Create score_focal_labels_only.py

scoring_scripts/score_focal_labels_only.py

@@ -0,0 +1,53 @@
+# imports
+from sklearn.preprocessing import MultiLabelBinarizer 
+from sklearn.metrics import classification_report
+
+# global param
+label_list = ['Background', 'Motivation', 'Uses', 'Extends', 'Similarities', 'Differences', 'Compare/Contrast', 'Future Work', 'Unclear']
+
+# start of function
+def evaluate_FOCAL_labels(references_jsonl, predictions_jsonl, print_report=False):
+    '''
+    Computes precision, recall and f1-scores for the labels of citations, 
+    without looking at the location of these labels in the paragraph, 
+    between two datasets loaded from jsonl (list of dicts with same keys).
+    In plain English, this check that you correctly predicted the reason(s) a given citation was made,
+    without checking if you correctly find the parts of the paragraph that explain the function of the citation.
+    '''
+    
+    # sort the refs and pred by unique ID
+    references_jsonl = sorted(references_jsonl, key=lambda x:x['Identifier'])
+    predictions_jsonl = sorted(predictions_jsonl, key=lambda x:x['Identifier'])
+
+    # assert that paragraphs match
+    ref_paragraphs = [e['Paragraph'] for e in references_jsonl]
+    pred_paragraphs = [e['Paragraph'] for e in predictions_jsonl]
+    assert(ref_paragraphs==pred_paragraphs)
+
+
+    # build y_true and y_pred
+    mlb = MultiLabelBinarizer(classes=label_list)
+    y_true = mlb.fit_transform([e['Functions Label'] for e in references_jsonl])
+    y_pred = mlb.fit_transform([e['Functions Label'] for e in predictions_jsonl])
+
+    # build report for printing
+    report_string = classification_report(y_true=y_true,
+                                          y_pred=y_pred,
+                                          target_names=label_list,
+                                          zero_division=0.0,
+                                          output_dict=False
+                                         )
+
+    # return report as dict (can't do both at the same time? slight waste of compute)
+    report_dict = classification_report(y_true=y_true,
+                                        y_pred=y_pred,
+                                        target_names=label_list,
+                                        zero_division=0.0,
+                                        output_dict=True
+                                       )
+
+    if print_report:
+        print(report_string)
+    
+    
+    return(report_dict)