Python model_selection.PredefinedSplit方法代码示例

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def test_2d_y():
    # smoke test for 2d y and multi-label
    n_samples = 30
    rng = np.random.RandomState(1)
    X = rng.randint(0, 3, size=(n_samples, 2))
    y = rng.randint(0, 3, size=(n_samples,))
    y_2d = y.reshape(-1, 1)
    y_multilabel = rng.randint(0, 2, size=(n_samples, 3))
    groups = rng.randint(0, 3, size=(n_samples,))
    splitters = [LeaveOneOut(), LeavePOut(p=2), KFold(), StratifiedKFold(),
                 RepeatedKFold(), RepeatedStratifiedKFold(),
                 ShuffleSplit(), StratifiedShuffleSplit(test_size=.5),
                 GroupShuffleSplit(), LeaveOneGroupOut(),
                 LeavePGroupsOut(n_groups=2), GroupKFold(), TimeSeriesSplit(),
                 PredefinedSplit(test_fold=groups)]
    for splitter in splitters:
        list(splitter.split(X, y, groups))
        list(splitter.split(X, y_2d, groups))
        try:
            list(splitter.split(X, y_multilabel, groups))
        except ValueError as e:
            allowed_target_types = ('binary', 'multiclass')
            msg = "Supported target types are: {}. Got 'multilabel".format(
                allowed_target_types)
            assert msg in str(e) 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
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) 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def test_predefinedsplit_with_kfold_split():
    # Check that PredefinedSplit can reproduce a split generated by Kfold.
    folds = np.full(10, -1.)
    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 = PredefinedSplit(folds)
    # n_splits is simply the no of unique folds
    assert_equal(len(np.unique(folds)), ps.get_n_splits())
    ps_train, ps_test = zip(*ps.split())
    assert_array_equal(ps_train, kf_train)
    assert_array_equal(ps_test, kf_test) 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def test_predefined_split(self, cv_split_cls, data):
        from sklearn.model_selection import PredefinedSplit
        indices = (data.y > 0).astype(int)
        split = PredefinedSplit(indices)

        dataset_train, dataset_valid = cv_split_cls(split)(data)
        y_train = data_from_dataset(dataset_train)[1]
        y_valid = data_from_dataset(dataset_valid)[1]

        assert (y_train > 0).all()
        assert (y_valid == 0).all() 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def main(data_dir, log_dir, source='xl-1542M-k40', n_train=500000, n_valid=10000, n_jobs=None, verbose=False):
    train_texts, train_labels = load_split(data_dir, source, 'train', n=n_train)
    valid_texts, valid_labels = load_split(data_dir, source, 'valid', n=n_valid)
    test_texts, test_labels = load_split(data_dir, source, 'test')

    vect = TfidfVectorizer(ngram_range=(1, 2), min_df=5, max_features=2**21)
    train_features = vect.fit_transform(train_texts)
    valid_features = vect.transform(valid_texts)
    test_features = vect.transform(test_texts)

    model = LogisticRegression(solver='liblinear')
    params = {'C': [1/64, 1/32, 1/16, 1/8, 1/4, 1/2, 1, 2, 4, 8, 16, 32, 64]}
    split = PredefinedSplit([-1]*n_train+[0]*n_valid)
    search = GridSearchCV(model, params, cv=split, n_jobs=n_jobs, verbose=verbose, refit=False)
    search.fit(sparse.vstack([train_features, valid_features]), train_labels+valid_labels)
    model = model.set_params(**search.best_params_)
    model.fit(train_features, train_labels)
    valid_accuracy = model.score(valid_features, valid_labels)*100.
    test_accuracy = model.score(test_features, test_labels)*100.
    data = {
        'source':source,
        'n_train':n_train,
        'valid_accuracy':valid_accuracy,
        'test_accuracy':test_accuracy
    }
    print(data)
    json.dump(data, open(os.path.join(log_dir, f'{source}.json'), 'w')) 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def test_objectmapper(self):
        df = pdml.ModelFrame([])

        # Splitter Classes
        self.assertIs(df.model_selection.KFold, ms.KFold)
        self.assertIs(df.model_selection.GroupKFold, ms.GroupKFold)
        self.assertIs(df.model_selection.StratifiedKFold, ms.StratifiedKFold)

        self.assertIs(df.model_selection.LeaveOneGroupOut, ms.LeaveOneGroupOut)
        self.assertIs(df.model_selection.LeavePGroupsOut, ms.LeavePGroupsOut)
        self.assertIs(df.model_selection.LeaveOneOut, ms.LeaveOneOut)
        self.assertIs(df.model_selection.LeavePOut, ms.LeavePOut)

        self.assertIs(df.model_selection.ShuffleSplit, ms.ShuffleSplit)
        self.assertIs(df.model_selection.GroupShuffleSplit,
                      ms.GroupShuffleSplit)
        # self.assertIs(df.model_selection.StratifiedShuffleSplit,
        #               ms.StratifiedShuffleSplit)
        self.assertIs(df.model_selection.PredefinedSplit, ms.PredefinedSplit)
        self.assertIs(df.model_selection.TimeSeriesSplit, ms.TimeSeriesSplit)

        # Splitter Functions

        # Hyper-parameter optimizers
        self.assertIs(df.model_selection.GridSearchCV, ms.GridSearchCV)
        self.assertIs(df.model_selection.RandomizedSearchCV, ms.RandomizedSearchCV)
        self.assertIs(df.model_selection.ParameterGrid, ms.ParameterGrid)
        self.assertIs(df.model_selection.ParameterSampler, ms.ParameterSampler)

        # Model validation 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def test_objectmapper_abbr(self):
        df = pdml.ModelFrame([])

        # Splitter Classes
        self.assertIs(df.ms.KFold, ms.KFold)
        self.assertIs(df.ms.GroupKFold, ms.GroupKFold)
        self.assertIs(df.ms.StratifiedKFold, ms.StratifiedKFold)

        self.assertIs(df.ms.LeaveOneGroupOut, ms.LeaveOneGroupOut)
        self.assertIs(df.ms.LeavePGroupsOut, ms.LeavePGroupsOut)
        self.assertIs(df.ms.LeaveOneOut, ms.LeaveOneOut)
        self.assertIs(df.ms.LeavePOut, ms.LeavePOut)

        self.assertIs(df.ms.ShuffleSplit, ms.ShuffleSplit)
        self.assertIs(df.ms.GroupShuffleSplit,
                      ms.GroupShuffleSplit)
        # self.assertIs(df.ms.StratifiedShuffleSplit,
        #               ms.StratifiedShuffleSplit)
        self.assertIs(df.ms.PredefinedSplit, ms.PredefinedSplit)
        self.assertIs(df.ms.TimeSeriesSplit, ms.TimeSeriesSplit)

        # Splitter Functions

        # Hyper-parameter optimizers
        self.assertIs(df.ms.GridSearchCV, ms.GridSearchCV)
        self.assertIs(df.ms.RandomizedSearchCV, ms.RandomizedSearchCV)
        self.assertIs(df.ms.ParameterGrid, ms.ParameterGrid)
        self.assertIs(df.ms.ParameterSampler, ms.ParameterSampler)

        # Model validation 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def test_cross_validator_with_default_params():
    n_samples = 4
    n_unique_groups = 4
    n_splits = 2
    p = 2
    n_shuffle_splits = 10  # (the default value)

    X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
    X_1d = np.array([1, 2, 3, 4])
    y = np.array([1, 1, 2, 2])
    groups = np.array([1, 2, 3, 4])
    loo = LeaveOneOut()
    lpo = LeavePOut(p)
    kf = KFold(n_splits)
    skf = StratifiedKFold(n_splits)
    lolo = LeaveOneGroupOut()
    lopo = LeavePGroupsOut(p)
    ss = ShuffleSplit(random_state=0)
    ps = PredefinedSplit([1, 1, 2, 2])  # n_splits = np of unique folds = 2

    loo_repr = "LeaveOneOut()"
    lpo_repr = "LeavePOut(p=2)"
    kf_repr = "KFold(n_splits=2, random_state=None, shuffle=False)"
    skf_repr = "StratifiedKFold(n_splits=2, random_state=None, shuffle=False)"
    lolo_repr = "LeaveOneGroupOut()"
    lopo_repr = "LeavePGroupsOut(n_groups=2)"
    ss_repr = ("ShuffleSplit(n_splits=10, random_state=0, "
               "test_size=None, train_size=None)")
    ps_repr = "PredefinedSplit(test_fold=array([1, 1, 2, 2]))"

    n_splits_expected = [n_samples, comb(n_samples, p), n_splits, n_splits,
                         n_unique_groups, comb(n_unique_groups, p),
                         n_shuffle_splits, 2]

    for i, (cv, cv_repr) in enumerate(zip(
            [loo, lpo, kf, skf, lolo, lopo, ss, ps],
            [loo_repr, lpo_repr, kf_repr, skf_repr, lolo_repr, lopo_repr,
             ss_repr, ps_repr])):
        # Test if get_n_splits works correctly
        assert_equal(n_splits_expected[i], cv.get_n_splits(X, y, groups))

        # Test if the cross-validator works as expected even if
        # the data is 1d
        np.testing.assert_equal(list(cv.split(X, y, groups)),
                                list(cv.split(X_1d, y, groups)))
        # Test that train, test indices returned are integers
        for train, test in cv.split(X, y, groups):
            assert_equal(np.asarray(train).dtype.kind, 'i')
            assert_equal(np.asarray(train).dtype.kind, 'i')

        # Test if the repr works without any errors
        assert_equal(cv_repr, repr(cv))

    # ValueError for get_n_splits methods
    msg = "The 'X' parameter should not be None."
    assert_raise_message(ValueError, msg,
                         loo.get_n_splits, None, y, groups)
    assert_raise_message(ValueError, msg,
                         lpo.get_n_splits, None, y, groups) 

# 需要导入模块: from sklearn import model_selection [as 别名]
# 或者: from sklearn.model_selection import PredefinedSplit [as 别名]
def test_cross_validator_with_default_params():
    n_samples = 4
    n_unique_groups = 4
    n_splits = 2
    p = 2
    n_shuffle_splits = 10  # (the default value)

    X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
    X_1d = np.array([1, 2, 3, 4])
    y = np.array([1, 1, 2, 2])
    groups = np.array([1, 2, 3, 4])
    loo = LeaveOneOut()
    lpo = LeavePOut(p)
    kf = KFold(n_splits)
    skf = StratifiedKFold(n_splits)
    lolo = LeaveOneGroupOut()
    lopo = LeavePGroupsOut(p)
    ss = ShuffleSplit(random_state=0)
    ps = PredefinedSplit([1, 1, 2, 2])  # n_splits = np of unique folds = 2

    loo_repr = "LeaveOneOut()"
    lpo_repr = "LeavePOut(p=2)"
    kf_repr = "KFold(n_splits=2, random_state=None, shuffle=False)"
    skf_repr = "StratifiedKFold(n_splits=2, random_state=None, shuffle=False)"
    lolo_repr = "LeaveOneGroupOut()"
    lopo_repr = "LeavePGroupsOut(n_groups=2)"
    ss_repr = ("ShuffleSplit(n_splits=10, random_state=0, "
               "test_size='default',\n       train_size=None)")
    ps_repr = "PredefinedSplit(test_fold=array([1, 1, 2, 2]))"

    n_splits_expected = [n_samples, comb(n_samples, p), n_splits, n_splits,
                         n_unique_groups, comb(n_unique_groups, p),
                         n_shuffle_splits, 2]

    for i, (cv, cv_repr) in enumerate(zip(
            [loo, lpo, kf, skf, lolo, lopo, ss, ps],
            [loo_repr, lpo_repr, kf_repr, skf_repr, lolo_repr, lopo_repr,
             ss_repr, ps_repr])):
        # Test if get_n_splits works correctly
        assert_equal(n_splits_expected[i], cv.get_n_splits(X, y, groups))

        # Test if the cross-validator works as expected even if
        # the data is 1d
        np.testing.assert_equal(list(cv.split(X, y, groups)),
                                list(cv.split(X_1d, y, groups)))
        # Test that train, test indices returned are integers
        for train, test in cv.split(X, y, groups):
            assert_equal(np.asarray(train).dtype.kind, 'i')
            assert_equal(np.asarray(train).dtype.kind, 'i')

        # Test if the repr works without any errors
        assert_equal(cv_repr, repr(cv))

    # ValueError for get_n_splits methods
    msg = "The 'X' parameter should not be None."
    assert_raise_message(ValueError, msg,
                         loo.get_n_splits, None, y, groups)
    assert_raise_message(ValueError, msg,
                         lpo.get_n_splits, None, y, groups)