Python 3 compatibility + error in import

scikits/crab/base.py

@@ -8,7 +8,7 @@
 #          Bruno Melo <bruno@muricoca.com>
 # License: BSD Style.
 
-from scikits.learn.base import BaseEstimator
+from sklearn.base import BaseEstimator
 
 
 class BaseRecommender(BaseEstimator):

scikits/crab/datasets/book_crossing.py

@@ -40,7 +40,7 @@
 from os.path import dirname
 from os.path import join
 import numpy as np
-from base import Bunch
+from .base import Bunch
 import csv
 
 logger = logging.getLogger(__name__)
@@ -116,19 +116,19 @@ def load_bookcrossings(data_home=None, download_if_missing=True,
         if not os.path.exists(os.path.join(data_home, 'BX-Book-Ratings.csv')) \
             and not open(os.path.join(data_home, 'BX-Books.csv')):
             raise IOError
-    except Exception, e:
-        print 80 * '_'
-        print 'Loading files failed'
-        print 80 * '_'
-        print e
+    except Exception as e:
+        print ( 80 * '_')
+        print ('Loading files failed')
+        print (80 * '_')
+        print (e)
 
         if download_if_missing:
-            print 'downloading the dataset...'
+            print ('downloading the dataset...')
             try:
                 download_book_crossings(data_home)
             except:
                 raise Exception('FAIL: Problems during the download.')
-            print 'dataset downloaded.'
+            print ('dataset downloaded.')
         else:
             raise IOError('Book-Crossing dataset not found')
 

scikits/crab/models/classes.py

@@ -105,13 +105,17 @@ def build_model(self):
              Build the data model
         '''
         #Is it important to store as numpy array ?
-        self._user_ids = np.asanyarray(self.dataset.keys())
+        myListOKeys =[k for k,_ in self.dataset.items()]
+        self._user_ids = np.asanyarray(myListOKeys)
         self._user_ids.sort()
 
         #Is it important to store as numpy array ?
         self._item_ids = []
-        for items in self.dataset.itervalues():
-            self._item_ids.extend(items.keys())
+
+
+        #for dictVal in self.dataset.values():
+         #   self._item_ids.extend(dictVal.keys())
+        self._item_ids = [k for k,v in self.dataset.items() ]    
 
         self._item_ids = np.unique(np.array(self._item_ids))
         self._item_ids.sort()

scikits/crab/recommenders/knn/classes.py

@@ -13,10 +13,10 @@
 # Author: Marcel Caraciolo <marcel@muricoca.com>
 #
 # License: BSD Style.
-
-from base import ItemRecommender, UserRecommender
-from item_strategies import ItemsNeighborhoodStrategy
-from neighborhood_strategies import NearestNeighborsStrategy
+from sklearn.base import BaseEstimator
+from .base import ItemRecommender, UserRecommender
+from .item_strategies import ItemsNeighborhoodStrategy
+from .neighborhood_strategies import NearestNeighborsStrategy
 import numpy as np
 
 
@@ -533,8 +533,9 @@ def estimate_preference(self, user_id, item_id, **params):
         prefs = np.array([self.model.preference_value(to_user_id, item_id)
                  for to_user_id in nearest_neighbors])
 
-        prefs = prefs[~np.isnan(prefs)]
-        similarities = similarities[~np.isnan(prefs)]
+
+      #  prefs = prefs[~np.isnan(prefs)]
+      #  similarities = similarities[~np.isnan(prefs)]
 
         prefs_sim = np.sum(prefs[~np.isnan(similarities)] *
                              similarities[~np.isnan(similarities)])
@@ -597,7 +598,7 @@ def recommend(self, user_id, how_many=None, **params):
 
         '''
 
-        self._set_params(**params)
+        self.set_params(**params)
 
         candidate_items = self.all_other_items(user_id, **params)
 

scikits/crab/recommenders/knn/item_strategies.py

@@ -11,7 +11,7 @@
 #
 # License: BSD Style.
 
-from base import BaseCandidateItemsStrategy
+from .base import BaseCandidateItemsStrategy
 import numpy as np
 
 

scikits/crab/recommenders/knn/neighborhood_strategies.py

@@ -11,7 +11,7 @@
 #
 # License: BSD Style.
 
-from base import BaseUserNeighborhoodStrategy
+from .base import BaseUserNeighborhoodStrategy
 import numpy as np
 from ...similarities.basic_similarities import UserSimilarity
 from ...metrics.pairwise import euclidean_distances

scikits/crab/recommenders/svd/classes.py

@@ -15,7 +15,7 @@
 # License: BSD Style.
 import random
 
-from base import SVDRecommender
+from .base import SVDRecommender
 from ..knn.item_strategies import ItemsNeighborhoodStrategy
 import numpy as np
 from math import sqrt

scikits/crab/similarities/basic_similarities.py

@@ -9,7 +9,7 @@
 
 
 import numpy as np
-from base import BaseSimilarity
+from .base import BaseSimilarity
 from ..metrics.pairwise import loglikehood_coefficient
 
 
@@ -18,10 +18,15 @@ def find_common_elements(source_preferences, target_preferences):
     src = dict(source_preferences)
     tgt = dict(target_preferences)
 
-    inter = np.intersect1d(src.keys(), tgt.keys())
+    srcKeys  = [k for k,_ in src.items()]
+    tgtKeys  = [k for k,_ in tgt.items()]
+
+    inter = np.intersect1d(srcKeys, tgtKeys)
 
     common_preferences = zip(*[(src[item], tgt[item]) for item in inter \
             if not np.isnan(src[item]) and not np.isnan(tgt[item])])
+    common_preferences = list(common_preferences )
+
     if common_preferences:
         return np.asarray([common_preferences[0]]), np.asarray([common_preferences[1]])
     else: