Folders and files
| Name | Name | Last commit date | ||
|---|---|---|---|---|
Tree-based machine learning library for Go. "Metee" means "trees" in Swahili/Kikuyu.
Metee provides LightGBM and XGBoost bindings, ensemble methods, cross-validation, hyperparameter tuning, and a full feature-engineering pipeline — all behind clean Go interfaces. It is a standalone complement to the Zerfoo neural network framework with no dependency on it.
- Model backends — LightGBM and XGBoost via CGO bindings (optional build tags), with stubs for CGO-free builds
- Ensemble methods — Rank-normalized blending and out-of-fold stacking
- Cross-validation — Era-aware KFold and WalkForward splits
- Hyperparameter tuning — Grid search and random search over typed parameter spaces
- Feature transforms — Rank normalization, Gaussianization, SVD-based neutralization, exposure computation
- Metrics — Pearson, Spearman, Sharpe ratio, max drawdown, feature-neutral correlation (FNC), per-era reports
- Data loading — CSV and Parquet with streaming support
- Config — Generic YAML loader with environment variable overrides and struct validation
- Training orchestrator — Checkpointing, early stopping, and callback hooks
go get github.com/zerfoo/meteeRequires Go 1.25+.
package main
import (
"context"
"fmt"
"log"
"github.com/zerfoo/metee/cv"
"github.com/zerfoo/metee/data"
"github.com/zerfoo/metee/metrics"
"github.com/zerfoo/metee/registry"
_ "github.com/zerfoo/metee/lightgbm" // register backend (requires -tags lightgbm)
)
func main() {
ctx := context.Background()
// Load data
ds, err := data.LoadCSV("train.csv", data.CSVOptions{
TargetColumn: "target",
IDColumn: "id",
EraColumn: "era",
})
if err != nil {
log.Fatal(err)
}
// Get a model from the registry
m, err := registry.GetBackend("lightgbm")
if err != nil {
log.Fatal(err)
}
// Cross-validate with walk-forward splits
folds := cv.WalkForward(ds, 4, 2)
results, err := cv.CrossValidate(ctx, m, ds, folds, metrics.Spearman)
if err != nil {
log.Fatal(err)
}
for i, r := range results {
fmt.Printf("Fold %d: %.4f\n", i, r.Score)
}
}CGO backends are optional. Without build tags, stub implementations return descriptive errors and go build ./... always succeeds.
| Tag | Backend | Requirement |
|---|---|---|
| lightgbm | LightGBM | libLightGBM headers and shared library |
| xgboost | XGBoost | libxgboost headers and shared library |
# CPU-only, no CGO:
go test ./...
# With LightGBM:
CGO_CFLAGS="-I/usr/local/include" CGO_LDFLAGS="-L/usr/local/lib -lLightGBM" \
go test -tags lightgbm ./...
# With both backends:
go test -tags "lightgbm,xgboost" ./...| Package | Purpose |
|---|---|
| model/ | Model, Validator, and Configurable interfaces |
| registry/ | Thread-safe backend registry (RegisterBackend / GetBackend) |
| lightgbm/ | LightGBM CGO bindings + stub |
| xgboost/ | XGBoost CGO bindings + stub |
| data/ | Dataset type, CSV and Parquet loaders |
| transform/ | Rank normalization, Gaussianization, neutralization, exposure, pipeline |
| metrics/ | Pearson, Spearman, Sharpe, max drawdown, FNC, per-era reports |
| cv/ | KFold, WalkForward splits, CrossValidate |
| tuning/ | Parameter spaces (Discrete, Uniform, LogUniform, IntRange), grid/random search |
| trainer/ | Training orchestrator with checkpointing and callbacks |
| ensemble/ | Rank blending and out-of-fold stacking |
| config/ | Generic YAML loader with env overrides and validation |
All backends implement the core model interface:
type Model interface {
Train(ctx context.Context, features [][]float64, targets []float64) error
Predict(ctx context.Context, features [][]float64) ([]float64, error)
Save(ctx context.Context, path string) error
Load(ctx context.Context, path string) error
Importance() (map[string]float64, error)
Name() string
}type Validator interface {
Validate(ctx context.Context, features [][]float64, targets []float64) (map[string]float64, error)
}For runtime parameter updates during hyperparameter tuning:
type Configurable interface {
SetParams(params map[string]any) error
}| Dependency | Purpose |
|---|---|
| gonum.org/v1/gonum | Matrix operations (neutralization, FNC) |
| gopkg.in/yaml.v3 | YAML config parsing |
| github.com/parquet-go/parquet-go | Parquet data loading |