Created
April 23, 2021 20:27
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LGBM Inference Optimization
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import daal4py as d4p | |
from lightgbm import LGBMClassifier | |
model = LGBMClassifier() | |
model.fit(X_data, y_data) | |
y_pred_orig = model.predict_proba(x_data_dense)[:,1] | |
daal_model = d4p.get_gbt_model_from_lightgbm(model.booster_) | |
predictions_container = d4p.gbt_classification_prediction(nClasses=2, resultsToEvaluate='computeClassProbabilities', fptype='float') | |
y_pred_opti = predictions_container.compute(X_data, daal_model).probabilities[:,1] | |
np.allclose(y_pred_orig, y_pred_opti) |
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#https://treelite.readthedocs.io/en/latest/tutorials/import.html#importing-lightgbm-models | |
import treelite | |
from lightgbm import LGBMClassifier | |
model = LGBMClassifier() | |
model.fit(X_data, y_data) | |
model.booster_.save_model('my_model.txt') | |
treelite_model = treelite.Model.load('my_model.txt', model_format='lightgbm') | |
# for windows libpath='my_model.dll', toolchain = 'msvc' | |
treelite_model.export_lib(libpath='my_model.so', toolchain = 'gcc', verbose=True) | |
# Inference | |
import treelite_runtime | |
predictor = treelite_runtime.Predictor('./mymodel.so', verbose=True) | |
y_pred_opti = predictor.predict(X_data) |
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