1084 lines
41 KiB
Python
1084 lines
41 KiB
Python
"""Test the split module"""
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from __future__ import division
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import warnings
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import numpy as np
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from scipy.sparse import coo_matrix, csc_matrix, csr_matrix
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from scipy import stats
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from scipy.misc import comb
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from itertools import combinations
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from sklearn.utils.fixes import combinations_with_replacement
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_false
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_raises_regexp
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_greater_equal
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from sklearn.utils.testing import assert_not_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_warns_message
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from sklearn.utils.testing import assert_raise_message
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils.validation import _num_samples
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from sklearn.utils.mocking import MockDataFrame
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from sklearn.model_selection import cross_val_score
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from sklearn.model_selection import KFold
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from sklearn.model_selection import StratifiedKFold
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from sklearn.model_selection import GroupKFold
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from sklearn.model_selection import TimeSeriesSplit
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from sklearn.model_selection import LeaveOneOut
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from sklearn.model_selection import LeaveOneGroupOut
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from sklearn.model_selection import LeavePOut
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from sklearn.model_selection import LeavePGroupsOut
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from sklearn.model_selection import ShuffleSplit
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from sklearn.model_selection import GroupShuffleSplit
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from sklearn.model_selection import StratifiedShuffleSplit
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from sklearn.model_selection import PredefinedSplit
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from sklearn.model_selection import check_cv
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import GridSearchCV
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from sklearn.linear_model import Ridge
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from sklearn.model_selection._split import _safe_split
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from sklearn.model_selection._split import _validate_shuffle_split
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from sklearn.model_selection._split import _CVIterableWrapper
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from sklearn.model_selection._split import _build_repr
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from sklearn.datasets import load_digits
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from sklearn.datasets import load_iris
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from sklearn.datasets import make_classification
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from sklearn.externals import six
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from sklearn.externals.six.moves import zip
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from sklearn.svm import SVC
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X = np.ones(10)
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y = np.arange(10) // 2
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P_sparse = coo_matrix(np.eye(5))
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iris = load_iris()
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digits = load_digits()
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class MockClassifier(object):
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"""Dummy classifier to test the cross-validation"""
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def __init__(self, a=0, allow_nd=False):
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self.a = a
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self.allow_nd = allow_nd
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def fit(self, X, Y=None, sample_weight=None, class_prior=None,
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sparse_sample_weight=None, sparse_param=None, dummy_int=None,
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dummy_str=None, dummy_obj=None, callback=None):
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"""The dummy arguments are to test that this fit function can
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accept non-array arguments through cross-validation, such as:
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- int
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- str (this is actually array-like)
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- object
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- function
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"""
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self.dummy_int = dummy_int
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self.dummy_str = dummy_str
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self.dummy_obj = dummy_obj
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if callback is not None:
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callback(self)
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if self.allow_nd:
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X = X.reshape(len(X), -1)
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if X.ndim >= 3 and not self.allow_nd:
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raise ValueError('X cannot be d')
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if sample_weight is not None:
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assert_true(sample_weight.shape[0] == X.shape[0],
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'MockClassifier extra fit_param sample_weight.shape[0]'
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' is {0}, should be {1}'.format(sample_weight.shape[0],
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X.shape[0]))
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if class_prior is not None:
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assert_true(class_prior.shape[0] == len(np.unique(y)),
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'MockClassifier extra fit_param class_prior.shape[0]'
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' is {0}, should be {1}'.format(class_prior.shape[0],
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len(np.unique(y))))
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if sparse_sample_weight is not None:
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fmt = ('MockClassifier extra fit_param sparse_sample_weight'
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'.shape[0] is {0}, should be {1}')
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assert_true(sparse_sample_weight.shape[0] == X.shape[0],
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fmt.format(sparse_sample_weight.shape[0], X.shape[0]))
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if sparse_param is not None:
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fmt = ('MockClassifier extra fit_param sparse_param.shape '
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'is ({0}, {1}), should be ({2}, {3})')
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assert_true(sparse_param.shape == P_sparse.shape,
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fmt.format(sparse_param.shape[0],
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sparse_param.shape[1],
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P_sparse.shape[0], P_sparse.shape[1]))
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return self
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def predict(self, T):
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if self.allow_nd:
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T = T.reshape(len(T), -1)
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return T[:, 0]
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def score(self, X=None, Y=None):
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return 1. / (1 + np.abs(self.a))
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def get_params(self, deep=False):
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return {'a': self.a, 'allow_nd': self.allow_nd}
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@ignore_warnings
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def test_cross_validator_with_default_params():
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n_samples = 4
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n_unique_groups = 4
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n_splits = 2
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p = 2
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n_shuffle_splits = 10 # (the default value)
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X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
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X_1d = np.array([1, 2, 3, 4])
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y = np.array([1, 1, 2, 2])
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groups = np.array([1, 2, 3, 4])
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loo = LeaveOneOut()
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lpo = LeavePOut(p)
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kf = KFold(n_splits)
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skf = StratifiedKFold(n_splits)
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lolo = LeaveOneGroupOut()
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lopo = LeavePGroupsOut(p)
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ss = ShuffleSplit(random_state=0)
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ps = PredefinedSplit([1, 1, 2, 2]) # n_splits = np of unique folds = 2
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loo_repr = "LeaveOneOut()"
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lpo_repr = "LeavePOut(p=2)"
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kf_repr = "KFold(n_splits=2, random_state=None, shuffle=False)"
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skf_repr = "StratifiedKFold(n_splits=2, random_state=None, shuffle=False)"
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lolo_repr = "LeaveOneGroupOut()"
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lopo_repr = "LeavePGroupsOut(n_groups=2)"
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ss_repr = ("ShuffleSplit(n_splits=10, random_state=0, test_size=0.1, "
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"train_size=None)")
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ps_repr = "PredefinedSplit(test_fold=array([1, 1, 2, 2]))"
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n_splits_expected = [n_samples, comb(n_samples, p), n_splits, n_splits,
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n_unique_groups, comb(n_unique_groups, p),
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n_shuffle_splits, 2]
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for i, (cv, cv_repr) in enumerate(zip(
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[loo, lpo, kf, skf, lolo, lopo, ss, ps],
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[loo_repr, lpo_repr, kf_repr, skf_repr, lolo_repr, lopo_repr,
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ss_repr, ps_repr])):
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# Test if get_n_splits works correctly
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assert_equal(n_splits_expected[i], cv.get_n_splits(X, y, groups))
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# Test if the cross-validator works as expected even if
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# the data is 1d
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np.testing.assert_equal(list(cv.split(X, y, groups)),
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list(cv.split(X_1d, y, groups)))
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# Test that train, test indices returned are integers
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for train, test in cv.split(X, y, groups):
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assert_equal(np.asarray(train).dtype.kind, 'i')
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assert_equal(np.asarray(train).dtype.kind, 'i')
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# Test if the repr works without any errors
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assert_equal(cv_repr, repr(cv))
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def check_valid_split(train, test, n_samples=None):
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# Use python sets to get more informative assertion failure messages
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train, test = set(train), set(test)
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# Train and test split should not overlap
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assert_equal(train.intersection(test), set())
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if n_samples is not None:
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# Check that the union of train an test split cover all the indices
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assert_equal(train.union(test), set(range(n_samples)))
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def check_cv_coverage(cv, X, y, groups, expected_n_splits=None):
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n_samples = _num_samples(X)
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# Check that a all the samples appear at least once in a test fold
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if expected_n_splits is not None:
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assert_equal(cv.get_n_splits(X, y, groups), expected_n_splits)
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else:
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expected_n_splits = cv.get_n_splits(X, y, groups)
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collected_test_samples = set()
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iterations = 0
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for train, test in cv.split(X, y, groups):
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check_valid_split(train, test, n_samples=n_samples)
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iterations += 1
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collected_test_samples.update(test)
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# Check that the accumulated test samples cover the whole dataset
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assert_equal(iterations, expected_n_splits)
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if n_samples is not None:
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assert_equal(collected_test_samples, set(range(n_samples)))
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def test_kfold_valueerrors():
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X1 = np.array([[1, 2], [3, 4], [5, 6]])
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X2 = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
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# Check that errors are raised if there is not enough samples
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assert_raises(ValueError, next, KFold(4).split(X1))
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# Check that a warning is raised if the least populated class has too few
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# members.
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y = np.array([3, 3, -1, -1, 3])
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skf_3 = StratifiedKFold(3)
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assert_warns_message(Warning, "The least populated class",
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next, skf_3.split(X2, y))
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# Check that despite the warning the folds are still computed even
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# though all the classes are not necessarily represented at on each
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# side of the split at each split
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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check_cv_coverage(skf_3, X2, y, groups=None, expected_n_splits=3)
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# Check that errors are raised if all n_groups for individual
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# classes are less than n_splits.
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y = np.array([3, 3, -1, -1, 2])
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assert_raises(ValueError, next, skf_3.split(X2, y))
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# Error when number of folds is <= 1
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assert_raises(ValueError, KFold, 0)
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assert_raises(ValueError, KFold, 1)
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error_string = ("k-fold cross-validation requires at least one"
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" train/test split")
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assert_raise_message(ValueError, error_string,
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StratifiedKFold, 0)
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assert_raise_message(ValueError, error_string,
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StratifiedKFold, 1)
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# When n_splits is not integer:
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assert_raises(ValueError, KFold, 1.5)
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assert_raises(ValueError, KFold, 2.0)
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assert_raises(ValueError, StratifiedKFold, 1.5)
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assert_raises(ValueError, StratifiedKFold, 2.0)
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# When shuffle is not a bool:
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assert_raises(TypeError, KFold, n_splits=4, shuffle=None)
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def test_kfold_indices():
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# Check all indices are returned in the test folds
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X1 = np.ones(18)
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kf = KFold(3)
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check_cv_coverage(kf, X1, y=None, groups=None, expected_n_splits=3)
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# Check all indices are returned in the test folds even when equal-sized
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# folds are not possible
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X2 = np.ones(17)
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kf = KFold(3)
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check_cv_coverage(kf, X2, y=None, groups=None, expected_n_splits=3)
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# Check if get_n_splits returns the number of folds
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assert_equal(5, KFold(5).get_n_splits(X2))
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def test_kfold_no_shuffle():
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# Manually check that KFold preserves the data ordering on toy datasets
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X2 = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
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splits = KFold(2).split(X2[:-1])
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train, test = next(splits)
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assert_array_equal(test, [0, 1])
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assert_array_equal(train, [2, 3])
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train, test = next(splits)
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assert_array_equal(test, [2, 3])
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assert_array_equal(train, [0, 1])
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splits = KFold(2).split(X2)
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train, test = next(splits)
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assert_array_equal(test, [0, 1, 2])
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assert_array_equal(train, [3, 4])
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train, test = next(splits)
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assert_array_equal(test, [3, 4])
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assert_array_equal(train, [0, 1, 2])
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def test_stratified_kfold_no_shuffle():
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# Manually check that StratifiedKFold preserves the data ordering as much
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# as possible on toy datasets in order to avoid hiding sample dependencies
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# when possible
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X, y = np.ones(4), [1, 1, 0, 0]
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splits = StratifiedKFold(2).split(X, y)
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train, test = next(splits)
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assert_array_equal(test, [0, 2])
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assert_array_equal(train, [1, 3])
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train, test = next(splits)
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assert_array_equal(test, [1, 3])
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assert_array_equal(train, [0, 2])
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X, y = np.ones(7), [1, 1, 1, 0, 0, 0, 0]
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splits = StratifiedKFold(2).split(X, y)
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train, test = next(splits)
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assert_array_equal(test, [0, 1, 3, 4])
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assert_array_equal(train, [2, 5, 6])
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train, test = next(splits)
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assert_array_equal(test, [2, 5, 6])
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assert_array_equal(train, [0, 1, 3, 4])
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# Check if get_n_splits returns the number of folds
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assert_equal(5, StratifiedKFold(5).get_n_splits(X, y))
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def test_stratified_kfold_ratios():
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# Check that stratified kfold preserves class ratios in individual splits
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# Repeat with shuffling turned off and on
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n_samples = 1000
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X = np.ones(n_samples)
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y = np.array([4] * int(0.10 * n_samples) +
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[0] * int(0.89 * n_samples) +
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[1] * int(0.01 * n_samples))
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for shuffle in (False, True):
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for train, test in StratifiedKFold(5, shuffle=shuffle).split(X, y):
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assert_almost_equal(np.sum(y[train] == 4) / len(train), 0.10, 2)
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assert_almost_equal(np.sum(y[train] == 0) / len(train), 0.89, 2)
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assert_almost_equal(np.sum(y[train] == 1) / len(train), 0.01, 2)
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assert_almost_equal(np.sum(y[test] == 4) / len(test), 0.10, 2)
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assert_almost_equal(np.sum(y[test] == 0) / len(test), 0.89, 2)
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assert_almost_equal(np.sum(y[test] == 1) / len(test), 0.01, 2)
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def test_kfold_balance():
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# Check that KFold returns folds with balanced sizes
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for i in range(11, 17):
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kf = KFold(5).split(X=np.ones(i))
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sizes = []
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for _, test in kf:
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sizes.append(len(test))
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assert_true((np.max(sizes) - np.min(sizes)) <= 1)
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assert_equal(np.sum(sizes), i)
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def test_stratifiedkfold_balance():
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# Check that KFold returns folds with balanced sizes (only when
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# stratification is possible)
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# Repeat with shuffling turned off and on
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X = np.ones(17)
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y = [0] * 3 + [1] * 14
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for shuffle in (True, False):
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cv = StratifiedKFold(3, shuffle=shuffle)
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for i in range(11, 17):
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skf = cv.split(X[:i], y[:i])
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sizes = []
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for _, test in skf:
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sizes.append(len(test))
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assert_true((np.max(sizes) - np.min(sizes)) <= 1)
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assert_equal(np.sum(sizes), i)
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def test_shuffle_kfold():
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# Check the indices are shuffled properly
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kf = KFold(3)
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kf2 = KFold(3, shuffle=True, random_state=0)
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kf3 = KFold(3, shuffle=True, random_state=1)
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X = np.ones(300)
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all_folds = np.zeros(300)
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for (tr1, te1), (tr2, te2), (tr3, te3) in zip(
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kf.split(X), kf2.split(X), kf3.split(X)):
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for tr_a, tr_b in combinations((tr1, tr2, tr3), 2):
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# Assert that there is no complete overlap
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assert_not_equal(len(np.intersect1d(tr_a, tr_b)), len(tr1))
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# Set all test indices in successive iterations of kf2 to 1
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all_folds[te2] = 1
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# Check that all indices are returned in the different test folds
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assert_equal(sum(all_folds), 300)
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def test_shuffle_kfold_stratifiedkfold_reproducibility():
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# Check that when the shuffle is True multiple split calls produce the
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# same split when random_state is set
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X = np.ones(15) # Divisible by 3
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y = [0] * 7 + [1] * 8
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X2 = np.ones(16) # Not divisible by 3
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y2 = [0] * 8 + [1] * 8
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kf = KFold(3, shuffle=True, random_state=0)
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skf = StratifiedKFold(3, shuffle=True, random_state=0)
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for cv in (kf, skf):
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np.testing.assert_equal(list(cv.split(X, y)), list(cv.split(X, y)))
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np.testing.assert_equal(list(cv.split(X2, y2)), list(cv.split(X2, y2)))
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kf = KFold(3, shuffle=True)
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skf = StratifiedKFold(3, shuffle=True)
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for cv in (kf, skf):
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for data in zip((X, X2), (y, y2)):
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try:
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np.testing.assert_equal(list(cv.split(*data)),
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list(cv.split(*data)))
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except AssertionError:
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pass
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else:
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raise AssertionError("The splits for data, %s, are same even "
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"when random state is not set" % data)
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def test_shuffle_stratifiedkfold():
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# Check that shuffling is happening when requested, and for proper
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# sample coverage
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X_40 = np.ones(40)
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y = [0] * 20 + [1] * 20
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kf0 = StratifiedKFold(5, shuffle=True, random_state=0)
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kf1 = StratifiedKFold(5, shuffle=True, random_state=1)
|
|
for (_, test0), (_, test1) in zip(kf0.split(X_40, y),
|
|
kf1.split(X_40, y)):
|
|
assert_not_equal(set(test0), set(test1))
|
|
check_cv_coverage(kf0, X_40, y, groups=None, expected_n_splits=5)
|
|
|
|
|
|
def test_kfold_can_detect_dependent_samples_on_digits(): # see #2372
|
|
# The digits samples are dependent: they are apparently grouped by authors
|
|
# although we don't have any information on the groups segment locations
|
|
# for this data. We can highlight this fact by computing k-fold cross-
|
|
# validation with and without shuffling: we observe that the shuffling case
|
|
# wrongly makes the IID assumption and is therefore too optimistic: it
|
|
# estimates a much higher accuracy (around 0.93) than that the non
|
|
# shuffling variant (around 0.81).
|
|
|
|
X, y = digits.data[:600], digits.target[:600]
|
|
model = SVC(C=10, gamma=0.005)
|
|
|
|
n_splits = 3
|
|
|
|
cv = KFold(n_splits=n_splits, shuffle=False)
|
|
mean_score = cross_val_score(model, X, y, cv=cv).mean()
|
|
assert_greater(0.92, mean_score)
|
|
assert_greater(mean_score, 0.80)
|
|
|
|
# Shuffling the data artificially breaks the dependency and hides the
|
|
# overfitting of the model with regards to the writing style of the authors
|
|
# by yielding a seriously overestimated score:
|
|
|
|
cv = KFold(n_splits, shuffle=True, random_state=0)
|
|
mean_score = cross_val_score(model, X, y, cv=cv).mean()
|
|
assert_greater(mean_score, 0.92)
|
|
|
|
cv = KFold(n_splits, shuffle=True, random_state=1)
|
|
mean_score = cross_val_score(model, X, y, cv=cv).mean()
|
|
assert_greater(mean_score, 0.92)
|
|
|
|
# Similarly, StratifiedKFold should try to shuffle the data as little
|
|
# as possible (while respecting the balanced class constraints)
|
|
# and thus be able to detect the dependency by not overestimating
|
|
# the CV score either. As the digits dataset is approximately balanced
|
|
# the estimated mean score is close to the score measured with
|
|
# non-shuffled KFold
|
|
|
|
cv = StratifiedKFold(n_splits)
|
|
mean_score = cross_val_score(model, X, y, cv=cv).mean()
|
|
assert_greater(0.93, mean_score)
|
|
assert_greater(mean_score, 0.80)
|
|
|
|
|
|
def test_shuffle_split():
|
|
ss1 = ShuffleSplit(test_size=0.2, random_state=0).split(X)
|
|
ss2 = ShuffleSplit(test_size=2, random_state=0).split(X)
|
|
ss3 = ShuffleSplit(test_size=np.int32(2), random_state=0).split(X)
|
|
for typ in six.integer_types:
|
|
ss4 = ShuffleSplit(test_size=typ(2), random_state=0).split(X)
|
|
for t1, t2, t3, t4 in zip(ss1, ss2, ss3, ss4):
|
|
assert_array_equal(t1[0], t2[0])
|
|
assert_array_equal(t2[0], t3[0])
|
|
assert_array_equal(t3[0], t4[0])
|
|
assert_array_equal(t1[1], t2[1])
|
|
assert_array_equal(t2[1], t3[1])
|
|
assert_array_equal(t3[1], t4[1])
|
|
|
|
|
|
def test_stratified_shuffle_split_init():
|
|
X = np.arange(7)
|
|
y = np.asarray([0, 1, 1, 1, 2, 2, 2])
|
|
# Check that error is raised if there is a class with only one sample
|
|
assert_raises(ValueError, next,
|
|
StratifiedShuffleSplit(3, 0.2).split(X, y))
|
|
|
|
# Check that error is raised if the test set size is smaller than n_classes
|
|
assert_raises(ValueError, next, StratifiedShuffleSplit(3, 2).split(X, y))
|
|
# Check that error is raised if the train set size is smaller than
|
|
# n_classes
|
|
assert_raises(ValueError, next,
|
|
StratifiedShuffleSplit(3, 3, 2).split(X, y))
|
|
|
|
X = np.arange(9)
|
|
y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2])
|
|
# Check that errors are raised if there is not enough samples
|
|
assert_raises(ValueError, StratifiedShuffleSplit, 3, 0.5, 0.6)
|
|
assert_raises(ValueError, next,
|
|
StratifiedShuffleSplit(3, 8, 0.6).split(X, y))
|
|
assert_raises(ValueError, next,
|
|
StratifiedShuffleSplit(3, 0.6, 8).split(X, y))
|
|
|
|
# Train size or test size too small
|
|
assert_raises(ValueError, next,
|
|
StratifiedShuffleSplit(train_size=2).split(X, y))
|
|
assert_raises(ValueError, next,
|
|
StratifiedShuffleSplit(test_size=2).split(X, y))
|
|
|
|
|
|
def test_stratified_shuffle_split_respects_test_size():
|
|
y = np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2])
|
|
test_size = 5
|
|
train_size = 10
|
|
sss = StratifiedShuffleSplit(6, test_size=test_size, train_size=train_size,
|
|
random_state=0).split(np.ones(len(y)), y)
|
|
for train, test in sss:
|
|
assert_equal(len(train), train_size)
|
|
assert_equal(len(test), test_size)
|
|
|
|
|
|
def test_stratified_shuffle_split_iter():
|
|
ys = [np.array([1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3]),
|
|
np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]),
|
|
np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2] * 2),
|
|
np.array([1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4]),
|
|
np.array([-1] * 800 + [1] * 50),
|
|
np.concatenate([[i] * (100 + i) for i in range(11)])
|
|
]
|
|
|
|
for y in ys:
|
|
sss = StratifiedShuffleSplit(6, test_size=0.33,
|
|
random_state=0).split(np.ones(len(y)), y)
|
|
# this is how test-size is computed internally
|
|
# in _validate_shuffle_split
|
|
test_size = np.ceil(0.33 * len(y))
|
|
train_size = len(y) - test_size
|
|
for train, test in sss:
|
|
assert_array_equal(np.unique(y[train]), np.unique(y[test]))
|
|
# Checks if folds keep classes proportions
|
|
p_train = (np.bincount(np.unique(y[train],
|
|
return_inverse=True)[1]) /
|
|
float(len(y[train])))
|
|
p_test = (np.bincount(np.unique(y[test],
|
|
return_inverse=True)[1]) /
|
|
float(len(y[test])))
|
|
assert_array_almost_equal(p_train, p_test, 1)
|
|
assert_equal(len(train) + len(test), y.size)
|
|
assert_equal(len(train), train_size)
|
|
assert_equal(len(test), test_size)
|
|
assert_array_equal(np.lib.arraysetops.intersect1d(train, test), [])
|
|
|
|
|
|
def test_stratified_shuffle_split_even():
|
|
# Test the StratifiedShuffleSplit, indices are drawn with a
|
|
# equal chance
|
|
n_folds = 5
|
|
n_splits = 1000
|
|
|
|
def assert_counts_are_ok(idx_counts, p):
|
|
# Here we test that the distribution of the counts
|
|
# per index is close enough to a binomial
|
|
threshold = 0.05 / n_splits
|
|
bf = stats.binom(n_splits, p)
|
|
for count in idx_counts:
|
|
prob = bf.pmf(count)
|
|
assert_true(prob > threshold,
|
|
"An index is not drawn with chance corresponding "
|
|
"to even draws")
|
|
|
|
for n_samples in (6, 22):
|
|
groups = np.array((n_samples // 2) * [0, 1])
|
|
splits = StratifiedShuffleSplit(n_splits=n_splits,
|
|
test_size=1. / n_folds,
|
|
random_state=0)
|
|
|
|
train_counts = [0] * n_samples
|
|
test_counts = [0] * n_samples
|
|
n_splits_actual = 0
|
|
for train, test in splits.split(X=np.ones(n_samples), y=groups):
|
|
n_splits_actual += 1
|
|
for counter, ids in [(train_counts, train), (test_counts, test)]:
|
|
for id in ids:
|
|
counter[id] += 1
|
|
assert_equal(n_splits_actual, n_splits)
|
|
|
|
n_train, n_test = _validate_shuffle_split(
|
|
n_samples, test_size=1. / n_folds, train_size=1. - (1. / n_folds))
|
|
|
|
assert_equal(len(train), n_train)
|
|
assert_equal(len(test), n_test)
|
|
assert_equal(len(set(train).intersection(test)), 0)
|
|
|
|
group_counts = np.unique(groups)
|
|
assert_equal(splits.test_size, 1.0 / n_folds)
|
|
assert_equal(n_train + n_test, len(groups))
|
|
assert_equal(len(group_counts), 2)
|
|
ex_test_p = float(n_test) / n_samples
|
|
ex_train_p = float(n_train) / n_samples
|
|
|
|
assert_counts_are_ok(train_counts, ex_train_p)
|
|
assert_counts_are_ok(test_counts, ex_test_p)
|
|
|
|
|
|
def test_stratified_shuffle_split_overlap_train_test_bug():
|
|
# See https://github.com/scikit-learn/scikit-learn/issues/6121 for
|
|
# the original bug report
|
|
y = [0, 1, 2, 3] * 3 + [4, 5] * 5
|
|
X = np.ones_like(y)
|
|
|
|
sss = StratifiedShuffleSplit(n_splits=1,
|
|
test_size=0.5, random_state=0)
|
|
|
|
train, test = next(iter(sss.split(X=X, y=y)))
|
|
|
|
assert_array_equal(np.intersect1d(train, test), [])
|
|
|
|
|
|
def test_predefinedsplit_with_kfold_split():
|
|
# Check that PredefinedSplit can reproduce a split generated by Kfold.
|
|
folds = -1 * np.ones(10)
|
|
kf_train = []
|
|
kf_test = []
|
|
for i, (train_ind, test_ind) in enumerate(KFold(5, shuffle=True).split(X)):
|
|
kf_train.append(train_ind)
|
|
kf_test.append(test_ind)
|
|
folds[test_ind] = i
|
|
ps_train = []
|
|
ps_test = []
|
|
ps = PredefinedSplit(folds)
|
|
# n_splits is simply the no of unique folds
|
|
assert_equal(len(np.unique(folds)), ps.get_n_splits())
|
|
for train_ind, test_ind in ps.split():
|
|
ps_train.append(train_ind)
|
|
ps_test.append(test_ind)
|
|
assert_array_equal(ps_train, kf_train)
|
|
assert_array_equal(ps_test, kf_test)
|
|
|
|
|
|
def test_group_shuffle_split():
|
|
groups = [np.array([1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3]),
|
|
np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]),
|
|
np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2]),
|
|
np.array([1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4])]
|
|
|
|
for l in groups:
|
|
X = y = np.ones(len(l))
|
|
n_splits = 6
|
|
test_size = 1./3
|
|
slo = GroupShuffleSplit(n_splits, test_size=test_size, random_state=0)
|
|
|
|
# Make sure the repr works
|
|
repr(slo)
|
|
|
|
# Test that the length is correct
|
|
assert_equal(slo.get_n_splits(X, y, groups=l), n_splits)
|
|
|
|
l_unique = np.unique(l)
|
|
|
|
for train, test in slo.split(X, y, groups=l):
|
|
# First test: no train group is in the test set and vice versa
|
|
l_train_unique = np.unique(l[train])
|
|
l_test_unique = np.unique(l[test])
|
|
assert_false(np.any(np.in1d(l[train], l_test_unique)))
|
|
assert_false(np.any(np.in1d(l[test], l_train_unique)))
|
|
|
|
# Second test: train and test add up to all the data
|
|
assert_equal(l[train].size + l[test].size, l.size)
|
|
|
|
# Third test: train and test are disjoint
|
|
assert_array_equal(np.intersect1d(train, test), [])
|
|
|
|
# Fourth test:
|
|
# unique train and test groups are correct, +- 1 for rounding error
|
|
assert_true(abs(len(l_test_unique) -
|
|
round(test_size * len(l_unique))) <= 1)
|
|
assert_true(abs(len(l_train_unique) -
|
|
round((1.0 - test_size) * len(l_unique))) <= 1)
|
|
|
|
|
|
def test_leave_group_out_changing_groups():
|
|
# Check that LeaveOneGroupOut and LeavePGroupsOut work normally if
|
|
# the groups variable is changed before calling split
|
|
groups = np.array([0, 1, 2, 1, 1, 2, 0, 0])
|
|
X = np.ones(len(groups))
|
|
groups_changing = np.array(groups, copy=True)
|
|
lolo = LeaveOneGroupOut().split(X, groups=groups)
|
|
lolo_changing = LeaveOneGroupOut().split(X, groups=groups)
|
|
lplo = LeavePGroupsOut(n_groups=2).split(X, groups=groups)
|
|
lplo_changing = LeavePGroupsOut(n_groups=2).split(X, groups=groups)
|
|
groups_changing[:] = 0
|
|
for llo, llo_changing in [(lolo, lolo_changing), (lplo, lplo_changing)]:
|
|
for (train, test), (train_chan, test_chan) in zip(llo, llo_changing):
|
|
assert_array_equal(train, train_chan)
|
|
assert_array_equal(test, test_chan)
|
|
|
|
# n_splits = no of 2 (p) group combinations of the unique groups = 3C2 = 3
|
|
assert_equal(3, LeavePGroupsOut(n_groups=2).get_n_splits(X, y, groups))
|
|
# n_splits = no of unique groups (C(uniq_lbls, 1) = n_unique_groups)
|
|
assert_equal(3, LeaveOneGroupOut().get_n_splits(X, y, groups))
|
|
|
|
|
|
def test_train_test_split_errors():
|
|
assert_raises(ValueError, train_test_split)
|
|
assert_raises(ValueError, train_test_split, range(3), train_size=1.1)
|
|
assert_raises(ValueError, train_test_split, range(3), test_size=0.6,
|
|
train_size=0.6)
|
|
assert_raises(ValueError, train_test_split, range(3),
|
|
test_size=np.float32(0.6), train_size=np.float32(0.6))
|
|
assert_raises(ValueError, train_test_split, range(3),
|
|
test_size="wrong_type")
|
|
assert_raises(ValueError, train_test_split, range(3), test_size=2,
|
|
train_size=4)
|
|
assert_raises(TypeError, train_test_split, range(3),
|
|
some_argument=1.1)
|
|
assert_raises(ValueError, train_test_split, range(3), range(42))
|
|
|
|
|
|
def test_train_test_split():
|
|
X = np.arange(100).reshape((10, 10))
|
|
X_s = coo_matrix(X)
|
|
y = np.arange(10)
|
|
|
|
# simple test
|
|
split = train_test_split(X, y, test_size=None, train_size=.5)
|
|
X_train, X_test, y_train, y_test = split
|
|
assert_equal(len(y_test), len(y_train))
|
|
# test correspondence of X and y
|
|
assert_array_equal(X_train[:, 0], y_train * 10)
|
|
assert_array_equal(X_test[:, 0], y_test * 10)
|
|
|
|
# don't convert lists to anything else by default
|
|
split = train_test_split(X, X_s, y.tolist())
|
|
X_train, X_test, X_s_train, X_s_test, y_train, y_test = split
|
|
assert_true(isinstance(y_train, list))
|
|
assert_true(isinstance(y_test, list))
|
|
|
|
# allow nd-arrays
|
|
X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)
|
|
y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)
|
|
split = train_test_split(X_4d, y_3d)
|
|
assert_equal(split[0].shape, (7, 5, 3, 2))
|
|
assert_equal(split[1].shape, (3, 5, 3, 2))
|
|
assert_equal(split[2].shape, (7, 7, 11))
|
|
assert_equal(split[3].shape, (3, 7, 11))
|
|
|
|
# test stratification option
|
|
y = np.array([1, 1, 1, 1, 2, 2, 2, 2])
|
|
for test_size, exp_test_size in zip([2, 4, 0.25, 0.5, 0.75],
|
|
[2, 4, 2, 4, 6]):
|
|
train, test = train_test_split(y, test_size=test_size,
|
|
stratify=y,
|
|
random_state=0)
|
|
assert_equal(len(test), exp_test_size)
|
|
assert_equal(len(test) + len(train), len(y))
|
|
# check the 1:1 ratio of ones and twos in the data is preserved
|
|
assert_equal(np.sum(train == 1), np.sum(train == 2))
|
|
|
|
|
|
@ignore_warnings
|
|
def train_test_split_pandas():
|
|
# check train_test_split doesn't destroy pandas dataframe
|
|
types = [MockDataFrame]
|
|
try:
|
|
from pandas import DataFrame
|
|
types.append(DataFrame)
|
|
except ImportError:
|
|
pass
|
|
for InputFeatureType in types:
|
|
# X dataframe
|
|
X_df = InputFeatureType(X)
|
|
X_train, X_test = train_test_split(X_df)
|
|
assert_true(isinstance(X_train, InputFeatureType))
|
|
assert_true(isinstance(X_test, InputFeatureType))
|
|
|
|
|
|
def train_test_split_sparse():
|
|
# check that train_test_split converts scipy sparse matrices
|
|
# to csr, as stated in the documentation
|
|
X = np.arange(100).reshape((10, 10))
|
|
sparse_types = [csr_matrix, csc_matrix, coo_matrix]
|
|
for InputFeatureType in sparse_types:
|
|
X_s = InputFeatureType(X)
|
|
X_train, X_test = train_test_split(X_s)
|
|
assert_true(isinstance(X_train, csr_matrix))
|
|
assert_true(isinstance(X_test, csr_matrix))
|
|
|
|
|
|
def train_test_split_mock_pandas():
|
|
# X mock dataframe
|
|
X_df = MockDataFrame(X)
|
|
X_train, X_test = train_test_split(X_df)
|
|
assert_true(isinstance(X_train, MockDataFrame))
|
|
assert_true(isinstance(X_test, MockDataFrame))
|
|
X_train_arr, X_test_arr = train_test_split(X_df)
|
|
|
|
|
|
def test_shufflesplit_errors():
|
|
# When the {test|train}_size is a float/invalid, error is raised at init
|
|
assert_raises(ValueError, ShuffleSplit, test_size=None, train_size=None)
|
|
assert_raises(ValueError, ShuffleSplit, test_size=2.0)
|
|
assert_raises(ValueError, ShuffleSplit, test_size=1.0)
|
|
assert_raises(ValueError, ShuffleSplit, test_size=0.1, train_size=0.95)
|
|
assert_raises(ValueError, ShuffleSplit, train_size=1j)
|
|
|
|
# When the {test|train}_size is an int, validation is based on the input X
|
|
# and happens at split(...)
|
|
assert_raises(ValueError, next, ShuffleSplit(test_size=11).split(X))
|
|
assert_raises(ValueError, next, ShuffleSplit(test_size=10).split(X))
|
|
assert_raises(ValueError, next, ShuffleSplit(test_size=8,
|
|
train_size=3).split(X))
|
|
|
|
|
|
def test_shufflesplit_reproducible():
|
|
# Check that iterating twice on the ShuffleSplit gives the same
|
|
# sequence of train-test when the random_state is given
|
|
ss = ShuffleSplit(random_state=21)
|
|
assert_array_equal(list(a for a, b in ss.split(X)),
|
|
list(a for a, b in ss.split(X)))
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def test_safe_split_with_precomputed_kernel():
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clf = SVC()
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clfp = SVC(kernel="precomputed")
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X, y = iris.data, iris.target
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K = np.dot(X, X.T)
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cv = ShuffleSplit(test_size=0.25, random_state=0)
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tr, te = list(cv.split(X))[0]
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X_tr, y_tr = _safe_split(clf, X, y, tr)
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K_tr, y_tr2 = _safe_split(clfp, K, y, tr)
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assert_array_almost_equal(K_tr, np.dot(X_tr, X_tr.T))
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X_te, y_te = _safe_split(clf, X, y, te, tr)
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K_te, y_te2 = _safe_split(clfp, K, y, te, tr)
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assert_array_almost_equal(K_te, np.dot(X_te, X_tr.T))
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def test_train_test_split_allow_nans():
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# Check that train_test_split allows input data with NaNs
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X = np.arange(200, dtype=np.float64).reshape(10, -1)
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X[2, :] = np.nan
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y = np.repeat([0, 1], X.shape[0] / 2)
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train_test_split(X, y, test_size=0.2, random_state=42)
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def test_check_cv():
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X = np.ones(9)
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cv = check_cv(3, classifier=False)
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# Use numpy.testing.assert_equal which recursively compares
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# lists of lists
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np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
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y_binary = np.array([0, 1, 0, 1, 0, 0, 1, 1, 1])
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cv = check_cv(3, y_binary, classifier=True)
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np.testing.assert_equal(list(StratifiedKFold(3).split(X, y_binary)),
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list(cv.split(X, y_binary)))
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y_multiclass = np.array([0, 1, 0, 1, 2, 1, 2, 0, 2])
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cv = check_cv(3, y_multiclass, classifier=True)
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np.testing.assert_equal(list(StratifiedKFold(3).split(X, y_multiclass)),
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list(cv.split(X, y_multiclass)))
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X = np.ones(5)
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y_multilabel = np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1],
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[1, 1, 0, 1], [0, 0, 1, 0]])
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cv = check_cv(3, y_multilabel, classifier=True)
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np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
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y_multioutput = np.array([[1, 2], [0, 3], [0, 0], [3, 1], [2, 0]])
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cv = check_cv(3, y_multioutput, classifier=True)
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np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
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# Check if the old style classes are wrapped to have a split method
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X = np.ones(9)
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y_multiclass = np.array([0, 1, 0, 1, 2, 1, 2, 0, 2])
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cv1 = check_cv(3, y_multiclass, classifier=True)
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with warnings.catch_warnings(record=True):
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from sklearn.cross_validation import StratifiedKFold as OldSKF
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cv2 = check_cv(OldSKF(y_multiclass, n_folds=3))
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np.testing.assert_equal(list(cv1.split(X, y_multiclass)),
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list(cv2.split()))
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assert_raises(ValueError, check_cv, cv="lolo")
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|
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def test_cv_iterable_wrapper():
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y_multiclass = np.array([0, 1, 0, 1, 2, 1, 2, 0, 2])
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with warnings.catch_warnings(record=True):
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from sklearn.cross_validation import StratifiedKFold as OldSKF
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|
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cv = OldSKF(y_multiclass, n_folds=3)
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wrapped_old_skf = _CVIterableWrapper(cv)
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|
|
|
# Check if split works correctly
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np.testing.assert_equal(list(cv), list(wrapped_old_skf.split()))
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|
|
|
# Check if get_n_splits works correctly
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|
assert_equal(len(cv), wrapped_old_skf.get_n_splits())
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|
|
|
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|
def test_group_kfold():
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|
rng = np.random.RandomState(0)
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|
|
|
# Parameters of the test
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n_groups = 15
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|
n_samples = 1000
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|
n_splits = 5
|
|
|
|
X = y = np.ones(n_samples)
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|
|
|
# Construct the test data
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|
tolerance = 0.05 * n_samples # 5 percent error allowed
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|
groups = rng.randint(0, n_groups, n_samples)
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|
|
|
ideal_n_groups_per_fold = n_samples // n_splits
|
|
|
|
len(np.unique(groups))
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|
# Get the test fold indices from the test set indices of each fold
|
|
folds = np.zeros(n_samples)
|
|
lkf = GroupKFold(n_splits=n_splits)
|
|
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
|
|
folds[test] = i
|
|
|
|
# Check that folds have approximately the same size
|
|
assert_equal(len(folds), len(groups))
|
|
for i in np.unique(folds):
|
|
assert_greater_equal(tolerance,
|
|
abs(sum(folds == i) - ideal_n_groups_per_fold))
|
|
|
|
# Check that each group appears only in 1 fold
|
|
for group in np.unique(groups):
|
|
assert_equal(len(np.unique(folds[groups == group])), 1)
|
|
|
|
# Check that no group is on both sides of the split
|
|
groups = np.asarray(groups, dtype=object)
|
|
for train, test in lkf.split(X, y, groups):
|
|
assert_equal(len(np.intersect1d(groups[train], groups[test])), 0)
|
|
|
|
# Construct the test data
|
|
groups = np.array(['Albert', 'Jean', 'Bertrand', 'Michel', 'Jean',
|
|
'Francis', 'Robert', 'Michel', 'Rachel', 'Lois',
|
|
'Michelle', 'Bernard', 'Marion', 'Laura', 'Jean',
|
|
'Rachel', 'Franck', 'John', 'Gael', 'Anna', 'Alix',
|
|
'Robert', 'Marion', 'David', 'Tony', 'Abel', 'Becky',
|
|
'Madmood', 'Cary', 'Mary', 'Alexandre', 'David',
|
|
'Francis', 'Barack', 'Abdoul', 'Rasha', 'Xi', 'Silvia'])
|
|
|
|
n_groups = len(np.unique(groups))
|
|
n_samples = len(groups)
|
|
n_splits = 5
|
|
tolerance = 0.05 * n_samples # 5 percent error allowed
|
|
ideal_n_groups_per_fold = n_samples // n_splits
|
|
|
|
X = y = np.ones(n_samples)
|
|
|
|
# Get the test fold indices from the test set indices of each fold
|
|
folds = np.zeros(n_samples)
|
|
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
|
|
folds[test] = i
|
|
|
|
# Check that folds have approximately the same size
|
|
assert_equal(len(folds), len(groups))
|
|
for i in np.unique(folds):
|
|
assert_greater_equal(tolerance,
|
|
abs(sum(folds == i) - ideal_n_groups_per_fold))
|
|
|
|
# Check that each group appears only in 1 fold
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("ignore", DeprecationWarning)
|
|
for group in np.unique(groups):
|
|
assert_equal(len(np.unique(folds[groups == group])), 1)
|
|
|
|
# Check that no group is on both sides of the split
|
|
groups = np.asarray(groups, dtype=object)
|
|
for train, test in lkf.split(X, y, groups):
|
|
assert_equal(len(np.intersect1d(groups[train], groups[test])), 0)
|
|
|
|
# Should fail if there are more folds than groups
|
|
groups = np.array([1, 1, 1, 2, 2])
|
|
X = y = np.ones(len(groups))
|
|
assert_raises_regexp(ValueError, "Cannot have number of splits.*greater",
|
|
next, GroupKFold(n_splits=3).split(X, y, groups))
|
|
|
|
|
|
def test_time_series_cv():
|
|
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14]]
|
|
|
|
# Should fail if there are more folds than samples
|
|
assert_raises_regexp(ValueError, "Cannot have number of folds.*greater",
|
|
next,
|
|
TimeSeriesSplit(n_splits=7).split(X))
|
|
|
|
tscv = TimeSeriesSplit(2)
|
|
|
|
# Manually check that Time Series CV preserves the data
|
|
# ordering on toy datasets
|
|
splits = tscv.split(X[:-1])
|
|
train, test = next(splits)
|
|
assert_array_equal(train, [0, 1])
|
|
assert_array_equal(test, [2, 3])
|
|
|
|
train, test = next(splits)
|
|
assert_array_equal(train, [0, 1, 2, 3])
|
|
assert_array_equal(test, [4, 5])
|
|
|
|
splits = TimeSeriesSplit(2).split(X)
|
|
|
|
train, test = next(splits)
|
|
assert_array_equal(train, [0, 1, 2])
|
|
assert_array_equal(test, [3, 4])
|
|
|
|
train, test = next(splits)
|
|
assert_array_equal(train, [0, 1, 2, 3, 4])
|
|
assert_array_equal(test, [5, 6])
|
|
|
|
# Check get_n_splits returns the correct number of splits
|
|
splits = TimeSeriesSplit(2).split(X)
|
|
n_splits_actual = len(list(splits))
|
|
assert_equal(n_splits_actual, tscv.get_n_splits())
|
|
assert_equal(n_splits_actual, 2)
|
|
|
|
|
|
def test_nested_cv():
|
|
# Test if nested cross validation works with different combinations of cv
|
|
rng = np.random.RandomState(0)
|
|
|
|
X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
|
|
groups = rng.randint(0, 5, 15)
|
|
|
|
cvs = [LeaveOneGroupOut(), LeaveOneOut(), GroupKFold(), StratifiedKFold(),
|
|
StratifiedShuffleSplit(n_splits=3, random_state=0)]
|
|
|
|
for inner_cv, outer_cv in combinations_with_replacement(cvs, 2):
|
|
gs = GridSearchCV(Ridge(), param_grid={'alpha': [1, .1]},
|
|
cv=inner_cv)
|
|
cross_val_score(gs, X=X, y=y, groups=groups, cv=outer_cv,
|
|
fit_params={'groups': groups})
|
|
|
|
|
|
def test_build_repr():
|
|
class MockSplitter:
|
|
def __init__(self, a, b=0, c=None):
|
|
self.a = a
|
|
self.b = b
|
|
self.c = c
|
|
|
|
def __repr__(self):
|
|
return _build_repr(self)
|
|
|
|
assert_equal(repr(MockSplitter(5, 6)), "MockSplitter(a=5, b=6, c=None)")
|