超簡単Pythonで機械学習ライフサイクル管理（MLflow 利用）lightgbm（MLOps）

PythonでMLflowを利用して超簡単にlightgbmによるirisデータ実験管理（MLOps）

1. ツールインストール

$ pip install mlflow scikit-learn　lightgbm　matplotlib

2. ファイル作成

train.py

import argparse

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss
import lightgbm as lgb
import matplotlib as mpl

import mlflow
import mlflow.lightgbm

mpl.use("Agg")


def parse_args():
   parser = argparse.ArgumentParser(description="LightGBM example")
   parser.add_argument(
       "--learning-rate",
       type=float,
       default=0.1,
       help="learning rate to update step size at each boosting step (default: 0.3)",
   )
   parser.add_argument(
       "--colsample-bytree",
       type=float,
       default=1.0,
       help="subsample ratio of columns when constructing each tree (default: 1.0)",
   )
   parser.add_argument(
       "--subsample",
       type=float,
       default=1.0,
       help="subsample ratio of the training instances (default: 1.0)",
   )
   return parser.parse_args()


def main():
   # parse command-line arguments
   args = parse_args()

   # prepare train and test data
   iris = datasets.load_iris()
   X = iris.data
   y = iris.target
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

   # enable auto logging
   mlflow.lightgbm.autolog()

   train_set = lgb.Dataset(X_train, label=y_train)

   with mlflow.start_run():

       # train model
       params = {
           "objective": "multiclass",
           "num_class": 3,
           "learning_rate": args.learning_rate,
           "metric": "multi_logloss",
           "colsample_bytree": args.colsample_bytree,
           "subsample": args.subsample,
           "seed": 42,
       }
       model = lgb.train(
           params, train_set, num_boost_round=10, valid_sets=[train_set], valid_names=["train"]
       )

       # evaluate model
       y_proba = model.predict(X_test)
       y_pred = y_proba.argmax(axis=1)
       loss = log_loss(y_test, y_proba)
       acc = accuracy_score(y_test, y_pred)

       # log metrics
       mlflow.log_metrics({"log_loss": loss, "accuracy": acc})


if __name__ == "__main__":
   main()

3. 実行

$ python train.py --colsample-bytree 0.8 --subsample 0.9
$ python train.py --learning-rate 0.4 --colsample-bytree 0.7 --subsample 0.8

4. 結果

$ mlflow ui

以上、超簡単！

5. 参考

MLflow - A platform for the machine learning lifecycle