point_outliers

Classes

DensityOutliersTransform(in_column, ...)

Transform that uses get_anomalies_density() to find anomalies in data.

MedianOutliersTransform(in_column[, ...])

Transform that uses get_anomalies_median() to find anomalies in data.

PredictionIntervalOutliersTransform(...[, ...])

Transform that uses get_anomalies_prediction_interval() to find anomalies in data.
class DensityOutliersTransform(in_column: str, window_size: int = 15, distance_coef: float = 3, n_neighbors: int = 3, distance_func: typing.Callable[[float, float], float] = <function absolute_difference_distance>)[source]

Transform that uses get_anomalies_density() to find anomalies in data.

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Create instance of DensityOutliersTransform.

Parameters

  • in_column (str) – name of processed column

  • window_size (int) – size of windows to build

  • distance_coef (float) – factor for standard deviation that forms distance threshold to determine points are close to each other

  • n_neighbors (int) – min number of close neighbors of point not to be outlier

  • distance_func (Callable[[float, float], float]) – distance function

detect_outliers(ts: etna.datasets.tsdataset.TSDataset) → Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]][source]

Call get_anomalies_density() function with self parameters.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – dataset to process

Returns

dict of outliers in format {segment: [outliers_timestamps]}

Return type

Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]]

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.base.Transform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit the transform on.

Returns

The fitted transform instance.

Return type

etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Returns

List with regressors created by the transform.

Return type

List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to be inverse transformed.

Returns

TSDataset after applying inverse transformation.

Return type

etna.datasets.tsdataset.TSDataset

classmethod load(path: pathlib.Path) → typing_extensions.Self

Load an 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 (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution][source]

Get default grid for tuning hyperparameters.

This grid tunes parameters: window_size, distance_coef, n_neighbors. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) → etna.core.mixins.TMixin

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 – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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()

Collect all information about etna object in dict.

transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Transform TSDataset inplace.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

class MedianOutliersTransform(in_column: str, window_size: int = 10, alpha: float = 3)[source]

Transform that uses get_anomalies_median() to find anomalies in data.

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Create instance of MedianOutliersTransform.

Parameters

  • in_column (str) – name of processed column

  • window_size (int) – number of points in the window

  • alpha (float) – coefficient for determining the threshold

detect_outliers(ts: etna.datasets.tsdataset.TSDataset) → Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]][source]

Call get_anomalies_median() function with self parameters.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – dataset to process

Returns

dict of outliers in format {segment: [outliers_timestamps]}

Return type

Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]]

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.base.Transform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit the transform on.

Returns

The fitted transform instance.

Return type

etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Returns

List with regressors created by the transform.

Return type

List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to be inverse transformed.

Returns

TSDataset after applying inverse transformation.

Return type

etna.datasets.tsdataset.TSDataset

classmethod load(path: pathlib.Path) → typing_extensions.Self

Load an 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 (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution][source]

Get default grid for tuning hyperparameters.

This grid tunes parameters: window_size, alpha. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) → etna.core.mixins.TMixin

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 – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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()

Collect all information about etna object in dict.

transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Transform TSDataset inplace.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

class PredictionIntervalOutliersTransform(in_column: str, model: Union[Literal['prophet'], Literal['sarimax'], Type[etna.models.prophet.ProphetModel], Type[etna.models.sarimax.SARIMAXModel]], interval_width: float = 0.95, **model_kwargs)[source]

Transform that uses get_anomalies_prediction_interval() to find anomalies in data.

Create instance of PredictionIntervalOutliersTransform.

Parameters

  • in_column (str) – name of processed column

  • model (Union[Literal['prophet'], typing.Literal['sarimax'], typing.Type[ProphetModel], typing.Type[SARIMAXModel]]) – model for prediction interval estimation

  • interval_width (float) – width of the prediction interval

Notes

For not “target” column only column data will be used for learning.

detect_outliers(ts: etna.datasets.tsdataset.TSDataset) → Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]][source]

Call get_anomalies_prediction_interval() function with self parameters.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – dataset to process

Returns

dict of outliers in format {segment: [outliers_timestamps]}

Return type

Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]]

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.base.Transform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit the transform on.

Returns

The fitted transform instance.

Return type

etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Returns

List with regressors created by the transform.

Return type

List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to be inverse transformed.

Returns

TSDataset after applying inverse transformation.

Return type

etna.datasets.tsdataset.TSDataset

classmethod load(path: pathlib.Path) → typing_extensions.Self

Load an 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 (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution][source]

Get default grid for tuning hyperparameters.

This grid tunes parameters: interval_width, model. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) → etna.core.mixins.TMixin

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 – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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()

Collect all information about etna object in dict.

transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Transform TSDataset inplace.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset