Upload qa_pvalue.py

qa_pvalue.py

@@ -0,0 +1,48 @@
+import json
+from tqdm import tqdm
+import scipy.stats as stats
+def evaluate_retrieval(retrieval_file, topk):
+    retrieval = json.load(open(retrieval_file))
+    accuracy = { k : [] for k in topk }
+    max_k = max(topk)
+    for qid in tqdm(list(retrieval.keys())):
+        contexts = retrieval[qid]['contexts']
+        has_ans_idx = max_k  # first index in contexts that has answers
+
+        for idx, ctx in enumerate(contexts):
+            if idx >= max_k:
+                break
+
+            if ctx['has_answer']:
+                has_ans_idx = idx
+                break
+
+        for k in topk:
+            accuracy[k].append(0 if has_ans_idx >= k else 1)
+
+    return accuracy
+
+if __name__=="__main__":
+    topk = [5, 10, 20, 50, 100]
+    all_scores_a,all_scores_b=None,None
+    for DATA in ["nq","trivia","wq","curated","squad"]:
+        FileNameA="/data/t-junhanyang/InfoCSE/QA_TEST/InfoCSE_ICT_K1.{}.test.json".format(DATA)
+        FileNameB="/data/t-junhanyang/InfoCSE/QA_TEST/CONPONO.{}.test.json".format(DATA)
+        scores_a=evaluate_retrieval(FileNameA,topk)
+        if all_scores_a is None:
+            all_scores_a=scores_a
+        else:
+            for k in topk:
+                all_scores_a[k]+=scores_a[k]
+        print(FileNameB)
+        scores_b=evaluate_retrieval(FileNameB,topk)
+        if all_scores_b is None:
+            all_scores_b=scores_b
+        else:
+            for k in topk:
+                all_scores_b[k]+=scores_b[k]
+    print(len(all_scores_a[5]))
+    print(len(all_scores_a[100]))
+    for k in topk:
+        stat_val, p_val = stats.ttest_ind(all_scores_a[k], all_scores_b[k])
+        print(str(k)+': '+str(p_val/2))