diff --git a/.env copy b/.env copy index 5f89edb..d94bec1 100644 --- a/.env copy +++ b/.env copy @@ -1 +1 @@ -MODEL_REGISTRY = "local" \ No newline at end of file +MODEL_REGISTRY = "local" diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..98a9c19 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,31 @@ +name: CI + +on: + push: + branches: + - 0-Starter-productionizing-ml + - main + - master + pull_request: + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: code checkout + uses: actions/checkout@v4 + + - name: isntall Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install poetry + run: pip install poetry + + - name: Install dependencies + run: poetry install + + - name: launch Makefile + run: make lint-all \ No newline at end of file diff --git a/.gitignore b/.gitignore index 4869ac4..5c41d19 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ .venv/ .python-version .ipynb_checkpoints/ +__pycache__/ # Data data/ @@ -13,4 +14,9 @@ models/ # Env .env -.envrc \ No newline at end of file +.envrc + +# MLflow +mlartifacts/ +mlruns/ +mlflow.db \ No newline at end of file diff --git a/.mlflow.pid b/.mlflow.pid new file mode 100644 index 0000000..b817fd5 --- /dev/null +++ b/.mlflow.pid @@ -0,0 +1 @@ +78638 diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..c0fa2b9 --- /dev/null +++ b/Makefile @@ -0,0 +1,30 @@ +.PHONY: lint lint-fix format lint-all + +lint: + @echo "checking lint with ruff" + poetry run ruff check . + +lint-fix: + @echo "auto fix lint with ruff" + poetry run ruff check . --fix + +format: + @echo "checking format" + poetry run ruff format . + +lint-all: lint-fix lint format + +.PHONY: start-mlflow stop-mlflow +MLFLOW_PORT=5000 +MLFLOW_PID_FILE=.mlflow.pid + +start-mlflow: + mlflow ui --backend-store-uri sqlite:///models/mlflow.db -port $(MLFLOW_PORT) & echo $$! > $(MLFLOW_PID_FILE) + +stop-mlflow: + @if [ -f $(MLFLOW_PID_FILE) ]; then \ + kill `cat $(MLFLOW_PID_FILE)` && rm $(MLFLOW_PID_FILE); \ + echo "MLflow server stopped"; \ + else \ + echo "No MLflow server running"; \ + fi diff --git a/api/data_model.py b/api/data_model.py new file mode 100644 index 0000000..483b587 --- /dev/null +++ b/api/data_model.py @@ -0,0 +1,18 @@ +from pydantic import BaseModel + +class Diamond(BaseModel): + carat: float + cut: str + color: str + clarity: str + depth: float + table: float + x: float + y: float + z: float + +class DiamondResponse(Diamond): + price: float + +class Diamonds(BaseModel): + diamonds: list[Diamond] \ No newline at end of file diff --git a/api/rest.py b/api/rest.py new file mode 100644 index 0000000..dc1c9d1 --- /dev/null +++ b/api/rest.py @@ -0,0 +1,23 @@ +import pandas as pd +from fastapi import FastAPI +from src.diamonds.registry import load_model +from .data_model import Diamond, DiamondResponse + +app = FastAPI( + title="Diamond API", + description="api to get diamond price", + version="0.1.0" +) + +model = load_model("model", "local") + +@app.get("/") +def read_root(): + return {"Hello": "World"} + +@app.post("/price") +def predict_price(diamond: Diamond): + diamond_df = pd.Dataframe([diamond.model_dump()]) + prediction = model.predict(diamond_df) + diamond_reponse = DiamondResponse(**diamond.model_dump(), price=prediction[0]) + return diamond_reponse \ No newline at end of file diff --git a/comments.txt b/comments.txt new file mode 100644 index 0000000..996c25b --- /dev/null +++ b/comments.txt @@ -0,0 +1,2 @@ +hello, this is yufo +great jcc \ No newline at end of file diff --git a/data/preprocessed/.gitkeep b/data/preprocessed/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/data/raw/.gitkeep b/data/raw/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/models/.gitkeep b/models/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/notebooks/Experiement-Tracking.ipynb b/notebooks/Experiement-Tracking.ipynb new file mode 100644 index 0000000..aa3285b --- /dev/null +++ b/notebooks/Experiement-Tracking.ipynb @@ -0,0 +1,1208 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "e11c05bf", + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow \n", + "\n", + "import sklearn\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7aecb5ea", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026/03/10 14:12:18 INFO mlflow.tracking.fluent: Experiment with name 'Iris_Experiment' does not exist. Creating a new experiment.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1. Set the tracking URI to the MLflow server\n", + "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", + "# 2. Set the Experiment name\n", + "mlflow.set_experiment(\"Iris_Experiment\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6d746100", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.9333\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026/03/10 14:15:44 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🏃 View run omniscient-foal-439 at: http://localhost:5000/#/experiments/1/runs/8fc827ff373b47cf95bf4f1a6d4a0c5d\n", + "🧪 View experiment at: http://localhost:5000/#/experiments/1\n" + ] + } + ], + "source": [ + "import random\n", + "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n", + "\n", + "# Load data\n", + "iris = load_iris()\n", + "X, y = iris.data, iris.target\n", + "\n", + "# Split data\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42, stratify=y\n", + ")\n", + "n_estimators = random.randint(50, 200) # Randomly choose n_estimators for demonstration\n", + "depth = random.randint(5, 20) # Randomly choose max_depth for demonstration\n", + "# Train classifier\n", + "clf = RandomForestClassifier(n_estimators=n_estimators\n", + " , max_depth=depth)\n", + "clf.fit(X_train, y_train)\n", + "\n", + "# Evaluate\n", + "y_pred = clf.predict(X_test)\n", + "acc = accuracy_score(y_test, y_pred)\n", + "print(f\"Test accuracy: {acc:.4f}\")\n", + "with mlflow.start_run() as run:\n", + " mlflow.log_param(\"n_estimators\", n_estimators)\n", + " mlflow.log_param(\"max_depth\", depth)\n", + " mlflow.log_metric(\"accuracy\", acc)\n", + " mlflow.log_metric(\"recall\", recall_score(y_test, y_pred, average=\"weighted\"))\n", + " mlflow.log_metric(\"precision\", precision_score(y_test, y_pred, average=\"weighted\"))\n", + " mlflow.log_metric(\"f1_score\", f1_score(y_test, y_pred, average=\"weighted\"))\n", + " mlflow.sklearn.log_model(clf, name=\"random_forest_model\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "108fa9a0", + "metadata": {}, + "source": [ + "## Autolog" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70d96a88", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026/03/10 14:20:59 INFO mlflow.tracking.fluent: Autologging successfully enabled for sklearn.\n", + "2026/03/10 14:21:00 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.9333\n", + "🏃 View run monumental-cat-491 at: http://localhost:5000/#/experiments/1/runs/bc2d0b0d96ad4362ad2e7e10abdb41f3\n", + "🧪 View experiment at: http://localhost:5000/#/experiments/1\n" + ] + } + ], + "source": [ + "with mlflow.start_run():\n", + " mlflow.autolog(log_input_examples=True)\n", + " # Load data\n", + " iris = load_iris()\n", + " X, y = iris.data, iris.target\n", + "\n", + " # Split data\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42, stratify=y\n", + " )\n", + " n_estimators = random.randint(50, 200) # Randomly choose n_estimators for demonstration\n", + " depth = random.randint(5, 20) # Randomly choose max_depth for demonstration\n", + " # Train classifier\n", + " clf = RandomForestClassifier(n_estimators=n_estimators\n", + " , max_depth=depth)\n", + " clf.fit(X_train, y_train) \n", + "\n", + " # Evaluate\n", + " y_pred = clf.predict(X_test)\n", + " acc = accuracy_score(y_test, y_pred)\n", + " print(f\"Test accuracy: {acc:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ba4fd5", + "metadata": {}, + "source": [ + "## Loading a model from MLflow" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6d58a77e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier(max_depth=18, n_estimators=98)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier(max_depth=18, n_estimators=98)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "client = mlflow.MlflowClient()\n", + "model_name = \"iris\"\n", + "model_version_alias = \"prod\"\n", + "# Get the model version using a model URI\n", + "model_uri = f\"models:/{model_name}@{model_version_alias}\"\n", + "model = mlflow.sklearn.load_model(model_uri)\n", + "\n", + "(model)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "diamond_project_env", + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Exploration.ipynb b/notebooks/Exploration.ipynb index 4c7af4f..296bff5 100644 --- a/notebooks/Exploration.ipynb +++ b/notebooks/Exploration.ipynb @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "# NB : This reload your library after each edit, \n", + "# NB : This reload your library after each edit,\n", "# so you don't have to restart the kernel\n", "%load_ext autoreload\n", "%autoreload 2" @@ -29,7 +29,6 @@ "outputs": [], "source": [ "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "\n", "# ML\n", @@ -54,7 +53,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_diamonds = sns.load_dataset('diamonds')" + "df_diamonds = sns.load_dataset(\"diamonds\")" ] }, { @@ -437,8 +436,7 @@ } ], "source": [ - "sns.histplot(data=df_diamonds,\n", - " x=\"price\")" + "sns.histplot(data=df_diamonds, x=\"price\")" ] }, { @@ -1352,8 +1350,7 @@ } ], "source": [ - "px.scatter_3d(data_frame=df_diamonds,\n", - " x=\"x\",y=\"y\",z=\"z\", color=\"price\")" + "px.scatter_3d(data_frame=df_diamonds, x=\"x\", y=\"y\", z=\"z\", color=\"price\")" ] }, { @@ -1415,9 +1412,9 @@ } ], "source": [ - "sns.heatmap(df_diamonds.select_dtypes(include=\"number\").corr(),\n", - " annot=True,\n", - " cmap=\"coolwarm\")" + "sns.heatmap(\n", + " df_diamonds.select_dtypes(include=\"number\").corr(), annot=True, cmap=\"coolwarm\"\n", + ")" ] }, { @@ -1446,8 +1443,7 @@ } ], "source": [ - "df_diamonds.select_dtypes(include=\"category\")\\\n", - " [\"color\"].value_counts()" + "df_diamonds.select_dtypes(include=\"category\")[\"color\"].value_counts()" ] }, { @@ -1470,7 +1466,11 @@ ], "source": [ "import scipy.stats as stats\n", - "groups = [group[\"price\"].values for name, group in df_diamonds.groupby(\"color\", observed=False)]\n", + "\n", + "groups = [\n", + " group[\"price\"].values\n", + " for name, group in df_diamonds.groupby(\"color\", observed=False)\n", + "]\n", "\n", "\n", "print(\"--- Test ANOVA à un facteur ---\")\n", @@ -1479,9 +1479,13 @@ "print(f\"P-value : {p_value_anova:.2e}\")\n", "\n", "if p_value_anova < 0.05:\n", - " print(\"Conclusion : On rejette H0. La couleur a un impact significatif sur le prix moyen.\\n\")\n", + " print(\n", + " \"Conclusion : On rejette H0. La couleur a un impact significatif sur le prix moyen.\\n\"\n", + " )\n", "else:\n", - " print(\"Conclusion : On ne peut pas rejeter H0. La couleur ne semble pas impacter le prix.\\n\")" + " print(\n", + " \"Conclusion : On ne peut pas rejeter H0. La couleur ne semble pas impacter le prix.\\n\"\n", + " )" ] }, { @@ -1494,7 +1498,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "27c4ffbc-55f9-4970-9d4b-df1101c3134d", "metadata": {}, "outputs": [ @@ -1510,13 +1514,14 @@ } ], "source": [ - "def keep_not_null(row) :\n", - " if 0 in row.values : return False\n", + "def keep_not_null(row):\n", + " if 0 in row.values:\n", + " return False\n", " return True\n", "\n", + "\n", "# Pour le test sur une ligne :\n", - "keep_not_null(df_diamonds.loc[11182,:])\n", - " " + "keep_not_null(df_diamonds.loc[11182, :])" ] }, { @@ -1526,7 +1531,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_clean = df_diamonds[df_diamonds.apply(keep_not_null,axis=1)]" + "df_clean = df_diamonds[df_diamonds.apply(keep_not_null, axis=1)]" ] }, { @@ -1619,7 +1624,7 @@ "X = df_clean.drop(columns=[\"price\"])\n", "y = df_clean[\"price\"]\n", "\n", - "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n", "X_train.shape, X_test.shape, y_train.shape, y_test.shape" ] }, @@ -2464,9 +2469,11 @@ ], "source": [ "cat_pipe = Pipeline(\n", - " [ (\"cat_imp\",SimpleImputer(strategy=\"most_frequent\"))\n", - " ,(\"ohe\",OneHotEncoder(drop=\"first\",sparse_output=False))\n", - " ])\n", + " [\n", + " (\"cat_imp\", SimpleImputer(strategy=\"most_frequent\")),\n", + " (\"ohe\", OneHotEncoder(drop=\"first\", sparse_output=False)),\n", + " ]\n", + ")\n", "cat_pipe" ] }, @@ -3189,11 +3196,9 @@ ], "source": [ "num_pipe = Pipeline(\n", - " [(\"knn_imp\", KNNImputer(n_neighbors=5))\n", - " ,(\"scaler\", StandardScaler())\n", - " ])\n", - "num_pipe\n", - " " + " [(\"knn_imp\", KNNImputer(n_neighbors=5)), (\"scaler\", StandardScaler())]\n", + ")\n", + "num_pipe" ] }, { @@ -4135,9 +4140,11 @@ ], "source": [ "preprocessor = ColumnTransformer(\n", - " [(\"numeric\",num_pipe, make_column_selector(dtype_include=\"number\"))\n", - " ,(\"categorical\", cat_pipe, make_column_selector(dtype_exclude=\"number\"))\n", - " ]).set_output(transform=\"pandas\")\n", + " [\n", + " (\"numeric\", num_pipe, make_column_selector(dtype_include=\"number\")),\n", + " (\"categorical\", cat_pipe, make_column_selector(dtype_exclude=\"number\")),\n", + " ]\n", + ").set_output(transform=\"pandas\")\n", "preprocessor" ] }, @@ -4148,11 +4155,10 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "# Normalement j'ai pas besoin de preproc mais pour vérfier \n", + "# Normalement j'ai pas besoin de preproc mais pour vérfier\n", "preprocessor.fit(X_train)\n", "X_train_scaled = preprocessor.transform(X_train)\n", - "X_test_scaled = preprocessor.transform(X_test)" + "X_test_scaled = preprocessor.transform(X_test)" ] }, { @@ -4182,7 +4188,7 @@ } ], "source": [ - "from sklearn.linear_model import LinearRegression, ElasticNet\n", + "from sklearn.linear_model import LinearRegression\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.svm import SVR\n", @@ -4199,14 +4205,14 @@ "source": [ "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "\n", - "def evaluate_model(y_true,y_pred) -> dict[float] : \n", - " mae = mean_absolute_error(y_true,y_pred)\n", - " mse = mean_squared_error(y_true,y_pred)\n", - " r2 = r2_score(y_true,y_pred) \n", - " scores = {\"mae\":mae,\"mse\":mse,\"r2\":r2}\n", + "\n", + "def evaluate_model(y_true, y_pred) -> dict[float]:\n", + " mae = mean_absolute_error(y_true, y_pred)\n", + " mse = mean_squared_error(y_true, y_pred)\n", + " r2 = r2_score(y_true, y_pred)\n", + " scores = {\"mae\": mae, \"mse\": mse, \"r2\": r2}\n", " print(scores)\n", - " return scores\n", - " " + " return scores" ] }, { @@ -4235,9 +4241,9 @@ ], "source": [ "lin = LinearRegression()\n", - "lin.fit(X_train_scaled,y_train) \n", + "lin.fit(X_train_scaled, y_train)\n", "y_pred = lin.predict(X_test_scaled)\n", - "evaluate_model(y_test,y_pred)" + "evaluate_model(y_test, y_pred)" ] }, { @@ -4266,9 +4272,9 @@ ], "source": [ "forest = RandomForestRegressor()\n", - "forest.fit(X_train_scaled,y_train)\n", + "forest.fit(X_train_scaled, y_train)\n", "y_pred = forest.predict(X_test_scaled)\n", - "evaluate_model(y_test,y_pred)" + "evaluate_model(y_test, y_pred)" ] }, { @@ -4297,9 +4303,9 @@ ], "source": [ "knn = KNeighborsRegressor()\n", - "knn.fit(X_train_scaled,y_train)\n", + "knn.fit(X_train_scaled, y_train)\n", "y_pred = knn.predict(X_test_scaled)\n", - "evaluate_model(y_test,y_pred)" + "evaluate_model(y_test, y_pred)" ] }, { @@ -4328,9 +4334,9 @@ ], "source": [ "svm = SVR()\n", - "svm.fit(X_train_scaled,y_train)\n", + "svm.fit(X_train_scaled, y_train)\n", "y_pred = svm.predict(X_test_scaled)\n", - "evaluate_model(y_test,y_pred)" + "evaluate_model(y_test, y_pred)" ] }, { @@ -4339,9 +4345,7 @@ "id": "966abe76-ef40-490b-b043-d560525f6973", "metadata": {}, "outputs": [], - "source": [ - "\n" - ] + "source": [] }, { "cell_type": "code", @@ -5146,12 +5150,12 @@ "source": [ "from sklearn.model_selection import GridSearchCV\n", "\n", - "grid = {\"n_neighbors\": range(2,30)}\n", + "grid = {\"n_neighbors\": range(2, 30)}\n", "\n", "\n", "search = GridSearchCV(KNeighborsRegressor(), grid, verbose=1)\n", "\n", - "search.fit(X_train_scaled,y_train)" + "search.fit(X_train_scaled, y_train)" ] }, { @@ -5182,7 +5186,7 @@ "source": [ "y_pred = search.predict(X_test_scaled)\n", "print(search.best_params_)\n", - "evaluate_model(y_test,y_pred)" + "evaluate_model(y_test, y_pred)" ] }, { @@ -6072,13 +6076,14 @@ } ], "source": [ - "grid = {\"n_estimators\": [100,150,300,500]\n", - " #,\"max_depth\" : [None, 2,5,10]\n", - " #,\"min_samples_leaf\" : [1,5,10,50]\n", - " }\n", + "grid = {\n", + " \"n_estimators\": [100, 150, 300, 500]\n", + " # ,\"max_depth\" : [None, 2,5,10]\n", + " # ,\"min_samples_leaf\" : [1,5,10,50]\n", + "}\n", "\n", - "search = GridSearchCV(RandomForestRegressor(),grid,verbose=1, cv=3)\n", - "search.fit(X_train_scaled,y_train)\n" + "search = GridSearchCV(RandomForestRegressor(), grid, verbose=1, cv=3)\n", + "search.fit(X_train_scaled, y_train)" ] }, { @@ -6109,7 +6114,7 @@ "source": [ "y_pred = search.predict(X_test_scaled)\n", "print(search.best_params_)\n", - "evaluate_model(y_test,y_pred)" + "evaluate_model(y_test, y_pred)" ] }, { @@ -6146,9 +6151,9 @@ ], "source": [ "forest = RandomForestRegressor(**search.best_params_)\n", - "forest.fit(X_train_scaled,y_train)\n", + "forest.fit(X_train_scaled, y_train)\n", "y_pred = forest.predict(X_test_scaled)\n", - "evaluate_model(y_test,y_pred)" + "evaluate_model(y_test, y_pred)" ] }, { @@ -6166,8 +6171,8 @@ "metadata": {}, "outputs": [], "source": [ - "import pickle \n", - "import os " + "import pickle\n", + "import os" ] }, { @@ -6195,12 +6200,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Saving the preprocessor \n", + "# Saving the preprocessor\n", "model_path = \"models\"\n", - "if not os.path.exists(model_path) : \n", + "if not os.path.exists(model_path):\n", " os.mkdir(model_path)\n", - "with open(os.path.join(model_path,\"preproc.pkl\"),\"wb\") as f:\n", - " pickle.dump(preprocessor,f)" + "with open(os.path.join(model_path, \"preproc.pkl\"), \"wb\") as f:\n", + " pickle.dump(preprocessor, f)" ] }, { @@ -6210,9 +6215,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Saving the model \n", - "with open(os.path.join(model_path,\"model.pkl\"),\"wb\") as f:\n", - " pickle.dump(forest,f)" + "# Saving the model\n", + "with open(os.path.join(model_path, \"model.pkl\"), \"wb\") as f:\n", + " pickle.dump(forest, f)" ] }, { @@ -7177,7 +7182,7 @@ } ], "source": [ - "with open(os.path.join(model_path,\"preproc.pkl\"),\"rb\") as f:\n", + "with open(os.path.join(model_path, \"preproc.pkl\"), \"rb\") as f:\n", " new_preproc = pickle.load(f)\n", "new_preproc" ] diff --git a/poetry.lock b/poetry.lock index c991753..a4621ac 100644 --- 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index 38a3f2f..790e4ab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,17 +6,41 @@ authors = [ {name = "Your Name",email = "you@example.com"} ] readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.11,<4.0" dependencies = [ "scikit-learn (>=1.8.0,<2.0.0)", - "pandas (>=3.0.1,<4.0.0)" + "pandas (<3)", + "seaborn (>=0.13.2,<0.14.0)", + "loguru (>=0.7.3,<0.8.0)", ] [tool.poetry] [tool.poetry.group.dev.dependencies] notebook = "^7.5.4" +ruff = "^0.15.5" +mlflow = "^3.10.1" [build-system] requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" + +[tool.ruff] +line-length = 100 +target-version = "py311" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "F", # pyflakes + "I", # isort + "B", # bugbear + "UP", # pyupgrade +] +ignore = [ + "E501" +] + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" \ No newline at end of file diff --git a/src/diamonds/data.py b/src/diamonds/data.py index 7bc5d2e..a81bfa7 100644 --- a/src/diamonds/data.py +++ b/src/diamonds/data.py @@ -1,8 +1,20 @@ -import pandas as pd # Import other necessary libraries here +import os + +import loguru +import pandas as pd +import seaborn as sns + +from diamonds.model import create_preproc +from diamonds.params import DATA_PATH +from diamonds.registry import load_model, save_model +df_diamonds = sns.load_dataset("diamonds") -def load_data(cache = True) -> pd.DataFrame: +logger = loguru.logger + +def load_data() -> pd.DataFrame: + df_diamonds = sns.load_dataset("diamonds") """ Load the diamonds dataset. @@ -16,7 +28,17 @@ def load_data(cache = True) -> pd.DataFrame: pd.DataFrame The diamonds dataset """ - pass + logger.info("Loading diamonds dataset...") + csv_path = os.path.join(DATA_PATH, "raw", "diamonds.csv") + if not os.path.exists(csv_path): + logger.info("Caching the diamonds dataset...") + df_diamonds = sns.load_dataset("diamonds") + df_diamonds.to_csv(csv_path, index=False) + else: + logger.info("Loading diamonds dataset from cache...") + df_diamonds = pd.read_csv(csv_path) + return df_diamonds + def clean_data(df: pd.DataFrame) -> pd.DataFrame: """ @@ -32,9 +54,20 @@ def clean_data(df: pd.DataFrame) -> pd.DataFrame: pd.DataFrame The cleaned diamonds dataset """ - pass + rows = len(df) + + def keep_not_allow(rows): + if 0 in rows.values: + return False + return True + + df_clean = df[df.apply(keep_not_allow, axis=1)] + logger.info(f"Cleaned the diamonds dataset: {rows} rows -> {len(df_clean)} rows") -def preprocess_data(df: pd.DataFrame) -> pd.DataFrame: + return df_clean + + +def preprocess_data(df: pd.DataFrame, train: bool = True) -> pd.DataFrame: """ Preprocess the diamonds dataset. @@ -48,9 +81,22 @@ def preprocess_data(df: pd.DataFrame) -> pd.DataFrame: pd.DataFrame The preprocessed diamonds dataset """ - pass -def create_X_y(df: pd.DataFrame) ->tuple[pd.DataFrame, pd.Series]: + if train: + preprocessor = create_preproc() + preprocessor.fit(df) + save_model(preprocessor, "preprocessor") + + else: + preprocessor = load_model("preprocessor") + + df_preprocessed = preprocessor.transform(df) + logger.info(f"Preprocessed the diamonds dataset: {df.shape} -> {df_preprocessed.shape}") + return df_preprocessed + + +def create_X_y(df: pd.DataFrame, predict_value: str = "price") -> tuple[pd.DataFrame, pd.Series]: + # Split target first so preprocessing columns only reference feature columns """ Create the feature matrix X and target vector y from the diamonds dataset. @@ -64,12 +110,14 @@ def create_X_y(df: pd.DataFrame) ->tuple[pd.DataFrame, pd.Series]: (pd.DataFrame, pd.Series) The feature matrix X and target vector y """ - pass + X = df.drop(columns=predict_value) + y = df[predict_value] + + return X, y if __name__ == "__main__": df = load_data() - # df_clean = clean_data(df) - # df_preprocessed = preprocess_data(df_clean) - # X, y = create_X_y(df_preprocessed) \ No newline at end of file + df_clean = clean_data(df) + X, y = create_X_y(df_clean) diff --git a/src/diamonds/model.py b/src/diamonds/model.py index cd3ebcc..f75a2f0 100644 --- a/src/diamonds/model.py +++ b/src/diamonds/model.py @@ -1,7 +1,27 @@ -from sklearn.base import BaseEstimator, Pipeline +import loguru +import pandas as pd +from sklearn.base import BaseEstimator +from sklearn.compose import ColumnTransformer, make_column_selector +from sklearn.ensemble import RandomForestRegressor +from sklearn.impute import KNNImputer, SimpleImputer +from sklearn.linear_model import LinearRegression, Ridge +from sklearn.metrics import ( + mean_absolute_error, + mean_absolute_percentage_error, + mean_squared_error, + r2_score, +) +from sklearn.neighbors import KNeighborsRegressor +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import OneHotEncoder, StandardScaler +from diamonds.registry import save_model -def create_model(model_name: str) -> BaseEstimator: +logger = loguru.logger + +def create_model( + model_name: str, estimators: int = 200, max_depth: int = 10, random_state: int = 42 +) -> BaseEstimator: """ Create an untrained model with the best hyperparameters found during tuning. @@ -15,23 +35,128 @@ def create_model(model_name: str) -> BaseEstimator: BaseEstimator The model ready to be fitted """ - pass -def create_preproc() -> Pipeline: + models = { + "ridge": Ridge(alpha=1.0), + "random_forest": RandomForestRegressor( + n_estimators=estimators, max_depth=max_depth, random_state=random_state + ), + "knn": KNeighborsRegressor(n_neighbors=5), + "linear": LinearRegression(fit_intercept=True), + } + + if model_name not in models: + raise ValueError(f"Unknown model: {model_name}") + + return models[model_name] + + +def create_preproc(): + + num_pipeline = Pipeline( + [ + ("num_imp", KNNImputer()), + ("scaler", StandardScaler()), + ] + ) + + cat_pipeline = Pipeline( + [ + ("cat_imp", SimpleImputer(strategy="most_frequent")), + ("ohe", OneHotEncoder(drop="first", handle_unknown="ignore", sparse_output=False)), + ] + ) + + preprocessor = ColumnTransformer( + [ + ("numeric", num_pipeline, make_column_selector(dtype_include="number")), + ("categorical", cat_pipeline, make_column_selector(dtype_exclude="number")), + ] + ).set_output(transform="pandas") + + return preprocessor + + +def create_training_pipeline( + model_name: str, + estimators: int = 200, + max_depth: int = 10, + random_state: int = 42, +) -> Pipeline: + preprocessor = create_preproc() + model = create_model( + model_name=model_name, + estimators=estimators, + max_depth=max_depth, + random_state=random_state, + ) + + pipeline = Pipeline( + [ + ("preprocessor", preprocessor), + ("model", model), + ] + ) + + return pipeline + + +def train_model(model, X_train, y_train): """ - Create a preprocessing pipeline. + Train model and save it. + + Parameters + ---------- + model : any + The model to train + X_train : pd.DataFrame + The training data + y_train : pd.Series + The target variable """ - pass + logger.info("Training model...") + model.fit(X_train, y_train) + logger.info("Model trained. Saving...") + save_model(model, "model") + return model -def train_model(model, X_train, y_train): - pass def evaluate_model(model, X_test, y_test) -> dict[str, float]: + """ + Evaluate the model on the test set. + + Parameters + ---------- + model : any + The model to evaluate + X_test : pd.DataFrame + The test data + y_test : pd.Series + The target variable + + Returns + ------- + dict[str, float] + The metrics for the model. + """ # NB : mae, mse, r2_score, mape # Only print the metrics for now - pass + y_pred = model.predict(X_test) + + mae = mean_absolute_error(y_test, y_pred) + mse = mean_squared_error(y_test, y_pred) + r2 = r2_score(y_test, y_pred) + mape = mean_absolute_percentage_error(y_test, y_pred) + + metrics = {"mae": mae, "mse": mse, "r2": r2, "mape": mape} -def predict(model, X): + for name, value in metrics.items(): + print(f"{name}: {value:.4f}") + + return metrics + + +def predict(model, X: pd.DataFrame) -> pd.Series: """ Make predictions using the trained model. @@ -47,4 +172,6 @@ def predict(model, X): pd.Series The predicted values """ - + y_pred = model.predict(X) + + return pd.Series(y_pred, index=X.index, name="prediction") diff --git a/src/diamonds/params.py b/src/diamonds/params.py index 49702b5..e240dbc 100644 --- a/src/diamonds/params.py +++ b/src/diamonds/params.py @@ -1,6 +1,7 @@ import os -DATA_PATH= "data" +DATA_PATH = "data" MODEL_PATH = "models" -MODEL_REGISTRY = os.environ.get("MODEL_REGISTRY", "local") \ No newline at end of file +MODEL_REGISTRY = os.environ.get("MODEL_REGISTRY", "local") +MLFLOW_TRACKING_URI = os.environ.get("MLFLOW_TRACKING_URI", "http://localhost:5000") diff --git a/src/diamonds/registry.py b/src/diamonds/registry.py index e631010..12a872c 100644 --- a/src/diamonds/registry.py +++ b/src/diamonds/registry.py @@ -1,13 +1,34 @@ +import os +import pickle +import loguru +import mlflow from sklearn.base import BaseEstimator -from diamonds.params import MODEL_REGISTRY -def save_model(model, path): - """Save the model to the specified path.""" - # Implement the logic to save the model (e.g., using pickle, joblib, etc.) - pass +from diamonds.params import MODEL_PATH -def load_model(path) -> BaseEstimator: - """Load the model from the specified path.""" - # Implement the logic to load the model (e.g., using pickle, joblib, etc.) - pass \ No newline at end of file +logger = loguru.logger + +def save_model(model: BaseEstimator, name: str, model_registry: str = "local") -> None: + """Save a trained model to disk and return its path.""" + model_path = os.path.join(MODEL_PATH, f"{name}.pkl") + with open(model_path, "wb") as f: + pickle.dump(model, f) + if model_registry == "mlflow": + mlflow.sklearn.log_model(model, name) + logger.info(f"Model saved to MLflow registry with name: {name}") + + +def load_model(name: str, model_registry: str = "local") -> BaseEstimator: + """Load a previously saved model from disk.""" + model_path = os.path.join(MODEL_PATH, f"{name}.pkl") + if model_registry == "mlflow": + model_uri = f"models:/{name}/latest" + model = mlflow.sklearn.load_model(model_uri) + logger.info(f"Model loaded from MLflow registry with name: {name}") + else: + with open(model_path, "rb") as f: + model = pickle.load(f) + logger.info(f"Model loaded from local with name: {name}") + + return model diff --git a/src/diamonds/train.py b/src/diamonds/train.py index 024d31a..c3a8e40 100644 --- a/src/diamonds/train.py +++ b/src/diamonds/train.py @@ -1,6 +1,20 @@ +import random +import pandas as pd +import mlflow +from sklearn.metrics import ( + mean_absolute_error, + mean_squared_error, + r2_score, +) +from sklearn.model_selection import train_test_split +from diamonds.data import clean_data, create_X_y, load_data, preprocess_data +from diamonds.model import create_model, create_training_pipeline, evaluate_model, train_model +from diamonds.params import MLFLOW_TRACKING_URI -def train( + +def train_from_raw_data( + raw_data : pd.DataFrame = load_data, model_name: str = "baseline", test_size: float = 0.2, random_state: int = 42, @@ -8,21 +22,87 @@ def train( """ Simple end‑to‑end pipeline: - - load and clean the raw data + - clean the raw data - preprocess it and build X, y - split into train / test - build the model and preprocessing - train, evaluate, and save the trained model """ + n_estimators = random.randint(50, 200) # Randomly choose n_estimators for demonstration + depth = random.randint(5, 20) + # 1) Clean data + df_clean = clean_data(raw_data) + # 2) Preprocess data + X, y = create_X_y(df_clean) + X_train, _, y_train, _ = train_test_split( + X, y, test_size=test_size, random_state=random_state + ) + + pipeline = create_training_pipeline( + model_name=model_name, + estimators=n_estimators, + max_depth=depth, + random_state=random_state, + ) + # 3) Train model + train_model(pipeline, X_train, y_train) + + +def autolog_mlflow( + model_name: str = "random_forest", + test_size: float = 0.2, + random_state: int = 42, +): + if mlflow is None: + raise ModuleNotFoundError( + "mlflow is not installed in the active Python interpreter. " + "Install mlflow or run train() without experiment tracking." + ) + + # 1. Set the tracking URI to the MLflow server + mlflow.set_tracking_uri(MLFLOW_TRACKING_URI) + # 2. Set the Experiment name + mlflow.set_experiment("Diamonds_experiment") + mlflow.autolog(log_input_examples=True) + n_estimators = random.randint(50, 200) # Randomly choose n_estimators for demonstration + depth = random.randint(5, 20) # 1) Data - + df = load_data() + df_clean = clean_data(df) # 2) Model + preprocessing - - # 3) Evaluation - - # 4) Persistence + X, y = create_X_y(df_clean) + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_size, random_state=random_state + ) + pipeline = create_training_pipeline( + model_name=model_name, + estimators=n_estimators, + max_depth=depth, + random_state=random_state, + ) + with mlflow.start_run(): + pipeline.fit(X_train, y_train) + y_pred = pipeline.predict(X_test) + mae = mean_absolute_error(y_test, y_pred) + rmse = mean_squared_error(y_test, y_pred) ** 0.5 + r2 = r2_score(y_test, y_pred) + mlflow.log_metric("mae", mae) + mlflow.log_metric("rmse", rmse) + mlflow.log_metric("r2", r2) -if __name__ == "__main__": - train() + print(f"Model: {model_name}") + print(f"n_estimators: {n_estimators}") + print(f"max_depth: {depth}") + print(f"MAE: {mae:.2f}") + print(f"RMSE: {rmse:.2f}") + print(f"R2: {r2:.4f}") + return pipeline + +if __name__ == "__main__": + if mlflow is None: + print("mlflow not found in this interpreter. Running training without tracking.") + train_from_raw_data(raw_data=load_data(), model_name="random_forest") + else: + autolog_mlflow()