test_sklearn.py 86.2 KB
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# coding: utf-8
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import inspect
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import itertools
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import math
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import re
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from functools import partial
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from os import getenv
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from pathlib import Path
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import joblib
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import numpy as np
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import pytest
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import scipy.sparse
from scipy.stats import spearmanr
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from sklearn.base import clone
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from sklearn.datasets import load_svmlight_file, make_blobs, make_multilabel_classification
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from sklearn.ensemble import StackingClassifier, StackingRegressor
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from sklearn.metrics import accuracy_score, log_loss, mean_squared_error, r2_score
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from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split
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from sklearn.multioutput import ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, RegressorChain
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from sklearn.utils.estimator_checks import parametrize_with_checks as sklearn_parametrize_with_checks
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from sklearn.utils.validation import check_is_fitted
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import lightgbm as lgb
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from lightgbm.compat import (
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    DASK_INSTALLED,
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    DATATABLE_INSTALLED,
    PANDAS_INSTALLED,
    _sklearn_version,
    dt_DataTable,
    pd_DataFrame,
    pd_Series,
)
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from .utils import (
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    assert_silent,
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    load_breast_cancer,
    load_digits,
    load_iris,
    load_linnerud,
    make_ranking,
    make_synthetic_regression,
    sklearn_multiclass_custom_objective,
    softmax,
)
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SKLEARN_MAJOR, SKLEARN_MINOR, *_ = _sklearn_version.split(".")
SKLEARN_VERSION_GTE_1_6 = (int(SKLEARN_MAJOR), int(SKLEARN_MINOR)) >= (1, 6)

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decreasing_generator = itertools.count(0, -1)
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estimator_classes = (lgb.LGBMModel, lgb.LGBMClassifier, lgb.LGBMRegressor, lgb.LGBMRanker)
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task_to_model_factory = {
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    "ranking": lgb.LGBMRanker,
    "binary-classification": lgb.LGBMClassifier,
    "multiclass-classification": lgb.LGBMClassifier,
    "regression": lgb.LGBMRegressor,
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}
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all_tasks = tuple(task_to_model_factory.keys())
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def _create_data(task, n_samples=100, n_features=4):
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    if task == "ranking":
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        X, y, g = make_ranking(n_features=4, n_samples=n_samples)
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        g = np.bincount(g)
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    elif task.endswith("classification"):
        if task == "binary-classification":
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            centers = 2
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        elif task == "multiclass-classification":
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            centers = 3
        else:
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            raise ValueError(f"Unknown classification task '{task}'")
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        X, y = make_blobs(n_samples=n_samples, n_features=n_features, centers=centers, random_state=42)
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        g = None
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    elif task == "regression":
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        X, y = make_synthetic_regression(n_samples=n_samples, n_features=n_features)
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        g = None
    return X, y, g
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class UnpicklableCallback:
    def __reduce__(self):
        raise Exception("This class in not picklable")

    def __call__(self, env):
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        env.model.attr_set_inside_callback = env.iteration * 10
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class ExtendedLGBMClassifier(lgb.LGBMClassifier):
    """Class for testing that inheriting from LGBMClassifier works"""

    def __init__(self, *, some_other_param: str = "lgbm-classifier", **kwargs):
        self.some_other_param = some_other_param
        super().__init__(**kwargs)


class ExtendedLGBMRanker(lgb.LGBMRanker):
    """Class for testing that inheriting from LGBMRanker works"""

    def __init__(self, *, some_other_param: str = "lgbm-ranker", **kwargs):
        self.some_other_param = some_other_param
        super().__init__(**kwargs)


class ExtendedLGBMRegressor(lgb.LGBMRegressor):
    """Class for testing that inheriting from LGBMRegressor works"""

    def __init__(self, *, some_other_param: str = "lgbm-regressor", **kwargs):
        self.some_other_param = some_other_param
        super().__init__(**kwargs)


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def custom_asymmetric_obj(y_true, y_pred):
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    residual = (y_true - y_pred).astype(np.float64)
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    grad = np.where(residual < 0, -2 * 10.0 * residual, -2 * residual)
    hess = np.where(residual < 0, 2 * 10.0, 2.0)
    return grad, hess


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def objective_ls(y_true, y_pred):
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    grad = y_pred - y_true
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    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):
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    return "error", 0, False
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def decreasing_metric(y_true, y_pred):
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    return ("decreasing_metric", next(decreasing_generator), False)
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def mse(y_true, y_pred):
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    return "custom MSE", mean_squared_error(y_true, y_pred), False
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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)])


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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)
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    gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1)
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    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)])
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    ret = log_loss(y_test, gbm.predict_proba(X_test))
    assert ret < 0.12
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    assert gbm.evals_result_["valid_0"]["binary_logloss"][gbm.best_iteration_ - 1] == pytest.approx(ret)
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def test_regression():
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    X, y = make_synthetic_regression()
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1)
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    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)])
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    ret = mean_squared_error(y_test, gbm.predict(X_test))
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    assert ret < 174
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    assert gbm.evals_result_["valid_0"]["l2"][gbm.best_iteration_ - 1] == pytest.approx(ret)
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@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Skip due to differences in implementation details of CUDA version"
)
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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)
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    gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1)
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    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)])
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    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
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    assert gbm.evals_result_["valid_0"]["multi_logloss"][gbm.best_iteration_ - 1] == pytest.approx(ret)
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@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Skip due to differences in implementation details of CUDA version"
)
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def test_lambdarank():
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    rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank"
    X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train"))
    X_test, y_test = load_svmlight_file(str(rank_example_dir / "rank.test"))
    q_train = np.loadtxt(str(rank_example_dir / "rank.train.query"))
    q_test = np.loadtxt(str(rank_example_dir / "rank.test.query"))
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    gbm = lgb.LGBMRanker(n_estimators=50)
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    gbm.fit(
        X_train,
        y_train,
        group=q_train,
        eval_set=[(X_test, y_test)],
        eval_group=[q_test],
        eval_at=[1, 3],
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        callbacks=[lgb.early_stopping(10), lgb.reset_parameter(learning_rate=lambda x: max(0.01, 0.1 - 0.01 * x))],
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    )
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    assert gbm.best_iteration_ <= 24
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    assert gbm.best_score_["valid_0"]["ndcg@1"] > 0.5674
    assert gbm.best_score_["valid_0"]["ndcg@3"] > 0.578
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def test_xendcg():
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    xendcg_example_dir = Path(__file__).absolute().parents[2] / "examples" / "xendcg"
    X_train, y_train = load_svmlight_file(str(xendcg_example_dir / "rank.train"))
    X_test, y_test = load_svmlight_file(str(xendcg_example_dir / "rank.test"))
    q_train = np.loadtxt(str(xendcg_example_dir / "rank.train.query"))
    q_test = np.loadtxt(str(xendcg_example_dir / "rank.test.query"))
    gbm = lgb.LGBMRanker(n_estimators=50, objective="rank_xendcg", random_state=5, n_jobs=1)
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    gbm.fit(
        X_train,
        y_train,
        group=q_train,
        eval_set=[(X_test, y_test)],
        eval_group=[q_test],
        eval_at=[1, 3],
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        eval_metric="ndcg",
        callbacks=[lgb.early_stopping(10), lgb.reset_parameter(learning_rate=lambda x: max(0.01, 0.1 - 0.01 * x))],
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    )
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    assert gbm.best_iteration_ <= 24
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    assert gbm.best_score_["valid_0"]["ndcg@1"] > 0.6211
    assert gbm.best_score_["valid_0"]["ndcg@3"] > 0.6253
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def test_eval_at_aliases():
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    rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank"
    X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train"))
    X_test, y_test = load_svmlight_file(str(rank_example_dir / "rank.test"))
    q_train = np.loadtxt(str(rank_example_dir / "rank.train.query"))
    q_test = np.loadtxt(str(rank_example_dir / "rank.test.query"))
    for alias in lgb.basic._ConfigAliases.get("eval_at"):
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        gbm = lgb.LGBMRanker(n_estimators=5, **{alias: [1, 2, 3, 9]})
        with pytest.warns(UserWarning, match=f"Found '{alias}' in params. Will use it instead of 'eval_at' argument"):
            gbm.fit(X_train, y_train, group=q_train, eval_set=[(X_test, y_test)], eval_group=[q_test])
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        assert list(gbm.evals_result_["valid_0"].keys()) == ["ndcg@1", "ndcg@2", "ndcg@3", "ndcg@9"]
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@pytest.mark.parametrize("custom_objective", [True, False])
def test_objective_aliases(custom_objective):
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    X, y = make_synthetic_regression()
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    if custom_objective:
        obj = custom_dummy_obj
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        metric_name = "l2"  # default one
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    else:
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        obj = "mape"
        metric_name = "mape"
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    evals = []
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    for alias in lgb.basic._ConfigAliases.get("objective"):
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        gbm = lgb.LGBMRegressor(n_estimators=5, **{alias: obj})
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        if alias != "objective":
            with pytest.warns(
                UserWarning, match=f"Found '{alias}' in params. Will use it instead of 'objective' argument"
            ):
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                gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)])
        else:
            gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)])
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        assert list(gbm.evals_result_["valid_0"].keys()) == [metric_name]
        evals.append(gbm.evals_result_["valid_0"][metric_name])
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    evals_t = np.array(evals).T
    for i in range(evals_t.shape[0]):
        np.testing.assert_allclose(evals_t[i], evals_t[i][0])
    # check that really dummy objective was used and estimator didn't learn anything
    if custom_objective:
        np.testing.assert_allclose(evals_t, evals_t[0][0])


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def test_regression_with_custom_objective():
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    X, y = make_synthetic_regression()
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1, objective=objective_ls)
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    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)])
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    ret = mean_squared_error(y_test, gbm.predict(X_test))
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    assert ret < 174
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    assert gbm.evals_result_["valid_0"]["l2"][gbm.best_iteration_ - 1] == pytest.approx(ret)
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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)
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    gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1, objective=logregobj)
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    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)])
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    # 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():
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    X, y = make_synthetic_regression()
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    gbm = lgb.LGBMRegressor(boosting_type="dart", n_estimators=50)
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    gbm.fit(X_train, y_train)
    score = gbm.score(X_test, y_test)
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    assert 0.8 <= score <= 1.0
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def test_stacking_classifier():
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
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    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
    )
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    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    assert score >= 0.8
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    assert score <= 1.0
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    assert clf.n_features_in_ == 4  # number of input features
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    assert len(clf.named_estimators_["gbm1"].feature_importances_) == 4
    assert clf.named_estimators_["gbm1"].n_features_in_ == clf.named_estimators_["gbm2"].n_features_in_
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    assert clf.final_estimator_.n_features_in_ == 10  # number of concatenated features
    assert len(clf.final_estimator_.feature_importances_) == 10
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    assert all(clf.named_estimators_["gbm1"].classes_ == clf.named_estimators_["gbm2"].classes_)
    assert all(clf.classes_ == clf.named_estimators_["gbm1"].classes_)
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def test_stacking_regressor():
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    X, y = make_synthetic_regression(n_samples=200)
    n_features = X.shape[1]
    n_input_models = 2
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    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
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    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)
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    reg.fit(X_train, y_train)
    score = reg.score(X_test, y_test)
    assert score >= 0.2
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    assert score <= 1.0
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    assert reg.n_features_in_ == n_features  # number of input features
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    assert len(reg.named_estimators_["gbm1"].feature_importances_) == n_features
    assert reg.named_estimators_["gbm1"].n_features_in_ == reg.named_estimators_["gbm2"].n_features_in_
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    assert reg.final_estimator_.n_features_in_ == n_features + n_input_models  # number of concatenated features
    assert len(reg.final_estimator_.feature_importances_) == n_features + n_input_models
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def test_grid_search():
    X, y = load_iris(return_X_y=True)
    y = y.astype(str)  # utilize label encoder at it's max power
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    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)
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    params = {"subsample": 0.8, "subsample_freq": 1}
    grid_params = {"boosting_type": ["rf", "gbdt"], "n_estimators": [4, 6], "reg_alpha": [0.01, 0.005]}
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    evals_result = {}
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    fit_params = {
        "eval_set": [(X_val, y_val)],
        "eval_metric": constant_metric,
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        "callbacks": [lgb.early_stopping(2), lgb.record_evaluation(evals_result)],
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    }
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    grid = GridSearchCV(estimator=lgb.LGBMClassifier(**params), param_grid=grid_params, cv=2)
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    grid.fit(X_train, y_train, **fit_params)
    score = grid.score(X_test, y_test)  # utilizes GridSearchCV default refit=True
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    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_ <= 1.0
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    assert grid.best_estimator_.best_iteration_ == 1
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    assert grid.best_estimator_.best_score_["valid_0"]["multi_logloss"] < 0.25
    assert grid.best_estimator_.best_score_["valid_0"]["error"] == 0
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    assert score >= 0.2
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    assert score <= 1.0
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    assert evals_result == grid.best_estimator_.evals_result_
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def test_random_search(rng):
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    X, y = load_iris(return_X_y=True)
    y = y.astype(str)  # utilize label encoder at it's max power
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    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)
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    n_iter = 3  # Number of samples
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    params = {"subsample": 0.8, "subsample_freq": 1}
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    param_dist = {
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        "boosting_type": ["rf", "gbdt"],
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        "n_estimators": rng.integers(low=3, high=10, size=(n_iter,)).tolist(),
        "reg_alpha": rng.uniform(low=0.01, high=0.06, size=(n_iter,)).tolist(),
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    }
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    fit_params = {"eval_set": [(X_val, y_val)], "eval_metric": constant_metric, "callbacks": [lgb.early_stopping(2)]}
    rand = RandomizedSearchCV(
        estimator=lgb.LGBMClassifier(**params), param_distributions=param_dist, cv=2, n_iter=n_iter, random_state=42
    )
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    rand.fit(X_train, y_train, **fit_params)
    score = rand.score(X_test, y_test)  # utilizes RandomizedSearchCV default refit=True
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    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_ <= 1.0
    assert rand.best_estimator_.best_score_["valid_0"]["multi_logloss"] < 0.25
    assert rand.best_estimator_.best_score_["valid_0"]["error"] == 0
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    assert score >= 0.2
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    assert score <= 1.0
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def test_multioutput_classifier():
    n_outputs = 3
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    X, y = make_multilabel_classification(n_samples=100, n_features=20, n_classes=n_outputs, random_state=0)
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    y = y.astype(str)  # utilize label encoder at it's max power
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    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
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    assert score <= 1.0
    np.testing.assert_array_equal(np.tile(np.unique(y_train), n_outputs), np.concatenate(clf.classes_))
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    for classifier in clf.estimators_:
        assert isinstance(classifier, lgb.LGBMClassifier)
        assert isinstance(classifier.booster_, lgb.Booster)


def test_multioutput_regressor():
    bunch = load_linnerud(as_frame=True)  # returns a Bunch instance
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    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)
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    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
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    assert score <= 120.0
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    for regressor in reg.estimators_:
        assert isinstance(regressor, lgb.LGBMRegressor)
        assert isinstance(regressor.booster_, lgb.Booster)


def test_classifier_chain():
    n_outputs = 3
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    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)
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    order = [2, 0, 1]
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    clf = ClassifierChain(base_estimator=lgb.LGBMClassifier(n_estimators=10), order=order, random_state=42)
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    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    assert score >= 0.2
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    assert score <= 1.0
    np.testing.assert_array_equal(np.tile(np.unique(y_train), n_outputs), np.concatenate(clf.classes_))
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    assert order == clf.order_
    for classifier in clf.estimators_:
        assert isinstance(classifier, lgb.LGBMClassifier)
        assert isinstance(classifier.booster_, lgb.Booster)


def test_regressor_chain():
    bunch = load_linnerud(as_frame=True)  # returns a Bunch instance
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    X, y = bunch["data"], bunch["target"]
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    order = [2, 0, 1]
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    reg = RegressorChain(base_estimator=lgb.LGBMRegressor(n_estimators=10), order=order, random_state=42)
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    reg.fit(X_train, y_train)
    y_pred = reg.predict(X_test)
    _, score, _ = mse(y_test, y_pred)
    assert score >= 0.2
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    assert score <= 120.0
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    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():
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    X, y = make_synthetic_regression()
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    gbm = lgb.LGBMRegressor(n_estimators=10, verbose=-1)
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    gbm.fit(X, y)
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    gbm_clone = clone(gbm)
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    # original estimator is unaffected
    assert gbm.n_estimators == 10
    assert gbm.verbose == -1
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    assert isinstance(gbm.booster_, lgb.Booster)
    assert isinstance(gbm.feature_importances_, np.ndarray)

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    # new estimator is unfitted, but has the same parameters
    assert gbm_clone.__sklearn_is_fitted__() is False
    assert gbm_clone.n_estimators == 10
    assert gbm_clone.verbose == -1
    assert gbm_clone.get_params() == gbm.get_params()

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


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@pytest.mark.parametrize("estimator", (lgb.LGBMClassifier, lgb.LGBMRegressor, lgb.LGBMRanker))
def test_estimators_all_have_the_same_kwargs_and_defaults(estimator):
    base_spec = inspect.getfullargspec(lgb.LGBMModel)
    subclass_spec = inspect.getfullargspec(estimator)

    # should not allow for any varargs
    assert subclass_spec.varargs == base_spec.varargs
    assert subclass_spec.varargs is None

    # the only varkw should be **kwargs,
    assert subclass_spec.varkw == base_spec.varkw
    assert subclass_spec.varkw == "kwargs"

    # default values for all constructor arguments should be identical
    #
    # NOTE: if LGBMClassifier / LGBMRanker / LGBMRegressor ever override
    #       any of LGBMModel's constructor arguments, this will need to be updated
    assert subclass_spec.kwonlydefaults == base_spec.kwonlydefaults

    # only positional argument should be 'self'
    assert subclass_spec.args == base_spec.args
    assert subclass_spec.args == ["self"]
    assert subclass_spec.defaults is None

    # get_params() should be identical
    assert estimator().get_params() == lgb.LGBMModel().get_params()


def test_subclassing_get_params_works():
    expected_params = {
        "boosting_type": "gbdt",
        "class_weight": None,
        "colsample_bytree": 1.0,
        "importance_type": "split",
        "learning_rate": 0.1,
        "max_depth": -1,
        "min_child_samples": 20,
        "min_child_weight": 0.001,
        "min_split_gain": 0.0,
        "n_estimators": 100,
        "n_jobs": None,
        "num_leaves": 31,
        "objective": None,
        "random_state": None,
        "reg_alpha": 0.0,
        "reg_lambda": 0.0,
        "subsample": 1.0,
        "subsample_for_bin": 200000,
        "subsample_freq": 0,
    }

    # Overrides, used to test that passing through **kwargs works as expected.
    #
    # why these?
    #
    #  - 'n_estimators' directly matches a keyword arg for the scikit-learn estimators
    #  - 'eta' is a parameter alias for 'learning_rate'
    overrides = {"n_estimators": 13, "eta": 0.07}

    # lightgbm-official classes
    for est in [lgb.LGBMModel, lgb.LGBMClassifier, lgb.LGBMRanker, lgb.LGBMRegressor]:
        assert est().get_params() == expected_params
        assert est(**overrides).get_params() == {
            **expected_params,
            "eta": 0.07,
            "n_estimators": 13,
            "learning_rate": 0.1,
        }

    if DASK_INSTALLED:
        for est in [lgb.DaskLGBMClassifier, lgb.DaskLGBMRanker, lgb.DaskLGBMRegressor]:
            assert est().get_params() == {
                **expected_params,
                "client": None,
            }
            assert est(**overrides).get_params() == {
                **expected_params,
                "eta": 0.07,
                "n_estimators": 13,
                "learning_rate": 0.1,
                "client": None,
            }

    # custom sub-classes
    assert ExtendedLGBMClassifier().get_params() == {**expected_params, "some_other_param": "lgbm-classifier"}
    assert ExtendedLGBMClassifier(**overrides).get_params() == {
        **expected_params,
        "eta": 0.07,
        "n_estimators": 13,
        "learning_rate": 0.1,
        "some_other_param": "lgbm-classifier",
    }
    assert ExtendedLGBMRanker().get_params() == {
        **expected_params,
        "some_other_param": "lgbm-ranker",
    }
    assert ExtendedLGBMRanker(**overrides).get_params() == {
        **expected_params,
        "eta": 0.07,
        "n_estimators": 13,
        "learning_rate": 0.1,
        "some_other_param": "lgbm-ranker",
    }
    assert ExtendedLGBMRegressor().get_params() == {
        **expected_params,
        "some_other_param": "lgbm-regressor",
    }
    assert ExtendedLGBMRegressor(**overrides).get_params() == {
        **expected_params,
        "eta": 0.07,
        "n_estimators": 13,
        "learning_rate": 0.1,
        "some_other_param": "lgbm-regressor",
    }


@pytest.mark.parametrize("task", all_tasks)
def test_subclassing_works(task):
    # param values to make training deterministic and
    # just train a small, cheap model
    params = {
        "deterministic": True,
        "force_row_wise": True,
        "n_jobs": 1,
        "n_estimators": 5,
        "num_leaves": 11,
        "random_state": 708,
    }

    X, y, g = _create_data(task=task)
    if task == "ranking":
        est = lgb.LGBMRanker(**params).fit(X, y, group=g)
        est_sub = ExtendedLGBMRanker(**params).fit(X, y, group=g)
    elif task.endswith("classification"):
        est = lgb.LGBMClassifier(**params).fit(X, y)
        est_sub = ExtendedLGBMClassifier(**params).fit(X, y)
    else:
        est = lgb.LGBMRegressor(**params).fit(X, y)
        est_sub = ExtendedLGBMRegressor(**params).fit(X, y)

    np.testing.assert_allclose(est.predict(X), est_sub.predict(X))


@pytest.mark.parametrize(
    "estimator_to_task",
    [
        (lgb.LGBMClassifier, "binary-classification"),
        (ExtendedLGBMClassifier, "binary-classification"),
        (lgb.LGBMRanker, "ranking"),
        (ExtendedLGBMRanker, "ranking"),
        (lgb.LGBMRegressor, "regression"),
        (ExtendedLGBMRegressor, "regression"),
    ],
)
def test_parameter_aliases_are_handled_correctly(estimator_to_task):
    estimator, task = estimator_to_task
    # scikit-learn estimators should remember every parameter passed
    # via keyword arguments in the estimator constructor, but then
    # only pass the correct value down to LightGBM's C++ side
    params = {
        "eta": 0.08,
        "num_iterations": 3,
        "num_leaves": 5,
    }
    X, y, g = _create_data(task=task)
    mod = estimator(**params)
    if task == "ranking":
        mod.fit(X, y, group=g)
    else:
        mod.fit(X, y)

    # scikit-learn get_params()
    p = mod.get_params()
    assert p["eta"] == 0.08
    assert p["learning_rate"] == 0.1

    # lgb.Booster's 'params' attribute
    p = mod.booster_.params
    assert p["eta"] == 0.08
    assert p["learning_rate"] == 0.1

    # Config in the 'LightGBM::Booster' on the C++ side
    p = mod.booster_._get_loaded_param()
    assert p["learning_rate"] == 0.1
    assert "eta" not in p


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def test_joblib(tmp_path):
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    X, y = make_synthetic_regression()
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    gbm = lgb.LGBMRegressor(n_estimators=10, objective=custom_asymmetric_obj, verbose=-1, importance_type="split")
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    gbm.fit(
        X_train,
        y_train,
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        eval_set=[(X_train, y_train), (X_test, y_test)],
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        eval_metric=mse,
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        callbacks=[lgb.early_stopping(5), lgb.reset_parameter(learning_rate=list(np.arange(1, 0, -0.1)))],
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    )
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    model_path_pkl = str(tmp_path / "lgb.pkl")
    joblib.dump(gbm, model_path_pkl)  # test model with custom functions
    gbm_pickle = joblib.load(model_path_pkl)
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    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]:
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            np.testing.assert_allclose(gbm.evals_result_[eval_set][metric], gbm_pickle.evals_result_[eval_set][metric])
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    pred_origin = gbm.predict(X_test)
    pred_pickle = gbm_pickle.predict(X_test)
    np.testing.assert_allclose(pred_origin, pred_pickle)


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def test_non_serializable_objects_in_callbacks(tmp_path):
    unpicklable_callback = UnpicklableCallback()

    with pytest.raises(Exception, match="This class in not picklable"):
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        joblib.dump(unpicklable_callback, tmp_path / "tmp.joblib")
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    X, y = make_synthetic_regression()
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    gbm = lgb.LGBMRegressor(n_estimators=5)
    gbm.fit(X, y, callbacks=[unpicklable_callback])
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    assert gbm.booster_.attr_set_inside_callback == 40
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@pytest.mark.parametrize("rng_constructor", [np.random.RandomState, np.random.default_rng])
def test_random_state_object(rng_constructor):
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    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)
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    state1 = rng_constructor(123)
    state2 = rng_constructor(123)
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    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)
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    clf.set_params(importance_type="split")
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    importances_split = clf.feature_importances_
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    clf.set_params(importance_type="gain")
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    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


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# why fixed seed?
# sometimes there is no difference how cols are treated (cat or not cat)
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def test_pandas_categorical(rng_fixed_seed, tmp_path):
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    pd = pytest.importorskip("pandas")
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    X = pd.DataFrame(
        {
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            "A": rng_fixed_seed.permutation(["a", "b", "c", "d"] * 75),  # str
            "B": rng_fixed_seed.permutation([1, 2, 3] * 100),  # int
            "C": rng_fixed_seed.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60),  # float
            "D": rng_fixed_seed.permutation([True, False] * 150),  # bool
            "E": pd.Categorical(rng_fixed_seed.permutation(["z", "y", "x", "w", "v"] * 60), ordered=True),
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        }
    )  # str and ordered categorical
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    y = rng_fixed_seed.permutation([0, 1] * 150)
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    X_test = pd.DataFrame(
        {
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            "A": rng_fixed_seed.permutation(["a", "b", "e"] * 20),  # unseen category
            "B": rng_fixed_seed.permutation([1, 3] * 30),
            "C": rng_fixed_seed.permutation([0.1, -0.1, 0.2, 0.2] * 15),
            "D": rng_fixed_seed.permutation([True, False] * 30),
            "E": pd.Categorical(rng_fixed_seed.permutation(["z", "y"] * 30), ordered=True),
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        }
    )
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    cat_cols_actual = ["A", "B", "C", "D"]
    cat_cols_to_store = cat_cols_actual + ["E"]
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    X[cat_cols_actual] = X[cat_cols_actual].astype("category")
    X_test[cat_cols_actual] = X_test[cat_cols_actual].astype("category")
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    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)
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    gbm2 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A"])
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    pred2 = gbm2.predict(X_test, raw_score=True)
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    gbm3 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A", "B", "C", "D"])
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    pred3 = gbm3.predict(X_test, raw_score=True)
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    categorical_model_path = tmp_path / "categorical.model"
    gbm3.booster_.save_model(categorical_model_path)
    gbm4 = lgb.Booster(model_file=categorical_model_path)
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    pred4 = gbm4.predict(X_test)
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    gbm5 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A", "B", "C", "D", "E"])
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    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


850
def test_pandas_sparse(rng):
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    pd = pytest.importorskip("pandas")
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    X = pd.DataFrame(
        {
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            "A": pd.arrays.SparseArray(rng.permutation([0, 1, 2] * 100)),
            "B": pd.arrays.SparseArray(rng.permutation([0.0, 0.1, 0.2, -0.1, 0.2] * 60)),
            "C": pd.arrays.SparseArray(rng.permutation([True, False] * 150)),
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        }
    )
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    y = pd.Series(pd.arrays.SparseArray(rng.permutation([0, 1] * 150)))
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    X_test = pd.DataFrame(
        {
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            "A": pd.arrays.SparseArray(rng.permutation([0, 2] * 30)),
            "B": pd.arrays.SparseArray(rng.permutation([0.0, 0.1, 0.2, -0.1] * 15)),
            "C": pd.arrays.SparseArray(rng.permutation([True, False] * 30)),
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        }
    )
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    for dtype in pd.concat([X.dtypes, X_test.dtypes, pd.Series(y.dtypes)]):
868
        assert isinstance(dtype, pd.SparseDtype)
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    gbm = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y)
    pred_sparse = gbm.predict(X_test, raw_score=True)
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    if hasattr(X_test, "sparse"):
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        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)
881
    X_train, X_test, y_train, _ = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
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883
    gbm = lgb.train({"objective": "multiclass", "num_class": 3, "verbose": -1}, lgb.Dataset(X_train, y_train))
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    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)
913
    res_sklearn_params = clf.predict_proba(X_test, pred_early_stop=True, pred_early_stop_margin=1.0)
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    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)
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    res_sklearn_params = clf.predict_proba(X_test, pred_early_stop=True, pred_early_stop_margin=1.0, start_iteration=10)
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    with pytest.raises(AssertionError):
        np.testing.assert_allclose(res_engine, res_sklearn_params)

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    # Test multiclass binary classification
    num_samples = 100
    num_classes = 2
    X_train = np.linspace(start=0, stop=10, num=num_samples * 3).reshape(num_samples, 3)
    y_train = np.concatenate([np.zeros(int(num_samples / 2 - 10)), np.ones(int(num_samples / 2 + 10))])

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

    res_engine = gbm.predict(X_train)
    res_sklearn = clf.predict_proba(X_train)

    assert res_engine.shape == (num_samples, num_classes)
    assert res_sklearn.shape == (num_samples, num_classes)
    np.testing.assert_allclose(res_engine, res_sklearn)

    res_class_sklearn = clf.predict(X_train)
    np.testing.assert_allclose(res_class_sklearn, y_train)

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def test_predict_with_params_from_init():
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)

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    predict_params = {"pred_early_stop": True, "pred_early_stop_margin": 1.0}
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    y_preds_no_params = lgb.LGBMClassifier(verbose=-1).fit(X_train, y_train).predict(X_test, raw_score=True)
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    y_preds_params_in_predict = (
        lgb.LGBMClassifier(verbose=-1).fit(X_train, y_train).predict(X_test, raw_score=True, **predict_params)
    )
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    with pytest.raises(AssertionError):
        np.testing.assert_allclose(y_preds_no_params, y_preds_params_in_predict)

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    y_preds_params_in_set_params_before_fit = (
        lgb.LGBMClassifier(verbose=-1)
        .set_params(**predict_params)
        .fit(X_train, y_train)
        .predict(X_test, raw_score=True)
    )
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    np.testing.assert_allclose(y_preds_params_in_predict, y_preds_params_in_set_params_before_fit)

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    y_preds_params_in_set_params_after_fit = (
        lgb.LGBMClassifier(verbose=-1)
        .fit(X_train, y_train)
        .set_params(**predict_params)
        .predict(X_test, raw_score=True)
    )
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    np.testing.assert_allclose(y_preds_params_in_predict, y_preds_params_in_set_params_after_fit)

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    y_preds_params_in_init = (
        lgb.LGBMClassifier(verbose=-1, **predict_params).fit(X_train, y_train).predict(X_test, raw_score=True)
    )
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    np.testing.assert_allclose(y_preds_params_in_predict, y_preds_params_in_init)

    # test that params passed in predict have higher priority
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    y_preds_params_overwritten = (
        lgb.LGBMClassifier(verbose=-1, **predict_params)
        .fit(X_train, y_train)
        .predict(X_test, raw_score=True, pred_early_stop=False)
    )
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    np.testing.assert_allclose(y_preds_no_params, y_preds_params_overwritten)


1013
def test_evaluate_train_set():
1014
    X, y = make_synthetic_regression()
1015
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
1016
    gbm = lgb.LGBMRegressor(n_estimators=10, verbose=-1)
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    gbm.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)])
1018
    assert len(gbm.evals_result_) == 2
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    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"]
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def test_metrics():
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    X, y = make_synthetic_regression()
    y = abs(y)
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    params = {"n_estimators": 2, "verbose": -1}
    params_fit = {"X": X, "y": y, "eval_set": (X, y)}
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    # no custom objective, no custom metric
    # default metric
    gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
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    assert len(gbm.evals_result_["training"]) == 1
    assert "l2" in gbm.evals_result_["training"]
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    # non-default metric
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    gbm = lgb.LGBMRegressor(metric="mape", **params).fit(**params_fit)
    assert len(gbm.evals_result_["training"]) == 1
    assert "mape" in gbm.evals_result_["training"]
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    # no metric
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    gbm = lgb.LGBMRegressor(metric="None", **params).fit(**params_fit)
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    assert gbm.evals_result_ == {}
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    # non-default metric in eval_metric
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    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"]
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    # non-default metric with non-default metric in eval_metric
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    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"]
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    # non-default metric with multiple metrics in eval_metric
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    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"]
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    # non-default metric with multiple metrics in eval_metric for LGBMClassifier
    X_classification, y_classification = load_breast_cancer(return_X_y=True)
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    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),
    }
    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"]
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    # default metric for non-default objective
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    gbm = lgb.LGBMRegressor(objective="regression_l1", **params).fit(**params_fit)
    assert len(gbm.evals_result_["training"]) == 1
    assert "l1" in gbm.evals_result_["training"]
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    # non-default metric for non-default objective
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    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"]
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    # no metric
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    gbm = lgb.LGBMRegressor(objective="regression_l1", metric="None", **params).fit(**params_fit)
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    assert gbm.evals_result_ == {}
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    # non-default metric in eval_metric for non-default objective
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    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"]
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    # non-default metric with non-default metric in eval_metric for non-default objective
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    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"]
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    # non-default metric with multiple metrics in eval_metric for non-default objective
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    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"]
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    # custom objective, no custom metric
    # default regression metric for custom objective
    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, **params).fit(**params_fit)
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    assert len(gbm.evals_result_["training"]) == 1
    assert "l2" in gbm.evals_result_["training"]
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    # non-default regression metric for custom objective
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    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"]
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    # multiple regression metrics for custom objective
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    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"]
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    # no metric
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    gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric="None", **params).fit(**params_fit)
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    assert gbm.evals_result_ == {}
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    # default regression metric with non-default metric in eval_metric for custom objective
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    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"]
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    # non-default regression metric with metric in eval_metric for custom objective
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    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"]
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    # multiple regression metrics with metric in eval_metric for custom objective
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    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"]
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    # multiple regression metrics with multiple metrics in eval_metric for custom objective
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    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"]
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    # no custom objective, custom metric
    # default metric with custom metric
    gbm = lgb.LGBMRegressor(**params).fit(eval_metric=constant_metric, **params_fit)
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    assert len(gbm.evals_result_["training"]) == 2
    assert "l2" in gbm.evals_result_["training"]
    assert "error" in gbm.evals_result_["training"]
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    # non-default metric with custom metric
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    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"]
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    # multiple metrics with custom metric
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    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"]
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    # custom metric (disable default metric)
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    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"]
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    # default metric for non-default objective with custom metric
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    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"]
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    # non-default metric for non-default objective with custom metric
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    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"]
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    # multiple metrics for non-default objective with custom metric
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    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"]
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    # custom metric (disable default metric for non-default objective)
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    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"]
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    # custom objective, custom metric
    # custom metric for custom objective
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    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"]
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    # non-default regression metric with custom metric for custom objective
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    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"]
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    # multiple regression metrics with custom metric for custom objective
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    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"]
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1246

    X, y = load_digits(n_class=3, return_X_y=True)
1247
    params_fit = {"X": X, "y": y, "eval_set": (X, y)}
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    # default metric and invalid binary metric is replaced with multiclass alternative
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    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"]
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    # invalid binary metric is replaced with multiclass alternative
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    gbm = lgb.LGBMClassifier(**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"]
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    # default metric for non-default multiclass objective
    # and invalid binary metric is replaced with multiclass alternative
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    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"]
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    X, y = load_digits(n_class=2, return_X_y=True)
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    params_fit = {"X": X, "y": y, "eval_set": (X, y)}
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    # default metric and invalid multiclass metric is replaced with binary alternative
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    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"]
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    # invalid multiclass metric is replaced with binary alternative for custom objective
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    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"]
1283

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    # the evaluation metric changes to multiclass metric even num classes is 2 for multiclass objective
    gbm = lgb.LGBMClassifier(objective="multiclass", num_classes=2, **params).fit(
        eval_metric="binary_logloss", **params_fit
    )
    assert len(gbm._evals_result["training"]) == 1
    assert "multi_logloss" in gbm.evals_result_["training"]

    # the evaluation metric changes to multiclass metric even num classes is 2 for ovr objective
    gbm = lgb.LGBMClassifier(objective="ovr", num_classes=2, **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"]

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def test_multiple_eval_metrics():
    X, y = load_breast_cancer(return_X_y=True)

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    params = {"n_estimators": 2, "verbose": -1, "objective": "binary", "metric": "binary_logloss"}
    params_fit = {"X": X, "y": y, "eval_set": (X, y)}
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    # Verify that can receive a list of metrics, only callable
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[constant_metric, decreasing_metric], **params_fit)
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    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"]
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    # Verify that can receive a list of custom and built-in metrics
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    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"]
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    # Verify that works as expected when eval_metric is empty
    gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[], **params_fit)
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    assert len(gbm.evals_result_["training"]) == 1
    assert "binary_logloss" in gbm.evals_result_["training"]
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    # Verify that can receive a list of metrics, only built-in
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    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"]
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    # Verify that eval_metric is robust to receiving a list with None
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    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"]
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def test_nan_handle(rng):
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    nrows = 100
    ncols = 10
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    X = rng.standard_normal(size=(nrows, ncols))
    y = rng.standard_normal(size=(nrows,)) + np.full(nrows, 1e30)
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    weight = np.zeros(nrows)
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    params = {"n_estimators": 20, "verbose": -1}
    params_fit = {"X": X, "y": y, "sample_weight": weight, "eval_set": (X, y), "callbacks": [lgb.early_stopping(5)]}
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    gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
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    np.testing.assert_allclose(gbm.evals_result_["training"]["l2"], np.nan)
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@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Skip due to differences in implementation details of CUDA version"
)
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def test_first_metric_only():
    def fit_and_check(eval_set_names, metric_names, assumed_iteration, first_metric_only):
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        params["first_metric_only"] = first_metric_only
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        gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
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        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])
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                expected = assumed_iteration + (
                    params["early_stopping_rounds"]
                    if eval_set_name != "training" and assumed_iteration != gbm.n_estimators
                    else 0
                )
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                assert expected == actual
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                if eval_set_name != "training":
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                    assert assumed_iteration == gbm.best_iteration_
                else:
                    assert gbm.n_estimators == gbm.best_iteration_

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    X, y = make_synthetic_regression(n_samples=300)
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    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)
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    params = {
        "n_estimators": 30,
        "learning_rate": 0.8,
        "num_leaves": 15,
        "verbose": -1,
        "seed": 123,
        "early_stopping_rounds": 5,
    }  # early stop should be supported via global LightGBM parameter
    params_fit = {"X": X_train, "y": y_train}
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    iter_valid1_l1 = 4
    iter_valid1_l2 = 4
    iter_valid2_l1 = 2
    iter_valid2_l2 = 2
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    assert len({iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2}) == 2
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    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])

    # feval
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    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)
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    # single eval_set
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    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)
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    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)
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    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)
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    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)
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    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)
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    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)
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    # two eval_set
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    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)
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    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)
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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)
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    y_train_str = y_train.astype("str")
    y_test_str = y_test.astype("str")
    gbm = lgb.LGBMClassifier(n_estimators=10, class_weight="balanced", verbose=-1)
    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}],
    )
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    for eval_set1, eval_set2 in itertools.combinations(gbm.evals_result_.keys(), 2):
        for metric in gbm.evals_result_[eval_set1]:
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            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", verbose=-1)
    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}],
    )
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    for eval_set1, eval_set2 in itertools.combinations(gbm_str.evals_result_.keys(), 2):
        for metric in gbm_str.evals_result_[eval_set1]:
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            np.testing.assert_raises(
                AssertionError,
                np.testing.assert_allclose,
                gbm_str.evals_result_[eval_set1][metric],
                gbm_str.evals_result_[eval_set2][metric],
            )
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    for eval_set in gbm.evals_result_:
        for metric in gbm.evals_result_[eval_set]:
1497
            np.testing.assert_allclose(gbm.evals_result_[eval_set][metric], gbm_str.evals_result_[eval_set][metric])
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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)
1503
    init_gbm = lgb.LGBMClassifier(n_estimators=5).fit(X_train, y_train, eval_set=(X_test, y_test))
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    gbm = lgb.LGBMClassifier(n_estimators=5).fit(X_train, y_train, eval_set=(X_test, y_test), 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]
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1510
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def test_actual_number_of_trees():
    X = [[1, 2, 3], [1, 2, 3]]
    y = [1, 1]
    n_estimators = 5
    gbm = lgb.LGBMRegressor(n_estimators=n_estimators).fit(X, y)
    assert gbm.n_estimators == n_estimators
    assert gbm.n_estimators_ == 1
    assert gbm.n_iter_ == 1
    np.testing.assert_array_equal(gbm.predict(np.array(X) * 10), y)


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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):
1530
            check_is_fitted(model)
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    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)
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1539
@pytest.mark.parametrize("estimator_class", estimator_classes)
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@pytest.mark.parametrize("max_depth", [3, 4, 5, 8])
def test_max_depth_warning_is_never_raised(capsys, estimator_class, max_depth):
    X, y = make_blobs(n_samples=1_000, n_features=1, centers=2)
    params = {"n_estimators": 1, "max_depth": max_depth, "verbose": 0}
    if estimator_class is lgb.LGBMModel:
        estimator_class(**{**params, "objective": "binary"}).fit(X, y)
    elif estimator_class is lgb.LGBMRanker:
        estimator_class(**params).fit(X, y, group=np.ones(X.shape[0]))
    else:
        estimator_class(**params).fit(X, y)
    assert "Provided parameters constrain tree depth" not in capsys.readouterr().out


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def test_verbosity_is_respected_when_using_custom_objective(capsys):
    X, y = make_synthetic_regression()
    params = {
        "objective": objective_ls,
        "nonsense": 123,
        "num_leaves": 3,
    }
    lgb.LGBMRegressor(**params, verbosity=-1, n_estimators=1).fit(X, y)
    assert capsys.readouterr().out == ""
    lgb.LGBMRegressor(**params, verbosity=0, n_estimators=1).fit(X, y)
    assert "[LightGBM] [Warning] Unknown parameter: nonsense" in capsys.readouterr().out


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def test_fit_only_raises_num_rounds_warning_when_expected(capsys):
    X, y = make_synthetic_regression()
    base_kwargs = {
        "num_leaves": 5,
        "verbosity": -1,
    }

    # no warning: no aliases, all defaults
    reg = lgb.LGBMRegressor(**base_kwargs).fit(X, y)
    assert reg.n_estimators_ == 100
    assert_silent(capsys)

    # no warning: no aliases, just n_estimators
    reg = lgb.LGBMRegressor(**base_kwargs, n_estimators=2).fit(X, y)
    assert reg.n_estimators_ == 2
    assert_silent(capsys)

    # no warning: 1 alias + n_estimators (both same value)
    reg = lgb.LGBMRegressor(**base_kwargs, n_estimators=3, n_iter=3).fit(X, y)
    assert reg.n_estimators_ == 3
    assert_silent(capsys)

    # no warning: 1 alias + n_estimators (different values... value from params should win)
    reg = lgb.LGBMRegressor(**base_kwargs, n_estimators=3, n_iter=4).fit(X, y)
    assert reg.n_estimators_ == 4
    assert_silent(capsys)

    # no warning: 2 aliases (both same value)
    reg = lgb.LGBMRegressor(**base_kwargs, n_iter=3, num_iterations=3).fit(X, y)
    assert reg.n_estimators_ == 3
    assert_silent(capsys)

    # no warning: 4 aliases (all same value)
    reg = lgb.LGBMRegressor(**base_kwargs, n_iter=3, num_trees=3, nrounds=3, max_iter=3).fit(X, y)
    assert reg.n_estimators_ == 3
    assert_silent(capsys)

    # warning: 2 aliases (different values... "num_iterations" wins because it's the main param name)
    with pytest.warns(UserWarning, match="LightGBM will perform up to 5 boosting rounds"):
        reg = lgb.LGBMRegressor(**base_kwargs, num_iterations=5, n_iter=6).fit(X, y)
    assert reg.n_estimators_ == 5
    # should not be any other logs (except the warning, intercepted by pytest)
    assert_silent(capsys)

    # warning: 2 aliases (different values... first one in the order from Config::parameter2aliases() wins)
    with pytest.warns(UserWarning, match="LightGBM will perform up to 4 boosting rounds"):
        reg = lgb.LGBMRegressor(**base_kwargs, n_iter=4, max_iter=5).fit(X, y)
    assert reg.n_estimators_ == 4
    # should not be any other logs (except the warning, intercepted by pytest)
    assert_silent(capsys)


1618
@pytest.mark.parametrize("estimator_class", estimator_classes)
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def test_getting_feature_names_in_np_input(estimator_class):
    # input is a numpy array, which doesn't have feature names. LightGBM adds
    # feature names to the fitted model, which is inconsistent with sklearn's behavior
    X, y = load_digits(n_class=2, return_X_y=True)
    params = {"n_estimators": 2, "num_leaves": 7}
    if estimator_class is lgb.LGBMModel:
        model = estimator_class(**{**params, "objective": "binary"})
    else:
        model = estimator_class(**params)
    with pytest.raises(lgb.compat.LGBMNotFittedError):
        check_is_fitted(model)
    if isinstance(model, lgb.LGBMRanker):
        model.fit(X, y, group=[X.shape[0]])
    else:
        model.fit(X, y)
    np.testing.assert_array_equal(model.feature_names_in_, np.array([f"Column_{i}" for i in range(X.shape[1])]))


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@pytest.mark.parametrize("estimator_class", estimator_classes)
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def test_getting_feature_names_in_pd_input(estimator_class):
    X, y = load_digits(n_class=2, return_X_y=True, as_frame=True)
    col_names = X.columns.to_list()
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    assert isinstance(col_names, list) and all(isinstance(c, str) for c in col_names), (
        "input data must have feature names for this test to cover the expected functionality"
    )
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    params = {"n_estimators": 2, "num_leaves": 7}
    if estimator_class is lgb.LGBMModel:
        model = estimator_class(**{**params, "objective": "binary"})
    else:
        model = estimator_class(**params)
    with pytest.raises(lgb.compat.LGBMNotFittedError):
        check_is_fitted(model)
    if isinstance(model, lgb.LGBMRanker):
        model.fit(X, y, group=[X.shape[0]])
    else:
        model.fit(X, y)
    np.testing.assert_array_equal(model.feature_names_in_, X.columns)


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# Starting with scikit-learn 1.6 (https://github.com/scikit-learn/scikit-learn/pull/30149),
# the only API for marking estimator tests as expected to fail is to pass a keyword argument
# to parametrize_with_checks(). That function didn't accept additional arguments in earlier
# versions.
#
# This block defines a patched version of parametrize_with_checks() so lightgbm's tests
# can be compatible with scikit-learn <1.6 and >=1.6.
#
# This should be removed once minimum supported scikit-learn version is at least 1.6.
if SKLEARN_VERSION_GTE_1_6:
    parametrize_with_checks = sklearn_parametrize_with_checks
else:

    def parametrize_with_checks(estimator, *args, **kwargs):
        return sklearn_parametrize_with_checks(estimator)


def _get_expected_failed_tests(estimator):
    return estimator._more_tags()["_xfail_checks"]


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@parametrize_with_checks(
    [ExtendedLGBMClassifier(), ExtendedLGBMRegressor(), lgb.LGBMClassifier(), lgb.LGBMRegressor()],
    expected_failed_checks=_get_expected_failed_tests,
)
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def test_sklearn_integration(estimator, check):
    estimator.set_params(min_child_samples=1, min_data_in_bin=1)
    check(estimator)
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@pytest.mark.parametrize("estimator_class", estimator_classes)
def test_sklearn_tags_should_correctly_reflect_lightgbm_specific_values(estimator_class):
    est = estimator_class()
    more_tags = est._more_tags()
    err_msg = "List of supported X_types has changed. Update LGBMModel.__sklearn_tags__() to match."
    assert more_tags["X_types"] == ["2darray", "sparse", "1dlabels"], err_msg
    # the try-except part of this should be removed once lightgbm's
    # minimum supported scikit-learn version is at least 1.6
    try:
        sklearn_tags = est.__sklearn_tags__()
    except AttributeError as err:
        # only the exact error we expected to be raised should be raised
        assert bool(re.search(r"__sklearn_tags__.* should not be called", str(err)))
    else:
        # if no AttributeError was thrown, we must be using scikit-learn>=1.6,
        # and so the actual effects of __sklearn_tags__() should be tested
        assert sklearn_tags.input_tags.allow_nan is True
        assert sklearn_tags.input_tags.sparse is True
        assert sklearn_tags.target_tags.one_d_labels is True
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        if estimator_class is lgb.LGBMClassifier:
            assert sklearn_tags.estimator_type == "classifier"
            assert sklearn_tags.classifier_tags.multi_class is True
            assert sklearn_tags.classifier_tags.multi_label is False
        elif estimator_class is lgb.LGBMRegressor:
            assert sklearn_tags.estimator_type == "regressor"
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@pytest.mark.parametrize("task", all_tasks)
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def test_training_succeeds_when_data_is_dataframe_and_label_is_column_array(task):
    pd = pytest.importorskip("pandas")
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    X, y, g = _create_data(task)
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    X = pd.DataFrame(X)
    y_col_array = y.reshape(-1, 1)
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    params = {"n_estimators": 1, "num_leaves": 3, "random_state": 0}
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    model_factory = task_to_model_factory[task]
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    with pytest.warns(UserWarning, match="column-vector"):
        if task == "ranking":
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            model_1d = model_factory(**params).fit(X, y, group=g)
            model_2d = model_factory(**params).fit(X, y_col_array, group=g)
        else:
            model_1d = model_factory(**params).fit(X, y)
            model_2d = model_factory(**params).fit(X, y_col_array)

    preds_1d = model_1d.predict(X)
    preds_2d = model_2d.predict(X)
    np.testing.assert_array_equal(preds_1d, preds_2d)
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@pytest.mark.parametrize("use_weight", [True, False])
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def test_multiclass_custom_objective(use_weight):
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    centers = [[-4, -4], [4, 4], [-4, 4]]
    X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42)
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    weight = np.full_like(y, 2) if use_weight else None
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    params = {"n_estimators": 10, "num_leaves": 7}
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    builtin_obj_model = lgb.LGBMClassifier(**params)
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    builtin_obj_model.fit(X, y, sample_weight=weight)
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    builtin_obj_preds = builtin_obj_model.predict_proba(X)

    custom_obj_model = lgb.LGBMClassifier(objective=sklearn_multiclass_custom_objective, **params)
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    custom_obj_model.fit(X, y, sample_weight=weight)
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    custom_obj_preds = softmax(custom_obj_model.predict(X, raw_score=True))

    np.testing.assert_allclose(builtin_obj_preds, custom_obj_preds, rtol=0.01)
    assert not callable(builtin_obj_model.objective_)
    assert callable(custom_obj_model.objective_)
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@pytest.mark.parametrize("use_weight", [True, False])
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def test_multiclass_custom_eval(use_weight):
    def custom_eval(y_true, y_pred, weight):
        loss = log_loss(y_true, y_pred, sample_weight=weight)
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        return "custom_logloss", loss, False
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    centers = [[-4, -4], [4, 4], [-4, 4]]
    X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42)
    train_test_split_func = partial(train_test_split, test_size=0.2, random_state=0)
    X_train, X_valid, y_train, y_valid = train_test_split_func(X, y)
    if use_weight:
        weight = np.full_like(y, 2)
        weight_train, weight_valid = train_test_split_func(weight)
    else:
        weight_train = None
        weight_valid = None
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    params = {"objective": "multiclass", "num_class": 3, "num_leaves": 7}
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    model = lgb.LGBMClassifier(**params)
    model.fit(
        X_train,
        y_train,
        sample_weight=weight_train,
        eval_set=[(X_train, y_train), (X_valid, y_valid)],
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        eval_names=["train", "valid"],
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        eval_sample_weight=[weight_train, weight_valid],
        eval_metric=custom_eval,
    )
    eval_result = model.evals_result_
    train_ds = (X_train, y_train, weight_train)
    valid_ds = (X_valid, y_valid, weight_valid)
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    for key, (X, y_true, weight) in zip(["train", "valid"], [train_ds, valid_ds]):
        np.testing.assert_allclose(eval_result[key]["multi_logloss"], eval_result[key]["custom_logloss"])
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        y_pred = model.predict_proba(X)
        _, metric_value, _ = custom_eval(y_true, y_pred, weight)
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        np.testing.assert_allclose(metric_value, eval_result[key]["custom_logloss"][-1])
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def test_negative_n_jobs(tmp_path):
    n_threads = joblib.cpu_count()
    if n_threads <= 1:
        return None
    # 'val_minus_two' here is the expected number of threads for n_jobs=-2
    val_minus_two = n_threads - 1
    X, y = load_breast_cancer(return_X_y=True)
    # Note: according to joblib's formula, a value of n_jobs=-2 means
    # "use all but one thread" (formula: n_cpus + 1 + n_jobs)
    gbm = lgb.LGBMClassifier(n_estimators=2, verbose=-1, n_jobs=-2).fit(X, y)
    gbm.booster_.save_model(tmp_path / "model.txt")
    with open(tmp_path / "model.txt", "r") as f:
        model_txt = f.read()
    assert bool(re.search(rf"\[num_threads: {val_minus_two}\]", model_txt))


def test_default_n_jobs(tmp_path):
    n_cores = joblib.cpu_count(only_physical_cores=True)
    X, y = load_breast_cancer(return_X_y=True)
    gbm = lgb.LGBMClassifier(n_estimators=2, verbose=-1, n_jobs=None).fit(X, y)
    gbm.booster_.save_model(tmp_path / "model.txt")
    with open(tmp_path / "model.txt", "r") as f:
        model_txt = f.read()
    assert bool(re.search(rf"\[num_threads: {n_cores}\]", model_txt))
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1818
@pytest.mark.skipif(not PANDAS_INSTALLED, reason="pandas is not installed")
1819
@pytest.mark.parametrize("task", all_tasks)
1820
def test_validate_features(task):
1821
    X, y, g = _create_data(task, n_features=4)
1822
    features = ["x1", "x2", "x3", "x4"]
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    df = pd_DataFrame(X, columns=features)
    model = task_to_model_factory[task](n_estimators=10, num_leaves=15, verbose=-1)
1825
    if task == "ranking":
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        model.fit(df, y, group=g)
    else:
        model.fit(df, y)
    assert model.feature_name_ == features

    # try to predict with a different feature
1832
    df2 = df.rename(columns={"x2": "z"})
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    with pytest.raises(lgb.basic.LightGBMError, match="Expected 'x2' at position 1 but found 'z'"):
        model.predict(df2, validate_features=True)

    # check that disabling the check doesn't raise the error
    model.predict(df2, validate_features=False)
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# LightGBM's 'predict_disable_shape_check' mechanism is intentionally not respected by
# its scikit-learn estimators, for consistency with scikit-learn's own behavior.
@pytest.mark.parametrize("task", all_tasks)
@pytest.mark.parametrize("predict_disable_shape_check", [True, False])
def test_predict_rejects_inputs_with_incorrect_number_of_features(predict_disable_shape_check, task):
    X, y, g = _create_data(task, n_features=4)
    model_factory = task_to_model_factory[task]
    fit_kwargs = {"X": X[:, :-1], "y": y}
    if task == "ranking":
        estimator_name = "LGBMRanker"
        fit_kwargs.update({"group": g})
    elif task == "regression":
        estimator_name = "LGBMRegressor"
    else:
        estimator_name = "LGBMClassifier"

    # train on the first 3 features
    model = model_factory(n_estimators=5, num_leaves=7, verbose=-1).fit(**fit_kwargs)

    # more cols in X than features: error
    err_msg = f"X has 4 features, but {estimator_name} is expecting 3 features as input"
    with pytest.raises(ValueError, match=err_msg):
        model.predict(X, predict_disable_shape_check=predict_disable_shape_check)

    if estimator_name == "LGBMClassifier":
        with pytest.raises(ValueError, match=err_msg):
            model.predict_proba(X, predict_disable_shape_check=predict_disable_shape_check)

    # fewer cols in X than features: error
    err_msg = f"X has 2 features, but {estimator_name} is expecting 3 features as input"
    with pytest.raises(ValueError, match=err_msg):
        model.predict(X[:, :-2], predict_disable_shape_check=predict_disable_shape_check)

    if estimator_name == "LGBMClassifier":
        with pytest.raises(ValueError, match=err_msg):
            model.predict_proba(X[:, :-2], predict_disable_shape_check=predict_disable_shape_check)

    # same number of columns in both: no error
    preds = model.predict(X[:, :-1], predict_disable_shape_check=predict_disable_shape_check)
    assert preds.shape == y.shape

    if estimator_name == "LGBMClassifier":
        preds = model.predict_proba(X[:, :-1], predict_disable_shape_check=predict_disable_shape_check)
        assert preds.shape[0] == y.shape[0]


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@pytest.mark.parametrize("X_type", ["dt_DataTable", "list2d", "numpy", "scipy_csc", "scipy_csr", "pd_DataFrame"])
@pytest.mark.parametrize("y_type", ["list1d", "numpy", "pd_Series", "pd_DataFrame"])
@pytest.mark.parametrize("task", ["binary-classification", "multiclass-classification", "regression"])
1889
def test_classification_and_regression_minimally_work_with_all_all_accepted_data_types(X_type, y_type, task, rng):
1890
    if any(t.startswith("pd_") for t in [X_type, y_type]) and not PANDAS_INSTALLED:
1891
        pytest.skip("pandas is not installed")
1892
    if any(t.startswith("dt_") for t in [X_type, y_type]) and not DATATABLE_INSTALLED:
1893
        pytest.skip("datatable is not installed")
1894
    X, y, g = _create_data(task, n_samples=2_000)
1895
    weights = np.abs(rng.standard_normal(size=(y.shape[0],)))
1896

1897
    if task == "binary-classification" or task == "regression":
1898
        init_score = np.full_like(y, np.mean(y))
1899
    elif task == "multiclass-classification":
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        init_score = np.outer(y, np.array([0.1, 0.2, 0.7]))
    else:
        raise ValueError(f"Unrecognized task '{task}'")

    X_valid = X * 2
1905
    if X_type == "dt_DataTable":
1906
        X = dt_DataTable(X)
1907
    elif X_type == "list2d":
1908
        X = X.tolist()
1909
    elif X_type == "scipy_csc":
1910
        X = scipy.sparse.csc_matrix(X)
1911
    elif X_type == "scipy_csr":
1912
        X = scipy.sparse.csr_matrix(X)
1913
    elif X_type == "pd_DataFrame":
1914
        X = pd_DataFrame(X)
1915
    elif X_type != "numpy":
1916
1917
        raise ValueError(f"Unrecognized X_type: '{X_type}'")

1918
1919
    # make weights and init_score same types as y, just to avoid
    # a huge number of combinations and therefore test cases
1920
    if y_type == "list1d":
1921
        y = y.tolist()
1922
1923
        weights = weights.tolist()
        init_score = init_score.tolist()
1924
    elif y_type == "pd_DataFrame":
1925
        y = pd_DataFrame(y)
1926
        weights = pd_Series(weights)
1927
        if task == "multiclass-classification":
1928
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            init_score = pd_DataFrame(init_score)
        else:
            init_score = pd_Series(init_score)
1931
    elif y_type == "pd_Series":
1932
        y = pd_Series(y)
1933
        weights = pd_Series(weights)
1934
        if task == "multiclass-classification":
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            init_score = pd_DataFrame(init_score)
        else:
            init_score = pd_Series(init_score)
1938
    elif y_type != "numpy":
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        raise ValueError(f"Unrecognized y_type: '{y_type}'")

    model = task_to_model_factory[task](n_estimators=10, verbose=-1)
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    model.fit(
        X=X,
        y=y,
        sample_weight=weights,
        init_score=init_score,
        eval_set=[(X_valid, y)],
        eval_sample_weight=[weights],
1949
        eval_init_score=[init_score],
1950
    )
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    preds = model.predict(X)
1953
    if task == "binary-classification":
1954
        assert accuracy_score(y, preds) >= 0.99
1955
    elif task == "multiclass-classification":
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        assert accuracy_score(y, preds) >= 0.99
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    elif task == "regression":
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        assert r2_score(y, preds) > 0.86
    else:
        raise ValueError(f"Unrecognized task: '{task}'")


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@pytest.mark.parametrize("X_type", ["dt_DataTable", "list2d", "numpy", "scipy_csc", "scipy_csr", "pd_DataFrame"])
@pytest.mark.parametrize("y_type", ["list1d", "numpy", "pd_DataFrame", "pd_Series"])
@pytest.mark.parametrize("g_type", ["list1d_float", "list1d_int", "numpy", "pd_Series"])
1966
def test_ranking_minimally_works_with_all_all_accepted_data_types(X_type, y_type, g_type, rng):
1967
    if any(t.startswith("pd_") for t in [X_type, y_type, g_type]) and not PANDAS_INSTALLED:
1968
        pytest.skip("pandas is not installed")
1969
    if any(t.startswith("dt_") for t in [X_type, y_type, g_type]) and not DATATABLE_INSTALLED:
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        pytest.skip("datatable is not installed")
    X, y, g = _create_data(task="ranking", n_samples=1_000)
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    weights = np.abs(rng.standard_normal(size=(y.shape[0],)))
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    init_score = np.full_like(y, np.mean(y))
    X_valid = X * 2

1976
    if X_type == "dt_DataTable":
1977
        X = dt_DataTable(X)
1978
    elif X_type == "list2d":
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        X = X.tolist()
1980
    elif X_type == "scipy_csc":
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        X = scipy.sparse.csc_matrix(X)
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    elif X_type == "scipy_csr":
1983
        X = scipy.sparse.csr_matrix(X)
1984
    elif X_type == "pd_DataFrame":
1985
        X = pd_DataFrame(X)
1986
    elif X_type != "numpy":
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        raise ValueError(f"Unrecognized X_type: '{X_type}'")

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    # make weights and init_score same types as y, just to avoid
    # a huge number of combinations and therefore test cases
1991
    if y_type == "list1d":
1992
        y = y.tolist()
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        weights = weights.tolist()
        init_score = init_score.tolist()
1995
    elif y_type == "pd_DataFrame":
1996
        y = pd_DataFrame(y)
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        weights = pd_Series(weights)
        init_score = pd_Series(init_score)
1999
    elif y_type == "pd_Series":
2000
        y = pd_Series(y)
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        weights = pd_Series(weights)
        init_score = pd_Series(init_score)
2003
    elif y_type != "numpy":
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        raise ValueError(f"Unrecognized y_type: '{y_type}'")

2006
    if g_type == "list1d_float":
2007
        g = g.astype("float").tolist()
2008
    elif g_type == "list1d_int":
2009
        g = g.astype("int").tolist()
2010
    elif g_type == "pd_Series":
2011
        g = pd_Series(g)
2012
    elif g_type != "numpy":
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        raise ValueError(f"Unrecognized g_type: '{g_type}'")

2015
    model = task_to_model_factory["ranking"](n_estimators=10, verbose=-1)
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    model.fit(
        X=X,
        y=y,
        sample_weight=weights,
        init_score=init_score,
        group=g,
        eval_set=[(X_valid, y)],
        eval_sample_weight=[weights],
        eval_init_score=[init_score],
2025
        eval_group=[g],
2026
    )
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    preds = model.predict(X)
    assert spearmanr(preds, y).correlation >= 0.99
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2034
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2036


def test_classifier_fit_detects_classes_every_time():
    rng = np.random.default_rng(seed=123)
    nrows = 1000
    ncols = 20

    X = rng.standard_normal(size=(nrows, ncols))
2037
    y_bin = (rng.random(size=nrows) <= 0.3).astype(np.float64)
2038
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2040
2041
2042
2043
2044
2045
    y_multi = rng.integers(4, size=nrows)

    model = lgb.LGBMClassifier(verbose=-1)
    for _ in range(2):
        model.fit(X, y_multi)
        assert model.objective_ == "multiclass"
        model.fit(X, y_bin)
        assert model.objective_ == "binary"