def rank(self, user_ratings: pd.DataFrame, items_directory: str, recs_number: int = None,
filter_list: List[str] = None) -> pd.DataFrame:
"""
Rank the top-n recommended items for the user. If the recs_number parameter isn't specified,
All items will be ranked.
One can specify which items must be ranked with the filter_list parameter,
in this case ONLY items in the filter_list will be used to calculate the rank.
One can also pass items already seen by the user with the filter_list parameter.
Otherwise, ALL unrated items will be used to calculate the rank.
Args:
user_ratings (pd.DataFrame): DataFrame containing ratings of a single user
items_directory (str): path of the directory where the items are stored
recs_number (int): number of the top items that will be present in the ranking
filter_list (list): list of the items to rank, if None all unrated items will be used to
calculate the rank
Returns:
pd.DataFrame: DataFrame containing one column with the items name,
one column with the rating predicted, sorted in descending order by the 'rating' column
"""
# Load items to predict
if filter_list is None:
items_to_predict = get_unrated_items(items_directory, user_ratings)
else:
items_to_predict = get_chosen_items(items_directory, filter_list)
# Extract features of the items to predict
id_items_to_predict = []
features_items_to_predict = []
for item in items_to_predict:
if item is not None:
id_items_to_predict.append(item.content_id)
features_items_to_predict.append(self.extract_features_item(item))
recsys_logger.info("Calculating rank")
if len(id_items_to_predict) > 0:
# Fuse the input if there are dicts, multiple representation, etc.
fused_features_items_to_pred = self.fuse_representations(features_items_to_predict, self.__embedding_combiner)
score_labels = self.__classifier.predict_proba(fused_features_items_to_pred)
else:
score_labels = []
result = {'to_id': [], 'score': []}
for item_id, score in zip(id_items_to_predict, score_labels):
result['to_id'].append(item_id)
result['score'].append(score[1])
result = pd.DataFrame(result, columns=['to_id', 'score'])
result.sort_values(by=['score'], ascending=False, inplace=True)
rank = result[:recs_number]
return rank
def predict(self,
user_ratings: pd.DataFrame,
items_directory: str,
filter_list: List[str] = None) -> pd.DataFrame:
"""
Predicts how much a user will like unrated items.
One can specify which items must be predicted with the filter_list parameter,
in this case ONLY items in the filter_list will be predicted.
One can also pass items already seen by the user with the filter_list parameter.
Otherwise, ALL unrated items will be predicted.
Args:
user_ratings (pd.DataFrame): DataFrame containing ratings of a single user
items_directory (str): path of the directory where the items are stored
filter_list (list): list of the items to predict, if None all unrated items will be predicted
Returns:
pd.DataFrame: DataFrame containing one column with the items name,
one column with the score predicted
"""
# Load items to predict
if filter_list is None:
items_to_predict = get_unrated_items(items_directory, user_ratings)
else:
items_to_predict = get_chosen_items(items_directory, filter_list)
# Extract features of the items to predict
id_items_to_predict = []
features_items_to_predict = []
for item in items_to_predict:
if item is not None:
# raises AttributeError if items are not present locally
id_items_to_predict.append(item.content_id)
features_items_to_predict.append(
self.extract_features_item(item))
recsys_logger.info("Calculating score predictions")
if len(id_items_to_predict) > 0:
# Fuse the input if there are dicts, multiple representation, etc.
fused_features_items_to_pred = self.fuse_representations(
features_items_to_predict, self.__embedding_combiner)
score_labels = self.__regressor.predict(
fused_features_items_to_pred)
else:
score_labels = []
# Build the score_frame to return
columns = ["to_id", "score"]
score_frame = pd.DataFrame(columns=columns)
score_frame["to_id"] = id_items_to_predict
score_frame["score"] = score_labels
return score_frame
def rank(self, user_ratings: pd.DataFrame, items_directory: str, recs_number: int = None,
filter_list: List[str] = None) -> pd.DataFrame:
"""
Rank the top-n recommended items for the user. If the recs_number parameter isn't specified,
All unrated items will be ranked (or only items in the filter list, if specified).
One can specify which items must be ranked with the filter_list parameter,
in this case ONLY items in the filter_list parameter will be ranked.
One can also pass items already seen by the user with the filter_list parameter.
Otherwise, ALL unrated items will be ranked.
Args:
user_ratings (pd.DataFrame): DataFrame containing ratings of a single user
items_directory (str): path of the directory where the items are stored
recs_number (int): number of the top items that will be present in the ranking, if None
all unrated items will be ranked
filter_list (list): list of the items to rank, if None all unrated items will be ranked
Returns:
pd.DataFrame: DataFrame containing one column with the items name,
one column with the rating predicted, sorted in descending order by the 'rating' column
"""
# Load items to predict
if filter_list is None:
items_to_predict = get_unrated_items(items_directory, user_ratings)
else:
items_to_predict = get_chosen_items(items_directory, filter_list)
# Extract features of the items to predict
id_items_to_predict = []
features_items_to_predict = []
for item in items_to_predict:
if item is not None:
id_items_to_predict.append(item.content_id)
features_items_to_predict.append(self.extract_features_item(item))
recsys_logger.info("Calculating rank")
if len(id_items_to_predict) > 0:
# Calculate predictions, they are the similarity of the new items with the centroid vector
features_fused = self.fuse_representations(features_items_to_predict, self.__embedding_combiner)
similarities = [self.__similarity.perform(self.__centroid, item) for item in features_fused]
else:
similarities = []
# Build the score frame
result = {'to_id': id_items_to_predict, 'score': similarities}
result = pd.DataFrame(result, columns=['to_id', 'score'])
# Sort them in descending order
result.sort_values(by=['score'], ascending=False, inplace=True)
rank = result[:recs_number]
return rank