Show in check message how to disable checks (#1855)

albumentations/check_version.py

@@ -59,8 +59,12 @@ def check_for_updates() -> None:
         latest_version = parse_version(data)
         if latest_version and latest_version != current_version:
             logger.info(
-                f"A new version of Albumentations is available: {latest_version} (you have {current_version})."
-                " Upgrade using: pip install --upgrade albumentations",
+                f"A new version of Albumentations is available: {latest_version} (you have {current_version})."  # noqa: S608
+                " Upgrade using: pip install -U albumentations."
+                " To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.",
             )
     except Exception as e:  # General exception catch to ensure silent failure  # noqa: BLE001
-        logger.info(f"Failed to check for updates due to an unexpected error: {e}")
+        logger.info(
+            f"Failed to check for updates due to an unexpected error: {e}. "  # noqa: S608
+            "To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.",
+        )

tests/test_check_version.py

@@ -44,7 +44,7 @@ def test_check_for_updates_new_version_available(mocker):
     log_mock = mocker.patch('logging.Logger.info')
     check_for_updates()
     log_mock.assert_called_once_with(
-        "A new version of Albumentations is available: 1.0.2 (you have 1.0.1). Upgrade using: pip install --upgrade albumentations"
+        'A new version of Albumentations is available: 1.0.2 (you have 1.0.1). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.'
     )
 
 def test_check_for_updates_no_update_needed(mocker):
@@ -58,7 +58,7 @@ def test_check_for_updates_exception_handling(mocker):
     mocker.patch('albumentations.check_version.fetch_version_info', side_effect=Exception("Error"))
     log_mock = mocker.patch('logging.Logger.info')
     check_for_updates()
-    log_mock.assert_called_once_with("Failed to check for updates due to an unexpected error: Error")
+    log_mock.assert_called_once_with("Failed to check for updates due to an unexpected error: Error. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.")
 
 def test_parse_version_correct_input():
     json_input = '{"info": {"version": "1.0.2"}}'

tests/test_domain_adaptation.py

@@ -0,0 +1,78 @@
+import pytest
+import numpy as np
+import cv2
+from albumentations.augmentations.domain_adaptation_functional import MinMaxScaler, StandardScaler, PCA
+
+
+
+@pytest.mark.parametrize("feature_range, data, expected", [
+    ((0.0, 1.0), np.array([[1, 2], [3, 4], [5, 6]]), np.array([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]])),
+    ((-1.0, 1.0), np.array([[1, 2], [3, 4], [5, 6]]), np.array([[-1.0, -1.0], [0.0, 0.0], [1.0, 1.0]])),
+    ((0.0, 1.0), np.array([[1, 1], [1, 1], [1, 1]]), np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]])),  # edge case: all values are the same
+])
+def test_minmax_scaler(feature_range, data, expected):
+    scaler = MinMaxScaler(feature_range)
+    result = scaler.fit_transform(data)
+    np.testing.assert_almost_equal(result, expected)
+
+@pytest.mark.parametrize("feature_range, data, data_scaled, expected", [
+    ((0.0, 1.0), np.array([[1, 2], [3, 4], [5, 6]]), np.array([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]]), np.array([[1, 2], [3, 4], [5, 6]])),
+    ((-1.0, 1.0), np.array([[1, 2], [3, 4], [5, 6]]), np.array([[-1.0, -1.0], [0.0, 0.0], [1.0, 1.0]]), np.array([[1, 2], [3, 4], [5, 6]])),
+])
+def test_minmax_inverse_transform(feature_range, data, data_scaled, expected):
+    scaler = MinMaxScaler(feature_range)
+    scaler.fit(data)
+    result = scaler.inverse_transform(data_scaled)
+    np.testing.assert_almost_equal(result, expected)
+
+@pytest.mark.parametrize("data, expected", [
+    (np.array([[1, 2], [3, 4], [5, 6]]), np.array([[-1.22474487, -1.22474487], [0.0, 0.0], [1.22474487, 1.22474487]])),
+    (np.array([[1, 1], [1, 1], [1, 1]]), np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]])),  # edge case: all values are the same
+])
+def test_standard_scaler(data, expected):
+    scaler = StandardScaler()
+    result = scaler.fit_transform(data)
+    np.testing.assert_almost_equal(result, expected)
+
+@pytest.mark.parametrize("data, data_scaled, expected", [
+    (np.array([[1, 2], [3, 4], [5, 6]]), np.array([[-1.22474487, -1.22474487], [0.0, 0.0], [1.22474487, 1.22474487]]), np.array([[1, 2], [3, 4], [5, 6]])),
+])
+def test_standard_inverse_transform(data, data_scaled, expected):
+    scaler = StandardScaler()
+    scaler.fit(data)
+    result = scaler.inverse_transform(data_scaled)
+    np.testing.assert_almost_equal(result, expected)
+
+
+@pytest.mark.parametrize("n_components, data, expected_shape", [
+    (1, np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0],
+                  [2.3, 2.7], [2.0, 1.6], [1.0, 1.1], [1.5, 1.6], [1.1, 0.9]]), (10, 1)),
+    (2, np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0],
+                  [2.3, 2.7], [2.0, 1.6], [1.0, 1.1], [1.5, 1.6], [1.1, 0.9]]), (10, 2)),
+])
+def test_pca_transform(n_components, data, expected_shape):
+    pca = PCA(n_components)
+    transformed = pca.fit_transform(data)
+    assert transformed.shape == expected_shape
+    assert np.all(np.isfinite(transformed)), "Transformed data contains non-finite values"
+
+
+@pytest.mark.parametrize("n_components, data, expected_transformed, expected_inverse",
+    [(1, np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0],
+                  [2.3, 2.7], [2.0, 1.6], [1.0, 1.1], [1.5, 1.6], [1.1, 0.9]]),
+    np.array([[0.8279701862010879], [-1.7775803252804292], [0.9921974944148886], [0.27421041597539936], [1.6758014186445398], [0.9129491031588081], [-0.09910943749844434], [-1.1445721637986601], [-0.4380461367624502], [-1.2238205550547405]]),
+    np.array([[2.3712589640000026, 2.518706008322169], [0.6050255837456271, 0.6031608863381424], [2.4825842875499986, 2.639442419978468], [1.995879946589024, 2.111593644953067], [2.9459812029146377, 3.142013433918504], [2.428863911241362, 2.5811806942407656], [1.7428163487767303, 1.837136856988131], [1.0341249774652423, 1.068534975444947], [1.5130601765607719, 1.58795783010856], [0.9804046011566055, 1.0102732497072444]])
+
+     ),
+    (2, np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0],
+                  [2.3, 2.7], [2.0, 1.6], [1.0, 1.1], [1.5, 1.6], [1.1, 0.9]]),
+     np.array([[0.8279701862010879, 0.17511530704691558], [-1.7775803252804292, -0.14285722654428068], [0.9921974944148886, -0.3843749888804125], [0.27421041597539936, -0.13041720657412711], [1.6758014186445398, 0.20949846125675342], [0.9129491031588081, -0.17528244362036988], [-0.09910943749844434, 0.34982469809712086], [-1.1445721637986601, -0.04641725818328135], [-0.4380461367624502, -0.017764629675083188], [-1.2238205550547405, 0.16267528707676182]]),
+     np.array([[2.5, 2.4], [0.4999999999999998, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1000000000000005, 3.0], [2.3, 2.7], [2.0, 1.6], [0.9999999999999999, 1.1], [1.5, 1.6], [1.0999999999999999, 0.9000000000000001]]),
+     ),
+])
+def test_pca_inverse_transform(n_components, data, expected_transformed, expected_inverse):
+    pca = PCA(n_components)
+    transformed = pca.fit_transform(data)
+    inversed = pca.inverse_transform(transformed)
+    assert np.array_equal(transformed, expected_transformed)
+    assert np.array_equal(inversed, expected_inverse)