7. Collectors & Extractors¶

7.1. 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¶

7.2. 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¶

7.3. miplearn.collectors.basic¶

class miplearn.collectors.basic.BasicCollector(*, time_limit_sec: float = inf)¶

Bases: object

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

MIPLearn 0.3

7. Collectors & Extractors¶

7.1. miplearn.classifiers.minprob¶

7.2. miplearn.classifiers.singleclass¶

7.3. miplearn.collectors.basic¶

7.4. miplearn.features.fields¶

7.5. miplearn.features.AlvLouWeh2017¶