def enumerate_visual_onnx_representation_into_rst(sub, fLOG=noLOG):
"""
Returns content for pages such as
:ref:`l-skl2onnx-linear_model`.
"""
logger = getLogger('skl2onnx')
logger.disabled = True
templ = Template(visual_rst_template())
done = set()
subsets = [_['name'] for _ in sklearn_operators(sub)]
subsets.sort()
for row in enumerate_validated_operator_opsets(
verbose=0,
debug=None,
fLOG=fLOG,
opset_min=get_opset_number_from_onnx(),
store_models=True,
models=subsets):
if 'ONNX' not in row:
continue
name = row['name']
scenario = row['scenario']
problem = row['problem']
model = row['MODEL']
method = row['method_name']
title = " - ".join([name, problem, scenario])
if title in done:
continue
done.add(title)
link = "-".join([name, problem, scenario])
oinf = OnnxInference(row['ONNX'], skip_run=True)
dot = oinf.to_dot()
res = templ.render(dot=dot,
model=repr(model),
method=method,
kind=problem,
title=title,
indent=indent,
len=len,
link=link)
yield res
def test_write_documentation_converters(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
subs = []
for sub in sorted(sklearn__all__):
models = sklearn_operators(sub)
if len(models) > 0:
rows = []
for row in enumerate_visual_onnx_representation_into_rst(sub):
self.assertIn("digraph", row)
rows.append(row)
if len(rows) == 0:
continue
rows = [
".. _l-skl2onnx-%s:" % sub, "", "=" * len(sub), sub,
"=" * len(sub), "", ".. contents::", " :local:", ""
] + rows
rows.append('')
subs.append(sub)
fLOG("subfolder '{}' - {} scenarios.".format(sub, len(models)))
self.assertGreater(len(subs), 2)
def test_sklearn_operators(self):
res = sklearn_operators()
self.assertGreater(len(res), 1)
self.assertEqual(len(res[0]), 4)
short = ['IsotonicRegression']
for model in res:
if model['name'] not in short:
continue
prob = find_suitable_problem(model['cl'])
self.assertNotEmpty(prob)
if model['name'] == 'IsotonicRegression':
self.assertEqual(prob, ['~num+y-tr-1d', '~b-reg-1d'])
names = set(_['name'] for _ in res)
self.assertIn('Perceptron', names)
self.assertIn('TfidfVectorizer', names)
ra = {
'BaseEnsemble', 'NearestNeighbors', 'AgglomerativeClustering',
'DBSCAN', 'OPTICS', 'SpectralClustering', 'SpectralBiclustering',
'SpectralCoclustering'
}
for model in res:
if model['name'] in ra:
self.assertRaise(
lambda m=model: find_suitable_problem(m['cl']),
RuntimeError)
continue
prob = find_suitable_problem(model['cl'])
self.assertNotEmpty(prob)
if model['name'] == 'IsotonicRegression':
self.assertEqual(prob, ['~num+y-tr-1d', '~b-reg-1d'])
elif model['name'] == 'NearestCentroid':
self.assertEqual(prob, ['~b-cl-nop', '~b-cl-nop-64'])
self.assertIsInstance(prob, list)
def test_sklearn_operators(self):
res = sklearn_operators()
self.assertGreater(len(res), 1)
self.assertEqual(len(res[0]), 4)