import mlflow
from mlflow.models import infer_signature

#set tracking uri
mlflow.set_tracking_uri(uri = "http://127.0.0.1:8080")

#create a new MLflow Experiment
mlflow.set_experiment("Logging Example")

#start an MLflow run
with mlflow.start_run(run_name = "salary_baseline_regression") as run:

    # Log the loss metric
    mlflow.log_metric("Adjusted R2", r2_score(y_test, y_pred))
    
    #log the hyperparameters
    mlflow.log_params(params)

    #set a tag that we can use to remind ourselves what this run was for
    mlflow.set_tag("Model Type", "Baseline")

    #log the model
    model_info = mlflow.sklearn.log_model(
        sk_model = regr,
        artifact_path = 'model',
        signature = infer_signature(X_train, regr.predict(X_train)),
        input_example = X_train,
        registered_model_name = "salary_baseline_regression")