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Zero count

This file is part of the TPOT library.

TPOT was primarily developed at the University of Pennsylvania by: - Randal S. Olson (rso@randalolson.com) - Weixuan Fu (weixuanf@upenn.edu) - Daniel Angell (dpa34@drexel.edu) - and many more generous open source contributors

TPOT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

TPOT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with TPOT. If not, see http://www.gnu.org/licenses/.

ZeroCount

Bases: BaseEstimator, TransformerMixin

Adds the count of zeros and count of non-zeros per sample as features.

Source code in tpot2/builtin_modules/zero_count.py 
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class ZeroCount(BaseEstimator, TransformerMixin):
    """Adds the count of zeros and count of non-zeros per sample as features."""

    def fit(self, X, y=None):
        """Dummy function to fit in with the sklearn API."""
        return self

    def transform(self, X, y=None):
        """Transform data by adding two virtual features.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components
            is the number of components.
        y: None
            Unused

        Returns
        -------
        X_transformed: array-like, shape (n_samples, n_features)
            The transformed feature set
        """
        X = check_array(X)
        n_features = X.shape[1]

        X_transformed = np.copy(X)

        non_zero_vector = np.count_nonzero(X_transformed, axis=1)
        non_zero = np.reshape(non_zero_vector, (-1, 1))
        zero_col = np.reshape(n_features - non_zero_vector, (-1, 1))

        X_transformed = np.hstack((non_zero, X_transformed))
        X_transformed = np.hstack((zero_col, X_transformed))

        return X_transformed

fit(X, y=None)

Dummy function to fit in with the sklearn API.

Source code in tpot2/builtin_modules/zero_count.py 
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def fit(self, X, y=None):
    """Dummy function to fit in with the sklearn API."""
    return self

transform(X, y=None)

Transform data by adding two virtual features.

Parameters:

Name Type Description Default
X

New data, where n_samples is the number of samples and n_components is the number of components.

 required
y

Unused

 None

Returns:

Name Type Description
X_transformed (array - like, shape(n_samples, n_features))

The transformed feature set
Source code in tpot2/builtin_modules/zero_count.py 
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def transform(self, X, y=None):
    """Transform data by adding two virtual features.

    Parameters
    ----------
    X: numpy ndarray, {n_samples, n_components}
        New data, where n_samples is the number of samples and n_components
        is the number of components.
    y: None
        Unused

    Returns
    -------
    X_transformed: array-like, shape (n_samples, n_features)
        The transformed feature set
    """
    X = check_array(X)
    n_features = X.shape[1]

    X_transformed = np.copy(X)

    non_zero_vector = np.count_nonzero(X_transformed, axis=1)
    non_zero = np.reshape(non_zero_vector, (-1, 1))
    zero_col = np.reshape(n_features - non_zero_vector, (-1, 1))

    X_transformed = np.hstack((non_zero, X_transformed))
    X_transformed = np.hstack((zero_col, X_transformed))

    return X_transformed