Run ready WMD_Matcher

1 files changed, 26 insertions, 26 deletions

diff --git a/WMD_matching.py b/WMD_matching.py
index 3b8b1a9..0b81696 100644
--- a/WMD_matching.py
+++ b/WMD_matching.py
@@ -7,22 +7,6 @@ from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
 from sklearn.preprocessing import normalize
 from Wass_Matcher import Wasserstein_Matcher
 
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser(description='matching using wmd and wasserstein distance')
-    parser.add_argument('source_lang', help='source language short name')
-    parser.add_argument('target_lang', help='target language short name')
-    parser.add_argument('source_vector', help='path of the source vector')
-    parser.add_argument('target_vector', help='path of the target vector')
-    parser.add_argument('source_defs', help='path of the source definitions')
-    parser.add_argument('target_defs', help='path of the target definitions')
-    parser.add_argument('-b', '--batch', action='store_true', help='running in batch (store results in csv) or running a single instance (output the results)')
-    parser.add_argument('mode', choices=['all', 'wmd', 'snk'], default='all', help='which methods to run')
-    parser.add_argument('-n', '--instances', help='number of instances in each language to retrieve', default=1000, type=int)
-    args = parser.parse_args()
-
-    main(args)
-
 def load_embeddings(path, dimension=300):
     """
     Loads the embeddings from a word2vec formatted file.
@@ -88,6 +72,7 @@ def mrr_precision_at_k(golden, preds, k_list=[1,]):
 
 def main(args):
 
+    numpy.seterr(divide='ignore') # POT has issues with divide by zero errors
     source_lang = args.source_lang
     target_lang = args.target_lang
 
@@ -162,7 +147,7 @@ def main(args):
         if (not batch):
             print(f'WMD - tfidf: {source_lang} - {target_lang}')
 
-        clf = WassersteinDistances(W_embed=W_common, n_neighbors=5, n_jobs=14)
+        clf = Wasserstein_Matcher(W_embed=W_common, n_neighbors=5, n_jobs=14)
         clf.fit(X_train_idf[:instances], np.ones(instances))
         row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
         result = zip(row_ind, col_ind)
@@ -183,20 +168,35 @@ def main(args):
         if (not batch):
             print(f'Sinkhorn - tfidf: {source_lang} - {target_lang}')
 
-        clf = WassersteinDistances(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=True)
+        clf = Wasserstein_Matcher(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=True)
         clf.fit(X_train_idf[:instances], np.ones(instances))
         row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
 
         result = zip(row_ind, col_ind)
         hit_one = len([x for x,y in result if x == y])
+        percentage = hit_one / instances * 100
 
-    if (not batch):
-        print(f'{hit_one} definitions have been mapped correctly')
+        if (not batch):
+            print(f'{hit_one} definitions have been mapped correctly, {percentage}%')
 
+        if (batch):
+            fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}']
+            with open('sinkhorn_matching_result.csv', 'a') as f:
+                writer = csv.writer(f)
+                writer.writerow(fields)
 
-    if (batch):
-        percentage = hit_one / instances * 100
-        fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}']
-        with open('sinkhorn_matching_result.csv', 'a') as f:
-            writer = csv.writer(f)
-            writer.writerow(fields)
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description='matching using wmd and wasserstein distance')
+    parser.add_argument('source_lang', help='source language short name')
+    parser.add_argument('target_lang', help='target language short name')
+    parser.add_argument('source_vector', help='path of the source vector')
+    parser.add_argument('target_vector', help='path of the target vector')
+    parser.add_argument('source_defs', help='path of the source definitions')
+    parser.add_argument('target_defs', help='path of the target definitions')
+    parser.add_argument('-b', '--batch', action='store_true', help='running in batch (store results in csv) or running a single instance (output the results)')
+    parser.add_argument('mode', choices=['all', 'wmd', 'snk'], default='all', help='which methods to run')
+    parser.add_argument('-n', '--instances', help='number of instances in each language to retrieve', default=1000, type=int)
+    args = parser.parse_args()
+
+    main(args)