def test_decisiontreeregressor_decision_path(self):
model = DecisionTreeRegressor(max_depth=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2].astype(numpy.float32)
model.fit(X, y)
model_onnx = to_onnx(model,
X,
options={id(model): {
'decision_path': True
}})
sess = OnnxInference(model_onnx)
res = sess.run({'X': X})
pred = model.predict(X)
self.assertEqualArray(pred, res['variable'].ravel())
dec = model.decision_path(X)
exp = binary_array_to_string(dec.todense())
self.assertEqual(exp, res['decision_path'].ravel().tolist())
def test_randomforestregressor_decision_path(self):
model = RandomForestRegressor(max_depth=2, n_estimators=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2].astype(numpy.float32)
model.fit(X, y)
model_onnx = to_onnx(model,
X,
options={id(model): {
'decision_path': True
}})
sess = OnnxInference(model_onnx)
res = sess.run({'X': X})
pred = model.predict(X)
self.assertEqualArray(pred, res['variable'].ravel())
dec = model.decision_path(X)
exp = binary_array_to_string(dec[0].todense())
got = numpy.array([''.join(row) for row in res['decision_path']])
self.assertEqual(exp, got.tolist())