tdaad.utils package

Submodules

tdaad.utils.local_elliptic_envelope module

Pandas Elliptic Envelope.

tdaad.utils.local_elliptic_envelope.pandas_mahalanobis(self, X)[source]

Compute the negative Mahalanobis distances of embedding matrix X.

Parameters:: X (array-like of shape (n_samples, n_features)) – The embedding matrix.
Returns:: negative_mahal_distances – Opposite of the Mahalanobis distances.
Return type:: pandas.DataFrame of shape (n_samples,)

tdaad.utils.local_elliptic_envelope.pandas_score_samples(self, X)[source]

Compute the negative Mahalanobis distances.

Parameters:: X (array-like of shape (n_samples, n_features)) – The data matrix.
Returns:: negative_mahal_distances – Opposite of the Mahalanobis distances.
Return type:: array-like of shape (n_samples,)

tdaad.utils.local_pipeline module

Window Functions.

class tdaad.utils.local_pipeline.LocalPipeline(steps, *, transform_input=None, memory=None, verbose=False)[source]

Bases: Pipeline

Local pipeline modification for added functionality.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LocalPipeline

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

False: metadata is not requested and the meta-estimator will not pass it to score.

None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.

selfobject
The updated object.

tdaad.utils.remapping_functions module

Remapping Functions.

tdaad.utils.remapping_functions.score_flat_fast_remapping(scores, window_size, stride, padding_length=0)[source]: Univariate score remapping without window indices and with precomputed padding_length.

tdaad.utils.window_functions module

Window Functions.

tdaad.utils.window_functions.sliding_window_ppl(data, pipeline, step=5, window_size=120)[source]

Applies a pipeline to timeseries data chunks using the Sliding Window algorithm.

@param data: pd.DataFrame with index to apply named_pipeline to. @param window_size: size of the sliding window algorithm to extract subsequences as input to named_pipeline. @param step: size of the sliding window steps between each window. @param pipeline: pipeline (sequence of operators that have a name attribute) to apply to each window. @return: pd.DataFrame that maps data to the result of applying named_pipeline to window view of data.