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test_onnxrt_python_runtime_ml_tree.py
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test_onnxrt_python_runtime_ml_tree.py
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"""
@brief test log(time=2s)
"""
import unittest
from logging import getLogger
import numpy
import pandas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, GradientBoostingRegressor
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
import skl2onnx
from pyquickhelper.pycode import ExtTestCase, unittest_require_at_least
from mlprodict.onnx_conv import to_onnx
from mlprodict.onnxrt import OnnxInference
class TestOnnxrtPythonRuntimeMlTree(ExtTestCase):
def setUp(self):
logger = getLogger('skl2onnx')
logger.disabled = True
def test_onnxrt_python_DecisionTreeClassifier(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = DecisionTreeClassifier()
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleClassifier", text)
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), [
'output_label', 'output_probability'])
lexp = clr.predict(X_test)
self.assertEqualArray(lexp, y['output_label'])
exp = clr.predict_proba(X_test)
got = pandas.DataFrame(list(y['output_probability'])).values
self.assertEqualArray(exp, got, decimal=5)
def test_onnxrt_python_DecisionTreeClassifier_plusten(self):
iris = load_iris()
X, y = iris.data, iris.target
y += 10
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = DecisionTreeClassifier()
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleClassifier", text)
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), [
'output_label', 'output_probability'])
lexp = clr.predict(X_test)
self.assertEqualArray(lexp, y['output_label'])
exp = clr.predict_proba(X_test)
got = pandas.DataFrame(list(y['output_probability'])).values
self.assertEqualArray(exp, got, decimal=5)
def test_onnxrt_python_GradientBoostingClassifier2(self):
iris = load_iris()
X, y = iris.data, iris.target
y[y == 2] = 1
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = GradientBoostingClassifier()
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleClassifier", text)
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), [
'output_label', 'output_probability'])
lexp = clr.predict(X_test)
self.assertEqualArray(lexp, y['output_label'])
exp = clr.predict_proba(X_test)
got = pandas.DataFrame(list(y['output_probability'])).values
self.assertEqualArray(exp, got, decimal=3)
def test_onnxrt_python_GradientBoostingClassifier3(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = GradientBoostingClassifier()
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleClassifier", text)
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), [
'output_label', 'output_probability'])
lexp = clr.predict(X_test)
self.assertEqualArray(lexp, y['output_label'])
exp = clr.predict_proba(X_test)
got = pandas.DataFrame(list(y['output_probability'])).values
self.assertEqualArray(exp, got, decimal=3)
@unittest_require_at_least(skl2onnx, '1.5.9999')
def test_onnxrt_python_DecisionTreeClassifier_mlabel(self):
iris = load_iris()
X, y_ = iris.data, iris.target
y = numpy.zeros((y_.shape[0], 3), dtype=int)
y[y_ == 0, 0] = 1
y[y_ == 1, 1] = 1
y[y_ == 2, 2] = 1
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = DecisionTreeClassifier()
clr.fit(X_train, y_train)
try:
model_def = to_onnx(clr, X_train.astype(numpy.float32))
except NotImplementedError:
# multi-label is not supported yet
return
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleClassifier", text)
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), [
'output_label', 'output_probability'])
exp = numpy.array(clr.predict_proba(X_test))
exp = exp.reshape(max(exp.shape), -1)
p = y['output_probability']
got = pandas.DataFrame(p.values, columns=p.columns)
self.assertEqualArray(exp, got, decimal=5)
lexp = clr.predict(X_test)
self.assertEqualArray(lexp, y['output_label'])
def test_onnxrt_python_DecisionTreeRegressor(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = DecisionTreeRegressor()
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleRegressor", text)
for i in range(0, 20):
y = oinf.run({'X': X_test.astype(numpy.float32)[i: i + 1]})
self.assertEqual(list(sorted(y)), ['variable'])
lexp = clr.predict(X_test[i: i + 1])
self.assertEqual(lexp.shape, y['variable'].shape)
self.assertEqualArray(lexp, y['variable'])
for i in range(0, 20):
y = oinf.run({'X': X_test.astype(numpy.float32)[i: i + 2]})
self.assertEqual(list(sorted(y)), ['variable'])
lexp = clr.predict(X_test[i: i + 2])
self.assertEqual(lexp.shape, y['variable'].shape)
self.assertEqualArray(lexp, y['variable'])
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), ['variable'])
lexp = clr.predict(X_test)
self.assertEqual(lexp.shape, y['variable'].shape)
self.assertEqualArray(lexp, y['variable'])
def test_onnxrt_python_DecisionTreeRegressor2(self):
iris = load_iris()
X, y = iris.data, iris.target
y = numpy.vstack([y, y]).T
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = DecisionTreeRegressor()
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleRegressor", text)
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), ['variable'])
lexp = clr.predict(X_test)
self.assertEqual(lexp.shape, y['variable'].shape)
self.assertEqualArray(lexp, y['variable'])
@unittest_require_at_least(skl2onnx, '1.5.9999')
def test_onnxrt_python_DecisionTreeRegressor64(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(
X, y, random_state=11) # pylint: disable=W0612
clr = DecisionTreeRegressor(min_samples_leaf=7)
clr.fit(X_train, y_train)
lexp = clr.predict(X_test)
model_def64 = to_onnx(clr, X_train.astype(
numpy.float64), dtype=numpy.float64, rewrite_ops=True)
oinf64 = OnnxInference(model_def64)
text = "\n".join(map(lambda x: str(x.ops_), oinf64.sequence_))
self.assertIn("TreeEnsembleRegressor", text)
self.assertIn("TreeEnsembleRegressorDouble", text)
smodel_def64 = str(model_def64)
self.assertIn('double_data', smodel_def64)
self.assertNotIn('floats', smodel_def64)
y64 = oinf64.run({'X': X_test.astype(numpy.float64)})
self.assertEqual(list(sorted(y64)), ['variable'])
self.assertEqual(lexp.shape, y64['variable'].shape)
self.assertEqualArray(lexp, y64['variable'])
model_def32 = to_onnx(clr, X_train.astype(
numpy.float32), dtype=numpy.float32, rewrite_ops=True)
oinf32 = OnnxInference(model_def32)
text = "\n".join(map(lambda x: str(x.ops_), oinf32.sequence_))
self.assertIn("TreeEnsembleRegressor", text)
self.assertNotIn("TreeEnsembleRegressorDouble", text)
smodel_def32 = str(model_def32)
self.assertNotIn('doubles', smodel_def32)
self.assertNotIn('double_data', smodel_def32)
self.assertIn('floats', smodel_def32)
y32 = oinf32.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y32)), ['variable'])
self.assertEqual(lexp.shape, y32['variable'].shape)
self.assertEqualArray(lexp, y32['variable'])
onx32 = model_def32.SerializeToString()
onx64 = model_def64.SerializeToString()
s32 = len(onx32)
s64 = len(onx64)
self.assertGreater(s64, s32 + 100)
self.assertNotEqual(y32['variable'].dtype, y64['variable'].dtype)
diff = numpy.max(numpy.abs(y32['variable'].astype(numpy.float64) -
y64['variable'].astype(numpy.float64)))
self.assertLesser(diff, 1e-5)
@unittest_require_at_least(skl2onnx, '1.5.9999')
def test_onnxrt_python_GradientBoostingRegressor64(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(
X, y, random_state=11) # pylint: disable=W0612
clr = GradientBoostingRegressor(n_estimators=20)
clr.fit(X_train, y_train)
lexp = clr.predict(X_test)
model_def64 = to_onnx(clr, X_train.astype(
numpy.float64), dtype=numpy.float64, rewrite_ops=True)
oinf64 = OnnxInference(model_def64)
text = "\n".join(map(lambda x: str(x.ops_), oinf64.sequence_))
self.assertIn("TreeEnsembleRegressor", text)
#self.assertIn("TreeEnsembleRegressorDouble", text)
smodel_def64 = str(model_def64)
self.assertIn('double_data', smodel_def64)
self.assertNotIn('floats', smodel_def64)
y64 = oinf64.run({'X': X_test.astype(numpy.float64)})
self.assertEqual(list(sorted(y64)), ['variable'])
self.assertEqual(lexp.shape, y64['variable'].shape)
self.assertEqualArray(lexp, y64['variable'])
model_def32 = to_onnx(clr, X_train.astype(
numpy.float32), dtype=numpy.float32, rewrite_ops=True)
oinf32 = OnnxInference(model_def32)
text = "\n".join(map(lambda x: str(x.ops_), oinf32.sequence_))
self.assertIn("TreeEnsembleRegressor", text)
self.assertNotIn("TreeEnsembleRegressorDouble", text)
smodel_def32 = str(model_def32)
self.assertNotIn('doubles', smodel_def32)
self.assertNotIn('double_data', smodel_def32)
self.assertIn('floats', smodel_def32)
y32 = oinf32.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y32)), ['variable'])
self.assertEqual(lexp.shape, y32['variable'].shape)
self.assertEqualArray(lexp, y32['variable'])
onx32 = model_def32.SerializeToString()
onx64 = model_def64.SerializeToString()
s32 = len(onx32)
s64 = len(onx64)
self.assertGreater(s64, s32 + 100)
self.assertNotEqual(y32['variable'].dtype, y64['variable'].dtype)
diff = numpy.max(numpy.abs(y32['variable'].astype(numpy.float64) -
y64['variable'].astype(numpy.float64)))
self.assertLesser(diff, 1e-5)
def test_onnxrt_python_DecisionTree_depth2(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = DecisionTreeClassifier(max_depth=2)
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleClassifier", text)
y = oinf.run({'X': X_test.astype(numpy.float32)})
self.assertEqual(list(sorted(y)), [
'output_label', 'output_probability'])
lexp = clr.predict(X_test)
self.assertEqualArray(lexp, y['output_label'])
exp = clr.predict_proba(X_test)
got = pandas.DataFrame(list(y['output_probability'])).values
self.assertEqualArray(exp, got, decimal=5)
def test_onnxrt_python_RandomForestClassifer5(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = RandomForestClassifier(
n_estimators=4, max_depth=2, random_state=11)
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32))
oinf = OnnxInference(model_def)
text = "\n".join(map(lambda x: str(x.ops_), oinf.sequence_))
self.assertIn("TreeEnsembleClassifier", text)
y = oinf.run({'X': X_test[:5].astype(numpy.float32)})
self.assertEqual(list(sorted(y)), [
'output_label', 'output_probability'])
lexp = clr.predict(X_test[:5])
self.assertEqualArray(lexp, y['output_label'])
exp = clr.predict_proba(X_test[:5])
got = pandas.DataFrame(list(y['output_probability'])).values
self.assertEqualArray(exp, got, decimal=5)
def test_openmp_compilation(self):
from mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_ import RuntimeTreeEnsembleRegressorFloat # pylint: disable=E0611
ru = RuntimeTreeEnsembleRegressorFloat()
r = ru.runtime_options()
self.assertEqual('OPENMP', r)
nb = ru.omp_get_max_threads()
self.assertGreater(nb, 0)
from mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_ import RuntimeTreeEnsembleClassifierFloat # pylint: disable=E0611
ru = RuntimeTreeEnsembleClassifierFloat()
r = ru.runtime_options()
self.assertEqual('OPENMP', r)
nb2 = ru.omp_get_max_threads()
self.assertEqual(nb2, nb)
if __name__ == "__main__":
# TestOnnxrtPythonRuntimeMlTree().test_onnxrt_python_DecisionTreeRegressor()
unittest.main()