[~] Refactor

python/tasks/jigsaw_toxic.py

@@ -136,19 +136,6 @@ def kernel_2():
     # %% [code]
     train['comment_text'].apply(lambda x:len(str(x).split())).max()
 
-	# %% [markdown]
-	# Writing a function for getting auc score for validation
-
-	# %% [code]
-	def roc_auc(predictions,target):
-		'''
-		This methods returns the AUC Score when given the Predictions
-		and Labels
-		'''
-
-		fpr, tpr, thresholds = metrics.roc_curve(target, predictions)
-		roc_auc = metrics.auc(fpr, tpr)
-		return roc_auc
 
     # %% [markdown]
     # ### Data Preparation
@@ -231,16 +218,64 @@ def kernel_2():
 
     model.summary()
 
-	# %% [code]
+    return dict(
-	model.fit(xtrain_pad, ytrain, nb_epoch=5, batch_size=64*strategy.num_replicas_in_sync) #Multiplying by Strategy to run on TPU's
+        model=model,
+        xtrain_pad=xtrain_pad,
+        ytrain=ytrain,
+        strategy=strategy,
+        xvalid_pad=xvalid_pad,
+        yvalid=yvalid,
+        xtrain_seq=xtrain_seq,
+    )
+
+
+def kernel_3(
+    o_2,
+):
+    # %% [markdown]
+    # Writing a function for getting auc score for validation
 
     # %% [code]
-	scores = model.predict(xvalid_pad)
+    def roc_auc(predictions,target):
-	print("Auc: %.2f%%" % (roc_auc(scores,yvalid)))
+        '''
+        This methods returns the AUC Score when given the Predictions
+        and Labels
+        '''
+
+        fpr, tpr, thresholds = metrics.roc_curve(target, predictions)
+        roc_auc = metrics.auc(fpr, tpr)
+        return roc_auc
+
+    # %% [code]
+    o_2['model'].fit(
+        o_2['xtrain_pad'],
+        o_2['ytrain'],
+        nb_epoch=5,
+        batch_size=64*o_2['strategy'].num_replicas_in_sync
+    ) #Multiplying by Strategy to run on TPU's
+
+    # %% [code]
+    scores = o_2['model'].predict(o_2['xvalid_pad'])
+    print(
+        "Auc: %.2f%%" % (
+            roc_auc(
+                scores,
+                o_2['yvalid']
+            )
+        )
+    )
 
     # %% [code]
     scores_model = []
-	scores_model.append({'Model': 'SimpleRNN','AUC_Score': roc_auc(scores,yvalid)})
+    scores_model.append(
+        {
+            'Model': 'SimpleRNN',
+            'AUC_Score': roc_auc(
+                scores,
+                o_2['yvalid']
+            )
+        }
+    )
 
     # %% [markdown]
     # ## Code Explanantion