def test_rf_classifier_decision_path_leaf(self):
model = RandomForestClassifier(n_estimators=3, max_depth=3)
X, y = make_classification(3, n_features=4, random_state=42)
X = X[:, :2]
model.fit(X, y)
initial_types = [('input', FloatTensorType((None, X.shape[1])))]
model_onnx = convert_sklearn(model,
initial_types=initial_types,
options={
id(model): {
'decision_leaf': True,
'decision_path': True,
'zipmap': False
}
},
target_opset=TARGET_OPSET)
sess = InferenceSession(model_onnx.SerializeToString())
res = sess.run(None, {'input': X.astype(numpy.float32)})
pred = model.predict(X)
assert_almost_equal(pred, res[0].ravel())
dec = model.decision_path(X)
exp_leaf = path_to_leaf(model.estimators_, dec[0].todense(), dec[1])
exp_path = binary_array_to_string(dec[0].todense())
got_path = numpy.array([''.join(row) for row in res[2]])
assert exp_path == got_path.ravel().tolist()
assert exp_leaf.tolist() == res[3].tolist()
def test_decisiontree_classifier_decision_path_leaf(self):
model = DecisionTreeClassifier(max_depth=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2]
model.fit(X, y)
initial_types = [('input', FloatTensorType((None, X.shape[1])))]
model_onnx = convert_sklearn(model,
initial_types=initial_types,
options={
id(model): {
'decision_leaf': True,
'decision_path': True,
'zipmap': False
}
},
target_opset=TARGET_OPSET)
sess = InferenceSession(model_onnx.SerializeToString())
res = sess.run(None, {'input': X.astype(np.float32)})
pred = model.predict(X)
assert_almost_equal(pred, res[0].ravel())
prob = model.predict_proba(X)
assert_almost_equal(prob, res[1])
dec = model.decision_path(X)
exp_path = binary_array_to_string(dec.todense())
exp_leaf = path_to_leaf(model.tree_, dec.todense())
assert exp_path == res[2].ravel().tolist()
assert exp_leaf.tolist() == res[3].ravel().tolist()
def test_decisiontree_regressor_decision_path(self):
model = DecisionTreeRegressor(max_depth=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2]
model.fit(X, y)
initial_types = [('input', FloatTensorType((None, X.shape[1])))]
model_onnx = convert_sklearn(
model, initial_types=initial_types,
options={id(model): {'decision_path': True}})
sess = InferenceSession(model_onnx.SerializeToString())
res = sess.run(None, {'input': X.astype(np.float32)})
pred = model.predict(X)
assert_almost_equal(pred, res[0].ravel())
dec = model.decision_path(X)
exp = binary_array_to_string(dec.todense())
assert exp == res[1].ravel().tolist()
def test_extratreesregressor_decision_path(self):
model = ExtraTreesRegressor(max_depth=2, n_estimators=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2]
model.fit(X, y)
initial_types = [('input', FloatTensorType((None, X.shape[1])))]
model_onnx = convert_sklearn(
model,
initial_types=initial_types,
options={id(model): {
'decision_path': True
}},
target_opset=TARGET_OPSET)
sess = InferenceSession(model_onnx.SerializeToString())
res = sess.run(None, {'input': X.astype(numpy.float32)})
pred = model.predict(X)
assert_almost_equal(pred, res[0].ravel())
dec = model.decision_path(X)
exp = binary_array_to_string(dec[0].todense())
got = numpy.array([''.join(row) for row in res[1]])
assert exp == got.ravel().tolist()
def test_randomforestclassifier_decision_path(self):
model = RandomForestClassifier(max_depth=2, n_estimators=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2]
model.fit(X, y)
initial_types = [('input', FloatTensorType((None, X.shape[1])))]
model_onnx = convert_sklearn(
model,
initial_types=initial_types,
options={id(model): {
'decision_path': True,
'zipmap': False
}})
sess = InferenceSession(model_onnx.SerializeToString())
res = sess.run(None, {'input': X.astype(numpy.float32)})
pred = model.predict(X)
assert_almost_equal(pred, res[0].ravel())
prob = model.predict_proba(X)
assert_almost_equal(prob, res[1])
dec = model.decision_path(X)
exp = binary_array_to_string(dec[0].todense())
got = numpy.array([''.join(row) for row in res[2]])
assert exp == got.ravel().tolist()
