fix(datasets): Refactor TensorFlowModelDataset to DataSet

kedro-datasets/RELEASE.md

@@ -7,6 +7,13 @@
 ## Bug fixes and other changes
 * Relaxed `delta-spark` upper bound to allow compatibility with Spark 3.1.x and 3.2.x.
 
+# Release 1.2.1:
+
+## Major features and improvements:
+
+## Bug fixes and other changes
+* Renamed `TensorFlowModelDataset` to `TensorFlowModelDataSet` to be consistent with all other plugins in kedro-datasets.
+
 # Release 1.2.0:
 
 ## Major features and improvements:

kedro-datasets/kedro_datasets/tensorflow/README.md

@@ -1,4 +1,4 @@
-# TensorFlowModelDataset
+# TensorFlowModelDataSet
 
 ``TensorflowModelDataset`` loads and saves TensorFlow models.
 The underlying functionality is supported by, and passes input arguments to TensorFlow 2.X load_model and save_model methods. Only TF2 is currently supported for saving and loading, V1 requires HDF5 and serialises differently.
@@ -8,9 +8,9 @@ The underlying functionality is supported by, and passes input arguments to Tens
 import numpy as np
 import tensorflow as tf
 
-from kedro_datasets.tensorflow import TensorFlowModelDataset
+from kedro_datasets.tensorflow import TensorFlowModelDataSet
 
-data_set = TensorFlowModelDataset("tf_model_dirname")
+data_set = TensorFlowModelDataSet("tf_model_dirname")
 
 model = tf.keras.Model()
 predictions = model.predict([...])
@@ -25,7 +25,7 @@ np.testing.assert_allclose(predictions, new_predictions, rtol=1e-6, atol=1e-6)
 #### Example catalog.yml:
 ```yaml
 example_tensorflow_data:
-  type: tensorflow.TensorFlowModelDataset
+  type: tensorflow.TensorFlowModelDataSet
   filepath: data/08_reporting/tf_model_dirname
   load_args:
     tf_device: "/CPU:0"  # optional

kedro-datasets/kedro_datasets/tensorflow/__init__.py

@@ -1,8 +1,8 @@
 """Provides I/O for TensorFlow Models."""
 
-__all__ = ["TensorFlowModelDataset"]
+__all__ = ["TensorFlowModelDataSet"]
 
 from contextlib import suppress
 
 with suppress(ImportError):
-    from .tensorflow_model_dataset import TensorFlowModelDataset
+    from .tensorflow_model_dataset import TensorFlowModelDataSet

kedro-datasets/kedro_datasets/tensorflow/tensorflow_model_dataset.py

@@ -1,4 +1,4 @@
-"""``TensorflowModelDataset`` is a data set implementation which can save and load
+"""``TensorFlowModelDataSet`` is a data set implementation which can save and load
 TensorFlow models.
 """
 import copy
@@ -19,8 +19,8 @@
 TEMPORARY_H5_FILE = "tmp_tensorflow_model.h5"
 
 
-class TensorFlowModelDataset(AbstractVersionedDataSet[tf.keras.Model, tf.keras.Model]):
-    """``TensorflowModelDataset`` loads and saves TensorFlow models.
+class TensorFlowModelDataSet(AbstractVersionedDataSet[tf.keras.Model, tf.keras.Model]):
+    """``TensorFlowModelDataSet`` loads and saves TensorFlow models.
     The underlying functionality is supported by, and passes input arguments through to,
     TensorFlow 2.X load_model and save_model methods.
 
@@ -31,7 +31,7 @@ class TensorFlowModelDataset(AbstractVersionedDataSet[tf.keras.Model, tf.keras.M
     .. code-block:: yaml
 
         tensorflow_model:
-          type: tensorflow.TensorFlowModelDataset
+          type: tensorflow.TensorFlowModelDataSet
           filepath: data/06_models/tensorflow_model.h5
           load_args:
             compile: False
@@ -45,11 +45,11 @@ class TensorFlowModelDataset(AbstractVersionedDataSet[tf.keras.Model, tf.keras.M
     data_catalog.html#use-the-data-catalog-with-the-code-api>`_:
     ::
 
-        >>> from kedro_datasets.tensorflow import TensorFlowModelDataset
+        >>> from kedro_datasets.tensorflow import TensorFlowModelDataSet
         >>> import tensorflow as tf
         >>> import numpy as np
         >>>
-        >>> data_set = TensorFlowModelDataset("data/06_models/tensorflow_model.h5")
+        >>> data_set = TensorFlowModelDataSet("data/06_models/tensorflow_model.h5")
         >>> model = tf.keras.Model()
         >>> predictions = model.predict([...])
         >>>
@@ -73,7 +73,7 @@ def __init__(
         credentials: Dict[str, Any] = None,
         fs_args: Dict[str, Any] = None,
     ) -> None:
-        """Creates a new instance of ``TensorFlowModelDataset``.
+        """Creates a new instance of ``TensorFlowModelDataSet``.
 
         Args:
             filepath: Filepath in POSIX format to a TensorFlow model directory prefixed with a

kedro-datasets/setup.py

@@ -59,7 +59,7 @@ def _collect_requirements(requires):
 }
 svmlight_require = {"svmlight.SVMLightDataSet": ["scikit-learn~=1.0.2", "scipy~=1.7.3"]}
 tensorflow_require = {
-    "tensorflow.TensorflowModelDataset": [
+    "tensorflow.TensorFlowModelDataSet": [
         # currently only TensorFlow V2 supported for saving and loading.
         # V1 requires HDF5 and serialises differently
         "tensorflow~=2.0; platform_system != 'Darwin' or platform_machine != 'arm64'",

kedro-datasets/tests/tensorflow/test_tensorflow_model_dataset.py

@@ -11,7 +11,7 @@
 from s3fs import S3FileSystem
 
 
-# In this test module, we wrap tensorflow and TensorFlowModelDataset imports into a module-scoped
+# In this test module, we wrap tensorflow and TensorFlowModelDataSet imports into a module-scoped
 # fixtures to avoid them being evaluated immediately when a new test process is spawned.
 # Specifically:
 #   - ParallelRunner spawns a new subprocess.
@@ -34,9 +34,9 @@ def tf():
 
 @pytest.fixture(scope="module")
 def tensorflow_model_dataset():
-    from kedro_datasets.tensorflow import TensorFlowModelDataset
+    from kedro_datasets.tensorflow import TensorFlowModelDataSet
 
-    return TensorFlowModelDataset
+    return TensorFlowModelDataSet
 
 
 @pytest.fixture
@@ -134,7 +134,7 @@ def call(self, inputs, training=None, mask=None):  # pragma: no cover
     return model
 
 
-class TestTensorFlowModelDataset:
+class TestTensorFlowModelDataSet:
     """No versioning passed to creator"""
 
     def test_save_and_load(self, tf_model_dataset, dummy_tf_base_model, dummy_x_test):
@@ -152,7 +152,7 @@ def test_save_and_load(self, tf_model_dataset, dummy_tf_base_model, dummy_x_test
     def test_load_missing_model(self, tf_model_dataset):
         """Test error message when trying to load missing model."""
         pattern = (
-            r"Failed while loading data from data set TensorFlowModelDataset\(.*\)"
+            r"Failed while loading data from data set TensorFlowModelDataSet\(.*\)"
         )
         with pytest.raises(DataSetError, match=pattern):
             tf_model_dataset.load()
@@ -166,7 +166,7 @@ def test_exists(self, tf_model_dataset, dummy_tf_base_model):
     def test_hdf5_save_format(
         self, dummy_tf_base_model, dummy_x_test, filepath, tensorflow_model_dataset
     ):
-        """Test TensorflowModelDataset can save TF graph models in HDF5 format"""
+        """Test TensorFlowModelDataSet can save TF graph models in HDF5 format"""
         hdf5_dataset = tensorflow_model_dataset(
             filepath=filepath, save_args={"save_format": "h5"}
         )
@@ -187,7 +187,7 @@ def test_unused_subclass_model_hdf5_save_format(
         filepath,
         tensorflow_model_dataset,
     ):
-        """Test TensorflowModelDataset cannot save subclassed user models in HDF5 format
+        """Test TensorFlowModelDataSet cannot save subclassed user models in HDF5 format
 
         Subclassed model
 
@@ -277,8 +277,8 @@ def test_save_and_overwrite_existing_model(
         assert len(dummy_tf_base_model_new.layers) == len(reloaded.layers)
 
 
-class TestTensorFlowModelDatasetVersioned:
-    """Test suite with versioning argument passed into TensorFlowModelDataset creator"""
+class TestTensorFlowModelDataSetVersioned:
+    """Test suite with versioning argument passed into TensorFlowModelDataSet creator"""
 
     @pytest.mark.parametrize(
         "load_version,save_version",
@@ -320,7 +320,7 @@ def test_hdf5_save_format(
         load_version,
         save_version,
     ):
-        """Test versioned TensorflowModelDataset can save TF graph models in
+        """Test versioned TensorFlowModelDataSet can save TF graph models in
         HDF5 format"""
         hdf5_dataset = tensorflow_model_dataset(
             filepath=filepath,
@@ -340,7 +340,7 @@ def test_prevent_overwrite(self, dummy_tf_base_model, versioned_tf_model_dataset
         corresponding file for a given save version already exists."""
         versioned_tf_model_dataset.save(dummy_tf_base_model)
         pattern = (
-            r"Save path \'.+\' for TensorFlowModelDataset\(.+\) must "
+            r"Save path \'.+\' for TensorFlowModelDataSet\(.+\) must "
             r"not exist if versioning is enabled\."
         )
         with pytest.raises(DataSetError, match=pattern):
@@ -362,7 +362,7 @@ def test_save_version_warning(
         the subsequent load path."""
         pattern = (
             rf"Save version '{save_version}' did not match load version '{load_version}' "
-            rf"for TensorFlowModelDataset\(.+\)"
+            rf"for TensorFlowModelDataSet\(.+\)"
         )
         with pytest.warns(UserWarning, match=pattern):
             versioned_tf_model_dataset.save(dummy_tf_base_model)
@@ -383,7 +383,7 @@ def test_exists(self, versioned_tf_model_dataset, dummy_tf_base_model):
 
     def test_no_versions(self, versioned_tf_model_dataset):
         """Check the error if no versions are available for load."""
-        pattern = r"Did not find any versions for TensorFlowModelDataset\(.+\)"
+        pattern = r"Did not find any versions for TensorFlowModelDataSet\(.+\)"
         with pytest.raises(DataSetError, match=pattern):
             versioned_tf_model_dataset.load()
 
@@ -408,7 +408,7 @@ def test_versioning_existing_dataset(
         self, tf_model_dataset, versioned_tf_model_dataset, dummy_tf_base_model
     ):
         """Check behavior when attempting to save a versioned dataset on top of an
-        already existing (non-versioned) dataset. Note: because TensorFlowModelDataset
+        already existing (non-versioned) dataset. Note: because TensorFlowModelDataSet
         saves to a directory even if non-versioned, an error is not expected."""
         tf_model_dataset.save(dummy_tf_base_model)
         assert tf_model_dataset.exists()
@@ -425,7 +425,7 @@ def test_save_and_load_with_device(
         load_version,
         save_version,
     ):
-        """Test versioned TensorflowModelDataset can load models using an explicit tf_device"""
+        """Test versioned TensorFlowModelDataSet can load models using an explicit tf_device"""
         hdf5_dataset = tensorflow_model_dataset(
             filepath=filepath,
             load_args={"tf_device": "/CPU:0"},