Cross val utils

This file is part of the TPOT library.

The current version of TPOT was developed at Cedars-Sinai by: - Pedro Henrique Ribeiro (https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/) - Anil Saini (anil.saini@cshs.org) - Jose Hernandez (jgh9094@gmail.com) - Jay Moran (jay.moran@cshs.org) - Nicholas Matsumoto (nicholas.matsumoto@cshs.org) - Hyunjun Choi (hyunjun.choi@cshs.org) - Gabriel Ketron (gabriel.ketron@cshs.org) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

The original version of 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) - Jason Moore (moorejh28@gmail.com) - 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/.

cross_val_score_objective(estimator, X, y, scorers, cv, fold=None)

Compute the cross validated scores for a estimator. Only fits the estimator once per fold, and loops over the scorers to evaluate the estimator.

Parameters:

Name	Type	Description	Default
estimator		The estimator to fit and score.	required
X		The feature matrix.	required
y		The target vector.	required
scorers		The scorers to use. If a list, will loop over the scorers and return a list of scorers. If a single scorer, will return a single score.	required
cv		The cross-validator to use. For example, sklearn.model_selection.KFold or sklearn.model_selection.StratifiedKFold.	required
fold		The fold to return the scores for. If None, will return the mean of all the scores (per scorer). Default is None.	None

Returns:

Name	Type	Description
scores	ndarray or float	The scores for the estimator per scorer. If fold is None, will return the mean of all the scores (per scorer). Returns a list if multiple scorers are used, otherwise returns a float for the single scorer.

Source code in tpot/tpot_estimator/cross_val_utils.py

def cross_val_score_objective(estimator, X, y, scorers, cv, fold=None):
    """
    Compute the cross validated scores for a estimator. Only fits the estimator once per fold, and loops over the scorers to evaluate the estimator.

    Parameters
    ----------
    estimator: sklearn.base.BaseEstimator
        The estimator to fit and score.
    X: np.ndarray or pd.DataFrame
        The feature matrix.
    y: np.ndarray or pd.Series
        The target vector.
    scorers: list or scorer
        The scorers to use. 
        If a list, will loop over the scorers and return a list of scorers.
        If a single scorer, will return a single score.
    cv: sklearn cross-validator
        The cross-validator to use. For example, sklearn.model_selection.KFold or sklearn.model_selection.StratifiedKFold.
    fold: int, optional
        The fold to return the scores for. If None, will return the mean of all the scores (per scorer). Default is None.

    Returns
    -------
    scores: np.ndarray or float
        The scores for the estimator per scorer. If fold is None, will return the mean of all the scores (per scorer).
        Returns a list if multiple scorers are used, otherwise returns a float for the single scorer.

    """

    #check if scores is not iterable
    if not isinstance(scorers, Iterable): 
        scorers = [scorers]
    scores = []
    if fold is None:
        for train_index, test_index in cv.split(X, y):
            this_fold_estimator = sklearn.base.clone(estimator)
            if isinstance(X, pd.DataFrame) or isinstance(X, pd.Series):
                X_train, X_test = X.iloc[train_index], X.iloc[test_index]
            else:
                X_train, X_test = X[train_index], X[test_index]

            if isinstance(y, pd.DataFrame) or isinstance(y, pd.Series):
                y_train, y_test = y.iloc[train_index], y.iloc[test_index]
            else:
                y_train, y_test = y[train_index], y[test_index]


            start = time.time()
            this_fold_estimator.fit(X_train,y_train)
            duration = time.time() - start

            this_fold_scores = [sklearn.metrics.get_scorer(scorer)(this_fold_estimator, X_test, y_test) for scorer in scorers] 
            scores.append(this_fold_scores)
            del this_fold_estimator
            del X_train
            del X_test
            del y_train
            del y_test


        return np.mean(scores,0)
    else:
        this_fold_estimator = sklearn.base.clone(estimator)
        train_index, test_index = list(cv.split(X, y))[fold]
        if isinstance(X, pd.DataFrame) or isinstance(X, pd.Series):
            X_train, X_test = X.iloc[train_index], X.iloc[test_index]
        else:
            X_train, X_test = X[train_index], X[test_index]

        if isinstance(y, pd.DataFrame) or isinstance(y, pd.Series):
            y_train, y_test = y.iloc[train_index], y.iloc[test_index]
        else:
            y_train, y_test = y[train_index], y[test_index]

        start = time.time()
        this_fold_estimator.fit(X_train,y_train)
        duration = time.time() - start
        this_fold_scores = [sklearn.metrics.get_scorer(scorer)(this_fold_estimator, X_test, y_test) for scorer in scorers] 
        return this_fold_scores