emb naming

code/binary_bpr/main.py

@@ -65,7 +65,7 @@ def read_file(dataTrain, dataTest):
     dico_items = { k:v for (k,v) in zip(items, range(num_items))} 
     print("NB Items", num_items, len(dico_items))    
     return dico_kc, dico_users, dico_items
-def save_embeddings(xpName: str, modelName: str, embeddings,userEmbDir : str,itemEmbDir : str, grid_search_id):
+def save_embeddings(xpName: str, modelName: str, embeddings,userEmbDir : str,itemEmbDir : str):
     """
     Saves all the metrics measured after the training process.
 
@@ -77,10 +77,11 @@ def save_embeddings(xpName: str, modelName: str, embeddings,userEmbDir : str,ite
 
     u_emb, i_emb = embeddings
 
-    results_name_file = (xpName + modelName + "_" + str(grid_search_id))
+    results_name_file = (xpName +"_"+ modelName)
 
     # save embeddings
     results_path = os.path.join(userEmbDir, results_name_file+".csv")
+    print(results_path)
     np.savetxt(results_path, u_emb, delimiter=',')
     # save embeddings
     results_path = os.path.join(itemEmbDir, results_name_file+".csv")
@@ -335,9 +336,7 @@ if __name__ == '__main__':
             emb = [bpr_model.user_embeddings.weight.clone().detach().cpu().numpy(),
                    bpr_model.item_embeddings.weight.clone().detach().cpu().numpy()]
 
-            print("save")
-            save_embeddings(xpName=datasetName, modelName="BPR", embeddings=emb,itemEmbDir=embPath+'items/',userEmbDir=embPath+'users/',
-                            grid_search_id=get_formatted_date())
+            save_embeddings(xpName=datasetName+"_"+str(i_fold), modelName="BPR", embeddings=emb,itemEmbDir=embPath+'items/',userEmbDir=embPath+'users/')
 
             write_file_doa(FileNameTrain_temp, emb[0], train, dico_kc, dico_users, dico_items)
             doa = compute_doa(FileNameTrain_temp)
@@ -398,7 +397,7 @@ if __name__ == '__main__':
             acc = bpr_model.train(train, len(dico_kc), epochs, batch_size, y_train,test,y_test)
         emb = [bpr_model.user_embeddings.weight.clone().detach().cpu().numpy(),bpr_model.item_embeddings.weight.clone().detach().cpu().numpy()]
 
-        save_embeddings(xpName=datasetName, modelName="BPR", embeddings=emb,itemEmbDir=embPath+'items/',userEmbDir=embPath+'users/', grid_search_id=get_formatted_date())
+        save_embeddings(xpName=datasetName, modelName="BPR", embeddings=emb,itemEmbDir=embPath+'items/',userEmbDir=embPath+'users/')
 
         write_file_doa(trainFileName, emb[0], train, dico_kc, dico_users, dico_items)
         doa = compute_doa(trainFileName)

code/binary_bpr/script.py

@@ -2,8 +2,8 @@ import os
 dPath = "../../data/cdbpr_format/"
 embDirPath = "../../results/table_2/"
 datasets = ['assist0910_tkde', 'assist17_tkde', 'algebra','math_1', 'math_2']
-epochs = [75, 95, 5, 90, 90]
-batchSize =[ 512, 512,512, 512,512]
+epochs = [1,75, 95, 5, 90, 90]
+batchSize =[ 512, 512,512, 512,512,4000]
 learningRate = [0.01,0.01,0.01,0.01,0.01]
 mode = [1,1,1,1,1]
 for i in range(len(datasets)):