Convert a pipeline with a LightGbm model

sklearn-onnx only converts scikit-learn models into ONNX but many libraries implement scikit-learn API so that their models can be included in a scikit-learn pipeline. This example considers a pipeline including a LightGbm model. sklearn-onnx can convert the whole pipeline as long as it knows the converter associated to a LGBMClassifier. Let’s see how to do it.

Train a LightGBM classifier

import lightgbm
import onnxmltools
import skl2onnx
import onnx
import sklearn
import matplotlib.pyplot as plt
import os
from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
import onnxruntime as rt
from onnxruntime.capi.onnxruntime_pybind11_state import Fail as OrtFail
from skl2onnx import convert_sklearn, update_registered_converter
from skl2onnx.common.shape_calculator import (
    calculate_linear_classifier_output_shapes,
)  # noqa
from onnxmltools.convert.lightgbm.operator_converters.LightGbm import (
    convert_lightgbm,
)  # noqa
import onnxmltools.convert.common.data_types
from skl2onnx.common.data_types import FloatTensorType
import numpy
from sklearn.datasets import load_iris
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from lightgbm import LGBMClassifier

data = load_iris()
X = data.data[:, :2]
y = data.target

ind = numpy.arange(X.shape[0])
numpy.random.shuffle(ind)
X = X[ind, :].copy()
y = y[ind].copy()

pipe = Pipeline(
    [("scaler", StandardScaler()), ("lgbm", LGBMClassifier(n_estimators=3))]
)
pipe.fit(X, y)

[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000037 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 47
[LightGBM] [Info] Number of data points in the train set: 150, number of used features: 2
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

Pipeline(steps=[('scaler', StandardScaler()),
                ('lgbm', LGBMClassifier(n_estimators=3))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Register the converter for LGBMClassifier

The converter is implemented in onnxmltools: onnxmltools…LightGbm.py. and the shape calculator: onnxmltools…Classifier.py.

Then we import the converter and shape calculator.

Let’s register the new converter.

update_registered_converter(
    LGBMClassifier,
    "LightGbmLGBMClassifier",
    calculate_linear_classifier_output_shapes,
    convert_lightgbm,
    options={"nocl": [True, False], "zipmap": [True, False, "columns"]},
)

Convert again

model_onnx = convert_sklearn(
    pipe,
    "pipeline_lightgbm",
    [("input", FloatTensorType([None, 2]))],
    target_opset={"": 12, "ai.onnx.ml": 2},
)

# And save.
with open("pipeline_lightgbm.onnx", "wb") as f:
    f.write(model_onnx.SerializeToString())

Compare the predictions

Predictions with LightGbm.

print("predict", pipe.predict(X[:5]))
print("predict_proba", pipe.predict_proba(X[:1]))

predict [1 2 0 2 2]
predict_proba [[0.33024337 0.39747231 0.27228432]]

Predictions with onnxruntime.

try:
    sess = rt.InferenceSession(
        "pipeline_lightgbm.onnx", providers=["CPUExecutionProvider"]
    )
except OrtFail as e:
    print(e)
    print("The converter requires onnxmltools>=1.7.0")
    sess = None

if sess is not None:
    pred_onx = sess.run(None, {"input": X[:5].astype(numpy.float32)})
    print("predict", pred_onx[0])
    print("predict_proba", pred_onx[1][:1])

predict [1 2 0 2 2]
predict_proba [{0: 0.3302433490753174, 1: 0.3974723219871521, 2: 0.2722843289375305}]

Display the ONNX graph

pydot_graph = GetPydotGraph(
    model_onnx.graph,
    name=model_onnx.graph.name,
    rankdir="TB",
    node_producer=GetOpNodeProducer(
        "docstring", color="yellow", fillcolor="yellow", style="filled"
    ),
)
pydot_graph.write_dot("pipeline.dot")

os.system("dot -O -Gdpi=300 -Tpng pipeline.dot")

image = plt.imread("pipeline.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis("off")

(-0.5, 2549.5, 2558.5, -0.5)

Versions used for this example

print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", skl2onnx.__version__)
print("onnxmltools: ", onnxmltools.__version__)
print("lightgbm: ", lightgbm.__version__)

numpy: 1.23.5
scikit-learn: 1.4.dev0
onnx:  1.15.0
onnxruntime:  1.16.0+cu118
skl2onnx:  1.16.0
onnxmltools:  1.11.2
lightgbm:  4.0.0