def gen_rand_user_item_feature(user_num, item_num, class_num):
user_id = random.randint(1, user_num)
item_id = random.randint(1, item_num)
rating = random.randint(1, class_num)
sample = Sample.from_ndarray(np.array([user_id, item_id]),
np.array([rating]))
return UserItemFeature(user_id, item_id, sample)
def to_user_item_feature(row, column_info, model_type="wide_n_deep"):
"""
convert a row to UserItemFeature given column feature information of a WideAndDeep model
:param row: Row of userId, itemId, features and label
:param column_info: ColumnFeatureInfo specify information of different features
:return: UserItemFeature for recommender model
"""
return UserItemFeature(row["userId"], row["itemId"],
row_to_sample(row, column_info, model_type))
def to_user_item_feature(row, column_info, model_type="wide_n_deep"):
return UserItemFeature(row["userId"], row["itemId"],
row_to_sample(row, column_info, model_type))
def build_sample(user_id, item_id, rating):
sample = Sample.from_ndarray(np.array([user_id, item_id]), np.array([rating]))
return UserItemFeature(user_id, item_id, sample)