10. Collectors & Extractors¶

10.1. miplearn.classifiers.minprob¶

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

Bases: sklearn.base.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: numpy.ndarray, y: numpy.ndarray) → None¶

predict(x: numpy.ndarray) → numpy.ndarray¶

10.2. miplearn.classifiers.singleclass¶

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

Bases: sklearn.base.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: numpy.ndarray, y: numpy.ndarray) → None¶

predict(x: numpy.ndarray) → numpy.ndarray¶

10.3. miplearn.collectors.basic¶

class miplearn.collectors.basic.BasicCollector¶

Bases: object

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

10.4. miplearn.extractors.fields¶

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

Bases: miplearn.extractors.abstract.FeaturesExtractor

get_constr_features(h5: miplearn.h5.H5File) → numpy.ndarray¶

get_instance_features(h5: miplearn.h5.H5File) → numpy.ndarray¶

get_var_features(h5: miplearn.h5.H5File) → numpy.ndarray¶

10.5. miplearn.extractors.AlvLouWeh2017¶

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