syne_tune.optimizer.schedulers.searchers.conformal.surrogate.symmetric_conformalized_quantile_regression_model module

class syne_tune.optimizer.schedulers.searchers.conformal.surrogate.symmetric_conformalized_quantile_regression_model.SymmetricConformalQuantileCorrection(lower_quantile, upper_quantile, target_coverage=None, correction=None)[source]

Bases: object

lower_quantile: float
upper_quantile: float
target_coverage: float = None
correction: float = None
class syne_tune.optimizer.schedulers.searchers.conformal.surrogate.symmetric_conformalized_quantile_regression_model.SymmetricConformalizedGradientBoostingQuantileRegressor(quantiles=5, valid_fraction=0.1, min_samples_to_conformalize=32, verbose=False, **kwargs)[source]

Bases: GradientBoostingQuantileRegressor

fit(df_features, y, **kwargs)[source]

Fit the gradient boosting model.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

yarray-like of shape (n_samples,)

Target values (strings or integers in classification, real numbers in regression) For classification, labels must correspond to classes.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

monitorcallable, default=None

The monitor is called after each iteration with the current iteration, a reference to the estimator and the local variables of _fit_stages as keyword arguments callable(i, self, locals()). If the callable returns True the fitting procedure is stopped. The monitor can be used for various things such as computing held-out estimates, early stopping, model introspect, and snapshotting.

Returns

selfobject

Fitted estimator.

predict(df_test)[source]

Predict regression target for X.

Return type:

QuantileRegressorPredictions

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

Returns

yndarray of shape (n_samples,)

The predicted values.

set_fit_request(*, df_features: bool | None | str = '$UNCHANGED$') → SymmetricConformalizedGradientBoostingQuantileRegressor

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.

Added 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

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

Metadata routing for df_features parameter in fit.

Returns

selfobject

The updated object.

set_predict_request(*, df_test: bool | None | str = '$UNCHANGED$') → SymmetricConformalizedGradientBoostingQuantileRegressor

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.

Added 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

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

Metadata routing for df_test parameter in predict.

Returns

selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SymmetricConformalizedGradientBoostingQuantileRegressor

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.

Added 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 in score.

Returns

selfobject

The updated object.