Logging tuned models
You have been experimenting with different model hyperparameters and need to log your latest round of experiment results to MLflow, let's do it!
この演習はコースの一部です
Designing Forecasting Pipelines for Production
演習の手順
- Set the experiment name as
"hyperparameter_tuning". - Loop over the index and rows of
df. - Start an MLflow run.
実践的なインタラクティブ演習
このサンプルコードを完成させて、この演習に挑戦してみましょう。
# Set the experiment name
experiment_name = "____"
experiment_id = mlflow.create_experiment(experiment_name)
# Loop through the DataFrame
for idx, row in df.____():
# Start a run
with mlflow.____(experiment_id=____):
model_params = ml_models[row["model_label"]].get_params()
model_params["model_name"] = row["model_name"]
model_params["model_label"] = row["model_label"]
model_params["partition"] = row["partition"]
model_params["lags"] = list(range(1, 24))
model_params["date_features"] = ["month", "day", "dayofweek", "week", "hour"]
mlflow.log_params(model_params)
mlflow.log_metric("mape", row["mape"])
mlflow.log_metric("rmse", row["rmse"])