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
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