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.

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 = 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.