Add first implementation of scaling functions

Author: franflame

foqus_lib/framework/surrogate/scale_test/test_scaling.py

@@ -0,0 +1,232 @@
+import numpy as np
+import pytest
+from foqus_lib.framework.surrogate.scaling import (
+    scale_linear,
+    unscale_linear,
+    scale_log,
+    unscale_log,
+    scale_log2,
+    unscale_log2,
+    scale_power,
+    unscale_power,
+    scale_power2,
+    unscale_power2,
+    validate_for_scaling,
+)
+
+from hypothesis.extra.numpy import arrays as arrays_strat, array_shapes
+from hypothesis import given, example, assume
+from contextlib import contextmanager
+
+POSITIVE_VALS_ONLY = {scale_log}
+
+
+@contextmanager
+def does_not_raise():
+    yield
+
+
+def test_scale_linear():
+    # Test case 1: Basic scaling
+    input_array = np.array([1, 2, 3, 4, 5])
+    scaled_array = scale_linear(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.25, 0.5, 0.75, 1.0])
+
+    # Test case 2: Custom range scaling
+    input_array = np.array([10, 20, 30, 40, 50])
+    scaled_array = scale_linear(input_array, lo=10, hi=50)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.25, 0.5, 0.75, 1.0])
+
+    # Test case 3: Scaling with negative values
+    input_array = np.array([-5, 0, 5])
+    scaled_array = scale_linear(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.5, 1.0])
+
+    # Test case 4: Scaling with repeated values
+    input_array = np.array([2, 2, 2, 2])
+    scaled_array = scale_linear(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.0, 0.0, 0.0])
+
+
+def test_unscale_linear():
+    # Test case 1: Basic unscaling
+    input_array = np.array([0.0, 0.25, 0.5, 0.75, 1.0])
+    unscaled_array = unscale_linear(input_array, lo=1, hi=5)
+    assert np.allclose(unscaled_array, [1, 2, 3, 4, 5])
+
+    # Test case 2: Custom range unscaling
+    input_array = np.array([0.0, 0.25, 0.5, 0.75, 1.0])
+    unscaled_array = unscale_linear(input_array, lo=10, hi=50)
+    assert np.allclose(unscaled_array, [10, 20, 30, 40, 50])
+
+    # Test case 3: Unscaling with negative values
+    input_array = np.array([0.0, 0.5, 1.0])
+    unscaled_array = unscale_linear(input_array, lo=-5, hi=5)
+    assert np.allclose(unscaled_array, [-5, 0, 5])
+
+    # Test case 4: Unscaling with repeated values
+    input_array = np.array([0.0, 0.0, 0.0, 0.0])
+    unscaled_array = unscale_linear(input_array, lo=0, hi=5)
+    assert np.allclose(unscaled_array, [0, 0, 0, 0])
+
+
+def test_scale_log():
+    # Test case 1: Basic log scaling
+    input_array = np.array([1, 2, 3, 4, 5])
+    scaled_array = scale_log(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.43067656, 0.68260619, 0.86135312, 1.0])
+
+    # Test case 2: Custom range log scaling
+    input_array = np.array([10, 20, 30, 40, 50])
+    scaled_array = scale_log(input_array, lo=10, hi=50)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.43067656, 0.68260619, 0.86135312, 1.0])
+
+
+def test_scale_log2():
+    # Test case 1: Basic log2 scaling
+    input_array = np.array([1, 2, 3, 4, 5])
+    scaled_array = scale_log2(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.51188336, 0.74036269, 0.8893017, 1.0])
+
+    # Test case 2: Custom range log2 scaling
+    input_array = np.array([10, 20, 30, 40, 50])
+    scaled_array = scale_log2(input_array, lo=10, hi=50)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.51188336, 0.74036269, 0.8893017, 1.0])
+
+
+def test_scale_power():
+    # Test case 1: Basic power scaling
+    input_array = np.array([1, 2, 3, 4, 5])
+    scaled_array = scale_power(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(
+        scaled_array,
+        [0.00000000e00, 9.00090009e-04, 9.90099010e-03, 9.99099910e-02, 1.00000000e00],
+    )
+
+    # Test case 2: Custom range power scaling
+    input_array = np.array([1.0, 4.7, 4.8, 4.999, 5.0])
+    scaled_array = scale_power(input_array)
+    print(scaled_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.50113735, 0.63092044, 0.99769983, 1.0])
+
+
+def test_scale_power2():
+    # Test case 1: Basic power scaling
+    input_array = np.array([1, 2, 3, 4, 5])
+    scaled_array = scale_power2(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.08647549, 0.24025307, 0.51371258, 1.0])
+
+    # Test case 2: Custom range power scaling
+    input_array = np.array([1.0, 4.7, 4.8, 4.999, 5.0])
+    scaled_array = scale_power2(input_array)
+    assert np.all(scaled_array >= 0)
+    assert np.all(scaled_array <= 1)
+    assert np.allclose(scaled_array, [0.0, 0.82377238, 0.87916771, 0.99936058, 1.0])
+
+
+# @pytest.mark.xfail(reason="function formula is wrong", strict=True)
+def test_unscale_log():
+    input_array = np.array([0.0, 0.43067656, 0.68260619, 0.86135312, 1.0])
+    unscaled_array = unscale_log(input_array, lo=1, hi=5)
+    assert np.allclose(unscaled_array, [1, 2, 3, 4, 5])
+
+    input_array = np.array([0.0, 0.43067656, 0.68260619, 0.86135312, 1.0])
+    unscaled_array = unscale_log(input_array, lo=10, hi=50)
+    assert np.allclose(unscaled_array, [10, 20, 30, 40, 50])
+
+
+def test_unscale_log2():
+    input_array = np.array([0.0, 0.51188336, 0.74036269, 0.8893017, 1.0])
+    unscaled_array = unscale_log2(input_array, lo=1, hi=5)
+    assert np.allclose(unscaled_array, [1, 2, 3, 4, 5])
+
+    input_array = np.array([0.0, 0.51188336, 0.74036269, 0.8893017, 1.0])
+    unscaled_array = unscale_log2(input_array, lo=10, hi=50)
+    assert np.allclose(unscaled_array, [10, 20, 30, 40, 50])
+
+
+def test_unscale_power():
+    input_array = np.array(
+        [0.00000000e00, 9.00090009e-04, 9.90099010e-03, 9.99099910e-02, 1.00000000e00]
+    )
+    unscaled_array = unscale_power(input_array, lo=1, hi=5)
+    assert np.allclose(unscaled_array, [1, 2, 3, 4, 5])
+
+    input_array = np.array([0.0, 0.50113735, 0.63092044, 0.99769983, 1.0])
+    unscaled_array = unscale_power(input_array, lo=1.0, hi=5.0)
+    assert np.allclose(unscaled_array, [1.0, 4.7, 4.8, 4.999, 5.0])
+
+
+def test_unscale_power2():
+    input_array = np.array([0.0, 0.08647549, 0.24025307, 0.51371258, 1.0])
+    unscaled_array = unscale_power2(input_array, lo=1, hi=5)
+    assert np.allclose(unscaled_array, [1, 2, 3, 4, 5])
+
+    input_array = np.array([0.0, 0.82377238, 0.87916771, 0.99936058, 1.0])
+    unscaled_array = unscale_power2(input_array, lo=1.0, hi=5.0)
+    assert np.allclose(unscaled_array, [1.0, 4.7, 4.8, 4.999, 5.0])
+
+
+# fill in with more cases, parameters, functions
+@pytest.mark.parametrize("x", [np.array([1, 2, 3, 4, 5]), np.array([0, 7, 9, 10, 12])])
+# @given(x=arrays_strat(np.float32, array_shapes()))
+@pytest.mark.parametrize(
+    "scale,unscale",
+    [
+        (scale_linear, unscale_linear),
+        (scale_log, unscale_log),
+        (scale_log2, unscale_log2),
+        (scale_power, unscale_power),
+        (scale_power2, unscale_power2),
+    ],
+)
+def test_roundtrip(x, scale, unscale):
+
+    lo = np.min(x)
+    hi = np.max(x)
+    if not passes_validation(x, lo, hi):
+        expected_failure = pytest.raises(ValueError)
+    elif lo <= 0 and scale in POSITIVE_VALS_ONLY:
+        expected_failure = pytest.raises(ValueError, match="All values must be > 0.*")
+    else:
+        expected_failure = does_not_raise()
+    with expected_failure:
+        scaled = scale(x, lo=lo, hi=hi)
+        unscaled = unscale(scaled, lo=lo, hi=hi)
+        assert np.allclose(x, unscaled)
+
+
+def passes_validation(array_in, lo, hi):
+    try:
+        validate_for_scaling(array_in, lo, hi)
+    except Exception:
+        return False
+    else:
+        return True
+
+
+# Run the tests
+if __name__ == "__main__":
+    pytest.main()