mapie.regression
.MapieTimeSeriesRegressor¶
- class mapie.regression.MapieTimeSeriesRegressor(estimator: Optional[RegressorMixin] = None, method: str = 'enbpi', cv: Optional[Union[int, str, BaseCrossValidator]] = None, n_jobs: Optional[int] = None, agg_function: Optional[str] = 'mean', verbose: int = 0, conformity_score: Optional[BaseRegressionScore] = None, random_state: Optional[Union[int, RandomState]] = None)[source]¶
Prediction intervals with out-of-fold residuals for time series. This class only has two valid
method
:"enbpi"
or"aci"
The prediction intervals are calibrated on a split of the trained data. Both strategies are estimating prediction intervals on single-output time series.
EnbPI allows you to update conformal scores using the
partial_fit
function. It will replace the oldest one with the newest scores. It will keep the same amount of total scoresActually, EnbPI only corresponds to
MapieTimeSeriesRegressor
if thecv
argument is of typeBlockBootstrap
.The ACI strategy allows you to adapt the conformal inference (i.e the quantile). If the real values are not in the coverage, the size of the intervals will grow. Conversely, if the real values are in the coverage, the size of the intervals will decrease. You can use a gamma coefficient to adjust the strength of the correction. If the quantile is equal to zero, the method will produce an infinite set size.
References
Chen Xu, and Yao Xie. “Conformal prediction for dynamic time-series.” https://arxiv.org/abs/2010.09107
Isaac Gibbs, Emmanuel Candes “Adaptive conformal inference under distribution shift” https://proceedings.neurips.cc/paper/2021/file/0d441de75945e5acbc865406fc9a2559-Paper.pdf
Margaux Zaffran et al. “Adaptive Conformal Predictions for Time Series” https://arxiv.org/pdf/2202.07282.pdf
- __init__(estimator: Optional[RegressorMixin] = None, method: str = 'enbpi', cv: Optional[Union[int, str, BaseCrossValidator]] = None, n_jobs: Optional[int] = None, agg_function: Optional[str] = 'mean', verbose: int = 0, conformity_score: Optional[BaseRegressionScore] = None, random_state: Optional[Union[int, RandomState]] = None) None [source]¶
- adapt_conformal_inference(X: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], gamma: float, alpha: Optional[Union[float, Iterable[float]]] = None, ensemble: bool = False, optimize_beta: bool = False) MapieTimeSeriesRegressor [source]¶
Adapt the
alpha_t
attribute when new data with known labels are available.- Parameters
- X: ArrayLike of shape (n_samples, n_features)
Input data.
- y: ArrayLike of shape (n_samples_test,)
Input labels.
- 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. IfTrue
, predictions from perturbed models are aggregated by the aggregation function specified in theagg_function
attribute. Ifcv
is"prefit"
or"split"
,ensemble
is ignored.By default
False
.- gamma: float
Coefficient that decides the correction of the conformal inference. If it equals 0, there are no corrections.
- alpha: Optional[Union[float, Iterable[float]]]
Between
0
and1
, represents the uncertainty of the confidence interval.By default
None
.- optimize_beta: bool
Whether to optimize the PIs’ width or not.
By default
False
.
- Returns
- MapieTimeSeriesRegressor
The model itself.
- Raises
- ValueError
If the length of
y
is greater than the length of the training set.
- partial_fit(X: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], ensemble: bool = False) MapieTimeSeriesRegressor [source]¶
Update the
conformity_scores_
attribute when new data with known labels are available. Note: Don’t usepartial_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.
- 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. IfTrue
, predictions from perturbed models are aggregated by the aggregation function specified in theagg_function
attribute. Ifcv
is"prefit"
or"split"
,ensemble
is ignored.By default
False
.
- Returns
- MapieTimeSeriesRegressor
The model itself.
- Raises
- ValueError
If the length of
y
is greater than the length of the training set.
- predict(X: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], ensemble: bool = False, alpha: Optional[Union[float, Iterable[float]]] = None, optimize_beta: bool = False, allow_infinite_bounds: bool = False, **predict_params) Union[ndarray[Any, dtype[_ScalarType_co]], Tuple[ndarray[Any, dtype[_ScalarType_co]], ndarray[Any, dtype[_ScalarType_co]]]] [source]¶
Predict target on new samples with confidence intervals.
- 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. IfTrue
, predictions from perturbed models are aggregated by the aggregation function specified in theagg_function
attribute. Ifcv
is"prefit"
or"split"
,ensemble
is ignored.By default
False
.- alpha: Optional[Union[float, Iterable[float]]]
Between
0
and1
, represents the uncertainty of the confidence interval.By default
None
.- optimize_beta: bool
Whether to optimize the PIs’ width or not.
By default
False
.- allow_infinite_bounds: bool
Allow infinite prediction intervals to be produced.
- predict_paramsdict
Additional predict parameters.
- Returns
- Union[NDArray, Tuple[NDArray, NDArray]]
NDArray of shape (n_samples,) if
alpha
isNone
.Tuple[NDArray, NDArray] of shapes (n_samples,) and (n_samples, 2, n_alpha) if
alpha
is notNone
.[:, 0, :]: Lower bound of the prediction interval.
[:, 1, :]: Upper bound of the prediction interval.
- set_fit_request(*, groups: Union[bool, None, str] = '$UNCHANGED$', sample_weight: Union[bool, None, str] = '$UNCHANGED$') MapieTimeSeriesRegressor ¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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
. Otherwise it has no effect.- Parameters
- groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
groups
parameter infit
.- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns
- selfobject
The updated object.
- set_partial_fit_request(*, ensemble: Union[bool, None, str] = '$UNCHANGED$') MapieTimeSeriesRegressor ¶
Request metadata passed to the
partial_fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_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 topartial_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
. Otherwise it has no effect.- Parameters
- ensemblestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
ensemble
parameter inpartial_fit
.
- Returns
- selfobject
The updated object.
- set_predict_request(*, allow_infinite_bounds: Union[bool, None, str] = '$UNCHANGED$', alpha: Union[bool, None, str] = '$UNCHANGED$', ensemble: Union[bool, None, str] = '$UNCHANGED$', optimize_beta: Union[bool, None, str] = '$UNCHANGED$') MapieTimeSeriesRegressor ¶
Request metadata passed to the
predict
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topredict
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topredict
.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
. Otherwise it has no effect.- Parameters
- allow_infinite_boundsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
allow_infinite_bounds
parameter inpredict
.- alphastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
alpha
parameter inpredict
.- ensemblestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
ensemble
parameter inpredict
.- optimize_betastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
optimize_beta
parameter inpredict
.
- Returns
- selfobject
The updated object.
- set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') MapieTimeSeriesRegressor ¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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
. Otherwise it has no effect.- Parameters
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns
- selfobject
The updated object.
- update(X: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], ensemble: bool = False, alpha: Optional[Union[float, Iterable[float]]] = None, gamma: float = 0.0, optimize_beta: bool = False) MapieTimeSeriesRegressor [source]¶
Update with respect to the used
method
.method="enbpi"
will callpartial_fit
method andmethod="aci"
will calladapt_conformal_inference
method.- Parameters
- X: ArrayLike of shape (n_samples, n_features)
Input data.
- y: ArrayLike of shape (n_samples_test,)
Input labels.
- 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. IfTrue
, predictions from perturbed models are aggregated by the aggregation function specified in theagg_function
attribute. Ifcv
is"prefit"
or"split"
,ensemble
is ignored.By default
False
.- alpha: Optional[Union[float, Iterable[float]]]
Between
0
and1
, represents the uncertainty of the confidence interval.By default
None
.- gamma: float
Coefficient that decides the correction of the conformal inference. If it equals 0, there are no corrections.
By default
0.
.- optimize_beta: bool
Whether to optimize the PIs’ width or not.
By default
False
.
- Returns
- MapieTimeSeriesRegressor
The model itself.
- Raises
- ValueError
If the length of
y
is greater than the length of the training set.