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

#create an instance of a PandasDataset
dataset = mlflow.data.from_pandas(
    eval_data, source = 'kaggle', name = "ds_salaries", targets = "salary")

#create a new MLflow Experiment
mlflow.set_experiment("Salary Prediction")

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

    #log the dataset
    mlflow.log_input(dataset, context = "training")
    
    #log the hyperparameters
    mlflow.log_params(params)

    #log pngs
    mlflow.log_artifact('Result.png')

    #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")
    model_uri = mlflow.get_artifact_uri("model")

    #create the model uri and evalute the model
    result = mlflow.evaluate(model = model_uri, data = dataset, model_type = "regressor", evaluators=["default"])