"""

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"""

import os, json

from azureml.core import Workspace

from azureml.core import Experiment

from azureml.core.model import Model

import azureml.core

from azureml.core import Run

import argparse

# Get workspace

# ws = Workspace.from_config()

run = Run.get_context()

exp = run.experiment

ws = run.experiment.workspace

parser = argparse.ArgumentParser("evaluate")

parser.add_argument(

"--config_suffix", type=str, help="Datetime suffix for json config files"

)

parser.add_argument(

"--json_config",

type=str,

help="Directory to write all the intermediate json configs",

)

args = parser.parse_args()

print("Argument 1: %s" % args.config_suffix)

print("Argument 2: %s" % args.json_config)

if not (args.json_config is None):

os.makedirs(args.json_config, exist_ok=True)

print("%s created" % args.json_config)

# Paramaterize the matrics on which the models should be compared

# Add golden data set on which all the model performance can be evaluated

# Get the latest run_id

# with open("aml_config/run_id.json") as f:

# config = json.load(f)

train_run_id_json = "run_id_{}.json".format(args.config_suffix)

train_output_path = os.path.join(args.json_config, train_run_id_json)

with open(train_output_path) as f:

config = json.load(f)

# parser = argparse.ArgumentParser()

# parser.add_argument('--train_run_id',type=str,default='',help='Run id of the newly trained model')

# #parser.add_argument('--model_assets_path',type=str,default='outputs',help='Location of trained model.')

new_model_run_id = config["run_id"] # args.train_run_id

experiment_name = config["experiment_name"]

# exp = Experiment(workspace=ws, name=experiment_name)

try:

# Get most recently registered model, we assume that is the model in production. Download this model and compare it with the recently trained model by running test with same data set.

model_list = Model.list(ws)

production_model = next(

filter(

lambda x: x.created_time == max(model.created_time for model in model_list),

model_list,

)

)

production_model_run_id = production_model.tags.get("run_id")

run_list = exp.get_runs()

# production_model_run = next(filter(lambda x: x.id == production_model_run_id, run_list))

# Get the run history for both production model and newly trained model and compare mse

production_model_run = Run(exp, run_id=production_model_run_id)

new_model_run = Run(exp, run_id=new_model_run_id)

production_model_mse = production_model_run.get_metrics().get("mse")

new_model_mse = new_model_run.get_metrics().get("mse")

print(

"Current Production model mse: {}, New trained model mse: {}".format(

production_model_mse, new_model_mse

)

)

promote_new_model = False

if new_model_mse < production_model_mse:

promote_new_model = True

print("New trained model performs better, thus it will be registered")

except:

promote_new_model = True

print("This is the first model to be trained, thus nothing to evaluate for now")

run_id = {}

run_id["run_id"] = ""

# Writing the run id to /aml_config/run_id.json

if promote_new_model:

run_id["run_id"] = new_model_run_id

# register new model

# new_model_run.register_model(model_name='',model_path='outputs/sklearn_regression_model.pkl')

run_id["experiment_name"] = experiment_name

filename = "run_id_{}.json".format(args.config_suffix)

output_path = os.path.join(args.json_config, filename)

with open(output_path, "w") as outfile:

json.dump(run_id, outfile)