Source code for mlinsights.sklapi.sklearn_base_learner

# -*- coding: utf-8 -*-
"""
Implements a *learner* which follows the same API
as every :epkg:`scikit-learn` learner.


:githublink:`%|py|7`
"""
from .sklearn_base import SkBase


class SkBaseLearner(SkBase):

    """
    Pattern of a *learner* qui suit la même API que :epkg:`scikit-learn`.


    :githublink:`%|py|14`
    """

    def __init__(self, **kwargs):
        """
        constructor


        :githublink:`%|py|19`
        """
        SkBase.__init__(self, **kwargs)

    ###################
    # API scikit-learn
    ###################

    def fit(self, X, y=None, sample_weight=None):
        """
        Trains a model.

        :param      X:               features
        :param      y:               targets
        :param      sample_weight:   weight
        :return:                     self


        :githublink:`%|py|34`
        """
        raise NotImplementedError()  # pragma: no cover

    def predict(self, X):
        """
        Predicts.

        :param      X:   features
        :return:         prédictions


        :githublink:`%|py|43`
        """
        raise NotImplementedError()  # pragma: no cover

    def decision_function(self, X):
        """
        Output of the model in case of a regressor,
        matrix with a score for each class and each sample
        for a classifier.

        :param      X:   Samples, {array-like, sparse matrix}, shape = (n_samples, n_features)
        :return:         array, shape = (n_samples,.), Returns predicted values.


        :githublink:`%|py|54`
        """
        raise NotImplementedError()  # pragma: no cover

    def score(self, X, y=None, sample_weight=None):
        """
        Returns the mean accuracy on the given test data and labels.

        :param      X:               Training data, numpy array or sparse matrix of shape [n_samples,n_features]
        :param      y:               Target values, numpy array of shape [n_samples, n_targets] (optional)
        :param      sample_weight:   Weight values, numpy array of shape [n_samples, n_targets] (optional)
        :return:                     score : float, Mean accuracy of self.predict(X) wrt. y.


        :githublink:`%|py|65`
        """
        raise NotImplementedError()  # pragma: no cover