Class Vectorizer

mne/decoding/transformer.py:295–396

Transform n-dimensional array into 2D array of n_samples by n_features. This class reshapes an n-dimensional array into an n_samples * n_features array, usable by the estimators and transformers of scikit-learn. Attributes ---------- features_shape_ : tuple Stores the

class Vectorizer(MNETransformerMixin, BaseEstimator):
    """Transform n-dimensional array into 2D array of n_samples by n_features.

    This class reshapes an n-dimensional array into an n_samples * n_features
    array, usable by the estimators and transformers of scikit-learn.

    Attributes
    ----------
    features_shape_ : tuple
         Stores the original shape of data.

    Examples
    --------
    >>> from sklearn.linear_model import LogisticRegression
    >>> from sklearn.pipeline import make_pipeline
    >>> from sklearn.preprocessing import StandardScaler
    >>> clf = make_pipeline(Vectorizer(), StandardScaler(), LogisticRegression())
    """

    def fit(self, X, y=None):
        """Store the shape of the features of X.

        Parameters
        ----------
        X : array-like
            The data to fit. Can be, for example a list, or an array of at
            least 2d. The first dimension must be of length n_samples, where
            samples are the independent samples used by the estimator
            (e.g. n_epochs for epoched data).
        y : None | array, shape (n_samples,)
            Used for scikit-learn compatibility.

        Returns
        -------
        self : instance of Vectorizer
            Return the modified instance.
        """
        X = self._check_data(X, y=y, atleast_3d=False, fit=True, check_n_features=False)
        self.features_shape_ = X.shape[1:]
        return self

    def transform(self, X):
        """Convert given array into two dimensions.

        Parameters
        ----------
        X : array-like
            The data to fit. Can be, for example a list, or an array of at
            least 2d. The first dimension must be of length n_samples, where
            samples are the independent samples used by the estimator
            (e.g. n_epochs for epoched data).

        Returns
        -------
        X : array, shape (n_samples, n_features)
            The transformed data.
        """
        X = self._check_data(X, atleast_3d=False)