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0490ec5
Docs: Swapped CESNET_TimeSeries24 for CESNET_AGG23 in using dataset e…
milankures Dec 5, 2025
72d64a6
Docs: Updated old info regarding datasets
milankures Dec 5, 2025
c0d3410
Docs: Updated wrong information in benchmark docs
milankures Dec 5, 2025
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38 changes: 9 additions & 29 deletions cesnet_tszoo/benchmarks.py
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Expand Up @@ -28,37 +28,17 @@ class Benchmark:

**Intended usage:**

For time-based:
1. Call [`load_benchmark`][cesnet_tszoo.benchmarks.load_benchmark] with the desired benchmark. You can use your own saved benchmark or you can use already built-in one. This will download the dataset and annotations (if available) if they have not been previously downloaded.
2. Retrieve the initialized dataset using [`get_initialized_dataset`](reference_benchmarks.md#cesnet_tszoo.benchmarks.Benchmark.get_initialized_dataset). This will provide a dataset that is ready to use. Check beforehand what type of dataset is returned.
3. Use [`get_train_dataloader`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_train_dataloader)/[`get_train_df`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_train_df)/[`get_train_numpy`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_train_numpy) to get training data for chosen model.
4. [Optional] Modify used preprocessing steps with [`update_dataset_config_and_initialize`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.update_dataset_config_and_initialize).
5. Validate the model and perform the hyperparameter optimalization on [`get_val_dataloader`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_val_dataloader)/[`get_val_df`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_val_df)/[`get_val_numpy`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_val_numpy).
6. Evaluate the model on [`get_test_dataloader`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_test_dataloader)/[`get_test_df`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_test_df)/[`get_test_numpy`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.get_test_numpy).

When using [`TimeBasedCesnetDataset`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset) (`dataset_type` = `DatasetType.TIME_BASED`):
You can create custom benchmarks with [`save_benchmark`](reference_cesnet_dataset.md#cesnet_tszoo.datasets.cesnet_dataset.CesnetDataset.save_benchmark).
They will be saved to `"data_root"/tszoo/benchmarks/` directory, where `data_root` was passed as parameter to [`load_benchmark`][cesnet_tszoo.benchmarks.load_benchmark].

1. Create an instance of the dataset with the desired data root by calling [`get_dataset`](reference_cesnet_database.md#cesnet_tszoo.datasets.databases.cesnet_database.CesnetDatabase.get_dataset). This will download the dataset if it has not been previously downloaded and return instance of dataset.
2. Create an instance of [`TimeBasedConfig`](reference_time_based_config.md#references.TimeBasedConfig) and set it using [`set_dataset_config_and_initialize`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.set_dataset_config_and_initialize).
This initializes the dataset, including data splitting (train/validation/test), fitting transformers (if needed), selecting features, and more. This is cached for later use.
3. Use [`get_train_dataloader`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset)/[`get_train_df`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_train_df)/[`get_train_numpy`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_train_numpy) to get training data for chosen model.
4. Validate the model and perform the hyperparameter optimalization on [`get_val_dataloader`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_val_dataloader)/[`get_val_df`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_val_df)/[`get_val_numpy`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_val_numpy).
5. Evaluate the model on [`get_test_dataloader`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_test_dataloader)/[`get_test_df`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_test_df)/[`get_test_numpy`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_test_numpy).

When using [`SeriesBasedCesnetDataset`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset) (`dataset_type` = `DatasetType.SERIES_BASED`):

1. Create an instance of the dataset with the desired data root by calling [`get_dataset`](reference_cesnet_database.md#cesnet_tszoo.datasets.databases.cesnet_database.CesnetDatabase.get_dataset). This will download the dataset if it has not been previously downloaded and return instance of dataset.
2. Create an instance of [`SeriesBasedConfig`](reference_series_based_config.md#references.SeriesBasedConfig) and set it using [`set_dataset_config_and_initialize`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.set_dataset_config_and_initialize).
This initializes the dataset, including data splitting (train/validation/test), fitting transformers (if needed), selecting features, and more. This is cached for later use.
3. Use [`get_train_dataloader`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_train_dataloader)/[`get_train_df`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_train_df)/[`get_train_numpy`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_train_numpy) to get training data for chosen model.
4. Validate the model and perform the hyperparameter optimalization on [`get_val_dataloader`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_val_dataloader)/[`get_val_df`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_val_df)/[`get_val_numpy`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_val_numpy).
5. Evaluate the model on [`get_test_dataloader`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_test_dataloader)/[`get_test_df`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_test_df)/[`get_test_numpy`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_test_numpy).

When using [`DisjointTimeBasedCesnetDataset`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset) (`dataset_type` = `DatasetType.DISJOINT_TIME_BASED`):

1. Create an instance of the dataset with the desired data root by calling [`get_dataset`](reference_cesnet_database.md#cesnet_tszoo.datasets.databases.cesnet_database.CesnetDatabase.get_dataset). This will download the dataset if it has not been previously downloaded and return instance of dataset.
2. Create an instance of [`DisjointTimeBasedConfig`](reference_disjoint_time_based_config.md#references.DisjointTimeBasedConfig) and set it using [`set_dataset_config_and_initialize`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.set_dataset_config_and_initialize).
This initializes the dataset, including data splitting (train/validation/test), fitting transformers (if needed), selecting features, and more. This is cached for later use.
3. Use [`get_train_dataloader`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_train_dataloader)/[`get_train_df`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_train_df)/[`get_train_numpy`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_train_numpy) to get training data for chosen model.
4. Validate the model and perform the hyperparameter optimalization on [`get_val_dataloader`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_val_dataloader)/[`get_val_df`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_val_df)/[`get_val_numpy`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_val_numpy).
5. Evaluate the model on [`get_test_dataloader`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_test_dataloader)/[`get_test_df`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_test_df)/[`get_test_numpy`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_test_numpy).

You can create custom time-based benchmarks with [`save_benchmark`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.save_benchmark), series-based benchmarks with [`save_benchmark`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.save_benchmark) or disjoint-time-based with [`save_benchmark`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.save_benchmark).
They will be saved to `"data_root"/tszoo/benchmarks/` directory, where `data_root` was set when you created instance of dataset.
Above steps are practically the same for all dataset types, but there can be small differences is method parameters. Check each of them for info about that.
"""

def __init__(self, config: DatasetConfig, dataset: CesnetDataset, description: str = None):
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8 changes: 4 additions & 4 deletions cesnet_tszoo/datasets/disjoint_time_based_cesnet_dataset.py
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Expand Up @@ -37,7 +37,7 @@ class DisjointTimeBasedCesnetDataset(CesnetDataset):
- **Numpy array**: For loading the entire training, validation or test set at once.
- See [loading data][loading-data] for more details.

The dataset is stored in a [PyTables](https://www.pytables.org/) database. The internal `TimeBasedDataset`, `SplittedDataset`, `TimeBasedInitializerDataset` classes (used only when calling [`set_dataset_config_and_initialize`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.set_dataset_config_and_initialize)) act as wrappers that implement the PyTorch [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
The dataset is stored in a [PyTables](https://www.pytables.org/) dataset. The internal `DisjointTimeBasedSplittedDataset`, `DisjointTimeBasedSplitDataset` and `DisjointTimeBasedInitializerDataset` classes act as wrappers that implement the PyTorch [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
interface. These wrappers are compatible with PyTorch’s [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), providing efficient parallel data loading.

The dataset configuration is done through the [`DisjointTimeBasedConfig`](reference_disjoint_time_based_config.md#references.DisjointTimeBasedConfig) class.
Expand All @@ -64,11 +64,11 @@ class DisjointTimeBasedCesnetDataset(CesnetDataset):
"""Configuration of the dataset."""

train_dataset: Optional[DisjointTimeBasedSplittedDataset] = field(default=None, init=False)
"""Training set as a `SplittedDataset` instance wrapping multiple `TimeBasedDataset` that wrap the PyTables database."""
"""Training set as a `DisjointTimeBasedSplittedDataset` instance wrapping multiple `DisjointTimeBasedSplitDataset` that wrap the PyTables dataset."""
val_dataset: Optional[DisjointTimeBasedSplittedDataset] = field(default=None, init=False)
"""Validation set as a `SplittedDataset` instance wrapping multiple `TimeBasedDataset` that wrap the PyTables database."""
"""Validation set as a `DisjointTimeBasedSplittedDataset` instance wrapping multiple `DisjointTimeBasedSplitDataset` that wrap the PyTables dataset."""
test_dataset: Optional[DisjointTimeBasedSplittedDataset] = field(default=None, init=False)
"""Test set as a `SplittedDataset` instance wrapping multiple `TimeBasedDataset` that wrap the PyTables database. """
"""Test set as a `DisjointTimeBasedSplittedDataset` instance wrapping multiple `DisjointTimeBasedSplitDataset` that wrap the PyTables dataset. """

train_dataloader: Optional[DisjointTimeBasedDataloader] = field(default=None, init=False)
"""Iterable PyTorch [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) for training set."""
Expand Down
10 changes: 5 additions & 5 deletions cesnet_tszoo/datasets/series_based_cesnet_dataset.py
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Expand Up @@ -39,7 +39,7 @@ class SeriesBasedCesnetDataset(CesnetDataset):
- **Numpy array**: For loading the entire training, validation, test or all set at once.
- See [loading data][loading-data] for more details.

The dataset is stored in a [PyTables](https://www.pytables.org/) database. The internal `SeriesBasedDataset` and `SeriesBasedInitializerDataset` classes (used only when calling [`set_dataset_config_and_initialize`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.set_dataset_config_and_initialize)) act as wrappers that implement the PyTorch [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
The dataset is stored in a [PyTables](https://www.pytables.org/) dataset. The internal `SeriesBasedDataset` and `SeriesBasedInitializerDataset` classes act as wrappers that implement the PyTorch [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
interface. These wrappers are compatible with PyTorch’s [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), providing efficient parallel data loading.

The dataset configuration is done through the [`SeriesBasedConfig`](reference_series_based_config.md#references.SeriesBasedConfig) class.
Expand All @@ -66,13 +66,13 @@ class SeriesBasedCesnetDataset(CesnetDataset):
"""Configuration of the dataset."""

train_dataset: Optional[SeriesBasedDataset] = field(default=None, init=False)
"""Training set as a `SeriesBasedDataset` instance wrapping the PyTables database."""
"""Training set as a `SeriesBasedDataset` instance wrapping the PyTables dataset."""
val_dataset: Optional[SeriesBasedDataset] = field(default=None, init=False)
"""Validation set as a `SeriesBasedDataset` instance wrapping the PyTables database."""
"""Validation set as a `SeriesBasedDataset` instance wrapping the PyTables dataset."""
test_dataset: Optional[SeriesBasedDataset] = field(default=None, init=False)
"""Test set as a `SeriesBasedDataset` instance wrapping the PyTables database."""
"""Test set as a `SeriesBasedDataset` instance wrapping the PyTables dataset."""
all_dataset: Optional[SeriesBasedDataset] = field(default=None, init=False)
"""All set as a `SeriesBasedDataset` instance wrapping the PyTables database. """
"""All set as a `SeriesBasedDataset` instance wrapping the PyTables dataset. """

train_dataloader: Optional[SeriesBasedDataloader] = field(default=None, init=False)
"""Iterable PyTorch [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) for training set."""
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10 changes: 5 additions & 5 deletions cesnet_tszoo/datasets/time_based_cesnet_dataset.py
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Expand Up @@ -38,7 +38,7 @@ class TimeBasedCesnetDataset(CesnetDataset):
- **Numpy array**: For loading the entire training, validation, test or all set at once.
- See [loading data][loading-data] for more details.

The dataset is stored in a [PyTables](https://www.pytables.org/) database. The internal `TimeBasedDataset`, `SplittedDataset`, `TimeBasedInitializerDataset` classes (used only when calling [`set_dataset_config_and_initialize`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.set_dataset_config_and_initialize)) act as wrappers that implement the PyTorch [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
The dataset is stored in a [PyTables](https://www.pytables.org/) dataset. The internal `TimeBasedSplittedDataset`, `TimeSplitBasedDataset` and `TimeBasedInitializerDataset` classes act as wrappers that implement the PyTorch [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
interface. These wrappers are compatible with PyTorch’s [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), providing efficient parallel data loading.

The dataset configuration is done through the [`TimeBasedConfig`](reference_time_based_config.md#references.TimeBasedConfig) class.
Expand All @@ -65,16 +65,16 @@ class TimeBasedCesnetDataset(CesnetDataset):
"""Configuration of the dataset."""

train_dataset: Optional[TimeBasedSplittedDataset] = field(default=None, init=False)
"""Training set as a `SplittedDataset` instance wrapping multiple `TimeBasedDataset` that wrap the PyTables database."""
"""Training set as a `TimeBasedSplittedDataset` instance wrapping multiple `TimeSplitBasedDataset` that wrap the PyTables dataset."""

val_dataset: Optional[TimeBasedSplittedDataset] = field(default=None, init=False)
"""Validation set as a `SplittedDataset` instance wrapping multiple `TimeBasedDataset` that wrap the PyTables database."""
"""Validation set as a `TimeBasedSplittedDataset` instance wrapping multiple `TimeSplitBasedDataset` that wrap the PyTables dataset."""

test_dataset: Optional[TimeBasedSplittedDataset] = field(default=None, init=False)
"""Test set as a `SplittedDataset` instance wrapping multiple `TimeBasedDataset` that wrap the PyTables database. """
"""Test set as a `TimeBasedSplittedDataset` instance wrapping multiple `TimeSplitBasedDataset` that wrap the PyTables dataset. """

all_dataset: Optional[TimeBasedSplittedDataset] = field(default=None, init=False)
"""All set as a `SplittedDataset` instance wrapping multiple `TimeBasedDataset` that wrap the PyTables database. """
"""All set as a `TimeBasedSplittedDataset` instance wrapping multiple `TimeSplitBasedDataset` that wrap the PyTables dataset. """

train_dataloader: Optional[TimeBasedDataloader] = field(default=None, init=False)
"""Iterable PyTorch [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) for training set."""
Expand Down
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