COMPAT: Correct relative imports

Author: jseabold

mrec/item_similarity/precomputed.py

@@ -1,8 +1,8 @@
 """
 Make recommendations from a precomputed item similarity matrix.
 """
-
-from recommender import ItemSimilarityRecommender
+from __future__ import absolute_import
+from .recommender import ItemSimilarityRecommender
 
 class PrecomputedItemSimilarityRecommender(ItemSimilarityRecommender):
     """

mrec/item_similarity/slim.py

@@ -10,14 +10,14 @@
     X. Ning and G. Karypis, ICDM 2011.
     http://glaros.dtc.umn.edu/gkhome/fetch/papers/SLIM2011icdm.pdf
 """
-from __future__ import print_function
+from __future__ import print_function, absolute_import
 from six.moves import xrange
 from sklearn.linear_model import SGDRegressor, ElasticNet
 from sklearn.preprocessing import binarize
 import sklearn
 import numpy as np
 
-from recommender import ItemSimilarityRecommender
+from .recommender import ItemSimilarityRecommender
 
 
 def parse_version(version_string):

mrec/mf/evaluate.py

@@ -10,7 +10,7 @@ def retrain_recommender(model,dataset):
     except ImportError:
         from sklearn.grid_search import IterGrid as ParameterGrid
     from optparse import OptionParser
-    from warp import WARPMFRecommender
+    from .warp import WARPMFRecommender
 
     from mrec.evaluation.metrics import *
 
@@ -33,6 +33,7 @@ def retrain_recommender(model,dataset):
         # load em both up
         # put them into something that returns train,test.keys(),test in a generator()
         # test is a dict id->[id,id,...]
+        pass
 
     if opts.main_split_dir:
         generate_main_metrics = generate_metrics(get_known_items_from_dict,compute_main_metrics)

mrec/mf/model/warp2.py

@@ -2,8 +2,8 @@
 import scipy
 import random
 
-from warp import WARPBatchUpdate, WARPDecomposition, WARP
-from warp_fast import warp2_sample
+from .warp import WARPBatchUpdate, WARPDecomposition, WARP
+from .warp_fast import warp2_sample
 
 class WARP2BatchUpdate(WARPBatchUpdate):
     """Collection of arrays to hold a batch of sgd updates."""

mrec/mf/warp.py

@@ -5,8 +5,8 @@
 
 from mrec.evaluation import metrics
 
-from recommender import MatrixFactorizationRecommender
-from model.warp import WARP
+from .recommender import MatrixFactorizationRecommender
+from .model.warp import WARP
 
 class WARPMFRecommender(MatrixFactorizationRecommender):
     """

mrec/mf/warp2.py

@@ -1,7 +1,8 @@
+from __future__ import absolute_import
 import numpy as np
 
-from warp import WARPMFRecommender
-from model.warp2 import WARP2
+from .warp import WARPMFRecommender
+from .model.warp2 import WARP2
 
 class WARP2MFRecommender(WARPMFRecommender):
     """

mrec/popularity.py

@@ -2,11 +2,12 @@
 Trivial unpersonalized item popularity recommender
 intended to provide a baseline for evaluations.
 """
+from __future__ import absolute_import
 from six.moves import xrange
 import numpy as np
 
-from base_recommender import BaseRecommender
-from sparse import fast_sparse_matrix
+from .base_recommender import BaseRecommender
+from .sparse import fast_sparse_matrix
 
 class ItemPopularityRecommender(BaseRecommender):
     """

mrec/reranking_recommender.py

@@ -2,14 +2,14 @@
 Recommender that gets candidates using an item similarity model
 and then reranks them using a matrix factorization model.
 """
-
+from __future__ import absolute_import
 try:
     import cPickle as pickle
 except ImportError:
     import pickle
 import numpy as np
 
-from base_recommender import BaseRecommender
+from .base_recommender import BaseRecommender
 
 class RerankingRecommender(BaseRecommender):
     """