Python 3 compatibility and t-batch caching.

evaluate_interaction_prediction.py

@@ -27,7 +27,7 @@
 if args.train_proportion > 0.8:
     sys.exit('Training sequence proportion cannot be greater than 0.8.')
 if args.network == "mooc":
-    print "No interaction prediction for %s" % args.network
+    print("No interaction prediction for %s" % args.network)
     sys.exit(0)
 
 # SET GPU
@@ -43,7 +43,7 @@
     for l in f:
         l = l.strip()
         if search_string in l:
-            print "Output file already has results of epoch %d" % args.epoch
+            print("Output file already has results of epoch %d" % args.epoch)
             sys.exit(0)
     f.close()
 
@@ -58,7 +58,7 @@
 num_users = len(user2id)
 num_items = len(item2id) + 1
 true_labels_ratio = len(y_true)/(sum(y_true)+1)
-print "*** Network statistics:\n  %d users\n  %d items\n  %d interactions\n  %d/%d true labels ***\n\n" % (num_users, num_items, num_interactions, sum(y_true), len(y_true))
+print("*** Network statistics:\n  %d users\n  %d items\n  %d interactions\n  %d/%d true labels ***\n\n" % (num_users, num_items, num_interactions, sum(y_true), len(y_true)))
 
 # SET TRAIN, VALIDATION, AND TEST BOUNDARIES
 train_end_idx = validation_start_idx = int(num_interactions * args.train_proportion)
@@ -121,7 +121,7 @@
 tbatch_start_time = None
 loss = 0
 # FORWARD PASS
-print "*** Making interaction predictions by forward pass (no t-batching) ***"
+print("*** Making interaction predictions by forward pass (no t-batching) ***")
 with trange(train_end_idx, test_end_idx) as progress_bar:
     for j in progress_bar:
         progress_bar.set_description('%dth interaction for validation and testing' % j)
@@ -218,13 +218,13 @@
 fw = open(output_fname, "a")
 metrics = ['Mean Reciprocal Rank', 'Recall@10']
 
-print '\n\n*** Validation performance of epoch %d ***' % args.epoch
+print('\n\n*** Validation performance of epoch %d ***' % args.epoch)
 fw.write('\n\n*** Validation performance of epoch %d ***\n' % args.epoch)
 for i in xrange(len(metrics)):
     print(metrics[i] + ': ' + str(performance_dict['validation'][i]))
     fw.write("Validation: " + metrics[i] + ': ' + str(performance_dict['validation'][i]) + "\n")
 
-print '\n\n*** Test performance of epoch %d ***' % args.epoch
+print('\n\n*** Test performance of epoch %d ***' % args.epoch)
 fw.write('\n\n*** Test performance of epoch %d ***\n' % args.epoch)
 for i in xrange(len(metrics)):
     print(metrics[i] + ': ' + str(performance_dict['test'][i]))

evaluate_state_change_prediction.py

@@ -28,7 +28,7 @@
 if args.train_proportion > 0.8:
     sys.exit('Training sequence proportion cannot be greater than 0.8.')
 if args.network == "lastfm":
-    print "No state change prediction for %s" % args.network
+    print("No state change prediction for %s" % args.network)
     sys.exit(0)
 
 # SET GPU
@@ -44,7 +44,7 @@
     for l in f:
         l = l.strip()
         if search_string in l:
-            print "Output file already has results of epoch %d" % args.epoch
+            print("Output file already has results of epoch %d" % args.epoch)
             sys.exit(0)
     f.close()
 
@@ -59,7 +59,7 @@
 num_users = len(user2id)
 num_items = len(item2id) + 1
 true_labels_ratio = len(y_true)/(sum(y_true)+1)
-print "*** Network statistics:\n  %d users\n  %d items\n  %d interactions\n  %d/%d true labels ***\n\n" % (num_users, num_items, num_interactions, sum(y_true), len(y_true))
+print("*** Network statistics:\n  %d users\n  %d items\n  %d interactions\n  %d/%d true labels ***\n\n" % (num_users, num_items, num_interactions, sum(y_true), len(y_true)))
 
 # SET TRAIN, VALIDATION, AND TEST BOUNDARIES
 train_end_idx = validation_start_idx = int(num_interactions * args.train_proportion)
@@ -124,7 +124,7 @@
 tbatch_start_time = None
 loss = 0
 # FORWARD PASS
-print "*** Making state change predictions by forward pass (no t-batching) ***"
+print("*** Making state change predictions by forward pass (no t-batching) ***")
 with trange(train_end_idx, test_end_idx) as progress_bar:
     for j in progress_bar:
         progress_bar.set_description('%dth interaction for validation and testing' % j)
@@ -218,14 +218,14 @@
 fw = open(output_fname, "a")
 metrics = ['AUC']
 
-print '\n\n*** Validation performance of epoch %d ***' % args.epoch
+print('\n\n*** Validation performance of epoch %d ***' % args.epoch)
 fw.write('\n\n*** Validation performance of epoch %d ***\n' % args.epoch)
 
 for i in xrange(len(metrics)):
     print(metrics[i] + ': ' + str(performance_dict['validation'][i]))
     fw.write("Validation: " + metrics[i] + ': ' + str(performance_dict['validation'][i]) + "\n")
 
-print '\n\n*** Test performance of epoch %d ***' % args.epoch
+print('\n\n*** Test performance of epoch %d ***' % args.epoch)
 fw.write('\n\n*** Test performance of epoch %d ***\n' % args.epoch)
 for i in xrange(len(metrics)):
     print(metrics[i] + ': ' + str(performance_dict['test'][i]))

get_final_performance_numbers.py

@@ -43,13 +43,13 @@
 else:
     metrics = ['AUC']
 
-print '\n\n*** For file: %s ***' % fname
+print('\n\n*** For file: %s ***' % fname)
 best_val_idx = np.argmax(validation_performances[:,1])
-print "Best validation epoch: %d" % best_val_idx
-print '\n\n*** Best validation performance (epoch %d) ***' % best_val_idx
+print("Best validation epoch: %d" % best_val_idx)
+print('\n\n*** Best validation performance (epoch %d) ***' % best_val_idx)
 for i in xrange(len(metrics)):
     print(metrics[i] + ': ' + str(validation_performances[best_val_idx][i+1]))
 
-print '\n\n*** Final model performance on the test set, i.e., in epoch %d ***' % best_val_idx
+print('\n\n*** Final model performance on the test set, i.e., in epoch %d ***' % best_val_idx)
 for i in xrange(len(metrics)):
     print(metrics[i] + ': ' + str(test_performances[best_val_idx][i+1]))

jodie.py

@@ -20,10 +20,11 @@
 parser.add_argument('--epochs', default=50, type=int, help='Number of epochs to train the model')
 parser.add_argument('--embedding_dim', default=128, type=int, help='Number of dimensions of the dynamic embedding')
 parser.add_argument('--train_proportion', default=0.8, type=float, help='Fraction of interactions (from the beginning) that are used for training.The next 10% are used for validation and the next 10% for testing')
-parser.add_argument('--state_change', default=True, type=bool, help='True if training with state change of users along with interaction prediction. False otherwise. By default, set to True.') 
+parser.add_argument('--state_change', default=True, type=bool, help='True if training with state change of users along with interaction prediction. False otherwise. By default, set to True.')
+parser.add_argument('--cache_tbatches', default=False, type=bool, help='If True, will load pre-computed tbatches from data/${network}/tbatches. By default, set to False.')
 args = parser.parse_args()
 
-args.datapath = "data/%s.csv" % args.network 
+args.datapath = "data/%s.csv" % args.network
 if args.train_proportion > 0.8:
     sys.exit('Training sequence proportion cannot be greater than 0.8.')
 
@@ -42,7 +43,13 @@
 num_items = len(item2id) + 1 # one extra item for "none-of-these"
 num_features = len(feature_sequence[0])
 true_labels_ratio = len(y_true)/(1.0+sum(y_true)) # +1 in denominator in case there are no state change labels, which will throw an error. 
-print "*** Network statistics:\n  %d users\n  %d items\n  %d interactions\n  %d/%d true labels ***\n\n" % (num_users, num_items, num_interactions, sum(y_true), len(y_true))
+print("*** Network statistics:\n  %d users\n  %d items\n  %d interactions\n  %d/%d true labels ***\n\n" % (num_users, num_items, num_interactions, sum(y_true), len(y_true)))
+
+# ENSURE TBATCH DIRECTORY EXISTS IF NEEDED
+if args.cache_tbatches:
+  args.tbatchdir = "data/%s_tbatches" % args.network
+  if not os.path.isdir(args.tbatchdir):
+    os.mkdir(args.tbatchdir)
 
 # SET TRAINING, VALIDATION, TESTING, and TBATCH BOUNDARIES
 train_end_idx = validation_start_idx = int(num_interactions * args.train_proportion) 
@@ -85,7 +92,7 @@
 '''
 THE MODEL IS TRAINED FOR SEVERAL EPOCHS. IN EACH EPOCH, JODIES USES THE TRAINING SET OF INTERACTIONS TO UPDATE ITS PARAMETERS.
 '''
-print "*** Training the JODIE model for %d epochs ***" % args.epochs
+print("*** Training the JODIE model for %d epochs ***" % args.epochs)
 with trange(args.epochs) as progress_bar1:
     for ep in progress_bar1:
         progress_bar1.set_description('Epoch %d of %d' % (ep, args.epochs))
@@ -102,8 +109,8 @@
         tbatch_to_insert = -1
         tbatch_full = False
 
-        # TRAIN TILL THE END OF TRAINING INTERACTION IDX 
-        with trange(train_end_idx) as progress_bar2: 
+        # TRAIN TILL THE END OF TRAINING INTERACTION IDX
+        with trange(train_end_idx) as progress_bar2:
             for j in progress_bar2:
                 progress_bar2.set_description('Processed %dth interactions' % j)
 
@@ -114,18 +121,21 @@
                 user_timediff = user_timediffs_sequence[j]
                 item_timediff = item_timediffs_sequence[j]
 
-                # CREATE T-BATCHES: ADD INTERACTION J TO THE CORRECT T-BATCH
-                tbatch_to_insert = max(lib.tbatchid_user[userid], lib.tbatchid_item[itemid]) + 1 
-                lib.tbatchid_user[userid] = tbatch_to_insert 
-                lib.tbatchid_item[itemid] = tbatch_to_insert
-
-                lib.current_tbatches_user[tbatch_to_insert].append(userid)
-                lib.current_tbatches_item[tbatch_to_insert].append(itemid)
-                lib.current_tbatches_feature[tbatch_to_insert].append(feature)
-                lib.current_tbatches_interactionids[tbatch_to_insert].append(j)
-                lib.current_tbatches_user_timediffs[tbatch_to_insert].append(user_timediff)
-                lib.current_tbatches_item_timediffs[tbatch_to_insert].append(item_timediff)
-                lib.current_tbatches_previous_item[tbatch_to_insert].append(user_previous_itemid_sequence[j])
+                if not args.cache_tbatches or len(os.listdir(args.tbatchdir)) == 0:
+                    # CREATE T-BATCHES: ADD INTERACTION J TO THE CORRECT T-BATCH
+                    tbatch_to_insert = max(lib.tbatchid_user[userid], lib.tbatchid_item[itemid]) + 1
+                    lib.tbatchid_user[userid] = tbatch_to_insert
+                    lib.tbatchid_item[itemid] = tbatch_to_insert
+
+                    lib.current_tbatches_user[tbatch_to_insert].append(userid)
+                    lib.current_tbatches_item[tbatch_to_insert].append(itemid)
+                    lib.current_tbatches_feature[tbatch_to_insert].append(feature)
+                    lib.current_tbatches_interactionids[tbatch_to_insert].append(j)
+                    lib.current_tbatches_user_timediffs[tbatch_to_insert].append(user_timediff)
+                    lib.current_tbatches_item_timediffs[tbatch_to_insert].append(item_timediff)
+                    lib.current_tbatches_previous_item[tbatch_to_insert].append(user_previous_itemid_sequence[j])
+                if args.cache_tbatches and len(os.listdir(args.tbatchdir)) > 0:
+                    lib.load_tbatches()
 
                 timestamp = timestamp_sequence[j]
                 if tbatch_start_time is None:
@@ -196,20 +206,25 @@
                     user_embeddings_timeseries.detach_()
 
                     # REINITIALIZE
-                    reinitialize_tbatches()
-                    tbatch_to_insert = -1
+                    if not args.cache_tbatches:
+                        reinitialize_tbatches()
+                        tbatch_to_insert = -1
 
         # END OF ONE EPOCH 
-        print "\n\nTotal loss in this epoch = %f" % (total_loss)
+        print("\n\nTotal loss in this epoch = %f" % (total_loss))
         item_embeddings_dystat = torch.cat([item_embeddings, item_embedding_static], dim=1)
         user_embeddings_dystat = torch.cat([user_embeddings, user_embedding_static], dim=1)
         # SAVE CURRENT MODEL TO DISK TO BE USED IN EVALUATION.
         save_model(model, optimizer, args, ep, user_embeddings_dystat, item_embeddings_dystat, train_end_idx, user_embeddings_timeseries, item_embeddings_timeseries)
 
+        # SAVE TBATCHES IF NECESSARY
+        if args.cache_tbatches and len(os.listdir(args.tbatchdir)) == 0:
+            lib.save_tbatches()
+
         user_embeddings = initial_user_embedding.repeat(num_users, 1)
         item_embeddings = initial_item_embedding.repeat(num_items, 1)
 
 # END OF ALL EPOCHS. SAVE FINAL MODEL DISK TO BE USED IN EVALUATION.
-print "\n\n*** Training complete. Saving final model. ***\n\n"
+print("\n\n*** Training complete. Saving final model. ***\n\n")
 save_model(model, optimizer, args, ep, user_embeddings_dystat, item_embeddings_dystat, train_end_idx, user_embeddings_timeseries, item_embeddings_timeseries)
 

library_data.py

@@ -12,7 +12,6 @@
 import copy
 from collections import defaultdict
 import os, re
-import cPickle
 import argparse
 from sklearn.preprocessing import scale
 
@@ -40,7 +39,7 @@ def load_network(args, time_scaling=True):
     start_timestamp = None
     y_true_labels = []
 
-    print "\n\n**** Loading %s network from file: %s ****" % (network, datapath)
+    print("\n\n**** Loading %s network from file: %s ****" % (network, datapath))
     f = open(datapath,"r")
     f.readline()
     for cnt, l in enumerate(f):
@@ -52,14 +51,14 @@ def load_network(args, time_scaling=True):
             start_timestamp = float(ls[2])
         timestamp_sequence.append(float(ls[2]) - start_timestamp) 
         y_true_labels.append(int(ls[3])) # label = 1 at state change, 0 otherwise
-        feature_sequence.append(map(float,ls[4:]))
+        feature_sequence.append(list(map(float,ls[4:])))
     f.close()
 
     user_sequence = np.array(user_sequence) 
     item_sequence = np.array(item_sequence)
     timestamp_sequence = np.array(timestamp_sequence)
 
-    print "Formating item sequence"
+    print("Formating item sequence")
     nodeid = 0
     item2id = {}
     item_timedifference_sequence = []
@@ -74,7 +73,7 @@ def load_network(args, time_scaling=True):
     num_items = len(item2id)
     item_sequence_id = [item2id[item] for item in item_sequence]
 
-    print "Formating user sequence"
+    print("Formating user sequence")
     nodeid = 0
     user2id = {}
     user_timedifference_sequence = []
@@ -94,11 +93,11 @@ def load_network(args, time_scaling=True):
     user_sequence_id = [user2id[user] for user in user_sequence]
 
     if time_scaling:
-        print "Scaling timestamps"
+        print("Scaling timestamps")
         user_timedifference_sequence = scale(np.array(user_timedifference_sequence) + 1)
         item_timedifference_sequence = scale(np.array(item_timedifference_sequence) + 1)
 
-    print "*** Network loading completed ***\n\n"
+    print("*** Network loading completed ***\n\n")
     return [user2id, user_sequence_id, user_timedifference_sequence, user_previous_itemid_sequence, \
         item2id, item_sequence_id, item_timedifference_sequence, \
         timestamp_sequence, \