def test_onnxt_iris_random_forest_regressor(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, __ = train_test_split(X,
y,
random_state=11,
test_size=0.8)
clr = RandomForestRegressor(n_estimators=10, random_state=42)
clr.fit(X_train, y_train)
X_test = X_test.astype(numpy.float32)
X_test2 = make_n_rows(X_test, 10000)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def, runtime='python')
ti = timeit.repeat("oinf.run({'X': X_test2})",
number=100,
globals={
'oinf': oinf,
'X_test2': X_test2
},
repeat=10)
self.assertEqual(len(ti), 10)
# print("R",sum(ti), ti)
op = oinf.sequence_[0]
self.assertTrue(op.ops_.rt_.same_mode_)
if hasattr(op.ops_.rt_, 'consecutive_leaf_data_'):
self.assertFalse(op.ops_.rt_.consecutive_leaf_data_)
def test_split_xy(self):
X = numpy.arange(15).reshape(3, 5).astype(numpy.float32)
y = numpy.arange(3).astype(numpy.float32)
for k in [1, 2, 3, 4, 10]:
xs = make_n_rows(X, k)
self.assertIsInstance(xs, numpy.ndarray)
self.assertEqual(xs.shape[0], k)
self.assertEqual(xs.shape[1], X.shape[1])
rr = make_n_rows(X, k, y)
self.assertIsInstance(rr, tuple)
xs, ys = rr
self.assertIsInstance(xs, numpy.ndarray)
self.assertIsInstance(ys, numpy.ndarray)
self.assertEqual(xs.shape[0], k)
self.assertEqual(xs.shape[1], X.shape[1])
self.assertEqual(ys.shape[0], k)
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)