def train_model():
"""Training model."""
s3_obj = load_S3()
data = load_data(s3_obj)
hyper_params = load_hyper_params() or {}
LOWER_LIMIT = int(hyper_params.get('lower_limit', 13))
UPPER_LIMIT = int(hyper_params.get('upper_limit', 15))
LATENT_FACTOR = int(hyper_params.get('latent_factor', 300))
logger.info(
"Lower limit {}, Upper limit {} and latent factor {} are used.".format(
LOWER_LIMIT, UPPER_LIMIT, LATENT_FACTOR))
package_id_dict, manifest_id_dict = preprocess_raw_data(
data.get('package_dict', {}), LOWER_LIMIT, UPPER_LIMIT)
user_input_stacks = data.get('package_dict', {}).\
get('user_input_stack', [])
user_item_list = make_user_item_df(manifest_id_dict, package_id_dict,
user_input_stacks)
user_item_df = pd.DataFrame(user_item_list)
training_df, testing_df = train_test_split(user_item_df)
format_pkg_id_dict, format_mnf_id_dict = format_dict(
package_id_dict, manifest_id_dict)
del package_id_dict, manifest_id_dict
trained_recommender = run_recommender(training_df, LATENT_FACTOR)
precision_at_30, recall_at_30 = precision_recall_at_m(
30, testing_df, trained_recommender, user_item_df)
precision_at_50, recall_at_50 = precision_recall_at_m(
50, testing_df, trained_recommender, user_item_df)
try:
save_obj(s3_obj, trained_recommender, precision_at_30, recall_at_30,
format_pkg_id_dict, format_mnf_id_dict, precision_at_50,
recall_at_50, LOWER_LIMIT, UPPER_LIMIT, LATENT_FACTOR)
if GITHUB_TOKEN:
create_git_pr(s3_client=s3_obj,
model_version=MODEL_VERSION,
recall_at_30=recall_at_30)
except Exception as error:
logger.error(error)
raise
def test_load_hyper_params():
# mock command line args
helper.argv = ['helper.py', '{"a": 111, "b": "some text"}']
hyper_params = helper.load_hyper_params()
assert hyper_params.get('a') == 111
assert hyper_params.get('b') == "some text"