euclidean distance and unit tests

.github/pull_request_template.md

@@ -0,0 +1,7 @@
+## What's new?
+
+* **Problem**: Problem description
+* **Solution**: Solution description
+* **Related/Additional changes**: Related/Additional changes description
+
+Closes #

.github/workflows/test.yml

@@ -0,0 +1,26 @@
+name: Unit tests execution
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  tests:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+    - name: Test with pytest
+      run: |
+        pip install pytest
+        python -m pytest tests/ --disable-warnings

requirements.txt

@@ -0,0 +1,4 @@
+numpy
+scikit-learn
+pandas
+polars

src/core/distances.py

@@ -0,0 +1,4 @@
+import numpy as np
+
+def euclidean_distances(x_train: np.array, x_test_point: np.array):
+    return np.sqrt(np.sum((x_train - x_test_point) ** 2, axis=1))

tests/core/test_distances.py

@@ -0,0 +1,20 @@
+from unittest import TestCase
+import numpy as np
+from sklearn.metrics.pairwise import euclidean_distances as sk_ed
+from src.core.distances import euclidean_distances
+
+class TestDistances(TestCase):
+    def test_should_calculate_euclidean_distance(self):
+        x = np.array([
+            [0.5, 0.3, 0.23],
+            [0.4, 0.4, 0.43],
+            [0.3, 0.5, 0.33]
+        ])
+
+        target = np.array([0.3, 0.5, 0.33])
+
+        distances = euclidean_distances(x, target)
+        expected_distances = sk_ed(x, [target])
+
+        for distance_actual, distance_expected in zip(distances, expected_distances):
+            assert round(distance_actual, 4) == round(distance_expected[0], 4)

tests/tests.py

@@ -0,0 +1,4 @@
+import unittest
+
+if __name__ == "__main__":
+    unittest.main()