ADD Recall metrics

pyNTCIREVAL/metrics/__init__.py

@@ -16,4 +16,5 @@
 from .ms_ndcg import MSnDCG
 from .precision import Precision
 from .hit import Hit
+from .recall import Recall
 

pyNTCIREVAL/metrics/recall.py

@@ -0,0 +1,18 @@
+from .metric import Metric
+
+class Recall(Metric):
+    '''
+    Recall
+
+    Args:
+        xrelnum: the number of judged X-rel docs (including 0-rel=judged nonrel).
+        labeled_ranked_list: a list of tuples, where each tuple contains a document ID and its corresponding relevance score.
+    '''
+    def __init__(self, xrelnum):
+        self.total_positives = sum(xrelnum[1:])
+
+    def compute(self, labeled_ranked_list):
+        true_positives = sum(grade for docid, grade in labeled_ranked_list if grade is not None and grade > 0)
+        if true_positives == 0:
+            return 0.0
+        return true_positives / self.total_positives

tests/test_recall.py

@@ -0,0 +1,22 @@
+# -*- coding:utf-8 -*-
+import pytest
+
+class TestRecall(object):
+
+    def test_precision(self):
+        from pyNTCIREVAL import Labeler
+        from pyNTCIREVAL.metrics import Recall
+
+        qrels = {0: 1, 1: 0, 2: 0, 3: 0, 4: 1, 5: 0, 6: 0, 7: 1, 8: 0, 9: 0}
+        ranked_list = [0, 1, 3, 5, 8, 9] # a list of document IDs
+
+        # labeling: [doc_id] -> [(doc_id, rel_level)]
+        labeler = Labeler(qrels)
+        labeled_ranked_list = labeler.label(ranked_list)
+
+        xrelnum = labeler.compute_per_level_doc_num(2)
+        metric = Recall(xrelnum)
+        assert xrelnum == [7, 3]
+
+        result = metric.compute(labeled_ranked_list)
+        assert result == 0.3333333333333333