def transform(inputs):
transformed = inputs.copy()
for feature_transform_info in FEATURE_TRANSFORM_INFO_EXECUTE_ARRAY:
if feature_transform_info.op_type == TransformOpType.HASH:
transformed[feature_transform_info.input] = ToSparse()(
transformed[feature_transform_info.input]
)
transformed[feature_transform_info.output] = Hashing(
feature_transform_info.hash_bucket_size
)(transformed[feature_transform_info.input])
elif feature_transform_info.op_type == TransformOpType.BUCKETIZE:
transformed[feature_transform_info.input] = ToSparse()(
transformed[feature_transform_info.input]
)
transformed[feature_transform_info.output] = Discretization(
feature_transform_info.boundaries
)(transformed[feature_transform_info.input])
elif feature_transform_info.op_type == TransformOpType.LOOKUP:
transformed[feature_transform_info.input] = ToSparse()(
transformed[feature_transform_info.input]
)
transformed[feature_transform_info.output] = IndexLookup(
feature_transform_info.vocabulary_list
)(transformed[feature_transform_info.input])
elif feature_transform_info.op_type == TransformOpType.CONCAT:
inputs_to_concat = [
transformed[name] for name in feature_transform_info.input
]
transformed[feature_transform_info.output] = ConcatenateWithOffset(
feature_transform_info.id_offsets
)(inputs_to_concat)
elif feature_transform_info.op_type == TransformOpType.EMBEDDING:
transformed[feature_transform_info.output] = SparseEmbedding(
input_dim=feature_transform_info.input_dim,
output_dim=feature_transform_info.output_dim,
)(transformed[feature_transform_info.input])
elif feature_transform_info.op_type == TransformOpType.ARRAY:
transformed[feature_transform_info.output] = [
transformed[name] for name in feature_transform_info.input
]
return tuple([transformed[name] for name in TRANSFORM_OUTPUTS])
def test_to_sparse(self):
layer = ToSparse()
inp = tf.constant([["A", ""], ["B", "C"]], tf.string)
output = layer.call(inp)
expected_out = tf.SparseTensor(
indices=np.array([[0, 0], [1, 0], [1, 1]]),
values=np.array(["A", "B", "C"]),
dense_shape=(2, 2),
)
self.assertTrue(sparse_tensor_equal(output, expected_out))
layer = ToSparse()
inp = tf.constant([[12, -1], [45, 78]], tf.int64)
output = layer.call(inp)
expected_out = tf.SparseTensor(
indices=np.array([[0, 0], [1, 0], [1, 1]]),
values=np.array([12, 45, 78]),
dense_shape=(2, 2),
)
self.assertTrue(sparse_tensor_equal(output, expected_out))
def test_model_with_to_sparse(self):
inputs = tf.keras.Input(shape=(1, ), dtype=tf.int32)
sparse_inputs = ToSparse(ignore_value=-1)(inputs)
model = tf.keras.Model(inputs=inputs, outputs=sparse_inputs)
out = model.call(tf.constant([[1], [-1], [2], [3]]))
expect_out = tf.SparseTensor(
indices=tf.constant([[0, 0], [2, 0], [3, 0]], dtype=tf.int64),
values=tf.constant([1, 2, 3], dtype=tf.int32),
dense_shape=(4, 1),
)
self.assertTrue(sparse_tensor_equal(out, expect_out))
def transform_from_code_gen(source_inputs):
inputs = source_inputs.copy()
education_hash_out = Hashing(education_hash.hash_bucket_size)(
ToSparse()(inputs["education"])
)
occupation_hash_out = Hashing(occupation_hash.hash_bucket_size)(
ToSparse()(inputs["occupation"])
)
native_country_hash_out = Hashing(native_country_hash.hash_bucket_size)(
ToSparse()(inputs["native_country"])
)
workclass_lookup_out = IndexLookup(workclass_lookup.vocabulary_list)(
ToSparse()(inputs["workclass"])
)
marital_status_lookup_out = IndexLookup(
marital_status_lookup.vocabulary_list
)(ToSparse()(inputs["marital_status"]))
relationship_lookup_out = IndexLookup(relationship_lookup.vocabulary_list)(
ToSparse()(inputs["relationship"])
)
race_lookup_out = IndexLookup(race_lookup.vocabulary_list)(
ToSparse()(inputs["race"])
)
sex_lookup_out = IndexLookup(sex_lookup.vocabulary_list)(
ToSparse()(inputs["sex"])
)
age_bucketize_out = Discretization(age_bucketize.boundaries)(
ToSparse()(inputs["age"])
)
capital_gain_bucketize_out = Discretization(
capital_gain_bucketize.boundaries
)(ToSparse()(inputs["capital_gain"]))
capital_loss_bucketize_out = Discretization(
capital_loss_bucketize.boundaries
)(ToSparse()(inputs["capital_loss"]))
hours_per_week_bucketize_out = Discretization(
hours_per_week_bucketize.boundaries
)(ToSparse()(inputs["hours_per_week"]))
group1_out = ConcatenateWithOffset(group1.id_offsets)(
[
workclass_lookup_out,
hours_per_week_bucketize_out,
capital_gain_bucketize_out,
capital_loss_bucketize_out,
]
)
group2_out = ConcatenateWithOffset(group2.id_offsets)(
[
education_hash_out,
marital_status_lookup_out,
relationship_lookup_out,
occupation_hash_out,
]
)
group3_out = ConcatenateWithOffset(group3.id_offsets)(
[
age_bucketize_out,
sex_lookup_out,
race_lookup_out,
native_country_hash_out,
]
)
group1_embedding_wide_out = SparseEmbedding(
input_dim=group1_embedding_wide.input_dim,
output_dim=group1_embedding_wide.output_dim,
)(group1_out)
group2_embedding_wide_out = SparseEmbedding(
input_dim=group2_embedding_wide.input_dim,
output_dim=group2_embedding_wide.output_dim,
)(group2_out)
group1_embedding_deep_out = SparseEmbedding(
input_dim=group1_embedding_deep.input_dim,
output_dim=group1_embedding_deep.output_dim,
)(group1_out)
group2_embedding_deep_out = SparseEmbedding(
input_dim=group2_embedding_deep.input_dim,
output_dim=group2_embedding_deep.output_dim,
)(group2_out)
group3_embedding_deep_out = SparseEmbedding(
input_dim=group3_embedding_deep.input_dim,
output_dim=group3_embedding_deep.output_dim,
)(group3_out)
wide_embeddings_out = [
group1_embedding_wide_out,
group2_embedding_wide_out,
]
deep_embeddings_out = [
group1_embedding_deep_out,
group2_embedding_deep_out,
group3_embedding_deep_out,
]
return wide_embeddings_out, deep_embeddings_out