如何查看scikit学习ML类的源代码

Question

我想了解scikit-learn中各种ML类的源代码。比如，如果我想检查交叉验证模块的类KFold，我该如何检查它？。其他人也是如此。

感谢

Answer 1

您可以在他们的github:上查看它

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_validation.py

你可以下载它，但你不必

KFold看起来像这个

class KFold(_BaseKFold):
    """K-Folds cross validation iterator.
    Provides train/test indices to split data in train test sets. Split
    dataset into k consecutive folds (without shuffling).
    Each fold is then used a validation set once while the k - 1 remaining
    fold form the training set.
    Parameters
    ----------
    n : int
        Total number of elements.
    n_folds : int, default=3
        Number of folds. Must be at least 2.
    shuffle : boolean, optional
        Whether to shuffle the data before splitting into batches.
    random_state : None, int or RandomState
        Pseudo-random number generator state used for random
        sampling. If None, use default numpy RNG for shuffling
    Examples
    --------
    >>> from sklearn import cross_validation
    >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
    >>> y = np.array([1, 2, 3, 4])
    >>> kf = cross_validation.KFold(4, n_folds=2)
    >>> len(kf)
    2
    >>> print(kf)  # doctest: +NORMALIZE_WHITESPACE
    sklearn.cross_validation.KFold(n=4, n_folds=2, shuffle=False,
                                   random_state=None)
    >>> for train_index, test_index in kf:
    ...    print("TRAIN:", train_index, "TEST:", test_index)
    ...    X_train, X_test = X[train_index], X[test_index]
    ...    y_train, y_test = y[train_index], y[test_index]
    TRAIN: [2 3] TEST: [0 1]
    TRAIN: [0 1] TEST: [2 3]
    Notes
    -----
    The first n % n_folds folds have size n // n_folds + 1, other folds have
    size n // n_folds.
    See also
    --------
    StratifiedKFold: take label information into account to avoid building
    folds with imbalanced class distributions (for binary or multiclass
    classification tasks).
    """
    def __init__(self, n, n_folds=3, shuffle=False,
                 random_state=None):
        super(KFold, self).__init__(n, n_folds, shuffle, random_state)
        self.idxs = np.arange(n)
        if shuffle:
            rng = check_random_state(self.random_state)
            rng.shuffle(self.idxs)
    def _iter_test_indices(self):
        n = self.n
        n_folds = self.n_folds
        fold_sizes = (n // n_folds) * np.ones(n_folds, dtype=np.int)
        fold_sizes[:n % n_folds] += 1
        current = 0
        for fold_size in fold_sizes:
            start, stop = current, current + fold_size
            yield self.idxs[start:stop]
            current = stop
    def __repr__(self):
        return '%s.%s(n=%i, n_folds=%i, shuffle=%s, random_state=%s)' % (
            self.__class__.__module__,
            self.__class__.__name__,
            self.n,
            self.n_folds,
            self.shuffle,
            self.random_state,
        )
    def __len__(self):
        return self.n_folds