def testAccessFeaturesDict(self):
sparse = tf.SparseTensor(
indices=[[0, 0], [1, 1]],
values=['apple', 'banana'],
dense_shape=[2, 2])
dense = tf.constant([1.0, 2.0])
bad_dense = tf.constant([[1.0, 2.0], [3.0, 4.0]])
squeeze_needed = tf.constant([[2.0]])
sess = tf.Session()
sparse_value, dense_value, bad_dense_value, squeeze_needed_value = sess.run(
fetches=[sparse, dense, bad_dense, squeeze_needed])
features_dict = {
'sparse': {
encoding.NODE_SUFFIX: sparse_value
},
'dense': {
encoding.NODE_SUFFIX: dense_value
},
'squeeze_needed': {
encoding.NODE_SUFFIX: squeeze_needed_value
},
'bad_dense': {
encoding.NODE_SUFFIX: bad_dense_value
},
}
accessor = slice_accessor.SliceAccessor(features_dict)
self.assertEqual(['apple', 'banana'], list(accessor.get('sparse')))
self.assertEqual([1.0, 2.0], list(accessor.get('dense')))
self.assertEqual([2.0], list(accessor.get('squeeze_needed')))
with self.assertRaises(ValueError):
accessor.get('bad_dense')
with self.assertRaises(KeyError):
accessor.get('no_such_key')
def testAccessFeaturesDict(self, feature_value, slice_value):
accessor = slice_accessor.SliceAccessor([{'feature': feature_value}])
self.assertEqual(slice_value, accessor.get('feature'))
# Test with multiple dicts and duplicate values
accessor = slice_accessor.SliceAccessor([{
'feature': feature_value
}, {
'feature': feature_value
}])
self.assertEqual(slice_value, accessor.get('feature'))
# Test with default features dict
accessor = slice_accessor.SliceAccessor(
[{
'unmatched_feature': feature_value
}],
default_features_dict={'feature': feature_value})
self.assertEqual(slice_value, accessor.get('feature'))
def get_slices_for_features_dict(features_dict, slice_spec):
"""Generates the slice keys appropriate for the given features dictionary.
Args:
features_dict: Features dictionary.
slice_spec: slice specification.
Yields:
Slice keys appropriate for the given features dictionary.
"""
accessor = slice_accessor.SliceAccessor(features_dict)
for single_slice_spec in slice_spec:
for slice_key in single_slice_spec.generate_slices(accessor):
yield slice_key
def get_slices_for_features_dict(features_dict: types.DictOfFetchedTensorValues,
slice_spec: List[SingleSliceSpec]
) -> Iterable[SliceKeyType]:
"""Generates the slice keys appropriate for the given features dictionary.
Args:
features_dict: Features dictionary.
slice_spec: slice specification.
Yields:
Slice keys appropriate for the given features dictionary.
"""
accessor = slice_accessor.SliceAccessor(features_dict)
for single_slice_spec in slice_spec:
for slice_key in single_slice_spec.generate_slices(accessor):
yield slice_key
def get_slices_for_features_dicts(
features_dicts: Iterable[Union[types.DictOfTensorValue,
types.DictOfFetchedTensorValues]],
default_features_dict: Union[types.DictOfTensorValue,
types.DictOfFetchedTensorValues],
slice_spec: List[SingleSliceSpec]) -> Iterable[SliceKeyType]:
"""Generates the slice keys appropriate for the given features dictionaries.
Args:
features_dicts: Features dictionaries. For example a list of transformed
features dictionaries.
default_features_dict: Additional dict to search if a match is not found in
features dictionaries. For example the raw features.
slice_spec: slice specification.
Yields:
Slice keys appropriate for the given features dictionaries.
"""
accessor = slice_accessor.SliceAccessor(features_dicts, default_features_dict)
for single_slice_spec in slice_spec:
for slice_key in single_slice_spec.generate_slices(accessor):
yield slice_key
def testLegacyAccessFeaturesDict(self):
with tf.compat.v1.Session() as sess:
sparse = tf.SparseTensor(indices=[[0, 0], [1, 1]],
values=['apple', 'banana'],
dense_shape=[2, 2])
dense = tf.constant([1.0, 2.0])
dense_single = tf.constant([7.0])
dense_multidim = tf.constant([[1.0, 2.0], [3.0, 4.0]])
squeeze_needed = tf.constant([[2.0]])
(sparse_value, dense_value, dense_single_value,
dense_multidim_value, squeeze_needed_value) = sess.run(fetches=[
sparse, dense, dense_single, dense_multidim, squeeze_needed
])
features_dict = {
'sparse': {
encoding.NODE_SUFFIX: sparse_value
},
'dense': {
encoding.NODE_SUFFIX: dense_value
},
'dense_single': {
encoding.NODE_SUFFIX: dense_single_value
},
'squeeze_needed': {
encoding.NODE_SUFFIX: squeeze_needed_value
},
'dense_multidim': {
encoding.NODE_SUFFIX: dense_multidim_value
},
}
accessor = slice_accessor.SliceAccessor(features_dict)
self.assertEqual([b'apple', b'banana'], accessor.get('sparse'))
self.assertEqual([1.0, 2.0], accessor.get('dense'))
self.assertEqual([7.0], accessor.get('dense_single'))
self.assertEqual([1.0, 2.0, 3.0, 4.0],
accessor.get('dense_multidim'))
self.assertEqual([2.0], accessor.get('squeeze_needed'))
def testAccessFeaturesDict(self, feature_value, slice_value):
accessor = slice_accessor.SliceAccessor({'feature': feature_value})
self.assertEqual(slice_value, accessor.get('feature'))
def testRaisesKeyError(self):
accessor = slice_accessor.SliceAccessor({})
with self.assertRaises(KeyError):
accessor.get('no_such_key')
