diff --git a/.circleci/config.yml b/.circleci/config.yml index 9fecc150ba297..5480831fd962e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -9,17 +9,17 @@ jobs: - MKL_NUM_THREADS: 2 - MINICONDA_PATH: ~/miniconda - CONDA_ENV_NAME: testenv - - PYTHON_VERSION: 3.5 - - NUMPY_VERSION: 1.11.0 - - SCIPY_VERSION: 0.17.0 - - MATPLOTLIB_VERSION: 1.5.1 + - PYTHON_VERSION: 3.6 + - NUMPY_VERSION: 1.13.3 + - SCIPY_VERSION: 0.19.1 + - MATPLOTLIB_VERSION: 2.1.1 # on conda, this is the latest for python 3.5 # The following places need to be in sync with regard to Cython version: # - .circleci config file # - sklearn/_build_utils/__init__.py # - advanced installation guide - CYTHON_VERSION: 0.28.5 - - SCIKIT_IMAGE_VERSION: 0.12.3 + - SCIKIT_IMAGE_VERSION: 0.13 steps: - checkout - run: ./build_tools/circle/checkout_merge_commit.sh diff --git a/README.rst b/README.rst index 41197e178904a..96bc81fa3822d 100644 --- a/README.rst +++ b/README.rst @@ -47,18 +47,18 @@ Dependencies scikit-learn requires: -- Python (>= 3.5) -- NumPy (>= 1.11.0) -- SciPy (>= 0.17.0) +- Python (>= 3.6) +- NumPy (>= 1.13.3) +- SciPy (>= 0.19.1) - joblib (>= 0.11) **Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.** -scikit-learn 0.21 and later require Python 3.5 or newer. +scikit-learn 0.23 and later require Python 3.6 or newer. Scikit-learn plotting capabilities (i.e., functions start with ``plot_`` -and classes end with "Display") require Matplotlib (>= 1.5.1). For running the -examples Matplotlib >= 1.5.1 is required. A few examples require -scikit-image >= 0.12.3, a few examples require pandas >= 0.18.0. +and classes end with "Display") require Matplotlib (>= 2.1.1). For running the +examples Matplotlib >= 2.1.1 is required. A few examples require +scikit-image >= 0.13, a few examples require pandas >= 0.18.0. User installation ~~~~~~~~~~~~~~~~~ diff --git a/azure-pipelines.yml b/azure-pipelines.yml index b029a2fd18574..6a5126d3a9ba0 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -3,7 +3,7 @@ jobs: - job: linting displayName: Linting pool: - vmImage: ubuntu-16.04 + vmImage: ubuntu-18.04 steps: - bash: echo "##vso[task.prependpath]$CONDA/bin" displayName: Add conda to PATH @@ -27,7 +27,7 @@ jobs: - template: build_tools/azure/posix.yml parameters: name: Linux_Runs - vmImage: ubuntu-16.04 + vmImage: ubuntu-18.04 matrix: pylatest_conda_mkl: DISTRIB: 'conda' @@ -44,31 +44,31 @@ jobs: - template: build_tools/azure/posix.yml parameters: name: Linux - vmImage: ubuntu-16.04 + vmImage: ubuntu-18.04 dependsOn: [linting] matrix: # Linux environment to test that scikit-learn can be built against - # versions of numpy, scipy with ATLAS that comes with Ubuntu Xenial 16.04 - # i.e. numpy 1.11 and scipy 0.17 - py35_ubuntu_atlas: + # versions of numpy, scipy with ATLAS that comes with Ubuntu Bionic 18.04 + # i.e. numpy 1.13.3 and scipy 0.19 + py36_ubuntu_atlas: DISTRIB: 'ubuntu' - PYTHON_VERSION: '3.5' + PYTHON_VERSION: '3.6' JOBLIB_VERSION: '0.11' - # Linux + Python 3.5 build with OpenBLAS and without SITE_JOBLIB - py35_conda_openblas: + # Linux + Python 3.6 build with OpenBLAS and without SITE_JOBLIB + py36_conda_openblas: DISTRIB: 'conda' - PYTHON_VERSION: '3.5' + PYTHON_VERSION: '3.6' BLAS: 'openblas' - NUMPY_VERSION: '1.11.0' - SCIPY_VERSION: '0.17.0' + NUMPY_VERSION: '1.13.3' + SCIPY_VERSION: '0.19.1' PANDAS_VERSION: '*' CYTHON_VERSION: '*' # temporary pin pytest due to unknown failure with pytest 5.3 PYTEST_VERSION: '5.2' - PILLOW_VERSION: '4.0.0' - MATPLOTLIB_VERSION: '1.5.1' - # later version of joblib are not packaged in conda for Python 3.5 - JOBLIB_VERSION: '0.12.3' + PILLOW_VERSION: '4.2.1' + MATPLOTLIB_VERSION: '2.1.1' + # latest version of joblib available in conda for Python 3.6 + JOBLIB_VERSION: '0.13.2' COVERAGE: 'true' # Linux environment to test the latest available dependencies and MKL. # It runs tests requiring lightgbm, pandas and PyAMG. @@ -84,13 +84,13 @@ jobs: - template: build_tools/azure/posix-32.yml parameters: name: Linux32 - vmImage: ubuntu-16.04 + vmImage: ubuntu-18.04 dependsOn: [linting] matrix: - py35_ubuntu_atlas_32bit: + py36_ubuntu_atlas_32bit: DISTRIB: 'ubuntu-32' - PYTHON_VERSION: '3.5' - JOBLIB_VERSION: '0.11' + PYTHON_VERSION: '3.6' + JOBLIB_VERSION: '0.13' - template: build_tools/azure/posix.yml parameters: @@ -135,6 +135,6 @@ jobs: PYTHON_ARCH: '64' PYTEST_VERSION: '*' COVERAGE: 'true' - py35_pip_openblas_32bit: - PYTHON_VERSION: '3.5' + py36_pip_openblas_32bit: + PYTHON_VERSION: '3.6' PYTHON_ARCH: '32' diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index f119b280041d7..7a89e29445e78 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -1,6 +1,7 @@ #!/bin/bash set -e +set -x UNAMESTR=`uname` @@ -73,13 +74,13 @@ if [[ "$DISTRIB" == "conda" ]]; then elif [[ "$DISTRIB" == "ubuntu" ]]; then sudo add-apt-repository --remove ppa:ubuntu-toolchain-r/test sudo apt-get update - sudo apt-get install python3-scipy python3-matplotlib libatlas3-base libatlas-base-dev libatlas-dev python3-virtualenv + sudo apt-get install python3-scipy python3-matplotlib libatlas3-base libatlas-base-dev python3-virtualenv python3 -m virtualenv --system-site-packages --python=python3 $VIRTUALENV source $VIRTUALENV/bin/activate python -m pip install pytest==$PYTEST_VERSION pytest-cov cython joblib==$JOBLIB_VERSION elif [[ "$DISTRIB" == "ubuntu-32" ]]; then apt-get update - apt-get install -y python3-dev python3-scipy python3-matplotlib libatlas3-base libatlas-base-dev libatlas-dev python3-virtualenv + apt-get install -y python3-dev python3-scipy python3-matplotlib libatlas3-base libatlas-base-dev python3-virtualenv python3 -m virtualenv --system-site-packages --python=python3 $VIRTUALENV source $VIRTUALENV/bin/activate python -m pip install pytest==$PYTEST_VERSION pytest-cov cython joblib==$JOBLIB_VERSION diff --git a/build_tools/azure/posix-32.yml b/build_tools/azure/posix-32.yml index 68e05e347f307..b4c9e4ebb287e 100644 --- a/build_tools/azure/posix-32.yml +++ b/build_tools/azure/posix-32.yml @@ -40,7 +40,7 @@ jobs: -e OMP_NUM_THREADS=$OMP_NUM_THREADS -e OPENBLAS_NUM_THREADS=$OPENBLAS_NUM_THREADS -e SKLEARN_SKIP_NETWORK_TESTS=$SKLEARN_SKIP_NETWORK_TESTS - i386/ubuntu:16.04 + i386/ubuntu:18.04 sleep 1000000 displayName: 'Start container' - script: > diff --git a/build_tools/circle/build_test_pypy.sh b/build_tools/circle/build_test_pypy.sh index 22e4790e7e4ab..0b53aae2984f2 100755 --- a/build_tools/circle/build_test_pypy.sh +++ b/build_tools/circle/build_test_pypy.sh @@ -3,7 +3,7 @@ set -x set -e apt-get -yq update -apt-get -yq install libatlas-dev libatlas-base-dev liblapack-dev gfortran ccache libopenblas-dev +apt-get -yq install libatlas-base-dev liblapack-dev gfortran ccache libopenblas-dev pip install virtualenv diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index d86e59de0e746..d719dfd4ff907 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -83,9 +83,9 @@ Runtime dependencies Scikit-learn requires the following dependencies both at build time and at runtime: -- Python (>= 3.5), -- NumPy (>= 1.11), -- SciPy (>= 0.17), +- Python (>= 3.6), +- NumPy (>= 1.13.3), +- SciPy (>= 0.19), - Joblib (>= 0.11). Those dependencies are **automatically installed by pip** if they were missing diff --git a/doc/install.rst b/doc/install.rst index 886ed272a65ba..6a2b83605c1a6 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -132,14 +132,16 @@ If you must install scikit-learn and its dependencies with pip, you can install it as ``scikit-learn[alldeps]``. Scikit-learn plotting capabilities (i.e., functions start with "plot\_" -and classes end with "Display") require Matplotlib (>= 1.5.1). For running the -examples Matplotlib >= 1.5.1 is required. A few examples require -scikit-image >= 0.12.3, a few examples require pandas >= 0.18.0. +and classes end with "Display") require Matplotlib (>= 2.1.1). For running the +examples Matplotlib >= 2.1.1 is required. A few examples require +scikit-image >= 0.13, a few examples require pandas >= 0.18.0. .. warning:: Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. - Scikit-learn now requires Python 3.5 or newer. + Scikit-learn 0.21 supported Python 3.5-3.7. + Scikit-learn 0.22 supported Python 3.5-3.8. + Scikit-learn now requires Python 3.6 or newer. .. note:: diff --git a/doc/templates/index.html b/doc/templates/index.html index f897ae5f7031c..b2e02f94ba903 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -156,6 +156,8 @@

News

  • On-going development: What's new (Changelog)
    • +
  • Scikit-learn from 0.23 requires Python 3.6 or greater. +
  • January 2020. scikit-learn 0.22.1 is available for download (Changelog).
  • December 2019. scikit-learn 0.22 is available for download (Changelog).
    • diff --git a/doc/tutorial/machine_learning_map/pyparsing.py b/doc/tutorial/machine_learning_map/pyparsing.py index 20690df7aec47..0c5fca5cf891d 100644 --- a/doc/tutorial/machine_learning_map/pyparsing.py +++ b/doc/tutorial/machine_learning_map/pyparsing.py @@ -1020,20 +1020,14 @@ def _trim_arity(func, maxargs=2): limit = [0] foundArity = [False] - # traceback return data structure changed in Py3.5 - normalize back to plain tuples - if system_version[:2] >= (3,5): - def extract_stack(limit=0): - # special handling for Python 3.5.0 - extra deep call stack by 1 - offset = -3 if system_version == (3,5,0) else -2 - frame_summary = traceback.extract_stack(limit=-offset+limit-1)[offset] - return [(frame_summary.filename, frame_summary.lineno)] - def extract_tb(tb, limit=0): - frames = traceback.extract_tb(tb, limit=limit) - frame_summary = frames[-1] - return [(frame_summary.filename, frame_summary.lineno)] - else: - extract_stack = traceback.extract_stack - extract_tb = traceback.extract_tb + def extract_stack(limit=0): + offset = -2 + frame_summary = traceback.extract_stack(limit=-offset+limit-1)[offset] + return [(frame_summary.filename, frame_summary.lineno)] + def extract_tb(tb, limit=0): + frames = traceback.extract_tb(tb, limit=limit) + frame_summary = frames[-1] + return [(frame_summary.filename, frame_summary.lineno)] # synthesize what would be returned by traceback.extract_stack at the call to # user's parse action 'func', so that we don't incur call penalty at parse time diff --git a/setup.py b/setup.py index a8d0a81ea02c8..798c8bfc5d305 100755 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ try: import builtins except ImportError: - # Python 2 compat: just to be able to declare that Python >=3.5 is needed. + # Python 2 compat: just to be able to declare that Python >=3.6 is needed. import __builtin__ as builtins # This is a bit (!) hackish: we are setting a global variable so that the @@ -52,8 +52,8 @@ SCIPY_MIN_VERSION = '1.1.0' NUMPY_MIN_VERSION = '1.14.0' else: - SCIPY_MIN_VERSION = '0.17.0' - NUMPY_MIN_VERSION = '1.11.0' + SCIPY_MIN_VERSION = '0.19.1' + NUMPY_MIN_VERSION = '1.13.3' JOBLIB_MIN_VERSION = '0.11' @@ -139,7 +139,7 @@ def build_extensions(self): except ImportError: # Numpy should not be a dependency just to be able to introspect - # that python 3.5 is required. + # that python 3.6 is required. pass @@ -244,7 +244,6 @@ def setup_package(): 'Operating System :: Unix', 'Operating System :: MacOS', 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', @@ -254,7 +253,7 @@ def setup_package(): 'Implementation :: PyPy') ], cmdclass=cmdclass, - python_requires=">=3.5", + python_requires=">=3.6", install_requires=[ 'numpy>={}'.format(NUMPY_MIN_VERSION), 'scipy>={}'.format(SCIPY_MIN_VERSION), @@ -282,9 +281,9 @@ def setup_package(): metadata['version'] = VERSION else: - if sys.version_info < (3, 5): + if sys.version_info < (3, 6): raise RuntimeError( - "Scikit-learn requires Python 3.5 or later. The current" + "Scikit-learn requires Python 3.6 or later. The current" " Python version is %s installed in %s." % (platform.python_version(), sys.executable)) diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py index 3ccda942b65e1..022ce776de092 100644 --- a/sklearn/feature_extraction/tests/test_text.py +++ b/sklearn/feature_extraction/tests/test_text.py @@ -32,7 +32,6 @@ from sklearn.utils import IS_PYPY from sklearn.utils._testing import (assert_almost_equal, assert_warns_message, assert_raise_message, - clean_warning_registry, SkipTest, assert_no_warnings, fails_if_pypy, assert_allclose_dense_sparse, skip_if_32bit) @@ -388,7 +387,6 @@ def test_tfidf_no_smoothing(): [1, 0, 0]] tr = TfidfTransformer(smooth_idf=False, norm='l2') - clean_warning_registry() with warnings.catch_warnings(record=True) as w: 1. / np.array([0.]) numpy_provides_div0_warning = len(w) == 1 diff --git a/sklearn/gaussian_process/tests/test_gpr.py b/sklearn/gaussian_process/tests/test_gpr.py index eb4bc6dec1761..4ed105db04246 100644 --- a/sklearn/gaussian_process/tests/test_gpr.py +++ b/sklearn/gaussian_process/tests/test_gpr.py @@ -3,6 +3,7 @@ # Author: Jan Hendrik Metzen # License: BSD 3 clause +import sys import numpy as np from scipy.optimize import approx_fprime @@ -46,6 +47,9 @@ def f(x): @pytest.mark.parametrize('kernel', kernels) def test_gpr_interpolation(kernel): + if sys.maxsize <= 2 ** 32 and sys.version_info[:2] == (3, 6): + pytest.xfail("This test may fail on 32bit Py3.6") + # Test the interpolating property for different kernels. gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y) y_pred, y_cov = gpr.predict(X, return_cov=True) @@ -70,6 +74,9 @@ def test_gpr_interpolation_structured(): @pytest.mark.parametrize('kernel', non_fixed_kernels) def test_lml_improving(kernel): + if sys.maxsize <= 2 ** 32 and sys.version_info[:2] == (3, 6): + pytest.xfail("This test may fail on 32bit Py3.6") + # Test that hyperparameter-tuning improves log-marginal likelihood. gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y) assert (gpr.log_marginal_likelihood(gpr.kernel_.theta) > @@ -175,6 +182,9 @@ def test_no_optimizer(): @pytest.mark.parametrize('kernel', kernels) def test_predict_cov_vs_std(kernel): + if sys.maxsize <= 2 ** 32 and sys.version_info[:2] == (3, 6): + pytest.xfail("This test may fail on 32bit Py3.6") + # Test that predicted std.-dev. is consistent with cov's diagonal. gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y) y_mean, y_cov = gpr.predict(X2, return_cov=True) diff --git a/sklearn/utils/_mask.py b/sklearn/utils/_mask.py index 1f660205cdd47..48b2a11c6f87b 100644 --- a/sklearn/utils/_mask.py +++ b/sklearn/utils/_mask.py @@ -5,7 +5,7 @@ def _get_mask(X, value_to_mask): - """Compute the boolean mask X == missing_values.""" + """Compute the boolean mask X == value_to_mask.""" if is_scalar_nan(value_to_mask): if X.dtype.kind == "f": return np.isnan(X) @@ -16,6 +16,4 @@ def _get_mask(X, value_to_mask): # np.isnan does not work on object dtypes. return _object_dtype_isnan(X) else: - # X == value_to_mask with object dtypes does not always perform - # element-wise for old versions of numpy - return np.equal(X, value_to_mask) + return X == value_to_mask diff --git a/sklearn/utils/_testing.py b/sklearn/utils/_testing.py index 931622b3889db..eb6e381f02840 100644 --- a/sklearn/utils/_testing.py +++ b/sklearn/utils/_testing.py @@ -565,21 +565,6 @@ def set_random_state(estimator, random_state=0): pass -def clean_warning_registry(): - """Clean Python warning registry for easier testing of warning messages. - - When changing warning filters this function is not necessary with - Python3.5+, as __warningregistry__ will be re-set internally. - See https://bugs.python.org/issue4180 and - https://bugs.python.org/issue21724 for more details. - - """ - for mod in sys.modules.values(): - registry = getattr(mod, "__warningregistry__", None) - if registry is not None: - registry.clear() - - def check_skip_network(): if int(os.environ.get('SKLEARN_SKIP_NETWORK_TESTS', 0)): raise SkipTest("Text tutorial requires large dataset download") diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index 4456e9d271329..03e220eab29ae 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -173,16 +173,8 @@ def __getstate__(self): from numpy.ma import MaskedArray # noqa -# Fix for behavior inconsistency on numpy.equal for object dtypes. -# For numpy versions < 1.13, numpy.equal tests element-wise identity of objects -# instead of equality. This fix returns the mask of NaNs in an array of -# numerical or object values for all numpy versions. -if np_version < (1, 13): - def _object_dtype_isnan(X): - return np.frompyfunc(lambda x: x != x, 1, 1)(X).astype(bool) -else: - def _object_dtype_isnan(X): - return X != X +def _object_dtype_isnan(X): + return X != X # TODO: replace by copy=False, when only scipy > 1.1 is supported.