bugfixes fix #2

hpfrec/__init__.py

@@ -632,7 +632,7 @@ def _process_data_single(self, counts_df):
 		if self.reindex:
 			if self.produce_dicts:
 				try:
-					counts_df['ItemId'] = counts_df.ItemId.map(lambda x: self.item_dict_[user])
+					counts_df['ItemId'] = counts_df.ItemId.map(lambda x: self.item_dict_[x])
 				except:
 					raise ValueError("Can only make calculations for items that were in the training set.")
 			else:
@@ -1046,7 +1046,7 @@ def add_user(self, user_id, counts_df, update_existing=False, maxiter=10, ncores
 		if update_existing:
 			## checking that the user already exists
 			if self.produce_dicts and self.reindex:
-				user_id = self.user_dict_[user]
+				user_id = self.user_dict_[user_id]
 			else:
 				if self.reindex:
 					user_id = pd.Categorical(np.array([user_id]), self.user_mapping_).codes[0]

setup.py

@@ -27,12 +27,12 @@
    'scipy',
    'cython'
 ],
-  version = '0.2.2.1',
+  version = '0.2.2.2',
   description = 'Hierarchical Poisson matrix factorization for recommender systems',
   author = 'David Cortes',
   author_email = 'david.cortes.rivera@gmail.com',
   url = 'https://github.com/david-cortes/hpfrec',
-  download_url = 'https://github.com/david-cortes/hpfrec/archive/0.2.2.1.tar.gz',
+  download_url = 'https://github.com/david-cortes/hpfrec/archive/0.2.2.2.tar.gz',
   keywords = ['poisson', 'probabilistic', 'non-negative', 'factorization', 'variational inference', 'collaborative filtering'],
   classifiers = [],