PredictabilityAnalyzer#

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

Bases: TimeSeriesBinaryClassifier

Class for holding time series predictability prediction.

Init PredictabilityAnalyzer 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

`analyze_predictability`(ts)	Analyse the time series in the dataset for predictability.
`download_model`(model_name, dataset_freq, path)	Return the list of available models.
`dump`(path, args, *kwargs)	Save the object.
`fit`(x, y)	Fit the classifier.
`get_available_models`()	Return the list of available models.
`get_series_from_dataset`(ts)	Transform the dataset into the array with time series samples.
`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`

analyze_predictability(ts: TSDataset) → Dict[str, int][source]#

Analyse the time series in the dataset for predictability.

Parameters:: ts (TSDataset) – Dataset with time series.
Returns:: The indicators of predictability for the each segment in the dataset.
Return type:: Dict[str, int]

static download_model(model_name: str, dataset_freq: str, path: str)[source]#

Return the list of available models.

Parameters:

model_name (str) – Name of the pretrained model.
dataset_freq (str) – Frequency of the dataset.
path (str) – Path to save the file with model.

Raises:

ValueError: – If the model does not exist in s3.

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 get_available_models() → List[str][source]#

Return the list of available models.

Return type:: List[str]

static get_series_from_dataset(ts: TSDataset) → List[ndarray][source]#

Transform the dataset into the array with time series samples.

Series in the result array are sorted in the alphabetical order of the corresponding segment names.

Parameters:: ts (TSDataset) – TSDataset with the time series.
Returns:: Array with time series from TSDataset.
Return type:: List[ndarray]

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.