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"""

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

parser = argparse.ArgumentParser("train")

parser.add_argument(

"--release_id",

type=str,

help="The ID of the release triggering this pipeline run",

)

parser.add_argument(

"--model_name",

type=str,

help="Name of the Model",

default="sklearn_regression_model.pkl",

)

args = parser.parse_args()

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

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

model_name = args.model_name

release_id = args.release_id

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 = "."

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())

# Add properties to identify this specific training run

run.add_properties({"release_id": release_id, "run_type": "train"})

print(f"added properties: {run.properties}")

run.complete()