diff options
| -rw-r--r-- | .travis.yml | 58 | ||||
| -rw-r--r-- | MANIFEST.in | 1 | ||||
| -rw-r--r-- | README.rst | 10 | ||||
| -rw-r--r-- | gensvm/core.py | 2 | ||||
| -rw-r--r-- | gensvm/cython_wrapper/wrapper.pyx | 25 | ||||
| -rw-r--r-- | gensvm/gridsearch.py | 2 | ||||
| -rw-r--r-- | gensvm/sklearn_util.py | 15 | ||||
| -rw-r--r-- | gensvm/util.py | 13 | ||||
| -rw-r--r-- | poetry.lock | 250 | ||||
| -rw-r--r-- | pyproject.toml | 19 | ||||
| -rw-r--r-- | setup.py | 163 |
11 files changed, 230 insertions, 328 deletions
diff --git a/.travis.yml b/.travis.yml index 78aa334..0950aa6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,20 +1,50 @@ -language: python -python: - - "3.6" - - "2.7" +# Travis CI configuration for GenSVM -env: - - CC="gcc" +jobs: + include: + - language: python + dist: xenial + python: "3.7" + services: + - docker + before_install: + - sudo apt-get update + - sudo apt-get install -y libatlas-base-dev liblapack-dev liblapacke-dev; + env: + - CC="gcc" + - PYTHON="python3" + - PIP="pip3" + - CIBW_BEFORE_BUILD="yum install -y atlas-devel lapack-devel && pip install numpy Cython" + + - os: osx + osx_image: xcode11.3 + language: generic + before_install: + - brew update + - brew install openblas + env: + - CC="gcc" + - CFLAGS="-fcommon" # avoids build errors on OSx + - PYTHON="python3" + - PIP="pip3" + - CIBW_BEFORE_BUILD="pip install numpy Cython" -before_install: - - sudo apt-get update - - sudo apt-get install -y libatlas-base-dev liblapack-dev liblapacke-dev +env: + global: + # - No longer actively maintaining gensvm for Python 2.7 + # - Skipping 3.5 for now because scikit-learn is not available as wheel + # anymore, and this breaks testing. + - CIBW_SKIP="cp27-* cp35-*" + # Run the unit tests on the wheels that are created + - CIBW_TEST_REQUIRES="numpy Cython" + - CIBW_TEST_COMMAND="python -VV && python -m unittest discover -f -s {project}/test" install: - - pip install --upgrade pip - - pip install -U -r requirements.txt - - pip install green Cython - - python setup.py build_ext --inplace + - $PIP install numpy + - $PIP install -e .[dev] + - $PYTHON -m unittest discover -v -f -s ./test script: - - green -vv -f + - $PIP install cibuildwheel==1.0.0 + - cibuildwheel --output-dir wheelhouse + - ls wheelhouse diff --git a/MANIFEST.in b/MANIFEST.in index 5f5f17a..b1a1c22 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -12,6 +12,7 @@ exclude src/gensvm/.gitignore exclude .gitmodules exclude .gitignore exclude .travis.yml +exclude pyproject.toml exclude Makefile recursive-exclude test_human *.py recursive-exclude docs * @@ -36,16 +36,12 @@ J.J. van den Burg <https://gertjanvandenburg.com>`_ and `Patrick J.F. Groenen Installation ------------ -**Before** GenSVM can be installed, a working NumPy installation is required. -Please see `the installation instructions for NumPy -<https://docs.scipy.org/doc/numpy-1.13.0/user/install.html>`_, then install -GenSVM using the instructions below. - -GenSVM can be easily installed through pip: +**Before** GenSVM can be installed, a working NumPy installation is required, +so GenSVM can be installed using the following command: .. code:: bash - pip install gensvm + pip install numpy && pip install gensvm If you encounter any errors, please open an issue on `GitHub <https://github.com/GjjvdBurg/PyGenSVM>`_. diff --git a/gensvm/core.py b/gensvm/core.py index bfd5d9a..45d59ad 100644 --- a/gensvm/core.py +++ b/gensvm/core.py @@ -16,9 +16,9 @@ from sklearn.exceptions import ConvergenceWarning, FitFailedWarning from sklearn.preprocessing import LabelEncoder from sklearn.utils import check_array, check_X_y, check_random_state from sklearn.utils.multiclass import type_of_target -from sklearn.utils.validation import check_is_fitted from .cython_wrapper import wrapper +from .util import check_is_fitted def _fit_gensvm( diff --git a/gensvm/cython_wrapper/wrapper.pyx b/gensvm/cython_wrapper/wrapper.pyx index 009e70b..3a85e92 100644 --- a/gensvm/cython_wrapper/wrapper.pyx +++ b/gensvm/cython_wrapper/wrapper.pyx @@ -88,8 +88,7 @@ def train_wrap( raise ValueError(error_repl) # Do the actual training - with nogil: - gensvm_train(model, data, seed_model) + gensvm_train(model, data, seed_model) # update the number of variables (this may have changed due to kernel) n_var = get_m(model) @@ -134,12 +133,11 @@ def predict_wrap( # output vector cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions - predictions = np.empty((n_test_obs, ), dtype=np.int) + predictions = np.empty((n_test_obs, ), dtype=np.int_) # do the prediction - with nogil: - gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, - predictions.data) + gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, + predictions.data) return predictions @@ -174,10 +172,9 @@ def predict_kernels_wrap( cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions predictions = np.empty((n_obs_test, ), dtype=np.int) - with nogil: - gensvm_predict_kernels(Xtest.data, Xtrain.data, V.data, V_rows, - V_cols, n_obs_train, n_obs_test, n_var, n_class, kernel_idx, - gamma, coef, degree, kernel_eigen_cutoff, predictions.data) + gensvm_predict_kernels(Xtest.data, Xtrain.data, V.data, V_rows, V_cols, + n_obs_train, n_obs_test, n_var, n_class, kernel_idx, gamma, coef, + degree, kernel_eigen_cutoff, predictions.data) return predictions @@ -243,9 +240,7 @@ def grid_wrap( set_queue(queue, n_tasks, tasks) - with nogil: - gensvm_train_q_helper(queue, cv_idx.data, store_predictions, - verbosity) + gensvm_train_q_helper(queue, cv_idx.data, store_predictions, verbosity) cdef np.ndarray[np.int_t, ndim=1, mode='c'] pred cdef np.ndarray[np.double_t, ndim=1, mode='c'] dur @@ -264,12 +259,12 @@ def grid_wrap( results['params'].append(candidate_params[ID]) results['scores'].append(get_task_performance(tasks[ID])) if store_predictions: - pred = np.zeros((n_obs, ), dtype=np.int) + pred = np.zeros((n_obs, ), dtype=np.int_) copy_task_predictions(tasks[ID], pred.data, n_obs) results['predictions'].append(pred.copy()) dur = np.zeros((n_folds, ), dtype=np.double) copy_task_durations(tasks[ID], dur.data, n_folds) - results['durations'].append(dur.copy()) + results['durations'].append(dur) gensvm_free_queue(queue) free_data(data) diff --git a/gensvm/gridsearch.py b/gensvm/gridsearch.py index b27a347..22125a4 100644 --- a/gensvm/gridsearch.py +++ b/gensvm/gridsearch.py @@ -116,7 +116,7 @@ def _wrap_score(y_pred, y_true, scorers, is_multimetric): results["score"] = np.nan else: estimator = _MockEstimator(y_pred) - results = _score(estimator, None, y_true, scorers, is_multimetric) + results = _score(estimator, None, y_true, scorers) score_time = time.time() - start_time return results, score_time diff --git a/gensvm/sklearn_util.py b/gensvm/sklearn_util.py index 182f257..eb8ceb6 100644 --- a/gensvm/sklearn_util.py +++ b/gensvm/sklearn_util.py @@ -89,7 +89,9 @@ def _skl_format_cv_results( score_time, ) = zip(*out) else: - (test_score_dicts, test_sample_counts, fit_time, score_time) = zip(*out) + (test_score_dicts, test_sample_counts, fit_time, score_time) = zip( + *out + ) # test_score_dicts and train_score dicts are lists of dictionaries and # we make them into dict of lists @@ -160,7 +162,9 @@ def _skl_format_cv_results( ) if return_train_score: _store( - "train_%s" % scorer_name, train_scores[scorer_name], splits=True + "train_%s" % scorer_name, + train_scores[scorer_name], + splits=True, ) return results @@ -207,7 +211,12 @@ def _skl_check_is_fitted(estimator, method_name, refit): "attribute" % (type(estimator).__name__, method_name) ) else: - check_is_fitted(estimator, "best_estimator_") + if not hasattr(estimator, "best_estimator_"): + raise NotFittedError( + "This %s instance is not fitted yet. Call " + "'fit' with appropriate arguments before using this " + "estimator." % type(estimator).__name__ + ) def _skl_grid_score(X, y, scorer_, best_estimator_, refit, multimetric_): diff --git a/gensvm/util.py b/gensvm/util.py index 046f3be..40d0eb1 100644 --- a/gensvm/util.py +++ b/gensvm/util.py @@ -8,6 +8,7 @@ Utility functions for GenSVM import numpy as np +from sklearn.exceptions import NotFittedError def get_ranks(a): """ @@ -37,3 +38,15 @@ def get_ranks(a): ranks[~np.isnan(orig)] = count[dense - 1] + 1 ranks[np.isnan(orig)] = np.max(ranks) + 1 return list(ranks) + + +def check_is_fitted(estimator, attribute): + msg = ( + "This %(name)s instance is not fitted yet. Call 'fit' " + "with appropriate arguments before using this estimator." + ) + if not hasattr(estimator, "fit"): + raise TypeError("%s is not an estimator instance" % (estimator)) + + if not hasattr(estimator, attribute): + raise NotFittedError(msg % {"name": type(estimator).__name__}) diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 91337ff..0000000 --- a/poetry.lock +++ /dev/null @@ -1,250 +0,0 @@ -[[package]] -category = "dev" -description = "A configurable sidebar-enabled Sphinx theme" -name = "alabaster" -optional = false -python-versions = "*" -version = "0.7.12" - -[[package]] -category = "dev" -description = "Internationalization utilities" -name = "babel" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -version = "2.7.0" - -[package.dependencies] -pytz = ">=2015.7" - -[[package]] -category = "dev" -description = "Python package for providing Mozilla's CA Bundle." -name = "certifi" -optional = false -python-versions = "*" 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a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 1668a61..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,19 +0,0 @@ -[tool.poetry] -name = "gensvm" -version = "0.2.4" -description = "Python package for the GenSVM classifier" -authors = ["Gertjan van den Burg <gertjanvandenburg@gmail.com>"] -license = "GPL-2.0+" - -[tool.poetry.dependencies] -python = "^3.7" -scikit-learn = "^0.21.2" -numpy = "^1.16" - -[tool.poetry.dev-dependencies] -Sphinx = "=1.6.5" -sphinx_rtd_theme = "^0.4.3" - -[build-system] -requires = ["poetry>=0.12"] -build-backend = "poetry.masonry.api" @@ -3,6 +3,32 @@ import os import re +import sys + +# Package meta-data +AUTHOR = "Gertjan van den Burg" +DESCRIPTION = "Generalized Multiclass Support Vector Machines" +EMAIL = "gertjanvandenburg@gmail.com" +LICENSE = "GPLv2" +LICENSE_TROVE = ( + "License :: OSI Approved :: GNU General Public License v2 (GPLv2)" +) +NAME = "gensvm" +REQUIRES_PYTHON = ">=2.7" +URL = "https://github.com/GjjvdBurg/PyGenSVM" +VERSION = None + +REQUIRED = ["scikit-learn", "numpy"] + +docs_require = ["Sphinx==1.6.5", "sphinx_rtd_theme>=0.4.3"] +test_require = [] +dev_require = ["Cython"] + +EXTRAS = { + "docs": docs_require, + "tests": test_require, + "dev": docs_require + test_require + dev_require, +} # Set this to True to enable building extensions using Cython. Set it to False· # to build extensions from the C file (that was previously generated using· @@ -10,7 +36,7 @@ import re # the C file. USE_CYTHON = "auto" -# If we are in a release, we always never use Cython directly +# If we are in a release, we never use Cython directly IS_RELEASE = os.path.exists("PKG-INFO") if IS_RELEASE: USE_CYTHON = False @@ -25,15 +51,30 @@ if USE_CYTHON: else: raise -# Try to load setuptools, so that NumPy's distutils module that we use to -# provide the setup() function below comes from the setuptools package. If it -# fails, it'll use distutils' version, which doesn't support installing +# Try to load setuptools, so that NumPy's distutils module that we use to +# provide the setup() function below comes from the setuptools package. If it +# fails, it'll use distutils' version, which doesn't support installing # dependencies. try: - import setuptools + import setuptools except ImportError: - print("Warning: setuptools not found. You may have to install GenSVM's dependencies manually.") + print( + "Warning: setuptools not found. You may have to install GenSVM's dependencies manually." + ) + +def on_cibw_win(): + return ( + os.environ.get("CIBUILDWHEEL", "0") == "1" + and os.environ.get("TRAVIS_OS_NAME", "none") == "windows" + ) + + +def on_cibw_mac(): + return ( + os.environ.get("CIBUILDWHEEL", "0") == "1" + and os.environ.get("TRAVIS_OS_NAME", "none") == "osx" + ) def _skl_get_blas_info(): @@ -73,7 +114,7 @@ def _skl_get_blas_info(): from numpy.distutils.system_info import get_info def atlas_not_found(blas_info_): - def_macros = blas_info.get("define_macros", []) + def_macros = blas_info_.get("define_macros", []) for x in def_macros: if x[0] == "NO_ATLAS_INFO": # if x[1] != 1 we should have lapack @@ -85,13 +126,59 @@ def _skl_get_blas_info(): return True return False - blas_info = get_info("blas_opt", 0) + if on_cibw_win(): + blas_info = get_info("blas_opt", notfound_action=0) + blas_info = { + "define_macros": [("NO_ATLAS_INFO", 1), ("HAVE_CBLAS", None)], + "library_dirs": [ + os.sep.join( + [ + "C:", + "cibw", + "openblas", + "OpenBLAS.0.2.14.1", + "lib", + "native", + "lib", + ] + ) + ], + "include_dirs": [ + os.sep.join( + [ + "C:", + "cibw", + "openblas", + "OpenBLAS.0.2.14.1", + "lib", + "native", + "include", + ] + ) + ], + "language": "c", + } + return ["libopenblas"], blas_info + + blas_info = get_info("blas_opt", notfound_action=2) if (not blas_info) or atlas_not_found(blas_info): cblas_libs = ["cblas"] blas_info.pop("libraries", None) else: cblas_libs = blas_info.pop("libraries", []) + if os.environ.get("TRAVIS_OS_NAME", "none") == "osx": + libdir = blas_info.get("library_dirs", []) + libdir = libdir[0] if libdir else None + if libdir: + base = os.path.split(libdir)[0] + blas_info["include_dirs"] = [os.path.join(base, "include")] + + if on_cibw_mac(): + blas_info["include_dirs"] = [ + "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Versions/Current/Headers/" + ] + return cblas_libs, blas_info @@ -100,7 +187,7 @@ def get_lapack_info(): from numpy.distutils.system_info import get_info def atlas_not_found(lapack_info_): - def_macros = lapack_info.get("define_macros", []) + def_macros = lapack_info_.get("define_macros", []) for x in def_macros: if x[0] == "NO_ATLAS_INFO": return True @@ -109,9 +196,45 @@ def get_lapack_info(): return True return False - lapack_info = get_info("lapack_opt", 0) + if on_cibw_win(): + lapack_info = get_info("lapack_opt", notfound_action=0) + lapack_info = { + "define_macros": [("NO_ATLAS_INFO", 1), ("HAVE_CBLAS", None)], + "library_dirs": [ + os.sep.join( + [ + "C:", + "cibw", + "openblas", + "OpenBLAS.0.2.14.1", + "lib", + "native", + "lib", + ] + ) + ], + "include_dirs": [ + os.sep.join( + [ + "C:", + "cibw", + "openblas", + "OpenBLAS.0.2.14.1", + "lib", + "native", + "include", + ] + ) + ], + "language": "c", + } + print("***\nDefined lapack info: %r" % lapack_info) + return ["libopenblas"], lapack_info + + lapack_info = get_info("lapack_opt", notfound_action=2) + if (not lapack_info) or atlas_not_found(lapack_info): - # This is a guess, but seems to work in practice. Need more systems to + # This is a guess, but seems to work in practice. Need more systems to # test this fully. lapack_libs = ["lapack"] lapack_info.pop("libraries", None) @@ -182,6 +305,9 @@ def read(fname): def check_requirements(): numpy_instructions = ( + "\n" + "GenSVM Installation Error:" + "\n" "Numerical Python (NumPy) is not installed on your " "system. This package is required for GenSVM. Please install " "NumPy using the instructions available here: " @@ -206,14 +332,15 @@ if __name__ == "__main__": attr = configuration().todict() attr["version"] = version - attr["description"] = "Python package for the GenSVM classifier" + attr["description"] = DESCRIPTION attr["long_description"] = read("README.rst") - attr["packages"] = ["gensvm"] - attr["url"] = "https://github.com/GjjvdBurg/PyGenSVM" - attr["author"] = "G.J.J. van den Burg" - attr["author_email"] = "gertjanvandenburg@gmail.com" - attr["license"] = "GPL v2" - attr["install_requires"] = ["scikit-learn", "numpy"] + attr["packages"] = [NAME] + attr["url"] = URL + attr["author"] = AUTHOR + attr["author_email"] = EMAIL + attr["license"] = LICENSE + attr["install_requires"] = REQUIRED + attr["extras_require"] = EXTRAS from numpy.distutils.core import setup |