def init():
global model
model_path = Model.get_model_path('diabetes-model')
with open(model_path, 'rb') as file:
model = pickle.load(file)
# For demonstration purposes only
print(mylib.get_alphas())
from utils import mylib
os.makedirs('./outputs', exist_ok=True)
X, y = load_diabetes(return_X_y=True)
run = Run.get_context()
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}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = mylib.get_alphas()
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
preds = reg.predict(data["test"]["X"])
mse = mean_squared_error(preds, data["test"]["y"])
run.log('alpha', alpha)
run.log('MSE', mse)
# Save model in the outputs folder so it automatically get uploaded when running on AML Compute
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
with open(os.path.join('./outputs/', model_file_name), 'wb') as file:
pickle.dump(reg, file)