add dst unified evaluate

convlab2/dst/evaluate_unified_datasets.py

+import json
+from pprint import pprint
+
+
+def evaluate(predict_result):
+    predict_result = json.load(open(predict_result))
+
+    metrics = {'TP':0, 'FP':0, 'FN':0}
+    acc = []
+
+    for sample in predict_result:
+        pred_state = sample['predictions']['state']
+        gold_state = sample['state']
+        predicts = sorted(list({(domain, slot, ''.join(value.split()).lower()) for domain in pred_state for slot, value in pred_state[domain].items() if len(value)>0}))
+        labels = sorted(list({(domain, slot, ''.join(value.split()).lower()) for domain in gold_state for slot, value in gold_state[domain].items() if len(value)>0}))
+
+        flag = True
+        for ele in predicts:
+            if ele in labels:
+                metrics['TP'] += 1
+            else:
+                metrics['FP'] += 1
+        for ele in labels:
+            if ele not in predicts:
+                metrics['FN'] += 1
+        flag &= (predicts==labels)
+        acc.append(flag)
+    
+    TP = metrics.pop('TP')
+    FP = metrics.pop('FP')
+    FN = metrics.pop('FN')
+    precision = 1.0 * TP / (TP + FP) if TP + FP else 0.
+    recall = 1.0 * TP / (TP + FN) if TP + FN else 0.
+    f1 = 2.0 * precision * recall / (precision + recall) if precision + recall else 0.
+    metrics[f'slot_f1'] = f1
+    metrics[f'slot_precision'] = precision
+    metrics[f'slot_recall'] = recall
+    metrics['accuracy'] = sum(acc)/len(acc)
+
+    return metrics
+
+
+if __name__ == '__main__':
+    from argparse import ArgumentParser
+    parser = ArgumentParser(description="calculate DST metrics for unified datasets")
+    parser.add_argument('--predict_result', '-p', type=str, required=True, help='path to the prediction file that in the unified data format')
+    args = parser.parse_args()
+    print(args)
+    metrics = evaluate(args.predict_result)
+    pprint(metrics)