from azureml.core import Experiment, ScriptRunConfig, Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.train.hyperdrive import GridParameterSampling, HyperDriveConfig, PrimaryMetricGoal, choice
from azureml.widgets import RunDetails


# Sample a range of parameter values
params = GridParameterSampling(
    {
        # Hyperdrive will try 6 combinations, adding these as script arguments
        '--learning_rate': choice(0.01, 0.1, 1.0),
        '--n_estimators' : choice(10, 100)
    }
)

# Configure hyperdrive settings
hyperdrive = HyperDriveConfig(run_config=script_config, 
                          hyperparameter_sampling=params, 
                          policy=None, # No early stopping policy
                          primary_metric_name='AUC', # Find the highest AUC metric
                          primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, 
                          max_total_runs=6, # Restict the experiment to 6 iterations
                          max_concurrent_runs=2) # Run up to 2 iterations in parallel

# Run the experiment
experiment = Experiment(workspace=ws, name='mslearn-diabetes-hyperdrive')
run = experiment.submit(config=hyperdrive)

# Show the status in the notebook as the experiment runs
RunDetails(run).show()
run.wait_for_completion()