mlflow_block.py

if not Path('mlruns').exists():
    client = MlflowClient()
    n_experiments=0
elif not reuse_experiment:
    client = MlflowClient()
    n_experiments = len(client.list_experiments())
    experiment_name = 'experiment_' + str(n_experiments)
    client.create_experiment(name=experiment_name)
with mlflow.start_run(experiment_id=n_experiments):
    model = lgb.LGBMClassifier(**best)
    model.fit(X,y,
              feature_name=colnames,
              categorical_feature=categorical_columns
             )
    for name, value in best.items():
        mlflow.log_param(name, value)
    mlflow.log_metric('binary_logloss', trials.best_trial['result']['loss'])
    mlflow.sklearn.log_model(model, "model")