github.com/dmitryikh/leaves
Package leaves is pure Go implemetation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks. General All loaded models exibit the same interface from `Ensemble struct`. One can use method `Name` to get string representation of model origin. Possible name values are "lightgbm.gbdt", "lightgbm.rf", "xgboost.gbtree", "xgboost.gblinear", etc. Example: binary classification build_breast_cancer_model.py: predict_breast_cancer_model.go: Output: example: Multiclass Classification build_iris_model.py predict_iris_model.go: Output: Please note that one must not provide nEstimators = 0 when predict with DART models from xgboost. For more details see xgboost's documentation. Models trained with 'boosting_type': 'dart' options can be loaded with func `leaves.LGEnsembleFromFile`. But the name of the model (given by `Name()` method) will be 'lightgbm.gbdt', because LightGBM model format doesn't distinguish 'gbdt' and 'dart' models.
Readme
leaves is a library implementing prediction code for GBRT (Gradient Boosting Regression Trees) models in pure Go. The goal of the project - make it possible to use models from popular GBRT frameworks in Go programs without C API bindings.
NOTE: Before 1.0.0 release the API is a subject to change.
text format and from JSON formatgbdt, rf (random forest) and dart modelsgbtree, gblinear, dart modelsnan)0)sklearn.ensemble.GradientBoostingClassifierIn order to start, go get this repository:
go get github.com/dmitryikh/leaves
Minimal example:
package main
import (
"fmt"
"github.com/dmitryikh/leaves"
)
func main() {
// 1. Read model
useTransformation := true
model, err := leaves.LGEnsembleFromFile("lightgbm_model.txt", useTransformation)
if err != nil {
panic(err)
}
// 2. Do predictions!
fvals := []float64{1.0, 2.0, 3.0}
p := model.PredictSingle(fvals, 0)
fmt.Printf("Prediction for %v: %f\n", fvals, p)
}
In order to use XGBoost model, just change leaves.LGEnsembleFromFile, to leaves.XGEnsembleFromFile.
Documentation is hosted on godoc (link). Documentation contains complex usage examples and full API reference. Some additional information about usage examples can be found in leaves_test.go.
Most leaves features are tested to be compatible with old and coming versions of GBRT libraries. In compatibility.md one can found detailed report about leaves correctness against different versions of external GBRT libraries.
Some additional information on new features and backward compatibility can be found in NOTES.md.
Below are comparisons of prediction speed on batches (~1000 objects in 1 API
call). Hardware: MacBook Pro (15-inch, 2017), 2,9 GHz Intel Core i7, 16 ГБ
2133 MHz LPDDR3. C API implementations were called from python bindings. But
large batch size should neglect overhead of python bindings. leaves
benchmarks were run by means of golang test framework: go test -bench. See
benchmark for mode details on measurments. See
testdata/README.md for data preparation pipelines.
Single thread:
| Test Case | Features | Trees | Batch size | C API | leaves |
|---|---|---|---|---|---|
| LightGBM MS LTR | 137 | 500 | 1000 | 49ms | 51ms |
| LightGBM Higgs | 28 | 500 | 1000 | 50ms | 50ms |
| LightGBM KDD Cup 99* | 41 | 1200 | 1000 | 70ms | 85ms |
| XGBoost Higgs | 28 | 500 | 1000 | 44ms | 50ms |
4 threads:
| Test Case | Features | Trees | Batch size | C API | leaves |
|---|---|---|---|---|---|
| LightGBM MS LTR | 137 | 500 | 1000 | 14ms | 14ms |
| LightGBM Higgs | 28 | 500 | 1000 | 14ms | 14ms |
| LightGBM KDD Cup 99* | 41 | 1200 | 1000 | 19ms | 24ms |
| XGBoost Higgs | 28 | 500 | 1000 | ? | 14ms |
(?) - currenly I'm unable to utilize multithreading form XGBoost predictions by means of python bindings
(*) - KDD Cup 99 problem involves continuous and categorical features simultaneously
GradientBoostingClassifier.decision_function)0 is supportedIn case if you are interested in the project or if you have questions, please contact with me by
email: khdmitryi at gmail.com
FAQs
Package leaves is pure Go implemetation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks. General All loaded models exibit the same interface from `Ensemble struct`. One can use method `Name` to get string representation of model origin. Possible name values are "lightgbm.gbdt", "lightgbm.rf", "xgboost.gbtree", "xgboost.gblinear", etc. Example: binary classification build_breast_cancer_model.py: predict_breast_cancer_model.go: Output: example: Multiclass Classification build_iris_model.py predict_iris_model.go: Output: Please note that one must not provide nEstimators = 0 when predict with DART models from xgboost. For more details see xgboost's documentation. Models trained with 'boosting_type': 'dart' options can be loaded with func `leaves.LGEnsembleFromFile`. But the name of the model (given by `Name()` method) will be 'lightgbm.gbdt', because LightGBM model format doesn't distinguish 'gbdt' and 'dart' models.
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