def test_create_asv_benchmark_rf(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
self.assertNotEmpty(mlprodict)
temp = get_temp_folder(__file__, "temp_create_asv_benchmark_rf")
created = create_asv_benchmark(location=temp,
verbose=1,
fLOG=fLOG,
runtime=('scikit-learn', 'python',
'onnxruntime1'),
exc=False,
execute=True,
models={'RandomForestRegressor'})
self.assertNotEmpty(created)
reg = re.compile("class ([a-zA-Z0-9_]+)[(]")
verif = False
allnames = []
for path, _, files in os.walk(os.path.join(temp, 'benches')):
for zoo in files:
if '__init__' in zoo:
continue
fLOG("process '{}'".format(zoo))
fullname = os.path.join(path, zoo)
with open(fullname, 'r', encoding='utf-8') as f:
content = f.read()
names = reg.findall(content)
name = names[0]
content += "\n\ncl = %s()\ncl.setup_cache()\n" % name
allnames.append(fullname)
with open(fullname, 'w', encoding='utf-8') as f:
f.write(content)
__, err = run_script(fullname, wait=True)
lines = [_ for _ in err.split('\n') if _ and _[0] != ' ']
lines = [_ for _ in lines if "Warning" not in _]
lines = [
_ for _ in lines if "No module named 'mlprodict'" not in _
]
lines = [_ for _ in lines if "Traceback " not in _]
err = "\n".join(lines).strip(' \n\r')
if len(err) > 0:
raise RuntimeError("Issue with '{}'\n{}".format(
fullname, err))
if (zoo.endswith(
"bench_RandomForestReg_default_b_reg_nest100.py")
and compare_module_version(sklearn.__version__,
"0.21") >= 0):
if "random_state=42" not in content:
raise AssertionError(content)
else:
verif = True
if not verif:
raise AssertionError("Visited files\n{}".format(
"\n".join(allnames)))
def test_python(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
file = os.path.join(
os.path.split(__file__)[0], "..", "..", "src", "pyquickhelper",
"loghelper", "flog_fake_classes.py")
out, err = run_script(file)
assert out is not None
assert err is None
out.__exit__(None, None, None)
def test_create_asv_benchmark_tiny_same(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
self.assertNotEmpty(mlprodict)
temp = get_temp_folder(
__file__, "temp_create_asv_benchmark_all_tiny_same")
created = create_asv_benchmark(
location=temp, verbose=1, fLOG=fLOG,
skip_models={
'DictVectorizer', 'FeatureHasher', # 'CountVectorizer'
}, runtime=('scikit-learn', 'python', 'onnxruntime1'),
exc=False, execute=True, models={
'SelectFromModel', 'NMF', 'LatentDirichletAllocation'
}, env='same')
self.assertNotEmpty(created)
reg = re.compile("class ([a-zA-Z0-9_]+)[(]")
for path, _, files in os.walk(os.path.join(temp, 'benches')):
for zoo in files:
if '__init__' in zoo:
continue
fLOG("process '{}'".format(zoo))
fullname = os.path.join(path, zoo)
with open(fullname, 'r', encoding='utf-8') as f:
content = f.read()
names = reg.findall(content)
name = names[0]
content += "\n\ncl = %s()\ncl.setup_cache()\n" % name
with open(fullname, 'w', encoding='utf-8') as f:
f.write(content)
__, err = run_script(fullname, wait=True)
lines = [_ for _ in err.split('\n') if _ and _[0] != ' ']
lines = [_ for _ in lines if "Warning" not in _]
lines = [
_ for _ in lines if "No module named 'mlprodict'" not in _]
lines = [_ for _ in lines if "Traceback " not in _]
err = "\n".join(lines).strip(' \n\r')
if len(err) > 0:
raise RuntimeError(
"Issue with '{}'\n{}".format(fullname, err))
if (zoo.endswith("bench_NMF_default_num_tr_pos.py") and
compare_module_version(sklearn.__version__, "0.22") >= 0):
if ("from sklearn.decomposition.nmf import NMF" not in content and
"from sklearn.decomposition import NMF" not in content):
raise AssertionError(
"Unable to find 'import NMF' in\n{}".format(content))
def test_create_asv_benchmark_all(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
self.assertNotEmpty(mlprodict)
temp = get_temp_folder(__file__, "temp_create_asv_benchmark_all")
created = create_asv_benchmark(
location=temp,
verbose=1,
fLOG=fLOG,
skip_models={
'DictVectorizer',
'FeatureHasher', # 'CountVectorizer'
},
runtime=('scikit-learn', 'python', 'onnxruntime1'),
exc=False,
execute=True)
self.assertGreater(len(created), 2)
name = os.path.join(
temp, 'benches', 'linear_model', 'LogisticRegression',
'bench_LogReg_liblinear_b_cl_solverliblinear_onnx.py')
self.assertExists(name)
with open(name, "r", encoding="utf-8") as f:
content = f.read()
self.assertIn(
"class LogReg_liblinear_b_cl_solverliblinear_onnx_benchClassifier(",
content)
self.assertIn("solver='liblinear'", content)
self.assertIn("return onnx_optimisations(onx)", content)
try:
self.assertIn(
"from sklearn.linear_model._logistic import LogisticRegression",
content)
except AssertionError:
try:
self.assertIn(
"from sklearn.linear_model.logistic import LogisticRegression",
content)
except AssertionError:
self.assertIn(
"from sklearn.linear_model import LogisticRegression",
content)
if __name__ == "__main__":
fLOG("[] checks setup_cache")
reg = re.compile("class ([a-zA-Z0-9_]+)[(]")
checked = []
folder = os.path.join(temp, 'benches')
subsets_test = [
'Stacking', 'ovariance', 'bench_LogReg_liblinear', 'Latent'
]
for path, _, files in os.walk(folder):
for zoo in files:
if '__init__' in zoo:
continue
if 'chain' in zoo.lower():
continue
if not any(map(lambda x, z=zoo: x in z, subsets_test)):
continue
checked.append(zoo)
fLOG("process '{}'".format(zoo))
fullname = os.path.join(path, zoo)
with open(fullname, 'r', encoding='utf-8') as f:
content = f.read()
names = reg.findall(content)
name = names[0]
content += "\n\ncl = %s()\ncl.setup_cache()\n" % name
with open(fullname, 'w', encoding='utf-8') as f:
f.write(content)
__, err = run_script(fullname, wait=True)
lines = [_ for _ in err.split('\n') if _ and _[0] != ' ']
lines = [_ for _ in lines if "Warning" not in _]
err = "\n".join(lines).strip(' \n\r')
if len(err) > 0:
raise RuntimeError("Issue with '{}'\n{}".format(
fullname, err))
if len(checked) == 0:
raise AssertionError("Nothing found in '{}'.".format(folder))
def test_create_asv_benchmark_logreg(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
self.assertNotEmpty(mlprodict)
temp = get_temp_folder(__file__, "temp_create_asv_benchmark_logreg")
created = create_asv_benchmark(location=temp,
verbose=3,
fLOG=fLOG,
runtime=('scikit-learn', 'python',
'onnxruntime1'),
exc=False,
execute=True,
models={'LogisticRegression'})
if len(created) < 6:
raise AssertionError(
"Number of created files is too small.\n{}".format("\n".join(
sorted(created))))
reg = re.compile("class ([a-zA-Z0-9_]+)[(]")
verif = 0
allnames = []
for path, _, files in os.walk(os.path.join(temp, 'benches')):
for zoo in files:
if '__init__' in zoo:
continue
fLOG("process '{}'".format(zoo))
fullname = os.path.join(path, zoo)
with open(fullname, 'r', encoding='utf-8') as f:
content = f.read()
names = reg.findall(content)
name = names[0]
content += "\n\ncl = %s()\ncl.setup_cache()\n" % name
allnames.append(fullname)
with open(fullname, 'w', encoding='utf-8') as f:
f.write(content)
__, err = run_script(fullname, wait=True)
lines = [_ for _ in err.split('\n') if _ and _[0] != ' ']
lines = [_ for _ in lines if "Warning" not in _]
lines = [
_ for _ in lines if "No module named 'mlprodict'" not in _
]
lines = [_ for _ in lines if "Traceback " not in _]
err = "\n".join(lines).strip(' \n\r')
if len(err) > 0:
raise RuntimeError("Issue with '{}'\n{}".format(
fullname, err))
if (zoo.endswith(
"bench_LogReg_liblinear_m_cl_solverliblinear.py")
and compare_module_version(sklearn.__version__,
"0.21") >= 0):
if "{LogisticRegression: {'zipmap': False}}" in content:
raise AssertionError(content)
elif "'nozipmap'" not in content:
raise AssertionError(content)
if 'predict_proba' not in content:
raise AssertionError(content)
verif += 1
if (zoo.endswith(
"bench_LogReg_liblinear_dec_b_cl_dec_solverliblinear.py"
) and compare_module_version(sklearn.__version__, "0.21") >=
0):
if "{LogisticRegression: {'raw_scores': True}}" in content:
raise AssertionError(content)
elif "'raw_scores'" not in content:
raise AssertionError(content)
if 'decision_function' not in content:
raise AssertionError(content)
verif += 1
if not verif:
raise AssertionError("Visited files\n{}".format(
"\n".join(allnames)))
