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Feature transformers

Copyright 2015-Present Randal S. Olson.

This file is part of the TPOT library.

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/.

CategoricalSelector

Bases: BaseEstimator, TransformerMixin

Meta-transformer for selecting categorical features and transform them using OneHotEncoder.

Parameters:

Name Type Description Default
threshold int

Maximum number of unique values per feature to consider the feature to be categorical.

 10
minimum_fraction

Minimum fraction of unique values in a feature to consider the feature to be categorical.

 None
Source code in tpot2/builtin_modules/feature_transformers.py 
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class CategoricalSelector(BaseEstimator, TransformerMixin):
    """Meta-transformer for selecting categorical features and transform them using OneHotEncoder.

    Parameters
    ----------

    threshold : int, default=10
        Maximum number of unique values per feature to consider the feature
        to be categorical.

    minimum_fraction: float, default=None
        Minimum fraction of unique values in a feature to consider the feature
        to be categorical.
    """

    def __init__(self, threshold=10, minimum_fraction=None):
        """Create a CategoricalSelector object."""
        self.threshold = threshold
        self.minimum_fraction = minimum_fraction


    def fit(self, X, y=None):
        """Do nothing and return the estimator unchanged
        This method is just there to implement the usual API and hence
        work in pipelines.
        Parameters
        ----------
        X : array-like
        """
        X = check_array(X, accept_sparse='csr')
        return self


    def transform(self, X):
        """Select categorical features and transform them using OneHotEncoder.

        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.

        Returns
        -------
        array-like, {n_samples, n_components}
        """
        selected = auto_select_categorical_features(X, threshold=self.threshold)
        X_sel, _, n_selected, _ = _X_selected(X, selected)

        if n_selected == 0:
            # No features selected.
            raise ValueError('No categorical feature was found!')
        else:
            ohe = OneHotEncoder(categorical_features='all', sparse=False, minimum_fraction=self.minimum_fraction)
            return ohe.fit_transform(X_sel)

__init__(threshold=10, minimum_fraction=None)

Create a CategoricalSelector object.

Source code in tpot2/builtin_modules/feature_transformers.py 
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def __init__(self, threshold=10, minimum_fraction=None):
    """Create a CategoricalSelector object."""
    self.threshold = threshold
    self.minimum_fraction = minimum_fraction

fit(X, y=None)

Do nothing and return the estimator unchanged This method is just there to implement the usual API and hence work in pipelines.

Parameters:

Name Type Description Default
X array - like
required
Source code in tpot2/builtin_modules/feature_transformers.py 
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def fit(self, X, y=None):
    """Do nothing and return the estimator unchanged
    This method is just there to implement the usual API and hence
    work in pipelines.
    Parameters
    ----------
    X : array-like
    """
    X = check_array(X, accept_sparse='csr')
    return self

transform(X)

Select categorical features and transform them using OneHotEncoder.

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

Returns:

Type Description
(array - like, {n_samples, n_components})
Source code in tpot2/builtin_modules/feature_transformers.py 
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def transform(self, X):
    """Select categorical features and transform them using OneHotEncoder.

    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.

    Returns
    -------
    array-like, {n_samples, n_components}
    """
    selected = auto_select_categorical_features(X, threshold=self.threshold)
    X_sel, _, n_selected, _ = _X_selected(X, selected)

    if n_selected == 0:
        # No features selected.
        raise ValueError('No categorical feature was found!')
    else:
        ohe = OneHotEncoder(categorical_features='all', sparse=False, minimum_fraction=self.minimum_fraction)
        return ohe.fit_transform(X_sel)

ContinuousSelector

Bases: BaseEstimator, TransformerMixin

Meta-transformer for selecting continuous features and transform them using PCA.

Parameters:

Name Type Description Default
threshold int

Maximum number of unique values per feature to consider the feature to be categorical.

 10
svd_solver string {'auto', 'full', 'arpack', 'randomized'}

auto : the solver is selected by a default policy based on X.shape and n_components: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient 'randomized' method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards. full : run exact full SVD calling the standard LAPACK solver via scipy.linalg.svd and select the components by postprocessing arpack : run SVD truncated to n_components calling ARPACK solver via scipy.sparse.linalg.svds. It requires strictly 0 < n_components < X.shape[1] randomized : run randomized SVD by the method of Halko et al.

 'randomized'
iterated_power int >= 0, or 'auto', (default 'auto')

Number of iterations for the power method computed by svd_solver == 'randomized'.

 'auto'
Source code in tpot2/builtin_modules/feature_transformers.py 
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class ContinuousSelector(BaseEstimator, TransformerMixin):
    """Meta-transformer for selecting continuous features and transform them using PCA.

    Parameters
    ----------

    threshold : int, default=10
        Maximum number of unique values per feature to consider the feature
        to be categorical.

    svd_solver : string {'auto', 'full', 'arpack', 'randomized'}
        auto :
            the solver is selected by a default policy based on `X.shape` and
            `n_components`: if the input data is larger than 500x500 and the
            number of components to extract is lower than 80% of the smallest
            dimension of the data, then the more efficient 'randomized'
            method is enabled. Otherwise the exact full SVD is computed and
            optionally truncated afterwards.
        full :
            run exact full SVD calling the standard LAPACK solver via
            `scipy.linalg.svd` and select the components by postprocessing
        arpack :
            run SVD truncated to n_components calling ARPACK solver via
            `scipy.sparse.linalg.svds`. It requires strictly
            0 < n_components < X.shape[1]
        randomized :
            run randomized SVD by the method of Halko et al.

    iterated_power : int >= 0, or 'auto', (default 'auto')
        Number of iterations for the power method computed by
        svd_solver == 'randomized'.

    """

    def __init__(self, threshold=10, svd_solver='randomized' ,iterated_power='auto', random_state=42):
        """Create a ContinuousSelector object."""
        self.threshold = threshold
        self.svd_solver = svd_solver
        self.iterated_power = iterated_power
        self.random_state = random_state


    def fit(self, X, y=None):
        """Do nothing and return the estimator unchanged
        This method is just there to implement the usual API and hence
        work in pipelines.
        Parameters
        ----------
        X : array-like
        """
        X = check_array(X)
        return self


    def transform(self, X):
        """Select continuous features and transform them using PCA.

        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.

        Returns
        -------
        array-like, {n_samples, n_components}
        """
        selected = auto_select_categorical_features(X, threshold=self.threshold)
        _, X_sel, n_selected, _ = _X_selected(X, selected)

        if n_selected == 0:
            # No features selected.
            raise ValueError('No continuous feature was found!')
        else:
            pca = PCA(svd_solver=self.svd_solver, iterated_power=self.iterated_power, random_state=self.random_state)
            return pca.fit_transform(X_sel)

__init__(threshold=10, svd_solver='randomized', iterated_power='auto', random_state=42)

Create a ContinuousSelector object.

Source code in tpot2/builtin_modules/feature_transformers.py 
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def __init__(self, threshold=10, svd_solver='randomized' ,iterated_power='auto', random_state=42):
    """Create a ContinuousSelector object."""
    self.threshold = threshold
    self.svd_solver = svd_solver
    self.iterated_power = iterated_power
    self.random_state = random_state

fit(X, y=None)

Do nothing and return the estimator unchanged This method is just there to implement the usual API and hence work in pipelines.

Parameters:

Name Type Description Default
X array - like
required
Source code in tpot2/builtin_modules/feature_transformers.py 
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def fit(self, X, y=None):
    """Do nothing and return the estimator unchanged
    This method is just there to implement the usual API and hence
    work in pipelines.
    Parameters
    ----------
    X : array-like
    """
    X = check_array(X)
    return self

transform(X)

Select continuous features and transform them using PCA.

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

Returns:

Type Description
(array - like, {n_samples, n_components})
Source code in tpot2/builtin_modules/feature_transformers.py 
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def transform(self, X):
    """Select continuous features and transform them using PCA.

    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.

    Returns
    -------
    array-like, {n_samples, n_components}
    """
    selected = auto_select_categorical_features(X, threshold=self.threshold)
    _, X_sel, n_selected, _ = _X_selected(X, selected)

    if n_selected == 0:
        # No features selected.
        raise ValueError('No continuous feature was found!')
    else:
        pca = PCA(svd_solver=self.svd_solver, iterated_power=self.iterated_power, random_state=self.random_state)
        return pca.fit_transform(X_sel)