mapie.regression.MapieTimeSeriesRegressor

class mapie.regression.MapieTimeSeriesRegressor(estimator: Optional[sklearn.base.RegressorMixin] = None, method: str = 'enbpi', cv: Optional[Union[int, str, sklearn.model_selection._split.BaseCrossValidator]] = None, n_jobs: Optional[int] = None, agg_function: Optional[str] = 'mean', verbose: int = 0, random_state: Optional[Union[int, numpy.random.mtrand.RandomState]] = None)[source]

Prediction intervals with out-of-fold residuals for time series.

This class implements the EnbPI strategy for estimating prediction intervals on single-output time series. The only valid method is "enbpi".

Actually, EnbPI only corresponds to MapieTimeSeriesRegressor if the cv argument is of type BlockBootstrap.

References

Chen Xu, and Yao Xie. “Conformal prediction for dynamic time-series.” https://arxiv.org/abs/2010.09107

__init__(estimator: Optional[sklearn.base.RegressorMixin] = None, method: str = 'enbpi', cv: Optional[Union[int, str, sklearn.model_selection._split.BaseCrossValidator]] = None, n_jobs: Optional[int] = None, agg_function: Optional[str] = 'mean', verbose: int = 0, random_state: Optional[Union[int, numpy.random.mtrand.RandomState]] = None) → None[source]
fit(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], sample_weight: Optional[Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]] = None) → mapie.regression.time_series_regression.MapieTimeSeriesRegressor[source]

Compared to the method fit of MapieRegressor, the fit method of MapieTimeSeriesRegressor computes the conformity_scores_ with relative values.

Parameters
X: ArrayLike of shape (n_samples, n_features)

Training data.

y: ArrayLike of shape (n_samples,)

Training labels.

sample_weight: Optional[ArrayLike] of shape (n_samples,)

Sample weights for fitting the out-of-fold models. If None, then samples are equally weighted. If some weights are null, their corresponding observations are removed before the fitting process and hence have no conformity scores. If weights are non-uniform, conformity scores are still uniformly weighted.

By default None.

Returns
MapieTimeSeriesRegressor

The model itself.

partial_fit(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → mapie.regression.time_series_regression.MapieTimeSeriesRegressor[source]

Update the conformity_scores_ attribute when new data with known labels are available. Note: Don’t use partial_fit with samples of the training set.

Parameters
X: ArrayLike of shape (n_samples_test, n_features)

Input data.

y: ArrayLike of shape (n_samples_test,)

Input labels.

Returns
MapieTimeSeriesRegressor

The model itself.

Raises
ValueError

If the length of y is greater than the length of the training set.

predict(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], ensemble: bool = False, alpha: Optional[Union[float, Iterable[float]]] = None, optimize_beta: bool = True) → Union[numpy.ndarray[Any, numpy.dtype[numpy._typing._array_like._ScalarType_co]], Tuple[numpy.ndarray[Any, numpy.dtype[numpy._typing._array_like._ScalarType_co]], numpy.ndarray[Any, numpy.dtype[numpy._typing._array_like._ScalarType_co]]]][source]

Correspond to ‘Conformal prediction for dynamic time-series’.

Parameters
X: ArrayLike of shape (n_samples, n_features)

Test data.

ensemble: bool

Boolean determining whether the predictions are ensembled or not. If False, predictions are those of the model trained on the whole training set. If True, predictions from perturbed models are aggregated by the aggregation function specified in the agg_function attribute.

If cv is "prefit" or "split", ensemble is ignored.

By default False.

alpha: Optional[Union[float, Iterable[float]]]

Can be a float, a list of floats, or a ArrayLike of floats. Between 0 and 1, represents the uncertainty of the confidence interval. Lower alpha produce larger (more conservative) prediction intervals. alpha is the complement of the target coverage level.

By default None.

optimize_beta: bool

Whether to optimize the PIs’ width or not.

Returns
Union[NDArray, Tuple[NDArray, NDArray]]

  • NDArray of shape (n_samples,) if alpha is None.

  • Tuple[NDArray, NDArray] of shapes (n_samples,) and (n_samples, 2, n_alpha) if alpha is not None.

    • [:, 0, :]: Lower bound of the prediction interval.

    • [:, 1, :]: Upper bound of the prediction interval.

set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → mapie.regression.time_series_regression.MapieTimeSeriesRegressor

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns
selfobject

The updated object.

set_predict_request(*, alpha: Union[bool, None, str] = '$UNCHANGED$', ensemble: Union[bool, None, str] = '$UNCHANGED$', optimize_beta: Union[bool, None, str] = '$UNCHANGED$') → mapie.regression.time_series_regression.MapieTimeSeriesRegressor

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters
alphastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for alpha parameter in predict.

ensemblestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for ensemble parameter in predict.

optimize_betastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for optimize_beta parameter in predict.

Returns
selfobject

The updated object.

set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → mapie.regression.time_series_regression.MapieTimeSeriesRegressor

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns
selfobject

The updated object.