updated simple classifier results after rerun

output/Simple Classifier/1labelPredictionsStats.txt

@@ -2,37 +2,37 @@ Performance measures
 
 Train set:
 
-Precision macro: 0.48
-Precision Individually: [0.563 0.381 0.876 0.28  0.301]
-Recall macro: 0.527
-Recall Individually: [0.542 0.571 0.591 0.271 0.658]
-F1 Score micro: 0.541
-F1 Score macro: 0.481
-F1 Score weighted: 0.565
-F1 Score Individually: [0.552 0.457 0.706 0.275 0.413]
+Precision macro: 0.477
+Precision Individually: [0.563 0.381 0.857 0.28  0.301]
+Recall macro: 0.518
+Recall Individually: [0.542 0.571 0.55  0.271 0.658]
+F1 Score micro: 0.52
+F1 Score macro: 0.474
+F1 Score weighted: 0.541
+F1 Score Individually: [0.552 0.457 0.67  0.275 0.413]
 
 
 Dev set:
 
-Precision macro: 0.399
-Precision Individually: [0.533 0.    0.88  0.25  0.333]
-Recall macro: 0.441
-Recall Individually: [0.615 0.    0.629 0.182 0.778]
-F1 Score micro: 0.557
-F1 Score macro: 0.396
-F1 Score weighted: 0.566
-F1 Score Individually: [0.571 0.    0.733 0.211 0.467]
+Precision macro: 0.398
+Precision Individually: [0.533 0.    0.875 0.25  0.333]
+Recall macro: 0.439
+Recall Individually: [0.615 0.    0.618 0.182 0.778]
+F1 Score micro: 0.551
+F1 Score macro: 0.395
+F1 Score weighted: 0.559
+F1 Score Individually: [0.571 0.    0.724 0.211 0.467]
 
 
 Test set:
 
-Precision macro: 0.424
-Precision Individually: [0.6   0.    0.885 0.333 0.3  ]
-Recall macro: 0.438
-Recall Individually: [0.643 0.    0.676 0.273 0.6  ]
-F1 Score micro: 0.586
-F1 Score macro: 0.417
-F1 Score weighted: 0.601
-F1 Score Individually: [0.621 0.    0.767 0.3   0.4  ]
+Precision macro: 0.421
+Precision Individually: [0.6   0.    0.87  0.333 0.3  ]
+Recall macro: 0.432
+Recall Individually: [0.643 0.    0.645 0.273 0.6  ]
+F1 Score micro: 0.567
+F1 Score macro: 0.412
+F1 Score weighted: 0.581
+F1 Score Individually: [0.621 0.    0.741 0.3   0.4  ]
 
 

simpleclassifier.py

@@ -84,27 +84,28 @@ def simple_classifier(sents_ref_json):
 test_pred_array = [sent['label'] for sent in test_pred_dict]
 test_pred_first_label = [label[0] for label in test_pred_array]
 
+labels=['Commit to privacy', 'Declare opinion about privacy','Not applicable','Related to privacy','Violate privacy']
 # Confusion Matrixes
 
 path='output/Simple Classifier/1Label_confusion_matrix_TrainNormTrue.png'
-create_confusion_matrix(train_ref_primary_label, train_pred_first_label, "true", path, None, None)
+create_confusion_matrix(train_ref_primary_label, train_pred_first_label, "true", path, labels, labels)
 path='output/Simple Classifier/1Label_confusion_matrix_DevNormTrue.png'
-create_confusion_matrix(dev_ref_primary_label, dev_pred_first_label, "true", path, None, None)
+create_confusion_matrix(dev_ref_primary_label, dev_pred_first_label, "true", path, labels, labels)
 path='output/Simple Classifier/1Label_confusion_matrix_TestNormTrue.png'
-create_confusion_matrix(test_ref_primary_label, test_pred_first_label, "true", path, None, None)
+create_confusion_matrix(test_ref_primary_label, test_pred_first_label, "true", path, labels, labels)
 path='output/Simple Classifier/1Label_confusion_matrix_TrainNonNorm.png'
-create_confusion_matrix(train_ref_primary_label, train_pred_first_label, None, path, None, None)
+create_confusion_matrix(train_ref_primary_label, train_pred_first_label, None, path, labels, labels)
 path='output/Simple Classifier/1Label_confusion_matrix_DevNonNorm.png'
-create_confusion_matrix(dev_ref_primary_label, dev_pred_first_label, None, path, None, None)
+create_confusion_matrix(dev_ref_primary_label, dev_pred_first_label, None, path, labels, labels)
 path='output/Simple Classifier/1Label_confusion_matrix_TestNonNorm.png'
-create_confusion_matrix(test_ref_primary_label, test_pred_first_label, None, path, None, None)
+create_confusion_matrix(test_ref_primary_label, test_pred_first_label, None, path, labels, labels)
 
 # Performance Measures
 
 path='output/Simple Classifier/1labelPredictionsStats.txt'
 os.makedirs(os.path.dirname(path), exist_ok=True)
 with open(path, 'w') as file:
     print("Performance measures\n", file=file)
-write_output_stats_file(path, "Train", train_ref_primary_label, train_pred_first_label)
-write_output_stats_file(path, "Dev", dev_ref_primary_label, dev_pred_first_label)
-write_output_stats_file(path, "Test", test_ref_primary_label, test_pred_first_label)
+write_output_stats_file(path, "Train", train_ref_primary_label, train_pred_first_label, labels)
+write_output_stats_file(path, "Dev", dev_ref_primary_label, dev_pred_first_label, labels)
+write_output_stats_file(path, "Test", test_ref_primary_label, test_pred_first_label, labels)