From 50b28572cddc9872853c87d8abca3e0722d35f24 Mon Sep 17 00:00:00 2001
From: goldpulpy <sawsani1928@gmail.com>
Date: Wed, 9 Oct 2024 04:08:12 +0300
Subject: [PATCH] Compute function tests

---
 tests/test_compute.py | 81 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 tests/test_compute.py

diff --git a/tests/test_compute.py b/tests/test_compute.py
new file mode 100644
index 0000000..085e68f
--- /dev/null
+++ b/tests/test_compute.py
@@ -0,0 +1,81 @@
+"""Test cases for the compute functions module."""
+import unittest
+import numpy as np
+from pysentence_similarity.compute import (
+    cosine,
+    euclidean,
+    manhattan,
+    jaccard,
+    pearson,
+    minkowski,
+    hamming,
+    kl_divergence,
+    chebyshev,
+    bregman
+)
+
+
+class TestСompute(unittest.TestCase):
+    """Test cases for the embedding metrics."""
+
+    def setUp(self) -> None:
+        """Set up some common embeddings for testing."""
+        self.embedding_1 = np.array([1, 2, 3])
+        self.embedding_2 = np.array([4, 5, 6])
+        self.embedding_3 = np.array([1, 0, 0])
+        self.embedding_prob_1 = np.array([0.2, 0.5, 0.3])
+        self.embedding_prob_2 = np.array([0.1, 0.7, 0.2])
+
+    def test_cosine(self) -> None:
+        """Test cosine similarity between two embedding vectors."""
+        result = cosine(self.embedding_1, self.embedding_2)
+        self.assertAlmostEqual(result, 0.974631846, places=6)
+
+    def test_euclidean(self) -> None:
+        """Test Euclidean distance between two embedding vectors."""
+        result = euclidean(self.embedding_1, self.embedding_2)
+        self.assertAlmostEqual(result, 5.196152422, places=6)
+
+    def test_manhattan(self) -> None:
+        """Test Manhattan distance between two embedding vectors."""
+        result = manhattan(self.embedding_1, self.embedding_2)
+        self.assertEqual(result, 9)
+
+    def test_jaccard(self) -> None:
+        """Test Jaccard similarity between two embedding vectors."""
+        result = jaccard(self.embedding_1, self.embedding_2)
+        self.assertAlmostEqual(result, 0.4, places=6)
+
+    def test_pearson(self) -> None:
+        """Test Pearson correlation between two embedding vectors."""
+        result = pearson(self.embedding_1, self.embedding_2)
+        self.assertAlmostEqual(result, 1.0, places=6)
+
+    def test_minkowski(self) -> None:
+        """Test Minkowski distance between two embedding vectors."""
+        result = minkowski(self.embedding_1, self.embedding_2, p=3)
+        self.assertAlmostEqual(result, 4.32674871, places=6)
+
+    def test_hamming(self) -> None:
+        """Test Hamming distance between two embedding vectors."""
+        result = hamming(self.embedding_1, self.embedding_2)
+        self.assertEqual(result, 1.0)
+
+    def test_kl_divergence(self) -> None:
+        """Test KL divergence between two embedding vectors."""
+        result = kl_divergence(self.embedding_prob_1, self.embedding_prob_2)
+        self.assertAlmostEqual(result, 0.0920328, places=6)
+
+    def test_chebyshev(self) -> None:
+        """Test Chebyshev distance between two embedding vectors."""
+        result = chebyshev(self.embedding_1, self.embedding_2)
+        self.assertEqual(result, 3)
+
+    def test_bregman(self) -> None:
+        """Test Bregman divergence between two embedding vectors."""
+        result = bregman(self.embedding_1, self.embedding_2)
+        self.assertAlmostEqual(result, 27, places=6)
+
+
+if __name__ == '__main__':
+    unittest.main()