From f9be43b827ea84a8c1512914cf5b4579391c85cf Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Wed, 11 Mar 2026 18:39:00 +0000
Subject: [PATCH] Add unit tests for walk_forward_splits

Created tests/test_model_training.py and added unit tests for the walk_forward_splits function in models/model_training.py. Tests cover basic chronological splitting logic, varying number of splits, dataset sizes that don't divide perfectly, and end bounds capping. Verified that the tests accurately catch regressions.

Co-authored-by: maghdam <63883156+maghdam@users.noreply.github.com>
---
 tests/__init__.py            |  0
 tests/test_model_training.py | 91 ++++++++++++++++++++++++++++++++++++
 2 files changed, 91 insertions(+)
 create mode 100644 tests/__init__.py
 create mode 100644 tests/test_model_training.py

diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_model_training.py b/tests/test_model_training.py
new file mode 100644
index 0000000..0c84d2f
--- /dev/null
+++ b/tests/test_model_training.py
@@ -0,0 +1,91 @@
+import unittest
+import pandas as pd
+import numpy as np
+from models.model_training import walk_forward_splits
+
+class TestModelTraining(unittest.TestCase):
+
+    def setUp(self):
+        # Create a simple dummy sequence (e.g., numbers 0 to 9)
+        self.X = pd.DataFrame({'feature': np.arange(10)})
+        self.y = pd.Series(np.arange(10))
+
+    def test_walk_forward_splits_basic(self):
+        """Test basic chronological splitting with default n_splits=3 on n=10."""
+        # For n=10, n_splits=3, fold_size = 10 // (3 + 1) = 2
+        # Fold 1: Train [0:2], Test [2:4]
+        # Fold 2: Train [0:4], Test [4:6]
+        # Fold 3: Train [0:6], Test [6:8]
+        folds = walk_forward_splits(self.X, self.y, n_splits=3)
+
+        self.assertEqual(len(folds), 3)
+
+        # Fold 1
+        X_train_1, y_train_1, X_test_1, y_test_1 = folds[0]
+        self.assertEqual(len(X_train_1), 2)
+        self.assertEqual(len(X_test_1), 2)
+        self.assertTrue(np.array_equal(X_train_1['feature'].values, [0, 1]))
+        self.assertTrue(np.array_equal(X_test_1['feature'].values, [2, 3]))
+
+        # Fold 2
+        X_train_2, y_train_2, X_test_2, y_test_2 = folds[1]
+        self.assertEqual(len(X_train_2), 4)
+        self.assertEqual(len(X_test_2), 2)
+        self.assertTrue(np.array_equal(X_train_2['feature'].values, [0, 1, 2, 3]))
+        self.assertTrue(np.array_equal(X_test_2['feature'].values, [4, 5]))
+
+        # Fold 3
+        X_train_3, y_train_3, X_test_3, y_test_3 = folds[2]
+        self.assertEqual(len(X_train_3), 6)
+        self.assertEqual(len(X_test_3), 2)
+        self.assertTrue(np.array_equal(X_train_3['feature'].values, [0, 1, 2, 3, 4, 5]))
+        self.assertTrue(np.array_equal(X_test_3['feature'].values, [6, 7]))
+
+    def test_walk_forward_splits_varying_n_splits(self):
+        """Test varying number of splits."""
+        # For n=10, n_splits=4, fold_size = 10 // (4 + 1) = 2
+        folds = walk_forward_splits(self.X, self.y, n_splits=4)
+        self.assertEqual(len(folds), 4)
+
+        # Last fold: Train [0:8], Test [8:10]
+        X_train_last, _, X_test_last, _ = folds[-1]
+        self.assertEqual(len(X_train_last), 8)
+        self.assertEqual(len(X_test_last), 2)
+        self.assertEqual(X_test_last['feature'].iloc[-1], 9)
+
+    def test_walk_forward_splits_remainder(self):
+        """Test dataset size that doesn't divide perfectly."""
+        # n=11, n_splits=3, fold_size = 11 // (3 + 1) = 2
+        # Fold 1: Train [0:2], Test [2:4]
+        # Fold 2: Train [0:4], Test [4:6]
+        # Fold 3: Train [0:6], Test [6:8]
+        X_11 = pd.DataFrame({'feature': np.arange(11)})
+        y_11 = pd.Series(np.arange(11))
+
+        folds = walk_forward_splits(X_11, y_11, n_splits=3)
+        self.assertEqual(len(folds), 3)
+
+        # Check last fold bounds
+        X_train_last, _, X_test_last, _ = folds[-1]
+        self.assertEqual(len(X_train_last), 6)
+        self.assertEqual(len(X_test_last), 2)
+        self.assertTrue(np.array_equal(X_test_last['feature'].values, [6, 7]))
+
+    def test_walk_forward_splits_end_bounds(self):
+        """Test case where end_test is capped at n."""
+        # n=5, n_splits=2, fold_size = 5 // 3 = 1
+        # Fold 1: Train [0:1], Test [1:2]
+        # Fold 2: Train [0:2], Test [2:3]
+        X_5 = pd.DataFrame({'feature': np.arange(5)})
+        y_5 = pd.Series(np.arange(5))
+        folds = walk_forward_splits(X_5, y_5, n_splits=2)
+        self.assertEqual(len(folds), 2)
+
+        X_train_last, _, X_test_last, _ = folds[-1]
+        self.assertEqual(len(X_train_last), 2)
+        self.assertEqual(len(X_test_last), 1)
+        self.assertTrue(np.array_equal(X_test_last['feature'].values, [2]))
+
+
+if __name__ == '__main__':
+    unittest.main()