projektAI/venv/Lib/site-packages/sklearn/ensemble/tests/test_gradient_boosting.py

"""
Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
"""
import warnings
import numpy as np

from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import coo_matrix
from scipy.special import expit

import pytest

from sklearn import datasets
from sklearn.base import clone
from sklearn.datasets import (make_classification,
                              make_regression)
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble._gradient_boosting import predict_stages
from sklearn.preprocessing import OneHotEncoder, scale
from sklearn.svm import LinearSVC
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.utils import check_random_state, tosequence
from sklearn.utils._mocking import NoSampleWeightWrapper
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import skip_if_32bit
from sklearn.exceptions import DataConversionWarning
from sklearn.exceptions import NotFittedError
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LinearRegression
from sklearn.svm import NuSVR


GRADIENT_BOOSTING_ESTIMATORS = [GradientBoostingClassifier,
                                GradientBoostingRegressor]

# toy sample
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
y = [-1, -1, -1, 1, 1, 1]
T = [[-1, -1], [2, 2], [3, 2]]
true_result = [-1, 1, 1]

# also make regression dataset
X_reg, y_reg = make_regression(
    n_samples=500, n_features=10, n_informative=8, noise=10, random_state=7
)
y_reg = scale(y_reg)

rng = np.random.RandomState(0)
# also load the iris dataset
# and randomly permute it
iris = datasets.load_iris()
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]


@pytest.mark.parametrize('loss', ('deviance', 'exponential'))
def test_classification_toy(loss):
    # Check classification on a toy dataset.
    clf = GradientBoostingClassifier(loss=loss, n_estimators=10,
                                     random_state=1)

    assert_raises(ValueError, clf.predict, T)

    clf.fit(X, y)
    assert_array_equal(clf.predict(T), true_result)
    assert 10 == len(clf.estimators_)

    deviance_decrease = (clf.train_score_[:-1] - clf.train_score_[1:])
    assert np.any(deviance_decrease >= 0.0)

    leaves = clf.apply(X)
    assert leaves.shape == (6, 10, 1)


@pytest.mark.parametrize(
    "params, err_msg",
    [({"n_estimators": 0}, "n_estimators must be greater than 0"),
     ({"n_estimators": -1}, "n_estimators must be greater than 0"),
     ({"learning_rate": 0}, "learning_rate must be greater than 0"),
     ({"learning_rate": -1.0}, "learning_rate must be greater than 0"),
     ({"loss": "foobar"}, "Loss 'foobar' not supported"),
     ({"min_samples_split": 0.0}, "min_samples_split must be an integer"),
     ({"min_samples_split": -1.0}, "min_samples_split must be an integer"),
     ({"min_samples_split": 1.1}, "min_samples_split must be an integer"),
     ({"min_samples_leaf": 0}, "min_samples_leaf must be at least 1 or"),
     ({"min_samples_leaf": -1.0}, "min_samples_leaf must be at least 1 or"),
     ({"min_weight_fraction_leaf": -1.0}, "min_weight_fraction_leaf must in"),
     ({"min_weight_fraction_leaf": 0.6}, "min_weight_fraction_leaf must in"),
     ({"subsample": 0.0}, r"subsample must be in \(0,1\]"),
     ({"subsample": 1.1}, r"subsample must be in \(0,1\]"),
     ({"subsample": -0.1}, r"subsample must be in \(0,1\]"),
     ({"max_depth": -0.1}, "max_depth must be greater than zero"),
     ({"max_depth": 0}, "max_depth must be greater than zero"),
     ({"init": {}}, "The init parameter must be an estimator or 'zero'"),
     ({"max_features": "invalid"}, "Invalid value for max_features:"),
     ({"max_features": 0}, r"max_features must be in \(0, n_features\]"),
     ({"max_features": 100}, r"max_features must be in \(0, n_features\]"),
     ({"max_features": -0.1}, r"max_features must be in \(0, n_features\]"),
     ({"n_iter_no_change": "invalid"}, "n_iter_no_change should either be")]
)
@pytest.mark.parametrize(
    "GradientBoosting, X, y",
    [(GradientBoostingRegressor, X_reg, y_reg),
     (GradientBoostingClassifier, iris.data, iris.target)]
)
def test_gbdt_parameter_checks(GradientBoosting, X, y, params, err_msg):
    # Check input parameter validation for GradientBoosting
    with pytest.raises(ValueError, match=err_msg):
        GradientBoosting(**params).fit(X, y)


@pytest.mark.parametrize(
    "params, err_msg",
    [({"loss": "huber", "alpha": 1.2}, r"alpha must be in \(0.0, 1.0\)"),
     ({"loss": "quantile", "alpha": 1.2}, r"alpha must be in \(0.0, 1.0\)")]
)
def test_gbdt_loss_alpha_error(params, err_msg):
    # check that an error is raised when alpha is not proper for quantile and
    # huber loss
    with pytest.raises(ValueError, match=err_msg):
        GradientBoostingRegressor(**params).fit(X_reg, y_reg)


@pytest.mark.parametrize(
    "GradientBoosting, loss",
    [(GradientBoostingClassifier, "ls"),
     (GradientBoostingClassifier, "lad"),
     (GradientBoostingClassifier, "quantile"),
     (GradientBoostingClassifier, "huber"),
     (GradientBoostingRegressor, "deviance"),
     (GradientBoostingRegressor, "exponential")]
)
def test_wrong_type_loss_function(GradientBoosting, loss):
    # check that we raise an error when not using the right type of loss
    # function
    with pytest.raises(ValueError):
        GradientBoosting(loss=loss).fit(X, y)


@pytest.mark.parametrize('loss', ('deviance', 'exponential'))
def test_classification_synthetic(loss):
    # Test GradientBoostingClassifier on synthetic dataset used by
    # Hastie et al. in ESLII Example 12.7.
    X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)

    X_train, X_test = X[:2000], X[2000:]
    y_train, y_test = y[:2000], y[2000:]

    gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=2,
                                      max_depth=1, loss=loss,
                                      learning_rate=1.0, random_state=0)
    gbrt.fit(X_train, y_train)
    error_rate = (1.0 - gbrt.score(X_test, y_test))
    assert error_rate < 0.09

    gbrt = GradientBoostingClassifier(n_estimators=200, min_samples_split=2,
                                      max_depth=1, loss=loss,
                                      learning_rate=1.0, subsample=0.5,
                                      random_state=0)
    gbrt.fit(X_train, y_train)
    error_rate = (1.0 - gbrt.score(X_test, y_test))
    assert error_rate < 0.08


@pytest.mark.parametrize('loss', ('ls', 'lad', 'huber'))
@pytest.mark.parametrize('subsample', (1.0, 0.5))
def test_regression_dataset(loss, subsample):
    # Check consistency on regression dataset with least squares
    # and least absolute deviation.
    ones = np.ones(len(y_reg))
    last_y_pred = None
    for sample_weight in [None, ones, 2 * ones]:
        reg = GradientBoostingRegressor(n_estimators=100,
                                        loss=loss,
                                        max_depth=4,
                                        subsample=subsample,
                                        min_samples_split=2,
                                        random_state=1)

        reg.fit(X_reg, y_reg, sample_weight=sample_weight)
        leaves = reg.apply(X_reg)
        assert leaves.shape == (500, 100)

        y_pred = reg.predict(X_reg)
        mse = mean_squared_error(y_reg, y_pred)
        assert mse < 0.04

        if last_y_pred is not None:
            # FIXME: We temporarily bypass this test. This is due to the fact
            # that GBRT with and without `sample_weight` do not use the same
            # implementation of the median during the initialization with the
            # `DummyRegressor`. In the future, we should make sure that both
            # implementations should be the same. See PR #17377 for more.
            # assert_allclose(last_y_pred, y_pred)
            pass

        last_y_pred = y_pred


@pytest.mark.parametrize('subsample', (1.0, 0.5))
@pytest.mark.parametrize('sample_weight', (None, 1))
def test_iris(subsample, sample_weight):
    if sample_weight == 1:
        sample_weight = np.ones(len(iris.target))
    # Check consistency on dataset iris.
    clf = GradientBoostingClassifier(n_estimators=100,
                                     loss="deviance",
                                     random_state=1,
                                     subsample=subsample)
    clf.fit(iris.data, iris.target, sample_weight=sample_weight)
    score = clf.score(iris.data, iris.target)
    assert score > 0.9

    leaves = clf.apply(iris.data)
    assert leaves.shape == (150, 100, 3)


def test_regression_synthetic():
    # Test on synthetic regression datasets used in Leo Breiman,
    # `Bagging Predictors?. Machine Learning 24(2): 123-140 (1996).
    random_state = check_random_state(1)
    regression_params = {'n_estimators': 100, 'max_depth': 4,
                         'min_samples_split': 2, 'learning_rate': 0.1,
                         'loss': 'ls'}

    # Friedman1
    X, y = datasets.make_friedman1(n_samples=1200,
                                   random_state=random_state,
                                   noise=1.0)
    X_train, y_train = X[:200], y[:200]
    X_test, y_test = X[200:], y[200:]

    clf = GradientBoostingRegressor()
    clf.fit(X_train, y_train)
    mse = mean_squared_error(y_test, clf.predict(X_test))
    assert mse < 5.0

    # Friedman2
    X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state)
    X_train, y_train = X[:200], y[:200]
    X_test, y_test = X[200:], y[200:]

    clf = GradientBoostingRegressor(**regression_params)
    clf.fit(X_train, y_train)
    mse = mean_squared_error(y_test, clf.predict(X_test))
    assert mse < 1700.0

    # Friedman3
    X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state)
    X_train, y_train = X[:200], y[:200]
    X_test, y_test = X[200:], y[200:]

    clf = GradientBoostingRegressor(**regression_params)
    clf.fit(X_train, y_train)
    mse = mean_squared_error(y_test, clf.predict(X_test))
    assert mse < 0.015


@pytest.mark.parametrize(
    "GradientBoosting, X, y",
    [(GradientBoostingRegressor, X_reg, y_reg),
     (GradientBoostingClassifier, iris.data, iris.target)]
)
def test_feature_importances(GradientBoosting, X, y):
    # smoke test to check that the gradient boosting expose an attribute
    # feature_importances_
    gbdt = GradientBoosting()
    assert not hasattr(gbdt, "feature_importances_")
    gbdt.fit(X, y)
    assert hasattr(gbdt, 'feature_importances_')


def test_probability_log():
    # Predict probabilities.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)

    assert_raises(ValueError, clf.predict_proba, T)

    clf.fit(X, y)
    assert_array_equal(clf.predict(T), true_result)

    # check if probabilities are in [0, 1].
    y_proba = clf.predict_proba(T)
    assert np.all(y_proba >= 0.0)
    assert np.all(y_proba <= 1.0)

    # derive predictions from probabilities
    y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
    assert_array_equal(y_pred, true_result)


def test_single_class_with_sample_weight():
    sample_weight = [0, 0, 0, 1, 1, 1]
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
    msg = (
        "y contains 1 class after sample_weight trimmed classes with "
        "zero weights, while a minimum of 2 classes are required."
    )
    with pytest.raises(ValueError, match=msg):
        clf.fit(X, y, sample_weight=sample_weight)


def test_check_inputs_predict_stages():
    # check that predict_stages through an error if the type of X is not
    # supported
    x, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    x_sparse_csc = csc_matrix(x)
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
    clf.fit(x, y)
    score = np.zeros((y.shape)).reshape(-1, 1)
    assert_raise_message(ValueError,
                         "When X is a sparse matrix, a CSR format is expected",
                         predict_stages, clf.estimators_, x_sparse_csc,
                         clf.learning_rate, score)
    x_fortran = np.asfortranarray(x)
    assert_raise_message(ValueError,
                         "X should be C-ordered np.ndarray",
                         predict_stages, clf.estimators_, x_fortran,
                         clf.learning_rate, score)


def test_max_feature_regression():
    # Test to make sure random state is set properly.
    X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)

    X_train, X_test = X[:2000], X[2000:]
    y_train, y_test = y[:2000], y[2000:]

    gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=5,
                                      max_depth=2, learning_rate=.1,
                                      max_features=2, random_state=1)
    gbrt.fit(X_train, y_train)
    deviance = gbrt.loss_(y_test, gbrt.decision_function(X_test))
    assert deviance < 0.5, "GB failed with deviance %.4f" % deviance


def test_feature_importance_regression(fetch_california_housing_fxt):
    """Test that Gini importance is calculated correctly.

    This test follows the example from [1]_ (pg. 373).

    .. [1] Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements
       of statistical learning. New York: Springer series in statistics.
    """
    california = fetch_california_housing_fxt()
    X, y = california.data, california.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    reg = GradientBoostingRegressor(loss='huber', learning_rate=0.1,
                                    max_leaf_nodes=6, n_estimators=100,
                                    random_state=0)
    reg.fit(X_train, y_train)
    sorted_idx = np.argsort(reg.feature_importances_)[::-1]
    sorted_features = [california.feature_names[s] for s in sorted_idx]

    # The most important feature is the median income by far.
    assert sorted_features[0] == 'MedInc'

    # The three subsequent features are the following. Their relative ordering
    # might change a bit depending on the randomness of the trees and the
    # train / test split.
    assert set(sorted_features[1:4]) == {'Longitude', 'AveOccup', 'Latitude'}


def test_max_feature_auto():
    # Test if max features is set properly for floats and str.
    X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
    _, n_features = X.shape

    X_train = X[:2000]
    y_train = y[:2000]

    gbrt = GradientBoostingClassifier(n_estimators=1, max_features='auto')
    gbrt.fit(X_train, y_train)
    assert gbrt.max_features_ == int(np.sqrt(n_features))

    gbrt = GradientBoostingRegressor(n_estimators=1, max_features='auto')
    gbrt.fit(X_train, y_train)
    assert gbrt.max_features_ == n_features

    gbrt = GradientBoostingRegressor(n_estimators=1, max_features=0.3)
    gbrt.fit(X_train, y_train)
    assert gbrt.max_features_ == int(n_features * 0.3)

    gbrt = GradientBoostingRegressor(n_estimators=1, max_features='sqrt')
    gbrt.fit(X_train, y_train)
    assert gbrt.max_features_ == int(np.sqrt(n_features))

    gbrt = GradientBoostingRegressor(n_estimators=1, max_features='log2')
    gbrt.fit(X_train, y_train)
    assert gbrt.max_features_ == int(np.log2(n_features))

    gbrt = GradientBoostingRegressor(n_estimators=1,
                                     max_features=0.01 / X.shape[1])
    gbrt.fit(X_train, y_train)
    assert gbrt.max_features_ == 1


def test_staged_predict():
    # Test whether staged decision function eventually gives
    # the same prediction.
    X, y = datasets.make_friedman1(n_samples=1200,
                                   random_state=1, noise=1.0)
    X_train, y_train = X[:200], y[:200]
    X_test = X[200:]
    clf = GradientBoostingRegressor()
    # test raise ValueError if not fitted
    assert_raises(ValueError, lambda X: np.fromiter(
        clf.staged_predict(X), dtype=np.float64), X_test)

    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)

    # test if prediction for last stage equals ``predict``
    for y in clf.staged_predict(X_test):
        assert y.shape == y_pred.shape

    assert_array_almost_equal(y_pred, y)


def test_staged_predict_proba():
    # Test whether staged predict proba eventually gives
    # the same prediction.
    X, y = datasets.make_hastie_10_2(n_samples=1200,
                                     random_state=1)
    X_train, y_train = X[:200], y[:200]
    X_test, y_test = X[200:], y[200:]
    clf = GradientBoostingClassifier(n_estimators=20)
    # test raise NotFittedError if not fitted
    assert_raises(NotFittedError, lambda X: np.fromiter(
        clf.staged_predict_proba(X), dtype=np.float64), X_test)

    clf.fit(X_train, y_train)

    # test if prediction for last stage equals ``predict``
    for y_pred in clf.staged_predict(X_test):
        assert y_test.shape == y_pred.shape

    assert_array_equal(clf.predict(X_test), y_pred)

    # test if prediction for last stage equals ``predict_proba``
    for staged_proba in clf.staged_predict_proba(X_test):
        assert y_test.shape[0] == staged_proba.shape[0]
        assert 2 == staged_proba.shape[1]

    assert_array_almost_equal(clf.predict_proba(X_test), staged_proba)


@pytest.mark.parametrize('Estimator', GRADIENT_BOOSTING_ESTIMATORS)
def test_staged_functions_defensive(Estimator):
    # test that staged_functions make defensive copies
    rng = np.random.RandomState(0)
    X = rng.uniform(size=(10, 3))
    y = (4 * X[:, 0]).astype(int) + 1  # don't predict zeros
    estimator = Estimator()
    estimator.fit(X, y)
    for func in ['predict', 'decision_function', 'predict_proba']:
        staged_func = getattr(estimator, "staged_" + func, None)
        if staged_func is None:
            # regressor has no staged_predict_proba
            continue
        with warnings.catch_warnings(record=True):
            staged_result = list(staged_func(X))
        staged_result[1][:] = 0
        assert np.all(staged_result[0] != 0)


def test_serialization():
    # Check model serialization.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)

    clf.fit(X, y)
    assert_array_equal(clf.predict(T), true_result)
    assert 100 == len(clf.estimators_)

    try:
        import cPickle as pickle
    except ImportError:
        import pickle

    serialized_clf = pickle.dumps(clf, protocol=pickle.HIGHEST_PROTOCOL)
    clf = None
    clf = pickle.loads(serialized_clf)
    assert_array_equal(clf.predict(T), true_result)
    assert 100 == len(clf.estimators_)


def test_degenerate_targets():
    # Check if we can fit even though all targets are equal.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)

    # classifier should raise exception
    assert_raises(ValueError, clf.fit, X, np.ones(len(X)))

    clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
    clf.fit(X, np.ones(len(X)))
    clf.predict([rng.rand(2)])
    assert_array_equal(np.ones((1,), dtype=np.float64),
                       clf.predict([rng.rand(2)]))


def test_quantile_loss():
    # Check if quantile loss with alpha=0.5 equals lad.
    clf_quantile = GradientBoostingRegressor(n_estimators=100, loss='quantile',
                                             max_depth=4, alpha=0.5,
                                             random_state=7)

    clf_quantile.fit(X_reg, y_reg)
    y_quantile = clf_quantile.predict(X_reg)

    clf_lad = GradientBoostingRegressor(n_estimators=100, loss='lad',
                                        max_depth=4, random_state=7)

    clf_lad.fit(X_reg, y_reg)
    y_lad = clf_lad.predict(X_reg)
    assert_array_almost_equal(y_quantile, y_lad, decimal=4)


def test_symbol_labels():
    # Test with non-integer class labels.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)

    symbol_y = tosequence(map(str, y))

    clf.fit(X, symbol_y)
    assert_array_equal(clf.predict(T), tosequence(map(str, true_result)))
    assert 100 == len(clf.estimators_)


def test_float_class_labels():
    # Test with float class labels.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)

    float_y = np.asarray(y, dtype=np.float32)

    clf.fit(X, float_y)
    assert_array_equal(clf.predict(T),
                       np.asarray(true_result, dtype=np.float32))
    assert 100 == len(clf.estimators_)


def test_shape_y():
    # Test with float class labels.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)

    y_ = np.asarray(y, dtype=np.int32)
    y_ = y_[:, np.newaxis]

    # This will raise a DataConversionWarning that we want to
    # "always" raise, elsewhere the warnings gets ignored in the
    # later tests, and the tests that check for this warning fail
    assert_warns(DataConversionWarning, clf.fit, X, y_)
    assert_array_equal(clf.predict(T), true_result)
    assert 100 == len(clf.estimators_)


def test_mem_layout():
    # Test with different memory layouts of X and y
    X_ = np.asfortranarray(X)
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
    clf.fit(X_, y)
    assert_array_equal(clf.predict(T), true_result)
    assert 100 == len(clf.estimators_)

    X_ = np.ascontiguousarray(X)
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
    clf.fit(X_, y)
    assert_array_equal(clf.predict(T), true_result)
    assert 100 == len(clf.estimators_)

    y_ = np.asarray(y, dtype=np.int32)
    y_ = np.ascontiguousarray(y_)
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
    clf.fit(X, y_)
    assert_array_equal(clf.predict(T), true_result)
    assert 100 == len(clf.estimators_)

    y_ = np.asarray(y, dtype=np.int32)
    y_ = np.asfortranarray(y_)
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
    clf.fit(X, y_)
    assert_array_equal(clf.predict(T), true_result)
    assert 100 == len(clf.estimators_)


def test_oob_improvement():
    # Test if oob improvement has correct shape and regression test.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
                                     subsample=0.5)
    clf.fit(X, y)
    assert clf.oob_improvement_.shape[0] == 100
    # hard-coded regression test - change if modification in OOB computation
    assert_array_almost_equal(clf.oob_improvement_[:5],
                              np.array([0.19, 0.15, 0.12, -0.12, -0.11]),
                              decimal=2)


def test_oob_improvement_raise():
    # Test if oob improvement has correct shape.
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
                                     subsample=1.0)
    clf.fit(X, y)
    assert_raises(AttributeError, lambda: clf.oob_improvement_)


def test_oob_multilcass_iris():
    # Check OOB improvement on multi-class dataset.
    clf = GradientBoostingClassifier(n_estimators=100, loss='deviance',
                                     random_state=1, subsample=0.5)
    clf.fit(iris.data, iris.target)
    score = clf.score(iris.data, iris.target)
    assert score > 0.9
    assert clf.oob_improvement_.shape[0] == clf.n_estimators
    # hard-coded regression test - change if modification in OOB computation
    # FIXME: the following snippet does not yield the same results on 32 bits
    # assert_array_almost_equal(clf.oob_improvement_[:5],
    #                           np.array([12.68, 10.45, 8.18, 6.43, 5.13]),
    #                           decimal=2)


def test_verbose_output():
    # Check verbose=1 does not cause error.
    from io import StringIO

    import sys
    old_stdout = sys.stdout
    sys.stdout = StringIO()
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
                                     verbose=1, subsample=0.8)
    clf.fit(X, y)
    verbose_output = sys.stdout
    sys.stdout = old_stdout

    # check output
    verbose_output.seek(0)
    header = verbose_output.readline().rstrip()
    # with OOB
    true_header = ' '.join(['%10s'] + ['%16s'] * 3) % (
        'Iter', 'Train Loss', 'OOB Improve', 'Remaining Time')
    assert true_header == header

    n_lines = sum(1 for l in verbose_output.readlines())
    # one for 1-10 and then 9 for 20-100
    assert 10 + 9 == n_lines


def test_more_verbose_output():
    # Check verbose=2 does not cause error.
    from io import StringIO
    import sys
    old_stdout = sys.stdout
    sys.stdout = StringIO()
    clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
                                     verbose=2)
    clf.fit(X, y)
    verbose_output = sys.stdout
    sys.stdout = old_stdout

    # check output
    verbose_output.seek(0)
    header = verbose_output.readline().rstrip()
    # no OOB
    true_header = ' '.join(['%10s'] + ['%16s'] * 2) % (
        'Iter', 'Train Loss', 'Remaining Time')
    assert true_header == header

    n_lines = sum(1 for l in verbose_output.readlines())
    # 100 lines for n_estimators==100
    assert 100 == n_lines


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start(Cls):
    # Test if warm start equals fit.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=200, max_depth=1)
    est.fit(X, y)

    est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
    est_ws.fit(X, y)
    est_ws.set_params(n_estimators=200)
    est_ws.fit(X, y)

    if Cls is GradientBoostingRegressor:
        assert_array_almost_equal(est_ws.predict(X), est.predict(X))
    else:
        # Random state is preserved and hence predict_proba must also be
        # same
        assert_array_equal(est_ws.predict(X), est.predict(X))
        assert_array_almost_equal(est_ws.predict_proba(X),
                                  est.predict_proba(X))


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_n_estimators(Cls):
    # Test if warm start equals fit - set n_estimators.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=300, max_depth=1)
    est.fit(X, y)

    est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
    est_ws.fit(X, y)
    est_ws.set_params(n_estimators=300)
    est_ws.fit(X, y)

    assert_array_almost_equal(est_ws.predict(X), est.predict(X))


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_max_depth(Cls):
    # Test if possible to fit trees of different depth in ensemble.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=100, max_depth=1, warm_start=True)
    est.fit(X, y)
    est.set_params(n_estimators=110, max_depth=2)
    est.fit(X, y)

    # last 10 trees have different depth
    assert est.estimators_[0, 0].max_depth == 1
    for i in range(1, 11):
        assert est.estimators_[-i, 0].max_depth == 2


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_clear(Cls):
    # Test if fit clears state.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=100, max_depth=1)
    est.fit(X, y)

    est_2 = Cls(n_estimators=100, max_depth=1, warm_start=True)
    est_2.fit(X, y)  # inits state
    est_2.set_params(warm_start=False)
    est_2.fit(X, y)  # clears old state and equals est

    assert_array_almost_equal(est_2.predict(X), est.predict(X))


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_zero_n_estimators(Cls):
    # Test if warm start with zero n_estimators raises error
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=100, max_depth=1, warm_start=True)
    est.fit(X, y)
    est.set_params(n_estimators=0)
    assert_raises(ValueError, est.fit, X, y)


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_smaller_n_estimators(Cls):
    # Test if warm start with smaller n_estimators raises error
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=100, max_depth=1, warm_start=True)
    est.fit(X, y)
    est.set_params(n_estimators=99)
    assert_raises(ValueError, est.fit, X, y)


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_equal_n_estimators(Cls):
    # Test if warm start with equal n_estimators does nothing
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=100, max_depth=1)
    est.fit(X, y)

    est2 = clone(est)
    est2.set_params(n_estimators=est.n_estimators, warm_start=True)
    est2.fit(X, y)

    assert_array_almost_equal(est2.predict(X), est.predict(X))


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_oob_switch(Cls):
    # Test if oob can be turned on during warm start.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=100, max_depth=1, warm_start=True)
    est.fit(X, y)
    est.set_params(n_estimators=110, subsample=0.5)
    est.fit(X, y)

    assert_array_equal(est.oob_improvement_[:100], np.zeros(100))
    # the last 10 are not zeros
    assert_array_equal(est.oob_improvement_[-10:] == 0.0,
                       np.zeros(10, dtype=bool))


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_oob(Cls):
    # Test if warm start OOB equals fit.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est = Cls(n_estimators=200, max_depth=1, subsample=0.5,
              random_state=1)
    est.fit(X, y)

    est_ws = Cls(n_estimators=100, max_depth=1, subsample=0.5,
                 random_state=1, warm_start=True)
    est_ws.fit(X, y)
    est_ws.set_params(n_estimators=200)
    est_ws.fit(X, y)

    assert_array_almost_equal(est_ws.oob_improvement_[:100],
                              est.oob_improvement_[:100])


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_sparse(Cls):
    # Test that all sparse matrix types are supported
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    sparse_matrix_type = [csr_matrix, csc_matrix, coo_matrix]
    est_dense = Cls(n_estimators=100, max_depth=1, subsample=0.5,
                    random_state=1, warm_start=True)
    est_dense.fit(X, y)
    est_dense.predict(X)
    est_dense.set_params(n_estimators=200)
    est_dense.fit(X, y)
    y_pred_dense = est_dense.predict(X)

    for sparse_constructor in sparse_matrix_type:
        X_sparse = sparse_constructor(X)

        est_sparse = Cls(n_estimators=100, max_depth=1, subsample=0.5,
                         random_state=1, warm_start=True)
        est_sparse.fit(X_sparse, y)
        est_sparse.predict(X)
        est_sparse.set_params(n_estimators=200)
        est_sparse.fit(X_sparse, y)
        y_pred_sparse = est_sparse.predict(X)

        assert_array_almost_equal(est_dense.oob_improvement_[:100],
                                  est_sparse.oob_improvement_[:100])
        assert_array_almost_equal(y_pred_dense, y_pred_sparse)


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_warm_start_fortran(Cls):
    # Test that feeding a X in Fortran-ordered is giving the same results as
    # in C-ordered
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    est_c = Cls(n_estimators=1, random_state=1, warm_start=True)
    est_fortran = Cls(n_estimators=1, random_state=1, warm_start=True)

    est_c.fit(X, y)
    est_c.set_params(n_estimators=11)
    est_c.fit(X, y)

    X_fortran = np.asfortranarray(X)
    est_fortran.fit(X_fortran, y)
    est_fortran.set_params(n_estimators=11)
    est_fortran.fit(X_fortran, y)

    assert_array_almost_equal(est_c.predict(X), est_fortran.predict(X))


def early_stopping_monitor(i, est, locals):
    """Returns True on the 10th iteration. """
    if i == 9:
        return True
    else:
        return False


@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
def test_monitor_early_stopping(Cls):
    # Test if monitor return value works.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)

    est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5)
    est.fit(X, y, monitor=early_stopping_monitor)
    assert est.n_estimators == 20  # this is not altered
    assert est.estimators_.shape[0] == 10
    assert est.train_score_.shape[0] == 10
    assert est.oob_improvement_.shape[0] == 10

    # try refit
    est.set_params(n_estimators=30)
    est.fit(X, y)
    assert est.n_estimators == 30
    assert est.estimators_.shape[0] == 30
    assert est.train_score_.shape[0] == 30

    est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5,
              warm_start=True)
    est.fit(X, y, monitor=early_stopping_monitor)
    assert est.n_estimators == 20
    assert est.estimators_.shape[0] == 10
    assert est.train_score_.shape[0] == 10
    assert est.oob_improvement_.shape[0] == 10

    # try refit
    est.set_params(n_estimators=30, warm_start=False)
    est.fit(X, y)
    assert est.n_estimators == 30
    assert est.train_score_.shape[0] == 30
    assert est.estimators_.shape[0] == 30
    assert est.oob_improvement_.shape[0] == 30


def test_complete_classification():
    # Test greedy trees with max_depth + 1 leafs.
    from sklearn.tree._tree import TREE_LEAF
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
    k = 4

    est = GradientBoostingClassifier(n_estimators=20, max_depth=None,
                                     random_state=1, max_leaf_nodes=k + 1)
    est.fit(X, y)

    tree = est.estimators_[0, 0].tree_
    assert tree.max_depth == k
    assert (tree.children_left[tree.children_left == TREE_LEAF].shape[0] ==
                 k + 1)


def test_complete_regression():
    # Test greedy trees with max_depth + 1 leafs.
    from sklearn.tree._tree import TREE_LEAF
    k = 4

    est = GradientBoostingRegressor(n_estimators=20, max_depth=None,
                                    random_state=1, max_leaf_nodes=k + 1)
    est.fit(X_reg, y_reg)

    tree = est.estimators_[-1, 0].tree_
    assert (tree.children_left[tree.children_left == TREE_LEAF].shape[0] ==
                 k + 1)


def test_zero_estimator_reg():
    # Test if init='zero' works for regression.

    est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
                                    random_state=1, init='zero')
    est.fit(X_reg, y_reg)
    y_pred = est.predict(X_reg)
    mse = mean_squared_error(y_reg, y_pred)
    assert_almost_equal(mse, 0.52, decimal=2)

    est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
                                    random_state=1, init='foobar')
    assert_raises(ValueError, est.fit, X_reg, y_reg)


def test_zero_estimator_clf():
    # Test if init='zero' works for classification.
    X = iris.data
    y = np.array(iris.target)

    est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
                                     random_state=1, init='zero')
    est.fit(X, y)

    assert est.score(X, y) > 0.96

    # binary clf
    mask = y != 0
    y[mask] = 1
    y[~mask] = 0
    est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
                                     random_state=1, init='zero')
    est.fit(X, y)
    assert est.score(X, y) > 0.96

    est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
                                     random_state=1, init='foobar')
    assert_raises(ValueError, est.fit, X, y)


@pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS)
def test_max_leaf_nodes_max_depth(GBEstimator):
    # Test precedence of max_leaf_nodes over max_depth.
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)

    k = 4

    est = GBEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
    tree = est.estimators_[0, 0].tree_
    assert tree.max_depth == 1

    est = GBEstimator(max_depth=1).fit(X, y)
    tree = est.estimators_[0, 0].tree_
    assert tree.max_depth == 1


@pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS)
def test_min_impurity_split(GBEstimator):
    # Test if min_impurity_split of base estimators is set
    # Regression test for #8006
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)

    est = GBEstimator(min_impurity_split=0.1)
    est = assert_warns_message(FutureWarning,
                               "min_impurity_decrease",
                               est.fit, X, y)
    for tree in est.estimators_.flat:
        assert tree.min_impurity_split == 0.1


@pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS)
def test_min_impurity_decrease(GBEstimator):
    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)

    est = GBEstimator(min_impurity_decrease=0.1)
    est.fit(X, y)
    for tree in est.estimators_.flat:
        # Simply check if the parameter is passed on correctly. Tree tests
        # will suffice for the actual working of this param
        assert tree.min_impurity_decrease == 0.1


def test_warm_start_wo_nestimators_change():
    # Test if warm_start does nothing if n_estimators is not changed.
    # Regression test for #3513.
    clf = GradientBoostingClassifier(n_estimators=10, warm_start=True)
    clf.fit([[0, 1], [2, 3]], [0, 1])
    assert clf.estimators_.shape[0] == 10
    clf.fit([[0, 1], [2, 3]], [0, 1])
    assert clf.estimators_.shape[0] == 10


def test_probability_exponential():
    # Predict probabilities.
    clf = GradientBoostingClassifier(loss='exponential',
                                     n_estimators=100, random_state=1)

    assert_raises(ValueError, clf.predict_proba, T)

    clf.fit(X, y)
    assert_array_equal(clf.predict(T), true_result)

    # check if probabilities are in [0, 1].
    y_proba = clf.predict_proba(T)
    assert np.all(y_proba >= 0.0)
    assert np.all(y_proba <= 1.0)
    score = clf.decision_function(T).ravel()
    assert_array_almost_equal(y_proba[:, 1], expit(2 * score))

    # derive predictions from probabilities
    y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
    assert_array_equal(y_pred, true_result)


def test_non_uniform_weights_toy_edge_case_reg():
    X = [[1, 0],
         [1, 0],
         [1, 0],
         [0, 1]]
    y = [0, 0, 1, 0]
    # ignore the first 2 training samples by setting their weight to 0
    sample_weight = [0, 0, 1, 1]
    for loss in ('huber', 'ls', 'lad', 'quantile'):
        gb = GradientBoostingRegressor(learning_rate=1.0, n_estimators=2,
                                       loss=loss)
        gb.fit(X, y, sample_weight=sample_weight)
        assert gb.predict([[1, 0]])[0] > 0.5


def test_non_uniform_weights_toy_edge_case_clf():
    X = [[1, 0],
         [1, 0],
         [1, 0],
         [0, 1]]
    y = [0, 0, 1, 0]
    # ignore the first 2 training samples by setting their weight to 0
    sample_weight = [0, 0, 1, 1]
    for loss in ('deviance', 'exponential'):
        gb = GradientBoostingClassifier(n_estimators=5, loss=loss)
        gb.fit(X, y, sample_weight=sample_weight)
        assert_array_equal(gb.predict([[1, 0]]), [1])


@skip_if_32bit
@pytest.mark.parametrize(
        'EstimatorClass',
        (GradientBoostingClassifier, GradientBoostingRegressor)
)
@pytest.mark.parametrize('sparse_matrix', (csr_matrix, csc_matrix, coo_matrix))
def test_sparse_input(EstimatorClass, sparse_matrix):
    y, X = datasets.make_multilabel_classification(random_state=0,
                                                   n_samples=50,
                                                   n_features=1,
                                                   n_classes=20)
    y = y[:, 0]
    X_sparse = sparse_matrix(X)

    dense = EstimatorClass(n_estimators=10, random_state=0,
                           max_depth=2, min_impurity_decrease=1e-7).fit(X, y)
    sparse = EstimatorClass(n_estimators=10, random_state=0,
                            max_depth=2,
                            min_impurity_decrease=1e-7).fit(X_sparse, y)

    assert_array_almost_equal(sparse.apply(X), dense.apply(X))
    assert_array_almost_equal(sparse.predict(X), dense.predict(X))
    assert_array_almost_equal(sparse.feature_importances_,
                              dense.feature_importances_)

    assert_array_almost_equal(sparse.predict(X_sparse), dense.predict(X))
    assert_array_almost_equal(dense.predict(X_sparse), sparse.predict(X))

    if issubclass(EstimatorClass, GradientBoostingClassifier):
        assert_array_almost_equal(sparse.predict_proba(X),
                                  dense.predict_proba(X))
        assert_array_almost_equal(sparse.predict_log_proba(X),
                                  dense.predict_log_proba(X))

        assert_array_almost_equal(sparse.decision_function(X_sparse),
                                  sparse.decision_function(X))
        assert_array_almost_equal(dense.decision_function(X_sparse),
                                  sparse.decision_function(X))
        for res_sparse, res in zip(sparse.staged_decision_function(X_sparse),
                                   sparse.staged_decision_function(X)):
            assert_array_almost_equal(res_sparse, res)


def test_gradient_boosting_early_stopping():
    X, y = make_classification(n_samples=1000, random_state=0)

    gbc = GradientBoostingClassifier(n_estimators=1000,
                                     n_iter_no_change=10,
                                     learning_rate=0.1, max_depth=3,
                                     random_state=42)

    gbr = GradientBoostingRegressor(n_estimators=1000, n_iter_no_change=10,
                                    learning_rate=0.1, max_depth=3,
                                    random_state=42)

    X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                        random_state=42)
    # Check if early_stopping works as expected
    for est, tol, early_stop_n_estimators in ((gbc, 1e-1, 28), (gbr, 1e-1, 13),
                                              (gbc, 1e-3, 70),
                                              (gbr, 1e-3, 28)):
        est.set_params(tol=tol)
        est.fit(X_train, y_train)
        assert est.n_estimators_ == early_stop_n_estimators
        assert est.score(X_test, y_test) > 0.7

    # Without early stopping
    gbc = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1,
                                     max_depth=3, random_state=42)
    gbc.fit(X, y)
    gbr = GradientBoostingRegressor(n_estimators=200, learning_rate=0.1,
                                    max_depth=3, random_state=42)
    gbr.fit(X, y)

    assert gbc.n_estimators_ == 100
    assert gbr.n_estimators_ == 200


def test_gradient_boosting_validation_fraction():
    X, y = make_classification(n_samples=1000, random_state=0)

    gbc = GradientBoostingClassifier(n_estimators=100,
                                     n_iter_no_change=10,
                                     validation_fraction=0.1,
                                     learning_rate=0.1, max_depth=3,
                                     random_state=42)
    gbc2 = clone(gbc).set_params(validation_fraction=0.3)
    gbc3 = clone(gbc).set_params(n_iter_no_change=20)

    gbr = GradientBoostingRegressor(n_estimators=100, n_iter_no_change=10,
                                    learning_rate=0.1, max_depth=3,
                                    validation_fraction=0.1,
                                    random_state=42)
    gbr2 = clone(gbr).set_params(validation_fraction=0.3)
    gbr3 = clone(gbr).set_params(n_iter_no_change=20)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    # Check if validation_fraction has an effect
    gbc.fit(X_train, y_train)
    gbc2.fit(X_train, y_train)
    assert gbc.n_estimators_ != gbc2.n_estimators_

    gbr.fit(X_train, y_train)
    gbr2.fit(X_train, y_train)
    assert gbr.n_estimators_ != gbr2.n_estimators_

    # Check if n_estimators_ increase monotonically with n_iter_no_change
    # Set validation
    gbc3.fit(X_train, y_train)
    gbr3.fit(X_train, y_train)
    assert gbr.n_estimators_ < gbr3.n_estimators_
    assert gbc.n_estimators_ < gbc3.n_estimators_


def test_early_stopping_stratified():
    # Make sure data splitting for early stopping is stratified
    X = [[1, 2], [2, 3], [3, 4], [4, 5]]
    y = [0, 0, 0, 1]

    gbc = GradientBoostingClassifier(n_iter_no_change=5)
    with pytest.raises(
            ValueError,
            match='The least populated class in y has only 1 member'):
        gbc.fit(X, y)


def _make_multiclass():
    return make_classification(n_classes=3, n_clusters_per_class=1)


@pytest.mark.parametrize(
    "gb, dataset_maker, init_estimator",
    [(GradientBoostingClassifier, make_classification, DummyClassifier),
     (GradientBoostingClassifier, _make_multiclass, DummyClassifier),
     (GradientBoostingRegressor, make_regression, DummyRegressor)],
    ids=["binary classification", "multiclass classification", "regression"])
def test_gradient_boosting_with_init(gb, dataset_maker, init_estimator):
    # Check that GradientBoostingRegressor works when init is a sklearn
    # estimator.
    # Check that an error is raised if trying to fit with sample weight but
    # initial estimator does not support sample weight

    X, y = dataset_maker()
    sample_weight = np.random.RandomState(42).rand(100)

    # init supports sample weights
    init_est = init_estimator()
    gb(init=init_est).fit(X, y, sample_weight=sample_weight)

    # init does not support sample weights
    init_est = NoSampleWeightWrapper(init_estimator())
    gb(init=init_est).fit(X, y)  # ok no sample weights
    with pytest.raises(ValueError,
                       match="estimator.*does not support sample weights"):
        gb(init=init_est).fit(X, y, sample_weight=sample_weight)


def test_gradient_boosting_with_init_pipeline():
    # Check that the init estimator can be a pipeline (see issue #13466)

    X, y = make_regression(random_state=0)
    init = make_pipeline(LinearRegression())
    gb = GradientBoostingRegressor(init=init)
    gb.fit(X, y)  # pipeline without sample_weight works fine

    with pytest.raises(
            ValueError,
            match='The initial estimator Pipeline does not support sample '
                  'weights'):
        gb.fit(X, y, sample_weight=np.ones(X.shape[0]))

    # Passing sample_weight to a pipeline raises a ValueError. This test makes
    # sure we make the distinction between ValueError raised by a pipeline that
    # was passed sample_weight, and a ValueError raised by a regular estimator
    # whose input checking failed.
    with pytest.raises(
            ValueError,
            match='nu <= 0 or nu > 1'):
        # Note that NuSVR properly supports sample_weight
        init = NuSVR(gamma='auto', nu=1.5)
        gb = GradientBoostingRegressor(init=init)
        gb.fit(X, y, sample_weight=np.ones(X.shape[0]))


@pytest.mark.parametrize('estimator, missing_method', [
    (GradientBoostingClassifier(init=LinearSVC()), 'predict_proba'),
    (GradientBoostingRegressor(init=OneHotEncoder()), 'predict')
])
def test_gradient_boosting_init_wrong_methods(estimator, missing_method):
    # Make sure error is raised if init estimators don't have the required
    # methods (fit, predict, predict_proba)

    message = ("The init parameter must be a valid estimator and support "
               "both fit and " + missing_method)
    with pytest.raises(ValueError, match=message):
        estimator.fit(X, y)


def test_early_stopping_n_classes():
    # when doing early stopping (_, , y_train, _ = train_test_split(X, y))
    # there might be classes in y that are missing in y_train. As the init
    # estimator will be trained on y_train, we need to raise an error if this
    # happens.

    X = [[1]] * 10
    y = [0, 0] + [1] * 8  # only 2 negative class over 10 samples
    gb = GradientBoostingClassifier(n_iter_no_change=5, random_state=0,
                                    validation_fraction=8)
    with pytest.raises(
                ValueError,
                match='The training data after the early stopping split'):
        gb.fit(X, y)

    # No error if we let training data be big enough
    gb = GradientBoostingClassifier(n_iter_no_change=5, random_state=0,
                                    validation_fraction=4)


def test_gbr_degenerate_feature_importances():
    # growing an ensemble of single node trees. See #13620
    X = np.zeros((10, 10))
    y = np.ones((10,))
    gbr = GradientBoostingRegressor().fit(X, y)
    assert_array_equal(gbr.feature_importances_,
                       np.zeros(10, dtype=np.float64))


# TODO: Remove in 1.1 when `n_classes_` is deprecated
def test_gbr_deprecated_attr():
    # check that accessing n_classes_ in GradientBoostingRegressor raises
    # a deprecation warning
    X = np.zeros((10, 10))
    y = np.ones((10,))
    gbr = GradientBoostingRegressor().fit(X, y)
    msg = "Attribute n_classes_ was deprecated"
    with pytest.warns(FutureWarning, match=msg):
        gbr.n_classes_


# TODO: Remove in 1.1 when `n_classes_` is deprecated
@pytest.mark.filterwarnings("ignore:Attribute n_classes_ was deprecated")
def test_attr_error_raised_if_not_fitted():
    # check that accessing n_classes_ in not fitted GradientBoostingRegressor
    # raises an AttributeError
    gbr = GradientBoostingRegressor()
    # test raise AttributeError if not fitted
    msg = (
        f"{GradientBoostingRegressor.__name__} object has no n_classes_ "
        f"attribute."
    )
    with pytest.raises(AttributeError, match=msg):
        gbr.n_classes_


# TODO: Update in 1.1 to check for the error raised
@pytest.mark.parametrize('estimator', [
    GradientBoostingClassifier(criterion='mae'),
    GradientBoostingRegressor(criterion='mae')
])
def test_criterion_mae_deprecation(estimator):
    # checks whether a deprecation warning is issues when criterion='mae'
    # is used.
    msg = ("criterion='mae' was deprecated in version 0.24 and "
           "will be removed in version 1.1")
    with pytest.warns(FutureWarning, match=msg):
        estimator.fit(X, y)