add CLUENER_assessment

knlp/seq_labeling/hmm-ner/CLUENER_assessment.py

@@ -0,0 +1,80 @@
+tags = [("B-add", "I-add"), ("B-boo", "I-boo"), ("B-com", "I-com"), ("B-gam", "I-gam"), ("B-gov", "I-gov"),
+        ("B-mov", "I-mov"), ("B-nam", "I-nam"), ("B-org", "I-org"), ("B-pos", "I-pos"), ("B-sce", "I-sce")]
+
+
+def find_tag(labels, B_label, I_label):
+    result = []
+    if isinstance(labels, str):
+        labels = labels.strip().split()
+    for i in range(len(labels)):
+        if labels[i] == B_label:
+            B_pos0 = i
+        if labels[i] == I_label and labels[i - 1] == B_label:
+            length = 2
+            for i2 in range(i, len(labels)):
+                if labels[i2] == I_label and labels[i2 - 1] == I_label:
+                    length += 1
+                if labels[i2] == "O":
+                    result.append((B_pos0, length))
+                    break
+    return result
+
+
+def find_all_tag(labels):
+    result = {}
+    for tag in tags:
+        res = find_tag(labels, B_label=tag[0], I_label=tag[1])
+        result[tag[0].split("-")[1]] = res
+    return result
+
+
+def precision(pre_labels, true_labels):
+    pre = []
+    if isinstance(pre_labels, str):
+        pre_labels = pre_labels.strip().split()
+    if isinstance(true_labels, str):
+        true_labels = true_labels.strip().split()
+
+    pre_result = find_all_tag(pre_labels)
+    for name in pre_result:
+        for x in pre_result[name]:
+            if x:
+                if pre_labels[x[0]:x[0]+x[1]] == true_labels[x[0]:x[0] + x[1]]:
+                    pre.append(1)
+                else:
+                    pre.append(0)
+    return sum(pre) / len(pre)
+
+
+def recall(pre_labels, true_labels):
+    recall = []
+    if isinstance(pre_labels, str):
+        pre_labels = pre_labels.strip().split()
+    if isinstance(true_labels, str):
+        true_labels = true_labels.strip().split()
+    true_result = find_all_tag(true_labels)
+    for name in true_result:
+        for x in true_result[name]:
+            if x:
+                if pre_labels[x[0]:x[0] + x[1]] == true_labels[x[0]:x[0] + x[1]]:
+                    recall.append(1)
+                else:
+                    recall.append(0)
+    return sum(recall) / len(recall)
+
+
+def f1_score(precision, recall):
+    return (2 * precision * recall) / (precision + recall)
+
+
+if __name__ == '__main__':
+    fp = open('dev_pre.json', 'r')
+    pre = fp.readline()
+    ft = open('dev_true.json', 'r')
+    true = ft.readline()
+    precision = precision(pre, true)
+    recall = recall(pre, true)
+    f1 = f1_score(precision, recall)
+    print(precision)
+    print(recall)
+    print(f1)