|
|
@@ -220,17 +220,26 @@ from candidate_item
|
|
|
item_h_dict = {}
|
|
|
k_col = 'i_id'
|
|
|
dt = datetime
|
|
|
- key_name_prefix = f"{config_.KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_ITEM}{model_key}:"
|
|
|
+ key_name_prefix = f"{config_.KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_ITEM}{model_key}"
|
|
|
print(key_name_prefix)
|
|
|
+ mean_item_h = 0.0
|
|
|
+ count_item_h = 0
|
|
|
with data.open_reader() as reader:
|
|
|
for row in reader:
|
|
|
- k = row['i_id']
|
|
|
+ k = str(row['i_id'])
|
|
|
item_features = get_item_features(row)
|
|
|
item_h = lr_model.predict_h(item_features)
|
|
|
redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", item_h, 28 * 3600)
|
|
|
item_h_dict[k] = item_h
|
|
|
+ mean_item_h += item_h
|
|
|
+ count_item_h += 1
|
|
|
# print(item_features)
|
|
|
# print(item_h)
|
|
|
+ mean_item_h = mean_item_h / count_item_h
|
|
|
+ item_h_dict['mean'] = mean_item_h
|
|
|
+ print(mean_item_h)
|
|
|
+ k = 'mean'
|
|
|
+ redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", mean_item_h, 28 * 3600)
|
|
|
with open('{}.json'.format(key_name_prefix), 'w') as fout:
|
|
|
json.dump(item_h_dict, fout, indent=2, ensure_ascii=False, sort_keys=True)
|
|
|
|
