Changed output to save after every module number for continuous validation

bayespairing/src/validation.py

@@ -460,56 +460,69 @@ def two_fold_CV(modules_to_test, output_file, shuffle, ss):
     :return:
     """
 
-    cv_results = {}
+
     TO_SKIP = []
 
     for module_number in modules_to_test:
-
-        module_results = []
-
         if module_number in TO_SKIP:  # we have done a sibling
             print("Module", module_number, "skipped.")
             continue
 
-        module_sequences = mod_sequences[module_number]
-        target_sequences = test_sequences[module_number]
+        # Idea - open pickle results, load previous and continue
+        try:
+            cv_results = pickle.load(open(output_file, "rb"))
+        except IOError:
+            cv_results = {}
+
+
+        # If key doesn't exists, validate, otherwise skip it
+        if module_number not in cv_results:
+
+
+            module_results = []
+
+
+
+            module_sequences = mod_sequences[module_number]
+            target_sequences = test_sequences[module_number]
+
+            num_sequences = len(module_sequences)
+            if num_sequences < 2:
+                print("Module", module_number, "does not have enough data for Cross Validation, skipped.")
 
-        num_sequences = len(module_sequences)
-        if num_sequences < 2:
-            print("Module", module_number, "does not have enough data for Cross Validation, skipped.")
+            indexes = list(range(0, num_sequences))
 
-        indexes = list(range(0, num_sequences))
+            # Shuffle and split indexes in half
+            random.shuffle(indexes)
 
-        # Shuffle and split indexes in half
-        random.shuffle(indexes)
+            # TODO: Fix this so it's more logical with higher K-folds
+            split = [indexes[:num_sequences//2], indexes[num_sequences//2:]]
 
-        # TODO: Fix this so it's more logical with higher K-folds
-        split = [indexes[:num_sequences//2], indexes[num_sequences//2:]]
+            for j in [0,1]:
 
-        for j in [0,1]:
+                test_indexes = split[1 - j]
+                train_indexes = split[0 + 1]
+                modules_train = [module_sequences[i] for i in train_indexes]
+                modules_test = [module_sequences[i] for i in test_indexes]
+                target_test = [target_sequences[i] for i in test_indexes]
 
-            test_indexes = split[1 - j]
-            train_indexes = split[0 + 1]
-            modules_train = [module_sequences[i] for i in train_indexes]
-            modules_test = [module_sequences[i] for i in test_indexes]
-            target_test = [target_sequences[i] for i in test_indexes]
+                # Train the BN
+                BNs = train_BN(module_number, motif_sequences=modules_train)
 
-            # Train the BN
-            BNs = train_BN(module_number, motif_sequences=modules_train)
+                # Results = list of scores
+                module_results = module_results + run_validation(module_number, BNs=BNs, module_sequences=modules_test, target_sequences=target_test,test_indexes=test_indexes, shuffle=shuffle, ss=ss)
 
-            # Results = list of scores
-            module_results = module_results + run_validation(module_number, BNs=BNs, module_sequences=modules_test, target_sequences=target_test,test_indexes=test_indexes, shuffle=shuffle, ss=ss)
+            # Set results in dict
+            cv_results[module_number] = module_results
+            # Save to output file
 
-        # Set results in dict
-        cv_results[module_number] = module_results
+            pickle.dump(cv_results, open(output_file, "wb"))
 
         # If module is in siblings, skip the rest
         if module_number in siblings:
             for sib in siblings[module_number]:
                 TO_SKIP.append(sib)
 
-    # Save to output file
-    pickle.dump(cv_results, open(output_file, "wb"))
 
 
 
@@ -540,7 +553,7 @@ if __name__ == "__main__":
 
     # Test all the modules
     modules_to_test = list(range(0, len(graphs)))
-    #modules_to_test = [0]
+    #modules_to_test = [0,1,2,3,4]
 
     # Output file name
     file_name = "2fold_cv_" + DATASET_NAME