def test_lightgbm_booster_multi_classifier(self):
X = [[0, 1], [1, 1], [2, 0], [1, 2], [-1, 2], [1, -2]]
X = numpy.array(X, dtype=numpy.float32)
y = [0, 1, 0, 1, 2, 2]
data = lightgbm.Dataset(X, label=y)
model = lightgbm.train(
{
'boosting_type': 'gbdt',
'objective': 'multiclass',
'n_estimators': 3,
'min_child_samples': 1,
'num_class': 3
}, data)
model_onnx, prefix = convert_model(
model, 'tree-based classifier',
[('input', FloatTensorType([None, 2]))])
dump_data_and_model(
X,
model,
model_onnx,
allow_failure=
"StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
basename=prefix + "BoosterBin" + model.__class__.__name__)
try:
from onnxruntime import InferenceSession
except ImportError:
# onnxruntime not installed (python 2.7)
return
sess = InferenceSession(model_onnx.SerializeToString())
out = sess.get_outputs()
names = [o.name for o in out]
assert names == ['label', 'probabilities']
def test_lightgbm_classifier_nozipmap(self):
X = [[0, 1], [1, 1], [2, 0], [1, 2], [1, 5], [6, 2]]
X = numpy.array(X, dtype=numpy.float32)
y = [0, 1, 0, 1, 1, 0]
model = LGBMClassifier(n_estimators=3,
min_child_samples=1,
max_depth=2)
model.fit(X, y)
onx = convert_model(model,
'dummy',
input_types=[('X',
FloatTensorType([None,
X.shape[1]]))],
zipmap=False)
assert "zipmap" not in str(onx).lower()
onxs = onx[0].SerializeToString()
try:
sess = onnxruntime.InferenceSession(onxs)
except Exception as e:
raise AssertionError(
"Model cannot be loaded by onnxruntime due to %r\n%s." %
(e, onx[0]))
exp = model.predict(X), model.predict_proba(X)
got = sess.run(None, {'X': X})
assert_almost_equal(exp[0], got[0])
assert_almost_equal(exp[1], got[1])
def test_lightgbm_booster_regressor(self):
X = [[0, 1], [1, 1], [2, 0]]
X = numpy.array(X, dtype=numpy.float32)
y = [0, 1, 1.1]
data = lightgbm.Dataset(X, label=y)
model = lightgbm.train(
{
'boosting_type': 'gbdt',
'objective': 'regression',
'n_estimators': 3,
'min_child_samples': 1,
'max_depth': 1
}, data)
model_onnx, prefix = convert_model(
model,
'tree-based binary classifier',
[('input', FloatTensorType([None, 2]))],
without_onnx_ml=True)
dump_data_and_model(
X,
model,
model_onnx,
allow_failure=
"StrictVersion(onnx.__version__) < StrictVersion('1.0.0')",
basename=prefix + "BoosterBin" + model.__class__.__name__)
def _test_lgbm(self, X, model, extra_config={}):
# Create ONNX-ML model
onnx_ml_model = convert_model(
model, 'lgbm-onnxml',
[("input", FloatTensorType([X.shape[0], X.shape[1]]))])[0]
# Create ONNX model
onnx_model = convert_model(
model,
'lgbm-onnx',
[("input", FloatTensorType([X.shape[0], X.shape[1]]))],
without_onnx_ml=True)[0]
try:
from onnxruntime import InferenceSession
except ImportError:
# onnxruntime not installed (python 2.7)
return
# Get the predictions for the ONNX-ML model
session = InferenceSession(onnx_ml_model.SerializeToString())
output_names = [
session.get_outputs()[i].name
for i in range(len(session.get_outputs()))
]
onnx_ml_pred = [[] for i in range(len(output_names))]
inputs = {session.get_inputs()[0].name: X}
pred = session.run(output_names, inputs)
for i in range(len(output_names)):
if output_names[i] == "label":
onnx_ml_pred[1] = pred[i]
else:
onnx_ml_pred[0] = pred[i]
# Get the predictions for the ONNX model
session = InferenceSession(onnx_model.SerializeToString())
onnx_pred = [[] for i in range(len(output_names))]
pred = session.run(output_names, inputs)
for i in range(len(output_names)):
if output_names[i] == "label":
onnx_pred[1] = pred[i]
else:
onnx_pred[0] = pred[i]
return onnx_ml_pred, onnx_pred, output_names
def test_lightgbm_classifier_zipmap(self):
X = [[0, 1], [1, 1], [2, 0], [1, 2]]
X = numpy.array(X, dtype=numpy.float32)
y = [0, 1, 0, 1]
model = LGBMClassifier(n_estimators=3, min_child_samples=1)
model.fit(X, y)
onx = convert_model(
model, 'dummy', input_types=[('X', FloatTensorType([None, X.shape[1]]))])
assert "zipmap" in str(onx).lower()
def test_lightgbm_booster_classifier_zipmap(self):
X = [[0, 1], [1, 1], [2, 0], [1, 2]]
X = numpy.array(X, dtype=numpy.float32)
y = [0, 1, 0, 1]
data = lightgbm.Dataset(X, label=y)
model = lightgbm.train({'boosting_type': 'gbdt', 'objective': 'binary',
'n_estimators': 3, 'min_child_samples': 1},
data)
model_onnx, prefix = convert_model(model, 'tree-based classifier',
[('input', FloatTensorType([None, 2]))])
assert "zipmap" in str(model_onnx).lower()
dump_data_and_model(X, model, model_onnx,
allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
basename=prefix + "BoosterBin" + model.__class__.__name__)
