specify the last supported version of scikit-learn (#2637)

.appveyor.yml

@@ -30,7 +30,7 @@ install:
   - activate
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q -y conda
-  - conda create -q -y -n test-env python=%PYTHON_VERSION% joblib matplotlib numpy pandas psutil pytest python-graphviz scikit-learn scipy
+  - conda create -q -y -n test-env python=%PYTHON_VERSION% joblib matplotlib numpy pandas psutil pytest python-graphviz "scikit-learn<=0.21.3" scipy
   - activate test-env
 
 build_script:

.ci/test.sh

@@ -83,7 +83,7 @@ if [[ $TASK == "r-package" ]]; then
     exit 0
 fi
 
-conda install -q -y -n $CONDA_ENV joblib matplotlib numpy pandas psutil pytest python-graphviz scikit-learn scipy
+conda install -q -y -n $CONDA_ENV joblib matplotlib numpy pandas psutil pytest python-graphviz "scikit-learn<=0.21.3" scipy
 
 if [[ $OS_NAME == "macos" ]] && [[ $COMPILER == "clang" ]]; then
     # fix "OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized." (OpenMP library conflict due to conda's MKL)

.vsts-ci.yml

@@ -132,7 +132,7 @@ jobs:
   - powershell: Write-Host "##vso[task.prependpath]$env:CONDA\Scripts"
     displayName: Enable conda
   - script: |
-      cmd /c "activate & conda config --set always_yes yes --set changeps1 no & conda update -q -y conda & conda create -q -y -n %CONDA_ENV% python=%PYTHON_VERSION% joblib matplotlib numpy pandas psutil pytest python-graphviz scikit-learn scipy"
+      cmd /c 'activate & conda config --set always_yes yes --set changeps1 no & conda update -q -y conda & conda create -q -y -n %CONDA_ENV% python=%PYTHON_VERSION% joblib matplotlib numpy pandas psutil pytest python-graphviz "scikit-learn<=0.21.3" scipy'
       cmd /c "activate %CONDA_ENV% & powershell -ExecutionPolicy Bypass -File %BUILD_SOURCESDIRECTORY%/.ci/test_windows.ps1"
     displayName: Test
   - task: PublishBuildArtifacts@1

docker/dockerfile-python

@@ -18,7 +18,7 @@ RUN apt-get update && \
     export PATH="$CONDA_DIR/bin:$PATH" && \
     conda config --set always_yes yes --set changeps1 no && \
     # lightgbm
-    conda install -q -y numpy scipy scikit-learn pandas && \
+    conda install -q -y numpy scipy "scikit-learn<=0.21.3" pandas && \
     git clone --recursive --branch stable --depth 1 https://github.com/Microsoft/LightGBM && \
     cd LightGBM/python-package && python setup.py install && \
     # clean

docker/gpu/dockerfile.gpu

@@ -75,8 +75,8 @@ RUN echo "export PATH=$CONDA_DIR/bin:"'$PATH' > /etc/profile.d/conda.sh && \
     rm ~/miniconda.sh
 
 RUN conda config --set always_yes yes --set changeps1 no && \
-    conda create -y -q -n py2 python=2.7 mkl numpy scipy scikit-learn jupyter notebook ipython pandas matplotlib && \
-    conda create -y -q -n py3 python=3.6 mkl numpy scipy scikit-learn jupyter notebook ipython pandas matplotlib
+    conda create -y -q -n py2 python=2.7 mkl numpy scipy "scikit-learn<=0.21.3" jupyter notebook ipython pandas matplotlib && \
+    conda create -y -q -n py3 python=3.6 mkl numpy scipy "scikit-learn<=0.21.3" jupyter notebook ipython pandas matplotlib
 
 #################################################################################################################
 #           LightGBM

docs/GPU-Tutorial.rst

-LightGBM GPU Tutorial
+LightGBM GPU Tutorial
 =====================
 
 The purpose of this document is to give you a quick step-by-step tutorial on GPU training.
@@ -78,7 +78,7 @@ If you want to use the Python interface of LightGBM, you can install it now (alo
 ::
 
     sudo apt-get -y install python-pip
-    sudo -H pip install setuptools numpy scipy scikit-learn -U
+    sudo -H pip install setuptools numpy scipy "scikit-learn<=0.21.3" -U
     cd python-package/
     sudo python setup.py install --precompile
     cd ..

docs/Python-API.rst

@@ -24,6 +24,10 @@ Training API
 Scikit-learn API
 ----------------
 
+.. warning::
+
+    The last supported version of scikit-learn is ``0.21.3``. Our estimators are incompatible with newer versions.
+
 .. autosummary::
     :toctree: pythonapi/
 

docs/Python-Intro.rst

@@ -15,11 +15,11 @@ Install
 -------
 
 Install Python-package dependencies,
-``setuptools``, ``wheel``, ``numpy`` and ``scipy`` are required, ``scikit-learn`` is required for sklearn interface and recommended:
+``setuptools``, ``wheel``, ``numpy`` and ``scipy`` are required, ``scikit-learn<=0.21.3`` is required for sklearn interface and recommended:
 
 ::
 
-    pip install setuptools wheel numpy scipy scikit-learn -U
+    pip install setuptools wheel numpy scipy "scikit-learn<=0.21.3" -U
 
 Refer to `Python-package`_ folder for the installation guide.
 

examples/python-guide/README.md

@@ -8,7 +8,7 @@ You should install LightGBM [Python-package](https://github.com/microsoft/LightG
 You also need scikit-learn, pandas, matplotlib (only for plot example), and scipy (only for logistic regression example) to run the examples, but they are not required for the package itself. You can install them with pip:
 
 ```
-pip install scikit-learn pandas matplotlib scipy -U
+pip install "scikit-learn<=0.21.3" pandas matplotlib scipy -U
 ```
 
 Now you can run examples in this folder, for example:

python-package/lightgbm/compat.py

@@ -125,6 +125,7 @@ try:
         from sklearn.cross_validation import StratifiedKFold, GroupKFold
         from sklearn.utils.validation import NotFittedError
     SKLEARN_INSTALLED = True
+    from sklearn import __version__ as SKLEARN_VERSION
     _LGBMModelBase = BaseEstimator
     _LGBMRegressorBase = RegressorMixin
     _LGBMClassifierBase = ClassifierMixin
@@ -140,6 +141,7 @@ try:
     _LGBMComputeSampleWeight = compute_sample_weight
 except ImportError:
     SKLEARN_INSTALLED = False
+    SKLEARN_VERSION = '0.0.0'
     _LGBMModelBase = object
     _LGBMClassifierBase = object
     _LGBMRegressorBase = object

python-package/lightgbm/sklearn.py

@@ -7,7 +7,7 @@ import warnings
 import numpy as np
 
 from .basic import Dataset, LightGBMError, _ConfigAliases
-from .compat import (SKLEARN_INSTALLED, _LGBMClassifierBase,
+from .compat import (SKLEARN_INSTALLED, SKLEARN_VERSION, _LGBMClassifierBase,
                      LGBMNotFittedError, _LGBMLabelEncoder, _LGBMModelBase,
                      _LGBMRegressorBase, _LGBMCheckXY, _LGBMCheckArray, _LGBMCheckConsistentLength,
                      _LGBMAssertAllFinite, _LGBMCheckClassificationTargets, _LGBMComputeSampleWeight,
@@ -294,6 +294,9 @@ class LGBMModel(_LGBMModelBase):
         """
         if not SKLEARN_INSTALLED:
             raise LightGBMError('Scikit-learn is required for this module')
+        elif SKLEARN_VERSION > '0.21.3':
+            raise RuntimeError("The last supported version of scikit-learn is 0.21.3.\n"
+                               "Found version: {0}.".format(SKLEARN_VERSION))
 
         self.boosting_type = boosting_type
         self.objective = objective

python-package/setup.py

@@ -314,7 +314,7 @@ if __name__ == "__main__":
           install_requires=[
               'numpy',
               'scipy',
-              'scikit-learn'
+              'scikit-learn<=0.21.3'
           ],
           maintainer='Guolin Ke',
           maintainer_email='guolin.ke@microsoft.com',