Feat: Various doc changes

Author: milankures

cesnet_tszoo/benchmarks.py

@@ -30,34 +30,34 @@ class Benchmark:
 
     For time-based:
 
-    When using [`TimeBasedCesnetDataset`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset] (`dataset_type` = `DatasetType.TIME_BASED`):
+    When using [`TimeBasedCesnetDataset`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset) (`dataset_type` = `DatasetType.TIME_BASED`):
 
-    1. Create an instance of the dataset with the desired data root by calling [`get_dataset`][cesnet_tszoo.datasets.databases.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`][cesnet_tszoo.references.configs.TimeBasedConfig] and set it using [`set_dataset_config_and_initialize`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.set_dataset_config_and_initialize]. 
+    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`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_train_dataloader]/[`get_train_df`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_train_df]/[`get_train_numpy`][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`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_val_dataloader]/[`get_val_df`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_val_df]/[`get_val_numpy`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_val_numpy].
-    5. Evaluate the model on [`get_test_dataloader`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_test_dataloader]/[`get_test_df`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_test_df]/[`get_test_numpy`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.get_test_numpy].     
+    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`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset] (`dataset_type` = `DatasetType.SERIES_BASED`):
+    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`][cesnet_tszoo.datasets.databases.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`][cesnet_tszoo.references.configs.SeriesBasedConfig] and set it using [`set_dataset_config_and_initialize`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.set_dataset_config_and_initialize]. 
+    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`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_train_dataloader]/[`get_train_df`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_train_df]/[`get_train_numpy`][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`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_val_dataloader]/[`get_val_df`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_val_df]/[`get_val_numpy`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_val_numpy].
-    5. Evaluate the model on [`get_test_dataloader`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_test_dataloader]/[`get_test_df`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_test_df]/[`get_test_numpy`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.get_test_numpy].   
+    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`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset] (`dataset_type` = `DatasetType.DISJOINT_TIME_BASED`):
+    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`][cesnet_tszoo.datasets.databases.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`][cesnet_tszoo.references.configs.DisjointTimeBasedConfig] and set it using [`set_dataset_config_and_initialize`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.set_dataset_config_and_initialize]. 
+    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`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_train_dataloader]/[`get_train_df`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_train_df]/[`get_train_numpy`][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`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_val_dataloader]/[`get_val_df`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_val_df]/[`get_val_numpy`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_val_numpy].
-    5. Evaluate the model on [`get_test_dataloader`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_test_dataloader]/[`get_test_df`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_test_df]/[`get_test_numpy`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.get_test_numpy].      
+    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`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset.save_benchmark], series-based benchmarks with [`save_benchmark`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset.save_benchmark] or disjoint-time-based with [`save_benchmark`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.save_benchmark].
+    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.
     """
 
@@ -79,11 +79,11 @@ def get_initialized_dataset(self, display_config_details: Optional[Literal["text
 
         This method uses following config attributes:
 
-        | Dataset config                    | Description                                                                                    |
-        | --------------------------------- | ---------------------------------------------------------------------------------------------- |
-        | `init_workers`                    | Specifies the number of workers to use for initialization. Applied when `workers` = "config". |
-        | `partial_fit_initialized_transformers` | Determines whether initialized transformers should be partially fitted on the training data.        |
-        | `nan_threshold`                   | Filters out time series with missing values exceeding the specified threshold.                 |
+        Dataset config | Description
+        -------------- | -----------
+        `init_workers` | Specifies the number of workers to use for initialization. Applied when `workers` = "config".
+        `partial_fit_initialized_transformers` | Determines whether initialized transformers should be partially fitted on the training data.
+        `nan_threshold` | Filters out time series with missing values exceeding the specified threshold.
 
         Parameters:
             display_config_details: Flag indicating whether to display the configuration values after initialization. `Default: True`   

docs/choosing_data.md

@@ -4,7 +4,7 @@ This tutorial will look at some configuration options for choosing data you wish
 
 Each dataset type will have its own part because of multiple differences of available configuration values.
 
-## [`TimeBasedCesnetDataset`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset] dataset
+## [`TimeBasedCesnetDataset`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset) dataset
 
 !!! info "Note"
     For every configuration and more detailed examples refer to Jupyter notebook [`time_based_choosing_data`](https://github.com/CESNET/cesnet-ts-zoo-tutorials/blob/main/time_based_choosing_data.ipynb)
@@ -113,7 +113,7 @@ time_based_dataset.set_dataset_config_and_initialize(config)
 
 ```
 
-## [`DisjointTimeBasedCesnetDataset`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset] dataset
+## [`DisjointTimeBasedCesnetDataset`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset) dataset
 
 !!! info "Note"
     For every configuration and more detailed examples refer to Jupyter notebook [`disjoint_time_based_choosing_data`](https://github.com/CESNET/cesnet-ts-zoo-tutorials/blob/main/disjoint_time_based_choosing_data.ipynb)
@@ -207,7 +207,7 @@ disjoint_dataset.set_dataset_config_and_initialize(config)
 
 ```
 
-## [`SeriesBasedCesnetDataset`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset] dataset
+## [`SeriesBasedCesnetDataset`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset) dataset
 
 !!! info "Note"
     For every configuration and more detailed examples refer to Jupyter notebook [`series_based_choosing_data`](https://github.com/CESNET/cesnet-ts-zoo-tutorials/blob/main/series_based_choosing_data.ipynb)