StratifiedShuffleSplit()函數
StratifiedShuffleSplit(n_splits=10, test_size=’default’, train_size=None, random_state=None)
class StratifiedShuffleSplit(BaseShuffleSplit):
"""Stratified Shuffle Split cross-validator
Provides train/test indices to split data in train/test sets.
This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.
Note: like the ShuffleSplit strategy, stratified random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets.
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
n_splits : int, default=10
Number of re-shuffling & splitting iterations.
test_size : float or int, default=None. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If ``train_size`` is also None, it will be set to 0.1.
train_size : float or int, default=None. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.
random_state : int or RandomState instance, default=None. Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
分層洗牌分裂交叉驗證器
提供訓練/測試索引來分割訓練/測試集中的資料。
這個交叉驗證對象是StratifiedKFold和ShuffleSplit的合并，它将傳回StratifiedKFold。折疊是通過儲存每個類的樣本百分比來實作的。
注意:就像ShuffleSplit政策一樣，分層随機分割不能保證所有的折疊都是不同的，盡管這對于相當大的資料集仍然很有可能。
更多資訊請參見:ref: ' User Guide <cross_validation> '。</cross_validation>
參數
----------
int，預設=10
重新洗牌和分裂疊代的數量。
test_size: float或int，預設=None。如果是浮動的，則應該在0.0和1.0之間，并表示要包含在測試分割中的資料集的比例。如果int，表示測試樣本的絕對數量。如果沒有，則将該值設定為train size的補集。如果' ' train_size ' '也是None，它将被設定為0.1。
train_size: float或int，預設=None。如果是浮點數，則應該在0.0和1.0之間，并表示要包含在分割序列中的資料集的比例。如果int，表示train樣本的絕對數量。如果沒有，該值将自動設定為train size的補集。
random_state: int或RandomState執行個體，預設為None。控制産生的訓練和測試名額的随機性。在多個函數調用之間傳遞可重複輸出的int。
看:術語:“術語表< random_state >”。
Examples
--------
>>> import numpy as np
>>> from sklearn.model_selection import StratifiedShuffleSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 0, 1, 1, 1])
>>> sss = StratifiedShuffleSplit(n_splits=5, test_size=0.5,
random_state=0)
>>> sss.get_n_splits(X, y)
5
>>> print(sss)
StratifiedShuffleSplit(n_splits=5, random_state=0, ...)
>>> for train_index, test_index in sss.split(X, y):
... 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: [5 2 3] TEST: [4 1 0]
TRAIN: [5 1 4] TEST: [0 2 3]
TRAIN: [5 0 2] TEST: [4 3 1]
TRAIN: [4 1 0] TEST: [2 3 5]
TRAIN: [0 5 1] TEST: [3 4 2]
"""
@_deprecate_positional_args
def __init__(self, n_splits=10, *, test_size=None, train_size=None,
random_state=None):
super().__init__(n_splits=n_splits, test_size=test_size,
train_size=train_size, random_state=random_state)
self._default_test_size = 0.1
def _iter_indices(self, X, y, groups=None):
n_samples = _num_samples(X)
y = check_array(y, ensure_2d=False, dtype=None)
n_train, n_test = _validate_shuffle_split(
n_samples, self.test_size, self.train_size,
default_test_size=self._default_test_size)
if y.ndim == 2:
# for multi-label y, map each distinct row to a string repr
# using join because str(row) uses an ellipsis if len(row) >
1000
y = np.array([' '.join(row.astype('str')) for row in y])
classes, y_indices = np.unique(y, return_inverse=True)
n_classes = classes.shape[0]
class_counts = np.bincount(y_indices)
if np.min(class_counts) < 2:
raise ValueError("The least populated class in y has only 1"
" member, which is too few. The minimum"
" number of groups for any class cannot"
" be less than 2.")
if n_train < n_classes:
raise ValueError(
'The train_size = %d should be greater or '
'equal to the number of classes = %d' %
(n_train, n_classes))
if n_test < n_classes:
raise ValueError('The test_size = %d should be greater or '
(n_test, n_classes)) # Find the sorted list of instances for
each class:
# (np.unique above performs a sort, so code is O(n logn)
already)
class_indices = np.split(np.argsort(y_indices,
kind='mergesort'), np.cumsum(class_counts)[:-1])
rng = check_random_state(self.random_state)
for _ in range(self.n_splits):
# if there are ties in the class-counts, we want
# to make sure to break them anew in each iteration
n_i = _approximate_mode(class_counts, n_train, rng)
class_counts_remaining = class_counts - n_i
t_i = _approximate_mode(class_counts_remaining, n_test,
rng)
train = []
test = []
for i in range(n_classes):
permutation = rng.permutation(class_counts[i])
perm_indices_class_i = class_indices[i].take(permutation,
mode='clip')
train.extend(perm_indices_class_i[:n_i[i]])
test.extend(perm_indices_class_i[n_i[i]:n_i[i] + t_i[i]])
train = rng.permutation(train)
test = rng.permutation(test)
yield train, test
def split(self, X, y, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Note that providing ``y`` is sufficient to generate the splits
and
hence ``np.zeros(n_samples)`` may be used as a placeholder
for
``X`` instead of actual training data.
y : array-like of shape (n_samples,) or (n_samples, n_labels)
The target variable for supervised learning problems.
Stratification is done based on the y labels.
groups : object
Always ignored, exists for compatibility.
Yields
------
train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
Notes
-----
Randomized CV splitters may return different results for each
call of
split. You can make the results identical by setting
`random_state`
to an integer.
"""
return super().split(X, y, groups)
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