Preparing for merging two WMD scripts

* Added align() to return p_at_one, percentage for
both metrics

2 files changed, 15 insertions, 3 deletions

diff --git a/WMD_retrieval.py b/WMD_retrieval.py
index fbf1b57..f32372f 100644
--- a/WMD_retrieval.py
+++ b/WMD_retrieval.py
@@ -93,9 +93,10 @@ def main(args):
 
         clf = Wasserstein_Retriever(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=(metric == 'snk'))
         clf.fit(X_train_idf[:instances], np.ones(instances))
-        dist, preds = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
-        mrr, p_at_one = mrr_precision_at_k(list(range(len(preds))), preds)
-        percentage = p_at_one * 100
+        # dist, preds = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
+        # mrr, p_at_one = mrr_precision_at_k(list(range(len(preds))), preds)
+        # percentage = p_at_one * 100
+        p_at_one, percentage  = clf.align(X_test_idf[:instances], n_neighbors=instances)
 
         if (not batch):
             print(f'P @ 1: {p_at_one}\ninstances: {instances}\n{percentage}%')
diff --git a/Wasserstein_Distance.py b/Wasserstein_Distance.py
index 925eca3..5965ed5 100644
--- a/Wasserstein_Distance.py
+++ b/Wasserstein_Distance.py
@@ -138,6 +138,17 @@ class Wasserstein_Retriever(KNeighborsClassifier):
         dist = self._pairwise_wmd(X)
         return super(Wasserstein_Retriever, self).kneighbors(dist, n_neighbors)
 
+    def align(self, X, n_neighbors=1)
+        """
+        Wrapper function over kneighbors to return
+        precision at one and percentage values
+
+        """
+        dist, preds = self.kneighbors(X, n_neighbors)
+        mrr, p_at_one = mrr_precision_at_k(list(range(len(preds))), preds)
+        percentage = p_at_one * 100
+        return (p_at_one, percentage)
+
 
 def load_embeddings(path, dimension=300):
     """