new scaling test

Author: PhilippSchmelter

tests/test_scaling.py

@@ -0,0 +1,60 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from src.preprocessing.scaling import discretize_power, normalize_power
+
+# --------------------------------------------------------------------------- #
+# normalize_power                                                             #
+# --------------------------------------------------------------------------- #
+
+
+def test_normalize_power_basic():
+    df = pd.DataFrame({"power": [0.0, 2.0, 4.0]})
+    out = normalize_power(df.copy())
+
+    expected = pd.Series([0.0, 0.5, 1.0], name="power")
+    pd.testing.assert_series_equal(out["power"], expected, check_dtype=False)
+
+
+def test_normalize_power_constant_values():
+    df = pd.DataFrame({"power": [3.3, 3.3, 3.3]})
+    out = normalize_power(df.copy(), eps=1e-12)
+
+    assert out["power"].eq(0.0).all()
+
+
+def test_normalize_power_empty_df_raises():
+    empty = pd.DataFrame(columns=["power"])
+    with pytest.raises(ValueError):
+        normalize_power(empty)
+
+
+# --------------------------------------------------------------------------- #
+# discretize_power                                                            #
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.parametrize(
+    "value,expected_state",
+    [
+        (0.00, 0),
+        (0.02, 1),
+        (0.05, 2),
+        (0.20, 4),
+        (0.83, 9),
+    ],
+)
+def test_discretize_power_states(value, expected_state):
+    df = pd.DataFrame({"power": [value]})
+    out = discretize_power(df.copy())
+
+    assert out.loc[0, "state"] == expected_state
+
+
+def test_discretize_power_preserves_power_column():
+    values = np.linspace(0, 1, 6)
+    df = pd.DataFrame({"power": values})
+    out = discretize_power(df.copy())
+
+    pd.testing.assert_series_equal(out["power"], pd.Series(values, name="power"))