def fit(self, X, y=None):
        """
        Fits the learning curve with the wrapped model to the specified data.
        Draws training and test score curves and saves the scores to the
        estimator.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape (n_samples) or (n_samples, n_features), optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

        Returns
        -------
        self : instance
            Returns the instance of the learning curve visualizer for use in
            pipelines and other sequential transformers.
        """
        # arguments to pass to sk_learning_curve
        sklc_kwargs = {
            key: self.get_params()[key]
            for key in (
                'groups', 'train_sizes', 'cv', 'scoring',
                'exploit_incremental_learning', 'n_jobs',
                'pre_dispatch', 'shuffle', 'random_state',
            )
        }

        # compute the learning curve and store the scores on the estimator
        curve = sk_learning_curve(self.estimator, X, y, **sklc_kwargs)
        self.train_sizes_, self.train_scores_, self.test_scores_ = curve

        # compute the mean and standard deviation of the training data
        self.train_scores_mean_ = np.mean(self.train_scores_, axis=1)
        self.train_scores_std_ = np.std(self.train_scores_, axis=1)

        # compute the mean and standard deviation of the test data
        self.test_scores_mean_ = np.mean(self.test_scores_, axis=1)
        self.test_scores_std_ = np.std(self.test_scores_, axis=1)

        # draw the curves on the current axes
        self.draw()
        return self
def fit ( self , X , y = None ) : sklc_kwargs = { key : self . get_params ( ) [ key ] for key in ( 'groups' , 'train_sizes' , 'cv' , 'scoring' , 'exploit_incremental_learning' , 'n_jobs' , 'pre_dispatch' , 'shuffle' , 'random_state' , ) } curve = sk_learning_curve ( self . estimator , X , y , ** sklc_kwargs ) self . train_sizes_ , self . train_scores_ , self . test_scores_ = curve self . train_scores_mean_ = np . mean ( self . train_scores_ , axis = 1 ) self . train_scores_std_ = np . std ( self . train_scores_ , axis = 1 ) self . test_scores_mean_ = np . mean ( self . test_scores_ , axis = 1 ) self . test_scores_std_ = np . std ( self . test_scores_ , axis = 1 ) self . draw ( ) return self
Fits the learning curve with the wrapped model to the specified data.
        Draws training and test score curves and saves the scores to the
        estimator.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape (n_samples) or (n_samples, n_features), optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

        Returns
        -------
        self : instance
            Returns the instance of the learning curve visualizer for use in
            pipelines and other sequential transformers.