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[numpy.random.mtrand.RandomState, int]] = None)

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[numpy.random.mtrand.RandomState, int]] = None) → None

fit(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], 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], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], 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], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], 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

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], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], 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], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → mapie.regression.time_series_regression.MapieTimeSeriesRegressor

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], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], 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._generic_alias.ScalarType]], Tuple[numpy.ndarray[Any, numpy.dtype[numpy._typing._generic_alias.ScalarType]], numpy.ndarray[Any, numpy.dtype[numpy._typing._generic_alias.ScalarType]]]]

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.

Examples using mapie.regression.MapieTimeSeriesRegressor

Estimating prediction intervals of time series forecast with EnbPI

Tutorial for time series