def recommend(self, users=None, k=100, exclude_known=True):
"""Give toplist recommendations for users.
Parameters
----------
users : list
List of users to give recommendation for.
k : int
Size of toplists
exclude_known : bool
Whether to exclude (user,item) pairs in the train dataset from the toplists.
Returns
-------
pandas.DataFrame
DataFrame of recommendations, with columns **user**, **item** and **rank**.
"""
rs.collect()
dummy_model_filter = rs.DummyModelFilter()
dummy_model_filter.set_items(self.items)
dummy_model_filter.set_users(self.users)
pred_creator = rs.PredictionCreatorPersonalized(
top_k=k, lookback=1 if exclude_known else 0)
pred_creator.set_filter(dummy_model_filter)
pred_creator.set_train_matrix(self.matrix)
pred_creator.set_model(self.model)
ranking_computer = rs.OfflineRankingComputer(top_k=k)
ranking_computer.set_items(self.items)
if users is None:
ranking_computer.set_users(self.users)
else:
ranking_computer.set_users(
rs.VectorInt(pd.Series(users).unique().tolist()))
ranking_computer.set_toplist_creator(pred_creator)
created_objects = rs.get_and_clean()
# rs.initialize_all(created_objects)
for i in created_objects:
rs.run_self_test(i)
preds = ranking_computer.compute()
preds_df = pd.DataFrame({
'user': preds.users,
'item': preds.items,
'rank': preds.ranks
}).sort_values(['user', 'rank'])[['user', 'item', 'rank']]
return preds_df
def fit(self, X, y=None, columns={}):
"""Fit the model to a dataset.
Parameters
----------
X : pandas.DataFrame
The input data, must contain the columns **user** and **item**. May contain the **score** column as well.
y : pandas.Series or list
The target values. If not set (and X doesn't contain the score column), it is assumed to be constant 1 (implicit recommendation).
columns : dict
Optionally the mapping of the input DataFrame's columns' names to the expected ones.
"""
rs.collect()
data = X
if y is None:
if 'score' not in X:
data['score'] = np.ones(len(X))
else:
if 'score' in X:
raise ValueError("y and score column both provided")
else:
data['score'] = y
recommender_data = DataframeData(data, columns=columns)
matrix = recommender_data.get_full_matrix()
users = rs.VectorInt([])
items = rs.VectorInt([])
recommender_data.get_users_into(users)
recommender_data.get_items_into(items)
(model, learner) = self._fit(recommender_data, users, items, matrix)
created_objects = rs.get_and_clean()
rs.initialize_all(created_objects)
for i in created_objects:
rs.run_self_test(i)
self.check_unused_parameters()
learner.fit(recommender_data)
self.objects = created_objects
self.model = model
self.items = items
self.users = users
self.matrix = matrix
self.recommender_data = recommender_data
