"""

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"""

import pickle

from azureml.core import Workspace

from azureml.core.run import Run

import os

import argparse

from sklearn.datasets import load_diabetes

from sklearn.linear_model import Ridge

from sklearn.metrics import mean_squared_error

from sklearn.model_selection import train_test_split

from sklearn.externals import joblib

import numpy as np

import json

import subprocess

from typing import Tuple, List

parser = argparse.ArgumentParser("train")

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)

run = Run.get_context()

exp = run.experiment

ws = run.experiment.workspace

X, y = load_diabetes(return_X_y=True)

columns = ["age", "gender", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

data = {"train": {"X": X_train, "y": y_train}, "test": {"X": X_test, "y": y_test}}

print("Running train.py")

# Randomly pic alpha

alphas = np.arange(0.0, 1.0, 0.05)

alpha = alphas[np.random.choice(alphas.shape[0], 1, replace=False)][0]

print(alpha)

run.log("alpha", alpha)

reg = Ridge(alpha=alpha)

reg.fit(data["train"]["X"], data["train"]["y"])

preds = reg.predict(data["test"]["X"])

run.log("mse", mean_squared_error(preds, data["test"]["y"]))

# Save model as part of the run history

model_name = "sklearn_regression_model.pkl"

# model_name = "."

with open(model_name, "wb") as file:

joblib.dump(value=reg, filename=model_name)

# upload the model file explicitly into artifacts

run.upload_file(name="./outputs/" + model_name, path_or_stream=model_name)

print("Uploaded the model {} to experiment {}".format(model_name, run.experiment.name))

dirpath = os.getcwd()

print(dirpath)

print("Following files are uploaded ")

print(run.get_file_names())

# register the model

# run.log_model(file_name = model_name)

# print('Registered the model {} to run history {}'.format(model_name, run.history.name))

run_id = {}

run_id["run_id"] = run.id

run_id["experiment_name"] = run.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)

run.complete()