Name change

deepSnapPred.py

@@ -227,7 +227,7 @@ def train(model, dataloaders, optimizer, epochs):
     plt.xlabel('Iteration')
     plt.xticks(range(0, epochs+1, int(epochs/10)))
     plt.yticks((0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85))
-    plt.savefig('plots/deepSnapPred/metrics/loss.svg', format='svg', dpi=1200)
+    plt.savefig('plots/behor/metrics/loss.svg', format='svg', dpi=1200)
     plt.clf()
     
     return t_accu, v_accu, e_accu, best_model
@@ -310,7 +310,7 @@ def main(epochs, hidden_dim, lr, weight_decay, dropout):
     _, _, _, model = train(model, dataloaders, optimizer, epochs)
     print("Finished training at", datetime.now().strftime("%H:%M:%S"))
 
-    torch.save(model.state_dict(),"./models/modelDeepSnapPred")
+    torch.save(model.state_dict(), "models/behor")
 
     # Testing
     model.eval()
@@ -322,8 +322,8 @@ def main(epochs, hidden_dim, lr, weight_decay, dropout):
     labels = [item for sublist in true_labels for item in sublist]
     pure_predictions = [item for sublist in pure_pred_labels for item in sublist]
     
-    plot_roc(labels, pure_predictions, keys[0], "deepSnapPred/")
-    plot_prc(torch.tensor(labels), pure_predictions, keys[0], "deepSnapPred/")
+    plot_roc(labels, pure_predictions, keys[0], "behor/")
+    plot_prc(torch.tensor(labels), pure_predictions, keys[0], "behor/")
     return model
 
 

testRepoDB.py

@@ -107,10 +107,10 @@ def deepSnapMetrics(model):
     pure_predictions = [item for sublist in [preds,predsN] for item in sublist]
     labels = torch.tensor([item for sublist in [labels1,labels2] for item in sublist])
     
-    fpr, tpr, label1 = plot_roc(labels, pure_predictions,('disorder', 'dis_dru_the', 'drug'), "deepSnapPred/", "RepoDB")
-    recall, precision, label2 = plot_prc(labels, pure_predictions, ('disorder', 'dis_dru_the', 'drug'), "deepSnapPred/", "RepoDB")
+    fpr, tpr, label1 = plot_roc(labels, pure_predictions,('disorder', 'dis_dru_the', 'drug'), "behor/", "RepoDB")
+    recall, precision, label2 = plot_prc(labels, pure_predictions, ('disorder', 'dis_dru_the', 'drug'), "behor/", "RepoDB")
     
-    plotMetrics("deepSnapPred", fpr, tpr, label1, recall, precision, label2)    
+    plotMetrics("behor", fpr, tpr, label1, recall, precision, label2)
     return label1, label2