import pandas as pd
from azureml.core import Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.train.estimator import Estimator
from azureml.core.run import Run


def run_inference(test_experiment, compute_target, script_folder, train_run,
                  test_dataset, target_column_name, model_name):

    train_run.download_file('outputs/conda_env_v_1_0_0.yml',
                            'inference/condafile.yml')

    inference_env = Environment("myenv")
    inference_env.docker.enabled = True
    inference_env.python.conda_dependencies = CondaDependencies(
        conda_dependencies_file_path='inference/condafile.yml')

    est = Estimator(source_directory=script_folder,
                    entry_script='infer.py',
                    script_params={
                        '--target_column_name': target_column_name,
                        '--model_name': model_name
                    },
                    inputs=[test_dataset.as_named_input('test_data')],
                    compute_target=compute_target,
                    environment_definition=inference_env)

    run = test_experiment.submit(
        est, tags={
            'training_run_id': train_run.id,
            'run_algorithm': train_run.properties['run_algorithm'],
            'valid_score': train_run.properties['score'],
            'primary_metric': train_run.properties['primary_metric']
        })

    run.log("run_algorithm", run.tags['run_algorithm'])
    return run


def get_result_df(remote_run):

    children = list(remote_run.get_children(recursive=True))
    summary_df = pd.DataFrame(index=['run_id', 'run_algorithm',
                                     'primary_metric', 'Score'])
    goal_minimize = False
    for run in children:
        if('run_algorithm' in run.properties and 'score' in run.properties):
            summary_df[run.id] = [run.id, run.properties['run_algorithm'],
                                  run.properties['primary_metric'],
                                  float(run.properties['score'])]
            if('goal' in run.properties):
                goal_minimize = run.properties['goal'].split('_')[-1] == 'min'

    summary_df = summary_df.T.sort_values(
        'Score',
        ascending=goal_minimize).drop_duplicates(['run_algorithm'])
    summary_df = summary_df.set_index('run_algorithm')

    return summary_df