def common_test_cast_regressor(self, dtype, input_type):
model = CastRegressor(DecisionTreeRegressor(max_depth=2), dtype=dtype)
data = numpy.array(
[[0.1, 0.2, 3.1], [1, 1, 0], [0, 2, 1], [1, 0, 2], [0.1, 2.1, 1.1],
[1.1, 0.1, 2.2], [-0.1, -2.1, -1.1], [-1.1, -0.1, -2.2],
[0.2, 2.2, 1.2], [1.2, 0.2, 2.2]],
dtype=numpy.float32)
y = (numpy.sum(data, axis=1, keepdims=0) +
numpy.random.randn(data.shape[0]))
model.fit(data, y)
pred = model
assert pred.dtype == dtype
model_onnx = convert_sklearn(model,
"cast",
[("input", FloatTensorType([None, 3]))],
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
dump_data_and_model(data,
model,
model_onnx,
basename="SklearnCastRegressor{}".format(
input_type.__class__.__name__))
############################################
# Perfect, no discrepencies at all.
print(diff(skl5, ort5))
##############################################
# CastRegressor
# +++++++++++++
#
# The previous example demonstrated the type difference for
# the predicted values explains the small differences between
# :epkg:`scikit-learn` and :epkg:`onnxruntime`. But it does not
# with the current ONNX. Another option is to cast the
# the predictions into floats in the :epkg:`scikit-learn` pipeline.
ctree = CastRegressor(DecisionTreeRegressor(max_depth=max_depth))
ctree.fit(Xi_train, yi_train)
onx6 = to_onnx(ctree, Xi_train[:1].astype(numpy.float32))
sess6 = InferenceSession(onx6.SerializeToString(),
providers=['CPUExecutionProvider'])
skl6 = ctree.predict(X32)
ort6 = sess6.run(None, {'X': X32})[0]
print(diff(skl6, ort6))
##############################
# Success!