def _main():
import argparse
# Adding custom extend action for support all python versions
class ExtendAction(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
items = getattr(namespace, self.dest) or []
items.extend(values)
setattr(namespace, self.dest, items)
parser = argparse.ArgumentParser(
prog="python -m sklearnex.glob",
description="""
Patch all your Scikit-learn applications using Intel(R) Extension for
scikit-learn.""",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.register('action', 'extend', ExtendAction)
parser.add_argument('action', choices=["patch_sklearn", "unpatch_sklearn"],
help="Enable or Disable patching")
parser.add_argument('--no-verbose', '-nv', action='store_false',
help="Disable additional information about enabling patching")
parser.add_argument('--algorithm', '-a', action='extend', type=str, nargs="+",
help="The name of an algorithm to be patched globally")
args = parser.parse_args()
if args.action == "patch_sklearn":
patch_sklearn(name=args.algorithm, verbose=args.no_verbose, global_patch=True)
elif args.action == "unpatch_sklearn":
unpatch_sklearn(global_unpatch=True)
else:
raise RuntimeError("Invalid choice for the action attribute."
" Expected: patch_sklearn or unpatch_sklearn."
f" Got {args.action}")
def test_unpatch_by_list_many_estimators():
sklearnex.patch_sklearn()
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
assert RandomForestRegressor.__module__.startswith('daal4py')
assert KNeighborsRegressor.__module__.startswith('daal4py')
assert LogisticRegression.__module__.startswith('daal4py')
assert SVC.__module__.startswith('daal4py') or SVC.__module__.startswith(
'sklearnex')
sklearnex.unpatch_sklearn(["KNeighborsRegressor", "RandomForestRegressor"])
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
assert RandomForestRegressor.__module__.startswith('sklearn')
assert KNeighborsRegressor.__module__.startswith('sklearn')
assert LogisticRegression.__module__.startswith('daal4py')
assert SVC.__module__.startswith('daal4py') or SVC.__module__.startswith(
'sklearnex')
def test_monkey_patching():
_tokens = sklearnex.get_patch_names()
_values = sklearnex.get_patch_map().values()
_classes = list()
for v in _values:
for c in v:
_classes.append(c[0])
sklearnex.patch_sklearn()
for i, _ in enumerate(_tokens):
t = _tokens[i]
p = _classes[i][0]
n = _classes[i][1]
class_module = getattr(p, n).__module__
assert \
class_module.startswith('daal4py') or class_module.startswith('sklearnex'), \
"Patching has completed with error."
for i, _ in enumerate(_tokens):
t = _tokens[i]
p = _classes[i][0]
n = _classes[i][1]
sklearnex.unpatch_sklearn(t)
class_module = getattr(p, n).__module__
assert class_module.startswith('sklearn'), \
"Unpatching has completed with error."
sklearnex.unpatch_sklearn()
for i, _ in enumerate(_tokens):
t = _tokens[i]
p = _classes[i][0]
n = _classes[i][1]
class_module = getattr(p, n).__module__
assert class_module.startswith('sklearn'), \
"Unpatching has completed with error."
sklearnex.unpatch_sklearn()
for i, _ in enumerate(_tokens):
t = _tokens[i]
p = _classes[i][0]
n = _classes[i][1]
sklearnex.patch_sklearn(t)
class_module = getattr(p, n).__module__
assert \
class_module.startswith('daal4py') or class_module.startswith('sklearnex'), \
"Patching has completed with error."
sklearnex.unpatch_sklearn()
def test_monkey_patching():
_tokens = sklearnex.get_patch_names()
_values = sklearn_patch_map().values()
_classes = list()
for v in _values:
_classes.append(v[0][0])
assert len(_tokens) == len(_classes)
assert isinstance(_tokens, list) and len(_tokens) > 0, \
"Internal Error: list of patched names has unexcepable format."
sklearnex.patch_sklearn()
for i, _ in enumerate(_tokens):
t = _tokens[i]
p = _classes[i][0]
n = _classes[i][1]
class_module = getattr(p, n).__module__
assert class_module.startswith('daal4py'), \
"Patching has completed with error."
sklearnex.unpatch_sklearn(t)
print(p, n)
class_module = getattr(p, n).__module__
assert class_module.startswith('sklearn'), \
"Unpatching has completed with error."
sklearnex.unpatch_sklearn()
for i, _ in enumerate(_tokens):
t = _tokens[i]
p = _classes[i][0]
n = _classes[i][1]
class_module = getattr(p, n).__module__
assert class_module.startswith('sklearn'), \
"Unpatching has completed with error."
sklearnex.patch_sklearn(t)
class_module = getattr(p, n).__module__
assert class_module.startswith('daal4py'), \
"Patching has completed with error."
sklearnex.unpatch_sklearn()
err_code = subprocess.call(
[sys.executable, "-m", "sklearnex.glob", "patch_sklearn", "-a", "svc"])
assert not err_code
from sklearn.svm import SVC, SVR
assert SVC.__module__.startswith('daal4py') or SVC.__module__.startswith(
'sklearnex')
assert not SVR.__module__.startswith('daal4py') and \
not SVR.__module__.startswith('sklearnex')
from sklearnex import patch_sklearn, unpatch_sklearn
# test unpatching from command line
err_code = subprocess.call(
[sys.executable, "-m", "sklearnex.glob", "unpatch_sklearn"])
assert not err_code
unpatch_sklearn()
from sklearn.svm import SVC, SVR
assert not SVR.__module__.startswith('daal4py') and \
not SVR.__module__.startswith('sklearnex')
assert not SVR.__module__.startswith('daal4py') and \
not SVR.__module__.startswith('sklearnex')
# test patching from function
patch_sklearn(name=['svc'], global_patch=True)
from sklearn.svm import SVC, SVR
assert SVC.__module__.startswith('daal4py') or \
SVC.__module__.startswith('sklearnex')
assert not SVR.__module__.startswith('daal4py') and \
not SVR.__module__.startswith('sklearnex')
# test unpatching from function
print(
f"Execution time with Intel(R) Extension for Scikit-learn: {(time() - start):.2f} s"
)
# Make predictions with SVC classifier and print a report of the main classification metrics:
predicted = classifier.predict(x_test)
report = metrics.classification_report(y_test, predicted)
print(
f"Classification report for SVC trained with Intel(R) extension for Scikit-learn:\n{report}\n"
)
# To cancel optimizations, use *unpatch_sklearn* and reimport the SVC class.
from sklearnex import unpatch_sklearn
unpatch_sklearn()
# "Train SVC algorithm with original scikit-learn:"
start = time()
from sklearn.svm import SVC
classifier = SVC(**params).fit(x_train, y_train)
print(
f"Execution time with the original Scikit-learn: {(time() - start):.2f} s")
# Make predictions with SVC classifier and print a report of the main classification metrics:
predicted = classifier.predict(x_test)
report = metrics.classification_report(y_test, predicted)
print(
f"Classification report for SVC trained with the original scikit-learn:\n{report}\n"