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443 | class DisjointTimeBasedConfig(SeriesBasedHandler, TimeBasedHandler, DatasetConfig):
"""
This class is used for configuring the [`DisjointTimeBasedCesnetDataset`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset].
Used to configure the following:
- Train, validation, test, all sets (time period, sizes, features, window size)
- Handling missing values (default values, [`fillers`][cesnet_tszoo.utils.filler])
- Handling anomalies ([`anomaly handlers`][cesnet_tszoo.utils.anomaly_handler])
- Data transformation using [`transformers`][cesnet_tszoo.utils.transformer]
- Dataloader options (train/val/test/all/init workers, batch sizes)
- Plotting
**Important Notes:**
- Custom fillers must inherit from the [`fillers`][cesnet_tszoo.utils.filler.Filler] base class.
- Custom anomaly handlers must inherit from the [`anomaly handlers`][cesnet_tszoo.utils.anomaly_handler.AnomalyHandler] base class.
- Selected anomaly handler is only used for train set.
- It is recommended to use the [`transformers`][cesnet_tszoo.utils.transformer.Transformer] base class, though this is not mandatory as long as it meets the required methods.
- If a transformer is already initialized and `partial_fit_initialized_transformers` is `False`, the transformer does not require `partial_fit`.
- Otherwise, the transformer must support `partial_fit`.
- Transformers must implement `transform` method.
- Both `partial_fit` and `transform` methods must accept an input of type `np.ndarray` with shape `(times, features)`.
- `train_time_period`, `val_time_period`, `test_time_period` can overlap, but they should keep order of `train_time_period` < `val_time_period` < `test_time_period`
For available configuration options, refer to [here][cesnet_tszoo.configs.disjoint_time_based_config.DisjointTimeBasedConfig--configuration-options].
Attributes:
used_train_workers: Tracks the number of train workers in use. Helps determine if the train dataloader should be recreated based on worker changes.
used_val_workers: Tracks the number of validation workers in use. Helps determine if the validation dataloader should be recreated based on worker changes.
used_test_workers: Tracks the number of test workers in use. Helps determine if the test dataloader should be recreated based on worker changes.
uses_all_time_period: Whether all time period set should be used.
uses_all_ts: Whether all time series set should be used.
import_identifier: Tracks the name of the config upon import. None if not imported.
logger: Logger for displaying information.
The following attributes are initialized when [`set_dataset_config_and_initialize`][cesnet_tszoo.datasets.disjoint_time_based_cesnet_dataset.DisjointTimeBasedCesnetDataset.set_dataset_config_and_initialize] is called:
Attributes:
display_train_time_period: Used to display the configured value of `train_time_period`.
display_val_time_period: Used to display the configured value of `val_time_period`.
display_test_time_period: Used to display the configured value of `test_time_period`.
train_ts_row_ranges: Initialized when `train_ts` is set. Contains time series IDs in train set with their respective time ID ranges.
val_ts_row_ranges: Initialized when `val_ts` is set. Contains time series IDs in validation set with their respective time ID ranges.
test_ts_row_ranges: Initialized when `test_ts` is set. Contains time series IDs in test set with their respective time ID ranges.
all_time_period: Contains total used time period.
all_ts: Contains all used time series.
all_ts_row_ranges: Contains time series IDs in all set with their respective time ID ranges.
aggregation: The aggregation period used for the data.
source_type: The source type of the data.
database_name: Specifies which database this config applies to.
transform_with_display: Used to display the configured type of `transform_with`.
fill_missing_with_display: Used to display the configured type of `fill_missing_with`.
handle_anomalies_with_display: Used to display the configured type of `handle_anomalies_with`.
features_to_take_without_ids: Features to be returned, excluding time or time series IDs.
indices_of_features_to_take_no_ids: Indices of non-ID features in `features_to_take`.
is_transformer_custom: Flag indicating whether the transformer is custom.
is_filler_custom: Flag indicating whether the filler is custom.
is_anomaly_handler_custom: Flag indicating whether the anomaly handler is custom.
ts_id_name: Name of the time series ID, dependent on `source_type`.
used_times: List of all times used in the configuration.
used_ts_ids: List of all time series IDs used in the configuration.
used_ts_row_ranges: List of time series IDs with their respective time ID ranges.
used_fillers: List of all fillers used in the configuration.
used_anomaly_handlers: List of all anomaly handlers used in the configuration.
used_singular_train_time_series: Currently used singular train set time series for dataloader.
used_singular_val_time_series: Currently used singular validation set time series for dataloader.
used_singular_test_time_series: Currently used singular test set time series for dataloader.
transformers: Prepared transformers for fitting/transforming. Can be one transformer, array of transformers or `None`.
are_transformers_premade: Indicates whether the transformers are premade.
train_fillers: Fillers used in the train set. `None` if no filler is used or train set is not used.
val_fillers: Fillers used in the validation set. `None` if no filler is used or validation set is not used.
test_fillers: Fillers used in the test set. `None` if no filler is used or test set is not used.
all_fillers: Fillers used for the all set.
anomaly_handlers: Prepared anomaly handlers for fitting/handling anomalies. Can be array of anomaly handlers or `None`.
is_initialized: Flag indicating if the configuration has already been initialized. If true, config initialization will be skipped.
version: Version of cesnet-tszoo this config was made in.
export_update_needed: Whether config was updated to newer version and should be exported.
# Configuration options
Attributes:
train_ts: Defines which time series IDs are used in the training set. Can be a list of IDs, or an integer/float to specify a random selection. An `int` specifies the number of random time series, and a `float` specifies the proportion of available time series.
`int` and `float` must be greater than 0, and a float should be smaller or equal to 1.0. Using `int` or `float` guarantees that no time series from other sets will be used. Must be used with `train_time_period`.
val_ts: Defines which time series IDs are used in the validation set. Same as `train_ts` but for the validation set. Must be used with `val_time_period`.
test_ts: Defines which time series IDs are used in the test set. Same as `train_ts` but for the test set. Must be used with `test_time_period`.
train_time_period: Defines the time period for training set. Can be a range of time IDs or a tuple of datetime objects. Float value is equivalent to percentage of available times with offseted position from previous used set. Must be used with `train_ts`. `Default: None`
val_time_period: Defines the time period for validation set. Can be a range of time IDs or a tuple of datetime objects. Float value is equivalent to percentage of available times with offseted position from previous used set. Must be used with `val_ts`. `Default: None`
test_time_period: Defines the time period for test set. Can be a range of time IDs or a tuple of datetime objects. Must be used with `test_ts`. `Default: None`
features_to_take: Defines which features are used. `Default: "all"`
default_values: Default values for missing data, applied before fillers. Can set one value for all features or specify for each feature. `Default: "default"`
sliding_window_size: Number of times in one window. Impacts dataloader behavior. Batch sizes affects how much data will be cached for creating windows. `Default: None`
sliding_window_prediction_size: Number of times to predict from sliding_window_size. Impacts dataloader behavior. Batch sizes affects how much data will be cached for creating windows. `Default: None`
sliding_window_step: Number of times to move by after each window. `Default: 1`
set_shared_size: How much times should time periods share. Order of sharing is training set < validation set < test set. Only in effect if sets share less values than set_shared_size. Use float value for percentage of total times or int for count. `Default: 0`
train_batch_size: Batch size for the train dataloader. Affects number of returned times in one batch. `Default: 32`
val_batch_size: Batch size for the validation dataloader. Affects number of returned times in one batch. `Default: 64`
test_batch_size: Batch size for the test dataloader. Affects number of returned times in one batch. `Default: 128`
fill_missing_with: Defines how to fill missing values in the dataset. Can pass enum [`FillerType`][cesnet_tszoo.utils.enums.FillerType] for built-in filler or pass a type of custom filler that must derive from [`Filler`][cesnet_tszoo.utils.filler.Filler] base class. `Default: None`
transform_with: Defines the transformer used to transform the dataset. Can pass enum [`TransformerType`][cesnet_tszoo.utils.enums.TransformerType] for built-in transformer, pass a type of custom transformer or instance of already fitted transformer(s). `Default: None`
handle_anomalies_with: Defines the anomaly handler for handling anomalies in the train set. Can pass enum [`AnomalyHandlerType`][cesnet_tszoo.utils.enums.AnomalyHandlerType] for built-in anomaly handler or a type of custom anomaly handler. `Default: None`
partial_fit_initialized_transformers: If `True`, partial fitting on train set is performed when using initiliazed transformers. `Default: False`
include_time: If `True`, time data is included in the returned values. `Default: True`
include_ts_id: If `True`, time series IDs are included in the returned values. `Default: True`
time_format: Format for the returned time data. When using TimeFormat.DATETIME, time will be returned as separate list along rest of the values. `Default: TimeFormat.ID_TIME`
train_workers: Number of workers for loading training data. `0` means that the data will be loaded in the main process. `Default: 4`
val_workers: Number of workers for loading validation data. `0` means that the data will be loaded in the main process. `Default: 3`
test_workers: Number of workers for loading test. `0` means that the data will be loaded in the main process. `Default: 2`
init_workers: Number of workers for initial dataset processing during configuration. `0` means that the data will be loaded in the main process. `Default: 4`
nan_threshold: Maximum allowable percentage of missing data. Time series exceeding this threshold are excluded. Time series over the threshold will not be used. Used for `train/val/test/all` separately. `Default: 1.0`
random_state: Fixes randomness for reproducibility during configuration and dataset initialization. `Default: None`
"""
def __init__(self,
train_ts: list[int] | npt.NDArray[np.int_] | float | int | None,
val_ts: list[int] | npt.NDArray[np.int_] | float | int | None,
test_ts: list[int] | npt.NDArray[np.int_] | float | int | None,
train_time_period: tuple[datetime, datetime] | range | float | None = None,
val_time_period: tuple[datetime, datetime] | range | float | None = None,
test_time_period: tuple[datetime, datetime] | range | float | None = None,
features_to_take: list[str] | Literal["all"] = "all",
default_values: list[Number] | npt.NDArray[np.number] | dict[str, Number] | Number | Literal["default"] | None = "default",
sliding_window_size: int | None = None,
sliding_window_prediction_size: int | None = None,
sliding_window_step: int = 1,
set_shared_size: float | int = 0,
train_batch_size: int = 32,
val_batch_size: int = 64,
test_batch_size: int = 128,
fill_missing_with: type | FillerType | Literal["mean_filler", "forward_filler", "linear_interpolation_filler"] | None = None,
transform_with: type | list[Transformer] | np.ndarray[Transformer] | TransformerType | Transformer | Literal["min_max_scaler", "standard_scaler", "max_abs_scaler", "log_transformer", "l2_normalizer"] | None = None,
handle_anomalies_with: type | AnomalyHandlerType | Literal["z-score", "interquartile_range"] | None = None,
partial_fit_initialized_transformer: bool = False,
include_time: bool = True,
include_ts_id: bool = True,
time_format: TimeFormat | Literal["id_time", "datetime", "unix_time", "shifted_unix_time"] = TimeFormat.ID_TIME,
train_workers: int = 4,
val_workers: int = 3,
test_workers: int = 2,
init_workers: int = 4,
nan_threshold: float = 1.0,
random_state: int | None = None):
self.allow_ts_id_overlap = False
self.logger = logging.getLogger("disjoint_time_based_config")
TimeBasedHandler.__init__(self, self.logger, train_batch_size, val_batch_size, test_batch_size, 1, True, sliding_window_size, sliding_window_prediction_size, sliding_window_step, set_shared_size, train_time_period, val_time_period, test_time_period)
SeriesBasedHandler.__init__(self, self.logger, True, train_ts, val_ts, test_ts)
DatasetConfig.__init__(self, features_to_take, default_values, train_batch_size, val_batch_size, test_batch_size, 1, fill_missing_with, transform_with, handle_anomalies_with, partial_fit_initialized_transformer, include_time, include_ts_id, time_format,
train_workers, val_workers, test_workers, 1, init_workers, nan_threshold, False, DatasetType.DISJOINT_TIME_BASED, DataloaderOrder.SEQUENTIAL, random_state, self.logger)
def _validate_construction(self) -> None:
"""Performs basic parameter validation to ensure correct configuration. More comprehensive validation, which requires dataset-specific data, is handled in [`_dataset_init`][cesnet_tszoo.configs.disjoint_time_based_config.DisjointTimeBasedConfig._dataset_init]. """
DatasetConfig._validate_construction(self)
if self.train_ts is None or self.train_time_period is None:
if self.train_ts is not None:
self.logger.error("When train_ts is not None you must set train_time_period or set train_ts as None.")
raise ValueError("When train_ts is not None you must set train_time_period or set train_ts as None.")
if self.train_time_period is not None:
self.logger.error("When train_time_period is not None you must set train_ts or set train_time_period as None.")
raise ValueError("When train_time_period is not None you must set train_ts or set train_time_period as None.")
if self.val_ts is None or self.val_time_period is None:
if self.val_ts is not None:
self.logger.error("When val_ts is not None you must set val_time_period or set val_ts as None.")
raise ValueError("When val_ts is not None you must set val_time_period or set val_ts as None.")
if self.val_time_period is not None:
self.logger.error("When val_time_period is not None you must set val_ts or set val_time_period as None.")
raise ValueError("When val_time_period is not None you must set val_ts or set val_time_period as None.")
if self.test_ts is None or self.test_time_period is None:
if self.test_ts is not None:
self.logger.error("When test_ts is not None you must set test_time_period or set test_ts as None.")
raise ValueError("When test_ts is not None you must set test_time_period or set test_ts as None.")
if self.test_time_period is not None:
self.logger.error("When test_time_period is not None you must set test_ts or set test_time_period as None.")
raise ValueError("When test_time_period is not None you must set test_ts or set test_time_period as None.")
if self.train_ts is None and self.val_ts is None and self.test_ts is None:
self.logger.error("No set for time series has been set. You must set at least one time series set and its respective time period.")
raise ValueError("No set for time series has been set. You must set at least one time series set and its respective time period.")
self._validate_time_periods_init()
self._validate_ts_init()
self._validate_set_shared_size_init()
self._validate_sliding_window_init()
self._update_batch_sizes(self.train_batch_size, self.val_batch_size, self.test_batch_size, self.all_batch_size)
self.logger.debug("Disjoint-time-based configuration validated successfully.")
def _update_batch_sizes(self, train_batch_size: int, val_batch_size: int, test_batch_size: int, all_batch_size: int) -> None:
# Adjust batch sizes based on sliding_window_size
if self.sliding_window_size is not None:
if self.sliding_window_step <= 0:
raise ValueError("sliding_window_step must be greater or equal to 1.")
total_window_size = self.sliding_window_size + self.sliding_window_prediction_size
if isinstance(self.train_batch_size, int) and total_window_size > self.train_batch_size:
self.train_batch_size = self.sliding_window_size + self.sliding_window_prediction_size
self.logger.info("train_batch_size adjusted to %s as it should be greater than or equal to sliding_window_size + sliding_window_prediction_size.", total_window_size)
if isinstance(self.val_batch_size, int) and total_window_size > self.val_batch_size:
self.val_batch_size = self.sliding_window_size + self.sliding_window_prediction_size
self.logger.info("val_batch_size adjusted to %s as it should be greater than or equal to sliding_window_size + sliding_window_prediction_size.", total_window_size)
if isinstance(self.test_batch_size, int) and total_window_size > self.test_batch_size:
self.test_batch_size = self.sliding_window_size + self.sliding_window_prediction_size
self.logger.info("test_batch_size adjusted to %s as it should be greater than or equal to sliding_window_size + sliding_window_prediction_size.", total_window_size)
DatasetConfig._update_batch_sizes(self, train_batch_size, val_batch_size, test_batch_size, all_batch_size)
def _update_sliding_window(self, sliding_window_size: int | None, sliding_window_prediction_size: int | None, sliding_window_step: int | None, set_shared_size: float | int, all_time_ids: np.ndarray):
"""Updates values related to sliding window. """
TimeBasedHandler._update_sliding_window(self, sliding_window_size, sliding_window_prediction_size, sliding_window_step, set_shared_size, all_time_ids, self.has_train(), self.has_val(), self.has_test(), self.has_all())
def _get_train(self) -> tuple[np.ndarray, np.ndarray] | tuple[None, None]:
"""Returns the indices corresponding to the training set. """
return self.train_ts, self.train_time_period
def _get_val(self) -> tuple[np.ndarray, np.ndarray] | tuple[None, None]:
"""Returns the indices corresponding to the validation set. """
return self.val_ts, self.val_time_period
def _get_test(self) -> tuple[np.ndarray, np.ndarray] | tuple[None, None]:
"""Returns the indices corresponding to the test set. """
return self.test_ts, self.test_time_period
def _get_all(self) -> tuple[np.ndarray, np.ndarray] | tuple[None, None]:
"""Returns the indices corresponding to the all set. """
return None, None
def has_train(self) -> bool:
"""Returns whether training set is used. """
return self.train_ts is not None and self.train_time_period is not None
def has_val(self) -> bool:
"""Returns whether validation set is used. """
return self.val_ts is not None and self.val_time_period is not None
def has_test(self) -> bool:
"""Returns whether test set is used. """
return self.test_ts is not None and self.test_time_period is not None
def has_all(self) -> bool:
"""Returns whether all set is used. """
return False
def _set_time_period(self, all_time_ids: np.ndarray) -> None:
"""Validates and filters `train_time_period`, `val_time_period`, `test_time_period` and `all_time_period` based on `dataset` and `aggregation`. """
self._prepare_and_set_time_period_sets(all_time_ids, self.time_format)
def _set_ts(self, all_ts_ids: np.ndarray, all_ts_row_ranges: np.ndarray) -> None:
""" Validates and filters inputted time series id from `train_ts`, `val_ts` and `test_ts` based on `dataset` and `source_type`. Handles random set."""
self._prepare_and_set_ts_sets(all_ts_ids, all_ts_row_ranges, self.ts_id_name, self.random_state)
def _set_feature_transformers(self) -> None:
"""Creates and/or validates transformers based on the `transform_with` parameter. """
self.create_transformer_per_time_series = False
if self.transform_with is None:
self.transform_with_display = None
self.are_transformers_premade = False
self.transformers = None
self.is_transformer_custom = None
self.logger.debug("No transformer will be used because transform_with is not set.")
return
if not self.has_train():
if self.partial_fit_initialized_transformers:
self.logger.warning("partial_fit_initialized_transformers will be ignored because train set is not used.")
self.partial_fit_initialized_transformers = False
# Treat transform_with as already initialized transformer
if not isinstance(self.transform_with, (type, TransformerType)):
self.transformers = self.transform_with
self.transform_with, self.transform_with_display = transformer_from_input_to_transformer_type(type(self.transform_with), check_for_fit=False, check_for_partial_fit=self.partial_fit_initialized_transformers)
self.are_transformers_premade = True
self.is_transformer_custom = "Custom" in self.transform_with_display
self.logger.debug("Using initialized transformer of type: %s", self.transform_with_display)
# Treat transform_with as uninitialized transformer
else:
if not self.has_train():
self.transform_with = None
self.transform_with_display = None
self.are_transformers_premade = False
self.transformers = None
self.is_transformer_custom = None
self.logger.warning("No transformer will be used because train set is not used.")
return
self.transform_with, self.transform_with_display = transformer_from_input_to_transformer_type(self.transform_with, check_for_fit=self.create_transformer_per_time_series, check_for_partial_fit=not self.create_transformer_per_time_series)
self.are_transformers_premade = False
self.is_transformer_custom = "Custom" in self.transform_with_display
self.transformers = self.transform_with()
self.logger.debug("Using uninitialized transformer of type: %s", self.transform_with_display)
def _set_fillers(self) -> None:
"""Creates and/or validates fillers based on the `fill_missing_with` parameter. """
self.fill_missing_with, self.fill_missing_with_display = filler_from_input_to_type(self.fill_missing_with)
self.is_filler_custom = "Custom" in self.fill_missing_with_display if self.fill_missing_with is not None else None
if self.fill_missing_with is None:
self.logger.debug("No filler is used because fill_missing_with is set to None.")
return
# Set the fillers for the training set
if self.has_train():
self.train_fillers = np.array([self.fill_missing_with(self.features_to_take_without_ids) for _ in self.train_ts])
self.logger.debug("Fillers for training set are set.")
# Set the fillers for the validation set
if self.has_val():
self.val_fillers = np.array([self.fill_missing_with(self.features_to_take_without_ids) for _ in self.val_ts])
self.logger.debug("Fillers for validation set are set.")
# Set the fillers for the test set
if self.has_test():
self.test_fillers = np.array([self.fill_missing_with(self.features_to_take_without_ids) for _ in self.test_ts])
self.logger.debug("Fillers for test set are set.")
# Set the fillers for the all set
self.all_fillers = np.array([self.fill_missing_with(self.features_to_take_without_ids) for _ in self.all_ts])
self.logger.debug("Fillers for all set are set.")
def _set_anomaly_handlers(self):
"""Creates and/or validates anomaly handlers based on the `handle_anomalies_with` parameter. """
if self.handle_anomalies_with is None:
self.logger.debug("No anomaly handler is used because handle_anomalies_with is set to None.")
return
if not self.has_train():
self.logger.error("Anomaly handler cannot be used without train set. Either set train set or set handle_anomalies_with to None")
raise ValueError("Anomaly handler cannot be used without train set. Either set train set or set handle_anomalies_with to None")
self.logger.info("Anomaly handler will only be used for train set.")
self.handle_anomalies_with, self.handle_anomalies_with_display = anomaly_handler_from_input_to_anomaly_handler_type(self.handle_anomalies_with)
self.is_anomaly_handler_custom = "Custom" in self.handle_anomalies_with_display
self.anomaly_handlers = np.array([self.handle_anomalies_with() for _ in self.train_ts])
def _validate_finalization(self) -> None:
""" Performs final validation of the configuration. Validates whether `train/val/test` are continuos."""
self._validate_time_periods_overlap()
if not self.allow_ts_id_overlap:
self._validate_ts_overlap()
def __str__(self) -> str:
if self.transform_with is None:
transformer_part = f"Transformer type: {str(self.transform_with_display)}"
else:
transformer_part = f'''Transformer type: {str(self.transform_with_display)}
Are transformers premade: {self.are_transformers_premade}
Are premade transformers partial_fitted: {self.partial_fit_initialized_transformers}'''
if self.include_time:
time_part = f'''Time included: {str(self.include_time)}
Time format: {str(self.time_format)}'''
else:
time_part = f"Time included: {str(self.include_time)}"
return f'''
Config Details
Used for database: {self.database_name}
Aggregation: {str(self.aggregation)}
Source: {str(self.source_type)}
Time series
Train time series IDs: {get_abbreviated_list_string(self.train_ts)}
Val time series IDs: {get_abbreviated_list_string(self.val_ts)}
Test time series IDs: {get_abbreviated_list_string(self.test_ts)}
Time periods
Train time periods: {str(self.display_train_time_period)}
Val time periods: {str(self.display_val_time_period)}
Test time periods: {str(self.display_test_time_period)}
Features
Taken features: {str(self.features_to_take_without_ids)}
Default values: {self.default_values}
Time series ID included: {str(self.include_ts_id)}
{time_part}
Sliding window
Sliding window size: {self.sliding_window_size}
Sliding window prediction size: {self.sliding_window_prediction_size}
Sliding window step size: {self.sliding_window_step}
Fillers
Filler type: {str(self.fill_missing_with_display)}
Transformers
{transformer_part}
Anomaly handler
Anomaly handler type (train set): {str(self.handle_anomalies_with_display)}
Batch sizes
Train batch size: {self.train_batch_size}
Val batch size: {self.val_batch_size}
Test batch size: {self.test_batch_size}
Default workers
Init worker count: {str(self.init_workers)}
Train worker count: {str(self.train_workers)}
Val worker count: {str(self.val_workers)}
Test worker count: {str(self.test_workers)}
Other
Nan threshold: {str(self.nan_threshold)}
Random state: {self.random_state}
Version: {self.version}
'''
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