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smly/lightgbm_tuner_with_mlflow.py

Last active April 27, 2020 08:35
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Revisions

  1. smly revised this gist Apr 27, 2020. 1 changed file with 2 additions and 0 deletions.
    2 changes: 2 additions & 0 deletions lightgbm_tuner_with_mlflow.py
    View file
    Original file line number Diff line number Diff line change
    @@ -16,7 +16,9 @@ def mlflow_callback(study, trial):

    with mlflow.start_run(run_name=study.study_name):
    mlflow.log_params(trial.params)
    mlflow.log_params({"step_name": trial.user_attrs["lightgbm_tuner:step_name"]}
    mlflow.log_metrics({
    "trial_number": trial.number,
    "elapsed_time": trial_runtime,
    "mean_squared_error": trial_value,
    })
  2. smly created this gist Apr 24, 2020.
    51 changes: 51 additions & 0 deletions lightgbm_tuner_with_mlflow.py
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,51 @@
    import numpy as np
    import sklearn.datasets
    from sklearn.metrics import accuracy_score
    from sklearn.model_selection import train_test_split
    import mlflow

    import optuna
    import optuna.integration.lightgbm as lgb


    def mlflow_callback(study, trial):
    trial_value = trial.value if trial.value is not None else float("nan")
    trial_runtime = float("nan")
    if trial.datetime_start and trial.datetime_complete:
    trial_runtime = (trial.datetime_complete - trial.datetime_start).total_seconds()

    with mlflow.start_run(run_name=study.study_name):
    mlflow.log_params(trial.params)
    mlflow.log_metrics({
    "elapsed_time": trial_runtime,
    "mean_squared_error": trial_value,
    })


    if __name__ == "__main__":
    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
    train_x, val_x, train_y, val_y = train_test_split(data, target, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)
    dval = lgb.Dataset(val_x, label=val_y)

    params = {
    "objective": "binary",
    "metric": "binary_logloss",
    "verbosity": -1,
    "boosting_type": "gbdt",
    }
    study = optuna.create_study(study_name="lightgbm_tuner_breast_cancer", direction="minimize")

    model = lgb.train(
    params, dtrain, valid_sets=[dtrain, dval], verbose_eval=100, early_stopping_rounds=100, optuna_callbacks=[mlflow_callback]
    )

    prediction = np.rint(model.predict(val_x, num_iteration=model.best_iteration))
    accuracy = accuracy_score(val_y, prediction)

    best_params = model.params
    print("Best params:", best_params)
    print(" Accuracy = {}".format(accuracy))
    print(" Params: ")
    for key, value in best_params.items():
    print(" {}: {}".format(key, value))