4209b72b12f6a36ee8d04d3ba88c0d8858ea2f312 + Relevant documents must include information about how screen use in low-light or dark environments affects eye health. Relevant details include descriptions of eye strain, digital eye fatigue, or retinal effects, explanations of how light exposure or contrast in darkness influences vision, and any studies, expert opinions, or medical guidance related to these effects. Documents discussing preventive measures (e.g., screen filters, lighting conditions, or usage guidelines) are also relevant. + d0067eca4a7ca790a8f8d13512c8be8d2d297a4feaed3cddffae5e4177e2933f,68dc8eef5495e81f3c077b8d8c420c792e253b58a6086a69d4fa91cc991a4f87,8f9cf4c2022674b0207a49bfd31a0e111f736e47a5b73e0f008f07fcded521b8 + screen use in the dark and eye health + + + What is the "getting things done" method of David Allen about? + 9f9c7e9674275ecb46b34407ce60004864f6145939dc2245fdacdb7648361c78,418833400109c4076ab43a7d490ab64b0853aac4054408ae86ea0e2931ac778f + Any relevant documents should have info about the book “Getting Things Done” of David Allen or his methodology. Anything related to getting things done or productivity that is not connected to David Allen is not relevant. + 6ab1e3a0a829e007bfec7b709d15961bfe58fcc651207f2fdee237a66ee82564,496f2f54c125c405eba7215f731ca306208677170e7bb8707bad402f4cdfb329 + getting things done + + + The user is seeking information on how to maintain the health and well-being of senior dogs, focusing on the best nutritional practices and health care approaches for older dogs. The user is particularly interested in diet recommendations, including the types of food that are most beneficial for older dogs and how their nutritional needs change as they age. Additionally, the user is looking for information on common health issues that affect senior dogs and how these can be managed or prevented. Veterinary advice and research studies that provide guidelines on age-appropriate care are also important. + 927cc237a60eab035519c7f9627e650623a8c7a03f6489a2c8488bbe055191ec,444c8e7fe80ea2cae50cd9f68ae405e2598df47bd8dbe67daecb220b16113fe3 + Relevant Content: +- Veterinary advice and research studies on the health care needs of senior dogs, including preventative measures and management of common age-related conditions. +- Guidelines on dog nutrition, specifically for senior dogs, including the types of foods that provide the necessary nutrients for older dogs. +- Articles or books on senior dog care, focusing on dietary needs and physical exercise for old dogs. +- Case studies or expert opinions on managing the health and nutrition of senior dogs. + +Non-relevant Content: +- General dog care tips not specific to senior dogs (e.g., puppy care or basic training). +- Promotional content for dog food or supplements that does not include scientifically backed information or recommendations from veterinary experts. +- Information on specific dog breeds that can't be translated and applied to all senior dogs. +- Personal anecdotal stories or non-scientific advice about senior dogs, unless it includes professional or veterinary recommendations. + 10ce1eabe50a1fc88047822100133c6f2a5dd50193515c0aed7025de9f96dfc0,b2b357f1839434c8b837caedcdf6c654ceabb006a917695038253ad2b1ad7d5f,a09f5c244e70e89a13a3dac241a795c9c7cc3a7ba79e160a597c0ec690754583 + Best nutrition and health care for senior dogs + + + What are the works of J. R. R. Tolkien? Which books, poems etc. did he pen? + 4e4285c651c9f2bc9ce6af5b062249d3ff9a19ee9566017049b1b93169e8e387 + Relevant documents must include a list of J. R. R. Tolkiens works. Irrelevant are documents that focus on something else like his life for example. + 34c571ec36716900b644dcabc9de9f910a384273961321c9ba61f994d1782484 + J. R. R. Tolkien + + + Which companies produce great chocolate pudding? What are some chocolate pudding recipes? How can good chocolate pudding be made? + 426d3490a0b256965c0b4c89d9edc5be9af48e578245a88cab6399ef32e17692 + Relevant documents include recipes for chocolate pudding or recommendations for specific chocolate pudding producers. Relevant details for the recipes include the ingredients and instructions for making chocolate pudding. Relevant details for recommendations include information about what companies are recommended or in which stores the recommended chocolate pudding can be found. +Not relevant are other flavours of pudding or other food made with chocolate. + 9eb910a9ac7b9c6478201c9fca991e792cfa8a98107e9ad471223f1e35fee313 + Best chocolate pudding + + + How is energy from renewable sources such as solar and wind stored, and what are the main technologies and challenges involved? + 2548da6a768e38c9b3d472c9eae6de82c87038e7fe4a22c47b3a8d300ae59d96 + Relevant documents must describe or discuss how energy generated from renewable sources is stored for later use. This includes documents that explain or evaluate energy storage technologies, their applications, performance, costs, or policies influencing their use. Documents that analyze case studies of renewable energy systems integrating storage, compare storage methods, or discuss technological or economic barriers are also relevant. + +Documents not relevant include those focusing solely on fossil fuel energy storage, electrical grid management without mention of renewable sources, or general discussions of renewable energy generation that do not address storage mechanisms. Documents describing small-scale storage unrelated to renewable energy (e.g., consumer electronics batteries) or purely theoretical energy concepts without practical connection to renewable storage applications are also not relevant. + eb5e5d83c5742af735a9d6452805ee00d960c7bafa4afb59523d61410ba84a53 + renewable energy storage + + + What is Alcoholics Anonymous, how is the organisation structered and how does it help people with alcohol-related problems? + d1bd7ffa3d3d6cc65e3ff30c9fe8af47cdaa2852e7f22541134de50d442e209a + Relevant documents should give information about Alcoholics Anonymous' purpose and their concepts. Relevant details include information about how the meetings proceed, what the goals are, how high the success rate is and how to find a near Alcoholics Anonymous group. Information about other unrelated organisations, personal stories or alcohol use without connection to the Alcoholics Anonymous are irrelevant. + a0c931da4cc3a8dc4609c291f312f44dca86461b0b3467b48069e083769e03b1,b5861cf98ac8d31ce303e111b78530fdd70a811842d4075e070dfd6d12287d43 + Alcoholics Anonymous + + diff --git a/ecir26/corpus-construction/collection-radboud/README.md b/ecir26/corpus-construction/collection-radboud/README.md index 9b75de0..908cc72 100644 --- a/ecir26/corpus-construction/collection-radboud/README.md +++ b/ecir26/corpus-construction/collection-radboud/README.md @@ -1,49 +1,49 @@ -# Labeling Step By Step - -We configured the pooling so that the number of documents is on mean and on median slighly below 50 (mean: 47.7, median 46.5). - -- Go to [https://doccano.web.webis.de](https://doccano.web.webis.de) -- Click on login, enter your credentials -- You see a single project (named after your account), click on it, then click on "Start Annotation" - -# Setting Up Doccano Step by Step - -## 1. Install teaching-ir locally - -Step 1: clone the repository: - -``` -git clone git@github.com:tira-io/teaching-ir-with-shared-tasks.git -``` - -cd into the directory, and install it: - -``` -pip3 install --no-deps -e . --break-system-packages -``` - -Check it works: - -``` -teaching-ir --help -``` - -## 2. Run pooling - -After playing around a bit, we decided to choose a depth of 11 for the pooling. - -``` -teaching-ir pool-documents --pooling-depth 11 . -``` - -## 3. Upload to Doccano - -``` -teaching-ir prepare-relevance-judgments --doccano-url https://doccano.web.webis.de/ --doccano-username admin --doccano-password TODO wows doccano-judgment-pool.jsonl -``` - -## 4. Export QRELS - -``` -teaching-ir export-relevance-judgments --doccano-url https://doccano.web.webis.de/ --doccano-username admin --doccano-password TODO wows . -`` +# Labeling Step By Step + +We configured the pooling so that the number of documents is on mean and on median slighly below 50 (mean: 47.7, median 46.5). + +- Go to [https://doccano.web.webis.de](https://doccano.web.webis.de) +- Click on login, enter your credentials +- You see a single project (named after your account), click on it, then click on "Start Annotation" + +# Setting Up Doccano Step by Step + +## 1. Install teaching-ir locally + +Step 1: clone the repository: + +``` +git clone git@github.com:tira-io/teaching-ir-with-shared-tasks.git +``` + +cd into the directory, and install it: + +``` +pip3 install --no-deps -e . --break-system-packages +``` + +Check it works: + +``` +teaching-ir --help +``` + +## 2. Run pooling + +After playing around a bit, we decided to choose a depth of 11 for the pooling. + +``` +teaching-ir pool-documents --pooling-depth 11 . +``` + +## 3. Upload to Doccano + +``` +teaching-ir prepare-relevance-judgments --doccano-url https://doccano.web.webis.de/ --doccano-username admin --doccano-password TODO wows doccano-judgment-pool.jsonl +``` + +## 4. Export QRELS + +``` +teaching-ir export-relevance-judgments --doccano-url https://doccano.web.webis.de/ --doccano-username admin --doccano-password TODO wows . +`` diff --git a/ecir26/corpus-construction/collection-radboud/config.json b/ecir26/corpus-construction/collection-radboud/config.json index 56648a5..34f6747 100644 --- a/ecir26/corpus-construction/collection-radboud/config.json +++ b/ecir26/corpus-construction/collection-radboud/config.json @@ -1,6 +1,6 @@ -{ - "topics": "topics-final.csv", - "runs": "runs", - "team-mapping": "topic-mapping.jsonl", - "chatnoir-index": "wows-owi/2025" -} +{ + "topics": "topics-final.csv", + "runs": "runs", + "team-mapping": "topic-mapping.jsonl", + "chatnoir-index": "wows-owi/2025" +} diff --git a/ecir26/corpus-construction/collection-radboud/doccano-label-configs.json b/ecir26/corpus-construction/collection-radboud/doccano-label-configs.json index a507f9e..e9e3d35 100644 --- a/ecir26/corpus-construction/collection-radboud/doccano-label-configs.json +++ b/ecir26/corpus-construction/collection-radboud/doccano-label-configs.json @@ -1,42 +1,42 @@ -[ - { - "id": 11349, - "text": "Spam (-2)", - "prefixKey": null, - "suffixKey": null, - "backgroundColor": "#9F0500", - "textColor": "#ffffff" - }, - { - "id": 11340, - "text": "Irrelevant (0)", - "prefixKey": null, - "suffixKey": "0", - "backgroundColor": "#D33115", - "textColor": "#ffffff" - }, - { - "id": 11339, - "text": "Related (1)", - "prefixKey": null, - "suffixKey": "1", - "backgroundColor": "#73D8FF", - "textColor": "#ffffff" - }, - { - "id": 11350, - "text": "Highly Relevant (2)", - "prefixKey": null, - "suffixKey": "2", - "backgroundColor": "#A4DD00", - "textColor": "#ffffff" - }, - { - "id": 11351, - "text": "Perfectly Relevant (3)", - "prefixKey": null, - "suffixKey": "3", - "backgroundColor": "#194D33", - "textColor": "#ffffff" - } -] +[ + { + "id": 11349, + "text": "Spam (-2)", + "prefixKey": null, + "suffixKey": null, + "backgroundColor": "#9F0500", + "textColor": "#ffffff" + }, + { + "id": 11340, + "text": "Irrelevant (0)", + "prefixKey": null, + "suffixKey": "0", + "backgroundColor": "#D33115", + "textColor": "#ffffff" + }, + { + "id": 11339, + "text": "Related (1)", + "prefixKey": null, + "suffixKey": "1", + "backgroundColor": "#73D8FF", + "textColor": "#ffffff" + }, + { + "id": 11350, + "text": "Highly Relevant (2)", + "prefixKey": null, + "suffixKey": "2", + "backgroundColor": "#A4DD00", + "textColor": "#ffffff" + }, + { + "id": 11351, + "text": "Perfectly Relevant (3)", + "prefixKey": null, + "suffixKey": "3", + "backgroundColor": "#194D33", + "textColor": "#ffffff" + } +] diff --git a/ecir26/corpus-construction/collection-radboud/extract-validation-dataset.py b/ecir26/corpus-construction/collection-radboud/extract-validation-dataset.py index 2762660..0115ec0 100755 --- a/ecir26/corpus-construction/collection-radboud/extract-validation-dataset.py +++ b/ecir26/corpus-construction/collection-radboud/extract-validation-dataset.py @@ -1,46 +1,46 @@ -#!/usr/bin/env python3 -import pandas as pd -from statistics import mean - -TOPIC_IDS = set(["3", "4", "7", "8", "13", "15", "16", "18", "20", "23", "24", "31", "32", "34", "39", "42", "43", "44", "46", "48", "52", "54", "58", "60", "64", "68", "69", "74"]) - -topics = pd.read_csv("topics-final.csv") -reformatted_topics = [] - -for _, topic in topics.iterrows(): - if str(topic["qid"]) not in TOPIC_IDS: - continue - reformatted_topics.append(f'\n {topic["qid"]}{topic["query"]}\n {topic["description"]}\n {topic["narrative"]}\n\n') - -assert len(reformatted_topics) == len(TOPIC_IDS) - -with open("../subsampled-corpora/radboud-validation/topics.xml", "wt") as f: - f.write("\n" + ("".join(reformatted_topics)) + "\n") - -qid_to_doc_to_judgments = {} -qrels_raw = pd.read_json("raw-exported-doccano-judgments.jsonl", lines=True) - -for _, i in qrels_raw.iterrows(): - qid = str(i["query_id"]) - docid = str(i["doc_id"]) - if qid not in TOPIC_IDS: - continue - - if qid not in qid_to_doc_to_judgments: - qid_to_doc_to_judgments[qid] = {} - if docid not in qid_to_doc_to_judgments[qid]: - qid_to_doc_to_judgments[qid][docid] = [] - - for label in i["label"]: - qid_to_doc_to_judgments[qid][docid].append(int(label.split("(")[-1].split(")")[0])) - -assert len(qid_to_doc_to_judgments.keys()) == len(TOPIC_IDS) - -with open("../subsampled-corpora/radboud-validation/qrels.txt", "wt") as f: - for qid in qid_to_doc_to_judgments.keys(): - for docid in qid_to_doc_to_judgments[qid].keys(): - labels = qid_to_doc_to_judgments[qid][docid] - if len(labels) == 0: - continue - label = int(mean(labels)) - f.write(f"{qid} 0 {docid} {label}\n") +#!/usr/bin/env python3 +import pandas as pd +from statistics import mean + +TOPIC_IDS = set(["3", "4", "7", "8", "13", "15", "16", "18", "20", "23", "24", "31", "32", "34", "39", "42", "43", "44", "46", "48", "52", "54", "58", "60", "64", "68", "69", "74"]) + +topics = pd.read_csv("topics-final.csv") +reformatted_topics = [] + +for _, topic in topics.iterrows(): + if str(topic["qid"]) not in TOPIC_IDS: + continue + reformatted_topics.append(f'\n {topic["qid"]}{topic["query"]}\n {topic["description"]}\n {topic["narrative"]}\n\n') + +assert len(reformatted_topics) == len(TOPIC_IDS) + +with open("../subsampled-corpora/radboud-validation/topics.xml", "wt") as f: + f.write("\n" + ("".join(reformatted_topics)) + "\n") + +qid_to_doc_to_judgments = {} +qrels_raw = pd.read_json("raw-exported-doccano-judgments.jsonl", lines=True) + +for _, i in qrels_raw.iterrows(): + qid = str(i["query_id"]) + docid = str(i["doc_id"]) + if qid not in TOPIC_IDS: + continue + + if qid not in qid_to_doc_to_judgments: + qid_to_doc_to_judgments[qid] = {} + if docid not in qid_to_doc_to_judgments[qid]: + qid_to_doc_to_judgments[qid][docid] = [] + + for label in i["label"]: + qid_to_doc_to_judgments[qid][docid].append(int(label.split("(")[-1].split(")")[0])) + +assert len(qid_to_doc_to_judgments.keys()) == len(TOPIC_IDS) + +with open("../subsampled-corpora/radboud-validation/qrels.txt", "wt") as f: + for qid in qid_to_doc_to_judgments.keys(): + for docid in qid_to_doc_to_judgments[qid].keys(): + labels = qid_to_doc_to_judgments[qid][docid] + if len(labels) == 0: + continue + label = int(mean(labels)) + f.write(f"{qid} 0 {docid} {label}\n") diff --git a/ecir26/corpus-construction/collection-radboud/index.py b/ecir26/corpus-construction/collection-radboud/index.py index 5f81689..8facdd2 100644 --- a/ecir26/corpus-construction/collection-radboud/index.py +++ b/ecir26/corpus-construction/collection-radboud/index.py @@ -1,9 +1,9 @@ -import os -import pyterrier as pt - -pt.init(packages=["com.github.terrierteam:terrier-ciff:-SNAPSHOT"]) - -for representation in ["title", "description", "content"]: - index_path = f"./index-{representation}/data.properties" - if not os.path.exists(index_path): - pt.run('ciff-ingest', ['-I', index_path, f"index-{representation}.ciff"]) +import os +import pyterrier as pt + +pt.init(packages=["com.github.terrierteam:terrier-ciff:-SNAPSHOT"]) + +for representation in ["title", "description", "content"]: + index_path = f"./index-{representation}/data.properties" + if not os.path.exists(index_path): + pt.run('ciff-ingest', ['-I', index_path, f"index-{representation}.ciff"]) diff --git a/ecir26/corpus-construction/collection-radboud/manual.csv b/ecir26/corpus-construction/collection-radboud/manual.csv index 237181d..0a47c53 100644 --- a/ecir26/corpus-construction/collection-radboud/manual.csv +++ b/ecir26/corpus-construction/collection-radboud/manual.csv @@ -1,278 +1,278 @@ -qid,doc_id,label -1,36573058460fd834571449ef00bfbf1964b6cc72d898ddfb50c7db6a163058f3,0 -1,806eeed09416f945b8d43e19066d43d8810839b6f9342d0b1e3ee6cfe063fbab,1 -1,a691ee68812244f7d2ba253e7699edc4414b430c42d0120d2515a9f78b8fb115,1 -1,cc66d933b926c4b7f7dc47745b79a2e826f779cba72d9a28c77eb718aa8d4187,0 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"author"]].to_csv("authors.csv", index=False) - -topics.drop(columns=["author"], inplace=True) - -topics.to_csv("topics-final.csv", index=False) - - -for representation in ["title", "description", "content"]: - print(representation) - index = pt.IndexFactory.of(f"./index-{representation}", memory=representation != "content") - - for wmodel in ["BM25", "PL2", "Hiemstra_LM"]: - print(wmodel) - - retr = pt.rewrite.tokenise() >> pt.terrier.Retriever(index, wmodel=wmodel, properties={"termpipelines": ""}) - - run = retr.transform(topics) - - pt.io.write_results(run, f"runs/ows-{representation}-{wmodel.lower().replace('_', '')}.txt.gz") +import random +import pyterrier as pt +import pandas as pd + + +df = pd.read_csv("topics.csv") + +topics = df[df["selected"] | df["additional"]][["title", "description", "narrative", "snumber"]].rename(columns={"title": "query", "snumber": "author"}).sample(frac=1) +topics["qid"] = range(1, len(topics) + 1) + +print(topics) + +topics[["qid", "author"]].to_csv("authors.csv", index=False) + +topics.drop(columns=["author"], inplace=True) + +topics.to_csv("topics-final.csv", index=False) + + +for representation in ["title", "description", "content"]: + print(representation) + index = pt.IndexFactory.of(f"./index-{representation}", memory=representation != "content") + + for wmodel in ["BM25", "PL2", "Hiemstra_LM"]: + print(wmodel) + + retr = pt.rewrite.tokenise() >> pt.terrier.Retriever(index, wmodel=wmodel, properties={"termpipelines": ""}) + + run = retr.transform(topics) + + pt.io.write_results(run, f"runs/ows-{representation}-{wmodel.lower().replace('_', '')}.txt.gz") diff --git a/ecir26/corpus-construction/collection-radboud/topic-mapping.jsonl b/ecir26/corpus-construction/collection-radboud/topic-mapping.jsonl index faafdf4..a6051e6 100644 --- a/ecir26/corpus-construction/collection-radboud/topic-mapping.jsonl +++ b/ecir26/corpus-construction/collection-radboud/topic-mapping.jsonl @@ -1,78 +1,78 @@ -{"account": "ir-25-ru-01", "topics": ["42", "8", "58"]} -{"account": "ir-25-ru-02", "topics": ["39", "10", "65"]} -{"account": "ir-25-ru-03", "topics": ["22", "27", "19"]} -{"account": "ir-25-ru-04", "topics": ["3", "74", "62"]} -{"account": "ir-25-ru-05", "topics": ["71", "57", "17"]} -{"account": "ir-25-ru-06", "topics": ["16", "23", "64"]} -{"account": "ir-25-ru-07", "topics": ["52", "55", "43"]} -{"account": "ir-25-ru-08", "topics": ["53", "16", "39"]} -{"account": "ir-25-ru-09", "topics": ["1", "24", "35"]} -{"account": "ir-25-ru-10", "topics": ["45", "68", "41"]} -{"account": "ir-25-ru-11", "topics": ["68", "20", "13"]} -{"account": "ir-25-ru-12", "topics": ["28", "63", "56"]} -{"account": "ir-25-ru-13", "topics": ["30", "28", "72"]} -{"account": "ir-25-ru-14", "topics": ["9", "77", "66"]} -{"account": "ir-25-ru-15", "topics": ["62", "9", "37"]} -{"account": "ir-25-ru-16", "topics": ["43", "31", "78"]} -{"account": "ir-25-ru-17", "topics": ["10", "59", "53"]} -{"account": "ir-25-ru-18", "topics": ["11", "63", "30"]} -{"account": "ir-25-ru-19", "topics": ["27", "40", "46"]} -{"account": "ir-25-ru-20", "topics": ["61", "71", "24"]} -{"account": "ir-25-ru-21", "topics": ["8", "32", "67"]} -{"account": "ir-25-ru-22", "topics": ["57", "47", "50"]} -{"account": "ir-25-ru-23", "topics": ["72", "33", "1"]} -{"account": "ir-25-ru-24", "topics": ["33", "5", "21"]} -{"account": "ir-25-ru-25", "topics": ["42", "29", "34"]} -{"account": "ir-25-ru-26", "topics": ["75", "11", "26"]} -{"account": "ir-25-ru-27", "topics": ["36", "73", "52"]} -{"account": "ir-25-ru-28", "topics": ["60", "69", "2"]} -{"account": "ir-25-ru-29", "topics": ["40", "51", "76"]} -{"account": "ir-25-ru-30", "topics": ["64", "14", "22"]} -{"account": "ir-25-ru-31", "topics": ["44", "36", "45"]} -{"account": "ir-25-ru-32", "topics": ["77", "48", "15"]} -{"account": "ir-25-ru-33", "topics": ["7", "3", "4"]} -{"account": "ir-25-ru-34", "topics": ["59", "12", "23"]} -{"account": "ir-25-ru-35", "topics": ["58", 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"11", "26"]} +{"account": "ir-25-ru-27", "topics": ["36", "73", "52"]} +{"account": "ir-25-ru-28", "topics": ["60", "69", "2"]} +{"account": "ir-25-ru-29", "topics": ["40", "51", "76"]} +{"account": "ir-25-ru-30", "topics": ["64", "14", "22"]} +{"account": "ir-25-ru-31", "topics": ["44", "36", "45"]} +{"account": "ir-25-ru-32", "topics": ["77", "48", "15"]} +{"account": "ir-25-ru-33", "topics": ["7", "3", "4"]} +{"account": "ir-25-ru-34", "topics": ["59", "12", "23"]} +{"account": "ir-25-ru-35", "topics": ["58", "18", "74"]} +{"account": "ir-25-ru-36", "topics": ["76", "60", "38"]} +{"account": "ir-25-ru-37", "topics": ["2", "75", "6"]} +{"account": "ir-25-ru-38", "topics": ["32", "70", "55"]} +{"account": "ir-25-ru-39", "topics": ["20", "54", "44"]} +{"account": "ir-25-ru-40", "topics": ["12", "73", "54"]} +{"account": "ir-25-ru-41", "topics": ["35", "38", "25"]} +{"account": "ir-25-ru-42", "topics": ["37", "49", "13"]} +{"account": "ir-25-ru-43", "topics": ["47", "51", "21"]} +{"account": "ir-25-ru-44", "topics": ["6", "22", "67"]} +{"account": "ir-25-ru-45", "topics": ["21", "66", "70"]} +{"account": "ir-25-ru-46", "topics": ["31", "35", "19"]} +{"account": "ir-25-ru-47", "topics": ["54", "69", "57"]} +{"account": "ir-25-ru-48", "topics": ["71", "62", "15"]} +{"account": "ir-25-ru-49", "topics": ["63", "20", "12"]} +{"account": "ir-25-ru-50", "topics": ["41", "66", "49"]} +{"account": "ir-25-ru-51", "topics": ["5", "34", "11"]} +{"account": "ir-25-ru-52", "topics": ["69", "14", "16"]} +{"account": "ir-25-ru-53", "topics": ["15", "74", "32"]} +{"account": "ir-25-ru-54", "topics": ["53", "8", "28"]} +{"account": "ir-25-ru-55", "topics": ["7", "29", "65"]} +{"account": "ir-25-ru-56", "topics": ["44", "78", "61"]} +{"account": "ir-25-ru-57", "topics": ["18", "7", "31"]} +{"account": "ir-25-ru-58", "topics": ["64", "3", "17"]} +{"account": "ir-25-ru-59", "topics": ["68", "29", "72"]} +{"account": "ir-25-ru-60", "topics": ["33", "50", "46"]} +{"account": "ir-25-ru-61", "topics": ["25", "30", "2"]} +{"account": "ir-25-ru-62", "topics": ["24", "59", "42"]} +{"account": "ir-25-ru-63", "topics": ["4", "19", "76"]} +{"account": "ir-25-ru-64", "topics": ["14", "26", "48"]} +{"account": "ir-25-ru-65", "topics": ["61", "41", "1"]} +{"account": "ir-25-ru-66", "topics": ["58", "47", "56"]} +{"account": "ir-25-ru-67", "topics": ["56", "78", "6"]} +{"account": "ir-25-ru-68", "topics": ["23", "36", "9"]} +{"account": "ir-25-ru-69", "topics": ["50", "70", "37"]} +{"account": "ir-25-ru-70", "topics": ["17", "55", "10"]} +{"account": "ir-25-ru-71", "topics": ["13", "46", "18"]} +{"account": "ir-25-ru-72", "topics": ["34", "60", "43"]} +{"account": "ir-25-ru-73", "topics": ["4", "26", "27"]} +{"account": "ir-25-ru-74", "topics": ["49", "73", "75"]} +{"account": "ir-25-ru-75", "topics": ["48", "51", "38"]} +{"account": "ir-25-ru-76", "topics": ["67", "52", "39"]} +{"account": "ir-25-ru-77", "topics": ["65", "5", "40"]} +{"account": "ir-25-ru-78", "topics": ["45", "25", "77"]} diff --git a/ecir26/corpus-construction/ir_datasets_wows/ir_datasets_owi.py b/ecir26/corpus-construction/ir_datasets_wows/ir_datasets_owi.py index 72ba72e..068b0a0 100755 --- a/ecir26/corpus-construction/ir_datasets_wows/ir_datasets_owi.py +++ b/ecir26/corpus-construction/ir_datasets_wows/ir_datasets_owi.py @@ -1,127 +1,127 @@ -#!/usr/bin/env python3 -from typing import NamedTuple -import duckdb -import ir_datasets -from ir_datasets.formats.base import BaseDocs, GenericDoc -from ir_datasets.indices.base import Docstore -from ir_datasets.datasets.base import Dataset -from glob import glob -import gzip -import json -from tqdm import tqdm - -con = duckdb.connect() -con.install_extension('httpfs') -con.load_extension('httpfs') -con.execute(""" - SET s3_access_key_id = ''; - SET s3_secret_access_key = ''; - SET s3_session_token = ''; - SET s3_endpoint = 'ir-teach-wise-2025.s3.data.webis.de'; - SET s3_url_style = 'virtual_hosted'; - SET s3_use_ssl = true; - """) - - -class OWIDoc(NamedTuple): - doc_id: str - url: str - main_content: str - title: str - description: str - - def default_text(self): - ret = '' - if self.title: - ret += ' ' + self.title - if self.main_content: - ret += ' ' + self.main_content - - return ret.strip() - - -class ParquetDocs(BaseDocs): - def __init__(self, path, doc_cls=GenericDoc): - super().__init__() - self._path = path - self._doc_cls = doc_cls - - def docs_path(self, force=True): - return self._path - - def docs_iter(self): - fields = ", ".join(self._doc_cls._fields) - results = con.query(f"SELECT id, url, main_content, title, description FROM {self._path};") - - while batch := results.fetchmany(): - for row in batch: - row = list(row) - row[2] = ir_datasets.util.sax_html_parser(row[2], fields=[None])[0] - yield self._doc_cls(*row) - - def docs_cls(self): - return self._doc_cls - - def docs_count(self): - return con.query(f"SELECT COUNT(*) FROM {self._path};").fetchone()[0] - - -class ParquetDocstore(Docstore): - def __init__(self, path, doc_cls, id_field='doc_id'): - super().__init__(doc_cls, id_field) - self._path = path - - def get_many_iter(self, doc_ids): - fields = ", ".join(self._doc_cls._fields) - results = con.query(f"SELECT {fields} FROM {self._path} WHERE {self._id_field} IN ?;", params=[tuple(doc_ids)]) - - while batch := results.fetchmany(): - for row in batch: - yield self._doc_cls(*row) - - -def register_to_ir_datasets(): - from ir_datasets import registry - #docs = ParquetDocs("read_parquet('s3://ir-teach-wise-2025/*.parquet')", doc_cls=OWIDoc) - # temporary on gammaweb08 for fast access - docs = ParquetDocs("read_parquet('/home/webis/owi/*.parquet')", doc_cls=OWIDoc) - registry.register("wows/owi/2025", Dataset(docs)) - - -if __name__ == '__main__': - store = ParquetDocstore("read_parquet('s3://ir-teach-wise-2025/*.parquet')", doc_cls=OWIDoc, id_field="id") - - docs_to_collect = set() - import pandas as pd - for doc_id in pd.read_csv('../collection/manual.csv')['doc_id']: - docs_to_collect.add(doc_id) - - docs = {} - -# for run in glob('../collection/runs/*'): -# parsed_run = pd.read_csv(run, header=0, names=['qid', 'q0', 'doc_id', 'rank', 'score', 'system'], sep=" ") -# for doc_id in parsed_run['doc_id']: -# docs_to_collect.add(doc_id) - - print(len(docs_to_collect)) - - with gzip.open('corpus.jsonl.gz', 'wt') as f: - for doc in tqdm(store.get_many_iter(docs_to_collect)): - default_text = '' - if doc.title: - default_text += ' ' + doc.title - if doc.main_content: - default_text += ' ' + doc.main_content - if doc.description: - default_text += ' ' + doc.description - - f.write(json.dumps({"doc_id": doc.id, "url": doc.url, "default_text": default_text.strip(), "main_content": doc.main_content, "title": doc.title, "description": doc.description}) + '\n') - f.flush() - - #print(docs.docs_count()) - - #for doc in docs.docs_iter(): - # print(doc) - # break - - #print(store.get("00000072d6e64d4a5e05fabdb1fa94ee06cd30e697163ba12ae22f2771026465")) +#!/usr/bin/env python3 +from typing import NamedTuple +import duckdb +import ir_datasets +from ir_datasets.formats.base import BaseDocs, GenericDoc +from ir_datasets.indices.base import Docstore +from ir_datasets.datasets.base import Dataset +from glob import glob +import gzip +import json +from tqdm import tqdm + +con = duckdb.connect() +con.install_extension('httpfs') +con.load_extension('httpfs') +con.execute(""" + SET s3_access_key_id = ''; + SET s3_secret_access_key = ''; + SET s3_session_token = ''; + SET s3_endpoint = 'ir-teach-wise-2025.s3.data.webis.de'; + SET s3_url_style = 'virtual_hosted'; + SET s3_use_ssl = true; + """) + + +class OWIDoc(NamedTuple): + doc_id: str + url: str + main_content: str + title: str + description: str + + def default_text(self): + ret = '' + if self.title: + ret += ' ' + self.title + if self.main_content: + ret += ' ' + self.main_content + + return ret.strip() + + +class ParquetDocs(BaseDocs): + def __init__(self, path, doc_cls=GenericDoc): + super().__init__() + self._path = path + self._doc_cls = doc_cls + + def docs_path(self, force=True): + return self._path + + def docs_iter(self): + fields = ", ".join(self._doc_cls._fields) + results = con.query(f"SELECT id, url, main_content, title, description FROM {self._path};") + + while batch := results.fetchmany(): + for row in batch: + row = list(row) + row[2] = ir_datasets.util.sax_html_parser(row[2], fields=[None])[0] + yield self._doc_cls(*row) + + def docs_cls(self): + return self._doc_cls + + def docs_count(self): + return con.query(f"SELECT COUNT(*) FROM {self._path};").fetchone()[0] + + +class ParquetDocstore(Docstore): + def __init__(self, path, doc_cls, id_field='doc_id'): + super().__init__(doc_cls, id_field) + self._path = path + + def get_many_iter(self, doc_ids): + fields = ", ".join(self._doc_cls._fields) + results = con.query(f"SELECT {fields} FROM {self._path} WHERE {self._id_field} IN ?;", params=[tuple(doc_ids)]) + + while batch := results.fetchmany(): + for row in batch: + yield self._doc_cls(*row) + + +def register_to_ir_datasets(): + from ir_datasets import registry + #docs = ParquetDocs("read_parquet('s3://ir-teach-wise-2025/*.parquet')", doc_cls=OWIDoc) + # temporary on gammaweb08 for fast access + docs = ParquetDocs("read_parquet('/home/webis/owi/*.parquet')", doc_cls=OWIDoc) + registry.register("wows/owi/2025", Dataset(docs)) + + +if __name__ == '__main__': + store = ParquetDocstore("read_parquet('s3://ir-teach-wise-2025/*.parquet')", doc_cls=OWIDoc, id_field="id") + + docs_to_collect = set() + import pandas as pd + for doc_id in pd.read_csv('../collection/manual.csv')['doc_id']: + docs_to_collect.add(doc_id) + + docs = {} + +# for run in glob('../collection/runs/*'): +# parsed_run = pd.read_csv(run, header=0, names=['qid', 'q0', 'doc_id', 'rank', 'score', 'system'], sep=" ") +# for doc_id in parsed_run['doc_id']: +# docs_to_collect.add(doc_id) + + print(len(docs_to_collect)) + + with gzip.open('corpus.jsonl.gz', 'wt') as f: + for doc in tqdm(store.get_many_iter(docs_to_collect)): + default_text = '' + if doc.title: + default_text += ' ' + doc.title + if doc.main_content: + default_text += ' ' + doc.main_content + if doc.description: + default_text += ' ' + doc.description + + f.write(json.dumps({"doc_id": doc.id, "url": doc.url, "default_text": default_text.strip(), "main_content": doc.main_content, "title": doc.title, "description": doc.description}) + '\n') + f.flush() + + #print(docs.docs_count()) + + #for doc in docs.docs_iter(): + # print(doc) + # break + + #print(store.get("00000072d6e64d4a5e05fabdb1fa94ee06cd30e697163ba12ae22f2771026465")) diff --git a/ecir26/corpus-construction/subsampled-corpora/README.md b/ecir26/corpus-construction/subsampled-corpora/README.md index c22201f..02193b0 100644 --- a/ecir26/corpus-construction/subsampled-corpora/README.md +++ b/ecir26/corpus-construction/subsampled-corpora/README.md @@ -1,24 +1,24 @@ -# Subsampled Corpora - -We apply [corpus subsampling]() to allow for Green experimentation using the subsampling implementation of the [lsr-benchmark](https://github.com/reneuir/lsr-benchmark/). - -The subsamples are created via: - -``` -lsr-benchmark create-lsr-corpus radboud-validation -``` - -Then, upload the subsamples to TIRA via: - -``` -tira-cli dataset-submission --path radboud-validation/ --task ir-lab-wise-2025 --split train -``` - -## Spot Check - -There exists a tiny spot-check dataset that you can use to verify that your approaches work. - -``` -tira-cli dataset-submission --path spot-check/ --task ir-lab-wise-2025 --split train -``` - +# Subsampled Corpora + +We apply [corpus subsampling]() to allow for Green experimentation using the subsampling implementation of the [lsr-benchmark](https://github.com/reneuir/lsr-benchmark/). + +The subsamples are created via: + +``` +lsr-benchmark create-lsr-corpus radboud-validation +``` + +Then, upload the subsamples to TIRA via: + +``` +tira-cli dataset-submission --path radboud-validation/ --task ir-lab-wise-2025 --split train +``` + +## Spot Check + +There exists a tiny spot-check dataset that you can use to verify that your approaches work. + +``` +tira-cli dataset-submission --path spot-check/ --task ir-lab-wise-2025 --split train +``` + diff --git a/ecir26/corpus-construction/subsampled-corpora/radboud-validation/README.md b/ecir26/corpus-construction/subsampled-corpora/radboud-validation/README.md index 95561ce..314f441 100644 --- a/ecir26/corpus-construction/subsampled-corpora/radboud-validation/README.md +++ b/ecir26/corpus-construction/subsampled-corpora/radboud-validation/README.md @@ -1,32 +1,32 @@ ---- -configs: -- config_name: inputs - data_files: - - split: train - path: ["corpus.jsonl.gz", "queries.jsonl"] -- config_name: truths - data_files: - - split: train - path: ["qrels.txt", "queries.jsonl", "dataset-metadata.json", "config.json", "subsample.json"] - -tira_configs: - resolve_inputs_to: "." - resolve_truths_to: "." - baseline: - link: https://github.com/reneuir/lsr-benchmark/tree/main/step-03-retrieval-approaches/lexical/pyterrier-pisa - command: /run-pyterrier-pisa.py --dataset $inputDataset --output $outputDir - format: - name: ["run-with-metadata", "lightning-ir-document-embeddings", "lightning-ir-query-embeddings"] - input_format: - name: "lsr-benchmark-inputs" - config: - max_size_mb: 500 - truth_format: - name: "qrels.txt" - evaluator: - measures: ["nDCG@10","P@10"] ---- - -# The Validation Dataset created in the Radboud IR course - - +--- +configs: +- config_name: inputs + data_files: + - split: train + path: ["corpus.jsonl.gz", "queries.jsonl"] +- config_name: truths + data_files: + - split: train + path: ["qrels.txt", "queries.jsonl", "dataset-metadata.json", "config.json", "subsample.json"] + +tira_configs: + resolve_inputs_to: "." + resolve_truths_to: "." + baseline: + link: https://github.com/reneuir/lsr-benchmark/tree/main/step-03-retrieval-approaches/lexical/pyterrier-pisa + command: /run-pyterrier-pisa.py --dataset $inputDataset --output $outputDir + format: + name: ["run-with-metadata", "lightning-ir-document-embeddings", "lightning-ir-query-embeddings"] + input_format: + name: "lsr-benchmark-inputs" + config: + max_size_mb: 500 + truth_format: + name: "qrels.txt" + evaluator: + measures: ["nDCG@10","P@10"] +--- + +# The Validation Dataset created in the Radboud IR course + + diff --git a/ecir26/corpus-construction/subsampled-corpora/radboud-validation/config.json b/ecir26/corpus-construction/subsampled-corpora/radboud-validation/config.json index e7667a4..e581b5a 100644 --- a/ecir26/corpus-construction/subsampled-corpora/radboud-validation/config.json +++ b/ecir26/corpus-construction/subsampled-corpora/radboud-validation/config.json @@ -1,8 +1,8 @@ -{ - "runs": "../collection/runs/", - "chatnoir-index": "wows-owi/2025", - "subsample_depth": 500, - "qrels": "radboud-validation/qrels.txt", - "topics": "radboud-validation/topics.xml", - "include-fields": ["title", "url", "description"] -} +{ + "runs": "../collection/runs/", + "chatnoir-index": "wows-owi/2025", + "subsample_depth": 500, + "qrels": "radboud-validation/qrels.txt", + "topics": "radboud-validation/topics.xml", + "include-fields": ["title", "url", "description"] +} diff --git a/ecir26/corpus-construction/subsampled-corpora/radboud-validation/dataset-metadata.json b/ecir26/corpus-construction/subsampled-corpora/radboud-validation/dataset-metadata.json index 60962c5..721fda2 100644 --- a/ecir26/corpus-construction/subsampled-corpora/radboud-validation/dataset-metadata.json +++ b/ecir26/corpus-construction/subsampled-corpora/radboud-validation/dataset-metadata.json @@ -1,2971 +1,2971 @@ -{ - "implementation": { - "executable": null, - "python": { - "interactive": false, - "modules": "3.13.7", - "packages": [ - "Automat==25.4.16", - "Beaker==1.13.0", - "Brotli==1.1.0", - "CacheControl==0.14.3", - "CherryPy==18.10.0", - "Cython==0.29.37", - "Deprecated==1.2.15", - "Django==5.2.7", - "FastWARC==0.15.1", - "Flask-RESTful==0.3.10", - "Flask==3.1.2", - "GitPython==3.1.44", - "HeapDict==1.0.1", - "Jinja2==3.1.6", - "Mako==1.3.10.dev0", - "Markdown==3.10", - "MarkupSafe==3.0.2", - "PyGObject==3.54.5", - "PyJWT==2.10.1", - "PyNaCl==1.6.0", - "PyQt5==5.15.11", - "PyQt5_sip==12.17.1", - "PyQtWebEngine==5.15.7", - "PyYAML==6.0.3", - "Pygments==2.19.2", - "RapidFuzz==3.13.0", - "Resiliparse==0.15.1", - "Routes==2.5.1", - "Rx==3.2.0", - "SecretStorage==3.3.3", - "Sphinx==8.2.3", - "TBB==0.2", - "Twisted==24.7.0", - "WebOb==1.8.9", - "WebTest==3.0.3", - "Werkzeug==3.1.3", - "accelerate==1.10.1", - "aiohappyeyeballs==2.6.1", - "aiohttp==3.13.2", - "aiosignal==1.4.0", - "alabaster==1.0.0", - "aligned_ir_eval==0.0.1rc1", - "allpairspy==2.5.1", - "amqp==5.3.1", - "aniso8601==10.0.0", - "annotated-types==0.7.0", - "anyio==4.11.0", - "anytree==2.13.0", - "apipkg==3.0.2", - "appdirs==1.4.4", - "approval_utilities==14.3.0", - "approvaltests==14.3.0", - "argcomplete==3.6.2", - "asgiref==3.10.0", - "asn1crypto==1.5.1", - "astroid==4.0.2", - "asttokens==3.0.0", - "async-timeout==5.0.1", - "attrs==25.4.0", - "autocommand==2.2.2", - "autoqrels==0.0.1", - "babel==2.17.0", - "bandit==1.8.3", - "bcrypt==5.0.0", - "beautifulsoup4==4.14.2", - "billiard==4.2.2", - "bleach==6.2.0", - "blinker==1.9.0", - "blis==1.3.0", - "boltons==25.0.0", - "boto3==1.37.37", - "botocore==1.37.37", - "bs4==0.0.2", - "btrfsutil==6.17", - "build==1.3.0", - "cachetools==6.2.1", - "catalogue==2.0.10", - "cattrs==24.1.3", - "cbor==1.0.0", - "celery==5.5.3", - "certifi==2025.10.5", - "cffi==2.0.0", - "chardet==5.2.0", - "charset-normalizer==3.4.4", - "chatnoir-api==3.4.3.dev4+gf1b304697", - "chatnoir-pyterrier==2.0.6.dev142+g8392b4ca1", - "cheroot==11.1.2", - "chest==0.2.3", - "cleo==2.1.0", - "click-didyoumean==0.3.1", - "click-plugins==1.1.1.2", - "click-repl==0.3.0", - "click==8.2.1", - "cloudpathlib==0.22.0", - "cmd2==2.6.2", - "colaburl==0.1.0", - "colorama==0.4.6", - "comm==0.2.2", - 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100644 --- a/ecir26/corpus-construction/subsampled-corpora/radboud-validation/dataset-metadata.yml +++ b/ecir26/corpus-construction/subsampled-corpora/radboud-validation/dataset-metadata.yml @@ -1,2927 +1,2927 @@ -implementation: - executable: null - python: - interactive: false - modules: 3.13.7 - packages: - - Automat==25.4.16 - - Beaker==1.13.0 - - Brotli==1.1.0 - - CacheControl==0.14.3 - - CherryPy==18.10.0 - - Cython==0.29.37 - - Deprecated==1.2.15 - - Django==5.2.7 - - FastWARC==0.15.1 - - Flask-RESTful==0.3.10 - - Flask==3.1.2 - - GitPython==3.1.44 - - HeapDict==1.0.1 - - Jinja2==3.1.6 - - Mako==1.3.10.dev0 - - Markdown==3.10 - - MarkupSafe==3.0.2 - - PyGObject==3.54.5 - - PyJWT==2.10.1 - - PyNaCl==1.6.0 - - PyQt5==5.15.11 - - PyQt5_sip==12.17.1 - - PyQtWebEngine==5.15.7 - - PyYAML==6.0.3 - - Pygments==2.19.2 - - RapidFuzz==3.13.0 - - Resiliparse==0.15.1 - - Routes==2.5.1 - - Rx==3.2.0 - - SecretStorage==3.3.3 - - Sphinx==8.2.3 - - TBB==0.2 - - Twisted==24.7.0 - - WebOb==1.8.9 - 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modules: 3.13.7 + packages: + - Automat==25.4.16 + - Beaker==1.13.0 + - Brotli==1.1.0 + - CacheControl==0.14.3 + - CherryPy==18.10.0 + - Cython==0.29.37 + - Deprecated==1.2.15 + - Django==5.2.7 + - FastWARC==0.15.1 + - Flask-RESTful==0.3.10 + - Flask==3.1.2 + - GitPython==3.1.44 + - HeapDict==1.0.1 + - Jinja2==3.1.6 + - Mako==1.3.10.dev0 + - Markdown==3.10 + - MarkupSafe==3.0.2 + - PyGObject==3.54.5 + - PyJWT==2.10.1 + - PyNaCl==1.6.0 + - PyQt5==5.15.11 + - PyQt5_sip==12.17.1 + - PyQtWebEngine==5.15.7 + - PyYAML==6.0.3 + - Pygments==2.19.2 + - RapidFuzz==3.13.0 + - Resiliparse==0.15.1 + - Routes==2.5.1 + - Rx==3.2.0 + - SecretStorage==3.3.3 + - Sphinx==8.2.3 + - TBB==0.2 + - Twisted==24.7.0 + - WebOb==1.8.9 + - WebTest==3.0.3 + - Werkzeug==3.1.3 + - accelerate==1.10.1 + - aiohappyeyeballs==2.6.1 + - aiohttp==3.13.2 + - aiosignal==1.4.0 + - alabaster==1.0.0 + - aligned_ir_eval==0.0.1rc1 + - allpairspy==2.5.1 + - amqp==5.3.1 + - aniso8601==10.0.0 + - annotated-types==0.7.0 + - anyio==4.11.0 + - anytree==2.13.0 + - apipkg==3.0.2 + - appdirs==1.4.4 + - approval_utilities==14.3.0 + - approvaltests==14.3.0 + - argcomplete==3.6.2 + - asgiref==3.10.0 + - asn1crypto==1.5.1 + - astroid==4.0.2 + - asttokens==3.0.0 + - async-timeout==5.0.1 + - attrs==25.4.0 + - autocommand==2.2.2 + - autoqrels==0.0.1 + - babel==2.17.0 + - bandit==1.8.3 + - bcrypt==5.0.0 + - beautifulsoup4==4.14.2 + - billiard==4.2.2 + - bleach==6.2.0 + - blinker==1.9.0 + - blis==1.3.0 + - boltons==25.0.0 + - boto3==1.37.37 + - botocore==1.37.37 + - bs4==0.0.2 + - btrfsutil==6.17 + - build==1.3.0 + - cachetools==6.2.1 + - catalogue==2.0.10 + - cattrs==24.1.3 + - cbor==1.0.0 + - celery==5.5.3 + - certifi==2025.10.5 + - cffi==2.0.0 + - chardet==5.2.0 + - charset-normalizer==3.4.4 + - chatnoir-api==3.4.3.dev4+gf1b304697 + - chatnoir-pyterrier==2.0.6.dev142+g8392b4ca1 + - cheroot==11.1.2 + - chest==0.2.3 + - cleo==2.1.0 + - click-didyoumean==0.3.1 + - click-plugins==1.1.1.2 + - click-repl==0.3.0 + - click==8.2.1 + - cloudpathlib==0.22.0 + - cmd2==2.6.2 + - colaburl==0.1.0 + - colorama==0.4.6 + - comm==0.2.2 + - commonmark==0.9.1 + - conf26-reliability-analysis==0.1.0 + - confection==0.1.5 + - configobj==5.0.9 + - constantly==23.10.4 + - contourpy==1.3.1 + - coverage==7.11.2 + - crashtest==0.4.1 + - crit==4.1.1 + - cryptography==46.0.3 + - cssselect==1.3.0 + - cuda-bindings==13.0.1 + - cuda-core==0.3.2 + - cuda-pathfinder==1.2.2 + - cuda-python==13.0.1 + - cycler==0.12.1 + - cymem==2.0.11 + - dataclasses-json==0.5.9 + - datasets==3.5.0 + - dbus-python==1.4.0 + - de_core_news_sm==3.8.0 + - debugpy==1.8.12 + - decorator==5.1.1 + - defusedxml==0.7.1 + - dill==0.3.8 + - dill==0.4.0 + - distlib==0.4.0 + - distro==1.9.0 + - django-minify-html==1.13.0 + - djangorestframework==3.15.1 + - dnspython==2.8.0 + - doccano-client==1.2.8 + - docker==7.1.0 + - docopt==0.6.2 + - docstring_parser==0.17.0 + - docutils==0.22.3 + - duckdb==1.4.0 + - dulwich==0.22.8 + - elastic-transport==8.17.1 + - elastic-transport==9.2.0 + - elasticsearch-dsl==8.18.0 + - elasticsearch==8.6.1 + - elasticsearch==9.2.0 + - empty-files==0.0.9 + - en_core_web_sm==3.8.0 + - entrypoints==0.4 + - et_xmlfile==2.0.0 + - evaluate==0.4.3 + - exceptiongroup==1.3.0 + - executing==2.2.0 + - face==24.0.0 + - fastjsonschema==2.21.2 + - filelock==3.20.0 + - findpython==0.6.3 + - flake8==7.3.0 + - flit_core==3.12.0 + - fluidity-sm==0.2.1 + - fonttools==4.56.0 + - freezegun==1.5.5 + - frozenlist==1.8.0 + - fsspec==2024.12.0 + - ftfy==6.3.1 + - furo==2024.8.6 + - gitdb==4.0.12 + - glom==24.11.0 + - google-api-core==2.19.1 + - google-api-python-client==2.140.0 + - google-auth-httplib2==0.2.1 + - google-auth==2.43.0 + - googleapis-common-protos==1.72.0 + - graphql-core==3.2.6 + - grpcio-tools==1.76.0 + - grpcio==1.76.0 + - h11==0.16.0 + - html5lib==1.1 + - httpcore==1.0.9 + - httplib2==0.31.0 + - httpx==0.28.1 + - huggingface-hub==0.29.3 + - hyperlink==21.0.0 + - hypothesis==6.147.0 + - idna==3.11 + - ijson==3.3.0 + - imagesize==1.4.1 + - importlib_metadata==8.6.1 + - importlib_metadata==8.7.0 + - importlib_resources==6.5.2 + - incremental==24.7.2 + - inflect==7.5.0 + - iniconfig==2.1.0 + - inscriptis==2.5.1 + - installer==0.7.0 + - internetarchive==5.7.1 + - invoke==2.2.1 + - ipykernel==6.29.5 + - ipython==8.32.0 + - ir-datasets-longeval==0.1.dev39+g6ee2fef + - ir-datasets-subsample==0.0.0 + - ir_datasets==0.5.11 + - ir_measures==0.4.1 + - isodate==0.7.2 + - isort==7.0.0 + - itsdangerous==2.2.0 + - jaraco.classes==3.4.0 + - jaraco.collections==5.1.0 + - jaraco.context==6.0.1 + - jaraco.functools==4.1.0 + - jaraco.text==4.0.0 + - jedi==0.19.2 + - jeepney==0.8.0 + - jiter==0.10.0 + - jmespath==1.0.1 + - joblib==1.4.2 + - jsonargparse==4.41.0 + - jsonpatch==1.33 + - jsonpointer==3.0.0 + - jsonschema-specifications==2025.9.1 + - jsonschema==4.25.1 + - jupyter_client==8.6.3 + - jupyter_core==5.7.2 + - jupyterlab_pygments==0.3.0 + - keyring==25.6.0 + - kiwisolver==1.4.8 + - kombu==5.5.4 + - langcodes==3.5.0 + - language_data==1.3.0 + - lark==1.3.1 + - lazy-object-proxy==1.12.0 + - legacy-cgi==2.6.4 + - lensfun==0.3.4 + - lexicon==3.0.0 + - libtmux==0.46.2 + - lightning-ir==0.0.5 + - lightning-utilities==0.15.2 + - lightning==2.5.5 + - lit==21.1.5.dev0 + - llvmlite==0.44.0 + - lockfile==0.12.2 + - logutils==0.3.5 + - louis==3.34.0 + - lsr_benchmark==0.0.1rc3 + - lxml==6.0.2 + - lz4==4.3.3 + - mallard-ducktype==1.0.2 + - marisa-trie==1.3.1 + - markdown-it-py==4.0.0 + - marshmallow-enum==1.5.1 + - marshmallow==3.26.1 + - matchpy==0.5.5 + - matplotlib-inline==0.1.7 + - matplotlib==3.10.0 + - mccabe==0.7.0 + - mdit-py-plugins==0.4.2 + - mdurl==0.1.2 + - minify_html==0.18.1 + - mistune==3.1.1 + - mock==5.1.0 + - more-itertools==10.8.0 + - mpmath==1.3.0 + - mrjob==0.7.4 + - msgpack==1.0.5 + - multidict==6.7.0 + - multiprocess==0.70.16 + - multiset==2.1.1 + - murmurhash==1.0.13 + - mypy-extensions==1.0.0 + - mypy==1.15.0 + - myst-parser==4.0.0 + - nbclient==0.10.2 + - nbconvert==7.16.6 + - nbformat==5.10.4 + - necessary==0.4.3 + - nest-asyncio==1.6.0 + - networkx==3.4.2 + - nftables==0.1 + - nh3==0.2.20 + - nltk==3.9.1 + - nose==1.3.7 + - npids==0.1.3 + - nspektr==0.5.1 + - numba==0.61.2 + - numpy==1.26.4 + - numpy==2.3.4 + - nvidia-cublas-cu12==12.4.5.8 + - nvidia-cuda-cccl==13.0.85 + - nvidia-cuda-cupti-cu12==12.4.127 + - nvidia-cuda-nvrtc-cu12==12.4.127 + - nvidia-cuda-nvrtc==13.0.88 + - nvidia-cuda-runtime-cu12==12.4.127 + - nvidia-cuda-runtime==13.0.88 + - nvidia-cudnn-cu12==9.1.0.70 + - nvidia-cufft-cu12==11.2.1.3 + - nvidia-curand-cu12==10.3.5.147 + - nvidia-cusolver-cu12==11.6.1.9 + - nvidia-cusparse-cu12==12.3.1.170 + - nvidia-cusparselt-cu12==0.6.2 + - nvidia-nccl-cu12==2.21.5 + - nvidia-nvjitlink-cu12==12.4.127 + - nvidia-nvjitlink==13.0.88 + - nvidia-nvtx-cu12==12.4.127 + - nvidia-nvvm==13.0.88 + - oauth2client==4.1.3 + - openai==1.93.0 + - openpyxl==3.1.5 + - openshot-qt==3.3.0 + - ordered-set==4.1.0 + - orjson==3.11.4 + - packaging==25.0 + - pandas==2.3.3 + - pandocfilters==1.5.1 + - parameterized==0.9.0 + - paramiko==4.0.0 + - parso==0.8.4 + - patsy==1.0.1 + - pbr==7.0.1 + - pbs-installer==2025.4.9 + - pdf2image==1.17.0 + - pecan==1.7.0 + - pew==1.2.0 + - pexpect==4.9.0 + - pillow==12.0.0 + - pip==25.2 + - pkgconfig==1.5.5 + - pkginfo==1.12.0 + - platformdirs==4.5.0 + - pluggy==1.6.0 + - ply==3.11 + - poetry-core==2.1.3 + - poetry-core==2.2.1 + - poetry==2.1.3 + - pooch==1.8.2 + - portend==3.2.1 + - preshed==3.0.10 + - prettytable==3.16.0 + - progress==1.6.1 + - prometheus_client==0.23.1 + - promise==2.3 + - prompt_toolkit==3.0.50 + - propcache==0.4.1 + - protobuf==6.33.0 + - psutil==7.1.3 + - ptyprocess==0.7.0 + - pure_eval==0.2.3 + - pwquality==1.4.5 + - py-cpuinfo==9.0.0 + - py==1.11.1.dev0+g447bac51.d20241130 + - pyOpenSSL==25.3.0 + - pyaml==24.12.0 + - pyaml_env==1.2.2 + - pyarrow==19.0.1 + - pyasn1==0.6.1 + - pyasn1_modules==0.4.2 + - pycairo==1.28.0 + - pycodestyle==2.14.0 + - pycosat==0.6.6 + - pycparser==2.23 + - pycriu==4.1.1 + - pydantic==2.12.3 + - pydantic==2.12.4 + - pydantic_core==2.41.4 + - pydantic_core==2.41.5 + - pyfailsafe==0.6.0 + - pyflakes==3.4.0 + - pygdbmi==0.11.0.0 + - pyjnius==1.7.0 + - pylint==4.0.2 + - pyparsing==3.2.5 + - pyperclip==1.11.0 + - pyproject_hooks==1.2.0 + - pyrate-limiter==2.10.0 + - pyrsistent==0.20.0 + - pyserial==3.5 + - pyterrier-alpha==0.12.6 + - pyterrier-dr==0.3.0 + - pyterrier-t5==0.0.1 + - pytesseract==0.3.13 + - pytest-asyncio==0.26.0 + - pytest-cov==5.0.0 + - pytest-cov==6.1.1 + - pytest-flake8==1.3.0 + - pytest==8.4.2 + - python-dateutil==2.9.0 + - python-dotenv==1.2.1 + - python-gitlab==5.6.0 + - python-magic==0.4.27 + - python-slugify==8.0.4 + - python-terrier==0.13.2 + - python-xlib==0.33 + - pytorch-lightning==2.5.5 + - pytrec_eval-terrier==0.5.8 + - pytrec_eval==0.5 + - pytz==2025.2 + - pyzmq==27.1.0 + - rank-bm25==0.2.2 + - readme_renderer==44.0 + - referencing==0.37.0 + - regex==2024.11.6 + - reliability_tests==0.0.1rc1 + 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config_name: inputs - data_files: - - split: train - path: ["corpus.jsonl.gz", "queries.jsonl"] -- config_name: truths - data_files: - - split: train - path: ["qrels.txt", "queries.jsonl", "dataset-metadata.json", "config.json", "subsample.json"] - -tira_configs: - resolve_inputs_to: "." - resolve_truths_to: "." - baseline: - link: https://github.com/reneuir/lsr-benchmark/tree/main/step-03-retrieval-approaches/lexical/pyterrier-pisa - command: /run-pyterrier-pisa.py --dataset $inputDataset --output $outputDir - format: - name: ["run-with-metadata", "lightning-ir-document-embeddings", "lightning-ir-query-embeddings"] - input_format: - name: "lsr-benchmark-inputs" - config: - max_size_mb: 500 - truth_format: - name: "qrels.txt" - evaluator: - measures: ["nDCG@10","P@10"] ---- - -# The Spot-Check Dataset created in the Radboud IR course - - +--- +configs: +- config_name: inputs + data_files: + - split: train + path: ["corpus.jsonl.gz", "queries.jsonl"] +- config_name: truths + data_files: + - split: train + path: ["qrels.txt", "queries.jsonl", "dataset-metadata.json", "config.json", "subsample.json"] + +tira_configs: + resolve_inputs_to: "." + resolve_truths_to: "." + baseline: + link: https://github.com/reneuir/lsr-benchmark/tree/main/step-03-retrieval-approaches/lexical/pyterrier-pisa + command: /run-pyterrier-pisa.py --dataset $inputDataset --output $outputDir + format: + name: ["run-with-metadata", "lightning-ir-document-embeddings", "lightning-ir-query-embeddings"] + input_format: + name: "lsr-benchmark-inputs" + config: + max_size_mb: 500 + truth_format: + name: "qrels.txt" + evaluator: + measures: ["nDCG@10","P@10"] +--- + +# The Spot-Check Dataset created in the Radboud IR course + + diff --git a/ecir26/corpus-construction/subsampled-corpora/spot-check/config.json b/ecir26/corpus-construction/subsampled-corpora/spot-check/config.json index b8e40e9..4b01d03 100644 --- a/ecir26/corpus-construction/subsampled-corpora/spot-check/config.json +++ b/ecir26/corpus-construction/subsampled-corpora/spot-check/config.json @@ -1,8 +1,8 @@ -{ - "runs": "../collection/runs/", - "chatnoir-index": "wows-owi/2025", - "subsample_depth": 20, - "qrels": "spot-check/qrels.txt", - "topics": "spot-check/topics.xml", - "include-fields": ["title", "url", "description"] -} +{ + "runs": "../collection/runs/", + "chatnoir-index": "wows-owi/2025", + "subsample_depth": 20, + "qrels": "spot-check/qrels.txt", + "topics": "spot-check/topics.xml", + "include-fields": ["title", "url", "description"] +} diff --git a/ecir26/corpus-construction/subsampled-corpora/spot-check/dataset-metadata.json b/ecir26/corpus-construction/subsampled-corpora/spot-check/dataset-metadata.json index cc01087..fb61938 100644 --- a/ecir26/corpus-construction/subsampled-corpora/spot-check/dataset-metadata.json +++ b/ecir26/corpus-construction/subsampled-corpora/spot-check/dataset-metadata.json @@ -1,3789 +1,3789 @@ -{ - "implementation": { - "executable": null, - "python": { - 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start time: 2025-11-22T09:21:54.458710659+0000 - stop time: 2025-11-22T09:22:32.054819467+0000 - system: 2480 ms - user: 35090 ms - wallclock: 37596 ms -schema version: 0.2 +implementation: + executable: null + python: + interactive: false + modules: 3.13.7 + packages: + - Automat==25.4.16 + - Beaker==1.13.0 + - Brotli==1.1.0 + - CacheControl==0.14.4 + - CherryPy==18.10.0 + - Cython==0.29.37 + - Deprecated==1.2.15 + - Deprecated==1.3.1 + - Django==5.2.7 + - FastWARC==0.15.1 + - Flask-RESTful==0.3.10 + - Flask==3.1.2 + - GitPython==3.1.44 + - GitPython==3.1.45 + - HeapDict==1.0.1 + - Jinja2==3.1.6 + - Mako==1.3.10.dev0 + - Markdown==3.10 + - MarkupSafe==3.0.2 + - PyGObject==3.54.5 + - PyJWT==2.10.1 + - PyNaCl==1.6.1 + - PyQt5==5.15.11 + - PyQt5_sip==12.17.1 + - PyQtWebEngine==5.15.7 + - PyYAML==6.0.3 + - Pygments==2.19.2 + - RapidFuzz==3.13.0 + - Resiliparse==0.15.1 + - Routes==2.5.1 + - Rx==3.2.0 + - SecretStorage==3.3.3 + - Sphinx==8.2.3 + - TBB==0.2 + - Twisted==24.7.0 + - WebOb==1.8.9 + - WebTest==3.0.3 + - Werkzeug==3.1.3 + - accelerate==1.10.1 + - aiohappyeyeballs==2.6.1 + - aiohttp==3.13.2 + - aiosignal==1.4.0 + - alabaster==1.0.0 + - aligned_ir_eval==0.0.1rc1 + - allpairspy==2.5.1 + - amqp==5.3.1 + - aniso8601==10.0.1 + - annotated-types==0.7.0 + - anyio==4.11.0 + - anytree==2.13.0 + - apipkg==3.0.2 + - appdirs==1.4.4 + - approval_utilities==14.3.0 + - approvaltests==14.3.0 + - argcomplete==3.6.2 + - asgiref==3.10.0 + - asn1crypto==1.5.1 + - astroid==4.0.2 + - asttokens==3.0.0 + - asttokens==3.0.1 + - async-timeout==5.0.1 + - attrs==25.4.0 + - autocommand==2.2.2 + - autoqrels==0.0.1 + - babel==2.17.0 + - bandit==1.8.3 + - bcrypt==5.0.0 + - beautifulsoup4==4.14.2 + - billiard==4.2.2 + - bleach==6.2.0 + - blinker==1.9.0 + - blis==1.3.0 + - boltons==25.0.0 + - boto3==1.37.37 + - botocore==1.37.37 + - bs4==0.0.2 + - btrfsutil==6.17.1 + - build==1.3.0 + - cachetools==6.2.2 + - catalogue==2.0.10 + - cattrs==24.1.3 + - cbor==1.0.0 + - celery==5.5.3 + - certifi==2025.11.12 + - cffi==2.0.0 + - chardet==5.2.0 + - charset-normalizer==3.4.4 + - chatnoir-api==3.3.0 + - chatnoir-api==3.4.3.dev4+gf1b304697 + - chatnoir-pyterrier==2.0.6.dev142+g8392b4ca1 + - cheroot==11.1.2 + - chest==0.2.3 + - cleo==2.1.0 + - click-didyoumean==0.3.1 + - click-plugins==1.1.1.2 + - click-repl==0.3.0 + - click==8.2.1 + - cloudpathlib==0.22.0 + - cmd2==2.6.2 + - colaburl==0.1.0 + - colorama==0.4.6 + - comm==0.2.2 + - commonmark==0.9.1 + - conf26-reliability-analysis==0.1.0 + - confection==0.1.5 + - configobj==5.0.9 + - constantly==23.10.4 + - contourpy==1.3.1 + - coverage==7.12.0 + - crashtest==0.4.1 + - crit==4.2 + - cryptography==46.0.3 + - cssselect==1.3.0 + - cuda-bindings==13.0.1 + - cuda-core==0.3.2 + - cuda-pathfinder==1.2.2 + - cuda-python==13.0.1 + - cycler==0.12.1 + - cymem==2.0.11 + - dataclasses-json==0.5.9 + - dataclasses-json==0.6.7 + - datasets==3.5.0 + - dbus-python==1.4.0 + - de_core_news_sm==3.8.0 + - debugpy==1.8.12 + - decorator==5.1.1 + - decorator==5.2.1 + - defusedxml==0.7.1 + - dill==0.3.8 + - dill==0.4.0 + - distlib==0.4.0 + - distro==1.9.0 + - django-minify-html==1.13.0 + - djangorestframework==3.15.1 + - dnspython==2.8.0 + - doccano-client==1.2.8 + - docker==7.1.0 + - docopt==0.6.2 + - docstring_parser==0.17.0 + - docutils==0.22.3 + - duckdb==1.4.0 + - dulwich==0.22.8 + - elastic-transport==8.17.1 + - elastic-transport==9.2.0 + - elasticsearch-dsl==8.18.0 + - elasticsearch==8.6.1 + - elasticsearch==9.2.0 + - empty-files==0.0.9 + - en_core_web_sm==3.8.0 + - entrypoints==0.4 + - et_xmlfile==2.0.0 + - evaluate==0.4.3 + - exceptiongroup==1.3.0 + - executing==2.2.0 + - executing==2.2.1 + - face==24.0.0 + - fastjsonschema==2.21.2 + - filelock==3.20.0 + - findpython==0.6.3 + - flake8==7.3.0 + - flit_core==3.12.0 + - fluidity-sm==0.2.1 + - fonttools==4.56.0 + - freezegun==1.5.5 + - frozenlist==1.8.0 + - fsspec==2024.12.0 + - ftfy==6.3.1 + - furo==2024.8.6 + - gitdb==4.0.12 + - glom==24.11.0 + - google-api-core==2.19.1 + - google-api-python-client==2.140.0 + - google-auth-httplib2==0.2.1 + - google-auth==2.43.0 + - googleapis-common-protos==1.72.0 + - graphql-core==3.2.6 + - grpcio-tools==1.76.0 + - grpcio==1.76.0 + - h11==0.16.0 + - html5lib==1.1 + - httpcore==1.0.9 + - httplib2==0.31.0 + - httpx==0.28.1 + - huggingface-hub==0.29.3 + - hyperlink==21.0.0 + - hypothesis==6.148.1 + - idna==3.11 + - ijson==3.3.0 + - ijson==3.4.0.post0 + - imagesize==1.4.1 + - importlib_metadata==8.6.1 + - importlib_metadata==8.7.0 + - importlib_resources==6.5.2 + - incremental==24.7.2 + - inflect==7.5.0 + - iniconfig==2.1.0 + - inscriptis==2.5.1 + - inscriptis==2.7.0 + - installer==0.7.0 + - internetarchive==5.7.1 + - invoke==2.2.1 + - ipykernel==6.29.5 + - ipython==8.32.0 + - ipython==9.7.0 + - ipython_pygments_lexers==1.1.1 + - ir-datasets-longeval==0.1.dev39+g6ee2fef + - ir-datasets-subsample==0.0.0 + - ir_datasets==0.5.11 + - ir_measures==0.4.1 + - ir_measures==0.4.2 + - isodate==0.7.2 + - isort==7.0.0 + - itsdangerous==2.2.0 + - jaraco.classes==3.4.0 + - jaraco.collections==5.1.0 + - jaraco.context==6.0.1 + - jaraco.functools==4.1.0 + - jaraco.text==4.0.0 + - jedi==0.19.2 + - jeepney==0.8.0 + - jiter==0.10.0 + - jmespath==1.0.1 + - joblib==1.4.2 + - jsonargparse==4.41.0 + - jsonpatch==1.33 + - jsonpointer==3.0.0 + - jsonschema-specifications==2025.9.1 + - jsonschema==4.25.1 + - jupyter_client==8.6.3 + - jupyter_core==5.7.2 + - jupyterlab_pygments==0.3.0 + - keyring==25.6.0 + - kiwisolver==1.4.8 + - kombu==5.5.4 + - langcodes==3.5.0 + - language_data==1.3.0 + - lark==1.3.1 + - lazy-object-proxy==1.12.0 + - legacy-cgi==2.6.4 + - lensfun==0.3.4 + - lexicon==3.0.0 + - libtmux==0.47.0 + - lightning-ir==0.0.5 + - lightning-utilities==0.15.2 + - lightning==2.5.5 + - lit==21.1.5.dev0 + - llvmlite==0.44.0 + - lockfile==0.12.2 + - logutils==0.3.5 + - louis==3.34.0 + - lsr_benchmark==0.0.1rc3 + - lxml==5.4.0 + - lz4==4.3.3 + - lz4==4.4.5 + - mallard-ducktype==1.0.2 + - marisa-trie==1.3.1 + - markdown-it-py==4.0.0 + - marshmallow-enum==1.5.1 + - marshmallow==3.26.1 + - matchpy==0.5.5 + - matplotlib-inline==0.1.7 + - matplotlib-inline==0.2.1 + - matplotlib==3.10.0 + - mccabe==0.7.0 + - mdit-py-plugins==0.4.2 + - mdurl==0.1.2 + - minify_html==0.18.1 + - mistune==3.1.1 + - mock==5.1.0 + - more-itertools==10.8.0 + - mpmath==1.3.0 + - mrjob==0.7.4 + - msgpack==1.1.2 + - multidict==6.7.0 + - multiprocess==0.70.16 + - multiset==2.1.1 + - murmurhash==1.0.13 + - mypy-extensions==1.0.0 + - mypy==1.15.0 + - mypy_extensions==1.1.0 + - myst-parser==4.0.0 + - nbclient==0.10.2 + - nbconvert==7.16.6 + - nbformat==5.10.4 + - necessary==0.4.3 + - nest-asyncio==1.6.0 + - networkx==3.4.2 + - nftables==0.1 + - nh3==0.2.20 + - nltk==3.9.1 + - nose==1.3.7 + - npids==0.1.3 + - nspektr==0.5.1 + - numba==0.61.2 + - numpy==1.26.4 + - numpy==2.3.5 + - nvidia-cublas-cu12==12.4.5.8 + - nvidia-cuda-cccl==13.0.85 + - nvidia-cuda-cupti-cu12==12.4.127 + - nvidia-cuda-nvrtc-cu12==12.4.127 + - nvidia-cuda-nvrtc==13.0.88 + - nvidia-cuda-runtime-cu12==12.4.127 + - nvidia-cuda-runtime==13.0.88 + - nvidia-cudnn-cu12==9.1.0.70 + - nvidia-cufft-cu12==11.2.1.3 + - nvidia-curand-cu12==10.3.5.147 + - nvidia-cusolver-cu12==11.6.1.9 + - nvidia-cusparse-cu12==12.3.1.170 + - nvidia-cusparselt-cu12==0.6.2 + - nvidia-nccl-cu12==2.21.5 + - nvidia-nvjitlink-cu12==12.4.127 + - nvidia-nvjitlink==13.0.88 + - nvidia-nvtx-cu12==12.4.127 + - nvidia-nvvm==13.0.88 + - oauth2client==4.1.3 + - openai==1.93.0 + - openpyxl==3.1.5 + - openshot-qt==3.3.0 + - ordered-set==4.1.0 + - orjson==3.11.4 + - packaging==25.0 + - pandas==2.3.3 + - pandocfilters==1.5.1 + - parameterized==0.9.0 + - paramiko==4.0.0 + - parso==0.8.4 + - parso==0.8.5 + - patsy==1.0.1 + - patsy==1.0.2 + - pbr==7.0.1 + - pbs-installer==2025.4.9 + - pdf2image==1.17.0 + - pecan==1.7.0 + - pew==1.2.0 + - pexpect==4.9.0 + - pillow==12.0.0 + - pip==25.3 + - pkgconfig==1.5.5 + - pkginfo==1.12.0 + - platformdirs==4.5.0 + - pluggy==1.6.0 + - ply==3.11 + - poetry-core==2.1.3 + - poetry-core==2.2.1 + - poetry==2.1.3 + - pooch==1.8.2 + - portend==3.2.1 + - preshed==3.0.10 + - prettytable==3.16.0 + - progress==1.6.1 + - prometheus_client==0.23.1 + - promise==2.3 + - prompt_toolkit==3.0.50 + - prompt_toolkit==3.0.52 + - propcache==0.4.1 + - protobuf==6.33.0 + - psutil==7.1.3 + - ptyprocess==0.7.0 + - pure_eval==0.2.3 + - pwquality==1.4.5 + - py-cpuinfo==9.0.0 + - py==1.11.1.dev0+g447bac51.d20241130 + - pyOpenSSL==25.3.0 + - pyaml==24.12.0 + - pyaml_env==1.2.2 + - pyarrow==19.0.1 + - pyarrow==22.0.0 + - pyasn1==0.6.1 + - pyasn1_modules==0.4.2 + - pycairo==1.29.0 + - pycodestyle==2.14.0 + - pycosat==0.6.6 + - pycparser==2.23 + - pycriu==4.2 + - pydantic==2.12.4 + - pydantic_core==2.41.5 + - pyfailsafe==0.6.0 + - pyflakes==3.4.0 + - pygdbmi==0.11.0.0 + - pyjnius==1.7.0 + - pylint==4.0.3 + - pyparsing==3.2.5 + - pyperclip==1.11.0 + - pyproject_hooks==1.2.0 + - pyrate-limiter==2.10.0 + - pyrsistent==0.20.0 + - pyserial==3.5 + - pyterrier-alpha==0.12.6 + - pyterrier-alpha==0.16.4 + - pyterrier-dr==0.3.0 + - pyterrier-t5==0.0.1 + - pytesseract==0.3.13 + - pytest-asyncio==0.26.0 + - pytest-cov==5.0.0 + - pytest-cov==6.1.1 + - pytest-flake8==1.3.0 + - pytest==8.4.2 + - python-dateutil==2.9.0 + - python-dotenv==1.2.1 + - python-gitlab==5.6.0 + - python-magic==0.4.27 + - python-slugify==8.0.4 + - python-terrier==0.13.2 + - python-xlib==0.33 + - pytorch-lightning==2.5.5 + - pytrec_eval-terrier==0.5.10 + - pytrec_eval-terrier==0.5.8 + - pytrec_eval==0.5 + - pytz==2025.2 + - pyzmq==27.1.0 + - rank-bm25==0.2.2 + - readme_renderer==44.0 + - referencing==0.37.0 + - regex==2024.11.6 + - reliability_tests==0.0.1rc1 + - repoze.lru==0.7 + - repro-eval==0.4.0 + - requests-cache==1.2.1 + - requests-ratelimiter==0.7.0 + - requests-toolbelt==1.0.0 + - requests==2.32.2 + - requests==2.32.5 + - requirements-parser==0.11.0 + - resolvelib==1.2.1 + - responses==0.25.8 + - retrying==1.3.4 + - rfc3986==2.0.0 + - rfc3987==1.3.8 + - 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0 +3 0 0613590b24db271a692b0e0eea44ce4ac48080d5c230d7071c00cc1724b1613f 2 diff --git a/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.jsonl b/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.jsonl index b495a85..d748563 100644 --- a/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.jsonl +++ b/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.jsonl @@ -1,3 +1,3 @@ -{"qid": "3", "query": "split ergo keyboard", "original_query": {"query_id": "3", "title": "split ergo keyboard", "description": "The user wants to know about split ergonomic keyboards, descriptions on what they are and where you might buy them", "narrative": "The relevant documents would describe details about split keyboards, not just ergonomic keyboards. There exists keyboards that are labeled ergonomic but that are in one piece, which should not be relevant."}} -{"qid": "4", "query": "metoo Hollywood", "original_query": {"query_id": "4", "title": "metoo Hollywood", "description": "The metoo movement and aftermath, specifically in Hollywood.", "narrative": "Relevant documents should contain information about how the metoo movement impacts Hollywood, could be a retrospective view, case study, opinion from an industry star etc. If the document does not mention Hollywood or filmmaking, it is not considered relevant. If the document is focused on Hollywood culture and only mentions metoo briefly, that too is not enough to make the document relevant."}} -{"qid": "7", "query": "gastritis symptoms", "original_query": {"query_id": "7", "title": "gastritis symptoms", "description": "I want to know more about what symptoms are linked to gastritis", "narrative": "Relevant documents would be documents that mention and list the symptoms patients with gastritis would have. Irrelevant documents are primarily documents that do not have any list of symptoms, so they could include a lot of information about gastritis, what could cause gastritis. But, if it never mentions the symptoms than it is not relevant."}} +{"qid": "3", "query": "split ergo keyboard", "original_query": {"query_id": "3", "title": "split ergo keyboard", "description": "The user wants to know about split ergonomic keyboards, descriptions on what they are and where you might buy them", "narrative": "The relevant documents would describe details about split keyboards, not just ergonomic keyboards. There exists keyboards that are labeled ergonomic but that are in one piece, which should not be relevant."}} +{"qid": "4", "query": "metoo Hollywood", "original_query": {"query_id": "4", "title": "metoo Hollywood", "description": "The metoo movement and aftermath, specifically in Hollywood.", "narrative": "Relevant documents should contain information about how the metoo movement impacts Hollywood, could be a retrospective view, case study, opinion from an industry star etc. If the document does not mention Hollywood or filmmaking, it is not considered relevant. If the document is focused on Hollywood culture and only mentions metoo briefly, that too is not enough to make the document relevant."}} +{"qid": "7", "query": "gastritis symptoms", "original_query": {"query_id": "7", "title": "gastritis symptoms", "description": "I want to know more about what symptoms are linked to gastritis", "narrative": "Relevant documents would be documents that mention and list the symptoms patients with gastritis would have. Irrelevant documents are primarily documents that do not have any list of symptoms, so they could include a lot of information about gastritis, what could cause gastritis. But, if it never mentions the symptoms than it is not relevant."}} diff --git a/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.xml b/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.xml index 47f4dea..a029f0f 100644 --- a/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.xml +++ b/ecir26/corpus-construction/subsampled-corpora/spot-check/queries.xml @@ -1,3 +1,3 @@ -split ergo keyboard3split ergo keyboardThe user wants to know about split ergonomic keyboards, descriptions on what they are and where you might buy themThe relevant documents would describe details about split keyboards, not just ergonomic keyboards. There exists keyboards that are labeled ergonomic but that are in one piece, which should not be relevant. -metoo Hollywood4metoo HollywoodThe metoo movement and aftermath, specifically in Hollywood.Relevant documents should contain information about how the metoo movement impacts Hollywood, could be a retrospective view, case study, opinion from an industry star etc. If the document does not mention Hollywood or filmmaking, it is not considered relevant. If the document is focused on Hollywood culture and only mentions metoo briefly, that too is not enough to make the document relevant. -gastritis symptoms7gastritis symptomsI want to know more about what symptoms are linked to gastritisRelevant documents would be documents that mention and list the symptoms patients with gastritis would have. Irrelevant documents are primarily documents that do not have any list of symptoms, so they could include a lot of information about gastritis, what could cause gastritis. But, if it never mentions the symptoms than it is not relevant. +split ergo keyboard3split ergo keyboardThe user wants to know about split ergonomic keyboards, descriptions on what they are and where you might buy themThe relevant documents would describe details about split keyboards, not just ergonomic keyboards. There exists keyboards that are labeled ergonomic but that are in one piece, which should not be relevant. +metoo Hollywood4metoo HollywoodThe metoo movement and aftermath, specifically in Hollywood.Relevant documents should contain information about how the metoo movement impacts Hollywood, could be a retrospective view, case study, opinion from an industry star etc. If the document does not mention Hollywood or filmmaking, it is not considered relevant. If the document is focused on Hollywood culture and only mentions metoo briefly, that too is not enough to make the document relevant. +gastritis symptoms7gastritis symptomsI want to know more about what symptoms are linked to gastritisRelevant documents would be documents that mention and list the symptoms patients with gastritis would have. Irrelevant documents are primarily documents that do not have any list of symptoms, so they could include a lot of information about gastritis, what could cause gastritis. But, if it never mentions the symptoms than it is not relevant. diff --git a/ecir26/corpus-construction/subsampled-corpora/spot-check/topics.xml b/ecir26/corpus-construction/subsampled-corpora/spot-check/topics.xml index 24dcba1..137b4e6 100644 --- a/ecir26/corpus-construction/subsampled-corpora/spot-check/topics.xml +++ b/ecir26/corpus-construction/subsampled-corpora/spot-check/topics.xml @@ -1,17 +1,17 @@ - - - 3split ergo keyboard - The user wants to know about split ergonomic keyboards, descriptions on what they are and where you might buy them - The relevant documents would describe details about split keyboards, not just ergonomic keyboards. There exists keyboards that are labeled ergonomic but that are in one piece, which should not be relevant. - - - 4metoo Hollywood - The metoo movement and aftermath, specifically in Hollywood. - Relevant documents should contain information about how the metoo movement impacts Hollywood, could be a retrospective view, case study, opinion from an industry star etc. If the document does not mention Hollywood or filmmaking, it is not considered relevant. If the document is focused on Hollywood culture and only mentions metoo briefly, that too is not enough to make the document relevant. - - - 7gastritis symptoms - I want to know more about what symptoms are linked to gastritis - Relevant documents would be documents that mention and list the symptoms patients with gastritis would have. Irrelevant documents are primarily documents that do not have any list of symptoms, so they could include a lot of information about gastritis, what could cause gastritis. But, if it never mentions the symptoms than it is not relevant. - - + + + 3split ergo keyboard + The user wants to know about split ergonomic keyboards, descriptions on what they are and where you might buy them + The relevant documents would describe details about split keyboards, not just ergonomic keyboards. There exists keyboards that are labeled ergonomic but that are in one piece, which should not be relevant. + + + 4metoo Hollywood + The metoo movement and aftermath, specifically in Hollywood. + Relevant documents should contain information about how the metoo movement impacts Hollywood, could be a retrospective view, case study, opinion from an industry star etc. If the document does not mention Hollywood or filmmaking, it is not considered relevant. If the document is focused on Hollywood culture and only mentions metoo briefly, that too is not enough to make the document relevant. + + + 7gastritis symptoms + I want to know more about what symptoms are linked to gastritis + Relevant documents would be documents that mention and list the symptoms patients with gastritis would have. Irrelevant documents are primarily documents that do not have any list of symptoms, so they could include a lot of information about gastritis, what could cause gastritis. But, if it never mentions the symptoms than it is not relevant. + + diff --git a/ecir26/pyterrier-baseline/.devcontainer.json b/ecir26/pyterrier-baseline/.devcontainer.json index c4699ca..b4924d1 100644 --- a/ecir26/pyterrier-baseline/.devcontainer.json +++ b/ecir26/pyterrier-baseline/.devcontainer.json @@ -1,17 +1,17 @@ -{ - "dockerFile": "Dockerfile", - "customizations": { - "vscode": { - "extensions": [ - "ms-python.python", - "ms-python.vscode-pylance", - "ms-toolsai.jupyter", - "ms-toolsai.datawrangler", - "ms-azuretools.vscode-docker" - ] - } - }, - "runArgs": [ - "--network=host", "--privileged" - ] -} +{ + "dockerFile": "Dockerfile", + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "ms-toolsai.jupyter", + "ms-toolsai.datawrangler", + "ms-azuretools.vscode-docker" + ] + } + }, + "runArgs": [ + "--network=host", "--privileged" + ] +} diff --git a/ecir26/pyterrier-baseline/Dockerfile b/ecir26/pyterrier-baseline/Dockerfile index 7ccb8f2..417c77a 100644 --- a/ecir26/pyterrier-baseline/Dockerfile +++ b/ecir26/pyterrier-baseline/Dockerfile @@ -1,15 +1,15 @@ -# the repo already contains everything pre-installed. In case you want to modify something, you can still do this below -# The base image is build from https://github.com/OpenWebSearch/wows-code/blob/main/ecir26/template-new-approach/Dockerfile.dev -FROM ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest - -RUN apt-get update \ - && apt-get install -y git openjdk-11-jdk python3 python3-pip - -ADD requirements.txt /tmp/requirements.txt - -RUN pip3 install -r /tmp/requirements.txt - -RUN python3 -c 'from tira.third_party_integrations import ensure_pyterrier_is_loaded; ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False)' - -ADD index.py /index.py - +# the repo already contains everything pre-installed. In case you want to modify something, you can still do this below +# The base image is build from https://github.com/OpenWebSearch/wows-code/blob/main/ecir26/template-new-approach/Dockerfile.dev +FROM ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest + +RUN apt-get update \ + && apt-get install -y git openjdk-11-jdk python3 python3-pip + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt + +RUN python3 -c 'from tira.third_party_integrations import ensure_pyterrier_is_loaded; ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False)' + +ADD index.py /index.py + diff --git a/ecir26/pyterrier-baseline/README.md b/ecir26/pyterrier-baseline/README.md index 1a8be25..777821d 100644 --- a/ecir26/pyterrier-baseline/README.md +++ b/ecir26/pyterrier-baseline/README.md @@ -1,62 +1,62 @@ -# PyTerrier Baseline - - -Get an overview of all possibilities to execute this baseline via: - -``` -./retrieve.py --help -``` - -This should yield an output like: - -``` -Usage: retrieve.py [OPTIONS] - -Options: - --dataset [radboud-validation-20251114-training|spot-check-20251122-training] - The dataset. [required] - --output PATH The output directory. [required] - --retrieval-model TEXT The retrieval model. - --index [default|title|description] - The text field of the index on which to - retrieve. - --help Show this message and exit. -``` - -## Upload some run via: - -``` -tira-cli upload --directory DIRECTORY-WITH-RUN -``` - -## Run All Variants - -We run retrieval via: - -``` -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model BM25 --index default -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model PL2 --index default -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model DirichletLM --index default - -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model BM25 --index title -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model PL2 --index title -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model DirichletLM --index title - -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model BM25 --index description -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model PL2 --index description -./retrieve.py --dataset DATASET-ID --output runs --retrieval-model DirichletLM --index description -``` - -## Indexing - -Submit the indexing as code submission to tira - -``` -tira-cli code-submission \ - --path . \ - --task ir-lab-wise-2025 \ - --tira-vm-id ows \ - --dataset spot-check-20251122-training \ - --command '/index.py --dataset $inputDataset --output $outputDir/index --text-field default_text' \ - --dry-run -``` +# PyTerrier Baseline + + +Get an overview of all possibilities to execute this baseline via: + +``` +./retrieve.py --help +``` + +This should yield an output like: + +``` +Usage: retrieve.py [OPTIONS] + +Options: + --dataset [radboud-validation-20251114-training|spot-check-20251122-training] + The dataset. [required] + --output PATH The output directory. [required] + --retrieval-model TEXT The retrieval model. + --index [default|title|description] + The text field of the index on which to + retrieve. + --help Show this message and exit. +``` + +## Upload some run via: + +``` +tira-cli upload --directory DIRECTORY-WITH-RUN +``` + +## Run All Variants + +We run retrieval via: + +``` +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model BM25 --index default +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model PL2 --index default +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model DirichletLM --index default + +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model BM25 --index title +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model PL2 --index title +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model DirichletLM --index title + +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model BM25 --index description +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model PL2 --index description +./retrieve.py --dataset DATASET-ID --output runs --retrieval-model DirichletLM --index description +``` + +## Indexing + +Submit the indexing as code submission to tira + +``` +tira-cli code-submission \ + --path . \ + --task ir-lab-wise-2025 \ + --tira-vm-id ows \ + --dataset spot-check-20251122-training \ + --command '/index.py --dataset $inputDataset --output $outputDir/index --text-field default_text' \ + --dry-run +``` diff --git a/ecir26/pyterrier-baseline/index.py b/ecir26/pyterrier-baseline/index.py index 0f838e1..245c632 100755 --- a/ecir26/pyterrier-baseline/index.py +++ b/ecir26/pyterrier-baseline/index.py @@ -1,34 +1,34 @@ -#!/usr/bin/env python3 -import click -import pyterrier as pt -from pathlib import Path -from tira.third_party_integrations import ensure_pyterrier_is_loaded -from tirex_tracker import tracking, ExportFormat -from tira.rest_api_client import Client -import pandas as pd -import json -from tqdm import tqdm - - -@click.command() -@click.option("--dataset", type=str, required=True, help="The dataset.") -@click.option("--output", type=Path, required=True, help="The output directory.") -@click.option("--text-field", type=click.Choice(["default_text", "title", "description"]), required=False, default="title", help="The text field to index.") -def main(dataset, text_field, output): - ensure_pyterrier_is_loaded() - tag = f"terrier-index-on-{text_field}" - description = f"This is a PyTerrier index that indexes the field {text_field} with default settings." - corpus_file = Client().download_dataset(None, dataset) / "corpus.jsonl.gz" - lines = pd.read_json(corpus_file, lines=True) - - docs = [] - for _, l in tqdm(lines.iterrows(), "load docs"): - docs.append({"docno": l["doc_id"], "text": l[text_field]}) - - with tracking(export_file_path=output / "index-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): - pt.IterDictIndexer(str(output.absolute()), meta={'docno' : 100}).index(docs) - - -if __name__ == '__main__': - main() - +#!/usr/bin/env python3 +import click +import pyterrier as pt +from pathlib import Path +from tira.third_party_integrations import ensure_pyterrier_is_loaded +from tirex_tracker import tracking, ExportFormat +from tira.rest_api_client import Client +import pandas as pd +import json +from tqdm import tqdm + + +@click.command() +@click.option("--dataset", type=str, required=True, help="The dataset.") +@click.option("--output", type=Path, required=True, help="The output directory.") +@click.option("--text-field", type=click.Choice(["default_text", "title", "description"]), required=False, default="title", help="The text field to index.") +def main(dataset, text_field, output): + ensure_pyterrier_is_loaded() + tag = f"terrier-index-on-{text_field}" + description = f"This is a PyTerrier index that indexes the field {text_field} with default settings." + corpus_file = Client().download_dataset(None, dataset) / "corpus.jsonl.gz" + lines = pd.read_json(corpus_file, lines=True) + + docs = [] + for _, l in tqdm(lines.iterrows(), "load docs"): + docs.append({"docno": l["doc_id"], "text": l[text_field]}) + + with tracking(export_file_path=output / "index-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): + pt.IterDictIndexer(str(output.absolute()), meta={'docno' : 100}).index(docs) + + +if __name__ == '__main__': + main() + diff --git a/ecir26/pyterrier-baseline/requirements.txt b/ecir26/pyterrier-baseline/requirements.txt index 8c61ea9..fbec619 100644 --- a/ecir26/pyterrier-baseline/requirements.txt +++ b/ecir26/pyterrier-baseline/requirements.txt @@ -1,5 +1,5 @@ -python-terrier -tira -ir_datasets -tirex-tracker==0.2.15 -click +python-terrier +tira +ir_datasets +tirex-tracker==0.2.15 +click diff --git a/ecir26/pyterrier-baseline/retrieve.py b/ecir26/pyterrier-baseline/retrieve.py index df219d6..82089c6 100755 --- a/ecir26/pyterrier-baseline/retrieve.py +++ b/ecir26/pyterrier-baseline/retrieve.py @@ -1,31 +1,31 @@ -#!/usr/bin/env python3 -import click -import pyterrier as pt -from pathlib import Path -from tira.third_party_integrations import ensure_pyterrier_is_loaded -from tirex_tracker import tracking, ExportFormat - - -@click.command() -@click.option("--dataset", type=click.Choice(["radboud-validation-20251114-training", "spot-check-20251122-training"]), required=True, help="The dataset.") -@click.option("--output", type=Path, required=True, help="The output directory.") -@click.option("--retrieval-model", type=str, default="BM25", required=False, help="The retrieval model.") -@click.option("--index", type=click.Choice(["default", "title", "description"]), required=False, default="default", help="The text field of the index on which to retrieve.") -def main(dataset, index, retrieval_model, output): - ensure_pyterrier_is_loaded() - pt_index = pt.Artifact.from_url(f"tira:{dataset}/ows/pyterrier-index-{index}") - topics = pt.datasets.get_dataset(f"irds:ir-lab-wise-2025/{dataset}").get_topics("title") - retriever = pt.terrier.Retriever(pt_index, wmodel=retrieval_model) - - tag = f"pyterrier-{retrieval_model}-on-{index}" - target_dir = output / dataset / tag - description = f"This is a PyTerrier retriever using the retrieval model {retriever} retrieving on the {index} text representation of the documents. Everything is set to the defaults." - - with tracking(export_file_path=target_dir / "retrieval-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): - run = retriever(topics) - - pt.io.write_results(run, target_dir / "run.txt.gz") - -if __name__ == '__main__': - main() - +#!/usr/bin/env python3 +import click +import pyterrier as pt +from pathlib import Path +from tira.third_party_integrations import ensure_pyterrier_is_loaded +from tirex_tracker import tracking, ExportFormat + + +@click.command() +@click.option("--dataset", type=click.Choice(["radboud-validation-20251114-training", "spot-check-20251122-training"]), required=True, help="The dataset.") +@click.option("--output", type=Path, required=True, help="The output directory.") +@click.option("--retrieval-model", type=str, default="BM25", required=False, help="The retrieval model.") +@click.option("--index", type=click.Choice(["default", "title", "description"]), required=False, default="default", help="The text field of the index on which to retrieve.") +def main(dataset, index, retrieval_model, output): + ensure_pyterrier_is_loaded() + pt_index = pt.Artifact.from_url(f"tira:{dataset}/ows/pyterrier-index-{index}") + topics = pt.datasets.get_dataset(f"irds:ir-lab-wise-2025/{dataset}").get_topics("title") + retriever = pt.terrier.Retriever(pt_index, wmodel=retrieval_model) + + tag = f"pyterrier-{retrieval_model}-on-{index}" + target_dir = output / dataset / tag + description = f"This is a PyTerrier retriever using the retrieval model {retriever} retrieving on the {index} text representation of the documents. Everything is set to the defaults." + + with tracking(export_file_path=target_dir / "retrieval-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): + run = retriever(topics) + + pt.io.write_results(run, target_dir / "run.txt.gz") + +if __name__ == '__main__': + main() + diff --git a/ecir26/submission-skeleton/ir-metadata.yml b/ecir26/submission-skeleton/ir-metadata.yml index 5090328..a7aaf6e 100644 --- a/ecir26/submission-skeleton/ir-metadata.yml +++ b/ecir26/submission-skeleton/ir-metadata.yml @@ -1,45 +1,45 @@ -# We use the ir-metadata standard to describe and process runs. -# The fields below are mandatory, you can add additional metadata if you like. -# There are two libraries that automate and simplify tracking of experimental metadata that you can use optionally: -# -# - The metadata module of [repro_eval](https://www.ir-metadata.org/software/): -# Tracks the platform and implementation of your experiments. -# -# - The [tirex_tracker](https://github.com/tira-io/tirex-tracker/): -# Tracks the platform, implementation, and resources (e.g., CPU, GPU, RAM, emissions, etc.) of your experiments. -# -# See https://www.ir-metadata.org for more details. - -method: - description: | - ENTER_VALUE_HERE - ... - This is a multi line string - ... - ENTER_VALUE_HERE - - name: ENTER_VALUE_HERE - -actor: - team: ENTER_VALUE_HERE - -data: - test collection: - name: ENTER_VALUE_HERE - -platform: - software: - # Which software and tools did you use for training, tunning and running your system? - # You can maintain the software that you used manually. - # Alternatively, you can use repro_eval or the tirex_tracker to track this. - libraries: - - ENTER_VALUE_HERE - - ENTER_VALUE_HERE - -implementation: - source: - # Please provide a reference to your code if possible. - # If you can not share your code, you can delete the implementation section. - # The repro_eval and/or tirex_tracker tools can track this automatically, including commits, branches, etc. - repository: ENTER_VALUE_HERE - +# We use the ir-metadata standard to describe and process runs. +# The fields below are mandatory, you can add additional metadata if you like. +# There are two libraries that automate and simplify tracking of experimental metadata that you can use optionally: +# +# - The metadata module of [repro_eval](https://www.ir-metadata.org/software/): +# Tracks the platform and implementation of your experiments. +# +# - The [tirex_tracker](https://github.com/tira-io/tirex-tracker/): +# Tracks the platform, implementation, and resources (e.g., CPU, GPU, RAM, emissions, etc.) of your experiments. +# +# See https://www.ir-metadata.org for more details. + +method: + description: | + ENTER_VALUE_HERE + ... + This is a multi line string + ... + ENTER_VALUE_HERE + + name: ENTER_VALUE_HERE + +actor: + team: ENTER_VALUE_HERE + +data: + test collection: + name: ENTER_VALUE_HERE + +platform: + software: + # Which software and tools did you use for training, tunning and running your system? + # You can maintain the software that you used manually. + # Alternatively, you can use repro_eval or the tirex_tracker to track this. + libraries: + - ENTER_VALUE_HERE + - ENTER_VALUE_HERE + +implementation: + source: + # Please provide a reference to your code if possible. + # If you can not share your code, you can delete the implementation section. + # The repro_eval and/or tirex_tracker tools can track this automatically, including commits, branches, etc. + repository: ENTER_VALUE_HERE + diff --git a/ecir26/template-new-approach/.devcontainer.json b/ecir26/template-new-approach/.devcontainer.json index c4699ca..b4924d1 100644 --- a/ecir26/template-new-approach/.devcontainer.json +++ b/ecir26/template-new-approach/.devcontainer.json @@ -1,17 +1,17 @@ -{ - "dockerFile": "Dockerfile", - "customizations": { - "vscode": { - "extensions": [ - "ms-python.python", - "ms-python.vscode-pylance", - "ms-toolsai.jupyter", - "ms-toolsai.datawrangler", - "ms-azuretools.vscode-docker" - ] - } - }, - "runArgs": [ - "--network=host", "--privileged" - ] -} +{ + "dockerFile": "Dockerfile", + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "ms-toolsai.jupyter", + "ms-toolsai.datawrangler", + "ms-azuretools.vscode-docker" + ] + } + }, + "runArgs": [ + "--network=host", "--privileged" + ] +} diff --git a/ecir26/template-new-approach/.gitignore b/ecir26/template-new-approach/.gitignore index b77bbb3..3a47b27 100644 --- a/ecir26/template-new-approach/.gitignore +++ b/ecir26/template-new-approach/.gitignore @@ -1,2 +1,2 @@ -indexes -runs +indexes +runs diff --git a/ecir26/template-new-approach/Dockerfile b/ecir26/template-new-approach/Dockerfile index 7ec6ad1..23cded1 100644 --- a/ecir26/template-new-approach/Dockerfile +++ b/ecir26/template-new-approach/Dockerfile @@ -1,7 +1,7 @@ -# the repo already contains everything pre-installed. In case you want to modify something, you can still do this below -# The base image is build from https://github.com/OpenWebSearch/wows-code/blob/main/ecir26/template-new-approach/Dockerfile.dev -FROM ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest - -ADD requirements.txt /tmp/requirements.txt - -RUN pip3 install -r /tmp/requirements.txt +# the repo already contains everything pre-installed. In case you want to modify something, you can still do this below +# The base image is build from https://github.com/OpenWebSearch/wows-code/blob/main/ecir26/template-new-approach/Dockerfile.dev +FROM ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt diff --git a/ecir26/template-new-approach/Dockerfile.dev b/ecir26/template-new-approach/Dockerfile.dev index bf0656f..9711797 100644 --- a/ecir26/template-new-approach/Dockerfile.dev +++ b/ecir26/template-new-approach/Dockerfile.dev @@ -1,12 +1,12 @@ -# docker build -f Dockerfile.dev -t ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest . -FROM ubuntu:jammy - - -RUN apt-get update \ - && apt-get install -y git openjdk-11-jdk python3 python3-pip - -ADD requirements.txt /tmp/requirements.txt - -RUN pip3 install -r /tmp/requirements.txt \ - && python3 -c 'from tira.third_party_integrations import ensure_pyterrier_is_loaded; ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False)' - +# docker build -f Dockerfile.dev -t ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest . +FROM ubuntu:jammy + + +RUN apt-get update \ + && apt-get install -y git openjdk-11-jdk python3 python3-pip + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt \ + && python3 -c 'from tira.third_party_integrations import ensure_pyterrier_is_loaded; ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False)' + diff --git a/ecir26/template-new-approach/README.md b/ecir26/template-new-approach/README.md index 73a91ea..989db98 100644 --- a/ecir26/template-new-approach/README.md +++ b/ecir26/template-new-approach/README.md @@ -1,64 +1,64 @@ -# An Artifact-Free Approach - - -Get an overview of all possibilities to execute this baseline via: - -``` -./retrieve.py --help -``` - -This should yield an output like: - -``` -Usage: retrieve.py [OPTIONS] - -Options: - --dataset [radboud-validation-20251114-training|spot-check-20251122-training] - The dataset. [required] - --output PATH The output directory. [required] - --retrieval-model TEXT The retrieval model. - --text-field-to-retrieve [default|title|description] - The text field of the index on which to - retrieve. - --help Show this message and exit. -``` - -## Upload some run via: - -``` -tira-cli upload --directory DIRECTORY-WITH-RUN -``` - -## Run All Variants - -We run retrieval via: - -``` -./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve default -./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve default -./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve default - -./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve title -./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve title -./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve title - -./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve description -./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve description -./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve description -``` -For each run, a directory is created at `output/runs/DATASET-ID`, which includes a `run.txt.gz` file. -After these files are generated, further investigate them by running the `analysis.ipynb` notebook. - -## Indexing - -Submit the indexing as code submission to tira - -``` -tira-cli code-submission \ - --path . \ - --task ir-lab-wise-2025 \ - --tira-vm-id ows \ - --dataset spot-check-20251122-training \ - --command '/index.py --dataset $inputDataset --output $outputDir/index --text-field default_text' \ - --dry-run -``` +# An Artifact-Free Approach + + +Get an overview of all possibilities to execute this baseline via: + +``` +./retrieve.py --help +``` + +This should yield an output like: + +``` +Usage: retrieve.py [OPTIONS] + +Options: + --dataset [radboud-validation-20251114-training|spot-check-20251122-training] + The dataset. [required] + --output PATH The output directory. [required] + --retrieval-model TEXT The retrieval model. + --text-field-to-retrieve [default|title|description] + The text field of the index on which to + retrieve. + --help Show this message and exit. +``` + +## Upload some run via: + +``` +tira-cli upload --directory DIRECTORY-WITH-RUN +``` + +## Run All Variants + +We run retrieval via: + +``` +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve default +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve default +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve default + +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve title +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve title +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve title + +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve description +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve description +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve description +``` +For each run, a directory is created at `output/runs/DATASET-ID`, which includes a `run.txt.gz` file. +After these files are generated, further investigate them by running the `analysis.ipynb` notebook. + +## Indexing + +Submit the indexing as code submission to tira + +``` +tira-cli code-submission \ + --path . \ + --task ir-lab-wise-2025 \ + --tira-vm-id ows \ + --dataset spot-check-20251122-training \ + --command '/index.py --dataset $inputDataset --output $outputDir/index --text-field default_text' \ + --dry-run +``` diff --git a/ecir26/template-new-approach/analysis.ipynb b/ecir26/template-new-approach/analysis.ipynb index 9f96e16..238b826 100644 --- a/ecir26/template-new-approach/analysis.ipynb +++ b/ecir26/template-new-approach/analysis.ipynb @@ -1,244 +1,244 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "c5266eee", - "metadata": {}, - "outputs": [], - "source": [ - "from glob import glob\n", - "import pyterrier as pt\n", - "from tira.third_party_integrations import ensure_pyterrier_is_loaded" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4536052c", - "metadata": {}, - "outputs": [], - "source": [ - "ensure_pyterrier_is_loaded()\n", - "ds_id = \"radboud-validation-20251114-training\"\n", - "dataset = pt.datasets.get_dataset(f\"irds:ir-lab-wise-2025/{ds_id}\")\n", - "topics = dataset.get_topics(\"title\")\n", - "qrels = dataset.get_qrels()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3d15d8a4", - "metadata": {}, - "outputs": [], - "source": [ - "runs = {}\n", - "\n", - "for run_name in glob(f'output/runs/{ds_id}/*/run.txt.gz'):\n", - " runs[run_name.split('/')[-2]] = pt.io.read_results(run_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "78acfa9a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "name", - "rawType": "object", - "type": "string" - }, - { - "name": "ndcg_cut.10", - "rawType": "float64", - "type": "float" - } - ], - "ref": "19caa345-b967-466c-8088-03cd3049c8e1", - "rows": [ - [ - "0", - "pyterrier-BM25-on-default_text", - "0.45163465633411787" - ], - [ - "1", - "pyterrier-PL2-on-title", - "0.32316815366734686" - ], - [ - "2", - "pyterrier-DirichletLM-on-description", - "0.10784633195991518" - ], - [ - "3", - "pyterrier-BM25-on-title", - "0.3502689694059472" - ], - [ - "4", - "pyterrier-DirichletLM-on-default_text", - "0.35295185862782524" - ], - [ - "5", - "pyterrier-PL2-on-description", - "0.21274926689355075" - ], - [ - "6", - "pyterrier-BM25-on-description", - "0.23040444987949454" - ], - [ - "7", - "pyterrier-PL2-on-default_text", - "0.47268983180551777" - ], - [ - "8", - "pyterrier-DirichletLM-on-title", - "0.15208789262056968" - ] - ], - "shape": { - "columns": 2, - "rows": 9 - } - }, - "text/html": [ - "
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Everything is set to the defaults." - - with tracking(export_file_path=target_dir / "retrieval-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): - run = retriever(topics) - - pt.io.write_results(run, target_file) - -@click.command() -@click.option("--dataset", type=click.Choice(["radboud-validation-20251114-training", "spot-check-20251122-training"]), required=True, help="The dataset.") -@click.option("--output", type=Path, required=False, default=Path("output"), help="The output directory.") -@click.option("--retrieval-model", type=str, default="BM25", required=False, help="The retrieval model (e.g., BM25, PL2, DirichletLM).") -@click.option("--text-field-to-retrieve", type=click.Choice(["default_text", "title", "description"]), required=False, default="default_text", help="The text field of the documents on which to retrieve.") -def main(dataset, text_field_to_retrieve, retrieval_model, output): - ensure_pyterrier_is_loaded(is_offline=False) - - index = get_index(dataset, text_field_to_retrieve, output) - run_retrieval(output, index, dataset, retrieval_model, text_field_to_retrieve) - - -if __name__ == '__main__': - main() - +#!/usr/bin/env python3 +import click +import pyterrier as pt +from pathlib import Path +from tirex_tracker import tracking, ExportFormat +from tira.third_party_integrations import ir_datasets, ensure_pyterrier_is_loaded +from tqdm import tqdm + +def extract_text_of_document(doc, field): + # ToDo: here one can make modifications to the document representations + if field == "default_text": + return doc.default_text() + elif field == "title": + return doc.title + elif field == "description": + return doc.description + + +def get_index(dataset, field, output_path): + index_dir = output_path / "indexes" / f"{dataset}-on-{field}" + if not index_dir.is_dir(): + print("Build new index") + docs = [] + dataset = ir_datasets.load(f"ir-lab-wise-2025/{dataset}") + + for doc in tqdm(dataset.docs_iter(), "Pre-Process Documents"): + docs.append({"docno": doc.doc_id, "text": extract_text_of_document(doc, field)}) + + with tracking(export_file_path=index_dir / "index-metadata.yml", export_format=ExportFormat.IR_METADATA): + pt.IterDictIndexer(str(index_dir.absolute()), meta={'docno' : 100}, verbose=True).index(docs) + + return pt.IndexFactory.of(str(index_dir.absolute())) + + +def run_retrieval(output, index, dataset, retrieval_model, text_field_to_retrieve): + tag = f"pyterrier-{retrieval_model}-on-{text_field_to_retrieve}" + target_dir = output / "runs" / dataset / tag + target_file = target_dir / "run.txt.gz" + + if target_file.exists(): + return + + topics = pt.datasets.get_dataset(f"irds:ir-lab-wise-2025/{dataset}").get_topics("title") + retriever = pt.terrier.Retriever(index, wmodel=retrieval_model) + + description = f"This is a PyTerrier retriever using the retrieval model {retriever} retrieving on the {text_field_to_retrieve} text representation of the documents. Everything is set to the defaults." + + with tracking(export_file_path=target_dir / "retrieval-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): + run = retriever(topics) + + pt.io.write_results(run, target_file) + +@click.command() +@click.option("--dataset", type=click.Choice(["radboud-validation-20251114-training", "spot-check-20251122-training"]), required=True, help="The dataset.") +@click.option("--output", type=Path, required=False, default=Path("output"), help="The output directory.") +@click.option("--retrieval-model", type=str, default="BM25", required=False, help="The retrieval model (e.g., BM25, PL2, DirichletLM).") +@click.option("--text-field-to-retrieve", type=click.Choice(["default_text", "title", "description"]), required=False, default="default_text", help="The text field of the documents on which to retrieve.") +def main(dataset, text_field_to_retrieve, retrieval_model, output): + ensure_pyterrier_is_loaded(is_offline=False) + + index = get_index(dataset, text_field_to_retrieve, output) + run_retrieval(output, index, dataset, retrieval_model, text_field_to_retrieve) + + +if __name__ == '__main__': + main() + diff --git a/irixys23/search-engine-prototyping/README.md b/irixys23/search-engine-prototyping/README.md index 5e21b88..958a695 100644 --- a/irixys23/search-engine-prototyping/README.md +++ b/irixys23/search-engine-prototyping/README.md @@ -1,42 +1,42 @@ -![](img/banner.png) - -# Code for the Search Engine Prototyping Hackathon at [IRIXYS'23](https://irixys.uni-passau.de/workshops-summer-schools/) - -The International Research & Innovation Centre on Digital Intelligent Systems (IRIXYS) hosts a hackathon on Open Web Search from the 28th to 29th of November 2023. - -## Homepage - -For more information, please visit the [Prototype Search Application (Prosa) homepage](https://qnode.eu/ows/prosa/). - -## Task: Build your own conversational Search Engine - -This task is very much about creativity. Do you have an innovative idea for a new, exciting search application? The [OpenWebSearch.eu](https://openwebsearch.eu/) initiative encourages all teams to make their ideas come true and work on interesting new concepts around Web Search. The teams will not need to start from scratch, but they can build off of an existing [Prototype Search Application](https://opencode.it4i.eu/openwebsearcheu-public/prototype-search-application). This prototype relies on data provided by the Open Web Index, which makes the Search Application independent from proprietary Web Indices. It is up to your choice whether you want to focus on the search backend, which is implemented in Java, the web frontend or concentrate on the conceptual elaboration. In order open more opportunities for the participants, we additionally provide API endpoints for a Mistral 7b Instruct and Zephyr 7b model for assistant style tasks (suited for Chat Completion) and a Mistral 7b 128k Base model with longer context (suited for General Prompting). For further details, please visit the [LLM API documentation](https://llm-api.pads.fim.uni-passau.de/docs). - -## Local Index Creation Pipeline - -### Step 1: Perform crawling and create warc file -``` -mkdir data -wget --input-file urls.txt --recursive --level 2 --delete-after --no-directories --warc-file data/crawl -``` - -### Step 2: Extract metadata -``` -docker run --rm -v "$PWD/data":/data opencode.it4i.eu:5050/openwebsearcheu-public/preprocessing-pipeline /data/crawl.warc.gz /data/example-index.parquet.gz -``` - -### Step 3: Create index -``` -docker run --rm -v "$PWD/data":/data opencode.it4i.eu:5050/openwebsearcheu-public/spark-indexer --description "CIFF description" --input-format parquet --output-format ciff --id-col record_id --content-col plain_text /data/example- index.parquet.gz /data/index/ -``` - -### Step 4: Convert CIFF file to Lucene index -``` -gunzip data/index/index.ciff.gz -docker run --rm -v "$PWD/data":/data opencode.it4i.eu:5050/openwebsearcheu-public/prototype-search-application/ciff- lucene-converter /data/index/index.ciff /data/index/example-index -``` - -### Step 5: Run the search application -``` -docker run --rm -v "$PWD/data":/data -p 8000:8000 opencode.it4i.eu:5050/openwebsearcheu-public/prototype-search- application/search-service -default-index example-index --port 8000 --lucene-dir-path /data/index/ --parquet-dir-path /data/ -``` +![](img/banner.png) + +# Code for the Search Engine Prototyping Hackathon at [IRIXYS'23](https://irixys.uni-passau.de/workshops-summer-schools/) + +The International Research & Innovation Centre on Digital Intelligent Systems (IRIXYS) hosts a hackathon on Open Web Search from the 28th to 29th of November 2023. + +## Homepage + +For more information, please visit the [Prototype Search Application (Prosa) homepage](https://qnode.eu/ows/prosa/). + +## Task: Build your own conversational Search Engine + +This task is very much about creativity. Do you have an innovative idea for a new, exciting search application? The [OpenWebSearch.eu](https://openwebsearch.eu/) initiative encourages all teams to make their ideas come true and work on interesting new concepts around Web Search. The teams will not need to start from scratch, but they can build off of an existing [Prototype Search Application](https://opencode.it4i.eu/openwebsearcheu-public/prototype-search-application). This prototype relies on data provided by the Open Web Index, which makes the Search Application independent from proprietary Web Indices. It is up to your choice whether you want to focus on the search backend, which is implemented in Java, the web frontend or concentrate on the conceptual elaboration. In order open more opportunities for the participants, we additionally provide API endpoints for a Mistral 7b Instruct and Zephyr 7b model for assistant style tasks (suited for Chat Completion) and a Mistral 7b 128k Base model with longer context (suited for General Prompting). For further details, please visit the [LLM API documentation](https://llm-api.pads.fim.uni-passau.de/docs). + +## Local Index Creation Pipeline + +### Step 1: Perform crawling and create warc file +``` +mkdir data +wget --input-file urls.txt --recursive --level 2 --delete-after --no-directories --warc-file data/crawl +``` + +### Step 2: Extract metadata +``` +docker run --rm -v "$PWD/data":/data opencode.it4i.eu:5050/openwebsearcheu-public/preprocessing-pipeline /data/crawl.warc.gz /data/example-index.parquet.gz +``` + +### Step 3: Create index +``` +docker run --rm -v "$PWD/data":/data opencode.it4i.eu:5050/openwebsearcheu-public/spark-indexer --description "CIFF description" --input-format parquet --output-format ciff --id-col record_id --content-col plain_text /data/example- index.parquet.gz /data/index/ +``` + +### Step 4: Convert CIFF file to Lucene index +``` +gunzip data/index/index.ciff.gz +docker run --rm -v "$PWD/data":/data opencode.it4i.eu:5050/openwebsearcheu-public/prototype-search-application/ciff- lucene-converter /data/index/index.ciff /data/index/example-index +``` + +### Step 5: Run the search application +``` +docker run --rm -v "$PWD/data":/data -p 8000:8000 opencode.it4i.eu:5050/openwebsearcheu-public/prototype-search- application/search-service -default-index example-index --port 8000 --lucene-dir-path /data/index/ --parquet-dir-path /data/ +``` diff --git a/irixys23/webpage-classification/README.md b/irixys23/webpage-classification/README.md index 24ce0f8..fdd527d 100644 --- a/irixys23/webpage-classification/README.md +++ b/irixys23/webpage-classification/README.md @@ -1,73 +1,73 @@ -# Code for the Webpage Classification Task at [IRIXYS'23](https://irixys.uni-passau.de/workshops-summer-schools/) - -The International Research & Innovation Centre on Digital Intelligent Systems (IRIXYS) hosts a hackathon on Open Web Search from the 28th to 29th of November 2023. - -## Task - -The internet is full of fascinating web pages, but also flooded with spam content. In this task, we want to encourage the teams to separate the righteous from the wicked by applying state-of-the-art ML & NLP techniques. You will work with comprehensive [training and validation datasets](https://doi.org/10.5281/zenodo.10118828) containing URLs and content of benign, malicious and adult pages. The task is therefore to create an accurate Classification model. Note that it is recommended, but not required to use the programming language Python. The concrete software framework, e.g. scikit-learn, snorkel, or transformers, is up to your choice. We particularly encourage the teams to try [snorkel](https://www.snorkel.org/), as its intuitive and rule-based approach on creating Machine Learning models seems promising to achieve good and reproducible results in short time. The teams will use the [TIRA platform](https://github.com/tira-io/tira) for submitting their software solutions and compete with each other on a shared TIRA leaderboard. - - -## Getting Started: Step-By-Step Guide - -We also have a [12 minute screencast](https://cloud.uni-jena.de/s/6cbsJsWxfJFX4Wi) that shows the steps below in a video. - -**Step 1**: Use your TIRA invitation link to register on TIRA (your account will be already configured correctly) - -**Step 2**: Go to the [submission page in TIRA](https://www.tira.io/task-overview/webpage-classification) and click on "Submit" - -**Step 3**: On the submission page in TIRA: click on "Code Submission" and fill out the form with your Github account. This will create your dedicated git repository for this hackathon that already has predefined Github Actions and baselines to simplify the participation (the repository can be either public or private). - -**Step 4**: Go to your repository, [download the data](#dataset) and hack, hack, hack ... - -![Start Working](https://media.tenor.com/B6FrX7t3vHoAAAAC/kermit-the-frog-monday.gif) - -**Step 5**: Every time you have something for submitting, execute the corresponding Github Action. From here, Maik will take care that the submission will work and you can continue your hacking. - -![Submit Something](https://media.tenor.com/BtWHg2vubOEAAAAC/math-dancing.gif) - - -## Dataset - - -The training and validation dataset is available on [Zenodo](https://doi.org/10.5281/zenodo.10118828). You can download it via `wget 'https://zenodo.org/records/10118828/files/Hackathon_data.zip?download=1' -O hackathon-data.zip`. Please find a tutorial on Snorkel in the [baselines](baselines) directory, which showcases how to work with the dataset. - -## Submission with TIRA - -### Notes - -- The submission deadline is **29th of November 2023, 12:30 CET**. -- The leaderboard will be unblinded after the submission deadline. - -### Setup - -Please install `python` (version 3.7 or newer), `tira-run` and `Docker` on your machine. -To install `tira-run`, please use `pip3 install tira`. - -### Running the Evaluator - -If you have preditions in a file `tira-output/predictions.jsonl`, you can run the evaluator via: - -``` -tira-run \ - --image webis/irixys23:evaluator \ - --input-directory example-data/truth \ - --input-run ${PWD}/tira-output \ - --output-directory tira-evaluation \ - --command '/evaluation.py -o $outputDir/evaluation.prototext -t $inputDataset/truths.jsonl -p $inputRun/predictions.jsonl' -``` - -For more details, see the [code of the evaluator](evaluation). - -### Running Baselines - -You can find the baselines and their code in the [baselines](baselines) directory. - -To run the trivial baseline that always predicts `Benign`, please run (the `--evaluate true` flag indicates that the evaluator directly tests if the results of your submission are valid): - -``` -tira-run \ - --input-dataset webpage-classification/tiny-sample-20231023-training \ - --image webis/irixys23:trivial-baseline \ - --evaluate true \ - --command '/baseline.py -i $inputDataset/inputs.jsonl -o $outputDir/predictions.jsonl' -``` +# Code for the Webpage Classification Task at [IRIXYS'23](https://irixys.uni-passau.de/workshops-summer-schools/) + +The International Research & Innovation Centre on Digital Intelligent Systems (IRIXYS) hosts a hackathon on Open Web Search from the 28th to 29th of November 2023. + +## Task + +The internet is full of fascinating web pages, but also flooded with spam content. In this task, we want to encourage the teams to separate the righteous from the wicked by applying state-of-the-art ML & NLP techniques. You will work with comprehensive [training and validation datasets](https://doi.org/10.5281/zenodo.10118828) containing URLs and content of benign, malicious and adult pages. The task is therefore to create an accurate Classification model. Note that it is recommended, but not required to use the programming language Python. The concrete software framework, e.g. scikit-learn, snorkel, or transformers, is up to your choice. We particularly encourage the teams to try [snorkel](https://www.snorkel.org/), as its intuitive and rule-based approach on creating Machine Learning models seems promising to achieve good and reproducible results in short time. The teams will use the [TIRA platform](https://github.com/tira-io/tira) for submitting their software solutions and compete with each other on a shared TIRA leaderboard. + + +## Getting Started: Step-By-Step Guide + +We also have a [12 minute screencast](https://cloud.uni-jena.de/s/6cbsJsWxfJFX4Wi) that shows the steps below in a video. + +**Step 1**: Use your TIRA invitation link to register on TIRA (your account will be already configured correctly) + +**Step 2**: Go to the [submission page in TIRA](https://www.tira.io/task-overview/webpage-classification) and click on "Submit" + +**Step 3**: On the submission page in TIRA: click on "Code Submission" and fill out the form with your Github account. This will create your dedicated git repository for this hackathon that already has predefined Github Actions and baselines to simplify the participation (the repository can be either public or private). + +**Step 4**: Go to your repository, [download the data](#dataset) and hack, hack, hack ... + +![Start Working](https://media.tenor.com/B6FrX7t3vHoAAAAC/kermit-the-frog-monday.gif) + +**Step 5**: Every time you have something for submitting, execute the corresponding Github Action. From here, Maik will take care that the submission will work and you can continue your hacking. + +![Submit Something](https://media.tenor.com/BtWHg2vubOEAAAAC/math-dancing.gif) + + +## Dataset + + +The training and validation dataset is available on [Zenodo](https://doi.org/10.5281/zenodo.10118828). You can download it via `wget 'https://zenodo.org/records/10118828/files/Hackathon_data.zip?download=1' -O hackathon-data.zip`. Please find a tutorial on Snorkel in the [baselines](baselines) directory, which showcases how to work with the dataset. + +## Submission with TIRA + +### Notes + +- The submission deadline is **29th of November 2023, 12:30 CET**. +- The leaderboard will be unblinded after the submission deadline. + +### Setup + +Please install `python` (version 3.7 or newer), `tira-run` and `Docker` on your machine. +To install `tira-run`, please use `pip3 install tira`. + +### Running the Evaluator + +If you have preditions in a file `tira-output/predictions.jsonl`, you can run the evaluator via: + +``` +tira-run \ + --image webis/irixys23:evaluator \ + --input-directory example-data/truth \ + --input-run ${PWD}/tira-output \ + --output-directory tira-evaluation \ + --command '/evaluation.py -o $outputDir/evaluation.prototext -t $inputDataset/truths.jsonl -p $inputRun/predictions.jsonl' +``` + +For more details, see the [code of the evaluator](evaluation). + +### Running Baselines + +You can find the baselines and their code in the [baselines](baselines) directory. + +To run the trivial baseline that always predicts `Benign`, please run (the `--evaluate true` flag indicates that the evaluator directly tests if the results of your submission are valid): + +``` +tira-run \ + --input-dataset webpage-classification/tiny-sample-20231023-training \ + --image webis/irixys23:trivial-baseline \ + --evaluate true \ + --command '/baseline.py -i $inputDataset/inputs.jsonl -o $outputDir/predictions.jsonl' +``` diff --git a/irixys23/webpage-classification/baselines/sklearn-baseline/Dockerfile b/irixys23/webpage-classification/baselines/sklearn-baseline/Dockerfile index 0bbe08d..493c0bf 100644 --- a/irixys23/webpage-classification/baselines/sklearn-baseline/Dockerfile +++ b/irixys23/webpage-classification/baselines/sklearn-baseline/Dockerfile @@ -1,14 +1,14 @@ -FROM python:3.9 - -ADD requirements.txt /requirements.txt - -RUN pip3 install -r /requirements.txt \ - && rm -Rf /var/cache/apt \ - && rm -Rf /requirements.txt \ - && rm -Rf /root/.cache/pip - -ENV PYTHONPATH=/app - -ADD * /app/ - -ENTRYPOINT [ "python3", "/app/sklearn_baseline.py", "-i", "$inputDataset/inputs.jsonl", "-o", "$outputDir", "-m", "/app/sk_model.pkl" ] +FROM python:3.9 + +ADD requirements.txt /requirements.txt + +RUN pip3 install -r /requirements.txt \ + && rm -Rf /var/cache/apt \ + && rm -Rf /requirements.txt \ + && rm -Rf /root/.cache/pip + +ENV PYTHONPATH=/app + +ADD * /app/ + +ENTRYPOINT [ "python3", "/app/sklearn_baseline.py", "-i", "$inputDataset/inputs.jsonl", "-o", "$outputDir", "-m", "/app/sk_model.pkl" ] diff --git a/irixys23/webpage-classification/baselines/sklearn-baseline/README.md b/irixys23/webpage-classification/baselines/sklearn-baseline/README.md index 4fa777d..98affc1 100644 --- a/irixys23/webpage-classification/baselines/sklearn-baseline/README.md +++ b/irixys23/webpage-classification/baselines/sklearn-baseline/README.md @@ -1,19 +1,19 @@ -# Scikit-Learn Baseline for The Webpage Classification Task - -This is a scikit-learn baseline for the webpage classification task. - -## Run this baseline: - -To run this baseline with tira-run, please execute: - -``` -tira-run --image webis/irixys23:scikit-learn-baseline --input-dataset webpage-classification/tiny-sample-20231023-training -``` - -## Development - -``` -docker build -t webis/irixys23:scikit-learn-baseline . -docker push webis/irixys23:scikit-learn-baseline -``` - +# Scikit-Learn Baseline for The Webpage Classification Task + +This is a scikit-learn baseline for the webpage classification task. + +## Run this baseline: + +To run this baseline with tira-run, please execute: + +``` +tira-run --image webis/irixys23:scikit-learn-baseline --input-dataset webpage-classification/tiny-sample-20231023-training +``` + +## Development + +``` +docker build -t webis/irixys23:scikit-learn-baseline . +docker push webis/irixys23:scikit-learn-baseline +``` + diff --git a/irixys23/webpage-classification/baselines/sklearn-baseline/requirements.txt b/irixys23/webpage-classification/baselines/sklearn-baseline/requirements.txt index 16e64e4..8b36a44 100644 --- a/irixys23/webpage-classification/baselines/sklearn-baseline/requirements.txt +++ b/irixys23/webpage-classification/baselines/sklearn-baseline/requirements.txt @@ -1,4 +1,4 @@ -scikit-learn==1.1.1 -joblib -pandas -jsonlines +scikit-learn==1.1.1 +joblib +pandas +jsonlines diff --git a/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline.py b/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline.py index b1aa4aa..3ea9642 100644 --- a/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline.py +++ b/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline.py @@ -1,43 +1,43 @@ -#!/usr/bin/env python3 -import argparse -from sklearn.feature_extraction.text import TfidfVectorizer -from sklearn.linear_model import SGDClassifier -from sklearn.pipeline import Pipeline -from sklearn.metrics import classification_report -import os -import joblib -from sklearn_baseline_train import load_data, preprocess - -def parse_args(): - parser = argparse.ArgumentParser(description='Webpage classification with sklearn pipeline') - parser.add_argument("-i", "--input_data", help="Path to the jsonl file for which predictions should be made.", required=True) - parser.add_argument("-m", "--model", help="The sklearn SGDClassifier model to use for the predictions.", required=True) - parser.add_argument("-o", "--output", help="Path to the directory to write the results to.", required=True) - return parser.parse_args() - -def load_model(model_file): - # Load the trained model - pipeline = joblib.load(model_file) - return pipeline - -def main(input_file, output_dir, model_file): - # Load datasets - test_data = load_data(input_file) - - # Preprocess the content - test_data['url'] = test_data['url'].apply(preprocess) - - # Feature extraction and classifier pipeline - pipeline = load_model(model_file) - - # Make predictions on the test data - test_predictions = pipeline.predict(test_data['url']) - - # Save the predictions - test_data['prediction'] = test_predictions - output_path = os.path.join(output_dir, 'predictions.jsonl') - test_data[['uid', 'prediction']].to_json(output_path, orient='records', lines=True) - -if __name__ == "__main__": - args = parse_args() - main(args.input_data, args.output, args.model) +#!/usr/bin/env python3 +import argparse +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.linear_model import SGDClassifier +from sklearn.pipeline import Pipeline +from sklearn.metrics import classification_report +import os +import joblib +from sklearn_baseline_train import load_data, preprocess + +def parse_args(): + parser = argparse.ArgumentParser(description='Webpage classification with sklearn pipeline') + parser.add_argument("-i", "--input_data", help="Path to the jsonl file for which predictions should be made.", required=True) + parser.add_argument("-m", "--model", help="The sklearn SGDClassifier model to use for the predictions.", required=True) + parser.add_argument("-o", "--output", help="Path to the directory to write the results to.", required=True) + return parser.parse_args() + +def load_model(model_file): + # Load the trained model + pipeline = joblib.load(model_file) + return pipeline + +def main(input_file, output_dir, model_file): + # Load datasets + test_data = load_data(input_file) + + # Preprocess the content + test_data['url'] = test_data['url'].apply(preprocess) + + # Feature extraction and classifier pipeline + pipeline = load_model(model_file) + + # Make predictions on the test data + test_predictions = pipeline.predict(test_data['url']) + + # Save the predictions + test_data['prediction'] = test_predictions + output_path = os.path.join(output_dir, 'predictions.jsonl') + test_data[['uid', 'prediction']].to_json(output_path, orient='records', lines=True) + +if __name__ == "__main__": + args = parse_args() + main(args.input_data, args.output, args.model) diff --git a/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline_train.py b/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline_train.py index a5c0718..3fd4912 100644 --- a/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline_train.py +++ b/irixys23/webpage-classification/baselines/sklearn-baseline/sklearn_baseline_train.py @@ -1,55 +1,55 @@ -#!/usr/bin/env python3 -import argparse -import jsonlines -import pandas as pd -from sklearn.feature_extraction.text import TfidfVectorizer -from sklearn.linear_model import SGDClassifier -from sklearn.pipeline import Pipeline -import os -import joblib - -def load_data(file_path): - with jsonlines.open(file_path) as reader: - for obj in reader: - obj_keys = obj.keys() - break - data = {k: [] for k in obj_keys} - with jsonlines.open(file_path) as reader: - for obj in reader: - for k in obj_keys: - data[k].append(obj[k]) - return pd.DataFrame(data) - -def parse_args(): - parser = argparse.ArgumentParser(description='Train a webpage classification model') - parser.add_argument("-d", "--data_dir", help="Path to the directory containing the data subfolders.", required=True) - parser.add_argument("-m", "--model_output", help="Path to save the trained model.", required=True) - return parser.parse_args() - -def preprocess(content): - # Placeholder for the content preprocessing - return content - -def main(data_dir, model_output): - # Load datasets - train_data = load_data(os.path.join(data_dir, 'train/D1_train.jsonl')) - train_labels = load_data(os.path.join(data_dir, 'train/D1_train-truth.jsonl'))['label'] - - # Preprocess the content - train_data['url'] = train_data['url'].apply(preprocess) - - # Feature extraction and classifier pipeline - pipeline = Pipeline([ - ('tfidf', TfidfVectorizer()), - ('clf', SGDClassifier()) - ]) - - # Train the model - pipeline.fit(train_data['url'], train_labels) - - # Save the trained model - joblib.dump(pipeline, model_output) - -if __name__ == "__main__": - args = parse_args() - main(args.data_dir, args.model_output) +#!/usr/bin/env python3 +import argparse +import jsonlines +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.linear_model import SGDClassifier +from sklearn.pipeline import Pipeline +import os +import joblib + +def load_data(file_path): + with jsonlines.open(file_path) as reader: + for obj in reader: + obj_keys = obj.keys() + break + data = {k: [] for k in obj_keys} + with jsonlines.open(file_path) as reader: + for obj in reader: + for k in obj_keys: + data[k].append(obj[k]) + return pd.DataFrame(data) + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a webpage classification model') + parser.add_argument("-d", "--data_dir", help="Path to the directory containing the data subfolders.", required=True) + parser.add_argument("-m", "--model_output", help="Path to save the trained model.", required=True) + return parser.parse_args() + +def preprocess(content): + # Placeholder for the content preprocessing + return content + +def main(data_dir, model_output): + # Load datasets + train_data = load_data(os.path.join(data_dir, 'train/D1_train.jsonl')) + train_labels = load_data(os.path.join(data_dir, 'train/D1_train-truth.jsonl'))['label'] + + # Preprocess the content + train_data['url'] = train_data['url'].apply(preprocess) + + # Feature extraction and classifier pipeline + pipeline = Pipeline([ + ('tfidf', TfidfVectorizer()), + ('clf', SGDClassifier()) + ]) + + # Train the model + pipeline.fit(train_data['url'], train_labels) + + # Save the trained model + joblib.dump(pipeline, model_output) + +if __name__ == "__main__": + args = parse_args() + main(args.data_dir, args.model_output) diff --git a/irixys23/webpage-classification/baselines/snorkel-baseline/Dockerfile b/irixys23/webpage-classification/baselines/snorkel-baseline/Dockerfile index 19956fa..1660b1d 100644 --- a/irixys23/webpage-classification/baselines/snorkel-baseline/Dockerfile +++ b/irixys23/webpage-classification/baselines/snorkel-baseline/Dockerfile @@ -1,15 +1,15 @@ -FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime - -ADD requirements.txt /requirements.txt - -RUN pip3 install -r /requirements.txt \ - && rm -Rf /var/cache/apt \ - && rm -Rf /requirements.txt \ - && rm -Rf /root/.cache/pip - -ENV PYTHONPATH=/app - -ADD * /app/ - -ENTRYPOINT [ "python3", "/app/snorkel_baseline.py", "-i", "$inputDataset/inputs.jsonl", "-o", "$outputDir/predictions.jsonl", "-m", "/app/snrkl_model.pkl" ] - +FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime + +ADD requirements.txt /requirements.txt + +RUN pip3 install -r /requirements.txt \ + && rm -Rf /var/cache/apt \ + && rm -Rf /requirements.txt \ + && rm -Rf /root/.cache/pip + +ENV PYTHONPATH=/app + +ADD * /app/ + +ENTRYPOINT [ "python3", "/app/snorkel_baseline.py", "-i", "$inputDataset/inputs.jsonl", "-o", "$outputDir/predictions.jsonl", "-m", "/app/snrkl_model.pkl" ] + diff --git a/irixys23/webpage-classification/baselines/snorkel-baseline/README.md b/irixys23/webpage-classification/baselines/snorkel-baseline/README.md index 324406c..75aa8d2 100644 --- a/irixys23/webpage-classification/baselines/snorkel-baseline/README.md +++ b/irixys23/webpage-classification/baselines/snorkel-baseline/README.md @@ -1,19 +1,19 @@ -# Snorkel Baseline for The Webpage Classification Task - -This is a snorkel baseline for the webpage classification task. - -## Run this baseline: - -To run this baseline with tira-run, please execute: - -``` -tira-run --image webis/irixys23:snorkel-baseline --input-dataset webpage-classification/tiny-sample-20231023-training -``` - - -## Development - -``` -docker build -t webis/irixys23:snorkel-baseline . -docker push webis/irixys23:snorkel-baseline -``` +# Snorkel Baseline for The Webpage Classification Task + +This is a snorkel baseline for the webpage classification task. + +## Run this baseline: + +To run this baseline with tira-run, please execute: + +``` +tira-run --image webis/irixys23:snorkel-baseline --input-dataset webpage-classification/tiny-sample-20231023-training +``` + + +## Development + +``` +docker build -t webis/irixys23:snorkel-baseline . +docker push webis/irixys23:snorkel-baseline +``` diff --git a/irixys23/webpage-classification/baselines/snorkel-baseline/requirements.txt b/irixys23/webpage-classification/baselines/snorkel-baseline/requirements.txt index 68d29b7..e17397a 100644 --- a/irixys23/webpage-classification/baselines/snorkel-baseline/requirements.txt +++ b/irixys23/webpage-classification/baselines/snorkel-baseline/requirements.txt @@ -1,3 +1,3 @@ -joblib -jsonlines -snorkel +joblib +jsonlines +snorkel diff --git a/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline.py b/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline.py index 6fd3484..414c3e3 100644 --- a/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline.py +++ b/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline.py @@ -1,63 +1,63 @@ -#!/usr/bin/env python3 -import argparse -import jsonlines -import pandas as pd -import joblib -import os -import numpy as np -from snorkel_baseline_train import label_names, ABSTAIN, BENIGN, MALICIOUS, ADULT, get_snorkel_pandas_lf_applier, load_data - - -def predict_with_tie_break(label_model, L, tie_break_label=BENIGN): - # Get the probabilistic predictions - probas = label_model.predict_proba(L) - - # Initialize an array to store the final predictions - predictions = -1 * np.ones(L.shape[0]) - - # Iterate over each item - for i, prob in enumerate(probas): - # Find the maximum probability - max_prob = max(prob) - - # Check if there's a tie - if (prob == max_prob).sum() > 1: - # If there's a tie, predict BENIGN - predictions[i] = tie_break_label - else: - # Otherwise, choose the label with the highest probability - predictions[i] = np.argmax(prob) - - return predictions - -def parse_args(): - parser = argparse.ArgumentParser(description='Make predictions using a trained Snorkel labeling model') - parser.add_argument("-i", "--input_data", help="Path to the JSONL file for making predictions.", required=True) - parser.add_argument("-m", "--model", help="Path to the trained model.", required=True) - parser.add_argument("-o", "--output", help="Path to save the predictions.", required=True) - return parser.parse_args() - -def main(input_data, model_path, output_file): - # Load the test data - test_data = load_data(input_data) - - # Apply the labeling functions to the test dataset - applier = get_snorkel_pandas_lf_applier() - L_test = applier.apply(df=test_data) - - # Load the trained label model - label_model = joblib.load(model_path) - - # Use the custom prediction function with tie-break handling - test_predictions = predict_with_tie_break(label_model, L_test) - - # Map numeric labels to string labels - test_predictions_mapped = [label_names.get(label, 'Unknown') for label in test_predictions] - - # Save the predictions - test_data['prediction'] = test_predictions_mapped - test_data[['uid', 'prediction']].to_json(output_file, orient='records', lines=True) - -if __name__ == "__main__": - args = parse_args() - main(args.input_data, args.model, args.output) +#!/usr/bin/env python3 +import argparse +import jsonlines +import pandas as pd +import joblib +import os +import numpy as np +from snorkel_baseline_train import label_names, ABSTAIN, BENIGN, MALICIOUS, ADULT, get_snorkel_pandas_lf_applier, load_data + + +def predict_with_tie_break(label_model, L, tie_break_label=BENIGN): + # Get the probabilistic predictions + probas = label_model.predict_proba(L) + + # Initialize an array to store the final predictions + predictions = -1 * np.ones(L.shape[0]) + + # Iterate over each item + for i, prob in enumerate(probas): + # Find the maximum probability + max_prob = max(prob) + + # Check if there's a tie + if (prob == max_prob).sum() > 1: + # If there's a tie, predict BENIGN + predictions[i] = tie_break_label + else: + # Otherwise, choose the label with the highest probability + predictions[i] = np.argmax(prob) + + return predictions + +def parse_args(): + parser = argparse.ArgumentParser(description='Make predictions using a trained Snorkel labeling model') + parser.add_argument("-i", "--input_data", help="Path to the JSONL file for making predictions.", required=True) + parser.add_argument("-m", "--model", help="Path to the trained model.", required=True) + parser.add_argument("-o", "--output", help="Path to save the predictions.", required=True) + return parser.parse_args() + +def main(input_data, model_path, output_file): + # Load the test data + test_data = load_data(input_data) + + # Apply the labeling functions to the test dataset + applier = get_snorkel_pandas_lf_applier() + L_test = applier.apply(df=test_data) + + # Load the trained label model + label_model = joblib.load(model_path) + + # Use the custom prediction function with tie-break handling + test_predictions = predict_with_tie_break(label_model, L_test) + + # Map numeric labels to string labels + test_predictions_mapped = [label_names.get(label, 'Unknown') for label in test_predictions] + + # Save the predictions + test_data['prediction'] = test_predictions_mapped + test_data[['uid', 'prediction']].to_json(output_file, orient='records', lines=True) + +if __name__ == "__main__": + args = parse_args() + main(args.input_data, args.model, args.output) diff --git a/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline_train.py b/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline_train.py index cdcc18d..a6a4a7d 100644 --- a/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline_train.py +++ b/irixys23/webpage-classification/baselines/snorkel-baseline/snorkel_baseline_train.py @@ -1,275 +1,275 @@ -#!/usr/bin/env python3 -import argparse -import jsonlines -import pandas as pd -from snorkel.labeling import LabelingFunction, PandasLFApplier, LFAnalysis -from snorkel.labeling.model import LabelModel -import os -import joblib - -# Constants for the labels -ABSTAIN = -1 -BENIGN = 0 -MALICIOUS = 1 -ADULT = 2 - -# Label mapping from numeric to string -label_names = {BENIGN: 'Benign', MALICIOUS: 'Malicious', ADULT: 'Adult'} - -def lf_educational_government_domains(row): - edu_gov_domains = ['.edu', '.gov'] - url = row['url'] - if any(url.endswith(domain) for domain in edu_gov_domains): - return BENIGN - return ABSTAIN - -def lf_news_websites(row): - news_domains = ['bbc.com', 'cnn.com', 'nytimes.com', 'theguardian.com'] - url = row['url'] - if any(domain in url for domain in news_domains): - return BENIGN - return ABSTAIN - -def lf_health_related(row): - health_keywords = ['health', 'medical', 'hospital', 'clinic', 'doctor'] - url = row['url'] - if any(keyword in url for keyword in health_keywords): - return BENIGN - return ABSTAIN - -def lf_educational_content(row): - edu_keywords = ['course', 'learn', 'education', 'school', 'university'] - url = row['url'] - if any(keyword in url for keyword in edu_keywords): - return BENIGN - return ABSTAIN - -def lf_tech_companies(row): - tech_domains = ['microsoft.com', 'apple.com', 'google.com', 'amazon.com'] - url = row['url'] - if any(domain in url for domain in tech_domains): - return BENIGN - return ABSTAIN - -def lf_family_kids_related(row): - family_keywords = ['family', 'kids', 'child', 'parenting', 'education'] - url = row['url'] - if any(keyword in url for keyword in family_keywords): - return BENIGN - return ABSTAIN - -def lf_cultural_artistic_content(row): - culture_art_keywords = ['museum', 'art', 'gallery', 'theatre', 'culture'] - url = row['url'] - if any(keyword in url for keyword in culture_art_keywords): - return BENIGN - return ABSTAIN - -def lf_major_retailers(row): - retailer_domains = ['ebay.com', 'amazon.com', 'walmart.com', 'target.com'] - url = row['url'] - if any(domain in url for domain in retailer_domains): - return BENIGN - return ABSTAIN - -def lf_government_services(row): - gov_keywords = ['gov', 'government', 'state', 'federal', 'official'] - url = row['url'] - if any(keyword in url for keyword in gov_keywords): - return BENIGN - return ABSTAIN - -def lf_sports_recreation(row): - sports_recreation_keywords = ['sport', 'fitness', 'gym', 'recreation', 'outdoor'] - url = row['url'] - if any(keyword in url for keyword in sports_recreation_keywords): - return BENIGN - return ABSTAIN - -def lf_explicit_adult_keywords(row): - explicit_keywords = ['nude', 'hot', 'erotic', 'escort', 'camgirl'] - url = row['url'] - if any(keyword in url for keyword in explicit_keywords): - return ADULT - return ABSTAIN - -def lf_age_restriction(row): - age_keywords = ['18+', 'adults-only', 'mature'] - url = row['url'] - if any(keyword in url for keyword in age_keywords): - return ADULT - return ABSTAIN - -def lf_adult_industry_domains(row): - adult_domains = ['.xxx', '.adult', '.sex'] - url = row['url'] - if any(url.endswith(domain) for domain in adult_domains): - return ADULT - return ABSTAIN - -def lf_adult_url_structure(row): - url = row['url'] - if '/adult/' in url or '/sex/' in url: - return ADULT - return ABSTAIN - -def lf_euphemisms_for_adult(row): - euphemisms = ['nsfw', 'afterdark', 'kinky'] - url = row['url'] - if any(euphemism in url for euphemism in euphemisms): - return ADULT - return ABSTAIN - -def lf_common_adult_content_keywords(row): - keywords = ['pornography', 'fetish', 'bdsm', 'swinger'] - url = row['url'] - if any(keyword in url for keyword in keywords): - return ADULT - return ABSTAIN - -def lf_sexual_innuendos(row): - innuendos = ['booty', 'babe', 'milf', 'daddy'] - url = row['url'] - if any(innuendo in url for innuendo in innuendos): - return ADULT - return ABSTAIN - -def lf_adult_product_references(row): - products = ['dildo', 'vibrator', 'lingerie', 'condom'] - url = row['url'] - if any(product in url for product in products): - return ADULT - return ABSTAIN - -def lf_explicit_usernames(row): - usernames = ['sexy', 'slutty', 'horny', 'naughty'] - url = row['url'] - if any(username in url for username in usernames): - return ADULT - return ABSTAIN - -def lf_adult_forums_chatrooms(row): - forums = ['chatroom', 'forum', 'webcam', 'livecam'] - url = row['url'] - if any(forum in url for forum in forums): - return ADULT - return ABSTAIN - -def lf_common_benign_domains(row): - common_domains = ['google.com', 'wikipedia.org', 'youtube.com'] - url = row['url'] - if any(domain in url for domain in common_domains): - return BENIGN - return ABSTAIN - -def lf_malicious_keywords(row): - malicious_keywords = ['hack', 'phish', 'malware', 'spyware'] - url = row['url'] - if any(keyword in url for keyword in malicious_keywords): - return MALICIOUS - return ABSTAIN - -def lf_adult_keywords(row): - adult_keywords = ['adult', 'sex', 'xxx', 'porn'] - url = row['url'] - if any(keyword in url for keyword in adult_keywords): - return ADULT - return ABSTAIN - -def lf_shortened_url(row): - shortened_services = ['bit.ly', 'goo.gl', 'tinyurl.com'] - url = row['url'] - if any(service in url for service in shortened_services): - return MALICIOUS # Often used for malicious purposes - return ABSTAIN - -def lf_long_url(row): - url = row['url'] - if len(url) > 100: # Arbitrary threshold - return MALICIOUS - return ABSTAIN - -def lf_non_standard_port(row): - url = row['url'] - if ':8080' in url or ':8000' in url: - return MALICIOUS - return ABSTAIN - -def lf_https_protocol(row): - url = row['url'] - if url.startswith('https://'): - return BENIGN - return ABSTAIN - -def lf_numerical_url(row): - url = row['url'] - if any(char.isdigit() for char in url): - return MALICIOUS - return ABSTAIN - -def lf_suspicious_subdomain(row): - suspicious_subdomains = ['secure-', 'account-', 'login-'] - url = row['url'] - if any(subdomain in url for subdomain in suspicious_subdomains): - return MALICIOUS - return ABSTAIN - -def lf_uncommon_tld(row): - uncommon_tlds = ['.biz', '.info', '.top'] - url = row['url'] - if any(url.endswith(tld) for tld in uncommon_tlds): - return MALICIOUS - return ABSTAIN - -def load_data(file_path): - with jsonlines.open(file_path) as reader: - for obj in reader: - obj_keys = obj.keys() - break - - data = {k: [] for k in obj_keys} - - with jsonlines.open(file_path) as reader: - for obj in reader: - for k in obj_keys: - data[k].append(obj[k]) - return pd.DataFrame(data) - -def get_snorkel_pandas_lf_applier(): - lfs = [lf_educational_government_domains, lf_news_websites, lf_health_related, - lf_educational_content, lf_tech_companies, lf_family_kids_related, - lf_cultural_artistic_content, lf_major_retailers, lf_government_services, - lf_sports_recreation, lf_explicit_adult_keywords, lf_age_restriction, lf_adult_industry_domains, - lf_adult_url_structure, lf_euphemisms_for_adult, lf_common_adult_content_keywords, - lf_sexual_innuendos, lf_adult_product_references, lf_explicit_usernames, lf_adult_forums_chatrooms, - lf_common_benign_domains, lf_malicious_keywords, lf_adult_keywords, - lf_shortened_url, lf_long_url, lf_non_standard_port, lf_https_protocol, - lf_numerical_url, lf_suspicious_subdomain, lf_uncommon_tld - ] - - return PandasLFApplier(lfs=[LabelingFunction(name=func.__name__, f=func) for func in lfs]) - -def parse_args(): - parser = argparse.ArgumentParser(description='Train a Snorkel labeling model') - parser.add_argument("-d", "--data_dir", help="Path to the directory containing the data subfolders.", required=True) - parser.add_argument("-m", "--model_output", help="Path to save the trained model.", required=True) - return parser.parse_args() - -def main(data_dir, model_output): - # Load datasets - train_data = load_data(os.path.join(data_dir, 'train/D1_train.jsonl')) - - # Apply the labeling functions to the datasets - applier = get_snorkel_pandas_lf_applier() - L_train = applier.apply(df=train_data) - - # Train the label model - label_model = LabelModel(cardinality=3, verbose=True) - label_model.fit(L_train, n_epochs=1000, log_freq=100, seed=123) - - # Save the trained label model - joblib.dump(label_model, model_output) - -if __name__ == "__main__": - args = parse_args() - main(args.data_dir, args.model_output) +#!/usr/bin/env python3 +import argparse +import jsonlines +import pandas as pd +from snorkel.labeling import LabelingFunction, PandasLFApplier, LFAnalysis +from snorkel.labeling.model import LabelModel +import os +import joblib + +# Constants for the labels +ABSTAIN = -1 +BENIGN = 0 +MALICIOUS = 1 +ADULT = 2 + +# Label mapping from numeric to string +label_names = {BENIGN: 'Benign', MALICIOUS: 'Malicious', ADULT: 'Adult'} + +def lf_educational_government_domains(row): + edu_gov_domains = ['.edu', '.gov'] + url = row['url'] + if any(url.endswith(domain) for domain in edu_gov_domains): + return BENIGN + return ABSTAIN + +def lf_news_websites(row): + news_domains = ['bbc.com', 'cnn.com', 'nytimes.com', 'theguardian.com'] + url = row['url'] + if any(domain in url for domain in news_domains): + return BENIGN + return ABSTAIN + +def lf_health_related(row): + health_keywords = ['health', 'medical', 'hospital', 'clinic', 'doctor'] + url = row['url'] + if any(keyword in url for keyword in health_keywords): + return BENIGN + return ABSTAIN + +def lf_educational_content(row): + edu_keywords = ['course', 'learn', 'education', 'school', 'university'] + url = row['url'] + if any(keyword in url for keyword in edu_keywords): + return BENIGN + return ABSTAIN + +def lf_tech_companies(row): + tech_domains = ['microsoft.com', 'apple.com', 'google.com', 'amazon.com'] + url = row['url'] + if any(domain in url for domain in tech_domains): + return BENIGN + return ABSTAIN + +def lf_family_kids_related(row): + family_keywords = ['family', 'kids', 'child', 'parenting', 'education'] + url = row['url'] + if any(keyword in url for keyword in family_keywords): + return BENIGN + return ABSTAIN + +def lf_cultural_artistic_content(row): + culture_art_keywords = ['museum', 'art', 'gallery', 'theatre', 'culture'] + url = row['url'] + if any(keyword in url for keyword in culture_art_keywords): + return BENIGN + return ABSTAIN + +def lf_major_retailers(row): + retailer_domains = ['ebay.com', 'amazon.com', 'walmart.com', 'target.com'] + url = row['url'] + if any(domain in url for domain in retailer_domains): + return BENIGN + return ABSTAIN + +def lf_government_services(row): + gov_keywords = ['gov', 'government', 'state', 'federal', 'official'] + url = row['url'] + if any(keyword in url for keyword in gov_keywords): + return BENIGN + return ABSTAIN + +def lf_sports_recreation(row): + sports_recreation_keywords = ['sport', 'fitness', 'gym', 'recreation', 'outdoor'] + url = row['url'] + if any(keyword in url for keyword in sports_recreation_keywords): + return BENIGN + return ABSTAIN + +def lf_explicit_adult_keywords(row): + explicit_keywords = ['nude', 'hot', 'erotic', 'escort', 'camgirl'] + url = row['url'] + if any(keyword in url for keyword in explicit_keywords): + return ADULT + return ABSTAIN + +def lf_age_restriction(row): + age_keywords = ['18+', 'adults-only', 'mature'] + url = row['url'] + if any(keyword in url for keyword in age_keywords): + return ADULT + return ABSTAIN + +def lf_adult_industry_domains(row): + adult_domains = ['.xxx', '.adult', '.sex'] + url = row['url'] + if any(url.endswith(domain) for domain in adult_domains): + return ADULT + return ABSTAIN + +def lf_adult_url_structure(row): + url = row['url'] + if '/adult/' in url or '/sex/' in url: + return ADULT + return ABSTAIN + +def lf_euphemisms_for_adult(row): + euphemisms = ['nsfw', 'afterdark', 'kinky'] + url = row['url'] + if any(euphemism in url for euphemism in euphemisms): + return ADULT + return ABSTAIN + +def lf_common_adult_content_keywords(row): + keywords = ['pornography', 'fetish', 'bdsm', 'swinger'] + url = row['url'] + if any(keyword in url for keyword in keywords): + return ADULT + return ABSTAIN + +def lf_sexual_innuendos(row): + innuendos = ['booty', 'babe', 'milf', 'daddy'] + url = row['url'] + if any(innuendo in url for innuendo in innuendos): + return ADULT + return ABSTAIN + +def lf_adult_product_references(row): + products = ['dildo', 'vibrator', 'lingerie', 'condom'] + url = row['url'] + if any(product in url for product in products): + return ADULT + return ABSTAIN + +def lf_explicit_usernames(row): + usernames = ['sexy', 'slutty', 'horny', 'naughty'] + url = row['url'] + if any(username in url for username in usernames): + return ADULT + return ABSTAIN + +def lf_adult_forums_chatrooms(row): + forums = ['chatroom', 'forum', 'webcam', 'livecam'] + url = row['url'] + if any(forum in url for forum in forums): + return ADULT + return ABSTAIN + +def lf_common_benign_domains(row): + common_domains = ['google.com', 'wikipedia.org', 'youtube.com'] + url = row['url'] + if any(domain in url for domain in common_domains): + return BENIGN + return ABSTAIN + +def lf_malicious_keywords(row): + malicious_keywords = ['hack', 'phish', 'malware', 'spyware'] + url = row['url'] + if any(keyword in url for keyword in malicious_keywords): + return MALICIOUS + return ABSTAIN + +def lf_adult_keywords(row): + adult_keywords = ['adult', 'sex', 'xxx', 'porn'] + url = row['url'] + if any(keyword in url for keyword in adult_keywords): + return ADULT + return ABSTAIN + +def lf_shortened_url(row): + shortened_services = ['bit.ly', 'goo.gl', 'tinyurl.com'] + url = row['url'] + if any(service in url for service in shortened_services): + return MALICIOUS # Often used for malicious purposes + return ABSTAIN + +def lf_long_url(row): + url = row['url'] + if len(url) > 100: # Arbitrary threshold + return MALICIOUS + return ABSTAIN + +def lf_non_standard_port(row): + url = row['url'] + if ':8080' in url or ':8000' in url: + return MALICIOUS + return ABSTAIN + +def lf_https_protocol(row): + url = row['url'] + if url.startswith('https://'): + return BENIGN + return ABSTAIN + +def lf_numerical_url(row): + url = row['url'] + if any(char.isdigit() for char in url): + return MALICIOUS + return ABSTAIN + +def lf_suspicious_subdomain(row): + suspicious_subdomains = ['secure-', 'account-', 'login-'] + url = row['url'] + if any(subdomain in url for subdomain in suspicious_subdomains): + return MALICIOUS + return ABSTAIN + +def lf_uncommon_tld(row): + uncommon_tlds = ['.biz', '.info', '.top'] + url = row['url'] + if any(url.endswith(tld) for tld in uncommon_tlds): + return MALICIOUS + return ABSTAIN + +def load_data(file_path): + with jsonlines.open(file_path) as reader: + for obj in reader: + obj_keys = obj.keys() + break + + data = {k: [] for k in obj_keys} + + with jsonlines.open(file_path) as reader: + for obj in reader: + for k in obj_keys: + data[k].append(obj[k]) + return pd.DataFrame(data) + +def get_snorkel_pandas_lf_applier(): + lfs = [lf_educational_government_domains, lf_news_websites, lf_health_related, + lf_educational_content, lf_tech_companies, lf_family_kids_related, + lf_cultural_artistic_content, lf_major_retailers, lf_government_services, + lf_sports_recreation, lf_explicit_adult_keywords, lf_age_restriction, lf_adult_industry_domains, + lf_adult_url_structure, lf_euphemisms_for_adult, lf_common_adult_content_keywords, + lf_sexual_innuendos, lf_adult_product_references, lf_explicit_usernames, lf_adult_forums_chatrooms, + lf_common_benign_domains, lf_malicious_keywords, lf_adult_keywords, + lf_shortened_url, lf_long_url, lf_non_standard_port, lf_https_protocol, + lf_numerical_url, lf_suspicious_subdomain, lf_uncommon_tld + ] + + return PandasLFApplier(lfs=[LabelingFunction(name=func.__name__, f=func) for func in lfs]) + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a Snorkel labeling model') + parser.add_argument("-d", "--data_dir", help="Path to the directory containing the data subfolders.", required=True) + parser.add_argument("-m", "--model_output", help="Path to save the trained model.", required=True) + return parser.parse_args() + +def main(data_dir, model_output): + # Load datasets + train_data = load_data(os.path.join(data_dir, 'train/D1_train.jsonl')) + + # Apply the labeling functions to the datasets + applier = get_snorkel_pandas_lf_applier() + L_train = applier.apply(df=train_data) + + # Train the label model + label_model = LabelModel(cardinality=3, verbose=True) + label_model.fit(L_train, n_epochs=1000, log_freq=100, seed=123) + + # Save the trained label model + joblib.dump(label_model, model_output) + +if __name__ == "__main__": + args = parse_args() + main(args.data_dir, args.model_output) diff --git a/irixys23/webpage-classification/baselines/snorkel-jupyter-baseline/snorkel_tutorial.ipynb b/irixys23/webpage-classification/baselines/snorkel-jupyter-baseline/snorkel_tutorial.ipynb index e5a6298..f72de4e 100644 --- a/irixys23/webpage-classification/baselines/snorkel-jupyter-baseline/snorkel_tutorial.ipynb +++ b/irixys23/webpage-classification/baselines/snorkel-jupyter-baseline/snorkel_tutorial.ipynb @@ -1,744 +1,744 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "NYByJ-703szv" - }, - "source": [ - "The main aim of Snorkel is to allow to create more training data, because labelling large amounts of data manually is very hard and requires a lot of effort. Snorkel is using labelling functions to classify the data in different categories. Those labelling functions represent noisy, programmatic rules and heuristics that assign labels to unlabeled training data." - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0uAxCV7U-BJW", - "outputId": "a6d9fd7e-dcc0-47f9-a4a3-824f6475bfe8" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: snorkel in /usr/local/lib/python3.10/dist-packages (0.9.9)\n", - "Requirement already satisfied: munkres>=1.0.6 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.1.4)\n", - "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.23.5)\n", - "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.11.3)\n", - "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.5.3)\n", - "Requirement already satisfied: tqdm>=4.33.0 in /usr/local/lib/python3.10/dist-packages (from snorkel) (4.66.1)\n", - 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"Requirement already satisfied: jsonlines in /usr/local/lib/python3.10/dist-packages (4.0.0)\n", - "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonlines) (23.1.0)\n" - ] - } - ], - "source": [ - "!pip install snorkel\n", - "!pip install jsonlines" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": { - "id": "uCN6oPLs-gBC" - }, - "outputs": [], - "source": [ - "#importing all the necessary packages\n", - "import snorkel\n", - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "from snorkel.labeling import labeling_function" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PXqrDaPO1o0T" - }, - "source": [ - "As an example, we load train and validation data for spam detection here" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": { - "id": "PDAl6OOqBnoh" - }, - "outputs": [], - "source": [ - "file_train = '/content/drive/MyDrive/data_colab/D1_train.jsonl'\n", - "file_val = '/content/drive/MyDrive/data_colab/D1_validation.jsonl'\n", - "val_truth = '/content/drive/MyDrive/data_colab/D1_validation-truth.jsonl'\n", - "\n", - "def load_data(file_path):\n", - " data = []\n", - " with open(file_path, 'r') as file:\n", - " for line in file:\n", - " json_data = json.loads(line)\n", - " data.append(json_data)\n", - " df = pd.DataFrame(data)\n", - " return df\n" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "id": "Dr5YoMxPJT1L" - }, - "outputs": [], - "source": [ - "#loading train and validation data\n", - "df_train = load_data(file_train)\n", - "df_val = load_data(file_val)\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Usually, Snorkel returns -1 value for ABSTAIN category and 0 or 1 for labels, but, as we have 3 labels, we use 0, 1 and 2." - ], - "metadata": { - "id": "ahpPPs06EiYu" - } - }, - { - "cell_type": "code", - "source": [ - "ABSTAIN = -1\n", - "BENIGN = 0\n", - "MALICIOUS = 1\n", - "ADULT = 2" - ], - "metadata": { - "id": "U5y0BFLZEfQ9" - }, - "execution_count": 175, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dPf7je9R10ca" - }, - "source": [ - "We replace the labels by numerical values" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": { - "id": "tdL9cmm2B3Lx" - }, - "outputs": [], - "source": [ - "df_val_t = load_data(val_truth)\n", - "df_val_t = df_val_t.replace({'Benign': 0, 'Malicious': 1, 'Adult': 2})\n", - "\n", - "# we check if all the labeled items are in data\n", - "list_of_values = df_val['uid'].to_list()\n", - "df_val_t = df_val_t[df_val_t['uid'].isin(list_of_values)]\n", - "df_val_t\n", - "val_true = np.array(df_val_t['label'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FXTFmOF_6SQh" - }, - "source": [ - "Labeling functions in Snorkel are created with the @labeling_function decorator. In this case, the labelling function uses the keywords that are present in the urls and indicate that the content is adult, malicious or benign. The functions can search in different parts of the data (such as URL and html content)." - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": { - "id": "ZmrzTtOHcIBB" - }, - "outputs": [], - "source": [ - "@labeling_function()\n", - "def lf_educational_content(x):\n", - " edu_keywords = ['academic', 'research', 'conference', 'student','school','education', 'university']\n", - " url = x['url']\n", - " if any(keyword in url for keyword in edu_keywords):\n", - " return BENIGN\n", - " return ABSTAIN" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": { - "id": "2kwPcjmDcTQh" - }, - "outputs": [], - "source": [ - "@labeling_function()\n", - "def lf_sexual_innuendos(x):\n", - " innuendos = ['booty', 'babe', 'milf', 'daddy','chick']\n", - " url = x['url']\n", - " if any(innuendo in url for innuendo in innuendos):\n", - " return ADULT\n", - " return ABSTAIN" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": { - "id": "zvLxi5jicfyq" - }, - "outputs": [], - "source": [ - "@labeling_function()\n", - "def lf_malicious_keywords(x):\n", - " malicious_keywords = ['hack', 'phish', 'malware', 'spyware']\n", - " url = x['url']\n", - " if any(keyword in url for keyword in malicious_keywords):\n", - " return MALICIOUS\n", - " return ABSTAIN\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "This function searches in html content instead of URL" - ], - "metadata": { - "id": "dD_3XC5zPdFN" - } - }, - { - "cell_type": "code", - "source": [ - "@labeling_function()\n", - "def lf_adult_content(x):\n", - " adult_keywords = ['sex', 'porn', 'hot','erotic']\n", - " html = x['html']\n", - " if any(keyword in html for keyword in adult_keywords):\n", - " return ADULT\n", - " return ABSTAIN" - ], - "metadata": { - "id": "LnLVYSkDNCiq" - }, - "execution_count": 180, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "To apply one or more labelling functions that we’ve written to a collection of data points, we use an LFApplier. Because our data points are represented with a Pandas DataFrame, we use the PandasLFApplier. Correspondingly, a single data point x that’s passed into our LFs will be a Pandas Series object." - ], - "metadata": { - "id": "Uw0Dcf9LFmjE" - } - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "R0DSfvhvdCnz", - "outputId": "18e4c6fd-8dce-4adb-8f0d-1944b4c41d3b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 19783/19783 [00:07<00:00, 2657.97it/s]\n", - "100%|██████████| 2936/2936 [00:01<00:00, 1945.79it/s]\n" - ] - } - ], - "source": [ - "from snorkel.labeling import PandasLFApplier\n", - "\n", - "lfs = [lf_educational_content, lf_sexual_innuendos,lf_malicious_keywords,lf_adult_content]\n", - "\n", - "applier = PandasLFApplier(lfs=lfs)\n", - "L_train = applier.apply(df=df_train)\n", - "L_val = applier.apply(df=df_val)\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Here we have statistics about the labelling of our labelling functions. Polarity means what labels and how many labels does a function return (for example, Malicious and Abstain). Coverage indicates how much dataset is labeled by this function. Overlaps is when several functions give the same label to the data. Conflicts is when different functions labeled the same data differently." - ], - "metadata": { - "id": "SEk_fc3CGmzI" - } - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 175 - }, - "id": "1lwv7uEEe4Xs", - "outputId": "724c8b18-b54d-4314-e977-790dbd591d13" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " j Polarity Coverage Overlaps Conflicts\n", - "lf_educational_content 0 [0] 0.006723 0.003336 0.003336\n", - "lf_sexual_innuendos 1 [2] 0.012536 0.012384 0.000051\n", - "lf_malicious_keywords 2 [1] 0.001415 0.000202 0.000202\n", - "lf_adult_content 3 [2] 0.449527 0.015872 0.003538" - ], - "text/html": [ - "\n", - "
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jPolarityCoverageOverlapsConflicts
lf_educational_content0[0]0.0067230.0033360.003336
lf_sexual_innuendos1[2]0.0125360.0123840.000051
lf_malicious_keywords2[1]0.0014150.0002020.000202
lf_adult_content3[2]0.4495270.0158720.003538
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\n" - ] - }, - "metadata": {}, - "execution_count": 182 - } - ], - "source": [ - "from snorkel.labeling import LFAnalysis\n", - "\n", - "LFAnalysis(L=L_train, lfs=lfs).lf_summary()\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "The LabelModel is able to learn weights for the labeling functions using only the label matrix as input. We also specify the cardinality, or number of classes." - ], - "metadata": { - "id": "FdCNis5qK8f3" - } - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "T8d3f67PhvbZ", - "outputId": "ddf8d137-461d-414a-cfd3-25c9c1a15e65" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 500/500 [00:01<00:00, 293.99epoch/s]\n" - ] - } - ], - "source": [ - "from snorkel.labeling.model import LabelModel\n", - "\n", - "label_model = LabelModel(cardinality=3, verbose=True)\n", - "label_model.fit(L_train=L_val, n_epochs=500, log_freq=100, seed=123)\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "The majority vote model uses the information about how many functions predict that this data will have this label and how many predict that this data will have a different label. The final label is determined by the majority: if 3 functions predict that it will be a label 'a' and 2 that if will be a label 'b', the final label will be 'a'" - ], - "metadata": { - "id": "HZ5IsZWJLWsq" - } - }, - { - "cell_type": "code", - "source": [ - "from snorkel.labeling.model import MajorityLabelVoter\n", - "\n", - "majority_model = MajorityLabelVoter(cardinality=3, verbose=True)\n", - "preds_train = majority_model.predict(L=L_train)\n" - ], - "metadata": { - "id": "-8Tuv-a4L0Pd" - }, - "execution_count": 184, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Here we can see, what accuracy scores we get. For some tasks label model is better, for some majority label voter is better: for you to find it out." - ], - "metadata": { - "id": "Jce7tCSeN9CE" - } - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "G3LOuX1th_Kv", - "outputId": "83ccf7e2-d78b-4e0e-d0b9-79c84be52eaa" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Label Model Accuracy: 25.4%\n", - "Majority Vote Accuracy: 25.8%\n" - ] - } - ], - "source": [ - "label_model_acc = label_model.score(L=L_val, Y=val_true, tie_break_policy=\"random\")[\n", - " \"accuracy\"\n", - "]\n", - "print(f\"{'Label Model Accuracy:'} {label_model_acc * 100:.1f}%\")\n", - "\n", - "majority_acc = majority_model.score(L=L_val, Y=val_true, tie_break_policy=\"random\")[\n", - " \"accuracy\"\n", - "]\n", - "print(f\"{'Majority Vote Accuracy:'} {majority_acc * 100:.1f}%\")" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "NYByJ-703szv" + }, + "source": [ + "The main aim of Snorkel is to allow to create more training data, because labelling large amounts of data manually is very hard and requires a lot of effort. Snorkel is using labelling functions to classify the data in different categories. Those labelling functions represent noisy, programmatic rules and heuristics that assign labels to unlabeled training data." + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0uAxCV7U-BJW", + "outputId": "a6d9fd7e-dcc0-47f9-a4a3-824f6475bfe8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: snorkel in /usr/local/lib/python3.10/dist-packages (0.9.9)\n", + "Requirement already satisfied: munkres>=1.0.6 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.1.4)\n", + "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.23.5)\n", + "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.11.3)\n", + "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from snorkel) (1.5.3)\n", + 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in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard>=2.9.1->snorkel) (2.1.3)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.2.0->snorkel) (1.3.0)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.9.1->snorkel) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.9.1->snorkel) (3.2.2)\n", + "Requirement already satisfied: jsonlines in /usr/local/lib/python3.10/dist-packages (4.0.0)\n", + "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonlines) (23.1.0)\n" + ] + } + ], + "source": [ + "!pip install snorkel\n", + "!pip install jsonlines" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "id": "uCN6oPLs-gBC" + }, + "outputs": [], + "source": [ + "#importing all the necessary packages\n", + "import snorkel\n", + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "from snorkel.labeling import labeling_function" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PXqrDaPO1o0T" + }, + "source": [ + "As an example, we load train and validation data for spam detection here" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": { + "id": "PDAl6OOqBnoh" + }, + "outputs": [], + "source": [ + "file_train = '/content/drive/MyDrive/data_colab/D1_train.jsonl'\n", + "file_val = '/content/drive/MyDrive/data_colab/D1_validation.jsonl'\n", + "val_truth = '/content/drive/MyDrive/data_colab/D1_validation-truth.jsonl'\n", + "\n", + "def load_data(file_path):\n", + " data = []\n", + " with open(file_path, 'r') as file:\n", + " for line in file:\n", + " json_data = json.loads(line)\n", + " data.append(json_data)\n", + " df = pd.DataFrame(data)\n", + " return df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "id": "Dr5YoMxPJT1L" + }, + "outputs": [], + "source": [ + "#loading train and validation data\n", + "df_train = load_data(file_train)\n", + "df_val = load_data(file_val)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Usually, Snorkel returns -1 value for ABSTAIN category and 0 or 1 for labels, but, as we have 3 labels, we use 0, 1 and 2." + ], + "metadata": { + "id": "ahpPPs06EiYu" + } + }, + { + "cell_type": "code", + "source": [ + "ABSTAIN = -1\n", + "BENIGN = 0\n", + "MALICIOUS = 1\n", + "ADULT = 2" + ], + "metadata": { + "id": "U5y0BFLZEfQ9" + }, + "execution_count": 175, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dPf7je9R10ca" + }, + "source": [ + "We replace the labels by numerical values" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": { + "id": "tdL9cmm2B3Lx" + }, + "outputs": [], + "source": [ + "df_val_t = load_data(val_truth)\n", + "df_val_t = df_val_t.replace({'Benign': 0, 'Malicious': 1, 'Adult': 2})\n", + "\n", + "# we check if all the labeled items are in data\n", + "list_of_values = df_val['uid'].to_list()\n", + "df_val_t = df_val_t[df_val_t['uid'].isin(list_of_values)]\n", + "df_val_t\n", + "val_true = np.array(df_val_t['label'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FXTFmOF_6SQh" + }, + "source": [ + "Labeling functions in Snorkel are created with the @labeling_function decorator. In this case, the labelling function uses the keywords that are present in the urls and indicate that the content is adult, malicious or benign. The functions can search in different parts of the data (such as URL and html content)." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "id": "ZmrzTtOHcIBB" + }, + "outputs": [], + "source": [ + "@labeling_function()\n", + "def lf_educational_content(x):\n", + " edu_keywords = ['academic', 'research', 'conference', 'student','school','education', 'university']\n", + " url = x['url']\n", + " if any(keyword in url for keyword in edu_keywords):\n", + " return BENIGN\n", + " return ABSTAIN" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "id": "2kwPcjmDcTQh" + }, + "outputs": [], + "source": [ + "@labeling_function()\n", + "def lf_sexual_innuendos(x):\n", + " innuendos = ['booty', 'babe', 'milf', 'daddy','chick']\n", + " url = x['url']\n", + " if any(innuendo in url for innuendo in innuendos):\n", + " return ADULT\n", + " return ABSTAIN" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "id": "zvLxi5jicfyq" + }, + "outputs": [], + "source": [ + "@labeling_function()\n", + "def lf_malicious_keywords(x):\n", + " malicious_keywords = ['hack', 'phish', 'malware', 'spyware']\n", + " url = x['url']\n", + " if any(keyword in url for keyword in malicious_keywords):\n", + " return MALICIOUS\n", + " return ABSTAIN\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "This function searches in html content instead of URL" + ], + "metadata": { + "id": "dD_3XC5zPdFN" + } + }, + { + "cell_type": "code", + "source": [ + "@labeling_function()\n", + "def lf_adult_content(x):\n", + " adult_keywords = ['sex', 'porn', 'hot','erotic']\n", + " html = x['html']\n", + " if any(keyword in html for keyword in adult_keywords):\n", + " return ADULT\n", + " return ABSTAIN" + ], + "metadata": { + "id": "LnLVYSkDNCiq" + }, + "execution_count": 180, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "To apply one or more labelling functions that we’ve written to a collection of data points, we use an LFApplier. Because our data points are represented with a Pandas DataFrame, we use the PandasLFApplier. Correspondingly, a single data point x that’s passed into our LFs will be a Pandas Series object." + ], + "metadata": { + "id": "Uw0Dcf9LFmjE" + } + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R0DSfvhvdCnz", + "outputId": "18e4c6fd-8dce-4adb-8f0d-1944b4c41d3b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 19783/19783 [00:07<00:00, 2657.97it/s]\n", + "100%|██████████| 2936/2936 [00:01<00:00, 1945.79it/s]\n" + ] + } + ], + "source": [ + "from snorkel.labeling import PandasLFApplier\n", + "\n", + "lfs = [lf_educational_content, lf_sexual_innuendos,lf_malicious_keywords,lf_adult_content]\n", + "\n", + "applier = PandasLFApplier(lfs=lfs)\n", + "L_train = applier.apply(df=df_train)\n", + "L_val = applier.apply(df=df_val)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here we have statistics about the labelling of our labelling functions. Polarity means what labels and how many labels does a function return (for example, Malicious and Abstain). Coverage indicates how much dataset is labeled by this function. Overlaps is when several functions give the same label to the data. Conflicts is when different functions labeled the same data differently." + ], + "metadata": { + "id": "SEk_fc3CGmzI" + } + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "id": "1lwv7uEEe4Xs", + "outputId": "724c8b18-b54d-4314-e977-790dbd591d13" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " j Polarity Coverage Overlaps Conflicts\n", + "lf_educational_content 0 [0] 0.006723 0.003336 0.003336\n", + "lf_sexual_innuendos 1 [2] 0.012536 0.012384 0.000051\n", + "lf_malicious_keywords 2 [1] 0.001415 0.000202 0.000202\n", + "lf_adult_content 3 [2] 0.449527 0.015872 0.003538" + ], + "text/html": [ + "\n", + "
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jPolarityCoverageOverlapsConflicts
lf_educational_content0[0]0.0067230.0033360.003336
lf_sexual_innuendos1[2]0.0125360.0123840.000051
lf_malicious_keywords2[1]0.0014150.0002020.000202
lf_adult_content3[2]0.4495270.0158720.003538
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\n" + ] + }, + "metadata": {}, + "execution_count": 182 + } + ], + "source": [ + "from snorkel.labeling import LFAnalysis\n", + "\n", + "LFAnalysis(L=L_train, lfs=lfs).lf_summary()\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The LabelModel is able to learn weights for the labeling functions using only the label matrix as input. We also specify the cardinality, or number of classes." + ], + "metadata": { + "id": "FdCNis5qK8f3" + } + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T8d3f67PhvbZ", + "outputId": "ddf8d137-461d-414a-cfd3-25c9c1a15e65" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 500/500 [00:01<00:00, 293.99epoch/s]\n" + ] + } + ], + "source": [ + "from snorkel.labeling.model import LabelModel\n", + "\n", + "label_model = LabelModel(cardinality=3, verbose=True)\n", + "label_model.fit(L_train=L_val, n_epochs=500, log_freq=100, seed=123)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The majority vote model uses the information about how many functions predict that this data will have this label and how many predict that this data will have a different label. The final label is determined by the majority: if 3 functions predict that it will be a label 'a' and 2 that if will be a label 'b', the final label will be 'a'" + ], + "metadata": { + "id": "HZ5IsZWJLWsq" + } + }, + { + "cell_type": "code", + "source": [ + "from snorkel.labeling.model import MajorityLabelVoter\n", + "\n", + "majority_model = MajorityLabelVoter(cardinality=3, verbose=True)\n", + "preds_train = majority_model.predict(L=L_train)\n" + ], + "metadata": { + "id": "-8Tuv-a4L0Pd" + }, + "execution_count": 184, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Here we can see, what accuracy scores we get. For some tasks label model is better, for some majority label voter is better: for you to find it out." + ], + "metadata": { + "id": "Jce7tCSeN9CE" + } + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G3LOuX1th_Kv", + "outputId": "83ccf7e2-d78b-4e0e-d0b9-79c84be52eaa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Label Model Accuracy: 25.4%\n", + "Majority Vote Accuracy: 25.8%\n" + ] + } + ], + "source": [ + "label_model_acc = label_model.score(L=L_val, Y=val_true, tie_break_policy=\"random\")[\n", + " \"accuracy\"\n", + "]\n", + "print(f\"{'Label Model Accuracy:'} {label_model_acc * 100:.1f}%\")\n", + "\n", + "majority_acc = majority_model.score(L=L_val, Y=val_true, tie_break_policy=\"random\")[\n", + " \"accuracy\"\n", + "]\n", + "print(f\"{'Majority Vote Accuracy:'} {majority_acc * 100:.1f}%\")" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/irixys23/webpage-classification/baselines/trivial-baseline/Dockerfile b/irixys23/webpage-classification/baselines/trivial-baseline/Dockerfile index f379a33..fdce941 100644 --- a/irixys23/webpage-classification/baselines/trivial-baseline/Dockerfile +++ b/irixys23/webpage-classification/baselines/trivial-baseline/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.11 - -ADD baseline.py /baseline.py - +FROM python:3.11 + +ADD baseline.py /baseline.py + diff --git a/irixys23/webpage-classification/baselines/trivial-baseline/README.md b/irixys23/webpage-classification/baselines/trivial-baseline/README.md index e0aa71f..8cf3a03 100644 --- a/irixys23/webpage-classification/baselines/trivial-baseline/README.md +++ b/irixys23/webpage-classification/baselines/trivial-baseline/README.md @@ -1,25 +1,25 @@ -# Trivial Baseline The Webpage Classification Task - -This is a trivial baseline for the webpage classification task that always predicts the passed label (e.g., `Benign` for --prediction Benign). - -## Run this baseline: - -To run this baseline with tira-run, please execute: - -``` -tira-run --image webis/irixys23:trivial-baseline --input-dataset webpage-classification/tiny-sample-20231023-training --command '/baseline.py -i $inputDataset/inputs.jsonl -o $outputDir/predictions.jsonl' -``` - -or, to always predict malicious, please execute: - -``` -tira-run --image webis/irixys23:trivial-baseline --input-dataset webpage-classification/tiny-sample-20231023-training --command '/baseline.py -i $inputDataset/inputs.jsonl -o $outputDir/predictions.jsonl --prediction Malicious' -``` - -## Development - -``` -docker build -t webis/irixys23:trivial-baseline . -docker push webis/irixys23:trivial-baseline -``` - +# Trivial Baseline The Webpage Classification Task + +This is a trivial baseline for the webpage classification task that always predicts the passed label (e.g., `Benign` for --prediction Benign). + +## Run this baseline: + +To run this baseline with tira-run, please execute: + +``` +tira-run --image webis/irixys23:trivial-baseline --input-dataset webpage-classification/tiny-sample-20231023-training --command '/baseline.py -i $inputDataset/inputs.jsonl -o $outputDir/predictions.jsonl' +``` + +or, to always predict malicious, please execute: + +``` +tira-run --image webis/irixys23:trivial-baseline --input-dataset webpage-classification/tiny-sample-20231023-training --command '/baseline.py -i $inputDataset/inputs.jsonl -o $outputDir/predictions.jsonl --prediction Malicious' +``` + +## Development + +``` +docker build -t webis/irixys23:trivial-baseline . +docker push webis/irixys23:trivial-baseline +``` + diff --git a/irixys23/webpage-classification/baselines/trivial-baseline/baseline.py b/irixys23/webpage-classification/baselines/trivial-baseline/baseline.py index c845d6e..ef8a777 100755 --- a/irixys23/webpage-classification/baselines/trivial-baseline/baseline.py +++ b/irixys23/webpage-classification/baselines/trivial-baseline/baseline.py @@ -1,31 +1,31 @@ -#!/usr/bin/env python3 -import argparse -import json - - -def parse_args(): - parser = argparse.ArgumentParser(description='Webpage classification baseline') - parser.add_argument("-i", "--input", help="Path to the .jsonl file containing the inputs.", required=True) - parser.add_argument("-o", "--output", help="Path to the file to write the results to.", required=True) - parser.add_argument("-p", "--prediction", help="The to-be-predicted label. This classifier always predicts the passed label", choices=["Adult", "Benign", "Malicious"], default="Benign") - return parser.parse_args() - - -def predict_class(web_page, prediction): - # In reality, here would come the interesting part. - # But in this baseline, we just return the label passed via --prediction - return prediction - - -def main(input_file, output_file, prediction): - with open(input_file, 'r') as inp, open(output_file, 'w+') as outp: - for l in inp: - l = json.loads(l) - output = {'uid': l['uid'], 'prediction': predict_class(l, prediction)} - outp.write(json.dumps(output) + '\n') - - -if __name__ == "__main__": - args = parse_args() - main(args.input, args.output, args.prediction) - +#!/usr/bin/env python3 +import argparse +import json + + +def parse_args(): + parser = argparse.ArgumentParser(description='Webpage classification baseline') + parser.add_argument("-i", "--input", help="Path to the .jsonl file containing the inputs.", required=True) + parser.add_argument("-o", "--output", help="Path to the file to write the results to.", required=True) + parser.add_argument("-p", "--prediction", help="The to-be-predicted label. This classifier always predicts the passed label", choices=["Adult", "Benign", "Malicious"], default="Benign") + return parser.parse_args() + + +def predict_class(web_page, prediction): + # In reality, here would come the interesting part. + # But in this baseline, we just return the label passed via --prediction + return prediction + + +def main(input_file, output_file, prediction): + with open(input_file, 'r') as inp, open(output_file, 'w+') as outp: + for l in inp: + l = json.loads(l) + output = {'uid': l['uid'], 'prediction': predict_class(l, prediction)} + outp.write(json.dumps(output) + '\n') + + +if __name__ == "__main__": + args = parse_args() + main(args.input, args.output, args.prediction) + diff --git a/irixys23/webpage-classification/clueweb-test-data/README.md b/irixys23/webpage-classification/clueweb-test-data/README.md index fdf547b..9a90875 100644 --- a/irixys23/webpage-classification/clueweb-test-data/README.md +++ b/irixys23/webpage-classification/clueweb-test-data/README.md @@ -1,13 +1,13 @@ -I took a random sample of 1000 clueweb09 and 1000 clueweb12 documents. - -From the [official spam rank documentation](https://plg.uwaterloo.ca/~gvcormac/clueweb09spam/): - -The percentile score indicates the percentage of the documents in the corpus that are "spammier." That is, the spammiest 1% of the documents have percentile-score=0, the next spammiest have percentile-score=1, and so on. The least spammy 1% have percentile-score=99. - -If you just want the best of the four sets of scores, choose Fusion. If you just want to label pages as spam or not, label those with percentile-score<70 to be spam, and the rest non-spam. For more details, read the paper - -Run it via: - -./step-02-create-dataset.py -i cw09-spam-ranks -o cw-09 --dataset-id clueweb09 - -./step-02-create-dataset.py -i cw12-spam-ranks -o cw-12 --dataset-id clueweb12 +I took a random sample of 1000 clueweb09 and 1000 clueweb12 documents. + +From the [official spam rank documentation](https://plg.uwaterloo.ca/~gvcormac/clueweb09spam/): + +The percentile score indicates the percentage of the documents in the corpus that are "spammier." That is, the spammiest 1% of the documents have percentile-score=0, the next spammiest have percentile-score=1, and so on. The least spammy 1% have percentile-score=99. + +If you just want the best of the four sets of scores, choose Fusion. If you just want to label pages as spam or not, label those with percentile-score<70 to be spam, and the rest non-spam. For more details, read the paper + +Run it via: + +./step-02-create-dataset.py -i cw09-spam-ranks -o cw-09 --dataset-id clueweb09 + +./step-02-create-dataset.py -i cw12-spam-ranks -o cw-12 --dataset-id clueweb12 diff --git a/irixys23/webpage-classification/clueweb-test-data/step-01-cw09-sample-documents.sh b/irixys23/webpage-classification/clueweb-test-data/step-01-cw09-sample-documents.sh index d0ac454..1882de6 100755 --- a/irixys23/webpage-classification/clueweb-test-data/step-01-cw09-sample-documents.sh +++ b/irixys23/webpage-classification/clueweb-test-data/step-01-cw09-sample-documents.sh @@ -1,4 +1,4 @@ -#!/usr/bin/sh - -cat /mnt/ceph/storage/corpora/corpora-thirdparty/corpus-clueweb09/spam-ranks/clueweb09spam.Fusion |shuf |head -1000 > cw09-spam-ranks - +#!/usr/bin/sh + +cat /mnt/ceph/storage/corpora/corpora-thirdparty/corpus-clueweb09/spam-ranks/clueweb09spam.Fusion |shuf |head -1000 > cw09-spam-ranks + diff --git a/irixys23/webpage-classification/clueweb-test-data/step-01-cw12-sample-documents.sh b/irixys23/webpage-classification/clueweb-test-data/step-01-cw12-sample-documents.sh index c5bc661..833581f 100755 --- a/irixys23/webpage-classification/clueweb-test-data/step-01-cw12-sample-documents.sh +++ b/irixys23/webpage-classification/clueweb-test-data/step-01-cw12-sample-documents.sh @@ -1,4 +1,4 @@ -#!/usr/bin/sh - -zcat /mnt/ceph/storage/corpora/corpora-thirdparty/corpus-clueweb12/corpus-waterloo-spam-cw12/waterloo-spam-cw12-decoded/* |shuf |head -1000 > cw12-spam-ranks - +#!/usr/bin/sh + +zcat /mnt/ceph/storage/corpora/corpora-thirdparty/corpus-clueweb12/corpus-waterloo-spam-cw12/waterloo-spam-cw12-decoded/* |shuf |head -1000 > cw12-spam-ranks + diff --git a/irixys23/webpage-classification/clueweb-test-data/step-02-create-dataset.py b/irixys23/webpage-classification/clueweb-test-data/step-02-create-dataset.py index 9662354..cb6dae3 100755 --- a/irixys23/webpage-classification/clueweb-test-data/step-02-create-dataset.py +++ b/irixys23/webpage-classification/clueweb-test-data/step-02-create-dataset.py @@ -1,46 +1,46 @@ -#!/usr/bin/env python3 -import ir_datasets -import argparse -import json -import os -import chardet -from tqdm import tqdm - - -def parse_args(): - parser = argparse.ArgumentParser(description='Create ClueWeb09/ClueWeb12 webpage classification datasets.') - parser.add_argument("-i", "--input", help="Path to spam rank file.", required=True) - parser.add_argument("-o", "--output", help="Path to the file where the outputs are to be stored.", required=True) - parser.add_argument("--dataset-id", help="The id of the ir_dataset.", required=True, choices=['clueweb09', 'clueweb12']) - return parser.parse_args() - -def decode(body): - encoding = chardet.detect(body)['encoding'] - if encoding: - return body.decode(encoding, errors='replace').replace('\uFFFD', '?') - - - return body.decode(errors='replace').replace('\uFFFD', '?') - -def main(input_file, output_file, ir_datasets_id): - dataset = ir_datasets.load(ir_datasets_id) - docs_store = dataset.docs_store() - - os.mkdir(output_file) - os.mkdir(output_file + '/input') - os.mkdir(output_file + '/truth') - with open(input_file, 'r') as inp, open(output_file + '/input/inputs.jsonl', 'w+') as resulting_input, open(output_file + '/truth/truths.jsonl', 'w+') as resulting_truth: - - for line in tqdm(inp): - spam_rank, doc_id = line.strip().split(' ') - # From the spam rank documentation: percentile-score<70 is spam, and the rest non-spam. - label = 'Benign' if int(spam_rank) >= 70 else 'Malicious' - doc = docs_store.get(doc_id) - resulting_truth.write(json.dumps({"uid": doc_id, "label": label}) + '\n') - resulting_input.write(json.dumps({"uid": doc_id, "url": doc.url, "html": decode(doc.body)}) + '\n') - - -if __name__ == '__main__': - args = parse_args() - main(args.input, args.output, args.dataset_id) - +#!/usr/bin/env python3 +import ir_datasets +import argparse +import json +import os +import chardet +from tqdm import tqdm + + +def parse_args(): + parser = argparse.ArgumentParser(description='Create ClueWeb09/ClueWeb12 webpage classification datasets.') + parser.add_argument("-i", "--input", help="Path to spam rank file.", required=True) + parser.add_argument("-o", "--output", help="Path to the file where the outputs are to be stored.", required=True) + parser.add_argument("--dataset-id", help="The id of the ir_dataset.", required=True, choices=['clueweb09', 'clueweb12']) + return parser.parse_args() + +def decode(body): + encoding = chardet.detect(body)['encoding'] + if encoding: + return body.decode(encoding, errors='replace').replace('\uFFFD', '?') + + + return body.decode(errors='replace').replace('\uFFFD', '?') + +def main(input_file, output_file, ir_datasets_id): + dataset = ir_datasets.load(ir_datasets_id) + docs_store = dataset.docs_store() + + os.mkdir(output_file) + os.mkdir(output_file + '/input') + os.mkdir(output_file + '/truth') + with open(input_file, 'r') as inp, open(output_file + '/input/inputs.jsonl', 'w+') as resulting_input, open(output_file + '/truth/truths.jsonl', 'w+') as resulting_truth: + + for line in tqdm(inp): + spam_rank, doc_id = line.strip().split(' ') + # From the spam rank documentation: percentile-score<70 is spam, and the rest non-spam. + label = 'Benign' if int(spam_rank) >= 70 else 'Malicious' + doc = docs_store.get(doc_id) + resulting_truth.write(json.dumps({"uid": doc_id, "label": label}) + '\n') + resulting_input.write(json.dumps({"uid": doc_id, "url": doc.url, "html": decode(doc.body)}) + '\n') + + +if __name__ == '__main__': + args = parse_args() + main(args.input, args.output, args.dataset_id) + diff --git a/irixys23/webpage-classification/evaluation/Dockerfile b/irixys23/webpage-classification/evaluation/Dockerfile index 23ba32d..3fcb865 100644 --- a/irixys23/webpage-classification/evaluation/Dockerfile +++ b/irixys23/webpage-classification/evaluation/Dockerfile @@ -1,5 +1,5 @@ -FROM python:3.11 - -RUN pip3 install scikit-learn - -ADD evaluation.py /evaluation.py +FROM python:3.11 + +RUN pip3 install scikit-learn + +ADD evaluation.py /evaluation.py diff --git a/irixys23/webpage-classification/evaluation/README.md b/irixys23/webpage-classification/evaluation/README.md index 0226783..93cef98 100644 --- a/irixys23/webpage-classification/evaluation/README.md +++ b/irixys23/webpage-classification/evaluation/README.md @@ -1,13 +1,13 @@ -# The evaluator for the Spam Classification Task at IRIXYS'23 - -``` -tira-run --image webis/irixys23:evaluator-0.0.2 --input-directory example-data/truth --input-run ${PWD}/tira-output --output-directory tira-evaluation --command '/evaluation.py -o $outputDir/evaluation.prototext -t $inputDataset/truths.jsonl -p $inputRun/predictions.jsonl' -``` - -### Development - -``` -docker build -t webis/irixys23:evaluator-0.0.2 . -docker push webis/irixys23:evaluator-0.0.2 -``` - +# The evaluator for the Spam Classification Task at IRIXYS'23 + +``` +tira-run --image webis/irixys23:evaluator-0.0.2 --input-directory example-data/truth --input-run ${PWD}/tira-output --output-directory tira-evaluation --command '/evaluation.py -o $outputDir/evaluation.prototext -t $inputDataset/truths.jsonl -p $inputRun/predictions.jsonl' +``` + +### Development + +``` +docker build -t webis/irixys23:evaluator-0.0.2 . +docker push webis/irixys23:evaluator-0.0.2 +``` + diff --git a/irixys23/webpage-classification/evaluation/evaluation.py b/irixys23/webpage-classification/evaluation/evaluation.py index 9db1538..d62ccd9 100755 --- a/irixys23/webpage-classification/evaluation/evaluation.py +++ b/irixys23/webpage-classification/evaluation/evaluation.py @@ -1,74 +1,74 @@ -#!/usr/bin/env python3 -import argparse -import json -from sklearn.metrics import f1_score - -def read_jsonl_file(file_path: str, label_name: str, label_mapping: dict=None) -> dict: - """ - Reads the .jsonl file into a dictionary - param file_path: path to the .jsonl file - return: dictionary of UID to value from the file - """ - data = {} - with open(file_path, 'r') as f: - for line in f: - item = json.loads(line.strip()) - label = item[label_name] - - if label_mapping: - label = label_mapping[label] - - data[item['uid']] = label - return data - -def compute_f1_score(predictions: dict, truth: dict) -> float: - """ - Computes F1 score from the predictions and truth dictionaries - param predictions: dictionary of UUID to predicted value - param truth: dictionary of UUID to ground truth value - return: computed F1 score - """ - pred_list = [predictions[uuid] for uuid in sorted(predictions.keys())] - truth_list = [truth[uuid] for uuid in sorted(truth.keys())] - return f1_score(truth_list, pred_list, average='macro') - -def write_output(filename: str, k: str, v: float): - """ - print() and write the F1 score to the indicated output file - param filename: full path of the file, where to write to - param k: the name of the metric - param v: the value of the metric - return: None - """ - line = 'measure{{\n key: "{}"\n value: "{}"\n}}\n'.format(k, str(v)) - print(line) - open(filename, "w").write(line) - -def main(): - parser = argparse.ArgumentParser(description='Webpage classification | Evaluation: Compute F1 Score') - parser.add_argument("-p", "--predictions", help="path to the .jsonl file containing predictions", required=True) - parser.add_argument("-t", "--truth", help="path to the .jsonl file containing ground truth", required=True) - parser.add_argument("-o", "--output", help="path to the file to write the results to", required=True) - parser.add_argument("--evaluate-on-clueweb", action='store_true', help='Is the evaluation on the clueweb? (I.e., only two labels, Benign and Malicious)', required=False) - args = parser.parse_args() - - label_mapping = None - if args.evaluate_on_clueweb: - label_mapping = {"Benign": "Benign", "Malicious": "Malicious", "Adult": "Malicious"} - - # Read JSONL files - predictions = read_jsonl_file(args.predictions, 'prediction', label_mapping) - truth = read_jsonl_file(args.truth, 'label', label_mapping) - - # Ensure both dictionaries have the same UUIDs - if set(predictions.keys()) != set(truth.keys()): - print("Error: Some UUIDs in the Predictions and Truth files don't match.") - return - - # Compute F1 score - f1 = compute_f1_score(predictions, truth) - write_output(args.output, "f1_score", f1) - -if __name__ == "__main__": - main() - +#!/usr/bin/env python3 +import argparse +import json +from sklearn.metrics import f1_score + +def read_jsonl_file(file_path: str, label_name: str, label_mapping: dict=None) -> dict: + """ + Reads the .jsonl file into a dictionary + param file_path: path to the .jsonl file + return: dictionary of UID to value from the file + """ + data = {} + with open(file_path, 'r') as f: + for line in f: + item = json.loads(line.strip()) + label = item[label_name] + + if label_mapping: + label = label_mapping[label] + + data[item['uid']] = label + return data + +def compute_f1_score(predictions: dict, truth: dict) -> float: + """ + Computes F1 score from the predictions and truth dictionaries + param predictions: dictionary of UUID to predicted value + param truth: dictionary of UUID to ground truth value + return: computed F1 score + """ + pred_list = [predictions[uuid] for uuid in sorted(predictions.keys())] + truth_list = [truth[uuid] for uuid in sorted(truth.keys())] + return f1_score(truth_list, pred_list, average='macro') + +def write_output(filename: str, k: str, v: float): + """ + print() and write the F1 score to the indicated output file + param filename: full path of the file, where to write to + param k: the name of the metric + param v: the value of the metric + return: None + """ + line = 'measure{{\n key: "{}"\n value: "{}"\n}}\n'.format(k, str(v)) + print(line) + open(filename, "w").write(line) + +def main(): + parser = argparse.ArgumentParser(description='Webpage classification | Evaluation: Compute F1 Score') + parser.add_argument("-p", "--predictions", help="path to the .jsonl file containing predictions", required=True) + parser.add_argument("-t", "--truth", help="path to the .jsonl file containing ground truth", required=True) + parser.add_argument("-o", "--output", help="path to the file to write the results to", required=True) + parser.add_argument("--evaluate-on-clueweb", action='store_true', help='Is the evaluation on the clueweb? (I.e., only two labels, Benign and Malicious)', required=False) + args = parser.parse_args() + + label_mapping = None + if args.evaluate_on_clueweb: + label_mapping = {"Benign": "Benign", "Malicious": "Malicious", "Adult": "Malicious"} + + # Read JSONL files + predictions = read_jsonl_file(args.predictions, 'prediction', label_mapping) + truth = read_jsonl_file(args.truth, 'label', label_mapping) + + # Ensure both dictionaries have the same UUIDs + if set(predictions.keys()) != set(truth.keys()): + print("Error: Some UUIDs in the Predictions and Truth files don't match.") + return + + # Compute F1 score + f1 = compute_f1_score(predictions, truth) + write_output(args.output, "f1_score", f1) + +if __name__ == "__main__": + main() + diff --git a/irixys23/webpage-classification/example-data/input/inputs.jsonl b/irixys23/webpage-classification/example-data/input/inputs.jsonl index 961a917..8eec5de 100644 --- a/irixys23/webpage-classification/example-data/input/inputs.jsonl +++ b/irixys23/webpage-classification/example-data/input/inputs.jsonl @@ -1,10 +1,10 @@ -{"uid": 22084230, "url": "http://www.bound.org/", "html": "\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n"} -{"uid": 22075699, "url": "http://www.furious.com/perfect/moondog.html", "html": "\\\\n\\\\nMoondog interview- Perfect Sound Forever\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n
\\\\n\\\\n
\"Perfect
\\\\n\\\\n\\\\n\\\\n

MOONDOG

\\\\n
\\\\n

Interview by Jason Gross (May 1998)

\\\\n\\\\nSome artists are just so unique that it\\\\'s hard to pin them down or make some kind of comparison to them. Louis Hardin (aka Moondog) is just this kind of artist. He\\\\'s been active for over a half a century with everyone from symphony orchestras to Janis Joplin and Julie Andrews (yes, the actress!) performing his work. His work includes orchestra and small combo compositions, poetry, theology (as you\\\\'ll see below) and theory- his latest release is the wonderful Sax Pax For A Sax (Atlantic). As he notes, because he works with more tonal (or traditional or melodic, if you want) structures for his work, he\\\\'s some of an outcast and an anomoly. The closest analogy might be Cecil Taylor- a classical-minded composer who\\\\'s drawn to jazz (or vice versa). No, that still doesn\\\\'t explain away someone as complex as Moondog so maybe it\\\\'s best to read on and let him explain for himself.\\\\n

\\\\n\\\\n\\\\nPSF: With Sax Pax For A Sax, you\\\\'ve said that Charlie Parker was a big influence on the music. To me, it sounds more like big-band swing though.

\\\\n\\\\nI\\\\'m into swing. I get that from the American Indians like the Sioux, the Arapahoe and the Apache. They have this drum-beat, heart-beat. Bom, bom, bom. They had the walking beat, which is slower, and the running beat, which is faster. I used that almost all the time on this record. I claim that swing came from them. If you listen to Indian music from the plains, you\\\\'ll hear that there\\\\'s a steady swing beat and the big tom-tom is syncopated with swing-type melodies.

\\\\n\\\\nI got that influence when I was six-years-old in Wyoming. My father took us to an Arapahoe Indian reservation. The chief let me sit on his lap and let me beat the tom-tom for the Sun Dance. So, that goes back to the early \\\\'20\\\\'s for me.

\\\\n\\\\nPSF: You\\\\'re talking about drum rhythms here and on that album, it seems like a lot of the music is based on that.

\\\\n\\\\nThe beat is always in the background. I sit down at my desk and write as I get different ideas and phrases. I don\\\\'t compose at a piano, it\\\\'s mostly in my head. The classical masters, like Mozart and Hayden, would sit at their desk and write a string quartet like somebody would write a letter.

\\\\n\\\\nThey\\\\'re trained that way and it\\\\'s the best way to write because you\\\\'re not limited by your technique on an instrument. 95% of my music is done that way. Sometimes I test things on the piano, if it\\\\'s a complicated rhythm or a harmony.

\\\\n\\\\n
PSF: How did the Sax Pax project come together?

\\\\n\\\\nThey recorded it in Bath, England, mostly London musicians. We had been doing some concerts before so we had some music at our fingers and then we just put it together. Sax Pax is a play on the word \\\\'pax,\\\\' meaning peace. Ironically, the sax was invented (Adolf) Sax to, what some would saw, compete with the brass in military bands. So \\\\'peace\\\\' and \\\\'military,\\\\' that\\\\'s a little play on words there. The oboes and flutes wouldn\\\\'t compete with the trumpets and trombones in a military band. But the saxophone is very loud though it\\\\'s been abused and misused a lot. Made it sounding almost comical at times by jazz musiciains but it has a serious side too.

\\\\n\\\\n
PSF: How so?

\\\\n\\\\nA lot of the jazz and rock did abuse the saxophone as far as I\\\\'m concerned. I think I gave the saxophone a better deal. It can do very noble things.

\\\\n\\\\n
PSF: You\\\\'ve spent over a half-century as a composer. What\\\\'s kept you motivated to work?

\\\\n\\\\nIt\\\\'s just a desire and an urge to write. I like to compose music, it comes very natural to me. I write a lot of verse too. I have more ideas than I can use. It takes a lot of time just to develop one idea. Ideas come very fast and easily for me.

\\\\n\\\\n
PSF: What do you draw inspiration from for your work?

\\\\n\\\\nSilence mostly. I find a quiet place where I can work. That\\\\'s why I do a lot of work at night. Being in Munster, I have more peace at night though there\\\\'s a lot of bell ringing here. I like bells but my god, they really go at it here. I also hear all kinds of sirens too but they all sound the same. When I was in New York, you could tell the difference but here they all sound the same to me. Ambulence, police and fire all have the same siren. When that\\\\'s going on, I can\\\\'t work because that interferes with my thinking.

\\\\n\\\\n
PSF: Are there any composers or musicians today that you see as being your peers?

\\\\n\\\\nI\\\\'m not in the field of atonal music at all. I\\\\'m strictly tonal so I feel kind of lonely. That seems to be the big thing now and I have to close my ears to that because it\\\\'s far away from tonality. That\\\\'s the big thing for me, tonality.

\\\\n\\\\nI find that a lot of pop music is (though I don\\\\'t agree with how they use it) tonal. They use the basic one-four-five harmonies and at least that\\\\'s tonal. When it comes to the rules of counterpoint, they don\\\\'t pay any attention to that. That\\\\'s my big forte, to follow the basic rules of counterpoint. Everything on that album is pretty counterpointal.

\\\\n\\\\n\\\\n
PSF: Outside of pop or jazz, are there any composers that are favorites of yours?

\\\\n\\\\nI listen a lot to Bach, Beethoven, Brahams and Mozart. When I was a very young boy, I lost my site and I was at a school for the blind. I heard my first classical records. One was Tchaikovsky\\\\'s Fourth, Beethoven\\\\'s Fifth and things like that. We had a school orchestra and we played some Mozart and we played some other people like that. I got to hear quite a bit of music then and then a lot more later on recordings and radio.

\\\\n\\\\n\\\\n
PSF: Your parents were missionaries. Do you think that might have had an effect on your work?

\\\\n\\\\nAs far as religion, I went along with what they said. I didn\\\\'t know any different until I was in my late teens. My sister began reading a lot of philosophy to me and that undermined my belief in Christianity. The Greek thinkers and Pythagorus had a big influence on me. Shakespeare had a big influence on me and I was impressed with what Newton did. Then I gave religion up completely. That Christian influence didn\\\\'t last very long.

\\\\n\\\\nPSF: Pythagorus was also very interested in music.\\\\n

\\\\nHis basic rules of tone relation are the basis of counterpoint. I follow those very religiously. Those are physical laws of the universe which cannot be altered- that\\\\'s the way they are and the way that tones and scales are put together.

\\\\n\\\\n
PSF: Another lasting influence from your childhood seems to be nursery rhymes as you\\\\'ve done some recording of these.

\\\\n\\\\nI was approached by Julie Lawrence to do the music for the Mother Goose album with Julie Andrews and Martin Green. That\\\\'s how I got into the nursery rhymes. It was nice working with Julie. That was before she became a star in MY FAIR LADY. I only got into it because of that record.

\\\\n\\\\n
PSF: Part of your hertiage is Scandanavian. I know that for a while you were wearing a Viking costume. Was that a way for you to show pride in your heritage?

\\\\n\\\\nIt started out differently. When I first got to New York and I was attending rehearsals of the Philharmonic, they wrote me up as \\\\'a man with the face of Christ.\\\\' I put up with that for a few years, getting compared with a monk or Christ, then I said \\\\'that\\\\'s enough, I don\\\\'t want that connection. I must do something about my appearance to make it look un-Christian.\\\\' At that time, I was studying the Norse and I felt much closer to that than Christianity so I\\\\'d do something to make it look more Nordic. That\\\\'s what was behind it.

\\\\n\\\\n\\\\n
PSF: When you were first in Manhattan, you were performing on the streets. Why did you decide to do that rather than working in a club or hall?

\\\\n\\\\nI made my living that way and I got exposure. It really worked because I hadn\\\\'t been on the streets for more than a few weeks of the fall of \\\\'49 when I was written up. By January of \\\\'50, I was sitting in the doorway of Spanish Music Center on Sixth Avenue (which isn\\\\'t there anymore) owned by Gabriel Oller. He said \\\\'I like the music you\\\\'re making. I made records. Would you like to make some singles?\\\\' We did three singles together and then I got other offers including a Columbia album. The first one was with a forty-piece orchestra that (producer) Al Brown got together. He really did a lot for me.

\\\\n\\\\n
PSF: At that time, you got to meet Arturo Toscanini and Leonard Bernstein also.

\\\\n\\\\nIt was strange about Lenny. I came to New York in November of \\\\'43 and the next day, I went to the Sunday broadcast at Carnegie Hall. I wanted to sit right in front, not realizing that Bernstein was making his debut. Within a few feet, he was standing there and I thought \\\\'I\\\\'m going to be the first one to applaud\\\\' because I knew it was going over coast-to-coast. I didn\\\\'t know I was giving him the first clap of his coast-to-coast debut.

\\\\n\\\\nJoseph Shuster was playing the cello solo in DON QUIXOTE and he saw me. A few days later, I was standing at the stage door entrance and the man there said \\\\'you\\\\'re not getting any farther.\\\\' There was an open door leading to where the musicians where and they were taking a break. Joseph came over to me and said \\\\'I saw you Sunday, would you like to go to rehearsals?\\\\' He came back with (Artur) Rodzinski, the conductor. He took me and said \\\\'you sit here and enjoy yourself.\\\\' So, for years, I was allowed to go to all rehearsals.

\\\\n\\\\nI talked to Bernstein later and I told him about all of that. We got very friendly. The last time I met him was in Sante Fe, New Mexico. He was having his shoes shined and I was out on the street. He said \\\\'what are you doing in Sante Fe? I\\\\'m on my way to Israel.\\\\' It was 1948, just when they were forming the nation.

\\\\n\\\\nRodzinski introduced me to Arturo Toscanini. I was so impressed that I bent over to kiss his hand. He pulled it away and said \\\\'I\\\\'m not a beautiful woman!\\\\' (laughs) When they left, Rodzinski said \\\\'Louis, remember this.\\\\' And a few years later, they were both dead. That was at Madison Square Garden where the NBC Orchestra and the Philharmonic did a joint concert together. Over a hundred musicians there.

\\\\n\\\\n
PSF: It\\\\'s amazing that in such a short amount of time, you went from playing on the streets to having symphonies perform your works. Was that a dizzying pace for you?

\\\\n\\\\nI had been working for it, even on the street. I had problems like with Alan Freed, the rock and roll king. He was coming out of Ohio, using my name and one of my singles \\\\'cause it had a howling wolf in it. Word got around that he wanted me to work with him but then he was calling himself Moondog. Then, when he came to New York, he had a program and was calling it the Moondog Show. I took him to court. He tried to settle out of court but I told him no. He said \\\\'you might lose\\\\' and I said \\\\'you might lose too.\\\\'

\\\\n\\\\nIn the meantime, Igor Stravinsky heard about this thing and he knew about me because he must have seen me at the Carnegie rehearsals. He called up the judge in the case and said \\\\'Do right by this man, he\\\\'s a good musician.\\\\' I don\\\\'t know if that decided the case or not but I won the case against Freed and he stopped using the name. On the radio, he said \\\\'I can\\\\'t use the name Moondog anymore so now it\\\\'s be the Rock and Roll Show.\\\\'

\\\\n\\\\n
PSF: You\\\\'re spent a lot of time writing about and working with canons. What\\\\'s your fasincation about this?

\\\\n\\\\nIt\\\\'s the interplay of one part against another but the parts must fit. When you write a piece, it may take a day to write it but then, composers, especially Bach, would put it aside and say \\\\'it\\\\'s finished.\\\\' But that\\\\'s when my work really begins. It\\\\'s note by note and in a sixteen part canon, there\\\\'s 120 possibilities of making a mistake between any two notes. That\\\\'s the most boring labor you can imagine, comparing note against note for a whole composition. If you find a mistake, you can\\\\'t just change the note, you have to change the whole phrase because of the musical grammatics. My music on this album (Sax Pax), there\\\\'s not one mistake in there. I use it on a lot of other things too.

\\\\n\\\\nOne other thing I like about canon is that you start with one voice and then you add first counterpoint, then the second and so forth. If it\\\\'s a nine part canon, then you have this build-up which leads up to a climax. Harmonic music doesn\\\\'t do that- they start out full blast and goes on. But this builds up and builds up. That\\\\'s what I like. A lot of people like that effect of the gradual build-up.

\\\\n\\\\n
PSF: I\\\\'ve heard that you\\\\'ve also had special instruments made for your work.

\\\\n\\\\nYes, I had a cabinet maker in New York who had built instruments for me. I\\\\'d give him the specifications and he\\\\'d do a beautiful job on it. For the street playing, I needed a special kind of drum down close to the ground. I didn\\\\'t have a chair there so I\\\\'d just squat down and played. I had them specially built on a triangular shape. First I had square drums but I liked the triangle shaped better- I called them trimbas. Then I had a little harp that I played with my left hand. I\\\\'d beat the other drum with a maraca.

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PSF: You\\\\'ve called yourself a \\\\'European in exile\\\\' and you\\\\'d lived in Germany for about 25 years. What\\\\'s drawn you there?

\\\\n\\\\nI feel culturally and artistically closer to Europe than I do to America, although I was born there and I have fond memories of America. Especially Wyoming where I lived on ranch as a boy and had my horses. I was hunting and trapping and fishing. But New York has a big hold on me too. Europe is where my heart is, artistically with all the composers who have worked here and all the historical things that I\\\\'ve read about. It\\\\'s very fascinating.

\\\\n\\\\n
PSF: This is an interesting quote from you: \\\\'rhythmically I\\\\'m in the past. Melodically and harmonically, I\\\\'m in the present or avant-garde.\\\\' What did you mean by that?

\\\\n\\\\n\\\\'Rhythmically in the past,\\\\' that goes back to the Indians. Even counterpoint comes out of the past. It\\\\'s goes back to 1100 or 1200. Harmoncially, my music is the same as Bach and Beethoven and Brahms and those people. No difference really. Rhythmically, it also goes back to the past- the swing rhythms of the Indians. You really couldn\\\\'t find anything more syncopated. If you get a chance, listen to some Sioux Indian recordings or Cheyenne or Blackfoot Indians. You see how syncopated their rhythms are.

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PSF: You\\\\'re putting together a book of poetry now, right?

\\\\n\\\\nI started writing couplets in the \\\\'50\\\\'s. First, there were seven foot, which what\\\\'s called iambic septameter. The normal couplet is iambic pentameter but those lines are too short to get my ideas out. I\\\\'ve written a lot in nonameter, which is nine iambic feet to a line.

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PSF: You\\\\'re also working on Overtone Tree?

\\\\n\\\\nThat\\\\'s one of my symphonies that I\\\\'ve been working on for twenty years. I finally got it finished now. I\\\\'ve been doing a lot of other work but this is a major work. I don\\\\'t expect that it\\\\'ll be ever played. It\\\\'ll probably be buried with me. It\\\\'s written for four conductors- one main conductor and three assitants because at one time, there may be as many as sixty or seventy parts going and one conductor can\\\\'t hanlde that. It\\\\'s all counterpointal. So each conductor would have his own score and be in charge of a certain section of the orchestra. It\\\\'s based on the first nine overtones and I tried that as a theme in \\\\'73 on the northwest corner of 55th Street and Sixth Avenue- that\\\\'s where I got the idea to use overtones as a theme. After I worked on it, I went to Europe. I was in Hamburg in \\\\'75 and I was working on the overtones and I finally realized that I had something interesting here. It was a code. I didn\\\\'t know it was a code but I cracked the code and found out how to develop it. Ten years later, I found out what it was. I wasn\\\\'t looking for a code.

\\\\n\\\\nThat code proves that not only whoever created the universe does exist but he, I call him the mega-mind, wants us to know he exists. Within this code, it also proves that contraction must come before expansion. It also proves that a cause can become an effect and an effect can become a cause. It proves the two-directionality of time. It proves that the past is the future and the future is the past. All this flys in the face of what Hubble did, saying that there was a big bang and an expansion of the universe. His is a theory but mine is a fact. A fact incontrovertable, as I call it. They talked about these theories of mine on National Public Radio.

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PSF: Are you planning to tour and do performances again?

\\\\n\\\\nThere\\\\'s nothing that I\\\\'d like to do more. I have a 26-piece big band in England. They appeared at the Queen Elizabeth Hall, Elvis Costello put it on in \\\\'95. We got great reviews. I got great musicians- you can\\\\'t imagine how great these guys are. They cost so much so I haven\\\\'t got any work. It\\\\'s such an expensive bunch. It\\\\'s the Saxophonic plus the London Brass, they\\\\'re world famous. They liked me very much and they said \\\\'we hope we have the chance to work with you again.\\\\' But it would cost about 50,000 to bring them over. At least we made a recording of it.

\\\\n\\\\n\\\\nAnd now Moondog turns the tables for a theological discussion.

\\\\n\\\\nMOONDOG: I have a question for you. How do you feel about Christianity monopolizing the passing of time, when the Pope and the Church decided to stop the old Roman calendar of the Caesars and start all over again? How do you feel about how every religion must march to the Christian calendar?

\\\\n\\\\nJASON: I\\\\'m Jewish actually.

\\\\n\\\\nMOONDOG: I know but how do you feel about it?

\\\\n\\\\nJASON: I\\\\'ve always thought that it was pretty crazy that we had to decide to carve up time into such exact pieces. I never cared much for the Pope or the church.

\\\\n\\\\nMOONDOG: Well, I\\\\'m starting it all over again, going back 10,000 years, into the Ice Age or the Inter-Glacial Period when farming and civilization began. Starting there a new calendar. So by the time that Christ was born, it comes to (the year) 8001. This year will be 9998 and next year will be 10,000. All the numbers are going forward for all religions and, here\\\\'s one for you, Chirst was born 5 B.C. so his date would be 8996. I don\\\\'t know an exact date for Moses but he\\\\'s figured in there roughly. I can\\\\'t find a date for Abraham though. But I\\\\'m putting in things like that.

\\\\n\\\\nJASON: That might be tough to figure out. They didn\\\\'t always keep the birth certificates on floppies back then.

\\\\n\\\\n
\\\\n\\\\n

See some of Moondog\\\\'s favorite music

\\\\n\\\\nAlso, see some of his explanations of his theories on the Common Calendar and Canons\\\\n

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    "} -{"uid": 22070542, "url": "https://www.wurb.com/if/game/922", "html": "b\"Baf's Guide is currently down. Please try again later.\\\\n\""} -{"uid": 22040149, "url": "https://www.afropoets.net/jaynecortez.html", "html": "\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\njayne cortez, jayne cortez poetry, jayne cortez poem, jayne cortez poem There It Is, jayne cortez poem In The Morning - Welcome to African American Writer Jayne Cortez Poems Page. Collection of poems written by Black Writer Jayne Cortez can be found here. Picture, bio, poems, and so on. jayne cortez poem I am New York City, jayne cortez poem Jazz Fan Looks Back, jayne cortez poem PUSH BACK THE CATASTROPHES\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n \\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\t \\\\r\\\\n\\\\t
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     Jayne Cortez

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    AfroPoets Famous Black Writers
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    \\\\r\\\\n  o\\\\r\\\\n Home\\\\r\\\\n
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    \\\\r\\\\n  o\\\\r\\\\n PoeticQ.com - Famous Poets Gallery\\\\r\\\\n
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    \\\\r\\\\n Brief Bio\\\\r\\\\n

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    \\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t \\\\r\\\\n\\\\r\\\\nJayne Cortez was born in Arizona, grew up in Los Angeles, California, and currently lives in New York City. She is the author of ten books of poems and has performed her poetry with music on nine recordings. Her poetry is celebrated for its political, surrealistic, dynamic innovations in lyricism, and visceral sound. \\\\r\\\\n\\\\r\\\\n

    \\\\r\\\\nCortez has presented her works and ideas at universities, museums, and festivals including: the Museum of Modern Art New York, UNESCO. Paris, the Berlin Jazz Festival, Germany, 11 Perfil da Literatura, Sao Paulo, Brazil,. Fourth World Congress on Women, Beijing, China, the Arts Alive International Festival, Johannesburg, South Africa, Banlieues Bleues Festival, France, Tampere Happening, Finland and New York University. \\\\r\\\\n\\\\r\\\\n

    \\\\r\\\\nHer poems have been translated into many languages and widely published in anthologies, journals, and magazines including: Post Modern American Poetry, Daughters of Africa, Poems for the Millennium, The Jazz Poetry Anthology, Surrealist Women, Sulfur, Black Scholar, Presence Africaine and Mother Jones. She is the recipient of several awards: Arts International, New York Foundation on the Arts, the National Endowment for the Arts, the International African Festival Award, the Langston Hughes Award, and the American Book Award. \\\\r\\\\n\\\\r\\\\n

    \\\\r\\\\nCortez appears in the Canadian film \\\\'Poetry in Motion\\\\' and the music video \"Nelson Mandela is Coming\" produced by Globalvision. Her poem \"I Am New York City\" was used as the theme for \"Honor\" an episode of the Fox Television series \"Tribeca. \" She is also on screen in the film \"Women In Jazz.\" Her most recent books are \"Somewhere in Advance of Nowhere\" published by Serpent\\\\'s Tail/ High Risk Books, and soon to be released \"Jazz Fan Looks Back\" published by Hanging Loose Press. \\\\r\\\\n\\\\r\\\\n\\\\r\\\\nHer work as activist and organizer is typified by her presidency of the Organization of Women Writers of Africa, her coordination of the Yari Yari International Conference of Women Writers of African Descent, NYU, the direction of the film Yari Yari, her organization of the 1999 international symposium \"Slave Routes: The Long Memory,\" and her participation in the Round Table: Dialogue Among Civilizations at the United Nations Millennium Summit 2000.\\\\r\\\\n\\\\r\\\\n

    \\\\r\\\\n\"The Dictionary of Literary Biography\" asserts that Cortez has distinguished herself as \"a creative artist uniquely able to reach audiences for whom books of poetry have little appeal.\" And critic Barbara Christian notes, Cortez \"has forged connections in her work that help us see how our histories are related. The result is a poetry as wide in its scope as it is compelling in its craft.\"\\\\r\\\\n \\\\r\\\\n

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    \\\\r\\\\n Poems - 10 in all

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    Jayne Cortez
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    Poetry\\\\r\\\\n\\\\r\\\\n
    SUMMIT\\\\r\\\\n\\\\r\\\\n
    There It Is\\\\r\\\\n\\\\r\\\\n
    MAKE IFA\\\\r\\\\n\\\\r\\\\n
    Rose Solitude\\\\r\\\\n\\\\r\\\\n
    In The Morning\\\\r\\\\n\\\\r\\\\n
    I am New York City\\\\r\\\\n\\\\r\\\\n
    Jazz Fan Looks Back\\\\r\\\\n\\\\r\\\\n
    Under the Edge of February\\\\r\\\\n\\\\r\\\\n
    PUSH BACK THE CATASTROPHES\\\\r\\\\n\\\\r\\\\n

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    \\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\r\\\\n : Copyright © 2003-2022 : Site Maintained By MrAfrica@AfroPoets.Net :\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t
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    \\\\n \\\\n DeSantis, who blamed diversity initiatives for Silicon Valley Bank's downfall, pushed for rollback of bank regulations under renewed scrutiny\\\\n \\\\n

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    \\\\n \\\\n \\\\n \\\\n By Steve Contorno and Andrew Kaczynski, CNNIn the wake of Silicon Valley Bank\\\\xe2\\\\x80\\\\x99s demise, Florida Gov. Ron DeSantis pointed fingers at several culprits, including the company\\\\xe2\\\\x80\\\\x99s diversity initiatives and federal regulators who he said \\\\xe2\\\\x80\\\\x9calways seem to whiff\\\\xe2\\\\x80\\\\x9d at preventing a financial crisis. But left out of DeSantis\\\\xe2\\\\x80\\\\x99 comments was his own record on banking regulations.\\\\n \\\\n \\\\n \\\\n
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    \\\\n\\\\n\\\\n \\\\n \\\\n \\\\n \\\\n \\\\n  | \\\\n \\\\n \\\\n \\\\n\\\\n\\\\n\\\\n \\\\n \\\\n \\\\n smithmountainlake.com\\\\n \\\\n \\\\n \\\\n\\\\n\\\\n
    \\\\n \\\\n \\\\n \\\\n In the wake of Silicon Valley Bank's demise, Florida Gov. Ron DeSantis pointed fingers at several culprits, including the company's diversity initiatives and federal regulators who he said "always seem to whiff" at preventing a financial crisis. But left out of DeSantis' comments was his own record on banking regulations.\\\\n \\\\n \\\\n \\\\n
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    \\\\n \\\\n DeSantis, who blamed diversity initiatives for Silicon Valley Bank's downfall, pushed for rollback of bank regulations under renewed scrutiny\\\\n \\\\n

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    \\\\n \\\\n \\\\n \\\\n In the wake of Silicon Valley Bank's demise, Florida Gov. Ron DeSantis pointed fingers at several culprits, including the company's diversity initiatives and federal regulators who he said "always seem to whiff" at preventing a financial crisis. But left out of DeSantis' comments was his own record on banking regulations.\\\\n \\\\n \\\\n \\\\n
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    @KFILE — 432,346 followers, 13,233 tweets

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    \\\\n \\\\n Quite the scene in the Signature Bank collapse, in which total panic just spread in these WhatsApp groups.\\\\n\\\\n https://www.wsj.com/articles/signature-banks-quirky-mix-of-customers-fueled-its-rise-and-hastened-its-fall-8bc10cd2 \\\\xe2\\\\x80\\\\xa6 pic.twitter.com/8uN9q1C8Dn\\\\n \\\\n
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    \\\\n \\\\n One of our friends gifted us this Sesame Street book about love for Talia. And it always reminds me of her big sister when reading it. Life is amazing but it can be so fragile in how precious it is. pic.twitter.com/3IsCK4qb3W\\\\n \\\\n
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    \\\\n\\\\n
    \"Perfect
    \\\\n\\\\n\\\\n\\\\n

    MOONDOG

    \\\\n
    \\\\n

    Interview by Jason Gross (May 1998)

    \\\\n\\\\nSome artists are just so unique that it\\\\'s hard to pin them down or make some kind of comparison to them. Louis Hardin (aka Moondog) is just this kind of artist. He\\\\'s been active for over a half a century with everyone from symphony orchestras to Janis Joplin and Julie Andrews (yes, the actress!) performing his work. His work includes orchestra and small combo compositions, poetry, theology (as you\\\\'ll see below) and theory- his latest release is the wonderful Sax Pax For A Sax (Atlantic). As he notes, because he works with more tonal (or traditional or melodic, if you want) structures for his work, he\\\\'s some of an outcast and an anomoly. The closest analogy might be Cecil Taylor- a classical-minded composer who\\\\'s drawn to jazz (or vice versa). No, that still doesn\\\\'t explain away someone as complex as Moondog so maybe it\\\\'s best to read on and let him explain for himself.\\\\n

    \\\\n\\\\n\\\\nPSF: With Sax Pax For A Sax, you\\\\'ve said that Charlie Parker was a big influence on the music. To me, it sounds more like big-band swing though.

    \\\\n\\\\nI\\\\'m into swing. I get that from the American Indians like the Sioux, the Arapahoe and the Apache. They have this drum-beat, heart-beat. Bom, bom, bom. They had the walking beat, which is slower, and the running beat, which is faster. I used that almost all the time on this record. I claim that swing came from them. If you listen to Indian music from the plains, you\\\\'ll hear that there\\\\'s a steady swing beat and the big tom-tom is syncopated with swing-type melodies.

    \\\\n\\\\nI got that influence when I was six-years-old in Wyoming. My father took us to an Arapahoe Indian reservation. The chief let me sit on his lap and let me beat the tom-tom for the Sun Dance. So, that goes back to the early \\\\'20\\\\'s for me.

    \\\\n\\\\nPSF: You\\\\'re talking about drum rhythms here and on that album, it seems like a lot of the music is based on that.

    \\\\n\\\\nThe beat is always in the background. I sit down at my desk and write as I get different ideas and phrases. I don\\\\'t compose at a piano, it\\\\'s mostly in my head. The classical masters, like Mozart and Hayden, would sit at their desk and write a string quartet like somebody would write a letter.

    \\\\n\\\\nThey\\\\'re trained that way and it\\\\'s the best way to write because you\\\\'re not limited by your technique on an instrument. 95% of my music is done that way. Sometimes I test things on the piano, if it\\\\'s a complicated rhythm or a harmony.

    \\\\n\\\\n
    PSF: How did the Sax Pax project come together?

    \\\\n\\\\nThey recorded it in Bath, England, mostly London musicians. We had been doing some concerts before so we had some music at our fingers and then we just put it together. Sax Pax is a play on the word \\\\'pax,\\\\' meaning peace. Ironically, the sax was invented (Adolf) Sax to, what some would saw, compete with the brass in military bands. So \\\\'peace\\\\' and \\\\'military,\\\\' that\\\\'s a little play on words there. The oboes and flutes wouldn\\\\'t compete with the trumpets and trombones in a military band. But the saxophone is very loud though it\\\\'s been abused and misused a lot. Made it sounding almost comical at times by jazz musiciains but it has a serious side too.

    \\\\n\\\\n
    PSF: How so?

    \\\\n\\\\nA lot of the jazz and rock did abuse the saxophone as far as I\\\\'m concerned. I think I gave the saxophone a better deal. It can do very noble things.

    \\\\n\\\\n
    PSF: You\\\\'ve spent over a half-century as a composer. What\\\\'s kept you motivated to work?

    \\\\n\\\\nIt\\\\'s just a desire and an urge to write. I like to compose music, it comes very natural to me. I write a lot of verse too. I have more ideas than I can use. It takes a lot of time just to develop one idea. Ideas come very fast and easily for me.

    \\\\n\\\\n
    PSF: What do you draw inspiration from for your work?

    \\\\n\\\\nSilence mostly. I find a quiet place where I can work. That\\\\'s why I do a lot of work at night. Being in Munster, I have more peace at night though there\\\\'s a lot of bell ringing here. I like bells but my god, they really go at it here. I also hear all kinds of sirens too but they all sound the same. When I was in New York, you could tell the difference but here they all sound the same to me. Ambulence, police and fire all have the same siren. When that\\\\'s going on, I can\\\\'t work because that interferes with my thinking.

    \\\\n\\\\n
    PSF: Are there any composers or musicians today that you see as being your peers?

    \\\\n\\\\nI\\\\'m not in the field of atonal music at all. I\\\\'m strictly tonal so I feel kind of lonely. That seems to be the big thing now and I have to close my ears to that because it\\\\'s far away from tonality. That\\\\'s the big thing for me, tonality.

    \\\\n\\\\nI find that a lot of pop music is (though I don\\\\'t agree with how they use it) tonal. They use the basic one-four-five harmonies and at least that\\\\'s tonal. When it comes to the rules of counterpoint, they don\\\\'t pay any attention to that. That\\\\'s my big forte, to follow the basic rules of counterpoint. Everything on that album is pretty counterpointal.

    \\\\n\\\\n\\\\n
    PSF: Outside of pop or jazz, are there any composers that are favorites of yours?

    \\\\n\\\\nI listen a lot to Bach, Beethoven, Brahams and Mozart. When I was a very young boy, I lost my site and I was at a school for the blind. I heard my first classical records. One was Tchaikovsky\\\\'s Fourth, Beethoven\\\\'s Fifth and things like that. We had a school orchestra and we played some Mozart and we played some other people like that. I got to hear quite a bit of music then and then a lot more later on recordings and radio.

    \\\\n\\\\n\\\\n
    PSF: Your parents were missionaries. Do you think that might have had an effect on your work?

    \\\\n\\\\nAs far as religion, I went along with what they said. I didn\\\\'t know any different until I was in my late teens. My sister began reading a lot of philosophy to me and that undermined my belief in Christianity. The Greek thinkers and Pythagorus had a big influence on me. Shakespeare had a big influence on me and I was impressed with what Newton did. Then I gave religion up completely. That Christian influence didn\\\\'t last very long.

    \\\\n\\\\nPSF: Pythagorus was also very interested in music.\\\\n

    \\\\nHis basic rules of tone relation are the basis of counterpoint. I follow those very religiously. Those are physical laws of the universe which cannot be altered- that\\\\'s the way they are and the way that tones and scales are put together.

    \\\\n\\\\n
    PSF: Another lasting influence from your childhood seems to be nursery rhymes as you\\\\'ve done some recording of these.

    \\\\n\\\\nI was approached by Julie Lawrence to do the music for the Mother Goose album with Julie Andrews and Martin Green. That\\\\'s how I got into the nursery rhymes. It was nice working with Julie. That was before she became a star in MY FAIR LADY. I only got into it because of that record.

    \\\\n\\\\n
    PSF: Part of your hertiage is Scandanavian. I know that for a while you were wearing a Viking costume. Was that a way for you to show pride in your heritage?

    \\\\n\\\\nIt started out differently. When I first got to New York and I was attending rehearsals of the Philharmonic, they wrote me up as \\\\'a man with the face of Christ.\\\\' I put up with that for a few years, getting compared with a monk or Christ, then I said \\\\'that\\\\'s enough, I don\\\\'t want that connection. I must do something about my appearance to make it look un-Christian.\\\\' At that time, I was studying the Norse and I felt much closer to that than Christianity so I\\\\'d do something to make it look more Nordic. That\\\\'s what was behind it.

    \\\\n\\\\n\\\\n
    PSF: When you were first in Manhattan, you were performing on the streets. Why did you decide to do that rather than working in a club or hall?

    \\\\n\\\\nI made my living that way and I got exposure. It really worked because I hadn\\\\'t been on the streets for more than a few weeks of the fall of \\\\'49 when I was written up. By January of \\\\'50, I was sitting in the doorway of Spanish Music Center on Sixth Avenue (which isn\\\\'t there anymore) owned by Gabriel Oller. He said \\\\'I like the music you\\\\'re making. I made records. Would you like to make some singles?\\\\' We did three singles together and then I got other offers including a Columbia album. The first one was with a forty-piece orchestra that (producer) Al Brown got together. He really did a lot for me.

    \\\\n\\\\n
    PSF: At that time, you got to meet Arturo Toscanini and Leonard Bernstein also.

    \\\\n\\\\nIt was strange about Lenny. I came to New York in November of \\\\'43 and the next day, I went to the Sunday broadcast at Carnegie Hall. I wanted to sit right in front, not realizing that Bernstein was making his debut. Within a few feet, he was standing there and I thought \\\\'I\\\\'m going to be the first one to applaud\\\\' because I knew it was going over coast-to-coast. I didn\\\\'t know I was giving him the first clap of his coast-to-coast debut.

    \\\\n\\\\nJoseph Shuster was playing the cello solo in DON QUIXOTE and he saw me. A few days later, I was standing at the stage door entrance and the man there said \\\\'you\\\\'re not getting any farther.\\\\' There was an open door leading to where the musicians where and they were taking a break. Joseph came over to me and said \\\\'I saw you Sunday, would you like to go to rehearsals?\\\\' He came back with (Artur) Rodzinski, the conductor. He took me and said \\\\'you sit here and enjoy yourself.\\\\' So, for years, I was allowed to go to all rehearsals.

    \\\\n\\\\nI talked to Bernstein later and I told him about all of that. We got very friendly. The last time I met him was in Sante Fe, New Mexico. He was having his shoes shined and I was out on the street. He said \\\\'what are you doing in Sante Fe? I\\\\'m on my way to Israel.\\\\' It was 1948, just when they were forming the nation.

    \\\\n\\\\nRodzinski introduced me to Arturo Toscanini. I was so impressed that I bent over to kiss his hand. He pulled it away and said \\\\'I\\\\'m not a beautiful woman!\\\\' (laughs) When they left, Rodzinski said \\\\'Louis, remember this.\\\\' And a few years later, they were both dead. That was at Madison Square Garden where the NBC Orchestra and the Philharmonic did a joint concert together. Over a hundred musicians there.

    \\\\n\\\\n
    PSF: It\\\\'s amazing that in such a short amount of time, you went from playing on the streets to having symphonies perform your works. Was that a dizzying pace for you?

    \\\\n\\\\nI had been working for it, even on the street. I had problems like with Alan Freed, the rock and roll king. He was coming out of Ohio, using my name and one of my singles \\\\'cause it had a howling wolf in it. Word got around that he wanted me to work with him but then he was calling himself Moondog. Then, when he came to New York, he had a program and was calling it the Moondog Show. I took him to court. He tried to settle out of court but I told him no. He said \\\\'you might lose\\\\' and I said \\\\'you might lose too.\\\\'

    \\\\n\\\\nIn the meantime, Igor Stravinsky heard about this thing and he knew about me because he must have seen me at the Carnegie rehearsals. He called up the judge in the case and said \\\\'Do right by this man, he\\\\'s a good musician.\\\\' I don\\\\'t know if that decided the case or not but I won the case against Freed and he stopped using the name. On the radio, he said \\\\'I can\\\\'t use the name Moondog anymore so now it\\\\'s be the Rock and Roll Show.\\\\'

    \\\\n\\\\n
    PSF: You\\\\'re spent a lot of time writing about and working with canons. What\\\\'s your fasincation about this?

    \\\\n\\\\nIt\\\\'s the interplay of one part against another but the parts must fit. When you write a piece, it may take a day to write it but then, composers, especially Bach, would put it aside and say \\\\'it\\\\'s finished.\\\\' But that\\\\'s when my work really begins. It\\\\'s note by note and in a sixteen part canon, there\\\\'s 120 possibilities of making a mistake between any two notes. That\\\\'s the most boring labor you can imagine, comparing note against note for a whole composition. If you find a mistake, you can\\\\'t just change the note, you have to change the whole phrase because of the musical grammatics. My music on this album (Sax Pax), there\\\\'s not one mistake in there. I use it on a lot of other things too.

    \\\\n\\\\nOne other thing I like about canon is that you start with one voice and then you add first counterpoint, then the second and so forth. If it\\\\'s a nine part canon, then you have this build-up which leads up to a climax. Harmonic music doesn\\\\'t do that- they start out full blast and goes on. But this builds up and builds up. That\\\\'s what I like. A lot of people like that effect of the gradual build-up.

    \\\\n\\\\n
    PSF: I\\\\'ve heard that you\\\\'ve also had special instruments made for your work.

    \\\\n\\\\nYes, I had a cabinet maker in New York who had built instruments for me. I\\\\'d give him the specifications and he\\\\'d do a beautiful job on it. For the street playing, I needed a special kind of drum down close to the ground. I didn\\\\'t have a chair there so I\\\\'d just squat down and played. I had them specially built on a triangular shape. First I had square drums but I liked the triangle shaped better- I called them trimbas. Then I had a little harp that I played with my left hand. I\\\\'d beat the other drum with a maraca.

    \\\\n\\\\n\\\\n
    PSF: You\\\\'ve called yourself a \\\\'European in exile\\\\' and you\\\\'d lived in Germany for about 25 years. What\\\\'s drawn you there?

    \\\\n\\\\nI feel culturally and artistically closer to Europe than I do to America, although I was born there and I have fond memories of America. Especially Wyoming where I lived on ranch as a boy and had my horses. I was hunting and trapping and fishing. But New York has a big hold on me too. Europe is where my heart is, artistically with all the composers who have worked here and all the historical things that I\\\\'ve read about. It\\\\'s very fascinating.

    \\\\n\\\\n
    PSF: This is an interesting quote from you: \\\\'rhythmically I\\\\'m in the past. Melodically and harmonically, I\\\\'m in the present or avant-garde.\\\\' What did you mean by that?

    \\\\n\\\\n\\\\'Rhythmically in the past,\\\\' that goes back to the Indians. Even counterpoint comes out of the past. It\\\\'s goes back to 1100 or 1200. Harmoncially, my music is the same as Bach and Beethoven and Brahms and those people. No difference really. Rhythmically, it also goes back to the past- the swing rhythms of the Indians. You really couldn\\\\'t find anything more syncopated. If you get a chance, listen to some Sioux Indian recordings or Cheyenne or Blackfoot Indians. You see how syncopated their rhythms are.

    \\\\n\\\\n\\\\n
    PSF: You\\\\'re putting together a book of poetry now, right?

    \\\\n\\\\nI started writing couplets in the \\\\'50\\\\'s. First, there were seven foot, which what\\\\'s called iambic septameter. The normal couplet is iambic pentameter but those lines are too short to get my ideas out. I\\\\'ve written a lot in nonameter, which is nine iambic feet to a line.

    \\\\n\\\\n
    PSF: You\\\\'re also working on Overtone Tree?

    \\\\n\\\\nThat\\\\'s one of my symphonies that I\\\\'ve been working on for twenty years. I finally got it finished now. I\\\\'ve been doing a lot of other work but this is a major work. I don\\\\'t expect that it\\\\'ll be ever played. It\\\\'ll probably be buried with me. It\\\\'s written for four conductors- one main conductor and three assitants because at one time, there may be as many as sixty or seventy parts going and one conductor can\\\\'t hanlde that. It\\\\'s all counterpointal. So each conductor would have his own score and be in charge of a certain section of the orchestra. It\\\\'s based on the first nine overtones and I tried that as a theme in \\\\'73 on the northwest corner of 55th Street and Sixth Avenue- that\\\\'s where I got the idea to use overtones as a theme. After I worked on it, I went to Europe. I was in Hamburg in \\\\'75 and I was working on the overtones and I finally realized that I had something interesting here. It was a code. I didn\\\\'t know it was a code but I cracked the code and found out how to develop it. Ten years later, I found out what it was. I wasn\\\\'t looking for a code.

    \\\\n\\\\nThat code proves that not only whoever created the universe does exist but he, I call him the mega-mind, wants us to know he exists. Within this code, it also proves that contraction must come before expansion. It also proves that a cause can become an effect and an effect can become a cause. It proves the two-directionality of time. It proves that the past is the future and the future is the past. All this flys in the face of what Hubble did, saying that there was a big bang and an expansion of the universe. His is a theory but mine is a fact. A fact incontrovertable, as I call it. They talked about these theories of mine on National Public Radio.

    \\\\n\\\\n
    PSF: Are you planning to tour and do performances again?

    \\\\n\\\\nThere\\\\'s nothing that I\\\\'d like to do more. I have a 26-piece big band in England. They appeared at the Queen Elizabeth Hall, Elvis Costello put it on in \\\\'95. We got great reviews. I got great musicians- you can\\\\'t imagine how great these guys are. They cost so much so I haven\\\\'t got any work. It\\\\'s such an expensive bunch. It\\\\'s the Saxophonic plus the London Brass, they\\\\'re world famous. They liked me very much and they said \\\\'we hope we have the chance to work with you again.\\\\' But it would cost about 50,000 to bring them over. At least we made a recording of it.

    \\\\n\\\\n\\\\nAnd now Moondog turns the tables for a theological discussion.

    \\\\n\\\\nMOONDOG: I have a question for you. How do you feel about Christianity monopolizing the passing of time, when the Pope and the Church decided to stop the old Roman calendar of the Caesars and start all over again? How do you feel about how every religion must march to the Christian calendar?

    \\\\n\\\\nJASON: I\\\\'m Jewish actually.

    \\\\n\\\\nMOONDOG: I know but how do you feel about it?

    \\\\n\\\\nJASON: I\\\\'ve always thought that it was pretty crazy that we had to decide to carve up time into such exact pieces. I never cared much for the Pope or the church.

    \\\\n\\\\nMOONDOG: Well, I\\\\'m starting it all over again, going back 10,000 years, into the Ice Age or the Inter-Glacial Period when farming and civilization began. Starting there a new calendar. So by the time that Christ was born, it comes to (the year) 8001. This year will be 9998 and next year will be 10,000. All the numbers are going forward for all religions and, here\\\\'s one for you, Chirst was born 5 B.C. so his date would be 8996. I don\\\\'t know an exact date for Moses but he\\\\'s figured in there roughly. I can\\\\'t find a date for Abraham though. But I\\\\'m putting in things like that.

    \\\\n\\\\nJASON: That might be tough to figure out. They didn\\\\'t always keep the birth certificates on floppies back then.

    \\\\n\\\\n
    \\\\n\\\\n

    See some of Moondog\\\\'s favorite music

    \\\\n\\\\nAlso, see some of his explanations of his theories on the Common Calendar and Canons\\\\n

    \\\\n\\\\n\\\\n\\\\n    \\\\n
    \\\\n\\\\n\\\\n


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      "} +{"uid": 22070542, "url": "https://www.wurb.com/if/game/922", "html": "b\"Baf's Guide is currently down. Please try again later.\\\\n\""} +{"uid": 22040149, "url": "https://www.afropoets.net/jaynecortez.html", "html": "\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\njayne cortez, jayne cortez poetry, jayne cortez poem, jayne cortez poem There It Is, jayne cortez poem In The Morning - Welcome to African American Writer Jayne Cortez Poems Page. Collection of poems written by Black Writer Jayne Cortez can be found here. Picture, bio, poems, and so on. jayne cortez poem I am New York City, jayne cortez poem Jazz Fan Looks Back, jayne cortez poem PUSH BACK THE CATASTROPHES\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n \\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n\\\\t \\\\r\\\\n\\\\t
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       Jayne Cortez

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      AfroPoets Famous Black Writers
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      \\\\r\\\\n  o\\\\r\\\\n Home\\\\r\\\\n
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      \\\\r\\\\n \"Jayne\\\\r\\\\n
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      \\\\r\\\\n Brief Bio\\\\r\\\\n

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      \\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t \\\\r\\\\n\\\\r\\\\nJayne Cortez was born in Arizona, grew up in Los Angeles, California, and currently lives in New York City. She is the author of ten books of poems and has performed her poetry with music on nine recordings. Her poetry is celebrated for its political, surrealistic, dynamic innovations in lyricism, and visceral sound. \\\\r\\\\n\\\\r\\\\n

      \\\\r\\\\nCortez has presented her works and ideas at universities, museums, and festivals including: the Museum of Modern Art New York, UNESCO. Paris, the Berlin Jazz Festival, Germany, 11 Perfil da Literatura, Sao Paulo, Brazil,. Fourth World Congress on Women, Beijing, China, the Arts Alive International Festival, Johannesburg, South Africa, Banlieues Bleues Festival, France, Tampere Happening, Finland and New York University. \\\\r\\\\n\\\\r\\\\n

      \\\\r\\\\nHer poems have been translated into many languages and widely published in anthologies, journals, and magazines including: Post Modern American Poetry, Daughters of Africa, Poems for the Millennium, The Jazz Poetry Anthology, Surrealist Women, Sulfur, Black Scholar, Presence Africaine and Mother Jones. She is the recipient of several awards: Arts International, New York Foundation on the Arts, the National Endowment for the Arts, the International African Festival Award, the Langston Hughes Award, and the American Book Award. \\\\r\\\\n\\\\r\\\\n

      \\\\r\\\\nCortez appears in the Canadian film \\\\'Poetry in Motion\\\\' and the music video \"Nelson Mandela is Coming\" produced by Globalvision. Her poem \"I Am New York City\" was used as the theme for \"Honor\" an episode of the Fox Television series \"Tribeca. \" She is also on screen in the film \"Women In Jazz.\" Her most recent books are \"Somewhere in Advance of Nowhere\" published by Serpent\\\\'s Tail/ High Risk Books, and soon to be released \"Jazz Fan Looks Back\" published by Hanging Loose Press. \\\\r\\\\n\\\\r\\\\n\\\\r\\\\nHer work as activist and organizer is typified by her presidency of the Organization of Women Writers of Africa, her coordination of the Yari Yari International Conference of Women Writers of African Descent, NYU, the direction of the film Yari Yari, her organization of the 1999 international symposium \"Slave Routes: The Long Memory,\" and her participation in the Round Table: Dialogue Among Civilizations at the United Nations Millennium Summit 2000.\\\\r\\\\n\\\\r\\\\n

      \\\\r\\\\n\"The Dictionary of Literary Biography\" asserts that Cortez has distinguished herself as \"a creative artist uniquely able to reach audiences for whom books of poetry have little appeal.\" And critic Barbara Christian notes, Cortez \"has forged connections in her work that help us see how our histories are related. The result is a poetry as wide in its scope as it is compelling in its craft.\"\\\\r\\\\n \\\\r\\\\n

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      \\\\r\\\\n Poems - 10 in all

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      Jayne Cortez
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      Poetry\\\\r\\\\n\\\\r\\\\n
      SUMMIT\\\\r\\\\n\\\\r\\\\n
      There It Is\\\\r\\\\n\\\\r\\\\n
      MAKE IFA\\\\r\\\\n\\\\r\\\\n
      Rose Solitude\\\\r\\\\n\\\\r\\\\n
      In The Morning\\\\r\\\\n\\\\r\\\\n
      I am New York City\\\\r\\\\n\\\\r\\\\n
      Jazz Fan Looks Back\\\\r\\\\n\\\\r\\\\n
      Under the Edge of February\\\\r\\\\n\\\\r\\\\n
      PUSH BACK THE CATASTROPHES\\\\r\\\\n\\\\r\\\\n

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      \\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\r\\\\n : Copyright © 2003-2022 : Site Maintained By MrAfrica@AfroPoets.Net :\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t
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        \\\\n \\\\n
      • Phone call (Direct and/or Dial) - \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x94\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\x87 \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb0/\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb8\\\\xad \\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xa8\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb9\\\\x8c\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x87 Phone Application \\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xb8\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\x93\\\\xe0\\\\xb9\\\\x8c\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xb8\\\\xe0\\\\xb8\\\\x93 (Dial) \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb8\\\\x94\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\xb8\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x99 \\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xb3\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\x9d\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x94 \\\\xe0\\\\xb9\\\\x86 \\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x99
        • \\\\n \\\\n
      • Read Phone State - \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\x94\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x81 (\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\x9c\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x99) \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xb3\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\x94\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\x9c\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x87\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x87 Application
        • \\\\n
      • Google account access - \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb8\\\\xe0\\\\xb8\\\\x8d\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb8\\\\xb6\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x8d\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb5 Google \\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x9c\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x89 \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb3\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xb3\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x9f\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x8c\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99 Push Notification (\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x8c\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2)
        • \\\\n \\\\n
      • Camera - \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x9f\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x8c\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\xa0\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x9e \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb0/\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb8\\\\xad \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\x99 QRCode / Barcode \\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb3\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\x93\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x99 \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\x9f\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb9\\\\x8c,\\\\xe2\\\\x80\\\\x8b \\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\x9f\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb9\\\\x8c\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x87 \\\\xe0\\\\xb9\\\\x86 \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb0/\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb8\\\\xad \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\x99 QRCode / Barcode \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x94\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x87 \\\\xe0\\\\xb9\\\\x86 \\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99
        • \\\\n
      • Location - \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb8\\\\xb6\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\xb8\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xb8\\\\xe0\\\\xb8\\\\x93 \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xb3\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\x94\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xaa\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x87 \\\\xe0\\\\xb9\\\\x86 \\\\xe0\\\\xb8\\\\xa0\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99 \\\\xe0\\\\xb9\\\\x82\\\\xe0\\\\xb8\\\\x94\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb8\\\\xb3\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\xb8\\\\xe0\\\\xb8\\\\x93\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb8\\\\xb9\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x87\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb9\\\\x84\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x9a\\\\xe0\\\\xb8\\\\x99 Server \\\\xe0\\\\xb8\\\\xab\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x99 Application \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\xa2\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\x95\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\x88\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb9\\\\x83\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9b\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xb0\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xa7\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\x9c\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x97\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\x99
        • \\\\n
      • External Storage - \\\\xe0\\\\xb9\\\\x81\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xa5\\\\xe0\\\\xb8\\\\xb4\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x84\\\\xe0\\\\xb8\\\\x8a\\\\xe0\\\\xb8\\\\xb1\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\x99\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xa1\\\\xe0\\\\xb8\\\\xb5\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x87\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\xa3\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x82\\\\xe0\\\\xb9\\\\x89\\\\xe0\\\\xb8\\\\xb2\\\\xe0\\\\xb8\\\\x96\\\\xe0\\\\xb8\\\\xb6\\\\xe0\\\\xb8\\\\x87 External Storage \\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x9e\\\\xe0\\\\xb8\\\\xb7\\\\xe0\\\\xb9\\\\x88\\\\xe0\\\\xb8\\\\xad\\\\xe0\\\\xb9\\\\x80\\\\xe0\\\\xb8\\\\x81\\\\xe0\\\\xb9\\\\x87\\\\xe0\\\\xb8\\\\x9a Cache 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      \\\\n \\\\n DeSantis, who blamed diversity initiatives for Silicon Valley Bank's downfall, pushed for rollback of bank regulations under renewed scrutiny\\\\n \\\\n

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      \\\\n \\\\n \\\\n \\\\n By Steve Contorno and Andrew Kaczynski, CNNIn the wake of Silicon Valley Bank\\\\xe2\\\\x80\\\\x99s demise, Florida Gov. Ron DeSantis pointed fingers at several culprits, including the company\\\\xe2\\\\x80\\\\x99s diversity initiatives and federal regulators who he said \\\\xe2\\\\x80\\\\x9calways seem to whiff\\\\xe2\\\\x80\\\\x9d at preventing a financial crisis. But left out of DeSantis\\\\xe2\\\\x80\\\\x99 comments was his own record on banking regulations.\\\\n \\\\n \\\\n \\\\n
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      \\\\n \\\\n DeSantis, who blamed diversity initiatives for Silicon Valley Bank's downfall, pushed for rollback of bank regulations under renewed scrutiny\\\\n \\\\n

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      \\\\n \\\\n \\\\n \\\\n In the wake of Silicon Valley Bank's demise, Florida Gov. Ron DeSantis pointed fingers at several culprits, including the company's diversity initiatives and federal regulators who he said "always seem to whiff" at preventing a financial crisis. But left out of DeSantis' comments was his own record on banking regulations.\\\\n \\\\n \\\\n \\\\n
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      \\\\n \\\\n DeSantis, who blamed diversity initiatives for Silicon Valley Bank's downfall, pushed for rollback of bank regulations under renewed scrutiny\\\\n \\\\n

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      \\\\n \\\\n \\\\n \\\\n In the wake of Silicon Valley Bank's demise, Florida Gov. Ron DeSantis pointed fingers at several culprits, including the company's diversity initiatives and federal regulators who he said "always seem to whiff" at preventing a financial crisis. But left out of DeSantis' comments was his own record on banking regulations.\\\\n \\\\n \\\\n \\\\n
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      @KFILE — 432,346 followers, 13,233 tweets

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      \\\\n \\\\n @CheeseWhiz my grandfather was Arbereshe Albanian and Irish so it always rounds out to something around there on Ancestry depending on the update pic.twitter.com/uNP4vnwkbe\\\\n \\\\n
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      \\\\n \\\\n Quite the scene in the Signature Bank collapse, in which total panic just spread in these WhatsApp groups.\\\\n\\\\n https://www.wsj.com/articles/signature-banks-quirky-mix-of-customers-fueled-its-rise-and-hastened-its-fall-8bc10cd2 \\\\xe2\\\\x80\\\\xa6 pic.twitter.com/8uN9q1C8Dn\\\\n \\\\n
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      \\\\n \\\\n One of our friends gifted us this Sesame Street book about love for Talia. And it always reminds me of her big sister when reading it. Life is amazing but it can be so fragile in how precious it is. pic.twitter.com/3IsCK4qb3W\\\\n \\\\n
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      \\\\n \\\\n Celebrating my 16% Irish heritage pic.twitter.com/ypUtaFYF3M\\\\n \\\\n
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      \\\\n \\\\n often think about the discontinued Dunkin Donuts mascot pic.twitter.com/JG1ogHCgTk\\\\n \\\\n
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diff --git a/irixys23/webpage-classification/example-data/truth/truths.jsonl b/irixys23/webpage-classification/example-data/truth/truths.jsonl index caf12c1..77f012e 100644 --- a/irixys23/webpage-classification/example-data/truth/truths.jsonl +++ b/irixys23/webpage-classification/example-data/truth/truths.jsonl @@ -1,10 +1,10 @@ -{"uid": 22084230, "label": "Adult", "sublabel": "Adult"} -{"uid": 22075699, "label": "Benign", "sublabel": "Arts"} -{"uid": 22014284, "label": "Adult", "sublabel": "Adult"} -{"uid": 22070542, "label": "Benign", "sublabel": "Games"} -{"uid": 22040149, "label": "Benign", "sublabel": "Arts"} -{"uid": 22101207, "label": "Privacy_policy", "sublabel": "Privacy_policy"} -{"uid": 22022604, "label": "Malicious", "sublabel": "Spam"} -{"uid": 21998788, "label": "Malicious", "sublabel": "Defacement"} -{"uid": 22041498, "label": "Benign", "sublabel": "Arts"} -{"uid": 22072890, "label": "Benign", "sublabel": "Science"} +{"uid": 22084230, "label": "Adult", "sublabel": "Adult"} +{"uid": 22075699, "label": "Benign", "sublabel": "Arts"} +{"uid": 22014284, "label": "Adult", "sublabel": "Adult"} +{"uid": 22070542, "label": "Benign", "sublabel": "Games"} +{"uid": 22040149, "label": "Benign", "sublabel": "Arts"} +{"uid": 22101207, "label": "Privacy_policy", "sublabel": "Privacy_policy"} +{"uid": 22022604, "label": "Malicious", "sublabel": "Spam"} +{"uid": 21998788, "label": "Malicious", "sublabel": "Defacement"} +{"uid": 22041498, "label": "Benign", "sublabel": "Arts"} +{"uid": 22072890, "label": "Benign", "sublabel": "Science"} diff --git a/ks-ORAKEL/.gitkeep b/ks-ORAKEL/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/ows-webinar25/README.md b/ows-webinar25/README.md index a08e262..c0910b3 100644 --- a/ows-webinar25/README.md +++ b/ows-webinar25/README.md @@ -1,14 +1,14 @@ -# Code for the OWS Webinar on Search Components: Development and Evaluation - -This repository contains the code for the OWS webinar on ["Search Components: Development and Evaluation"](https://openwebsearch.eu/community/trainings-webinars/) that was held by Maik Fröbe on 3 February 2025. - -The [slides](https://downloads.webis.de/talks/froebe2025a.pdf) are available as well as the [hands-on notebook](search-components-hands-on-notebook.ipynb). - -## Description - -Search engines use retrieval pipelines to create a list of search results shown to users. Those retrieval pipelines combine components that process queries (e.g., query expansion, performance prediction, etc.), documents (e.g., spam classification, document expansion/reduction, etc.), or query-document pairs (e.g., neural re-rankers) - -## Recording - -The webinar was recorded and the recording is [available online](https://openwebsearch.eu/community/trainings-webinars/). - +# Code for the OWS Webinar on Search Components: Development and Evaluation + +This repository contains the code for the OWS webinar on ["Search Components: Development and Evaluation"](https://openwebsearch.eu/community/trainings-webinars/) that was held by Maik Fröbe on 3 February 2025. + +The [slides](https://downloads.webis.de/talks/froebe2025a.pdf) are available as well as the [hands-on notebook](search-components-hands-on-notebook.ipynb). + +## Description + +Search engines use retrieval pipelines to create a list of search results shown to users. Those retrieval pipelines combine components that process queries (e.g., query expansion, performance prediction, etc.), documents (e.g., spam classification, document expansion/reduction, etc.), or query-document pairs (e.g., neural re-rankers) + +## Recording + +The webinar was recorded and the recording is [available online](https://openwebsearch.eu/community/trainings-webinars/). + diff --git a/ows-webinar25/search-components-hands-on-notebook.ipynb b/ows-webinar25/search-components-hands-on-notebook.ipynb index dca05a2..f6ac70f 100644 --- a/ows-webinar25/search-components-hands-on-notebook.ipynb +++ b/ows-webinar25/search-components-hands-on-notebook.ipynb @@ -1,937 +1,937 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Install Dependencies\n", - "\n", - "We will use [TIREx](https://www.tira.io/tirex/components), an integration of [ir-datasets](https://ir-datasets.com/), [TIRA](https://www.tira.io/), and [PyTerrier](https://github.com/terrier-org/pyterrier) for fast experimentation.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install ir-datasets 'python-terrier==0.10.0' 'git+https://github.com/tira-io/tira.git@pyterrier-artifacts#egg=tira&subdirectory=python-client' 'git+https://github.com/webis-de/auto-ir-metadata@dev' 'git+https://github.com/mam10eks/autoqrels.git' 'git+https://github.com/OpenWebSearch/wows-code.git#egg=wows-eval&subdirectory=ecir25/wows-eval' --break-system-packages" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Import Libraries\n", - "\n", - "We create an API client to interact with the TIRA platform (e.g., to load datasets and submit runs).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "from tira.third_party_integrations import ensure_pyterrier_is_loaded\n", - "from tira.rest_api_client import Client\n", - "\n", - "ensure_pyterrier_is_loaded()\n", - "import pyterrier as pt\n", - "tira = Client()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Step 2: Load the dataset\n", - "\n", - "We load a small teaching-oriented dataset by its ir_datasets ID from TIRA. The dataset is a subsample of MS MARCO.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "pt_dataset = pt.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Build an index\n", - "\n", - "We will then create an index from the documents in the dataset we just loaded.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training documents: 38%|███▊ | 25706/68261 [00:03<00:04, 8580.47it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "09:44:51.646 [ForkJoinPool-2-worker-1] WARN org.terrier.structures.indexing.Indexer - Adding an empty document to the index (6114613) - further warnings are suppressed\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training documents: 100%|██████████| 68261/68261 [00:08<00:00, 7757.15it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "09:44:59.055 [ForkJoinPool-2-worker-1] WARN org.terrier.structures.indexing.Indexer - Indexed 1 empty documents\n" - ] - } - ], - "source": [ - "indexer = pt.IterDictIndexer(\n", - " # Store the index in the `index` directory.\n", - " \"../data/index\",\n", - " meta={'docno': 50, 'text': 4096},\n", - " # If an index already exists there, then overwrite it.\n", - " overwrite=True,\n", - ")\n", - "index = indexer.index(pt_dataset.get_corpus_iter())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Look at Evaluation Data\n", - "\n", - "We reduce the topics to two information needs to make the evaluation a bit faster:\n", - "\n", - "- `who sings monk theme song`\n", - "- `what is the most popular food in switzerland`" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      qiddocnolabeliteration
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      " - ], - "text/plain": [ - " qid docno label iteration\n", - "2790 833860 115142 1 0\n", - "2791 833860 1524401 0 0\n", - "2792 833860 1524402 0 0\n", - "2793 833860 1524403 0 0\n", - "2794 833860 1524406 0 0\n", - "... ... ... ... ...\n", - "10269 1051399 8815205 0 0\n", - "10270 1051399 8818330 0 0\n", - "10271 1051399 904304 1 0\n", - "10272 1051399 909463 0 0\n", - "10273 1051399 909464 0 0\n", - "\n", - "[239 rows x 4 columns]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qrels = pt_dataset.get_qrels()\n", - "qrels = qrels[qrels['qid'].isin(('1051399', '833860'))]\n", - "\n", - "qrels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Define the retrieval pipeline\n", - "\n", - "We will define a simple retrieval pipeline using just BM25 as a baseline. For details, refer to the PyTerrier [documentation](https://pyterrier.readthedocs.io/) or [tutorial](https://github.com/terrier-org/ecir2021tutorial)." - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "bm25 = pt.BatchRetrieve(index, wmodel=\"BM25\")\n", - "\n", - "query_expansion = pt.rewrite.RM3(index)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 6: Evaluate the Retrieval System" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " name P_10 recall_100\n", - "0 BM25 0.35 0.254545" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pt.Experiment(\n", - " [bm25],\n", - " names=['BM25'],\n", - " topics=topics,\n", - " qrels=qrels,\n", - " eval_metrics=['P_10', 'recall_100']\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 7: Improve the Retrieval System\n", - "\n", - "Lets use large language models to try to improve the effectiveness of our retrieval system.\n", - "\n", - "One of the fundamental tasks that large language models might help with is to assess [if a document is relevant to a given query](https://downloads.webis.de/publications/papers/faggioli_2023b.pdf).\n", - "\n", - "![Relevance Assessments with an LLM](relevance-assessments.png \"relevance-assessments.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [], - "source": [ - "from wows_eval import evaluate as wows_evaluate\n", - "\n", - "from autoqrels.zeroshot import GradedMonoPrompt\n", - "from auto_ir_metadata import Environment\n", - "import pandas as pd\n", - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "WOWS_DATASET_ID = 'wows-eval/pointwise-smoke-test-20250128-training'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
      2cb7b20d0-def6-46c4-ae44-a78f00b47735who sings monk theme songHowever, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman.
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      " - ], - "text/plain": [ - " id query \\\n", - "0 32d23068-7440-4891-9958-42325f98a604 who sings monk theme song \n", - "1 cde83146-ac3e-4bc5-a959-f2006ac7b8de who sings monk theme song \n", - "2 cb7b20d0-def6-46c4-ae44-a78f00b47735 who sings monk theme song \n", - "\n", - " unknown \n", - "0 This is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk. \n", - "1 Walker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself. \n", - "2 However, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman. " - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_data = tira.pd.inputs(WOWS_DATASET_ID)\n", - "input_data.head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How Do we solve this task?\n", - "\n", - "\n", - "\n", - "\n", - "### Step 1: Describe the task in the prompt:\n", - "\n", - "For instance:\n", - "\n", - "```\n", - "Instruction: Indicate if the passage answers the question.\n", - "```\n", - "\n", - "### Step 2: In Context Learning:\n", - "\n", - "Provide a few examples:\n", - "\n", - "```\n", - "###\n", - "Example 1:\n", - "Question: At what age kids start to read?\n", - "Passage: Most kids say 1–2 words by 15 months and 3 or more words by 18 months.\n", - "Answer: Not Relevant\n", - "```\n", - "\n", - "```\n", - "###\n", - "Example 2:\n", - "Question: What are the 5 P's of drawing?\n", - "Passage: The 5 P's of drawning are (1) Patience, (2) Positive feedback, (3) Perseverance, (4) Practicing, and (5) Passion.\n", - "Answer: Perfectly Relevant\n", - "```\n", - "\n", - "\n", - "### Step 3: Teamwork :)\n", - "\n", - "Please share your task descriptions and in-context-learning examples in the chat. We will combine and test them to evaluate your prompts.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "BACKBONE_MODEL = \"flan-t5-small\"\n", - "\n", - "PROMPT = \"\"\"Instruction: Indicate if the passage answers the question.\n", - "###\n", - "Example 1:\n", - "Question: At about what age do adults normally begin to lose bone mass?\n", - "Passage: For most people, bone mass peaks during the third decade of life. By this age, men typically have accumulated more bone mass than women. After this point, the amount of bone in the skeleton typically begins to decline slowly as removal of old bone exceeds formation of new bone.\n", - "Answer: Perfectly relevant\n", - "###\n", - "Example 2:\n", - "Question: when and where did the battle of manassas take place\n", - "Passage: Summary of the Battle of Bull Run. The conflict took place close to Manassas Junction, Virginia. Around 35,000 Union soldiers marched from Washing D.C. towards Bull Run (a small river) where a 20,000 troop Confederate force was stationed.\n", - "Answer: Irrelevant\n", - "###\n", - "###\n", - "Example 3:\n", - "Question: what is lbm in body composition\n", - "Passage: They also measured the participants body fat and clean body mass of muscle mass, obtained by subtracting the body fat weight from the total body weight.\n", - "Answer: Relevant\n", - "###\n", - "Example 4:\n", - "Question: {{ query_text }}\n", - "Passage: {{ unk_doc_text }}\n", - "Answer:\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "flan_t5_small_assessor = GradedMonoPrompt(\n", - " backbone=f'google/{BACKBONE_MODEL}',\n", - " prompt=PROMPT,\n", - " dataset=None\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.0.263609
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      2cb7b20d0-def6-46c4-ae44-a78f00b47735who sings monk theme songHowever, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman.0.280311
      \n", - "" - ], - "text/plain": [ - " id query \\\n", - "0 32d23068-7440-4891-9958-42325f98a604 who sings monk theme song \n", - "1 cde83146-ac3e-4bc5-a959-f2006ac7b8de who sings monk theme song \n", - "2 cb7b20d0-def6-46c4-ae44-a78f00b47735 who sings monk theme song \n", - "\n", - " unknown \\\n", - "0 This is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk. \n", - "1 Walker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself. \n", - "2 However, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman. \n", - "\n", - " probability_relevant \n", - "0 0.263609 \n", - "1 0.240609 \n", - "2 0.280311 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with Environment().measure() as tracked_experiment:\n", - " predictions = flan_t5_small_assessor.predict(input_data)\n", - "\n", - "predictions.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/a03c0610-e1fa-48fa-bb2e-1737742fb94a\n" - ] - }, - { - "data": { - "text/plain": [ - "{'tau_ap': 0.07944444444444437,\n", - " 'kendall': 0.29523809523809524,\n", - " 'spearman': 0.32500000000000007,\n", - " 'pearson': 0.32499999999999996}" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wows_evaluate(\n", - " predictions,\n", - " WOWS_DATASET_ID,\n", - " upload=True,\n", - " system_name=f'auto-qrels-pointwise-{BACKBONE_MODEL}',\n", - " system_description=\"We use autoqrels [1] with a custom in-context learning prompt for pointwise relevance judgments.\\n\\n[1] - https://github.com/seanmacavaney/autoqrels\",\n", - " environment=tracked_experiment\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 8: Build a Chain of Search Components\n", - "\n", - "\n", - "Retrieval pipelines easily become complex.\n", - "Here, we use the most simple operator `>>` to chain components." - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "def llm_predictions_to_ranking(predictions):\n", - " ids = {i['id']: i.to_dict() for _, i in tira.pd.truths(WOWS_DATASET_ID).iterrows()}\n", - " ret = []\n", - " for _, i in predictions.iterrows():\n", - " qid, docno = ids[i['id']]['query_id'], ids[i['id']]['unknown_doc_id']\n", - " ret += [{'qid': qid, 'docno': docno, 'score': i['probability_relevant']}]\n", - "\n", - " return pt.Transformer.from_df(pd.DataFrame(ret))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "improved_pipeline = bm25 >> \\\n", - " llm_predictions_to_ranking(predictions) >> \\\n", - " query_expansion >> \\\n", - " bm25" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 9: Understand what the Pipelines Does" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'who sings monk theme song'" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bm25(topics)['query'].iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'applypipeline:off theme^0.197192997 4^0.014035087 real^0.077192985 show^0.014035087 song^0.197192997 sing^0.155087724 search^0.014035087 lyric^0.028070174 monk^0.120000005 who^0.155087724 time^0.028070174'" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "improved_pipeline(topics)['query'].iloc[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 10: Evaluate and Compare" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " name P_10 recall_100\n", - "0 BM25 0.35 0.254545\n", - "1 Improved 0.45 0.268182" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pt.Experiment(\n", - " [bm25, improved_pipeline],\n", - " names=['BM25', 'Improved'],\n", - " topics=topics,\n", - " qrels=qrels,\n", - " eval_metrics=['P_10', 'recall_100']\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Dependencies\n", + "\n", + "We will use [TIREx](https://www.tira.io/tirex/components), an integration of [ir-datasets](https://ir-datasets.com/), [TIRA](https://www.tira.io/), and [PyTerrier](https://github.com/terrier-org/pyterrier) for fast experimentation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install ir-datasets 'python-terrier==0.10.0' 'git+https://github.com/tira-io/tira.git@pyterrier-artifacts#egg=tira&subdirectory=python-client' 'git+https://github.com/webis-de/auto-ir-metadata@dev' 'git+https://github.com/mam10eks/autoqrels.git' 'git+https://github.com/OpenWebSearch/wows-code.git#egg=wows-eval&subdirectory=ecir25/wows-eval' --break-system-packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Import Libraries\n", + "\n", + "We create an API client to interact with the TIRA platform (e.g., to load datasets and submit runs).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "from tira.third_party_integrations import ensure_pyterrier_is_loaded\n", + "from tira.rest_api_client import Client\n", + "\n", + "ensure_pyterrier_is_loaded()\n", + "import pyterrier as pt\n", + "tira = Client()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 2: Load the dataset\n", + "\n", + "We load a small teaching-oriented dataset by its ir_datasets ID from TIRA. The dataset is a subsample of MS MARCO.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pt_dataset = pt.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Build an index\n", + "\n", + "We will then create an index from the documents in the dataset we just loaded.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training documents: 38%|███▊ | 25706/68261 [00:03<00:04, 8580.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:44:51.646 [ForkJoinPool-2-worker-1] WARN org.terrier.structures.indexing.Indexer - Adding an empty document to the index (6114613) - further warnings are suppressed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training documents: 100%|██████████| 68261/68261 [00:08<00:00, 7757.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:44:59.055 [ForkJoinPool-2-worker-1] WARN org.terrier.structures.indexing.Indexer - Indexed 1 empty documents\n" + ] + } + ], + "source": [ + "indexer = pt.IterDictIndexer(\n", + " # Store the index in the `index` directory.\n", + " \"../data/index\",\n", + " meta={'docno': 50, 'text': 4096},\n", + " # If an index already exists there, then overwrite it.\n", + " overwrite=True,\n", + ")\n", + "index = indexer.index(pt_dataset.get_corpus_iter())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Look at Evaluation Data\n", + "\n", + "We reduce the topics to two information needs to make the evaluation a bit faster:\n", + "\n", + "- `who sings monk theme song`\n", + "- `what is the most popular food in switzerland`" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " qid docno label iteration\n", + "2790 833860 115142 1 0\n", + "2791 833860 1524401 0 0\n", + "2792 833860 1524402 0 0\n", + "2793 833860 1524403 0 0\n", + "2794 833860 1524406 0 0\n", + "... ... ... ... ...\n", + "10269 1051399 8815205 0 0\n", + "10270 1051399 8818330 0 0\n", + "10271 1051399 904304 1 0\n", + "10272 1051399 909463 0 0\n", + "10273 1051399 909464 0 0\n", + "\n", + "[239 rows x 4 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qrels = pt_dataset.get_qrels()\n", + "qrels = qrels[qrels['qid'].isin(('1051399', '833860'))]\n", + "\n", + "qrels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Define the retrieval pipeline\n", + "\n", + "We will define a simple retrieval pipeline using just BM25 as a baseline. For details, refer to the PyTerrier [documentation](https://pyterrier.readthedocs.io/) or [tutorial](https://github.com/terrier-org/ecir2021tutorial)." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "bm25 = pt.BatchRetrieve(index, wmodel=\"BM25\")\n", + "\n", + "query_expansion = pt.rewrite.RM3(index)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Evaluate the Retrieval System" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " name P_10 recall_100\n", + "0 BM25 0.35 0.254545" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.Experiment(\n", + " [bm25],\n", + " names=['BM25'],\n", + " topics=topics,\n", + " qrels=qrels,\n", + " eval_metrics=['P_10', 'recall_100']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7: Improve the Retrieval System\n", + "\n", + "Lets use large language models to try to improve the effectiveness of our retrieval system.\n", + "\n", + "One of the fundamental tasks that large language models might help with is to assess [if a document is relevant to a given query](https://downloads.webis.de/publications/papers/faggioli_2023b.pdf).\n", + "\n", + "![Relevance Assessments with an LLM](relevance-assessments.png \"relevance-assessments.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "from wows_eval import evaluate as wows_evaluate\n", + "\n", + "from autoqrels.zeroshot import GradedMonoPrompt\n", + "from auto_ir_metadata import Environment\n", + "import pandas as pd\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "WOWS_DATASET_ID = 'wows-eval/pointwise-smoke-test-20250128-training'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      idqueryunknown
      032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.
      1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.
      2cb7b20d0-def6-46c4-ae44-a78f00b47735who sings monk theme songHowever, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman.
      \n", + "
      " + ], + "text/plain": [ + " id query \\\n", + "0 32d23068-7440-4891-9958-42325f98a604 who sings monk theme song \n", + "1 cde83146-ac3e-4bc5-a959-f2006ac7b8de who sings monk theme song \n", + "2 cb7b20d0-def6-46c4-ae44-a78f00b47735 who sings monk theme song \n", + "\n", + " unknown \n", + "0 This is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk. \n", + "1 Walker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself. \n", + "2 However, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman. " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_data = tira.pd.inputs(WOWS_DATASET_ID)\n", + "input_data.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How Do we solve this task?\n", + "\n", + "\n", + "\n", + "\n", + "### Step 1: Describe the task in the prompt:\n", + "\n", + "For instance:\n", + "\n", + "```\n", + "Instruction: Indicate if the passage answers the question.\n", + "```\n", + "\n", + "### Step 2: In Context Learning:\n", + "\n", + "Provide a few examples:\n", + "\n", + "```\n", + "###\n", + "Example 1:\n", + "Question: At what age kids start to read?\n", + "Passage: Most kids say 1–2 words by 15 months and 3 or more words by 18 months.\n", + "Answer: Not Relevant\n", + "```\n", + "\n", + "```\n", + "###\n", + "Example 2:\n", + "Question: What are the 5 P's of drawing?\n", + "Passage: The 5 P's of drawning are (1) Patience, (2) Positive feedback, (3) Perseverance, (4) Practicing, and (5) Passion.\n", + "Answer: Perfectly Relevant\n", + "```\n", + "\n", + "\n", + "### Step 3: Teamwork :)\n", + "\n", + "Please share your task descriptions and in-context-learning examples in the chat. We will combine and test them to evaluate your prompts.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "BACKBONE_MODEL = \"flan-t5-small\"\n", + "\n", + "PROMPT = \"\"\"Instruction: Indicate if the passage answers the question.\n", + "###\n", + "Example 1:\n", + "Question: At about what age do adults normally begin to lose bone mass?\n", + "Passage: For most people, bone mass peaks during the third decade of life. By this age, men typically have accumulated more bone mass than women. After this point, the amount of bone in the skeleton typically begins to decline slowly as removal of old bone exceeds formation of new bone.\n", + "Answer: Perfectly relevant\n", + "###\n", + "Example 2:\n", + "Question: when and where did the battle of manassas take place\n", + "Passage: Summary of the Battle of Bull Run. The conflict took place close to Manassas Junction, Virginia. Around 35,000 Union soldiers marched from Washing D.C. towards Bull Run (a small river) where a 20,000 troop Confederate force was stationed.\n", + "Answer: Irrelevant\n", + "###\n", + "###\n", + "Example 3:\n", + "Question: what is lbm in body composition\n", + "Passage: They also measured the participants body fat and clean body mass of muscle mass, obtained by subtracting the body fat weight from the total body weight.\n", + "Answer: Relevant\n", + "###\n", + "Example 4:\n", + "Question: {{ query_text }}\n", + "Passage: {{ unk_doc_text }}\n", + "Answer:\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "flan_t5_small_assessor = GradedMonoPrompt(\n", + " backbone=f'google/{BACKBONE_MODEL}',\n", + " prompt=PROMPT,\n", + " dataset=None\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      idqueryunknownprobability_relevant
      032d23068-7440-4891-9958-42325f98a604who sings monk theme songThis is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk.0.263609
      1cde83146-ac3e-4bc5-a959-f2006ac7b8dewho sings monk theme songWalker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself.0.240609
      2cb7b20d0-def6-46c4-ae44-a78f00b47735who sings monk theme songHowever, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman.0.280311
      \n", + "" + ], + "text/plain": [ + " id query \\\n", + "0 32d23068-7440-4891-9958-42325f98a604 who sings monk theme song \n", + "1 cde83146-ac3e-4bc5-a959-f2006ac7b8de who sings monk theme song \n", + "2 cb7b20d0-def6-46c4-ae44-a78f00b47735 who sings monk theme song \n", + "\n", + " unknown \\\n", + "0 This is a reference to the minor controversy that brewed among Monk fans over the introduction of the new theme song It's A Jungle Out There written and performed by Randy Newman in the second season of Monk. \n", + "1 Walker, Texas Ranger. Chuck Norris thought “Eyes of a Ranger” would be the perfect theme song for his new show Walker, Texas Ranger. He wanted his friend Randy Travis should sing it, but CBS had a different idea: The network suggested Norris sing the theme himself. \n", + "2 However, as Brave 's soundtrack reveals, the movie is also noteworthy for being one of the studio's most musical films, especially for one not featuring music by Pixar's go-to songwriter Randy Newman. \n", + "\n", + " probability_relevant \n", + "0 0.263609 \n", + "1 0.240609 \n", + "2 0.280311 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with Environment().measure() as tracked_experiment:\n", + " predictions = flan_t5_small_assessor.predict(input_data)\n", + "\n", + "predictions.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run uploaded to TIRA. Claim ownership via: https://www.tira.io/claim-submission/a03c0610-e1fa-48fa-bb2e-1737742fb94a\n" + ] + }, + { + "data": { + "text/plain": [ + "{'tau_ap': 0.07944444444444437,\n", + " 'kendall': 0.29523809523809524,\n", + " 'spearman': 0.32500000000000007,\n", + " 'pearson': 0.32499999999999996}" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wows_evaluate(\n", + " predictions,\n", + " WOWS_DATASET_ID,\n", + " upload=True,\n", + " system_name=f'auto-qrels-pointwise-{BACKBONE_MODEL}',\n", + " system_description=\"We use autoqrels [1] with a custom in-context learning prompt for pointwise relevance judgments.\\n\\n[1] - https://github.com/seanmacavaney/autoqrels\",\n", + " environment=tracked_experiment\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8: Build a Chain of Search Components\n", + "\n", + "\n", + "Retrieval pipelines easily become complex.\n", + "Here, we use the most simple operator `>>` to chain components." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "def llm_predictions_to_ranking(predictions):\n", + " ids = {i['id']: i.to_dict() for _, i in tira.pd.truths(WOWS_DATASET_ID).iterrows()}\n", + " ret = []\n", + " for _, i in predictions.iterrows():\n", + " qid, docno = ids[i['id']]['query_id'], ids[i['id']]['unknown_doc_id']\n", + " ret += [{'qid': qid, 'docno': docno, 'score': i['probability_relevant']}]\n", + "\n", + " return pt.Transformer.from_df(pd.DataFrame(ret))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "improved_pipeline = bm25 >> \\\n", + " llm_predictions_to_ranking(predictions) >> \\\n", + " query_expansion >> \\\n", + " bm25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9: Understand what the Pipelines Does" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'who sings monk theme song'" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm25(topics)['query'].iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'applypipeline:off theme^0.197192997 4^0.014035087 real^0.077192985 show^0.014035087 song^0.197192997 sing^0.155087724 search^0.014035087 lyric^0.028070174 monk^0.120000005 who^0.155087724 time^0.028070174'" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "improved_pipeline(topics)['query'].iloc[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10: Evaluate and Compare" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " name P_10 recall_100\n", + "0 BM25 0.35 0.254545\n", + "1 Improved 0.45 0.268182" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.Experiment(\n", + " [bm25, improved_pipeline],\n", + " names=['BM25', 'Improved'],\n", + " topics=topics,\n", + " qrels=qrels,\n", + " eval_metrics=['P_10', 'recall_100']\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 36f1195ecd691b43a257382f9807cf408bd7d9fe Mon Sep 17 00:00:00 2001 From: Jonas Keller Date: Tue, 9 Dec 2025 08:59:17 +0100 Subject: [PATCH 2/5] feat: use LLM for query rewrite --- .../query-optimization.ipynb | 920 ++++++++++++++++++ 1 file changed, 920 insertions(+) create mode 100644 ecir26/template-new-approach/query-optimization.ipynb diff --git a/ecir26/template-new-approach/query-optimization.ipynb b/ecir26/template-new-approach/query-optimization.ipynb new file mode 100644 index 0000000..c29277a --- /dev/null +++ b/ecir26/template-new-approach/query-optimization.ipynb @@ -0,0 +1,920 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "b1d445c1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from glob import glob\n", + "import pyterrier as pt\n", + "from tira.third_party_integrations import ensure_pyterrier_is_loaded\n", + "from transformers import pipeline\n", + "from pathlib import Path\n", + "import ir_datasets\n", + "import torch\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "748de81b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Java started and loaded: pyterrier.java.colab, pyterrier.java, pyterrier.java.24, pyterrier.terrier.java [version=5.11 (build: craig.macdonald 2025-01-13 21:29), helper_version=0.0.8]\n" + ] + } + ], + "source": [ + "ensure_pyterrier_is_loaded()\n", + "ds_id = \"radboud-validation-20251114-training\"\n", + "dataset = pt.datasets.get_dataset(f\"irds:ir-lab-wise-2025/{ds_id}\")\n", + "topics = dataset.get_topics(\"title\")\n", + "qrels = dataset.get_qrels()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "86ae2048", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading existing index...\n", + "Index loaded: >\n" + ] + } + ], + "source": [ + "# Define where to store the index\n", + "index_path = \"./output/indexes/radboud-validation\"\n", + "abs_index_path = os.path.abspath(index_path)\n", + "\n", + "if not os.path.exists(abs_index_path + \"/data.properties\"):\n", + " print(\"Building index...\")\n", + " # Load the dataset directly to access documents\n", + " irds_ds = ir_datasets.load(f\"ir-lab-wise-2025/{ds_id}\")\n", + " \n", + " def doc_iter():\n", + " for doc in tqdm(irds_ds.docs_iter(), desc=\"Indexing\"):\n", + " yield {'docno': doc.doc_id, 'text': doc.default_text()}\n", + " \n", + " # Index the documents\n", + " indexer = pt.IterDictIndexer(abs_index_path, meta={'docno': 100})\n", + " index_ref = indexer.index(doc_iter())\n", + " index = pt.IndexFactory.of(index_ref)\n", + "else:\n", + " print(\"Loading existing index...\")\n", + " index = pt.IndexFactory.of(abs_index_path)\n", + "\n", + "print(f\"Index loaded: {index}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c5f03edc", + "metadata": {}, + "outputs": [], + "source": [ + "BACKBONE_MODEL = \"flan-t5-small\"\n", + "\n", + "PROMPT = \"\"\"Instruction: Rewrite the user's search query to be optimal for a keyword-based search engine (BM25). Remove unnecessary words, fix typos, and add relevant technical context or synonyms to improve retrieval.\n", + "\n", + "###\n", + "Example 1:\n", + "Input: why is my internet so slow today\n", + "Output: slow internet connection speed troubleshooting causes\n", + "###\n", + "Example 2:\n", + "Input: python list reverse\n", + "Output: python list reverse method syntax documentation\n", + "###\n", + "Example 3:\n", + "Input: symptoms of flu vs covid\n", + "Output: flu covid-19 symptoms difference comparison chart\n", + "###\n", + "Example 4:\n", + "Input: best italian food nyc near central park\n", + "Output: best italian restaurants new york city central park manhattan reviews\n", + "###\n", + "Example 5:\n", + "Input: {{ query_text }}\n", + "Output:\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "baa74eb8", + "metadata": {}, + "outputs": [], + "source": [ + "def optimize_query():\n", + " \"\"\"\n", + " Returns a PyTerrier transformer that rewrites the query using FLAN-T5.\n", + " \"\"\"\n", + " # Initialize the generation pipeline (device=-1 for CPU, 0 for GPU if available)\n", + " generator = pipeline(\"text2text-generation\", model=f\"google/{BACKBONE_MODEL}\", device=-1)\n", + "\n", + " def _rewrite_row(row):\n", + " input_text = PROMPT.replace(\"{{ query_text }}\", row['query'])\n", + " # Generate with specific parameters\n", + " results = generator(input_text, max_new_tokens=32, do_sample=False)\n", + " return results[0]['generated_text']\n", + "\n", + " return pt.apply.query(_rewrite_row)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8070d43b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Previewing query optimization:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Device set to use cpu\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "qid", + "rawType": "object", + "type": "string" + }, + { + "name": "query_0", + "rawType": "object", + "type": "string" + }, + { + "name": "query", + "rawType": "object", + "type": "string" + } + ], + "ref": "9f872df4-3de2-4019-8b65-3c54391ed28a", + "rows": [ + [ + "0", + "3", + "split ergo keyboard", + "split ergo keyboard" + ], + [ + "1", + "4", + "metoo Hollywood", + "metoo california" + ], + [ + "2", + "7", + "gastritis symptoms", + "stomach pain signs ###" + ], + [ + "3", + "8", + "What is privacy by design(PbD)?", + "What is privacy by design (PbD)?" + ], + [ + "4", + "13", + "Impact of Exercise on Depression", + "Impact of Exercise on Depression" + ], + [ + "5", + "15", + "Autonomous car ethics in unavoidable accidents", + "autonomous car ethics in unavoidable accidents" + ], + [ + "6", + "16", + "mahler sixth symphony concert 2025", + "mahler sixth symphony concert 2025" + ], + [ + "7", + "18", + "Climate change effects on agriculture", + "climate change effects on agriculture" + ], + [ + "8", + "20", + "History of urban rooftop gardening in European cities", + "history of urban rooftop gardening in european cities" + ], + [ + "9", + "23", + "excel sum cells", + "excel sum formula function add ###" + ], + [ + "10", + "24", + "Iceland nature", + "Iceland nature is a country in the southeastern part of the world" + ], + [ + "11", + "31", + "decision trees", + "Result in decision trees" + ], + [ + "12", + "32", + "Hyperion Dan Simmons review", + "Hyperion Dan Simmons review" + ], + [ + "13", + "34", + "early detection of fake news", + "early detection of fake news" + ], + [ + "14", + "39", + "Renewable Energy Storage Solutions", + "bbc renewable energy storage solutions" + ], + [ + "15", + "42", + "Automatic Speech Recognition", + "Detecting automatic speech recognition" + ], + [ + "16", + "43", + "Gothic architecture", + "bbc architecture style history cathedrals ###" + ], + [ + "17", + "44", + "The impact of online gaming on the brain", + "The impact of online gaming on the brain" + ], + [ + "18", + "46", + "Impact of Social Media on Adolescent Mental Health", + "Impact of Social Media on Adolescent Mental Health" + ], + [ + "19", + "48", + "Hypnosis for sleep", + "hypnosis for sleep" + ], + [ + "20", + "52", + "Protein shake before or after workout", + "protein shake before or after workout" + ], + [ + "21", + "54", + "Solar panel recycling regularions", + "solar panel recycling regularions" + ], + [ + "22", + "58", + "Growth of esports industry", + "Growth of esports industry" + ], + [ + "23", + "60", + "What can dogs eat", + "What can dogs eat?" + ], + [ + "24", + "64", + "Average data scientist salary", + "average data scientist salary" + ], + [ + "25", + "68", + "drinking water purification methods", + "drinking water purification methods" + ], + [ + "26", + "69", + "the rings of power", + "the rings of power" + ], + [ + "27", + "74", + "Homeassistant setup", + "homeassistant setup" + ] + ], + "shape": { + "columns": 3, + "rows": 28 + } + }, + "text/html": [ + "
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      923excel sum cellsexcel sum formula function add ###
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      1439Renewable Energy Storage Solutionsbbc renewable energy storage solutions
      1542Automatic Speech RecognitionDetecting automatic speech recognition
      1643Gothic architecturebbc architecture style history cathedrals ###
      1744The impact of online gaming on the brainThe impact of online gaming on the brain
      1846Impact of Social Media on Adolescent Mental He...Impact of Social Media on Adolescent Mental He...
      1948Hypnosis for sleephypnosis for sleep
      2052Protein shake before or after workoutprotein shake before or after workout
      2154Solar panel recycling regularionssolar panel recycling regularions
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      " + ], + "text/plain": [ + " qid query_0 \\\n", + "0 3 split ergo keyboard \n", + "1 4 metoo Hollywood \n", + "2 7 gastritis symptoms \n", + "3 8 What is privacy by design(PbD)? \n", + "4 13 Impact of Exercise on Depression \n", + "5 15 Autonomous car ethics in unavoidable accidents \n", + "6 16 mahler sixth symphony concert 2025 \n", + "7 18 Climate change effects on agriculture \n", + "8 20 History of urban rooftop gardening in European... \n", + "9 23 excel sum cells \n", + "10 24 Iceland nature \n", + "11 31 decision trees \n", + "12 32 Hyperion Dan Simmons review \n", + "13 34 early detection of fake news \n", + "14 39 Renewable Energy Storage Solutions \n", + "15 42 Automatic Speech Recognition \n", + "16 43 Gothic architecture \n", + "17 44 The impact of online gaming on the brain \n", + "18 46 Impact of Social Media on Adolescent Mental He... \n", + "19 48 Hypnosis for sleep \n", + "20 52 Protein shake before or after workout \n", + "21 54 Solar panel recycling regularions \n", + "22 58 Growth of esports industry \n", + "23 60 What can dogs eat \n", + "24 64 Average data scientist salary \n", + "25 68 drinking water purification methods \n", + "26 69 the rings of power \n", + "27 74 Homeassistant setup \n", + "\n", + " query \n", + "0 split ergo keyboard \n", + "1 metoo california \n", + "2 stomach pain signs ### \n", + "3 What is privacy by design (PbD)? \n", + "4 Impact of Exercise on Depression \n", + "5 autonomous car ethics in unavoidable accidents \n", + "6 mahler sixth symphony concert 2025 \n", + "7 climate change effects on agriculture \n", + "8 history of urban rooftop gardening in european... \n", + "9 excel sum formula function add ### \n", + "10 Iceland nature is a country in the southeaster... \n", + "11 Result in decision trees \n", + "12 Hyperion Dan Simmons review \n", + "13 early detection of fake news \n", + "14 bbc renewable energy storage solutions \n", + "15 Detecting automatic speech recognition \n", + "16 bbc architecture style history cathedrals ### \n", + "17 The impact of online gaming on the brain \n", + "18 Impact of Social Media on Adolescent Mental He... \n", + "19 hypnosis for sleep \n", + "20 protein shake before or after workout \n", + "21 solar panel recycling regularions \n", + "22 Growth of esports industry \n", + "23 What can dogs eat? \n", + "24 average data scientist salary \n", + "25 drinking water purification methods \n", + "26 the rings of power \n", + "27 homeassistant setup " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preview the optimization on the first few topics\n", + "print(\"Previewing query optimization:\")\n", + "llm_pipeline = optimize_query()\n", + "preview = llm_pipeline.transform(topics.head(50))\n", + "display(preview)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a2df4d03", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_15717/3392988863.py:2: DeprecationWarning: Call to deprecated class BatchRetrieve. (use pt.terrier.Retriever() instead) -- Deprecated since version 0.11.0.\n", + " bm25 = pt.BatchRetrieve(index, wmodel=\"BM25\")\n", + "Device set to use cpu\n" + ] + } + ], + "source": [ + "# Define the retrieval models\n", + "bm25 = pt.BatchRetrieve(index, wmodel=\"BM25\")\n", + "\n", + "# Create the pipeline: Rewrite -> Retrieve\n", + "retrieval_llm_pipeline = optimize_query() >> bm25" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "b21e789f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "name", + "rawType": "object", + "type": "string" + }, + { + "name": "map", + "rawType": "float64", + "type": "float" + }, + { + "name": "P_10", + "rawType": "float64", + "type": "float" + }, + { + "name": "recall_100", + "rawType": "float64", + "type": "float" + }, + { + "name": "ndcg_cut_10", + "rawType": "float64", + "type": "float" + } + ], + "ref": "76c19be9-29ce-4c6d-96b6-a6c8f01ca75f", + "rows": [ + [ + "0", + "BM25", + "0.38139460838085837", + "0.43214285714285705", + "0.6811976005445962", + "0.45163465633411787" + ], + [ + "1", + "LLM_optimized_queries", + "0.3353493199195746", + "0.3642857142857142", + "0.632991385676515", + "0.39313849193720296" + ] + ], + "shape": { + "columns": 5, + "rows": 2 + } + }, + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " name map P_10 recall_100 ndcg_cut_10\n", + "0 BM25 0.381395 0.432143 0.681198 0.451635\n", + "1 LLM_optimized_queries 0.335349 0.364286 0.632991 0.393138" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.Experiment(\n", + " [bm25, retrieval_llm_pipeline],\n", + " names=['BM25', 'LLM_optimized_queries'],\n", + " topics=topics,\n", + " qrels=qrels,\n", + " eval_metrics=['ndcg_cut_10', 'P_10', 'recall_100', 'map']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f2e59f2f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Starte: pyterrier-bm25-baseline ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Detected a hypervisor/virtualization technology. Some metrics might not be available due to configuration or availability of virtual hardware features.\n", + "\n", + "===== Processor information =====\n", + "Linux arch_perfmon flag : yes\n", + "Hybrid processor : no\n", + "IBRS and IBPB supported : yes\n", + "STIBP supported : yes\n", + "Spec arch caps supported : yes\n", + "Max CPUID level : 27\n", + "CPU model number : 140\n", + "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", + "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", + "Can not access CPUs Model Specific Registers (MSRs).\n", + "execute 'modprobe msr' as root user, then execute pcm as root user.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gespeichert in: output/runs/radboud-validation-20251114-training/pyterrier-bm25-baseline/run.txt.gz\n", + "--- Starte: pyterrier-flan-t5-bm25 ---\n", + "Gespeichert in: output/runs/radboud-validation-20251114-training/pyterrier-flan-t5-bm25/run.txt.gz\n" + ] + } + ], + "source": [ + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "# Output Basis-Pfad\n", + "output = Path(\"./output\")\n", + "\n", + "def run_tira_experiment(pipeline, pipeline_name, dataset_id, topics, description):\n", + " \"\"\"\n", + " Führt eine Pipeline aus und speichert sie exakt mit dem TIRA tracking context manager.\n", + " \"\"\"\n", + " # 1. Pfade definieren (Exakt wie in deinem Snippet)\n", + " target_dir = output / \"runs\" / dataset_id / pipeline_name\n", + " target_file = target_dir / \"run.txt.gz\"\n", + "\n", + " if target_file.exists():\n", + " print(f\"Run existiert bereits: {target_dir}\")\n", + " return\n", + "\n", + " # Ordner erstellen\n", + " target_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " print(f\"--- Starte: {pipeline_name} ---\")\n", + "\n", + " # 2. Der entscheidende Teil: Nutzung von tracking() für die metadata.yml\n", + " # Dies garantiert, dass die Daten exakt so gespeichert werden, wie TIRA es erwartet.\n", + " with tracking(\n", + " export_file_path=target_dir / \"retrieval-metadata.yml\", \n", + " export_format=ExportFormat.IR_METADATA, \n", + " system_description=description, \n", + " system_name=pipeline_name\n", + " ):\n", + " # Hier führen wir die vorher definierte Pipeline aus\n", + " run = pipeline.transform(topics)\n", + "\n", + " # 3. Ergebnis speichern\n", + " pt.io.write_results(run, str(target_file))\n", + " print(f\"Gespeichert in: {target_file}\")\n", + "\n", + "# --- Ausführung ---\n", + "\n", + "# 1. Speichere die BM25 Baseline\n", + "run_tira_experiment(\n", + " pipeline=bm25,\n", + " pipeline_name=\"pyterrier-bm25-baseline\",\n", + " dataset_id=ds_id,\n", + " topics=topics,\n", + " description=\"Standard BM25 retrieval without query expansion.\"\n", + ")\n", + "\n", + "# 2. Speichere die LLM Pipeline\n", + "run_tira_experiment(\n", + " pipeline=retrieval_llm_pipeline,\n", + " pipeline_name=\"pyterrier-flan-t5-bm25\",\n", + " dataset_id=ds_id,\n", + " topics=topics,\n", + " description=\"Query rewriting using FLAN-T5-small followed by BM25.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39fb720c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 497ecd438ed1678f9a7f55ad95a5a3effd10025d Mon Sep 17 00:00:00 2001 From: Sebko276 Date: Thu, 11 Dec 2025 18:54:50 +0100 Subject: [PATCH 3/5] added-orakel-02 --- ecir26/ks-orakel-02/.devcontainer.json | 17 +++ ecir26/ks-orakel-02/.gitignore | 2 + ecir26/ks-orakel-02/Dockerfile | 7 + ecir26/ks-orakel-02/Dockerfile.dev | 12 ++ ecir26/ks-orakel-02/README.md | 64 +++++++++ ecir26/ks-orakel-02/analysis.ipynb | 191 +++++++++++++++++++++++++ ecir26/ks-orakel-02/requirements.txt | 5 + ecir26/ks-orakel-02/retrieve.py | 77 ++++++++++ 8 files changed, 375 insertions(+) create mode 100644 ecir26/ks-orakel-02/.devcontainer.json create mode 100644 ecir26/ks-orakel-02/.gitignore create mode 100644 ecir26/ks-orakel-02/Dockerfile create mode 100644 ecir26/ks-orakel-02/Dockerfile.dev create mode 100644 ecir26/ks-orakel-02/README.md create mode 100644 ecir26/ks-orakel-02/analysis.ipynb create mode 100644 ecir26/ks-orakel-02/requirements.txt create mode 100644 ecir26/ks-orakel-02/retrieve.py diff --git a/ecir26/ks-orakel-02/.devcontainer.json b/ecir26/ks-orakel-02/.devcontainer.json new file mode 100644 index 0000000..c4699ca --- /dev/null +++ b/ecir26/ks-orakel-02/.devcontainer.json @@ -0,0 +1,17 @@ +{ + "dockerFile": "Dockerfile", + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "ms-toolsai.jupyter", + "ms-toolsai.datawrangler", + "ms-azuretools.vscode-docker" + ] + } + }, + "runArgs": [ + "--network=host", "--privileged" + ] +} diff --git a/ecir26/ks-orakel-02/.gitignore b/ecir26/ks-orakel-02/.gitignore new file mode 100644 index 0000000..b77bbb3 --- /dev/null +++ b/ecir26/ks-orakel-02/.gitignore @@ -0,0 +1,2 @@ +indexes +runs diff --git a/ecir26/ks-orakel-02/Dockerfile b/ecir26/ks-orakel-02/Dockerfile new file mode 100644 index 0000000..7ec6ad1 --- /dev/null +++ b/ecir26/ks-orakel-02/Dockerfile @@ -0,0 +1,7 @@ +# the repo already contains everything pre-installed. In case you want to modify something, you can still do this below +# The base image is build from https://github.com/OpenWebSearch/wows-code/blob/main/ecir26/template-new-approach/Dockerfile.dev +FROM ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt diff --git a/ecir26/ks-orakel-02/Dockerfile.dev b/ecir26/ks-orakel-02/Dockerfile.dev new file mode 100644 index 0000000..bf0656f --- /dev/null +++ b/ecir26/ks-orakel-02/Dockerfile.dev @@ -0,0 +1,12 @@ +# docker build -f Dockerfile.dev -t ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest . +FROM ubuntu:jammy + + +RUN apt-get update \ + && apt-get install -y git openjdk-11-jdk python3 python3-pip + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt \ + && python3 -c 'from tira.third_party_integrations import ensure_pyterrier_is_loaded; ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False)' + diff --git a/ecir26/ks-orakel-02/README.md b/ecir26/ks-orakel-02/README.md new file mode 100644 index 0000000..73a91ea --- /dev/null +++ b/ecir26/ks-orakel-02/README.md @@ -0,0 +1,64 @@ +# An Artifact-Free Approach + + +Get an overview of all possibilities to execute this baseline via: + +``` +./retrieve.py --help +``` + +This should yield an output like: + +``` +Usage: retrieve.py [OPTIONS] + +Options: + --dataset [radboud-validation-20251114-training|spot-check-20251122-training] + The dataset. [required] + --output PATH The output directory. [required] + --retrieval-model TEXT The retrieval model. + --text-field-to-retrieve [default|title|description] + The text field of the index on which to + retrieve. + --help Show this message and exit. +``` + +## Upload some run via: + +``` +tira-cli upload --directory DIRECTORY-WITH-RUN +``` + +## Run All Variants + +We run retrieval via: + +``` +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve default +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve default +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve default + +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve title +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve title +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve title + +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve description +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve description +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve description +``` +For each run, a directory is created at `output/runs/DATASET-ID`, which includes a `run.txt.gz` file. +After these files are generated, further investigate them by running the `analysis.ipynb` notebook. + +## Indexing + +Submit the indexing as code submission to tira + +``` +tira-cli code-submission \ + --path . \ + --task ir-lab-wise-2025 \ + --tira-vm-id ows \ + --dataset spot-check-20251122-training \ + --command '/index.py --dataset $inputDataset --output $outputDir/index --text-field default_text' \ + --dry-run +``` diff --git a/ecir26/ks-orakel-02/analysis.ipynb b/ecir26/ks-orakel-02/analysis.ipynb new file mode 100644 index 0000000..fd27ca9 --- /dev/null +++ b/ecir26/ks-orakel-02/analysis.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "c5266eee", + "metadata": {}, + "outputs": [], + "source": [ + "from glob import glob\n", + "import pyterrier as pt\n", + "from tira.third_party_integrations import ensure_pyterrier_is_loaded" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4536052c", + "metadata": {}, + "outputs": [], + "source": [ + "ensure_pyterrier_is_loaded()\n", + "ds_id = \"radboud-validation-20251114-training\"\n", + "dataset = pt.datasets.get_dataset(f\"irds:ir-lab-wise-2025/{ds_id}\")\n", + "topics = dataset.get_topics(\"title\")\n", + "qrels = dataset.get_qrels()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3d15d8a4", + "metadata": {}, + "outputs": [], + "source": [ + "runs = {}\n", + "\n", + "for run_name in glob(f'output/runs/{ds_id}/*/run.txt.gz'):\n", + " runs[run_name.split('/')[-2]] = pt.io.read_results(run_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "78acfa9a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "name", + "rawType": "object", + "type": "string" + }, + { + "name": "recip_rank", + "rawType": "float64", + "type": "float" + }, + { + "name": "P_10", + "rawType": "float64", + "type": "float" + }, + { + "name": "recall.1000", + "rawType": "float64", + "type": "float" + }, + { + "name": "ndcg_cut.2", + "rawType": "float64", + "type": "float" + }, + { + "name": "ndcg_cut.10", + "rawType": "float64", + "type": "float" + } + ], + "ref": "fa14c9c0-0485-4e3f-96d9-5624b9453e35", + "rows": [ + [ + "0", + "pyterrier-bm25-with-monoT5-Reranking-on-default_text", + "0.8166666666666665", + "0.475", + "0.5457338417920098", + "0.48656606535342556", + "0.5131372275182859" + ] + ], + "shape": { + "columns": 6, + "rows": 1 + } + }, + "text/html": [ + "
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The top 40 results are reranked with the monoT5ReRanker." + + with tracking(export_file_path=target_dir / "retrieval-metadata.yml", export_format=ExportFormat.IR_METADATA, system_description=description, system_name=tag): + run = res(topics) + + pt.io.write_results(run, target_file) + +@click.command() +@click.option("--dataset", type=click.Choice(["radboud-validation-20251114-training", "spot-check-20251122-training"]), required=True, help="The dataset.") +@click.option("--output", type=Path, required=False, default=Path("output"), help="The output directory.") +@click.option("--text-field-to-retrieve", type=click.Choice(["default_text", "title", "description"]), required=False, default="default_text", help="The text field of the documents on which to retrieve.") +def main(dataset, text_field_to_retrieve, output): + ensure_pyterrier_is_loaded(is_offline=False) + + index = get_index(dataset, text_field_to_retrieve, output) + run_retrieval(output, index, dataset, text_field_to_retrieve) + + +if __name__ == '__main__': + main() \ No newline at end of file From 19bec427382d44cb004bd596d65e335260e9b07c Mon Sep 17 00:00:00 2001 From: Jonas Keller Date: Fri, 12 Dec 2025 19:19:38 +0100 Subject: [PATCH 4/5] feat: add approach rewrite and rerank --- ks-ORAKEL-01/.devcontainer.json | 17 + ks-ORAKEL-01/.gitignore | 2 + ks-ORAKEL-01/Dockerfile | 7 + ks-ORAKEL-01/Dockerfile.dev | 12 + ks-ORAKEL-01/README.md | 64 ++ ks-ORAKEL-01/query-optimization.ipynb | 1216 +++++++++++++++++++++++++ ks-ORAKEL-01/requirements.txt | 9 + ks-ORAKEL/.gitkeep | 0 8 files changed, 1327 insertions(+) create mode 100644 ks-ORAKEL-01/.devcontainer.json create mode 100644 ks-ORAKEL-01/.gitignore create mode 100644 ks-ORAKEL-01/Dockerfile create mode 100644 ks-ORAKEL-01/Dockerfile.dev create mode 100644 ks-ORAKEL-01/README.md create mode 100644 ks-ORAKEL-01/query-optimization.ipynb create mode 100644 ks-ORAKEL-01/requirements.txt delete mode 100644 ks-ORAKEL/.gitkeep diff --git a/ks-ORAKEL-01/.devcontainer.json b/ks-ORAKEL-01/.devcontainer.json new file mode 100644 index 0000000..c4699ca --- /dev/null +++ b/ks-ORAKEL-01/.devcontainer.json @@ -0,0 +1,17 @@ +{ + "dockerFile": "Dockerfile", + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "ms-toolsai.jupyter", + "ms-toolsai.datawrangler", + "ms-azuretools.vscode-docker" + ] + } + }, + "runArgs": [ + "--network=host", "--privileged" + ] +} diff --git a/ks-ORAKEL-01/.gitignore b/ks-ORAKEL-01/.gitignore new file mode 100644 index 0000000..b77bbb3 --- /dev/null +++ b/ks-ORAKEL-01/.gitignore @@ -0,0 +1,2 @@ +indexes +runs diff --git a/ks-ORAKEL-01/Dockerfile b/ks-ORAKEL-01/Dockerfile new file mode 100644 index 0000000..7ec6ad1 --- /dev/null +++ b/ks-ORAKEL-01/Dockerfile @@ -0,0 +1,7 @@ +# the repo already contains everything pre-installed. In case you want to modify something, you can still do this below +# The base image is build from https://github.com/OpenWebSearch/wows-code/blob/main/ecir26/template-new-approach/Dockerfile.dev +FROM ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt diff --git a/ks-ORAKEL-01/Dockerfile.dev b/ks-ORAKEL-01/Dockerfile.dev new file mode 100644 index 0000000..bf0656f --- /dev/null +++ b/ks-ORAKEL-01/Dockerfile.dev @@ -0,0 +1,12 @@ +# docker build -f Dockerfile.dev -t ghcr.io/openwebsearch/wows-code/ecir-2026-pyterrier:latest . +FROM ubuntu:jammy + + +RUN apt-get update \ + && apt-get install -y git openjdk-11-jdk python3 python3-pip + +ADD requirements.txt /tmp/requirements.txt + +RUN pip3 install -r /tmp/requirements.txt \ + && python3 -c 'from tira.third_party_integrations import ensure_pyterrier_is_loaded; ensure_pyterrier_is_loaded(patch_ir_datasets=True, is_offline=False)' + diff --git a/ks-ORAKEL-01/README.md b/ks-ORAKEL-01/README.md new file mode 100644 index 0000000..73a91ea --- /dev/null +++ b/ks-ORAKEL-01/README.md @@ -0,0 +1,64 @@ +# An Artifact-Free Approach + + +Get an overview of all possibilities to execute this baseline via: + +``` +./retrieve.py --help +``` + +This should yield an output like: + +``` +Usage: retrieve.py [OPTIONS] + +Options: + --dataset [radboud-validation-20251114-training|spot-check-20251122-training] + The dataset. [required] + --output PATH The output directory. [required] + --retrieval-model TEXT The retrieval model. + --text-field-to-retrieve [default|title|description] + The text field of the index on which to + retrieve. + --help Show this message and exit. +``` + +## Upload some run via: + +``` +tira-cli upload --directory DIRECTORY-WITH-RUN +``` + +## Run All Variants + +We run retrieval via: + +``` +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve default +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve default +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve default + +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve title +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve title +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve title + +./retrieve.py --dataset DATASET-ID --output output --retrieval-model BM25 --text-field-to-retrieve description +./retrieve.py --dataset DATASET-ID --output output --retrieval-model PL2 --text-field-to-retrieve description +./retrieve.py --dataset DATASET-ID --output output --retrieval-model DirichletLM --text-field-to-retrieve description +``` +For each run, a directory is created at `output/runs/DATASET-ID`, which includes a `run.txt.gz` file. +After these files are generated, further investigate them by running the `analysis.ipynb` notebook. + +## Indexing + +Submit the indexing as code submission to tira + +``` +tira-cli code-submission \ + --path . \ + --task ir-lab-wise-2025 \ + --tira-vm-id ows \ + --dataset spot-check-20251122-training \ + --command '/index.py --dataset $inputDataset --output $outputDir/index --text-field default_text' \ + --dry-run +``` diff --git a/ks-ORAKEL-01/query-optimization.ipynb b/ks-ORAKEL-01/query-optimization.ipynb new file mode 100644 index 0000000..5b293da --- /dev/null +++ b/ks-ORAKEL-01/query-optimization.ipynb @@ -0,0 +1,1216 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b1d445c1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/workspaces/wows-code/ks-ORAKEL-01/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import pyterrier as pt\n", + "from tira.third_party_integrations import ensure_pyterrier_is_loaded\n", + "from transformers import pipeline\n", + "from pathlib import Path\n", + "import ir_datasets\n", + "import torch\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "748de81b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Java started and loaded: pyterrier.java.colab, pyterrier.java, pyterrier.java.24, pyterrier.terrier.java [version=5.11 (build: craig.macdonald 2025-01-13 21:29), helper_version=0.0.8]\n", + "Download: 4.30MiB [00:01, 4.29MiB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download finished. Extract...\n", + "Extraction finished: /root/.tira/extracted_datasets/ir-lab-wise-2025/radboud-validation-20251114-training/\n" + ] + } + ], + "source": [ + "ensure_pyterrier_is_loaded()\n", + "ds_id = \"radboud-validation-20251114-training\"\n", + "dataset = pt.datasets.get_dataset(f\"irds:ir-lab-wise-2025/{ds_id}\")\n", + "topics = dataset.get_topics(\"title\")\n", + "qrels = dataset.get_qrels()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "86ae2048", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building index...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Download: 383MiB [01:12, 5.53MiB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download finished. Extract...\n", + "Extraction finished: /root/.tira/extracted_datasets/ir-lab-wise-2025/radboud-validation-20251114-training/\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Indexing: 16763it [02:38, 237.73it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17:41:19.331 [ForkJoinPool-1-worker-3] WARN org.terrier.structures.indexing.Indexer -- Adding an empty document to the index (85024af06d66c78f9b6f9d02d888ed0dfd20edd9389d326f0635995d32dec9d9) - further warnings are suppressed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Indexing: 63621it [06:47, 156.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17:46:19.116 [ForkJoinPool-1-worker-3] WARN org.terrier.structures.indexing.Indexer -- Indexed 2 empty documents\n", + "Index loaded: >\n" + ] + } + ], + "source": [ + "index_path = \"./output/indexes/radboud-validation\"\n", + "abs_index_path = os.path.abspath(index_path)\n", + "\n", + "if not os.path.exists(abs_index_path + \"/data.properties\"):\n", + " print(\"Building index...\")\n", + " irds_ds = ir_datasets.load(f\"ir-lab-wise-2025/{ds_id}\")\n", + " \n", + " def doc_iter():\n", + " for doc in tqdm(irds_ds.docs_iter(), desc=\"Indexing\"):\n", + " yield {'docno': doc.doc_id, 'text': doc.default_text()}\n", + " \n", + " indexer = pt.IterDictIndexer(abs_index_path, meta={'docno': 100})\n", + " index_ref = indexer.index(doc_iter())\n", + " index = pt.IndexFactory.of(index_ref)\n", + "else:\n", + " print(\"Loading existing index...\")\n", + " index = pt.IndexFactory.of(abs_index_path)\n", + "\n", + "print(f\"Index loaded: {index}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c5f03edc", + "metadata": {}, + "outputs": [], + "source": [ + "BACKBONE_MODEL = \"flan-t5-base\"\n", + "\n", + "PROMPT = \"\"\"Instruction: Rewrite the user's search query to be optimal for a keyword-based search engine (BM25). Remove unnecessary words, fix typos, and add relevant technical context or synonyms to improve retrieval.\n", + "\n", + "###\n", + "Example 1:\n", + "Input: why is my internet so slow today\n", + "Output: slow internet connection speed troubleshooting causes\n", + "###\n", + "Example 2:\n", + "Input: python list reverse\n", + "Output: python list reverse method syntax documentation\n", + "###\n", + "Example 3:\n", + "Input: symptoms of flu vs covid\n", + "Output: flu covid-19 symptoms difference comparison chart\n", + "###\n", + "Example 4:\n", + "Input: best italian food nyc near central park\n", + "Output: best italian restaurants new york city central park manhattan reviews\n", + "###\n", + "Example 5:\n", + "Input: {{ query_text }}\n", + "Output:\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "baa74eb8", + "metadata": {}, + "outputs": [], + "source": [ + "def optimize_query():\n", + " generator = pipeline(\"text2text-generation\", model=f\"google/{BACKBONE_MODEL}\", device=-1)\n", + "\n", + " def _rewrite_row(row):\n", + " input_text = PROMPT.replace(\"{{ query_text }}\", row['query'])\n", + " results = generator(input_text, max_new_tokens=32, num_beams=5, early_stopping=True)\n", + " text = results[0]['generated_text']\n", + " return text.split(\"###\")[0].strip()\n", + "\n", + " return pt.apply.query(_rewrite_row)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8070d43b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Previewing query optimization:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Device set to use cpu\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "qid", + "rawType": "object", + "type": "string" + }, + { + "name": "query_0", + "rawType": "object", + "type": "string" + }, + { + "name": "query", + "rawType": "object", + "type": "string" + } + ], + "ref": "33524594-c889-4e19-b553-f4ae612798cb", + "rows": [ + [ + "0", + "3", + "split ergo keyboard", + "split ergo keyboard" + ], + [ + "1", + "4", + "metoo Hollywood", + "metoo Hollywood" + ], + [ + "2", + "7", + "gastritis symptoms", + "symptoms of gastritis" + ], + [ + "3", + "8", + "What is privacy by design(PbD)?", + "What is privacy by design(PbD)?" + ], + [ + "4", + "13", + "Impact of Exercise on Depression", + "Impact of Exercise on Depression" + ], + [ + "5", + "15", + "Autonomous car ethics in unavoidable accidents", + "Autonomous car ethics in unavoidable accidents" + ], + [ + "6", + "16", + "mahler sixth symphony concert 2025", + "mahler sixth symphony concert 2025" + ], + [ + "7", + "18", + "Climate change effects on agriculture", + "Climate change effects on agriculture" + ], + [ + "8", + "20", + "History of urban rooftop gardening in European cities", + "History of urban rooftop gardening in European cities" + ], + [ + "9", + "23", + "excel sum cells", + "Excel sum cells" + ], + [ + "10", + "24", + "Iceland nature", + "Iceland nature" + ], + [ + "11", + "31", + "decision trees", + "decision trees" + ], + [ + "12", + "32", + "Hyperion Dan Simmons review", + "Hyperion Dan Simmons review" + ], + [ + "13", + "34", + "early detection of fake news", + "Early detection of fake news" + ], + [ + "14", + "39", + "Renewable Energy Storage Solutions", + "Renewable Energy Storage Solutions" + ], + [ + "15", + "42", + "Automatic Speech Recognition", + "Automatic Speech Recognition" + ], + [ + "16", + "43", + "Gothic architecture", + "Gothic architecture" + ], + [ + "17", + "44", + "The impact of online gaming on the brain", + "The impact of online gaming on the brain" + ], + [ + "18", + "46", + "Impact of Social Media on Adolescent Mental Health", + "Impact of social media on adolescents mental health" + ], + [ + "19", + "48", + "Hypnosis for sleep", + "Hypnosis for sleep" + ], + [ + "20", + "52", + "Protein shake before or after workout", + "Protein shake before or after workout" + ], + [ + "21", + "54", + "Solar panel recycling regularions", + "Solar panel recycling regularions" + ], + [ + "22", + "58", + "Growth of esports industry", + "Growth of esports industry" + ], + [ + "23", + "60", + "What can dogs eat", + "What can dogs eat?" + ], + [ + "24", + "64", + "Average data scientist salary", + "Average salary for data scientist" + ], + [ + "25", + "68", + "drinking water purification methods", + "drinking water purification methods" + ], + [ + "26", + "69", + "the rings of power", + "The rings of power" + ], + [ + "27", + "74", + "Homeassistant setup", + "Home assistant setup" + ] + ], + "shape": { + "columns": 3, + "rows": 28 + } + }, + "text/html": [ + "
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      " + ], + "text/plain": [ + " name map P_10 recall_100 ndcg_cut_10\n", + "0 BM25 0.381395 0.432143 0.681198 0.451635\n", + "1 LLM_optimized_queries 0.381321 0.428571 0.681198 0.454508" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.Experiment(\n", + " [bm25, retrieval_llm_pipeline],\n", + " names=['BM25', 'LLM_optimized_queries'],\n", + " topics=topics,\n", + " qrels=qrels,\n", + " eval_metrics=['ndcg_cut_10', 'P_10', 'recall_100', 'map']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f2e59f2f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Starte: pyterrier-bm25-baseline ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Detected a hypervisor/virtualization technology. Some metrics might not be available due to configuration or availability of virtual hardware features.\n", + "\n", + "===== Processor information =====\n", + "Linux arch_perfmon flag : yes\n", + "Hybrid processor : no\n", + "IBRS and IBPB supported : yes\n", + "STIBP supported : yes\n", + "Spec arch caps supported : yes\n", + "Max CPUID level : 27\n", + "CPU model number : 140\n", + "PCM Error: can't open MSR handle for core 0 (No such file or directory)\n", + "Try no-MSR mode by setting env variable PCM_NO_MSR=1\n", + "Can not access CPUs Model Specific Registers (MSRs).\n", + "execute 'modprobe msr' as root user, then execute pcm as root user.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gespeichert in: output/runs/radboud-validation-20251114-training/pyterrier-bm25-baseline/run.txt.gz\n", + "--- Starte: pyterrier-flan-t5-bm25 ---\n", + "Gespeichert in: output/runs/radboud-validation-20251114-training/pyterrier-flan-t5-bm25/run.txt.gz\n" + ] + } + ], + "source": [ + "from tirex_tracker import tracking, ExportFormat\n", + "\n", + "output = Path(\"./output\")\n", + "\n", + "def run_tira_experiment(pipeline, pipeline_name, dataset_id, topics, description):\n", + " target_dir = output / \"runs\" / dataset_id / pipeline_name\n", + " target_file = target_dir / \"run.txt.gz\"\n", + "\n", + " if target_file.exists():\n", + " print(f\"Run existiert bereits: {target_dir}\")\n", + " return\n", + "\n", + " target_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " print(f\"--- Starte: {pipeline_name} ---\")\n", + "\n", + " with tracking(\n", + " export_file_path=target_dir / \"retrieval-metadata.yml\", \n", + " export_format=ExportFormat.IR_METADATA, \n", + " system_description=description, \n", + " system_name=pipeline_name\n", + " ):\n", + " run = pipeline.transform(topics)\n", + "\n", + " pt.io.write_results(run, str(target_file))\n", + " print(f\"Gespeichert in: {target_file}\")\n", + "\n", + "run_tira_experiment(\n", + " pipeline=bm25,\n", + " pipeline_name=\"pyterrier-bm25-baseline\",\n", + " dataset_id=ds_id,\n", + " topics=topics,\n", + " description=\"Standard BM25 retrieval without query expansion.\"\n", + ")\n", + "\n", + "run_tira_experiment(\n", + " pipeline=retrieval_llm_pipeline,\n", + " pipeline_name=\"pyterrier-flan-t5-bm25\",\n", + " dataset_id=ds_id,\n", + " topics=topics,\n", + " description=\"Query rewriting using FLAN-T5-small followed by BM25.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3c57a991", + "metadata": {}, + "source": [ + "## Reranker" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c626596a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading Docs: 63621it [00:27, 2273.79it/s]\n" + ] + } + ], + "source": [ + "irds_ds = ir_datasets.load(f\"ir-lab-wise-2025/{ds_id}\")\n", + "\n", + "doc_text_map = {doc.doc_id: doc.default_text() for doc in tqdm(irds_ds.docs_iter(), desc=\"Loading Docs\")}\n", + "\n", + "def _lookup_text(row):\n", + " return doc_text_map.get(row['docno'], \"\")\n", + "\n", + "text_getter = pt.apply.text(_lookup_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "61bb860b", + "metadata": {}, + "outputs": [], + "source": [ + "from sentence_transformers import CrossEncoder\n", + "\n", + "model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', device=\"cpu\")\n", + "\n", + "def _cross_encoder_score(row):\n", + " return float(model.predict([(row['query'], row['text'])])[0])\n", + "\n", + "pipeline_rerank = (bm25 % 100) >> text_getter >> pt.apply.doc_score(_cross_encoder_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e173ce09", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Device set to use cpu\n" + ] + } + ], + "source": [ + "combined_pipeline = optimize_query() >> (bm25 % 100) >> text_getter >> pt.apply.doc_score(_cross_encoder_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3c6bc624", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "name", + "rawType": "object", + "type": "string" + }, + { + "name": "map", + "rawType": "float64", + "type": "float" + }, + { + "name": "P_10", + "rawType": "float64", + "type": "float" + }, + { + "name": "recall_100", + "rawType": "float64", + "type": "float" + }, + { + "name": "ndcg_cut_10", + "rawType": "float64", + "type": "float" + } + ], + "ref": "1d4453ab-b1c2-4724-ab7a-5a14cd4a5102", + "rows": [ + [ + "0", + "BM25", + "0.38139460838085837", + "0.43214285714285705", + "0.6811976005445962", + "0.45163465633411787" + ], + [ + "1", + "LLM_optimized_queries", + "0.38132069260032536", + "0.4285714285714285", + "0.6811976005445962", + "0.4545081987962892" + ], + [ + "2", + "Rerank only", + "0.3832413919829099", + "0.4607142857142858", + "0.6811976005445962", + "0.49416968070966977" + ], + [ + "3", + "Rewrite_and_rerank", + "0.38993256146935534", + "0.46428571428571436", + "0.6811976005445962", + "0.5012757355749428" + ] + ], + "shape": { + "columns": 5, + "rows": 4 + } + }, + "text/html": [ + "
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      " + ], + "text/plain": [ + " name map P_10 recall_100 ndcg_cut_10\n", + "0 BM25 0.381395 0.432143 0.681198 0.451635\n", + "1 LLM_optimized_queries 0.381321 0.428571 0.681198 0.454508\n", + "2 Rerank only 0.383241 0.460714 0.681198 0.494170\n", + "3 Rewrite_and_rerank 0.389933 0.464286 0.681198 0.501276" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.Experiment(\n", + " [bm25, retrieval_llm_pipeline, pipeline_rerank, combined_pipeline],\n", + " names=['BM25', 'LLM_optimized_queries', \"Rerank only\", \"Rewrite_and_rerank\"],\n", + " topics=topics,\n", + " qrels=qrels,\n", + " eval_metrics=['ndcg_cut_10', 'P_10', 'recall_100', 'map']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "df5a9d22", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Starte: combined_rewrite_reranker ---\n", + "Gespeichert in: output/runs/radboud-validation-20251114-training/combined_rewrite_reranker/run.txt.gz\n" + ] + } + ], + "source": [ + "run_tira_experiment(\n", + " pipeline=combined_pipeline,\n", + " pipeline_name=\"combined_rewrite_reranker\",\n", + " dataset_id=ds_id,\n", + " topics=topics,\n", + " description=\"query rewrite with BM25 retrieval (top 100) followed by reranking.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef2025e5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ks-ORAKEL-01/requirements.txt b/ks-ORAKEL-01/requirements.txt new file mode 100644 index 0000000..648106e --- /dev/null +++ b/ks-ORAKEL-01/requirements.txt @@ -0,0 +1,9 @@ +python-terrier +tira>=0.0.188 +ir_datasets +tirex-tracker==0.2.15 +click +torch +transformers +sentence_transformers +nbformat \ No newline at end of file diff --git a/ks-ORAKEL/.gitkeep b/ks-ORAKEL/.gitkeep deleted file mode 100644 index e69de29..0000000 From 8925f00263447222c6a556617fe9793ed2df9cf4 Mon Sep 17 00:00:00 2001 From: Jonas Keller Date: Sat, 13 Dec 2025 06:18:29 +0100 Subject: [PATCH 5/5] refac: move oracle directory --- {ks-ORAKEL-01 => ecir26/ks-orakel-01}/.devcontainer.json | 0 {ks-ORAKEL-01 => ecir26/ks-orakel-01}/.gitignore | 0 {ks-ORAKEL-01 => ecir26/ks-orakel-01}/Dockerfile | 0 {ks-ORAKEL-01 => ecir26/ks-orakel-01}/Dockerfile.dev | 0 {ks-ORAKEL-01 => ecir26/ks-orakel-01}/README.md | 0 {ks-ORAKEL-01 => ecir26/ks-orakel-01}/query-optimization.ipynb | 0 {ks-ORAKEL-01 => ecir26/ks-orakel-01}/requirements.txt | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename {ks-ORAKEL-01 => ecir26/ks-orakel-01}/.devcontainer.json (100%) rename {ks-ORAKEL-01 => ecir26/ks-orakel-01}/.gitignore (100%) rename {ks-ORAKEL-01 => ecir26/ks-orakel-01}/Dockerfile (100%) rename {ks-ORAKEL-01 => ecir26/ks-orakel-01}/Dockerfile.dev (100%) rename {ks-ORAKEL-01 => ecir26/ks-orakel-01}/README.md (100%) rename {ks-ORAKEL-01 => ecir26/ks-orakel-01}/query-optimization.ipynb (100%) rename {ks-ORAKEL-01 => ecir26/ks-orakel-01}/requirements.txt (100%) diff --git a/ks-ORAKEL-01/.devcontainer.json b/ecir26/ks-orakel-01/.devcontainer.json similarity index 100% rename from ks-ORAKEL-01/.devcontainer.json rename to ecir26/ks-orakel-01/.devcontainer.json diff --git a/ks-ORAKEL-01/.gitignore b/ecir26/ks-orakel-01/.gitignore similarity index 100% rename from ks-ORAKEL-01/.gitignore rename to ecir26/ks-orakel-01/.gitignore diff --git a/ks-ORAKEL-01/Dockerfile b/ecir26/ks-orakel-01/Dockerfile similarity index 100% rename from ks-ORAKEL-01/Dockerfile rename to ecir26/ks-orakel-01/Dockerfile diff --git a/ks-ORAKEL-01/Dockerfile.dev b/ecir26/ks-orakel-01/Dockerfile.dev similarity index 100% rename from ks-ORAKEL-01/Dockerfile.dev rename to ecir26/ks-orakel-01/Dockerfile.dev diff --git a/ks-ORAKEL-01/README.md b/ecir26/ks-orakel-01/README.md similarity index 100% rename from ks-ORAKEL-01/README.md rename to ecir26/ks-orakel-01/README.md diff --git a/ks-ORAKEL-01/query-optimization.ipynb b/ecir26/ks-orakel-01/query-optimization.ipynb similarity index 100% rename from ks-ORAKEL-01/query-optimization.ipynb rename to ecir26/ks-orakel-01/query-optimization.ipynb diff --git a/ks-ORAKEL-01/requirements.txt b/ecir26/ks-orakel-01/requirements.txt similarity index 100% rename from ks-ORAKEL-01/requirements.txt rename to ecir26/ks-orakel-01/requirements.txt