TSFreshRelevant¶

class TSFreshRelevant(default_fc_parameters='efficient', kind_to_fc_parameters=None, chunksize=None, n_jobs=1, show_warnings=False, disable_progressbar=True, impute_function=None, profiling=None, profiling_filename=None, profiling_sorting=None, distributor=None, test_for_binary_target_binary_feature=None, test_for_binary_target_real_feature=None, test_for_real_target_binary_feature=None, test_for_real_target_real_feature=None, fdr_level=None, hypotheses_independent=None, ml_task='auto')[source]¶

Transformer for extracting time series features via tsfresh.extract_features.

Direct interface to tsfresh.extract_features [1] followed by the tsfresh FeatureSelector class as an aeon transformer.

Parameters:

default_fc_parametersstr, FCParameters object or None,: default=None = tsfresh default = “comprehensive” Specifies pre-defined feature sets to be extracted If str, should be in [“minimal”, “efficient”, “comprehensive”] See [3] for more details.
kind_to_fc_parameterslist or None, default=None: List containing strings specifying selected features to be extracted. The naming convention from tsfresh applies, i.e. the strings should be structured as: {time_series_name}__{feature_name}__{param name 1}_ {param value 1}__[..]__{param name k}_{param value k}. See [2] for more details and [4] for viable options. Either default_fc_parameters or kind_to_fc_parameters should be passed. If both are passed, only features specified in kind_to_fc_parameters are extracted. If neither is passed, it calculates the “comprehensive” feature set.
n_jobsint, default=1: The number of processes to use for parallelization. If zero, no parallelization is used.
chunksizeNone or int, default=None: The size of one chunk that is submitted to the worker process for the parallelisation. Where one chunk is defined as a singular time series for one id and one kind. If you set the chunksize to 10, then it means that one task is to calculate all features for 10 time series. If it is set it to None, depending on distributor, heuristics are used to find the optimal chunksize. If you get out of memory exceptions, you can try it with the dask distributor and a smaller chunksize.
show_warningsbool, default=False: Show warnings during the feature extraction (needed for debugging of calculators).
disable_progressbarbool, default=False: Do not show a progressbar while doing the calculation.
impute_functionNone or Callable, default=None: None, if no imputing should happen or the function to call for imputing the result dataframe. Imputing will never happen on the input data.
profilingbool, default=None: Turn on profiling during feature extraction.
profiling_sortingbasestring, default=None: How to sort the profiling results (see the documentation of the tsfresh profiling package for more information).
profiling_filenamebasestring, default=None: Where to save the profiling results.
distributordistributor class, default=None: Advanced parameter: set this to a class name that you want to use as a distributor. See the tsfresh package utilities/distribution.py for more information. Leave to None, if you want TSFresh to choose the best distributor.
test_for_binary_target_binary_featurestr or None, default=None: Which test to be used for binary target, binary feature (currently unused).
test_for_binary_target_real_featurestr or None, default=None: Which test to be used for binary target, real feature.
test_for_real_target_binary_featurestr or None, default=None: Which test to be used for real target, binary feature (currently unused).
test_for_real_target_binary_featurestr or None, default=None: Which test to be used for real target, real feature (currently unused)
fdr_level: float or None, default=None: The FDR level that should be respected, this is the theoretical expected percentage of irrelevant features among all created features.
hypotheses_independent: bool or None, default=None: Can the significance of the features be assumed to be independent? Normally, this should be set to False as the features are never independent (e.g. mean and median)
ml_task: sre, default=”auto”: The intended machine learning task. Either ‘classification’, ‘regression’ or ‘auto’. Defaults to ‘auto’, meaning the intended task is inferred from y. If y has a boolean, integer or object dtype, the task is assumed to be classification, else regression.

References

[1]

https://github.com/blue-yonder/tsfresh

[2]

https://tsfresh.readthedocs.io/en/v0.1.2/text/feature_naming.html

[3]

https://tsfresh.readthedocs.io/en/latest/text/ feature_extraction_settings.html

[4]

https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html #module-tsfresh.feature_extraction.feature_calculators

[5]

Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018). Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package). Neurocomputing 307 (2018) 72-77

Examples

>>> from sklearn.model_selection import train_test_split
>>> from aeon.datasets import load_arrow_head
>>> from aeon.transformations.collection.feature_based import (
...     TSFreshRelevant
... )
>>> X, y = load_arrow_head()
>>> X_train, X_test, y_train, y_test = train_test_split(X, y)
>>> ts_eff = TSFreshRelevant(
...     default_fc_parameters="efficient", disable_progressbar=True
... ) 
>>> X_transform1 = ts_eff.fit_transform(X_train, y_train) 
>>> features_to_calc = [
...     "dim_0__quantile__q_0.6",
...     "dim_0__longest_strike_above_mean",
...     "dim_0__variance",
... ]
>>> ts_custom = TSFreshRelevant(
...     kind_to_fc_parameters=features_to_calc, disable_progressbar=True
... ) 
>>> X_transform2 = ts_custom.fit_transform(X_train, y_train) 

Methods

`clone`([random_state])	Obtain a clone of the object with the same hyperparameters.
`fit`(X[, y])	Fit transformer to X, optionally using y if supervised.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, raise_error, ...])	Get tag value from estimator class (only class tags).
`get_class_tags`()	Get class tags from estimator class and all its parent classes.
`get_fitted_params`([deep])	Get fitted parameters.
`get_metadata_routing`()	Sklearn metadata routing.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, raise_error, ...])	Get tag value from estimator class.
`get_tags`()	Get tags from estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`reset`([keep])	Reset the object to a clean post-init state.
`set_params`(**params)	Set the parameters of this estimator.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`transform`(X[, y])	Transform X and return a transformed version.

clone(random_state=None)[source]¶

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None: Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject: Instance of type(self), clone of self (see above)

fit(X, y=None)[source]¶

Fit transformer to X, optionally using y if supervised.

State change:: Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

selfa fitted instance of the estimator

fit_transform(X, y=None)[source]¶

Fit to data, then transform it.

Fits the transformer to X and y and returns a transformed version of X.

State change:: Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator.

Parameters:

Xnp.ndarray or list

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

transformed version of X

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr: Name of tag value.
raise_errorbool, default=True: Whether a ValueError is raised when the tag is not found.
tag_value_defaultany type, default=None: Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value: Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError: if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()[source]¶

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)[source]¶

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

deepbool, default=True: If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict: Fitted parameter names mapped to their values.

get_metadata_routing()[source]¶

Sklearn metadata routing.

Not supported by aeon estimators.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

get_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr: Name of tag to be retrieved.
raise_errorbool, default=True: Whether a ValueError is raised when the tag is not found.
tag_value_defaultany type, default=None: Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError: if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()[source]¶

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

inverse_transform(X, y=None)[source]¶

Inverse transform X and return an inverse transformed version.

Currently it is assumed that only transformers with tags: “input_data_type”=”Series”, “output_data_type”=”Series”,

can have an inverse_transform.

State required:: Requires state to be “fitted”.
Accesses in self:: _is_fitted : must be True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:

Xnp.ndarray or list

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

inverse transformed version of X: of the same type as X

reset(keep=None)[source]¶

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:: hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None: If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject: Reference to self.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_tags(**tag_dict)[source]¶

Set dynamic tags to given values.

Parameters:

**tag_dictdict: Dictionary of tag name and tag value pairs.

Returns:

selfobject: Reference to self.

transform(X, y=None)[source]¶

Transform X and return a transformed version.

State required:: Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters:

Xnp.ndarray or list

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

transformed version of X