def test_configure():
# Smoke test the 'configure' step of setup, this tests all the
# 'configure' functions in the setup.pys in the scikit
cwd = os.getcwd()
setup_path = os.path.abspath(os.path.join(gplearn.__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)
if PY3:
with open('setup.py') as f:
exec(f.read(), dict(__name__='__main__'))
else:
execfile('setup.py', dict(__name__='__main__'))
finally:
sys.argv = old_argv
os.chdir(cwd)
def test_configure():
# Smoke test the 'configure' step of setup, this tests all the
# 'configure' functions in the setup.pys in the scikit
cwd = os.getcwd()
setup_path = os.path.abspath(os.path.join(gplearn.__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)
if PY3:
with open('setup.py') as f:
exec(f.read(), dict(__name__='__main__'))
else:
execfile('setup.py', dict(__name__='__main__'))
finally:
sys.argv = old_argv
os.chdir(cwd)
def test_class_weight_auto_linear_classifiers():
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
linear_classifiers = [(name, clazz) for name, clazz in classifiers
if 'class_weight' in clazz().get_params().keys()
and issubclass(clazz, LinearClassifierMixin)]
for name, Classifier in linear_classifiers:
if name == "LogisticRegressionCV":
# Contrary to RidgeClassifierCV, LogisticRegressionCV use actual
# CV folds and fit a model for each CV iteration before averaging
# the coef. Therefore it is expected to not behave exactly as the
# other linear model.
continue
yield check_class_weight_auto_linear_classifier, name, Classifier
def test_class_weight_classifiers():
# test that class_weight works and that the semantics are consistent
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
classifiers = [c for c in classifiers
if 'class_weight' in c[1]().get_params().keys()]
for name, Classifier in classifiers:
if name == "NuSVC":
# the sparse version has a parameter that doesn't do anything
continue
if name.endswith("NB"):
# NaiveBayes classifiers have a somewhat different interface.
# FIXME SOON!
continue
yield check_class_weight_classifiers, name, Classifier
def test_class_weight_auto_classifiers():
# Test that class_weight="auto" improves f1-score
# This test is broken; its success depends on:
# * a rare fortuitous RNG seed for make_classification; and
# * the use of binary F1 over a seemingly arbitrary positive class for two
# datasets, and weighted average F1 for the third.
# Its expectations need to be clarified and reimplemented.
raise SkipTest('This test requires redefinition')
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
classifiers = [
c for c in classifiers
if 'class_weight' in c[1]().get_params().keys()
]
for n_classes, weights in zip([2, 3], [[.8, .2], [.8, .1, .1]]):
# create unbalanced dataset
X, y = make_classification(n_classes=n_classes,
n_samples=200,
n_features=10,
weights=weights,
random_state=0,
n_informative=n_classes)
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.5,
random_state=0)
for name, Classifier in classifiers:
if (name != "NuSVC"
# the sparse version has a parameter that doesn't do anything
and not name.startswith("RidgeClassifier")
# RidgeClassifier behaves unexpected
# FIXME!
and not name.endswith("NB")):
# NaiveBayes classifiers have a somewhat different interface.
# FIXME SOON!
yield (check_class_weight_auto_classifiers, name, Classifier,
X_train, y_train, X_test, y_test, weights)
def test_class_weight_auto_linear_classifiers():
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
linear_classifiers = [
(name, clazz)
for name, clazz in classifiers
if 'class_weight' in clazz().get_params().keys()
and issubclass(clazz, LinearClassifierMixin)]
for name, Classifier in linear_classifiers:
if name == "LogisticRegressionCV":
# Contrary to RidgeClassifierCV, LogisticRegressionCV use actual
# CV folds and fit a model for each CV iteration before averaging
# the coef. Therefore it is expected to not behave exactly as the
# other linear model.
continue
yield check_class_weight_auto_linear_classifier, name, Classifier
def test_class_weight_classifiers():
# test that class_weight works and that the semantics are consistent
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
classifiers = [
c for c in classifiers
if 'class_weight' in c[1]().get_params().keys()
]
for name, Classifier in classifiers:
if name == "NuSVC":
# the sparse version has a parameter that doesn't do anything
continue
if name.endswith("NB"):
# NaiveBayes classifiers have a somewhat different interface.
# FIXME SOON!
continue
yield check_class_weight_classifiers, name, Classifier
def test_class_weight_auto_classifiers():
# Test that class_weight="auto" improves f1-score
# This test is broken; its success depends on:
# * a rare fortuitous RNG seed for make_classification; and
# * the use of binary F1 over a seemingly arbitrary positive class for two
# datasets, and weighted average F1 for the third.
# Its expectations need to be clarified and reimplemented.
raise SkipTest('This test requires redefinition')
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
classifiers = [c for c in classifiers
if 'class_weight' in c[1]().get_params().keys()]
for n_classes, weights in zip([2, 3], [[.8, .2], [.8, .1, .1]]):
# create unbalanced dataset
X, y = make_classification(n_classes=n_classes, n_samples=200,
n_features=10, weights=weights,
random_state=0, n_informative=n_classes)
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
random_state=0)
for name, Classifier in classifiers:
if (name != "NuSVC"
# the sparse version has a parameter that doesn't do anything
and not name.startswith("RidgeClassifier")
# RidgeClassifier behaves unexpected
# FIXME!
and not name.endswith("NB")):
# NaiveBayes classifiers have a somewhat different interface.
# FIXME SOON!
yield (check_class_weight_auto_classifiers, name, Classifier,
X_train, y_train, X_test, y_test, weights)
