matrix.py 文件源码

def transform(self, M,  **kwargs):
        """
        Takes a Takes a dataframe that has :code:`item_id` index, other
        'features' columns for prediction, and applies a Keras sequential
        model to it.

        :param M:
            a dataframe that has an :code:`item_id` index, and
            "features" columns.

        :type M: pandas.DataFrame
        :rtype: a tuple with trained Keras model and its keyword
            arguments
        """
        rows, columns = M.shape
        factors = M.merge(self.validation_matrix, left_index=True,
                          right_index=True)
        factors = factors.values

        if self.classification:
            kfold = StratifiedKFold(n_splits=self.kfold_n_splits,
                                    random_state=self.kfold_seed,
                                    shuffle=self.kfold_shuffle)
        else:
            kfold = KFold(n_splits=self.kfold_n_splits,
                          random_state=self.kfold_seed,
                          shuffle=self.kfold_shuffle)

        X = factors[:, :columns]
        Y = factors[:, columns:]
        for train_index, test_index in kfold.split(X, Y):
            self.keras_model.fit(
                X[train_index], Y[train_index],
                validation_data=[X[test_index], Y[train_index]],
                **self.keras_kwargs)

        return self.keras_model, kwargs