"""
General tests for all estimators in sklearn.
"""

# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
#          Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
from __future__ import print_function

import os
import warnings
import sys
import re
import pkgutil
import functools

import pytest

from sklearn.utils.testing import assert_false, clean_warning_registry
from sklearn.utils.testing import all_estimators
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import ignore_warnings
from sklearn.exceptions import ConvergenceWarning

import sklearn
from sklearn.cluster.bicluster import BiclusterMixin

from sklearn.linear_model.base import LinearClassifierMixin
from sklearn.utils import IS_PYPY
from sklearn.utils.estimator_checks import (
    _yield_all_checks,
    set_checking_parameters,
    check_parameters_default_constructible,
    check_no_attributes_set_in_init,
    check_class_weight_balanced_linear_classifier)


def test_all_estimator_no_base_class():
    # test that all_estimators doesn't find abstract classes.
    for name, Estimator in all_estimators():
        msg = ("Base estimators such as {0} should not be included"
               " in all_estimators").format(name)
        assert_false(name.lower().startswith('base'), msg=msg)


def test_all_estimators():
    estimators = all_estimators(include_meta_estimators=True)

    # Meta sanity-check to make sure that the estimator introspection runs
    # properly
    assert_greater(len(estimators), 0)


@pytest.mark.parametrize(
        'name, Estimator',
        all_estimators(include_meta_estimators=True)
)
def test_parameters_default_constructible(name, Estimator):
    # Test that estimators are default-constructible
    check_parameters_default_constructible(name, Estimator)


def _tested_non_meta_estimators():
    for name, Estimator in all_estimators():
        if issubclass(Estimator, BiclusterMixin):
            continue
        if name.startswith("_"):
            continue
        yield name, Estimator


def _generate_checks_per_estimator(check_generator, estimators):
    with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
        for name, Estimator in estimators:
            estimator = Estimator()
            for check in check_generator(name, estimator):
                yield name, Estimator, check


def _rename_partial(val):
    if isinstance(val, functools.partial):
        kwstring = "".join(["{}={}".format(k, v)
                            for k, v in val.keywords.items()])
        return "{}({})".format(val.func.__name__, kwstring)


@pytest.mark.parametrize(
        "name, Estimator, check",
        _generate_checks_per_estimator(_yield_all_checks,
                                       _tested_non_meta_estimators()),
        ids=_rename_partial
)
def test_non_meta_estimators(name, Estimator, check):
    # Common tests for non-meta estimators
    with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
                                   UserWarning, FutureWarning)):
        estimator = Estimator()
        set_checking_parameters(estimator)
        check(name, estimator)


@pytest.mark.parametrize("name, Estimator",
                         _tested_non_meta_estimators())
def test_no_attributes_set_in_init(name, Estimator):
    # input validation etc for non-meta estimators
    with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
                                   UserWarning, FutureWarning)):
        estimator = Estimator()
        # check this on class
        check_no_attributes_set_in_init(name, estimator)


@ignore_warnings(category=DeprecationWarning)
# ignore deprecated open(.., 'U') in numpy distutils
def test_configure():
    # Smoke test the 'configure' step of setup, this tests all the
    # 'configure' functions in the setup.pys in scikit-learn
    cwd = os.getcwd()
    setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], '..'))
    setup_filename = os.path.join(setup_path, 'setup.py')
    if not os.path.exists(setup_filename):
        return
    try:
        os.chdir(setup_path)
        old_argv = sys.argv
        sys.argv = ['setup.py', 'config']
        clean_warning_registry()
        with warnings.catch_warnings():
            # The configuration spits out warnings when not finding
            # Blas/Atlas development headers
            warnings.simplefilter('ignore', UserWarning)
            with open('setup.py') as f:
                exec(f.read(), dict(__name__='__main__'))
    finally:
        sys.argv = old_argv
        os.chdir(cwd)


def _tested_linear_classifiers():
    classifiers = all_estimators(type_filter='classifier')

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        for name, clazz in classifiers:
            if ('class_weight' in clazz().get_params().keys() and
                    issubclass(clazz, LinearClassifierMixin)):
                yield name, clazz


@pytest.mark.parametrize("name, Classifier",
                         _tested_linear_classifiers())
def test_class_weight_balanced_linear_classifiers(name, Classifier):
    check_class_weight_balanced_linear_classifier(name, Classifier)


@ignore_warnings
def test_import_all_consistency():
    # Smoke test to check that any name in a __all__ list is actually defined
    # in the namespace of the module or package.
    pkgs = pkgutil.walk_packages(path=sklearn.__path__, prefix='sklearn.',
                                 onerror=lambda _: None)
    submods = [modname for _, modname, _ in pkgs]
    for modname in submods + ['sklearn']:
        if ".tests." in modname:
            continue
        if IS_PYPY and ('_svmlight_format' in modname or
                        'feature_extraction._hashing' in modname):
            continue
        package = __import__(modname, fromlist="dummy")
        for name in getattr(package, '__all__', ()):
            if getattr(package, name, None) is None:
                raise AttributeError(
                    "Module '{0}' has no attribute '{1}'".format(
                        modname, name))


def test_root_import_all_completeness():
    EXCEPTIONS = ('utils', 'tests', 'base', 'setup')
    for _, modname, _ in pkgutil.walk_packages(path=sklearn.__path__,
                                               onerror=lambda _: None):
        if '.' in modname or modname.startswith('_') or modname in EXCEPTIONS:
            continue
        assert_in(modname, sklearn.__all__)


def test_all_tests_are_importable():
    # Ensure that for each contentful subpackage, there is a test directory
    # within it that is also a subpackage (i.e. a directory with __init__.py)

    HAS_TESTS_EXCEPTIONS = re.compile(r'''(?x)
                                      \.externals(\.|$)|
                                      \.tests(\.|$)|
                                      \.daal4sklearn(\.|$)|
                                      \._
                                      ''')
    lookup = dict((name, ispkg)
                  for _, name, ispkg
                  in pkgutil.walk_packages(sklearn.__path__,
                                           prefix='sklearn.'))
    missing_tests = [name for name, ispkg in lookup.items()
                     if ispkg
                     and not HAS_TESTS_EXCEPTIONS.search(name)
                     and name + '.tests' not in lookup]
    assert_equal(missing_tests, [],
                 '{0} do not have `tests` subpackages. Perhaps they require '
                 '__init__.py or an add_subpackage directive in the parent '
                 'setup.py'.format(missing_tests))
