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交叉验证工具

该文件是TPOT库的一部分。

当前版本的TPOT是由以下人员在Cedars-Sinai开发的： - 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) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

TPOT的原始版本主要由宾夕法尼亚大学的以下人员开发： - Randal S. Olson (rso@randalolson.com) - Weixuan Fu (weixuanf@upenn.edu) - Daniel Angell (dpa34@drexel.edu) - Jason Moore (moorejh28@gmail.com) - 以及许多慷慨的开源贡献者

TPOT 是免费软件：您可以根据自由软件基金会发布的 GNU 宽通用公共许可证的条款重新分发和/或修改它，许可证的版本可以是第 3 版，或者（根据您的选择）任何以后的版本。

TPOT 的发布是希望它能有用， 但没有任何保证；甚至没有对 适销性或特定用途适用性的暗示保证。更多详情请参阅 GNU 较宽松通用公共许可证。

您应该已经收到了一份GNU较宽松通用公共许可证的副本，随TPOT一起提供。如果没有，请参见http://www.gnu.org/licenses/。

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

计算估计器的交叉验证分数。每个折叠只拟合一次估计器，并循环遍历评分器以评估估计器。

参数:

名称 类型 描述 默认值
estimator

用于拟合和评分的估计器。

 必填
X

特征矩阵。

 必填
y

目标向量。

 必填
scorers

要使用的评分器。 如果是列表，将循环遍历评分器并返回评分器列表。 如果是单个评分器，将返回单个分数。

 required
cv

使用的交叉验证器。例如，sklearn.model_selection.KFold 或 sklearn.model_selection.StratifiedKFold。

 required
fold

要返回分数的折叠。如果为None，将返回所有分数的平均值（每个评分器）。默认值为None。

 None

返回:

名称 类型 描述
scores ndarray or float

每个评分器的估计器得分。如果 fold 为 None，将返回所有得分的平均值（每个评分器）。 如果使用多个评分器，则返回一个列表，否则返回单个评分器的浮点数。
Source code in tpot2/tpot_estimator/cross_val_utils.py 
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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