Prototype keras data loader (#73)

dev-requirements.txt

@@ -1,4 +1,5 @@
 pytest
+tensorflow
 torch
 coverage
 pytest-cov

doc/api.rst

@@ -30,3 +30,6 @@ Dataloaders
 
 .. autoclass:: xbatcher.loaders.torch.IterableDataset
    :members:
+
+.. autoclass:: xbatcher.loaders.keras.CustomTFDataset
+   :members:

doc/conf.py

@@ -70,7 +70,7 @@ def setup(app):
     app.connect('autodoc-skip-member', skip)
 
 
-autodoc_mock_imports = ['torch']
+autodoc_mock_imports = ['torch', 'tensorflow']
 
 # link to github issues
 extlinks = {

setup.cfg

@@ -7,7 +7,7 @@ select = B,C,E,F,W,T4,B9
 
 [isort]
 known_first_party=xbatcher
-known_third_party=numpy,pkg_resources,pytest,setuptools,sphinx_autosummary_accessors,torch,xarray
+known_third_party=numpy,pkg_resources,pytest,setuptools,sphinx_autosummary_accessors,tensorflow,torch,xarray
 multi_line_output=3
 include_trailing_comma=True
 force_grid_wrap=0

xbatcher/loaders/keras.py

@@ -0,0 +1,73 @@
+from typing import Any, Callable, Optional, Tuple
+
+import tensorflow as tf
+import xarray as xr
+
+# Notes:
+# This module includes one Keras dataset, which can be provided to model.fit().
+#  - The CustomTFDataset provides an indexable interface
+# Assumptions made:
+#  - The dataset takes pre-configured X/y xbatcher generators (may not always want two generators in a dataset)
+
+
+class CustomTFDataset(tf.keras.utils.Sequence):
+    def __init__(
+        self,
+        X_generator,
+        y_generator,
+        *,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        dim: str = 'new_dim',
+    ) -> None:
+        '''
+        Keras Dataset adapter for Xbatcher
+
+        Parameters
+        ----------
+        X_generator : xbatcher.BatchGenerator
+        y_generator : xbatcher.BatchGenerator
+        transform : callable, optional
+            A function/transform that takes in an array and returns a transformed version.
+        target_transform : callable, optional
+            A function/transform that takes in the target and transforms it.
+        dim : str, 'new_dim'
+            Name of dim to pass to :func:`xarray.concat` as the dimension
+            to concatenate all variables along.
+        '''
+        self.X_generator = X_generator
+        self.y_generator = y_generator
+        self.transform = transform
+        self.target_transform = target_transform
+        self.concat_dim = dim
+
+    def __len__(self) -> int:
+        return len(self.X_generator)
+
+    def __getitem__(self, idx: int) -> Tuple[Any, Any]:
+        X_batch = tf.convert_to_tensor(
+            xr.concat(
+                (
+                    self.X_generator[idx][key]
+                    for key in list(self.X_generator[idx].keys())
+                ),
+                self.concat_dim,
+            ).data
+        )
+        y_batch = tf.convert_to_tensor(
+            xr.concat(
+                (
+                    self.y_generator[idx][key]
+                    for key in list(self.y_generator[idx].keys())
+                ),
+                self.concat_dim,
+            ).data
+        )
+
+        # TODO: Should the transformations be applied before tensor conversion?
+        if self.transform:
+            X_batch = self.transform(X_batch)
+
+        if self.target_transform:
+            y_batch = self.target_transform(y_batch)
+        return X_batch, y_batch

xbatcher/tests/test_keras_loaders.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pytest
+import xarray as xr
+
+tf = pytest.importorskip('tensorflow')
+
+from xbatcher import BatchGenerator
+from xbatcher.loaders.keras import CustomTFDataset
+
+
+@pytest.fixture(scope='module')
+def ds_xy():
+    n_samples = 100
+    n_features = 5
+    ds = xr.Dataset(
+        {
+            'x': (
+                ['sample', 'feature'],
+                np.random.random((n_samples, n_features)),
+            ),
+            'y': (['sample'], np.random.random(n_samples)),
+        },
+    )
+    return ds
+
+
+def test_custom_dataset(ds_xy):
+
+    x = ds_xy['x']
+    y = ds_xy['y']
+
+    x_gen = BatchGenerator(x, {'sample': 10})
+    y_gen = BatchGenerator(y, {'sample': 10})
+
+    dataset = CustomTFDataset(x_gen, y_gen)
+
+    # test __getitem__
+    x_batch, y_batch = dataset[0]
+    assert len(x_batch) == len(y_batch)
+    assert tf.is_tensor(x_batch)
+    assert tf.is_tensor(y_batch)
+
+    # test __len__
+    assert len(dataset) == len(x_gen)
+
+
+def test_custom_dataset_with_transform(ds_xy):
+
+    x = ds_xy['x']
+    y = ds_xy['y']
+
+    x_gen = BatchGenerator(x, {'sample': 10})
+    y_gen = BatchGenerator(y, {'sample': 10})
+
+    def x_transform(batch):
+        return batch * 0 + 1
+
+    def y_transform(batch):
+        return batch * 0 - 1
+
+    dataset = CustomTFDataset(
+        x_gen, y_gen, transform=x_transform, target_transform=y_transform
+    )
+    x_batch, y_batch = dataset[0]
+    assert len(x_batch) == len(y_batch)
+    assert tf.is_tensor(x_batch)
+    assert tf.is_tensor(y_batch)
+    assert tf.experimental.numpy.all(x_batch == 1)
+    assert tf.experimental.numpy.all(y_batch == -1)