diff --git a/.github/workflows/UTESTS.yml b/.github/workflows/UTESTS.yml index 556844b..0e108b3 100644 --- a/.github/workflows/UTESTS.yml +++ b/.github/workflows/UTESTS.yml @@ -71,7 +71,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.9", "3.10", "3.11"] + python-version: ["3.10", "3.11", "3.12"] test-group: ["unit", "integration", "layers"] include: - python-version: "3.11" @@ -87,7 +87,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: Cache Poetry dependencies - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: | ~/.cache/pypoetry @@ -124,7 +124,7 @@ jobs: timeout-minutes: 8 - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: test-results-${{ matrix.python-version }}-${{ matrix.test-group }} @@ -157,7 +157,7 @@ jobs: timeout-minutes: 10 - name: Upload coverage to Codecov - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 with: file: ./coverage.xml flags: unittests @@ -165,7 +165,7 @@ jobs: fail_ci_if_error: false - name: Upload coverage report - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: coverage-report path: htmlcov/ @@ -194,7 +194,7 @@ jobs: timeout-minutes: 10 - name: Upload benchmark results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: benchmark-results diff --git a/kdp/layers/preserve_dtype.py b/kdp/layers/preserve_dtype.py new file mode 100644 index 0000000..517c72a --- /dev/null +++ b/kdp/layers/preserve_dtype.py @@ -0,0 +1,57 @@ +import tensorflow as tf +from tensorflow import keras + + +@tf.keras.utils.register_keras_serializable(package="kdp.layers") +class PreserveDtypeLayer(keras.layers.Layer): + """Custom Keras layer that preserves the original dtype of input tensors. + + This is useful for passthrough features where we want to maintain the original + data type without any casting. + """ + + def __init__(self, target_dtype=None, **kwargs): + """Initialize the layer. + + Args: + target_dtype: Optional target dtype to cast to. If None, preserves original dtype. + **kwargs: Additional keyword arguments + """ + super().__init__(**kwargs) + self.target_dtype = target_dtype + + def call(self, inputs, **kwargs): + """Process the input tensor, optionally casting to target_dtype. + + Args: + inputs: Input tensor of any dtype + **kwargs: Additional keyword arguments + + Returns: + Tensor with preserved or target dtype + """ + if self.target_dtype is not None: + return tf.cast(inputs, self.target_dtype) + return inputs + + def get_config(self): + """Return the config dictionary for serialization. + + Returns: + A dictionary with the layer configuration + """ + config = super().get_config() + config.update({"target_dtype": self.target_dtype}) + return config + + @classmethod + def from_config(cls, config): + """Create a new instance from the serialized configuration. + + Args: + config: Layer configuration dictionary + + Returns: + A new instance of the layer + """ + return cls(**config) diff --git a/kdp/layers_factory.py b/kdp/layers_factory.py index d6902b0..8312b1d 100644 --- a/kdp/layers_factory.py +++ b/kdp/layers_factory.py @@ -9,6 +9,7 @@ from kdp.layers.text_preprocessing_layer import TextPreprocessingLayer from kdp.layers.cast_to_float import CastToFloat32Layer +from kdp.layers.preserve_dtype import PreserveDtypeLayer from kdp.layers.date_parsing_layer import DateParsingLayer from kdp.layers.date_encoding_layer import DateEncodingLayer from kdp.layers.season_layer import SeasonLayer @@ -183,6 +184,27 @@ def cast_to_float32_layer( **kwargs, ) + @staticmethod + def preserve_dtype_layer( + name: str = "preserve_dtype", target_dtype=None, **kwargs: dict + ) -> tf.keras.layers.Layer: + """Create a PreserveDtypeLayer layer. + + Args: + name: The name of the layer. + target_dtype: Optional target dtype to cast to. If None, preserves original dtype. + **kwargs: Additional keyword arguments to pass to the layer constructor. + + Returns: + An instance of the PreserveDtypeLayer layer. + """ + return PreprocessorLayerFactory.create_layer( + layer_class=PreserveDtypeLayer, + name=name, + target_dtype=target_dtype, + **kwargs, + ) + @staticmethod def date_parsing_layer( name: str = "date_parsing_layer", **kwargs: dict diff --git a/kdp/processor.py b/kdp/processor.py index dacc49a..27dd444 100644 --- a/kdp/processor.py +++ b/kdp/processor.py @@ -1126,6 +1126,15 @@ def _add_categorical_lookup( if feature.category_encoding == CategoryEncodingOptions.HASHING: return + # Handle empty vocabulary by providing a fallback + if not vocab: + logger.warning( + f"Empty vocabulary for categorical feature '{feature_name}'. " + "Using fallback vocabulary with placeholder values." + ) + # Provide a minimal vocabulary with unknown/placeholder values + vocab = [""] + # Default behavior if no specific preprocessing is defined if feature.feature_type == FeatureType.STRING_CATEGORICAL: preprocessor.add_processing_step( @@ -1414,10 +1423,12 @@ def _add_pipeline_passthrough(self, feature_name: str, input_layer) -> None: feature_name=feature_name, ) else: - # For passthrough features, we only ensure type consistency by casting to float32 + # For passthrough features, preserve the original dtype or cast to specified dtype + target_dtype = getattr(_feature, "dtype", None) preprocessor.add_processing_step( - layer_creator=PreprocessorLayerFactory.cast_to_float32_layer, - name=f"cast_to_float_{feature_name}", + layer_creator=PreprocessorLayerFactory.preserve_dtype_layer, + name=f"preserve_dtype_{feature_name}", + target_dtype=target_dtype, ) # Optionally reshape if needed diff --git a/poetry.lock b/poetry.lock index 0ba680b..a8d0025 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,15 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. 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Markdown parsing, done right!" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, @@ -1032,6 +1189,7 @@ version = "3.0.2" description = "Safely add untrusted strings to HTML/XML markup." optional = false python-versions = ">=3.9" +groups = ["main", "dev", "doc"] files = [ {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8"}, {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"}, @@ -1102,6 +1260,7 @@ version = "0.1.7" description = "Inline Matplotlib backend for Jupyter" optional = false python-versions = ">=3.8" +groups = ["dev"] 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"jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.9,<4.0" content-hash = "eb72db6a5fd7df7e7cd7cf1352afad181bb430643227aedc38ff50fc0e89f852" diff --git a/pytest.ini b/pytest.ini index eaf4a69..1e35dc1 100644 --- a/pytest.ini +++ b/pytest.ini @@ -24,6 +24,7 @@ markers = integration: Integration tests that may take longer slow: Tests that take a long time to run fast: Tests that run very quickly + micro: Micro tests (fastest) time_series: Time series specific tests layers: Layer-specific tests processor: Processor-specific tests diff --git a/test/layers/test_layer_factory.py b/test/layers/test_layer_factory.py index 64d905c..de02493 100644 --- a/test/layers/test_layer_factory.py +++ b/test/layers/test_layer_factory.py @@ -1,6 +1,7 @@ import unittest import numpy as np import tensorflow as tf +import pytest from kdp.layers_factory import PreprocessorLayerFactory from kdp.layers.distribution_aware_encoder_layer import ( @@ -10,6 +11,7 @@ from kdp.layers.distribution_transform_layer import DistributionTransformLayer from kdp.layers.text_preprocessing_layer import TextPreprocessingLayer from kdp.layers.cast_to_float import CastToFloat32Layer +from kdp.layers.preserve_dtype import PreserveDtypeLayer from kdp.layers.date_parsing_layer import DateParsingLayer from kdp.layers.date_encoding_layer import DateEncodingLayer from kdp.layers.season_layer import SeasonLayer @@ -25,6 +27,10 @@ from kdp.layers.gated_residual_network_layer import GatedResidualNetwork +@pytest.mark.layers +@pytest.mark.unit +@pytest.mark.fast +@pytest.mark.micro class TestPreprocessorLayerFactory(unittest.TestCase): def setUp(self): # Set seeds for reproducibility @@ -142,6 +148,28 @@ def test_cast_to_float32_layer(self): output = cast_layer(int_data) self.assertEqual(output.dtype, tf.float32) + def test_preserve_dtype_layer(self): + # Test preserving original dtype + preserve_layer = PreprocessorLayerFactory.preserve_dtype_layer( + name="preserve_layer" + ) + self.assertIsInstance(preserve_layer, PreserveDtypeLayer) + + # Test with integer data - should preserve int32 + int_data = np.array([[1], [2], [3]], dtype=np.int32) + output = preserve_layer(int_data) + self.assertEqual(output.dtype, tf.int32) + + # Test with target dtype + cast_layer = PreprocessorLayerFactory.preserve_dtype_layer( + name="cast_layer", target_dtype=tf.float32 + ) + self.assertIsInstance(cast_layer, PreserveDtypeLayer) + + # Test casting to float32 + output = cast_layer(int_data) + self.assertEqual(output.dtype, tf.float32) + def test_date_parsing_layer(self): date_parser = PreprocessorLayerFactory.date_parsing_layer( name="date_parser", date_format="YYYY-MM-DD" diff --git a/test/layers/test_preserve_dtype_layer.py b/test/layers/test_preserve_dtype_layer.py new file mode 100644 index 0000000..8df2939 --- /dev/null +++ b/test/layers/test_preserve_dtype_layer.py @@ -0,0 +1,120 @@ +import unittest +import numpy as np +import tensorflow as tf +import pytest + +from kdp.layers.preserve_dtype import PreserveDtypeLayer + + +@pytest.mark.layers +@pytest.mark.unit +@pytest.mark.fast +@pytest.mark.micro +class TestPreserveDtypeLayer(unittest.TestCase): + """Test cases for PreserveDtypeLayer.""" + + def test_preserve_original_dtype(self): + """Test that the layer preserves original dtype when target_dtype is None.""" + layer = PreserveDtypeLayer() + + # Test with string input + string_input = tf.constant(["hello", "world"]) + output = layer(string_input) + self.assertEqual(output.dtype, tf.string) + np.testing.assert_array_equal(output.numpy(), string_input.numpy()) + + # Test with int input + int_input = tf.constant([1, 2, 3]) + output = layer(int_input) + self.assertEqual(output.dtype, tf.int32) + np.testing.assert_array_equal(output.numpy(), int_input.numpy()) + + # Test with float input + float_input = tf.constant([1.5, 2.7, 3.9]) + output = layer(float_input) + self.assertEqual(output.dtype, tf.float32) + np.testing.assert_array_equal(output.numpy(), float_input.numpy()) + + def test_cast_to_target_dtype(self): + """Test that the layer casts to target_dtype when specified.""" + # Test casting float to int + layer = PreserveDtypeLayer(target_dtype=tf.int32) + float_input = tf.constant([1.5, 2.7, 3.9]) + output = layer(float_input) + self.assertEqual(output.dtype, tf.int32) + np.testing.assert_array_equal(output.numpy(), [1, 2, 3]) + + # Test casting int to float + layer = PreserveDtypeLayer(target_dtype=tf.float32) + int_input = tf.constant([1, 2, 3]) + output = layer(int_input) + self.assertEqual(output.dtype, tf.float32) + np.testing.assert_array_equal(output.numpy(), [1.0, 2.0, 3.0]) + + # Test casting to float64 + layer = PreserveDtypeLayer(target_dtype=tf.float64) + float_input = tf.constant([1.5, 2.7, 3.9]) + output = layer(float_input) + self.assertEqual(output.dtype, tf.float64) + np.testing.assert_array_almost_equal(output.numpy(), [1.5, 2.7, 3.9]) + + def test_batch_processing(self): + """Test that the layer works correctly with batched inputs.""" + layer = PreserveDtypeLayer() + + # Test with 2D input + batch_input = tf.constant([[1, 2], [3, 4], [5, 6]]) + output = layer(batch_input) + self.assertEqual(output.dtype, tf.int32) + self.assertEqual(output.shape, (3, 2)) + np.testing.assert_array_equal(output.numpy(), batch_input.numpy()) + + # Test with 3D input + layer = PreserveDtypeLayer(target_dtype=tf.float32) + batch_input = tf.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) + output = layer(batch_input) + self.assertEqual(output.dtype, tf.float32) + self.assertEqual(output.shape, (2, 2, 2)) + + def test_serialization(self): + """Test that the layer can be serialized and deserialized.""" + layer = PreserveDtypeLayer(target_dtype=tf.int32, name="test_layer") + + # Serialize + config = layer.get_config() + + # Deserialize + new_layer = PreserveDtypeLayer.from_config(config) + + # Test that they behave the same + input_tensor = tf.constant([1.5, 2.7, 3.9]) + original_output = layer(input_tensor) + new_output = new_layer(input_tensor) + + self.assertEqual(original_output.dtype, new_output.dtype) + np.testing.assert_array_equal(original_output.numpy(), new_output.numpy()) + self.assertEqual(layer.name, new_layer.name) + self.assertEqual(layer.target_dtype, new_layer.target_dtype) + + def test_model_integration(self): + """Test that the layer works correctly within a Keras model.""" + layer = PreserveDtypeLayer(target_dtype=tf.float32) + + # Create a simple model + inputs = tf.keras.Input(shape=(3,), dtype=tf.int32) + outputs = layer(inputs) + model = tf.keras.Model(inputs=inputs, outputs=outputs) + + # Test the model + test_input = tf.constant([[1, 2, 3], [4, 5, 6]]) + output = model(test_input) + + self.assertEqual(output.dtype, tf.float32) + self.assertEqual(output.shape, (2, 3)) + np.testing.assert_array_equal( + output.numpy(), [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]] + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/test_processor.py b/test/test_processor.py index 15f1097..7651a3b 100644 --- a/test/test_processor.py +++ b/test/test_processor.py @@ -4,6 +4,7 @@ import numpy as np import pandas as pd +import pytest import tensorflow as tf from kdp.layers.distribution_aware_encoder_layer import DistributionType @@ -119,6 +120,8 @@ class instances (NumericalFeature, CategoricalFeature, TextFeature, DateFeature) return pd.DataFrame(data) +@pytest.mark.unit +@pytest.mark.fast class TestFeatureSpaceConverter(unittest.TestCase): def setUp(self): """Setup test case environment.""" @@ -322,6 +325,8 @@ def test_init_features_specs_extended(self): self.assertEqual(feat9_instance.feature_type, FeatureType.DATE) +@pytest.mark.processor +@pytest.mark.integration class TestPreprocessingModel(unittest.TestCase): @classmethod def setUpClass(cls): @@ -1628,6 +1633,8 @@ def test_preprocessor_all_features_with_distribution_types(self): ) # Periodic features add 2 additional dimensions (sin and cos) +@pytest.mark.processor +@pytest.mark.integration class TestPreprocessingModel_Combinations(unittest.TestCase): @classmethod def setUpClass(cls): @@ -1801,6 +1808,7 @@ def test_preprocessor_parameter_combinations(self): ) # (batch_size, feature_dim) # You can add more specific checks for each feature if needed + @pytest.mark.micro def test_preprocessor_with_passthrough_feature(self): """Test preprocessor with a passthrough feature.""" # Create features specs with a passthrough feature @@ -1857,7 +1865,197 @@ def test_preprocessor_with_passthrough_feature(self): outputs["raw_feature"].numpy(), test_data["raw_feature"].numpy() ) + def test_passthrough_feature_preserves_string_dtype(self): + """Test that passthrough features with string dtype preserve their original type.""" + # Create features specs with a string passthrough feature + features = { + "string_feature": PassthroughFeature( + name="string_feature", + feature_type=FeatureType.PASSTHROUGH, + dtype=tf.string, + ), + } + + # Generate and save fake data + df = generate_fake_data(features, num_rows=20) + df.to_csv(self._path_data, index=False) + + # Create preprocessor with string passthrough feature + ppr = PreprocessingModel( + path_data=str(self._path_data), + features_specs=features, + features_stats_path=self.features_stats_path, + overwrite_stats=True, + output_mode=OutputModeOptions.DICT, + ) + + result = ppr.build_preprocessor() + + # Create test data with string values + test_data = { + "string_feature": tf.constant([["hello"], ["world"], ["test"]]), + } + + # Run prediction + outputs = result["model"](test_data) + + # Check that the output is still string type + self.assertEqual(outputs["string_feature"].dtype, tf.string) + + # Verify that the string values are unchanged + np.testing.assert_array_equal( + outputs["string_feature"].numpy(), test_data["string_feature"].numpy() + ) + + def test_passthrough_feature_preserves_int_dtype(self): + """Test that passthrough features with int dtype preserve their original type.""" + # Create features specs with an int passthrough feature + features = { + "int_feature": PassthroughFeature( + name="int_feature", + feature_type=FeatureType.PASSTHROUGH, + dtype=tf.int32, + ), + } + + # Generate and save fake data + df = generate_fake_data(features, num_rows=20) + df.to_csv(self._path_data, index=False) + + # Create preprocessor with int passthrough feature + ppr = PreprocessingModel( + path_data=str(self._path_data), + features_specs=features, + features_stats_path=self.features_stats_path, + overwrite_stats=True, + output_mode=OutputModeOptions.DICT, + ) + + result = ppr.build_preprocessor() + + # Create test data with int values + test_data = { + "int_feature": tf.constant([[1], [2], [3]]), + } + + # Run prediction + outputs = result["model"](test_data) + + # Check that the output is still int32 type + self.assertEqual(outputs["int_feature"].dtype, tf.int32) + + # Verify that the int values are unchanged + np.testing.assert_array_equal( + outputs["int_feature"].numpy(), test_data["int_feature"].numpy() + ) + + def test_passthrough_feature_preserves_float_dtype(self): + """Test that passthrough features with float dtype preserve their original type.""" + # Create features specs with a float passthrough feature + features = { + "float_feature": PassthroughFeature( + name="float_feature", + feature_type=FeatureType.PASSTHROUGH, + dtype=tf.float64, + ), + } + + # Generate and save fake data + df = generate_fake_data(features, num_rows=20) + df.to_csv(self._path_data, index=False) + + # Create preprocessor with float passthrough feature + ppr = PreprocessingModel( + path_data=str(self._path_data), + features_specs=features, + features_stats_path=self.features_stats_path, + overwrite_stats=True, + output_mode=OutputModeOptions.DICT, + ) + + result = ppr.build_preprocessor() + + # Create test data with float64 values + test_data = { + "float_feature": tf.constant([[1.5], [2.7], [3.9]], dtype=tf.float64), + } + + # Run prediction + outputs = result["model"](test_data) + + # Check that the output is still float64 type + self.assertEqual(outputs["float_feature"].dtype, tf.float64) + + # Verify that the float values are unchanged + np.testing.assert_array_almost_equal( + outputs["float_feature"].numpy(), test_data["float_feature"].numpy() + ) + + def test_passthrough_feature_mixed_types(self): + """Test that multiple passthrough features with different dtypes preserve their types.""" + # Create features specs with mixed dtype passthrough features + features = { + "string_feature": PassthroughFeature( + name="string_feature", + feature_type=FeatureType.PASSTHROUGH, + dtype=tf.string, + ), + "int_feature": PassthroughFeature( + name="int_feature", + feature_type=FeatureType.PASSTHROUGH, + dtype=tf.int32, + ), + "float_feature": PassthroughFeature( + name="float_feature", + feature_type=FeatureType.PASSTHROUGH, + dtype=tf.float32, + ), + } + + # Generate and save fake data + df = generate_fake_data(features, num_rows=20) + df.to_csv(self._path_data, index=False) + + # Create preprocessor with mixed passthrough features + ppr = PreprocessingModel( + path_data=str(self._path_data), + features_specs=features, + features_stats_path=self.features_stats_path, + overwrite_stats=True, + output_mode=OutputModeOptions.DICT, + ) + + result = ppr.build_preprocessor() + + # Create test data with mixed types + test_data = { + "string_feature": tf.constant([["hello"], ["world"]]), + "int_feature": tf.constant([[1], [2]]), + "float_feature": tf.constant([[1.5], [2.7]]), + } + + # Run prediction + outputs = result["model"](test_data) + + # Check that each output preserves its original dtype + self.assertEqual(outputs["string_feature"].dtype, tf.string) + self.assertEqual(outputs["int_feature"].dtype, tf.int32) + self.assertEqual(outputs["float_feature"].dtype, tf.float32) + + # Verify that all values are unchanged + np.testing.assert_array_equal( + outputs["string_feature"].numpy(), test_data["string_feature"].numpy() + ) + np.testing.assert_array_equal( + outputs["int_feature"].numpy(), test_data["int_feature"].numpy() + ) + np.testing.assert_array_almost_equal( + outputs["float_feature"].numpy(), test_data["float_feature"].numpy() + ) + +@pytest.mark.processor +@pytest.mark.integration class TestPreprocessingModel_åNumericalEmbedding(unittest.TestCase): @classmethod def setUpClass(cls): @@ -2067,6 +2265,8 @@ def test_advanced_embedding_config_preservation(self): ) +@pytest.mark.processor +@pytest.mark.integration class TestPreprocessingModel_GlobalNumericalEmbedding(unittest.TestCase): @classmethod def setUpClass(cls): @@ -2534,6 +2734,8 @@ def test_batch_predict_parallel_functionality(self): next(invalid_ppr.batch_predict(dataset)) +@pytest.mark.processor +@pytest.mark.integration class TestPreprocessingModel_FeatureMoE(unittest.TestCase): @classmethod def setUpClass(cls):