def setUp(self):
logger = getLogger('skl2onnx')
logger.disabled = True
register_converters()
X = numpy.abs(numpy.random.randn(10, 200)).astype(numpy.float32)
for i in range(X.shape[1]):
X[:, i] *= (i + 1) * 10
y = X.sum(axis=1) / 1e3 + numpy.random.randn(X.shape[0]).astype(
numpy.float32)
X = X.astype(numpy.float32)
y = y.astype(numpy.float32)
self.data_X, self.data_y = X, y
def setup(self, runtime, N, nf, opset, dtype, optim):
"asv API"
logger = getLogger('skl2onnx')
logger.disabled = True
register_converters()
register_rewritten_operators()
with open(self._name(nf, opset, dtype), "rb") as f:
stored = pickle.load(f)
self.stored = stored
self.model = stored['model']
self.X, self.y = make_n_rows(stored['X'], N, stored['y'])
onx, rt_, rt_fct_, rt_fct_track_ = self._create_onnx_and_runtime(
runtime, self.model, self.X, opset, dtype, optim)
self.onx = onx
setattr(self, "rt_" + runtime, rt_)
setattr(self, "rt_fct_" + runtime, rt_fct_)
setattr(self, "rt_fct_track_" + runtime, rt_fct_track_)
set_config(assume_finite=True)
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
results['breast_cancer'] = [X_train, X_test, y_train, y_test]
X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=41)
results['digits'] = [X_train, X_test, y_train, y_test]
X, y = make_classification(20000, 20)
X_train, X_test, y_train, y_test = train_test_split(X, y)
results['rndbin100'] = [X_train, X_test, y_train, y_test]
return results
register_converters()
common_datasets = create_datasets()
def get_model(lib):
if lib == "sklh":
return HistGradientBoostingRegressor(max_depth=6, max_iter=100)
if lib == "skl":
return RandomForestRegressor(max_depth=6, n_estimators=100)
if lib == 'xgb':
return XGBRegressor(max_depth=6, n_estimators=100)
if lib == 'lgb':
return LGBMRegressor(max_depth=6, n_estimators=100)
raise ValueError("Unknown library '{}'.".format(lib))
def setUp(self):
logger = getLogger('skl2onnx')
logger.disabled = True
register_converters()
def test_register_converters(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore", ResourceWarning)
res = register_converters(True)
self.assertGreater(len(res), 2)
