Merge pull request #31 from CESNET/feat-decoupling-docs-from-code

Feat decoupling docs from code

Author: milankures

cesnet_tszoo/benchmarks.py

@@ -1,5 +1,6 @@
 from abc import ABC
 from logging import Logger
+from typing import Optional
 
 import numpy as np
 import numpy.typing as npt
@@ -16,19 +17,19 @@ def __init__(self,
                  val_ts: list[int] | npt.NDArray[np.int_] | float | int | None,
                  test_ts: list[int] | npt.NDArray[np.int_] | float | int | None):
 
-        self.train_ts = train_ts
-        self.val_ts = val_ts
-        self.test_ts = test_ts
-        self.all_ts = None
+        self.train_ts: Optional[np.ndarray] = train_ts
+        self.val_ts: Optional[np.ndarray] = val_ts
+        self.test_ts: Optional[np.ndarray] = test_ts
+        self.all_ts: Optional[np.ndarray] = None
 
-        self.uses_all_ts = uses_all_ts
+        self.uses_all_ts: bool = uses_all_ts
 
-        self.train_ts_row_ranges = None
-        self.val_ts_row_ranges = None
-        self.test_ts_row_ranges = None
-        self.all_ts_row_ranges = None
+        self.train_ts_row_ranges: Optional[np.ndarray] = None
+        self.val_ts_row_ranges: Optional[np.ndarray] = None
+        self.test_ts_row_ranges: Optional[np.ndarray] = None
+        self.all_ts_row_ranges: Optional[np.ndarray] = None
 
-        self.logger = logger
+        self.logger: Logger = logger
 
     def _prepare_and_set_ts_sets(self, all_ts_ids: np.ndarray, all_ts_row_ranges: np.ndarray, ts_id_name: str, random_state) -> None:
         """Validates and filters the input time series IDs based on the `dataset` and `source_type`. Handles random split."""

cesnet_tszoo/configs/base_config.py

@@ -1,6 +1,7 @@
 from abc import ABC
 from datetime import datetime, timezone
 from logging import Logger
+from typing import Optional
 
 import numpy as np
 
@@ -24,31 +25,31 @@ def __init__(self,
                  val_time_period: tuple[datetime, datetime] | range | float | None,
                  test_time_period: tuple[datetime, datetime] | range | float | None):
 
-        self.train_time_period = train_time_period
-        self.val_time_period = val_time_period
-        self.test_time_period = test_time_period
+        self.train_time_period: Optional[np.ndarray] = train_time_period
+        self.val_time_period: Optional[np.ndarray] = val_time_period
+        self.test_time_period: Optional[np.ndarray] = test_time_period
 
-        self.train_batch_size = train_batch_size
-        self.val_batch_size = val_batch_size
-        self.test_batch_size = test_batch_size
-        self.all_batch_size = all_batch_size
+        self.train_batch_size: int = train_batch_size
+        self.val_batch_size: int = val_batch_size
+        self.test_batch_size: int = test_batch_size
+        self.all_batch_size: int = all_batch_size
 
-        self.set_shared_size = set_shared_size
+        self.set_shared_size: int | float = set_shared_size
 
-        self.sliding_window_size = sliding_window_size
-        self.sliding_window_prediction_size = sliding_window_prediction_size
-        self.sliding_window_step = sliding_window_step
+        self.sliding_window_size: Optional[int] = sliding_window_size
+        self.sliding_window_prediction_size: Optional[int] = sliding_window_prediction_size
+        self.sliding_window_step: int = sliding_window_step
 
-        self.uses_all_time_period = uses_all_time_period
+        self.uses_all_time_period: bool = uses_all_time_period
 
-        self.display_train_time_period = None
-        self.display_val_time_period = None
-        self.display_test_time_period = None
-        self.display_all_time_period = None
+        self.display_train_time_period: Optional[range] = None
+        self.display_val_time_period: Optional[range] = None
+        self.display_test_time_period: Optional[range] = None
+        self.display_all_time_period: Optional[range] = None
 
-        self.all_time_period = None
+        self.all_time_period: Optional[np.ndarray] = None
 
-        self.logger = logger
+        self.logger: Logger = logger
 
     def _prepare_and_set_time_period_sets(self, all_time_ids: np.ndarray, time_format: TimeFormat) -> None:
         """Validates and filters `train_time_period`, `val_time_period`, `test_time_period` and `all_time_period` based on `dataset` and `aggregation`. """

cesnet_tszoo/configs/disjoint_time_based_config.py

@@ -50,12 +50,12 @@ def get_dataset(cls, data_root: str, source_type: SourceType | Literal["ip_addre
             data_root: Path to the folder where the dataset will be stored. Each database has its own subfolder `data_root/tszoo/databases/database_name/`.
             source_type: The source type of the desired dataset.
             aggregation: The aggregation type for the selected source type.
-            dataset_type: Type of a dataset you want to create. Can be [`TimeBasedCesnetDataset`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset], [`SeriesBasedCesnetDataset`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset] or [`DisjointTimeBasedCesnetDataset`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset].
+            dataset_type: Type of a dataset you want to create. Can be [`TimeBasedCesnetDataset`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset), [`SeriesBasedCesnetDataset`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset) or [`DisjointTimeBasedCesnetDataset`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset).
             check_errors: Whether to validate if the dataset is corrupted. `Default: False`
             display_details: Whether to display details about the available data in chosen dataset. `Default: False`
 
         Returns:
-            [`TimeBasedCesnetDataset`][cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset], [`SeriesBasedCesnetDataset`][cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset] or [`DisjointTimeBasedCesnetDataset`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset].
+            [`TimeBasedCesnetDataset`](reference_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.time_based_cesnet_dataset.TimeBasedCesnetDataset), [`SeriesBasedCesnetDataset`](reference_series_based_cesnet_dataset.md#cesnet_tszoo.datasets.series_based_cesnet_dataset.SeriesBasedCesnetDataset) or [`DisjointTimeBasedCesnetDataset`](reference_disjoint_time_based_cesnet_dataset.md#cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset).
         """
 
         return super(CESNET_TimeSeries24, cls).get_dataset(data_root, source_type, aggregation, dataset_type, check_errors, display_details)