etna.experimental.classification.TimeSeriesBinaryClassifier#

class TimeSeriesBinaryClassifier(feature_extractor: BaseTimeSeriesFeatureExtractor, classifier: ClassifierMixin, threshold: float = 0.5)[source]#

Bases: BaseMixin, PickleSerializable

Class for holding time series binary classification.

Note

This class requires classification extension to be installed. Read more about this at installation page.

Init TimeSeriesClassifier with given parameters.

Parameters:

feature_extractor (BaseTimeSeriesFeatureExtractor) – Instance of time series feature extractor.
classifier (ClassifierMixin) – Instance of classifier with sklearn interface.
threshold (float) – Positive class probability threshold.

Methods

`dump`(path, args, *kwargs)	Save the object.
`fit`(x, y)	Fit the classifier.
`load`(path, args, *kwargs)	Load the object.
`masked_crossval_score`(x, y, mask)	Calculate classification metrics on cross-validation.
`predict`(x)	Predict classes with threshold.
`predict_proba`(x)	Predict probabilities of the positive class.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

`NEGATIVE_CLASS`
`POSITIVE_CLASS`

dump(path: str, *args, **kwargs)[source]#

Save the object.

Parameters:: path (str) –

fit(x: List[ndarray], y: ndarray) → TimeSeriesBinaryClassifier[source]#

Fit the classifier.

Parameters:

x (List[ndarray]) – Array with time series.
y (ndarray) – Array of class labels.

Returns:

Fitted instance of classifier.

Return type:

TimeSeriesBinaryClassifier

static load(path: str, *args, **kwargs)[source]#

Load the object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:: path (str) –

masked_crossval_score(x: List[ndarray], y: ndarray, mask: ndarray) → Dict[str, list][source]#

Calculate classification metrics on cross-validation.

Parameters:

x (List[ndarray]) – Array with time series.
y (ndarray) – Array of class labels.
mask (ndarray) – Fold mask (array where for each element there is a label of its fold)

Returns:

Classification metrics for each fold

Return type:

Dict[str, list]

predict(x: List[ndarray]) → ndarray[source]#

Predict classes with threshold.

Parameters:: x (List[ndarray]) – Array with time series.
Returns:: Array with predicted labels.
Return type:: ndarray

predict_proba(x: List[ndarray]) → ndarray[source]#

Predict probabilities of the positive class.

Parameters:: x (List[ndarray]) – Array with time series.
Returns:: Probabilities for classes.
Return type:: ndarray

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.