tests: data transformers

Author: iraedeus

tests/mocks/core/data_transformers/simple.py

@@ -0,0 +1,42 @@
+# -*- coding: ascii -*-
+
+"""
+Dummy data transformer implementation for testing.
+
+This module provides a minimal concrete implementation of IDataTransformer
+used strictly to test the default behaviors of the abstract base class.
+"""
+
+__author__ = "Danil Totmyanin"
+__copyright__ = "Copyright (c) 2026 PySATL project"
+__license__ = "SPDX-License-Identifier: MIT"
+
+from typing import Any
+
+from pysatl_cpd.core.data_providers.idata_provider import DataProvider
+from pysatl_cpd.core.data_transformers.idata_transformer import IDataTransformer
+
+
+class DummyTransformer(IDataTransformer[Any, Any]):
+    """
+    Minimal concrete implementation of IDataTransformer.
+
+    Used for testing the default behaviors of the abstract base class,
+    such as the default `name` and `__hash__` properties.
+    """
+
+    def transform(self, provider: DataProvider[Any]) -> DataProvider[Any]:
+        """
+        Dummy implementation that just returns the input provider.
+
+        Parameters
+        ----------
+        provider : DataProvider[Any]
+            The source data provider.
+
+        Returns
+        -------
+        DataProvider[Any]
+            The unmodified input provider.
+        """
+        return provider

tests/unit/core/data_transformers/test_columns_selector_transformer.py

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+# -*- coding: ascii -*-
+
+"""
+Tests for Data Transformers.
+
+Covers IDataTransformer base class properties and ColumnsSelectorTransformer logic.
+"""
+
+__author__ = "Danil Totmyanin"
+__copyright__ = "Copyright (c) 2026 PySATL project"
+__license__ = "SPDX-License-Identifier: MIT"
+
+import numpy as np
+import pytest
+
+from pysatl_cpd.core.data_providers.numpy_data_provider import (
+    NDArrayMultivariateProvider,
+    NDArrayUnivariateProvider,
+)
+from pysatl_cpd.core.data_transformers.columns_selector_transformer import (
+    ColumnsSelectorTransformer,
+)
+
+
+class TestColumnsSelectorTransformer:
+    """Tests for ColumnsSelectorTransformer logic and naming."""
+
+    def test_name_single_column(self) -> None:
+        """Transformer name should be formatted as 'Col_X' for a single int."""
+        transformer = ColumnsSelectorTransformer(columns=2)
+        assert transformer.name == "Col_2"
+
+    def test_name_multiple_columns(self) -> None:
+        """Transformer name should be formatted as 'Cols_X_Y' for a list of ints."""
+        transformer = ColumnsSelectorTransformer(columns=[0, 2, 3])
+        assert transformer.name == "Cols_0_2_3"
+
+    def test_transform_int_to_univariate(self) -> None:
+        """Selecting a single int column should yield a Univariate provider."""
+        data: np.ndarray = np.array(
+            [
+                [1.0, 2.0, 3.0],
+                [4.0, 5.0, 6.0],
+                [7.0, 8.0, 9.0],
+            ]
+        )
+        provider = NDArrayMultivariateProvider(data=data, name="test_data")
+        transformer = ColumnsSelectorTransformer(columns=1)
+
+        result_provider = transformer.transform(provider)
+
+        # Check type and name
+        assert isinstance(result_provider, NDArrayUnivariateProvider)
+        assert result_provider.name == "test_data_Col_1"
+
+        # Check extracted data (column index 1 -> [2.0, 5.0, 8.0])
+        result_data: list[float] = list(result_provider)
+        np.testing.assert_array_equal(result_data, [2.0, 5.0, 8.0])
+
+    def test_transform_list_to_multivariate(self) -> None:
+        """Selecting a list of columns should yield a Multivariate provider."""
+        data: np.ndarray = np.array(
+            [
+                [1.0, 2.0, 3.0, 4.0],
+                [5.0, 6.0, 7.0, 8.0],
+            ]
+        )
+        provider = NDArrayMultivariateProvider(data=data, name="multidataset")
+        transformer = ColumnsSelectorTransformer(columns=[0, 3])
+
+        result_provider = transformer.transform(provider)
+
+        # Check type and name
+        assert isinstance(result_provider, NDArrayMultivariateProvider)
+        assert result_provider.name == "multidataset_Cols_0_3"
+
+        # Check extracted data (columns 0 and 3)
+        result_data: list[np.ndarray] = list(result_provider)
+        expected_data: list[np.ndarray] = [
+            np.array([1.0, 4.0]),
+            np.array([5.0, 8.0]),
+        ]
+
+        assert len(result_data) == 2
+        np.testing.assert_array_equal(result_data[0], expected_data[0])
+        np.testing.assert_array_equal(result_data[1], expected_data[1])
+
+    def test_transform_raises_value_error_on_1d_data(self) -> None:
+        """Attempting to select columns from 1D data should raise ValueError."""
+        data: np.ndarray = np.array([1.0, 2.0, 3.0])
+        provider = NDArrayUnivariateProvider(data=data, name="1d_data")
+        transformer = ColumnsSelectorTransformer(columns=0)
+
+        expected_msg = "ColumnsSelectorTransformer expects 2D data, got 1D data from provider '1d_data'."
+        with pytest.raises(ValueError, match=expected_msg):
+            transformer.transform(provider)  # type: ignore[arg-type]
+
+    def test_transform_raises_index_error_on_out_of_bounds(self) -> None:
+        """Passing an out-of-bounds column index should propagate an IndexError from NumPy."""
+        data: np.ndarray = np.array(
+            [
+                [1.0, 2.0],
+                [3.0, 4.0],
+            ]
+        )
+        provider = NDArrayMultivariateProvider(data=data, name="data")
+
+        # Array only has columns 0 and 1, index 5 is out of bounds
+        transformer = ColumnsSelectorTransformer(columns=5)
+
+        with pytest.raises(IndexError):
+            transformer.transform(provider)

tests/unit/core/data_transformers/test_idata_transformer.py

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+# -*- coding: ascii -*-
+
+"""
+Tests for Data Transformers.
+
+Covers IDataTransformer base class properties and ColumnsSelectorTransformer logic.
+"""
+
+__author__ = "Danil Totmyanin"
+__copyright__ = "Copyright (c) 2026 PySATL project"
+__license__ = "SPDX-License-Identifier: MIT"
+
+
+from tests.mocks.core.data_transformers.simple import DummyTransformer
+
+
+class TestIDataTransformer:
+    """Tests for the abstract IDataTransformer base class default behaviors."""
+
+    def test_default_name_is_class_name(self) -> None:
+        """The default name property should return the class name."""
+        transformer = DummyTransformer()
+        assert transformer.name == "DummyTransformer"
+
+    def test_default_hash_is_hash_of_name(self) -> None:
+        """The default hash should be equal to the hash of the transformer's name."""
+        transformer = DummyTransformer()
+        assert hash(transformer) == hash("DummyTransformer")