def test_onnxrt_python_lightgbm_categorical_iris(self):
iris = load_iris()
X, y = iris.data, iris.target
X = (X * 10).astype(numpy.int32)
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
other_x = numpy.random.randint(0,
high=10,
size=(1500, X_train.shape[1]))
X_train = numpy.vstack([X_train, other_x]).astype(dtype=numpy.int32)
y_train = numpy.hstack([
y_train,
numpy.zeros(500) + 3,
numpy.zeros(500) + 4,
numpy.zeros(500) + 5
]).astype(dtype=numpy.int32)
self.assertEqual(y_train.shape, (X_train.shape[0], ))
y_train = y_train % 2
# Classic
gbm = LGBMClassifier()
gbm.fit(X_train, y_train)
exp = gbm.predict_proba(X_test)
onx = to_onnx(gbm,
initial_types=[('X',
Int64TensorType([None,
X_train.shape[1]]))])
self.assertIn('ZipMap', str(onx))
oif = OnnxInference(onx)
got = oif.run({'X': X_test})
values = pandas.DataFrame(got['output_probability']).values
self.assertEqualArray(exp, values, decimal=5)
# categorical_feature=[0, 1]
train_data = Dataset(X_train,
label=y_train,
feature_name=['c1', 'c2', 'c3', 'c4'],
categorical_feature=['c1', 'c2'])
params = {
"boosting_type": "gbdt",
"learning_rate": 0.05,
"n_estimators": 2,
"objective": "binary",
"max_bin": 5,
"min_child_samples": 100,
'verbose': -1,
}
booster = lgb_train(params, train_data)
exp = booster.predict(X_test)
onx = to_onnx(booster,
initial_types=[('X',
Int64TensorType([None,
X_train.shape[1]]))])
self.assertIn('ZipMap', str(onx))
oif = OnnxInference(onx)
got = oif.run({'X': X_test})
values = pandas.DataFrame(got['output_probability']).values
self.assertEqualArray(exp, values[:, 1], decimal=5)
def test_lightgbm_booster_classifier(self):
from lightgbm import Dataset, train as lgb_train
X = numpy.array([[0, 1], [1, 1], [2, 0], [1, 2]], dtype=numpy.float32)
y = [0, 1, 0, 1]
data = Dataset(X, label=y)
model = lgb_train({'boosting_type': 'rf', 'objective': 'binary',
'n_estimators': 3, 'min_child_samples': 1,
'subsample_freq': 1, 'bagging_fraction': 0.5,
'feature_fraction': 0.5},
data)
model_onnx = to_onnx(model, X, verbose=0, rewrite_ops=True,
target_opset=TARGET_OPSET)
self.assertNotEmpty(model_onnx)
def test_onnxrt_python_lightgbm_categorical_iris_booster3_real(self):
from lightgbm import LGBMClassifier, Dataset, train as lgb_train
iris = load_iris()
X, y = iris.data, iris.target
X = (X * 10).astype(numpy.float32)
X_train, X_test, y_train, _ = train_test_split(
X, y, random_state=11)
# Classic
gbm = LGBMClassifier()
gbm.fit(X_train, y_train)
exp = gbm.predict_proba(X_test)
onx = to_onnx(gbm.booster_, initial_types=[
('X', FloatTensorType([None, X_train.shape[1]]))],
target_opset=TARGET_OPSET)
self.assertIn('ZipMap', str(onx))
oif = OnnxInference(onx)
got = oif.run({'X': X_test})
values = pandas.DataFrame(got['output_probability']).values
self.assertEqualArray(exp, values, decimal=5)
# categorical_feature=[0, 1]
train_data = Dataset(
X_train, label=y_train,
feature_name=['c1', 'c2', 'c3', 'c4'],
categorical_feature=['c1', 'c2'])
params = {
"boosting_type": "gbdt", "learning_rate": 0.05,
"n_estimators": 2, "objective": "multiclass",
"max_bin": 5, "min_child_samples": 100,
'verbose': -1, 'num_class': 3}
booster = lgb_train(params, train_data)
exp = booster.predict(X_test)
onx = to_onnx(booster, initial_types=[
('X', FloatTensorType([None, X_train.shape[1]]))],
target_opset=TARGET_OPSET)
self.assertIn('ZipMap', str(onx))
oif = OnnxInference(onx)
got = oif.run({'X': X_test})
values = pandas.DataFrame(got['output_probability']).values
self.assertEqualArray(exp, values, decimal=5)
def test_onnxrt_python_lightgbm_categorical_iris_dataframe(self):
iris = load_iris()
X, y = iris.data, iris.target
X = (X * 10).astype(numpy.int32)
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
other_x = numpy.random.randint(0,
high=10,
size=(1500, X_train.shape[1]))
X_train = numpy.vstack([X_train, other_x]).astype(dtype=numpy.int32)
y_train = numpy.hstack([
y_train,
numpy.zeros(500) + 3,
numpy.zeros(500) + 4,
numpy.zeros(500) + 5
]).astype(dtype=numpy.int32)
self.assertEqual(y_train.shape, (X_train.shape[0], ))
y_train = y_train % 2
df_train = pandas.DataFrame(X_train)
df_train.columns = ['c1', 'c2', 'c3', 'c4']
df_train['c1'] = df_train['c1'].astype('category')
df_train['c2'] = df_train['c2'].astype('category')
df_train['c3'] = df_train['c3'].astype('category')
df_train['c4'] = df_train['c4'].astype('category')
df_test = pandas.DataFrame(X_test)
df_test.columns = ['c1', 'c2', 'c3', 'c4']
df_test['c1'] = df_test['c1'].astype('category')
df_test['c2'] = df_test['c2'].astype('category')
df_test['c3'] = df_test['c3'].astype('category')
df_test['c4'] = df_test['c4'].astype('category')
# categorical_feature=[0, 1]
train_data = Dataset(df_train, label=y_train)
params = {
"boosting_type": "gbdt",
"learning_rate": 0.05,
"n_estimators": 2,
"objective": "binary",
"max_bin": 5,
"min_child_samples": 100,
'verbose': -1,
}
booster = lgb_train(params, train_data)
exp = booster.predict(X_test)
onx = to_onnx(booster, df_train)
self.assertIn('ZipMap', str(onx))
oif = OnnxInference(onx)
got = oif.run(df_test)
values = pandas.DataFrame(got['output_probability']).values
self.assertEqualArray(exp, values[:, 1], decimal=5)
onx.ir_version = get_ir_version_from_onnx()
oif = OnnxInference(onx, runtime='onnxruntime1')
got = oif.run(df_test)
values = pandas.DataFrame(got['output_probability']).values
self.assertEqualArray(exp, values[:, 1], decimal=5)
onx = to_onnx(booster,
df_train,
options={booster.__class__: {
'cast': True
}})
self.assertIn('op_type: "Cast"', str(onx))
oif = OnnxInference(onx)
got = oif.run(df_test)
values = pandas.DataFrame(got['output_probability']).values
self.assertEqualArray(exp, values[:, 1], decimal=5)