Merge remote-tracking branch 'origin/master' into cuda/metric-binary

2022-12-21 06:33:01 +00:00 · 2022-12-21 06:33:01 +00:00 · 9f13ccc8ec
--- a/.ci/setup.sh
+++ b/.ci/setup.sh
@ -42,13 +42,13 @@ else  # Linux
            iputils-ping \
            jq \
            libcurl4 \
-            libicu66 \
-            libssl1.1 \
+            libicu-dev \
+            libssl-dev \
            libunwind8 \
            locales \
            netcat \
            unzip \
-            zip
+            zip || exit -1
        if [[ $COMPILER == "clang" ]]; then
            sudo apt-get install --no-install-recommends -y \
                clang \
@ -60,6 +60,10 @@ else  # Linux
        sudo locale-gen ${LANG}
        sudo update-locale
    fi
+    if [[ $TASK == "r-package" ]] && [[ $COMPILER == "clang" ]]; then
+        sudo apt-get install --no-install-recommends -y \
+            libomp-dev
+    fi
    if [[ $TASK == "mpi" ]]; then
        if [[ $IN_UBUNTU_LATEST_CONTAINER == "true" ]]; then
            sudo apt-get update
@ -75,10 +79,10 @@ else  # Linux
    fi
    if [[ $TASK == "gpu" ]]; then
        if [[ $IN_UBUNTU_LATEST_CONTAINER == "true" ]]; then
-            sudo add-apt-repository ppa:mhier/libboost-latest -y
            sudo apt-get update
            sudo apt-get install --no-install-recommends -y \
                libboost1.74-dev \
+                libboost-filesystem1.74-dev \
                ocl-icd-opencl-dev
        else  # in manylinux image
            sudo yum update -y
--- a/.github/workflows/linkchecker.yml
+++ b/.github/workflows/linkchecker.yml
@ -17,7 +17,7 @@ env:
 jobs:
  check-links:
    timeout-minutes: 60
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3
--- a/.github/workflows/no-response.yml
+++ b/.github/workflows/no-response.yml
@ -9,7 +9,7 @@ on:

 jobs:
  noResponse:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    steps:
      - uses: lee-dohm/no-response@v0.5.0
        with:
--- a/.github/workflows/optional_checks.yml
+++ b/.github/workflows/optional_checks.yml
@ -9,7 +9,7 @@ on:
 jobs:
  all-successful:
    timeout-minutes: 120
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3
--- a/.github/workflows/r_configure.yml
+++ b/.github/workflows/r_configure.yml
@ -9,7 +9,7 @@ jobs:
    name: r-configure
    timeout-minutes: 60
    runs-on: ubuntu-latest
-    container: "ubuntu:20.04"
+    container: "ubuntu:22.04"
    steps:
      - name: Install essential software before checkout
        run: |
--- a/.github/workflows/r_package.yml
+++ b/.github/workflows/r_package.yml
@ -33,22 +33,22 @@ jobs:
          ################
          # CMake builds #
          ################
-          - os: ubuntu-latest
+          - os: ubuntu-22.04
            task: r-package
            compiler: gcc
            r_version: 3.6
            build_type: cmake
-          - os: ubuntu-latest
+          - os: ubuntu-22.04
            task: r-package
            compiler: gcc
            r_version: 4.2
            build_type: cmake
-          - os: ubuntu-latest
+          - os: ubuntu-22.04
            task: r-package
            compiler: clang
            r_version: 3.6
            build_type: cmake
-          - os: ubuntu-latest
+          - os: ubuntu-22.04
            task: r-package
            compiler: clang
            r_version: 4.2
@ -114,7 +114,7 @@ jobs:
            toolchain: MSYS
            r_version: 4.2
            build_type: cran
-          - os: ubuntu-latest
+          - os: ubuntu-22.04
            task: r-package
            compiler: gcc
            r_version: 4.2
@ -127,7 +127,7 @@ jobs:
          ################
          # Other checks #
          ################
-          - os: ubuntu-latest
+          - os: ubuntu-22.04
            task: r-rchk
            compiler: gcc
            r_version: 4.2
@ -151,7 +151,7 @@ jobs:
          CTAN_MIRROR: https://ctan.math.illinois.edu/systems/win32/miktex
          TINYTEX_INSTALLER: TinyTeX
      - name: Setup and run tests on Linux and macOS
-        if: matrix.os == 'macOS-latest' || matrix.os == 'ubuntu-latest'
+        if: matrix.os == 'macOS-latest' || matrix.os == 'ubuntu-22.04'
        shell: bash
        run: |
          export TASK="${{ matrix.task }}"
@ -159,7 +159,7 @@ jobs:
          export GITHUB_ACTIONS="true"
          if [[ "${{ matrix.os }}" == "macOS-latest" ]]; then
              export OS_NAME="macos"
-          elif [[ "${{ matrix.os }}" == "ubuntu-latest" ]]; then
+          elif [[ "${{ matrix.os }}" == "ubuntu-22.04" ]]; then
              export OS_NAME="linux"
          fi
          export BUILD_DIRECTORY="$GITHUB_WORKSPACE"
@ -181,9 +181,9 @@ jobs:
          $env:TASK = "${{ matrix.task }}"
          & "$env:GITHUB_WORKSPACE/.ci/test_windows.ps1"
  test-r-sanitizers:
-    name: r-sanitizers (ubuntu-latest, R-devel, ${{ matrix.compiler }} ASAN/UBSAN)
+    name: r-sanitizers (ubuntu-22.04, R-devel, ${{ matrix.compiler }} ASAN/UBSAN)
    timeout-minutes: 60
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    container: wch1/r-debug
    strategy:
      fail-fast: false
@ -219,7 +219,7 @@ jobs:
  test-r-debian-clang:
    name: r-package (debian, R-devel, clang)
    timeout-minutes: 60
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    container: rhub/debian-clang-devel
    steps:
      - name: Install Git before checkout
@ -248,7 +248,7 @@ jobs:
          fi
  all-successful:
    # https://github.community/t/is-it-possible-to-require-all-github-actions-tasks-to-pass-without-enumerating-them/117957/4?u=graingert
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    needs: [test, test-r-sanitizers, test-r-debian-clang]
    steps:
    - name: Note that all tests succeeded
--- a/.github/workflows/static_analysis.yml
+++ b/.github/workflows/static_analysis.yml
@ -21,7 +21,7 @@ env:
 jobs:
  test:
    name: ${{ matrix.task }}
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    timeout-minutes: 60
    strategy:
      fail-fast: false
@ -47,7 +47,7 @@ jobs:
  r-check-docs:
    name: r-package-check-docs
    timeout-minutes: 60
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    container: rocker/verse
    steps:
      - name: Trust git cloning LightGBM
@ -82,7 +82,7 @@ jobs:
          fi
  all-successful:
    # https://github.community/t/is-it-possible-to-require-all-github-actions-tasks-to-pass-without-enumerating-them/117957/4?u=graingert
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    needs: [test, r-check-docs]
    steps:
    - name: Note that all tests succeeded
--- a/.vsts-ci.yml
+++ b/.vsts-ci.yml
@ -21,7 +21,7 @@ resources:
  - container: linux-artifact-builder
    image: lightgbm/vsts-agent:manylinux_2_28_x86_64
  - container: ubuntu-latest
-    image: 'ubuntu:20.04'
+    image: 'ubuntu:22.04'
    options: "--name ci-container -v /usr/bin/docker:/tmp/docker:ro"
  - container: rbase
    image: wch1/r-debug
@ -151,7 +151,7 @@ jobs:
    OS_NAME: 'linux'
    PRODUCES_ARTIFACTS: 'true'
  pool:
-    vmImage: ubuntu-latest
+    vmImage: ubuntu-22.04
  timeoutInMinutes: 180
  strategy:
    matrix:
@ -299,7 +299,7 @@ jobs:
 ###########################################
  condition: not(startsWith(variables['Build.SourceBranch'], 'refs/pull/'))
  pool:
-    vmImage: 'ubuntu-latest'
+    vmImage: 'ubuntu-22.04'
  container: rbase
  steps:
  - script: |
@ -330,7 +330,7 @@ jobs:
  - R_artifact
  condition: and(succeeded(), not(startsWith(variables['Build.SourceBranch'], 'refs/pull/')))
  pool:
-    vmImage: 'ubuntu-latest'
+    vmImage: 'ubuntu-22.04'
  steps:
  # Create archives with complete source code included (with git submodules)
  - task: ArchiveFiles@2
--- a/R-package/AUTOCONF_UBUNTU_VERSION
+++ b/R-package/AUTOCONF_UBUNTU_VERSION
@ -1 +1 @@
-2.69-11.1
+2.71-2
--- a/R-package/README.md
+++ b/R-package/README.md
@ -352,22 +352,22 @@ This section briefly explains the key files for building a CRAN package. To upda
 At build time, `configure` will be run and used to create a file `Makevars`, using `Makevars.in` as a template.

 1. Edit `configure.ac`.
-2. Create `configure` with `autoconf`. Do not edit it by hand. This file must be generated on Ubuntu 20.04.
+2. Create `configure` with `autoconf`. Do not edit it by hand. This file must be generated on Ubuntu 22.04.

-    If you have an Ubuntu 20.04 environment available, run the provided script from the root of the `LightGBM` repository.
+    If you have an Ubuntu 22.04 environment available, run the provided script from the root of the `LightGBM` repository.

    ```shell
    ./R-package/recreate-configure.sh
    ```

-    If you do not have easy access to an Ubuntu 20.04 environment, the `configure` script can be generated using Docker by running the code below from the root of this repo.
+    If you do not have easy access to an Ubuntu 22.04 environment, the `configure` script can be generated using Docker by running the code below from the root of this repo.

    ```shell
    docker run \
        --rm \
        -v $(pwd):/opt/LightGBM \
        -w /opt/LightGBM \
-        -t ubuntu:20.04 \
+        -t ubuntu:22.04 \
        ./R-package/recreate-configure.sh
    ```

--- a/R-package/configure
+++ b/R-package/configure
--- a/R-package/recreate-configure.sh
+++ b/R-package/recreate-configure.sh
@ -1,7 +1,7 @@
 #!/bin/bash

 # recreates 'configure' from 'configure.ac'
-# this script should run on Ubuntu 20.04
+# this script should run on Ubuntu 22.04
 AUTOCONF_VERSION=$(cat R-package/AUTOCONF_UBUNTU_VERSION)

 # R packages cannot have versions like 3.0.0rc1, but
--- a/tests/python_package_test/test_engine.py
+++ b/tests/python_package_test/test_engine.py
@ -21,7 +21,7 @@ from sklearn.model_selection import GroupKFold, TimeSeriesSplit, train_test_spli
 import lightgbm as lgb
 from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame

-from .utils import (SERIALIZERS, dummy_obj, load_boston, load_breast_cancer, load_digits, load_iris, logistic_sigmoid,
+from .utils import (SERIALIZERS, dummy_obj, load_breast_cancer, load_digits, load_iris, logistic_sigmoid,
                    make_synthetic_regression, mse_obj, pickle_and_unpickle_object, sklearn_multiclass_custom_objective,
                    softmax)

@ -114,7 +114,8 @@ def test_rf():

@pytest.mark.parametrize('objective', ['regression', 'regression_l1', 'huber', 'fair', 'poisson'])
 def test_regression(objective):
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
+    y = np.abs(y)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
        'objective': objective,
@ -133,13 +134,13 @@ def test_regression(objective):
    )
    ret = mean_squared_error(y_test, gbm.predict(X_test))
    if objective == 'huber':
-        assert ret < 35
+        assert ret < 430
    elif objective == 'fair':
-        assert ret < 17
+        assert ret < 296
    elif objective == 'poisson':
-        assert ret < 8
+        assert ret < 193
    else:
-        assert ret < 7
+        assert ret < 338
    assert evals_result['valid_0']['l2'][-1] == pytest.approx(ret)


@ -924,7 +925,7 @@ def test_early_stopping_min_delta(first_only, single_metric, greater_is_better):


 def test_continue_train():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
        'objective': 'regression',
@ -948,7 +949,7 @@ def test_continue_train():
        init_model='model.txt'
    )
    ret = mean_absolute_error(y_test, gbm.predict(X_test))
-    assert ret < 2.0
+    assert ret < 13.6
    assert evals_result['valid_0']['l1'][-1] == pytest.approx(ret)
    np.testing.assert_allclose(evals_result['valid_0']['l1'], evals_result['valid_0']['custom_mae'])

@ -968,7 +969,7 @@ def test_continue_train_reused_dataset():


 def test_continue_train_dart():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
        'boosting_type': 'dart',
@ -989,7 +990,7 @@ def test_continue_train_dart():
        init_model=init_gbm
    )
    ret = mean_absolute_error(y_test, gbm.predict(X_test))
-    assert ret < 2.0
+    assert ret < 13.6
    assert evals_result['valid_0']['l1'][-1] == pytest.approx(ret)


@ -1920,10 +1921,12 @@ def test_refit_dataset_params():
    np.testing.assert_allclose(stored_weights, refit_weight)


-def test_mape_rf():
-    X, y = load_boston(return_X_y=True)
+@pytest.mark.parametrize('boosting_type', ['rf', 'dart'])
+def test_mape_for_specific_boosting_types(boosting_type):
+    X, y = make_synthetic_regression()
+    y = abs(y)
    params = {
-        'boosting_type': 'rf',
+        'boosting_type': boosting_type,
        'objective': 'mape',
        'verbose': -1,
        'bagging_freq': 1,
@ -1935,25 +1938,9 @@ def test_mape_rf():
    gbm = lgb.train(params, lgb_train, num_boost_round=20)
    pred = gbm.predict(X)
    pred_mean = pred.mean()
-    assert pred_mean > 20
-
-
-def test_mape_dart():
-    X, y = load_boston(return_X_y=True)
-    params = {
-        'boosting_type': 'dart',
-        'objective': 'mape',
-        'verbose': -1,
-        'bagging_freq': 1,
-        'bagging_fraction': 0.8,
-        'feature_fraction': 0.8,
-        'boost_from_average': False
-    }
-    lgb_train = lgb.Dataset(X, y)
-    gbm = lgb.train(params, lgb_train, num_boost_round=40)
-    pred = gbm.predict(X)
-    pred_mean = pred.mean()
-    assert pred_mean > 18
+    # the following checks that dart and rf with mape can predict outside the 0-1 range
+    # https://github.com/microsoft/LightGBM/issues/1579
+    assert pred_mean > 8


 def check_constant_features(y_true, expected_pred, more_params):
@ -2667,19 +2654,22 @@ def test_model_size():

@pytest.mark.skipif(getenv('TASK', '') == 'cuda_exp', reason='Skip due to differences in implementation details of CUDA Experimental version')
 def test_get_split_value_histogram():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
+    X = np.repeat(X, 3, axis=0)
+    y = np.repeat(y, 3, axis=0)
+    X[:, 2] = np.random.default_rng(0).integers(0, 20, size=X.shape[0])
    lgb_train = lgb.Dataset(X, y, categorical_feature=[2])
    gbm = lgb.train({'verbose': -1}, lgb_train, num_boost_round=20)
    # test XGBoost-style return value
    params = {'feature': 0, 'xgboost_style': True}
-    assert gbm.get_split_value_histogram(**params).shape == (9, 2)
-    assert gbm.get_split_value_histogram(bins=999, **params).shape == (9, 2)
+    assert gbm.get_split_value_histogram(**params).shape == (12, 2)
+    assert gbm.get_split_value_histogram(bins=999, **params).shape == (12, 2)
    assert gbm.get_split_value_histogram(bins=-1, **params).shape == (1, 2)
    assert gbm.get_split_value_histogram(bins=0, **params).shape == (1, 2)
    assert gbm.get_split_value_histogram(bins=1, **params).shape == (1, 2)
    assert gbm.get_split_value_histogram(bins=2, **params).shape == (2, 2)
-    assert gbm.get_split_value_histogram(bins=6, **params).shape == (5, 2)
-    assert gbm.get_split_value_histogram(bins=7, **params).shape == (6, 2)
+    assert gbm.get_split_value_histogram(bins=6, **params).shape == (6, 2)
+    assert gbm.get_split_value_histogram(bins=7, **params).shape == (7, 2)
    if lgb.compat.PANDAS_INSTALLED:
        np.testing.assert_allclose(
            gbm.get_split_value_histogram(0, xgboost_style=True).values,
@ -2700,8 +2690,8 @@ def test_get_split_value_histogram():
        )
    # test numpy-style return value
    hist, bins = gbm.get_split_value_histogram(0)
-    assert len(hist) == 23
-    assert len(bins) == 24
+    assert len(hist) == 20
+    assert len(bins) == 21
    hist, bins = gbm.get_split_value_histogram(0, bins=999)
    assert len(hist) == 999
    assert len(bins) == 1000
@ -2790,7 +2780,7 @@ def test_early_stopping_for_only_first_metric():
        )
        assert assumed_iteration == len(ret[list(ret.keys())[0]])

-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
    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=73)
    lgb_train = lgb.Dataset(X_train, y_train)
@ -2798,16 +2788,16 @@ def test_early_stopping_for_only_first_metric():
    lgb_valid2 = lgb.Dataset(X_test2, y_test2, reference=lgb_train)

    iter_valid1_l1 = 3
-    iter_valid1_l2 = 14
-    iter_valid2_l1 = 2
+    iter_valid1_l2 = 3
+    iter_valid2_l1 = 3
    iter_valid2_l2 = 15
-    assert len(set([iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2])) == 4
+    assert len(set([iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2])) == 2
    iter_min_l1 = min([iter_valid1_l1, iter_valid2_l1])
    iter_min_l2 = min([iter_valid1_l2, iter_valid2_l2])
    iter_min_valid1 = min([iter_valid1_l1, iter_valid1_l2])

-    iter_cv_l1 = 4
-    iter_cv_l2 = 12
+    iter_cv_l1 = 15
+    iter_cv_l2 = 13
    assert len(set([iter_cv_l1, iter_cv_l2])) == 2
    iter_cv_min = min([iter_cv_l1, iter_cv_l2])

@ -3153,7 +3143,7 @@ def test_trees_to_dataframe():


 def test_interaction_constraints():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression(n_samples=200)
    num_features = X.shape[1]
    train_data = lgb.Dataset(X, label=y)
    # check that constraint containing all features is equivalent to no constraint
@ -3166,9 +3156,7 @@ def test_interaction_constraints():
    pred2 = est.predict(X)
    np.testing.assert_allclose(pred1, pred2)
    # check that constraint partitioning the features reduces train accuracy
-    est = lgb.train(dict(params, interaction_constraints=[list(range(num_features // 2)),
-                                                          list(range(num_features // 2, num_features))]),
-                    train_data, num_boost_round=10)
+    est = lgb.train(dict(params, interaction_constraints=[[0, 2], [1, 3]]), train_data, num_boost_round=10)
    pred3 = est.predict(X)
    assert mean_squared_error(y, pred1) < mean_squared_error(y, pred3)
    # check that constraints consisting of single features reduce accuracy further
@ -3568,7 +3556,7 @@ def test_dump_model_hook():

@pytest.mark.skipif(getenv('TASK', '') == 'cuda_exp', reason='Forced splits are not yet supported by CUDA Experimental version')
 def test_force_split_with_feature_fraction(tmp_path):
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    lgb_train = lgb.Dataset(X_train, y_train)

@ -3595,7 +3583,7 @@ def test_force_split_with_feature_fraction(tmp_path):

    gbm = lgb.train(params, lgb_train)
    ret = mean_absolute_error(y_test, gbm.predict(X_test))
-    assert ret < 2.0
+    assert ret < 15.7

    tree_info = gbm.dump_model()["tree_info"]
    assert len(tree_info) > 1
--- a/tests/python_package_test/test_sklearn.py
+++ b/tests/python_package_test/test_sklearn.py
@ -21,8 +21,8 @@ from sklearn.utils.validation import check_is_fitted
 import lightgbm as lgb
 from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame

-from .utils import (load_boston, load_breast_cancer, load_digits, load_iris, load_linnerud, make_ranking,
-                    make_synthetic_regression, sklearn_multiclass_custom_objective, softmax)
+from .utils import (load_breast_cancer, load_digits, load_iris, load_linnerud, make_ranking, make_synthetic_regression,
+                    sklearn_multiclass_custom_objective, softmax)

 decreasing_generator = itertools.count(0, -1)
 task_to_model_factory = {
@ -112,12 +112,12 @@ def test_binary():


 def test_regression():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1)
    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)])
    ret = mean_squared_error(y_test, gbm.predict(X_test))
-    assert ret < 7
+    assert ret < 174
    assert gbm.evals_result_['valid_0']['l2'][gbm.best_iteration_ - 1] == pytest.approx(ret)


@ -226,12 +226,12 @@ def test_objective_aliases(custom_objective):


 def test_regression_with_custom_objective():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1, objective=objective_ls)
    gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)])
    ret = mean_squared_error(y_test, gbm.predict(X_test))
-    assert ret < 7.0
+    assert ret < 174
    assert gbm.evals_result_['valid_0']['l2'][gbm.best_iteration_ - 1] == pytest.approx(ret)


@ -249,13 +249,12 @@ def test_binary_classification_with_custom_objective():


 def test_dart():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    gbm = lgb.LGBMRegressor(boosting_type='dart', n_estimators=50)
    gbm.fit(X_train, y_train)
    score = gbm.score(X_test, y_test)
-    assert score >= 0.8
-    assert score <= 1.
+    assert 0.8 <= score <= 1.0


 def test_stacking_classifier():
@ -280,7 +279,9 @@ def test_stacking_classifier():


 def test_stacking_regressor():
-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression(n_samples=200)
+    n_features = X.shape[1]
+    n_input_models = 2
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    regressors = [('gbm1', lgb.LGBMRegressor(n_estimators=3)),
                  ('gbm2', lgb.LGBMRegressor(n_estimators=3))]
@ -291,11 +292,11 @@ def test_stacking_regressor():
    score = reg.score(X_test, y_test)
    assert score >= 0.2
    assert score <= 1.
-    assert reg.n_features_in_ == 13  # number of input features
-    assert len(reg.named_estimators_['gbm1'].feature_importances_) == 13
+    assert reg.n_features_in_ == n_features  # number of input features
+    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_
-    assert reg.final_estimator_.n_features_in_ == 15  # number of concatenated features
-    assert len(reg.final_estimator_.feature_importances_) == 15
+    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


 def test_grid_search():
@ -765,7 +766,8 @@ def test_evaluate_train_set():


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

@ -1102,7 +1104,7 @@ def test_first_metric_only():
                else:
                    assert gbm.n_estimators == gbm.best_iteration_

-    X, y = load_boston(return_X_y=True)
+    X, y = make_synthetic_regression(n_samples=300)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    X_test1, X_test2, y_test1, y_test2 = train_test_split(X_test, y_test, test_size=0.5, random_state=72)
    params = {'n_estimators': 30,
@ -1114,11 +1116,11 @@ def test_first_metric_only():
    params_fit = {'X': X_train,
                  'y': y_train}

-    iter_valid1_l1 = 3
-    iter_valid1_l2 = 18
-    iter_valid2_l1 = 11
-    iter_valid2_l2 = 7
-    assert len(set([iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2])) == 4
+    iter_valid1_l1 = 4
+    iter_valid1_l2 = 4
+    iter_valid2_l1 = 2
+    iter_valid2_l2 = 2
+    assert len(set([iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2])) == 2
    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])
--- a/tests/python_package_test/utils.py
+++ b/tests/python_package_test/utils.py
@ -13,11 +13,6 @@ import lightgbm as lgb
 SERIALIZERS = ["pickle", "joblib", "cloudpickle"]


-@lru_cache(maxsize=None)
-def load_boston(**kwargs):
-    return sklearn.datasets.load_boston(**kwargs)
-
-
@lru_cache(maxsize=None)
 def load_breast_cancer(**kwargs):
    return sklearn.datasets.load_breast_cancer(**kwargs)