Collectors & Extractors

11. Collectors & Extractors#

11.1. miplearn.classifiers.minprob#

class miplearn.classifiers.minprob.MinProbabilityClassifier(base_clf: ~typing.Any, thresholds: ~typing.List[float], clone_fn: ~typing.Callable[[~typing.Any], ~typing.Any] = <function clone>)#

Bases: BaseEstimator

Meta-classifier that returns NaN for predictions made by a base classifier that have probability below a given threshold. More specifically, this meta-classifier calls base_clf.predict_proba and compares the result against the provided thresholds. If the probability for one of the classes is above its threshold, the meta-classifier returns that prediction. Otherwise, it returns NaN.

fit(x: ndarray, y: ndarray) → None#
predict(x: ndarray) → ndarray#
set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → MinProbabilityClassifier#

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:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, x: bool | None | str = '$UNCHANGED$') → MinProbabilityClassifier#

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:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in predict.

Returns:

self – The updated object.

Return type:

object

11.2. miplearn.classifiers.singleclass#

class miplearn.classifiers.singleclass.SingleClassFix(base_clf: ~sklearn.base.BaseEstimator, clone_fn: ~typing.Callable = <function clone>)#

Bases: BaseEstimator

Some sklearn classifiers, such as logistic regression, have issues with datasets that contain a single class. This meta-classifier fixes the issue. If the training data contains a single class, this meta-classifier always returns that class as a prediction. Otherwise, it fits the provided base classifier, and returns its predictions instead.

fit(x: ndarray, y: ndarray) → None#
predict(x: ndarray) → ndarray#
set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → SingleClassFix#

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:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, x: bool | None | str = '$UNCHANGED$') → SingleClassFix#

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:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in predict.

Returns:

self – The updated object.

Return type:

object

11.3. miplearn.collectors.basic#

class miplearn.collectors.basic.BasicCollector(skip_lp: bool = False, write_mps: bool = True)#

Bases: object

collect(filenames: List[str], build_model: Callable, n_jobs: int = 1, progress: bool = False, verbose: bool = False) → None#

11.4. miplearn.extractors.fields#

class miplearn.extractors.fields.H5FieldsExtractor(instance_fields: List[str] | None = None, var_fields: List[str] | None = None, constr_fields: List[str] | None = None)#

Bases: FeaturesExtractor

get_constr_features(h5: H5File) → ndarray#
get_instance_features(h5: H5File) → ndarray#
get_var_features(h5: H5File) → ndarray#

11.5. miplearn.extractors.AlvLouWeh2017#

class miplearn.extractors.AlvLouWeh2017.AlvLouWeh2017Extractor(with_m1: bool = True, with_m2: bool = True, with_m3: bool = True)#

Bases: FeaturesExtractor

get_constr_features(h5: H5File) → ndarray#
get_instance_features(h5: H5File) → ndarray#
get_var_features(h5: H5File) → ndarray#
Computes static variable features described in:

Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.

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