Fix: Testcases

tests/test_embedding_distance.py

@@ -1,6 +1,8 @@
 import unittest
 import numpy as np
-from langtest.metrics import EmbeddingDistance
+from langtest.metrics import (
+    EmbeddingDistance,
+)  # Replace 'your_module' with the actual module where EmbeddingDistance is defined
 
 
 class TestEmbeddingDistance(unittest.TestCase):
@@ -22,8 +24,7 @@ def test_cosine_similarity(self):
         This test checks the correctness of the cosine similarity distance metric.
         It ensures that the result is a float, within the range [-1, 1], and not None.
         """
-        ed = EmbeddingDistance()
-        result = ed["cosine"](self.vector1, self.vector2)
+        result = EmbeddingDistance._cosine_distance(self.vector1, self.vector2)
         self.assertIsInstance(result, float)
         self.assertGreaterEqual(result, -1)
         self.assertLessEqual(result, 1)
@@ -36,8 +37,7 @@ def test_euclidean_distance(self):
         This test checks the correctness of the Euclidean distance metric.
         It ensures that the result is a float, greater than or equal to 0, and not None.
         """
-        ed = EmbeddingDistance()
-        result = ed["euclidean"](self.vector1, self.vector2)
+        result = EmbeddingDistance._euclidean_distance(self.vector1, self.vector2)
         self.assertIsInstance(result, float)
         self.assertGreaterEqual(result, 0)
         self.assertIsNotNone(result)
@@ -49,8 +49,7 @@ def test_manhattan_distance(self):
         This test checks the correctness of the Manhattan distance metric.
         It ensures that the result is a float, greater than or equal to 0, and not None.
         """
-        ed = EmbeddingDistance()
-        result = ed["manhattan"](self.vector1, self.vector2)
+        result = EmbeddingDistance._manhattan_distance(self.vector1, self.vector2)
         self.assertIsInstance(result, float)
         self.assertGreaterEqual(result, 0)
         self.assertIsNotNone(result)
@@ -62,8 +61,7 @@ def test_chebyshev_distance(self):
         This test checks the correctness of the Chebyshev distance metric.
         It ensures that the result is a float, greater than or equal to 0, and not None.
         """
-        ed = EmbeddingDistance()
-        result = ed["chebyshev"](self.vector1, self.vector2)
+        result = EmbeddingDistance._chebyshev_distance(self.vector1, self.vector2)
         self.assertIsInstance(result, float)
         self.assertGreaterEqual(result, 0)
         self.assertIsNotNone(result)
@@ -75,8 +73,7 @@ def test_hamming_distance(self):
         This test checks the correctness of the Hamming distance metric.
         It ensures that the result is a float, greater than or equal to 0, and less than or equal to 1, and not None.
         """
-        ed = EmbeddingDistance()
-        result = ed["hamming"](self.vector1, self.vector2)
+        result = EmbeddingDistance._hamming_distance(self.vector1, self.vector2)
         self.assertIsInstance(result, float)
         self.assertGreaterEqual(result, 0)
         self.assertLessEqual(result, 1)

tests/test_string_distance.py

@@ -3,9 +3,10 @@
 
 
 class TestStringDistance(unittest.TestCase):
-    def setUp(self):
-        self.text1 = "hello"
-        self.text2 = "hola"
+    @classmethod
+    def setUpClass(cls):
+        cls.text1 = "hello"
+        cls.text2 = "hola"
 
     def assert_normalized_distance(self, distance_name, result):
         """
@@ -25,8 +26,7 @@ def test_normalized_jaro_distance(self):
 
         Ensure that the result is a float, not None, and falls within the range [0, 1].
         """
-        sd = StringDistance()
-        result = sd["jaro"](self.text1, self.text2)
+        result = StringDistance._normalized_jaro_distance(self.text1, self.text2)
         self.assert_normalized_distance("jaro", result)
 
     def test_normalized_jaro_winkler_distance(self):
@@ -35,8 +35,7 @@ def test_normalized_jaro_winkler_distance(self):
 
         Ensure that the result is a float, not None, and falls within the range [0, 1].
         """
-        sd = StringDistance()
-        result = sd["jaro_winkler"](self.text1, self.text2)
+        result = StringDistance._normalized_jaro_winkler_distance(self.text1, self.text2)
         self.assert_normalized_distance("jaro_winkler", result)
 
     def test_normalized_hamming_distance(self):
@@ -45,8 +44,7 @@ def test_normalized_hamming_distance(self):
 
         Ensure that the result is a float, not None, and falls within the range [0, 1].
         """
-        sd = StringDistance()
-        result = sd["hamming"](self.text1, self.text2)
+        result = StringDistance._normalized_hamming_distance(self.text1, self.text2)
         self.assert_normalized_distance("hamming", result)
 
     def test_normalized_levenshtein_distance(self):
@@ -55,8 +53,7 @@ def test_normalized_levenshtein_distance(self):
 
         Ensure that the result is a float, not None, and falls within the range [0, 1].
         """
-        sd = StringDistance()
-        result = sd["levenshtein"](self.text1, self.text2)
+        result = StringDistance._normalized_levenshtein_distance(self.text1, self.text2)
         self.assert_normalized_distance("levenshtein", result)
 
     def test_normalized_damerau_levenshtein_distance(self):
@@ -65,8 +62,9 @@ def test_normalized_damerau_levenshtein_distance(self):
 
         Ensure that the result is a float, not None, and falls within the range [0, 1].
         """
-        sd = StringDistance()
-        result = sd["damerau_levenshtein"](self.text1, self.text2)
+        result = StringDistance._normalized_damerau_levenshtein_distance(
+            self.text1, self.text2
+        )
         self.assert_normalized_distance("damerau_levenshtein", result)
 
     def test_normalized_indel_distance(self):
@@ -75,6 +73,5 @@ def test_normalized_indel_distance(self):
 
         Ensure that the result is a float, not None, and falls within the range [0, 1].
         """
-        sd = StringDistance()
-        result = sd["indel"](self.text1, self.text2)
+        result = StringDistance._normalized_indel_distance(self.text1, self.text2)
         self.assert_normalized_distance("indel", result)