test_sklearn.py

# coding: utf-8
import itertools
import joblib
import math
import os

import lightgbm as lgb
import numpy as np
import pytest
from pkg_resources import parse_version
from sklearn import __version__ as sk_version
from sklearn.base import clone
from sklearn.datasets import load_svmlight_file, make_multilabel_classification
from sklearn.utils.estimator_checks import check_parameters_default_constructible
from sklearn.metrics import log_loss, mean_squared_error
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split
from sklearn.multioutput import (MultiOutputClassifier, ClassifierChain, MultiOutputRegressor,
                                 RegressorChain)
from sklearn.utils.validation import check_is_fitted

from .utils import load_boston, load_breast_cancer, load_digits, load_iris, load_linnerud

sk_version = parse_version(sk_version)
if sk_version < parse_version("0.23"):
    import warnings
    from sklearn.exceptions import SkipTestWarning
    from sklearn.utils.estimator_checks import _yield_all_checks, SkipTest
else:
    from sklearn.utils.estimator_checks import parametrize_with_checks

decreasing_generator = itertools.count(0, -1)


def custom_asymmetric_obj(y_true, y_pred):
    residual = (y_true - y_pred).astype("float")
    grad = np.where(residual < 0, -2 * 10.0 * residual, -2 * residual)
    hess = np.where(residual < 0, 2 * 10.0, 2.0)
    return grad, hess


def objective_ls(y_true, y_pred):
    grad = (y_pred - y_true)
    hess = np.ones(len(y_true))
    return grad, hess


def logregobj(y_true, y_pred):
    y_pred = 1.0 / (1.0 + np.exp(-y_pred))
    grad = y_pred - y_true
    hess = y_pred * (1.0 - y_pred)
    return grad, hess


def custom_dummy_obj(y_true, y_pred):
    return np.ones(y_true.shape), np.ones(y_true.shape)


def constant_metric(y_true, y_pred):
    return 'error', 0, False


def decreasing_metric(y_true, y_pred):
    return ('decreasing_metric', next(decreasing_generator), False)


def mse(y_true, y_pred):
    return 'custom MSE', mean_squared_error(y_true, y_pred), False


def binary_error(y_true, y_pred):
    return np.mean((y_pred > 0.5) != y_true)


def multi_error(y_true, y_pred):
    return np.mean(y_true != y_pred)


def multi_logloss(y_true, y_pred):
    return np.mean([-math.log(y_pred[i][y]) for i, y in enumerate(y_true)])


def test_binary():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMClassifier(n_estimators=50, silent=True)
    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
    ret = log_loss(y_test, gbm.predict_proba(X_test))
    assert ret < 0.12
    assert gbm.evals_result_['valid_0']['binary_logloss'][gbm.best_iteration_ - 1] == pytest.approx(ret)


def test_regression():
    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(n_estimators=50, silent=True)
    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
    ret = mean_squared_error(y_test, gbm.predict(X_test))
    assert ret < 7
    assert gbm.evals_result_['valid_0']['l2'][gbm.best_iteration_ - 1] == pytest.approx(ret)


def test_multiclass():
    X, y = load_digits(n_class=10, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMClassifier(n_estimators=50, silent=True)
    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
    ret = multi_error(y_test, gbm.predict(X_test))
    assert ret < 0.05
    ret = multi_logloss(y_test, gbm.predict_proba(X_test))
    assert ret < 0.16
    assert gbm.evals_result_['valid_0']['multi_logloss'][gbm.best_iteration_ - 1] == pytest.approx(ret)


def test_lambdarank():
    X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                                       '../../examples/lambdarank/rank.train'))
    X_test, y_test = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                                     '../../examples/lambdarank/rank.test'))
    q_train = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                      '../../examples/lambdarank/rank.train.query'))
    q_test = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                     '../../examples/lambdarank/rank.test.query'))
    gbm = lgb.LGBMRanker(n_estimators=50)
    gbm.fit(X_train, y_train, group=q_train, eval_set=[(X_test, y_test)],
            eval_group=[q_test], eval_at=[1, 3], early_stopping_rounds=10, verbose=False,
            callbacks=[lgb.reset_parameter(learning_rate=lambda x: max(0.01, 0.1 - 0.01 * x))])
    assert gbm.best_iteration_ <= 24
    assert gbm.best_score_['valid_0']['ndcg@1'] > 0.5674
    assert gbm.best_score_['valid_0']['ndcg@3'] > 0.578


def test_xendcg():
    dir_path = os.path.dirname(os.path.realpath(__file__))
    X_train, y_train = load_svmlight_file(os.path.join(dir_path, '../../examples/xendcg/rank.train'))
    X_test, y_test = load_svmlight_file(os.path.join(dir_path, '../../examples/xendcg/rank.test'))
    q_train = np.loadtxt(os.path.join(dir_path, '../../examples/xendcg/rank.train.query'))
    q_test = np.loadtxt(os.path.join(dir_path, '../../examples/xendcg/rank.test.query'))
    gbm = lgb.LGBMRanker(n_estimators=50, objective='rank_xendcg', random_state=5, n_jobs=1)
    gbm.fit(X_train, y_train, group=q_train, eval_set=[(X_test, y_test)],
            eval_group=[q_test], eval_at=[1, 3], early_stopping_rounds=10, verbose=False,
            eval_metric='ndcg',
            callbacks=[lgb.reset_parameter(learning_rate=lambda x: max(0.01, 0.1 - 0.01 * x))])
    assert gbm.best_iteration_ <= 24
    assert gbm.best_score_['valid_0']['ndcg@1'] > 0.6211
    assert gbm.best_score_['valid_0']['ndcg@3'] > 0.6253


def test_regression_with_custom_objective():
    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(n_estimators=50, silent=True, objective=objective_ls)
    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
    ret = mean_squared_error(y_test, gbm.predict(X_test))
    assert ret < 7.0
    assert gbm.evals_result_['valid_0']['l2'][gbm.best_iteration_ - 1] == pytest.approx(ret)


def test_binary_classification_with_custom_objective():
    X, y = load_digits(n_class=2, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMClassifier(n_estimators=50, silent=True, objective=logregobj)
    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
    # prediction result is actually not transformed (is raw) due to custom objective
    y_pred_raw = gbm.predict_proba(X_test)
    assert not np.all(y_pred_raw >= 0)
    y_pred = 1.0 / (1.0 + np.exp(-y_pred_raw))
    ret = binary_error(y_test, y_pred)
    assert ret < 0.05


def test_dart():
    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(boosting_type='dart', n_estimators=50)
    gbm.fit(X_train, y_train)
    score = gbm.score(X_test, y_test)
    assert score >= 0.8
    assert score <= 1.


# sklearn <0.23 does not have a stacking classifier and n_features_in_ property
@pytest.mark.skipif(sk_version < parse_version("0.23"), reason='scikit-learn version is less than 0.23')
def test_stacking_classifier():
    from sklearn.ensemble import StackingClassifier

    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    classifiers = [('gbm1', lgb.LGBMClassifier(n_estimators=3)),
                   ('gbm2', lgb.LGBMClassifier(n_estimators=3))]
    clf = StackingClassifier(estimators=classifiers,
                             final_estimator=lgb.LGBMClassifier(n_estimators=3),
                             passthrough=True)
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    assert score >= 0.8
    assert score <= 1.
    assert clf.n_features_in_ == 4  # number of input features
    assert len(clf.named_estimators_['gbm1'].feature_importances_) == 4
    assert clf.named_estimators_['gbm1'].n_features_in_ == clf.named_estimators_['gbm2'].n_features_in_
    assert clf.final_estimator_.n_features_in_ == 10  # number of concatenated features
    assert len(clf.final_estimator_.feature_importances_) == 10
    assert all(clf.named_estimators_['gbm1'].classes_ == clf.named_estimators_['gbm2'].classes_)
    assert all(clf.classes_ == clf.named_estimators_['gbm1'].classes_)


# sklearn <0.23 does not have a stacking regressor and n_features_in_ property
@pytest.mark.skipif(sk_version < parse_version('0.23'), reason='scikit-learn version is less than 0.23')
def test_stacking_regressor():
    from sklearn.ensemble import StackingRegressor

    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    regressors = [('gbm1', lgb.LGBMRegressor(n_estimators=3)),
                  ('gbm2', lgb.LGBMRegressor(n_estimators=3))]
    reg = StackingRegressor(estimators=regressors,
                            final_estimator=lgb.LGBMRegressor(n_estimators=3),
                            passthrough=True)
    reg.fit(X_train, y_train)
    score = reg.score(X_test, y_test)
    assert score >= 0.2
    assert score <= 1.
    assert reg.n_features_in_ == 13  # number of input features
    assert len(reg.named_estimators_['gbm1'].feature_importances_) == 13
    assert reg.named_estimators_['gbm1'].n_features_in_ == reg.named_estimators_['gbm2'].n_features_in_
    assert reg.final_estimator_.n_features_in_ == 15  # number of concatenated features
    assert len(reg.final_estimator_.feature_importances_) == 15


def test_grid_search():
    X, y = load_iris(return_X_y=True)
    y = y.astype(str)  # utilize label encoder at it's max power
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                        random_state=42)
    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1,
                                                      random_state=42)
    params = dict(subsample=0.8,
                  subsample_freq=1)
    grid_params = dict(boosting_type=['rf', 'gbdt'],
                       n_estimators=[4, 6],
                       reg_alpha=[0.01, 0.005])
    fit_params = dict(verbose=False,
                      eval_set=[(X_val, y_val)],
                      eval_metric=constant_metric,
                      early_stopping_rounds=2)
    grid = GridSearchCV(estimator=lgb.LGBMClassifier(**params), param_grid=grid_params,
                        cv=2)
    grid.fit(X_train, y_train, **fit_params)
    score = grid.score(X_test, y_test)  # utilizes GridSearchCV default refit=True
    assert grid.best_params_['boosting_type'] in ['rf', 'gbdt']
    assert grid.best_params_['n_estimators'] in [4, 6]
    assert grid.best_params_['reg_alpha'] in [0.01, 0.005]
    assert grid.best_score_ <= 1.
    assert grid.best_estimator_.best_iteration_ == 1
    assert grid.best_estimator_.best_score_['valid_0']['multi_logloss'] < 0.25
    assert grid.best_estimator_.best_score_['valid_0']['error'] == 0
    assert score >= 0.2
    assert score <= 1.


def test_random_search():
    X, y = load_iris(return_X_y=True)
    y = y.astype(str)  # utilize label encoder at it's max power
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                        random_state=42)
    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1,
                                                      random_state=42)
    n_iter = 3  # Number of samples
    params = dict(subsample=0.8,
                  subsample_freq=1)
    param_dist = dict(boosting_type=['rf', 'gbdt'],
                      n_estimators=[np.random.randint(low=3, high=10) for i in range(n_iter)],
                      reg_alpha=[np.random.uniform(low=0.01, high=0.06) for i in range(n_iter)])
    fit_params = dict(verbose=False,
                      eval_set=[(X_val, y_val)],
                      eval_metric=constant_metric,
                      early_stopping_rounds=2)
    rand = RandomizedSearchCV(estimator=lgb.LGBMClassifier(**params),
                              param_distributions=param_dist, cv=2,
                              n_iter=n_iter, random_state=42)
    rand.fit(X_train, y_train, **fit_params)
    score = rand.score(X_test, y_test)  # utilizes RandomizedSearchCV default refit=True
    assert rand.best_params_['boosting_type'] in ['rf', 'gbdt']
    assert rand.best_params_['n_estimators'] in list(range(3, 10))
    assert rand.best_params_['reg_alpha'] >= 0.01  # Left-closed boundary point
    assert rand.best_params_['reg_alpha'] <= 0.06  # Right-closed boundary point
    assert rand.best_score_ <= 1.
    assert rand.best_estimator_.best_score_['valid_0']['multi_logloss'] < 0.25
    assert rand.best_estimator_.best_score_['valid_0']['error'] == 0
    assert score >= 0.2
    assert score <= 1.


# sklearn < 0.22 does not have the post fit attribute: classes_
@pytest.mark.skipif(sk_version < parse_version('0.22'), reason='scikit-learn version is less than 0.22')
def test_multioutput_classifier():
    n_outputs = 3
    X, y = make_multilabel_classification(n_samples=100, n_features=20,
                                          n_classes=n_outputs, random_state=0)
    y = y.astype(str)  # utilize label encoder at it's max power
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                        random_state=42)
    clf = MultiOutputClassifier(estimator=lgb.LGBMClassifier(n_estimators=10))
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    assert score >= 0.2
    assert score <= 1.
    np.testing.assert_array_equal(np.tile(np.unique(y_train), n_outputs),
                                  np.concatenate(clf.classes_))
    for classifier in clf.estimators_:
        assert isinstance(classifier, lgb.LGBMClassifier)
        assert isinstance(classifier.booster_, lgb.Booster)


# sklearn < 0.23 does not have as_frame parameter
@pytest.mark.skipif(sk_version < parse_version('0.23'), reason='scikit-learn version is less than 0.23')
def test_multioutput_regressor():
    bunch = load_linnerud(as_frame=True)  # returns a Bunch instance
    X, y = bunch['data'], bunch['target']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                        random_state=42)
    reg = MultiOutputRegressor(estimator=lgb.LGBMRegressor(n_estimators=10))
    reg.fit(X_train, y_train)
    y_pred = reg.predict(X_test)
    _, score, _ = mse(y_test, y_pred)
    assert score >= 0.2
    assert score <= 120.
    for regressor in reg.estimators_:
        assert isinstance(regressor, lgb.LGBMRegressor)
        assert isinstance(regressor.booster_, lgb.Booster)


# sklearn < 0.22 does not have the post fit attribute: classes_
@pytest.mark.skipif(sk_version < parse_version('0.22'), reason='scikit-learn version is less than 0.22')
def test_classifier_chain():
    n_outputs = 3
    X, y = make_multilabel_classification(n_samples=100, n_features=20,
                                          n_classes=n_outputs, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                        random_state=42)
    order = [2, 0, 1]
    clf = ClassifierChain(base_estimator=lgb.LGBMClassifier(n_estimators=10),
                          order=order, random_state=42)
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    assert score >= 0.2
    assert score <= 1.
    np.testing.assert_array_equal(np.tile(np.unique(y_train), n_outputs),
                                  np.concatenate(clf.classes_))
    assert order == clf.order_
    for classifier in clf.estimators_:
        assert isinstance(classifier, lgb.LGBMClassifier)
        assert isinstance(classifier.booster_, lgb.Booster)


# sklearn < 0.23 does not have as_frame parameter
@pytest.mark.skipif(sk_version < parse_version('0.23'), reason='scikit-learn version is less than 0.23')
def test_regressor_chain():
    bunch = load_linnerud(as_frame=True)  # returns a Bunch instance
    X, y = bunch['data'], bunch['target']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    order = [2, 0, 1]
    reg = RegressorChain(base_estimator=lgb.LGBMRegressor(n_estimators=10), order=order,
                         random_state=42)
    reg.fit(X_train, y_train)
    y_pred = reg.predict(X_test)
    _, score, _ = mse(y_test, y_pred)
    assert score >= 0.2
    assert score <= 120.
    assert order == reg.order_
    for regressor in reg.estimators_:
        assert isinstance(regressor, lgb.LGBMRegressor)
        assert isinstance(regressor.booster_, lgb.Booster)


def test_clone_and_property():
    X, y = load_boston(return_X_y=True)
    gbm = lgb.LGBMRegressor(n_estimators=10, silent=True)
    gbm.fit(X, y, verbose=False)

    gbm_clone = clone(gbm)
    assert isinstance(gbm.booster_, lgb.Booster)
    assert isinstance(gbm.feature_importances_, np.ndarray)

    X, y = load_digits(n_class=2, return_X_y=True)
    clf = lgb.LGBMClassifier(n_estimators=10, silent=True)
    clf.fit(X, y, verbose=False)
    assert sorted(clf.classes_) == [0, 1]
    assert clf.n_classes_ == 2
    assert isinstance(clf.booster_, lgb.Booster)
    assert isinstance(clf.feature_importances_, np.ndarray)


def test_joblib():
    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(n_estimators=10, objective=custom_asymmetric_obj,
                            silent=True, importance_type='split')
    gbm.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)],
            eval_metric=mse, early_stopping_rounds=5, verbose=False,
            callbacks=[lgb.reset_parameter(learning_rate=list(np.arange(1, 0, -0.1)))])

    joblib.dump(gbm, 'lgb.pkl')  # test model with custom functions
    gbm_pickle = joblib.load('lgb.pkl')
    assert isinstance(gbm_pickle.booster_, lgb.Booster)
    assert gbm.get_params() == gbm_pickle.get_params()
    np.testing.assert_array_equal(gbm.feature_importances_, gbm_pickle.feature_importances_)
    assert gbm_pickle.learning_rate == pytest.approx(0.1)
    assert callable(gbm_pickle.objective)

    for eval_set in gbm.evals_result_:
        for metric in gbm.evals_result_[eval_set]:
            np.testing.assert_allclose(gbm.evals_result_[eval_set][metric],
                                       gbm_pickle.evals_result_[eval_set][metric])
    pred_origin = gbm.predict(X_test)
    pred_pickle = gbm_pickle.predict(X_test)
    np.testing.assert_allclose(pred_origin, pred_pickle)


def test_random_state_object():
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    state1 = np.random.RandomState(123)
    state2 = np.random.RandomState(123)
    clf1 = lgb.LGBMClassifier(n_estimators=10, subsample=0.5, subsample_freq=1, random_state=state1)
    clf2 = lgb.LGBMClassifier(n_estimators=10, subsample=0.5, subsample_freq=1, random_state=state2)
    # Test if random_state is properly stored
    assert clf1.random_state is state1
    assert clf2.random_state is state2
    # Test if two random states produce identical models
    clf1.fit(X_train, y_train)
    clf2.fit(X_train, y_train)
    y_pred1 = clf1.predict(X_test, raw_score=True)
    y_pred2 = clf2.predict(X_test, raw_score=True)
    np.testing.assert_allclose(y_pred1, y_pred2)
    np.testing.assert_array_equal(clf1.feature_importances_, clf2.feature_importances_)
    df1 = clf1.booster_.model_to_string(num_iteration=0)
    df2 = clf2.booster_.model_to_string(num_iteration=0)
    assert df1 == df2
    # Test if subsequent fits sample from random_state object and produce different models
    clf1.fit(X_train, y_train)
    y_pred1_refit = clf1.predict(X_test, raw_score=True)
    df3 = clf1.booster_.model_to_string(num_iteration=0)
    assert clf1.random_state is state1
    assert clf2.random_state is state2
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(y_pred1, y_pred1_refit)
    assert df1 != df3


def test_feature_importances_single_leaf():
    data = load_iris(return_X_y=False)
    clf = lgb.LGBMClassifier(n_estimators=10)
    clf.fit(data.data, data.target)
    importances = clf.feature_importances_
    assert len(importances) == 4


def test_feature_importances_type():
    data = load_iris(return_X_y=False)
    clf = lgb.LGBMClassifier(n_estimators=10)
    clf.fit(data.data, data.target)
    clf.set_params(importance_type='split')
    importances_split = clf.feature_importances_
    clf.set_params(importance_type='gain')
    importances_gain = clf.feature_importances_
    # Test that the largest element is NOT the same, the smallest can be the same, i.e. zero
    importance_split_top1 = sorted(importances_split, reverse=True)[0]
    importance_gain_top1 = sorted(importances_gain, reverse=True)[0]
    assert importance_split_top1 != importance_gain_top1


def test_pandas_categorical():
    pd = pytest.importorskip("pandas")
    np.random.seed(42)  # sometimes there is no difference how cols are treated (cat or not cat)
    X = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'c', 'd'] * 75),  # str
                      "B": np.random.permutation([1, 2, 3] * 100),  # int
                      "C": np.random.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60),  # float
                      "D": np.random.permutation([True, False] * 150),  # bool
                      "E": pd.Categorical(np.random.permutation(['z', 'y', 'x', 'w', 'v'] * 60),
                                          ordered=True)})  # str and ordered categorical
    y = np.random.permutation([0, 1] * 150)
    X_test = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'e'] * 20),  # unseen category
                           "B": np.random.permutation([1, 3] * 30),
                           "C": np.random.permutation([0.1, -0.1, 0.2, 0.2] * 15),
                           "D": np.random.permutation([True, False] * 30),
                           "E": pd.Categorical(np.random.permutation(['z', 'y'] * 30),
                                               ordered=True)})
    np.random.seed()  # reset seed
    cat_cols_actual = ["A", "B", "C", "D"]
    cat_cols_to_store = cat_cols_actual + ["E"]
    X[cat_cols_actual] = X[cat_cols_actual].astype('category')
    X_test[cat_cols_actual] = X_test[cat_cols_actual].astype('category')
    cat_values = [X[col].cat.categories.tolist() for col in cat_cols_to_store]
    gbm0 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y)
    pred0 = gbm0.predict(X_test, raw_score=True)
    pred_prob = gbm0.predict_proba(X_test)[:, 1]
    gbm1 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, pd.Series(y), categorical_feature=[0])
    pred1 = gbm1.predict(X_test, raw_score=True)
    gbm2 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=['A'])
    pred2 = gbm2.predict(X_test, raw_score=True)
    gbm3 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=['A', 'B', 'C', 'D'])
    pred3 = gbm3.predict(X_test, raw_score=True)
    gbm3.booster_.save_model('categorical.model')
    gbm4 = lgb.Booster(model_file='categorical.model')
    pred4 = gbm4.predict(X_test)
    gbm5 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=['A', 'B', 'C', 'D', 'E'])
    pred5 = gbm5.predict(X_test, raw_score=True)
    gbm6 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=[])
    pred6 = gbm6.predict(X_test, raw_score=True)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(pred0, pred1)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(pred0, pred2)
    np.testing.assert_allclose(pred1, pred2)
    np.testing.assert_allclose(pred0, pred3)
    np.testing.assert_allclose(pred_prob, pred4)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(pred0, pred5)  # ordered cat features aren't treated as cat features by default
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(pred0, pred6)
    assert gbm0.booster_.pandas_categorical == cat_values
    assert gbm1.booster_.pandas_categorical == cat_values
    assert gbm2.booster_.pandas_categorical == cat_values
    assert gbm3.booster_.pandas_categorical == cat_values
    assert gbm4.pandas_categorical == cat_values
    assert gbm5.booster_.pandas_categorical == cat_values
    assert gbm6.booster_.pandas_categorical == cat_values


def test_pandas_sparse():
    pd = pytest.importorskip("pandas")
    try:
        from pandas.arrays import SparseArray
    except ImportError:  # support old versions
        from pandas import SparseArray
    X = pd.DataFrame({"A": SparseArray(np.random.permutation([0, 1, 2] * 100)),
                      "B": SparseArray(np.random.permutation([0.0, 0.1, 0.2, -0.1, 0.2] * 60)),
                      "C": SparseArray(np.random.permutation([True, False] * 150))})
    y = pd.Series(SparseArray(np.random.permutation([0, 1] * 150)))
    X_test = pd.DataFrame({"A": SparseArray(np.random.permutation([0, 2] * 30)),
                           "B": SparseArray(np.random.permutation([0.0, 0.1, 0.2, -0.1] * 15)),
                           "C": SparseArray(np.random.permutation([True, False] * 30))})
    if pd.__version__ >= '0.24.0':
        for dtype in pd.concat([X.dtypes, X_test.dtypes, pd.Series(y.dtypes)]):
            assert pd.api.types.is_sparse(dtype)
    gbm = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y)
    pred_sparse = gbm.predict(X_test, raw_score=True)
    if hasattr(X_test, 'sparse'):
        pred_dense = gbm.predict(X_test.sparse.to_dense(), raw_score=True)
    else:
        pred_dense = gbm.predict(X_test.to_dense(), raw_score=True)
    np.testing.assert_allclose(pred_sparse, pred_dense)


def test_predict():
    # With default params
    iris = load_iris(return_X_y=False)
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
                                                        test_size=0.2, random_state=42)

    gbm = lgb.train({'objective': 'multiclass',
                     'num_class': 3,
                     'verbose': -1},
                    lgb.Dataset(X_train, y_train))
    clf = lgb.LGBMClassifier(verbose=-1).fit(X_train, y_train)

    # Tests same probabilities
    res_engine = gbm.predict(X_test)
    res_sklearn = clf.predict_proba(X_test)
    np.testing.assert_allclose(res_engine, res_sklearn)

    # Tests same predictions
    res_engine = np.argmax(gbm.predict(X_test), axis=1)
    res_sklearn = clf.predict(X_test)
    np.testing.assert_equal(res_engine, res_sklearn)

    # Tests same raw scores
    res_engine = gbm.predict(X_test, raw_score=True)
    res_sklearn = clf.predict(X_test, raw_score=True)
    np.testing.assert_allclose(res_engine, res_sklearn)

    # Tests same leaf indices
    res_engine = gbm.predict(X_test, pred_leaf=True)
    res_sklearn = clf.predict(X_test, pred_leaf=True)
    np.testing.assert_equal(res_engine, res_sklearn)

    # Tests same feature contributions
    res_engine = gbm.predict(X_test, pred_contrib=True)
    res_sklearn = clf.predict(X_test, pred_contrib=True)
    np.testing.assert_allclose(res_engine, res_sklearn)

    # Tests other parameters for the prediction works
    res_engine = gbm.predict(X_test)
    res_sklearn_params = clf.predict_proba(X_test,
                                           pred_early_stop=True,
                                           pred_early_stop_margin=1.0)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(res_engine, res_sklearn_params)

    # Tests start_iteration
    # Tests same probabilities, starting from iteration 10
    res_engine = gbm.predict(X_test, start_iteration=10)
    res_sklearn = clf.predict_proba(X_test, start_iteration=10)
    np.testing.assert_allclose(res_engine, res_sklearn)

    # Tests same predictions, starting from iteration 10
    res_engine = np.argmax(gbm.predict(X_test, start_iteration=10), axis=1)
    res_sklearn = clf.predict(X_test, start_iteration=10)
    np.testing.assert_equal(res_engine, res_sklearn)

    # Tests same raw scores, starting from iteration 10
    res_engine = gbm.predict(X_test, raw_score=True, start_iteration=10)
    res_sklearn = clf.predict(X_test, raw_score=True, start_iteration=10)
    np.testing.assert_allclose(res_engine, res_sklearn)

    # Tests same leaf indices, starting from iteration 10
    res_engine = gbm.predict(X_test, pred_leaf=True, start_iteration=10)
    res_sklearn = clf.predict(X_test, pred_leaf=True, start_iteration=10)
    np.testing.assert_equal(res_engine, res_sklearn)

    # Tests same feature contributions, starting from iteration 10
    res_engine = gbm.predict(X_test, pred_contrib=True, start_iteration=10)
    res_sklearn = clf.predict(X_test, pred_contrib=True, start_iteration=10)
    np.testing.assert_allclose(res_engine, res_sklearn)

    # Tests other parameters for the prediction works, starting from iteration 10
    res_engine = gbm.predict(X_test, start_iteration=10)
    res_sklearn_params = clf.predict_proba(X_test,
                                           pred_early_stop=True,
                                           pred_early_stop_margin=1.0, start_iteration=10)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(res_engine, res_sklearn_params)


def test_evaluate_train_set():
    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(n_estimators=10, silent=True)
    gbm.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=False)
    assert len(gbm.evals_result_) == 2
    assert 'training' in gbm.evals_result_
    assert len(gbm.evals_result_['training']) == 1
    assert 'l2' in gbm.evals_result_['training']
    assert 'valid_1' in gbm.evals_result_
    assert len(gbm.evals_result_['valid_1']) == 1
    assert 'l2' in gbm.evals_result_['valid_1']


def test_metrics():
    X, y = load_boston(return_X_y=True)
    params = {'n_estimators': 2, 'verbose': -1}
    params_fit = {'X': X, 'y': y, 'eval_set': (X, y), 'verbose': False}

    # no custom objective, no custom metric
    # default metric
    gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'l2' in gbm.evals_result_['training']

    # non-default metric
    gbm = lgb.LGBMRegressor(metric='mape', **params).fit(**params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'mape' in gbm.evals_result_['training']

    # no metric
    gbm = lgb.LGBMRegressor(metric='None', **params).fit(**params_fit)
    assert gbm.evals_result_ is None

    # non-default metric in eval_metric
    gbm = lgb.LGBMRegressor(**params).fit(eval_metric='mape', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'l2' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # non-default metric with non-default metric in eval_metric
    gbm = lgb.LGBMRegressor(metric='gamma', **params).fit(eval_metric='mape', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'gamma' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # non-default metric with multiple metrics in eval_metric
    gbm = lgb.LGBMRegressor(metric='gamma',
                            **params).fit(eval_metric=['l2', 'mape'], **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'gamma' in gbm.evals_result_['training']
    assert 'l2' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # non-default metric with multiple metrics in eval_metric for LGBMClassifier
    X_classification, y_classification = load_breast_cancer(return_X_y=True)
    params_classification = {'n_estimators': 2, 'verbose': -1,
                             'objective': 'binary', 'metric': 'binary_logloss'}
    params_fit_classification = {'X': X_classification, 'y': y_classification,
                                 'eval_set': (X_classification, y_classification),
                                 'verbose': False}
    gbm = lgb.LGBMClassifier(**params_classification).fit(eval_metric=['fair', 'error'],
                                                          **params_fit_classification)
    assert len(gbm.evals_result_['training']) == 3
    assert 'fair' in gbm.evals_result_['training']
    assert 'binary_error' in gbm.evals_result_['training']
    assert 'binary_logloss' in gbm.evals_result_['training']

    # default metric for non-default objective
    gbm = lgb.LGBMRegressor(objective='regression_l1', **params).fit(**params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'l1' in gbm.evals_result_['training']

    # non-default metric for non-default objective
    gbm = lgb.LGBMRegressor(objective='regression_l1', metric='mape',
                            **params).fit(**params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'mape' in gbm.evals_result_['training']

    # no metric
    gbm = lgb.LGBMRegressor(objective='regression_l1', metric='None',
                            **params).fit(**params_fit)
    assert gbm.evals_result_ is None

    # non-default metric in eval_metric for non-default objective
    gbm = lgb.LGBMRegressor(objective='regression_l1',
                            **params).fit(eval_metric='mape', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'l1' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # non-default metric with non-default metric in eval_metric for non-default objective
    gbm = lgb.LGBMRegressor(objective='regression_l1', metric='gamma',
                            **params).fit(eval_metric='mape', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'gamma' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # non-default metric with multiple metrics in eval_metric for non-default objective
    gbm = lgb.LGBMRegressor(objective='regression_l1', metric='gamma',
                            **params).fit(eval_metric=['l2', 'mape'], **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'gamma' in gbm.evals_result_['training']
    assert 'l2' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # custom objective, no custom metric
    # default regression metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, **params).fit(**params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'l2' in gbm.evals_result_['training']

    # non-default regression metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric='mape', **params).fit(**params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'mape' in gbm.evals_result_['training']

    # multiple regression metrics for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=['l1', 'gamma'],
                            **params).fit(**params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'l1' in gbm.evals_result_['training']
    assert 'gamma' in gbm.evals_result_['training']

    # no metric
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric='None',
                            **params).fit(**params_fit)
    assert gbm.evals_result_ is None

    # default regression metric with non-default metric in eval_metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj,
                            **params).fit(eval_metric='mape', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'l2' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # non-default regression metric with metric in eval_metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric='mape',
                            **params).fit(eval_metric='gamma', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'mape' in gbm.evals_result_['training']
    assert 'gamma' in gbm.evals_result_['training']

    # multiple regression metrics with metric in eval_metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=['l1', 'gamma'],
                            **params).fit(eval_metric='l2', **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'l1' in gbm.evals_result_['training']
    assert 'gamma' in gbm.evals_result_['training']
    assert 'l2' in gbm.evals_result_['training']

    # multiple regression metrics with multiple metrics in eval_metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=['l1', 'gamma'],
                            **params).fit(eval_metric=['l2', 'mape'], **params_fit)
    assert len(gbm.evals_result_['training']) == 4
    assert 'l1' in gbm.evals_result_['training']
    assert 'gamma' in gbm.evals_result_['training']
    assert 'l2' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']

    # no custom objective, custom metric
    # default metric with custom metric
    gbm = lgb.LGBMRegressor(**params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'l2' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    # non-default metric with custom metric
    gbm = lgb.LGBMRegressor(metric='mape',
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'mape' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    # multiple metrics with custom metric
    gbm = lgb.LGBMRegressor(metric=['l1', 'gamma'],
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'l1' in gbm.evals_result_['training']
    assert 'gamma' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    # custom metric (disable default metric)
    gbm = lgb.LGBMRegressor(metric='None',
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'error' in gbm.evals_result_['training']

    # default metric for non-default objective with custom metric
    gbm = lgb.LGBMRegressor(objective='regression_l1',
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'l1' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    # non-default metric for non-default objective with custom metric
    gbm = lgb.LGBMRegressor(objective='regression_l1', metric='mape',
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'mape' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    # multiple metrics for non-default objective with custom metric
    gbm = lgb.LGBMRegressor(objective='regression_l1', metric=['l1', 'gamma'],
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'l1' in gbm.evals_result_['training']
    assert 'gamma' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    # custom metric (disable default metric for non-default objective)
    gbm = lgb.LGBMRegressor(objective='regression_l1', metric='None',
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'error' in gbm.evals_result_['training']

    # custom objective, custom metric
    # custom metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj,
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'error' in gbm.evals_result_['training']

    # non-default regression metric with custom metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric='mape',
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'mape' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    # multiple regression metrics with custom metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=['l2', 'mape'],
                            **params).fit(eval_metric=constant_metric, **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'l2' in gbm.evals_result_['training']
    assert 'mape' in gbm.evals_result_['training']
    assert 'error' in gbm.evals_result_['training']

    X, y = load_digits(n_class=3, return_X_y=True)
    params_fit = {'X': X, 'y': y, 'eval_set': (X, y), 'verbose': False}

    # default metric and invalid binary metric is replaced with multiclass alternative
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric='binary_error', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'multi_logloss' in gbm.evals_result_['training']
    assert 'multi_error' in gbm.evals_result_['training']

    # invalid objective is replaced with default multiclass one
    # and invalid binary metric is replaced with multiclass alternative
    gbm = lgb.LGBMClassifier(objective='invalid_obj',
                             **params).fit(eval_metric='binary_error', **params_fit)
    assert gbm.objective_ == 'multiclass'
    assert len(gbm.evals_result_['training']) == 2
    assert 'multi_logloss' in gbm.evals_result_['training']
    assert 'multi_error' in gbm.evals_result_['training']

    # default metric for non-default multiclass objective
    # and invalid binary metric is replaced with multiclass alternative
    gbm = lgb.LGBMClassifier(objective='ovr',
                             **params).fit(eval_metric='binary_error', **params_fit)
    assert gbm.objective_ == 'ovr'
    assert len(gbm.evals_result_['training']) == 2
    assert 'multi_logloss' in gbm.evals_result_['training']
    assert 'multi_error' in gbm.evals_result_['training']

    X, y = load_digits(n_class=2, return_X_y=True)
    params_fit = {'X': X, 'y': y, 'eval_set': (X, y), 'verbose': False}

    # default metric and invalid multiclass metric is replaced with binary alternative
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric='multi_error', **params_fit)
    assert len(gbm.evals_result_['training']) == 2
    assert 'binary_logloss' in gbm.evals_result_['training']
    assert 'binary_error' in gbm.evals_result_['training']

    # invalid multiclass metric is replaced with binary alternative for custom objective
    gbm = lgb.LGBMClassifier(objective=custom_dummy_obj,
                             **params).fit(eval_metric='multi_logloss', **params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'binary_logloss' in gbm.evals_result_['training']


def test_multiple_eval_metrics():

    X, y = load_breast_cancer(return_X_y=True)

    params = {'n_estimators': 2, 'verbose': -1, 'objective': 'binary', 'metric': 'binary_logloss'}
    params_fit = {'X': X, 'y': y, 'eval_set': (X, y), 'verbose': False}

    # Verify that can receive a list of metrics, only callable
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[constant_metric, decreasing_metric], **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'error' in gbm.evals_result_['training']
    assert 'decreasing_metric' in gbm.evals_result_['training']
    assert 'binary_logloss' in gbm.evals_result_['training']

    # Verify that can receive a list of custom and built-in metrics
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[constant_metric, decreasing_metric, 'fair'], **params_fit)
    assert len(gbm.evals_result_['training']) == 4
    assert 'error' in gbm.evals_result_['training']
    assert 'decreasing_metric' in gbm.evals_result_['training']
    assert 'binary_logloss' in gbm.evals_result_['training']
    assert 'fair' in gbm.evals_result_['training']

    # Verify that works as expected when eval_metric is empty
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[], **params_fit)
    assert len(gbm.evals_result_['training']) == 1
    assert 'binary_logloss' in gbm.evals_result_['training']

    # Verify that can receive a list of metrics, only built-in
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric=['fair', 'error'], **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'binary_logloss' in gbm.evals_result_['training']

    # Verify that eval_metric is robust to receiving a list with None
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric=['fair', 'error', None], **params_fit)
    assert len(gbm.evals_result_['training']) == 3
    assert 'binary_logloss' in gbm.evals_result_['training']


def test_inf_handle():
    nrows = 100
    ncols = 10
    X = np.random.randn(nrows, ncols)
    y = np.random.randn(nrows) + np.full(nrows, 1e30)
    weight = np.full(nrows, 1e10)
    params = {'n_estimators': 20, 'verbose': -1}
    params_fit = {'X': X, 'y': y, 'sample_weight': weight, 'eval_set': (X, y),
                  'verbose': False, 'early_stopping_rounds': 5}
    gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
    np.testing.assert_allclose(gbm.evals_result_['training']['l2'], np.inf)


def test_nan_handle():
    nrows = 100
    ncols = 10
    X = np.random.randn(nrows, ncols)
    y = np.random.randn(nrows) + np.full(nrows, 1e30)
    weight = np.zeros(nrows)
    params = {'n_estimators': 20, 'verbose': -1}
    params_fit = {'X': X, 'y': y, 'sample_weight': weight, 'eval_set': (X, y),
                  'verbose': False, 'early_stopping_rounds': 5}
    gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
    np.testing.assert_allclose(gbm.evals_result_['training']['l2'], np.nan)


def test_first_metric_only():

    def fit_and_check(eval_set_names, metric_names, assumed_iteration, first_metric_only):
        params['first_metric_only'] = first_metric_only
        gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
        assert len(gbm.evals_result_) == len(eval_set_names)
        for eval_set_name in eval_set_names:
            assert eval_set_name in gbm.evals_result_
            assert len(gbm.evals_result_[eval_set_name]) == len(metric_names)
            for metric_name in metric_names:
                assert metric_name in gbm.evals_result_[eval_set_name]

                actual = len(gbm.evals_result_[eval_set_name][metric_name])
                expected = assumed_iteration + (params_fit['early_stopping_rounds']
                                                if eval_set_name != 'training'
                                                and assumed_iteration != gbm.n_estimators else 0)
                assert expected == actual
                if eval_set_name != 'training':
                    assert assumed_iteration == gbm.best_iteration_
                else:
                    assert gbm.n_estimators == gbm.best_iteration_

    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    X_test1, X_test2, y_test1, y_test2 = train_test_split(X_test, y_test, test_size=0.5, random_state=72)
    params = {'n_estimators': 30,
              'learning_rate': 0.8,
              'num_leaves': 15,
              'verbose': -1,
              'seed': 123}
    params_fit = {'X': X_train,
                  'y': y_train,
                  'early_stopping_rounds': 5,
                  'verbose': False}

    iter_valid1_l1 = 3
    iter_valid1_l2 = 18
    iter_valid2_l1 = 11
    iter_valid2_l2 = 7
    assert len(set([iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2])) == 4
    iter_min_l1 = min([iter_valid1_l1, iter_valid2_l1])
    iter_min_l2 = min([iter_valid1_l2, iter_valid2_l2])
    iter_min = min([iter_min_l1, iter_min_l2])
    iter_min_valid1 = min([iter_valid1_l1, iter_valid1_l2])

    # training data as eval_set
    params_fit['eval_set'] = (X_train, y_train)
    fit_and_check(['training'], ['l2'], 30, False)
    fit_and_check(['training'], ['l2'], 30, True)

    # feval
    params['metric'] = 'None'
    params_fit['eval_metric'] = lambda preds, train_data: [decreasing_metric(preds, train_data),
                                                           constant_metric(preds, train_data)]
    params_fit['eval_set'] = (X_test1, y_test1)
    fit_and_check(['valid_0'], ['decreasing_metric', 'error'], 1, False)
    fit_and_check(['valid_0'], ['decreasing_metric', 'error'], 30, True)
    params_fit['eval_metric'] = lambda preds, train_data: [constant_metric(preds, train_data),
                                                           decreasing_metric(preds, train_data)]
    fit_and_check(['valid_0'], ['decreasing_metric', 'error'], 1, True)

    # single eval_set
    params.pop('metric')
    params_fit.pop('eval_metric')
    fit_and_check(['valid_0'], ['l2'], iter_valid1_l2, False)
    fit_and_check(['valid_0'], ['l2'], iter_valid1_l2, True)

    params_fit['eval_metric'] = "l2"
    fit_and_check(['valid_0'], ['l2'], iter_valid1_l2, False)
    fit_and_check(['valid_0'], ['l2'], iter_valid1_l2, True)

    params_fit['eval_metric'] = "l1"
    fit_and_check(['valid_0'], ['l1', 'l2'], iter_min_valid1, False)
    fit_and_check(['valid_0'], ['l1', 'l2'], iter_valid1_l1, True)

    params_fit['eval_metric'] = ["l1", "l2"]
    fit_and_check(['valid_0'], ['l1', 'l2'], iter_min_valid1, False)
    fit_and_check(['valid_0'], ['l1', 'l2'], iter_valid1_l1, True)

    params_fit['eval_metric'] = ["l2", "l1"]
    fit_and_check(['valid_0'], ['l1', 'l2'], iter_min_valid1, False)
    fit_and_check(['valid_0'], ['l1', 'l2'], iter_valid1_l2, True)

    params_fit['eval_metric'] = ["l2", "regression", "mse"]  # test aliases
    fit_and_check(['valid_0'], ['l2'], iter_valid1_l2, False)
    fit_and_check(['valid_0'], ['l2'], iter_valid1_l2, True)

    # two eval_set
    params_fit['eval_set'] = [(X_test1, y_test1), (X_test2, y_test2)]
    params_fit['eval_metric'] = ["l1", "l2"]
    fit_and_check(['valid_0', 'valid_1'], ['l1', 'l2'], iter_min_l1, True)
    params_fit['eval_metric'] = ["l2", "l1"]
    fit_and_check(['valid_0', 'valid_1'], ['l1', 'l2'], iter_min_l2, True)

    params_fit['eval_set'] = [(X_test2, y_test2), (X_test1, y_test1)]
    params_fit['eval_metric'] = ["l1", "l2"]
    fit_and_check(['valid_0', 'valid_1'], ['l1', 'l2'], iter_min, False)
    fit_and_check(['valid_0', 'valid_1'], ['l1', 'l2'], iter_min_l1, True)
    params_fit['eval_metric'] = ["l2", "l1"]
    fit_and_check(['valid_0', 'valid_1'], ['l1', 'l2'], iter_min, False)
    fit_and_check(['valid_0', 'valid_1'], ['l1', 'l2'], iter_min_l2, True)


def test_class_weight():
    X, y = load_digits(n_class=10, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    y_train_str = y_train.astype('str')
    y_test_str = y_test.astype('str')
    gbm = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', silent=True)
    gbm.fit(X_train, y_train,
            eval_set=[(X_train, y_train), (X_test, y_test), (X_test, y_test),
                      (X_test, y_test), (X_test, y_test)],
            eval_class_weight=['balanced', None, 'balanced', {1: 10, 4: 20}, {5: 30, 2: 40}],
            verbose=False)
    for eval_set1, eval_set2 in itertools.combinations(gbm.evals_result_.keys(), 2):
        for metric in gbm.evals_result_[eval_set1]:
            np.testing.assert_raises(AssertionError,
                                     np.testing.assert_allclose,
                                     gbm.evals_result_[eval_set1][metric],
                                     gbm.evals_result_[eval_set2][metric])
    gbm_str = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', silent=True)
    gbm_str.fit(X_train, y_train_str,
                eval_set=[(X_train, y_train_str), (X_test, y_test_str),
                          (X_test, y_test_str), (X_test, y_test_str), (X_test, y_test_str)],
                eval_class_weight=['balanced', None, 'balanced', {'1': 10, '4': 20}, {'5': 30, '2': 40}],
                verbose=False)
    for eval_set1, eval_set2 in itertools.combinations(gbm_str.evals_result_.keys(), 2):
        for metric in gbm_str.evals_result_[eval_set1]:
            np.testing.assert_raises(AssertionError,
                                     np.testing.assert_allclose,
                                     gbm_str.evals_result_[eval_set1][metric],
                                     gbm_str.evals_result_[eval_set2][metric])
    for eval_set in gbm.evals_result_:
        for metric in gbm.evals_result_[eval_set]:
            np.testing.assert_allclose(gbm.evals_result_[eval_set][metric],
                                       gbm_str.evals_result_[eval_set][metric])


def test_continue_training_with_model():
    X, y = load_digits(n_class=3, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    init_gbm = lgb.LGBMClassifier(n_estimators=5).fit(X_train, y_train, eval_set=(X_test, y_test),
                                                      verbose=False)
    gbm = lgb.LGBMClassifier(n_estimators=5).fit(X_train, y_train, eval_set=(X_test, y_test),
                                                 verbose=False, init_model=init_gbm)
    assert len(init_gbm.evals_result_['valid_0']['multi_logloss']) == len(gbm.evals_result_['valid_0']['multi_logloss'])
    assert len(init_gbm.evals_result_['valid_0']['multi_logloss']) == 5
    assert gbm.evals_result_['valid_0']['multi_logloss'][-1] < init_gbm.evals_result_['valid_0']['multi_logloss'][-1]


# sklearn < 0.22 requires passing "attributes" argument
@pytest.mark.skipif(sk_version < parse_version('0.22'), reason='scikit-learn version is less than 0.22')
def test_check_is_fitted():
    X, y = load_digits(n_class=2, return_X_y=True)
    est = lgb.LGBMModel(n_estimators=5, objective="binary")
    clf = lgb.LGBMClassifier(n_estimators=5)
    reg = lgb.LGBMRegressor(n_estimators=5)
    rnk = lgb.LGBMRanker(n_estimators=5)
    models = (est, clf, reg, rnk)
    for model in models:
        with pytest.raises(lgb.compat.LGBMNotFittedError):
            check_is_fitted(model)
    est.fit(X, y)
    clf.fit(X, y)
    reg.fit(X, y)
    rnk.fit(X, y, group=np.ones(X.shape[0]))
    for model in models:
        check_is_fitted(model)


def _tested_estimators():
    for Estimator in [lgb.sklearn.LGBMClassifier, lgb.sklearn.LGBMRegressor]:
        yield Estimator()


if sk_version < parse_version("0.23"):
    def _generate_checks_per_estimator(check_generator, estimators):
        for estimator in estimators:
            name = estimator.__class__.__name__
            for check in check_generator(name, estimator):
                yield estimator, check

    @pytest.mark.skipif(
        sk_version < parse_version("0.21"), reason="scikit-learn version is less than 0.21"
    )
    @pytest.mark.parametrize(
        "estimator, check",
        _generate_checks_per_estimator(_yield_all_checks, _tested_estimators()),
    )
    def test_sklearn_integration(estimator, check):
        xfail_checks = estimator._get_tags()["_xfail_checks"]
        check_name = check.__name__ if hasattr(check, "__name__") else check.func.__name__
        if xfail_checks and check_name in xfail_checks:
            warnings.warn(xfail_checks[check_name], SkipTestWarning)
            raise SkipTest
        estimator.set_params(min_child_samples=1, min_data_in_bin=1)
        name = estimator.__class__.__name__
        check(name, estimator)
else:
    @parametrize_with_checks(list(_tested_estimators()))
    def test_sklearn_integration(estimator, check, request):
        estimator.set_params(min_child_samples=1, min_data_in_bin=1)
        check(estimator)


@pytest.mark.skipif(
    sk_version >= parse_version("0.24"),
    reason="Default constructible check included in common check from 0.24"
)
@pytest.mark.parametrize("estimator", list(_tested_estimators()))
def test_parameters_default_constructible(estimator):
    name, Estimator = estimator.__class__.__name__, estimator.__class__
    # Test that estimators are default-constructible
    check_parameters_default_constructible(name, Estimator)