TensorFlow 1 version | View source on GitHub |
Returns a column for performing crosses of categorical features.
tf.feature_column.crossed_column(
keys, hash_bucket_size, hash_key=None
)
Crossed features will be hashed according to hash_bucket_size
. Conceptually,
the transformation can be thought of as:
Hash(cartesian product of features) % hash_bucket_size
For example, if the input features are:
- SparseTensor referred by first key:
shape = [2, 2] { [0, 0]: "a" [1, 0]: "b" [1, 1]: "c" }
- SparseTensor referred by second key:
shape = [2, 1] { [0, 0]: "d" [1, 0]: "e" }
then crossed feature will look like:
shape = [2, 2]
{
[0, 0]: Hash64("d", Hash64("a")) % hash_bucket_size
[1, 0]: Hash64("e", Hash64("b")) % hash_bucket_size
[1, 1]: Hash64("e", Hash64("c")) % hash_bucket_size
}
Here is an example to create a linear model with crosses of string features:
keywords_x_doc_terms = crossed_column(['keywords', 'doc_terms'], 50K)
columns = [keywords_x_doc_terms, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
You could also use vocabulary lookup before crossing:
keywords = categorical_column_with_vocabulary_file(
'keywords', '/path/to/vocabulary/file', vocabulary_size=1K)
keywords_x_doc_terms = crossed_column([keywords, 'doc_terms'], 50K)
columns = [keywords_x_doc_terms, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
If an input feature is of numeric type, you can use
categorical_column_with_identity
, or bucketized_column
, as in the example:
# vertical_id is an integer categorical feature.
vertical_id = categorical_column_with_identity('vertical_id', 10K)
price = numeric_column('price')
# bucketized_column converts numerical feature to a categorical one.
bucketized_price = bucketized_column(price, boundaries=[...])
vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
columns = [vertical_id_x_price, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
To use crossed column in DNN model, you need to add it in an embedding column as in this example:
vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
vertical_id_x_price_embedded = embedding_column(vertical_id_x_price, 10)
dense_tensor = input_layer(features, [vertical_id_x_price_embedded, ...])
Args | |
---|---|
keys
|
An iterable identifying the features to be crossed. Each element can
be either:
|
hash_bucket_size
|
An int > 1. The number of buckets. |
hash_key
|
Specify the hash_key that will be used by the FingerprintCat64
function to combine the crosses fingerprints on SparseCrossOp (optional).
|
Returns | |
---|---|
A CrossedColumn .
|
Raises | |
---|---|
ValueError
|
If len(keys) < 2 .
|
ValueError
|
If any of the keys is neither a string nor CategoricalColumn .
|
ValueError
|
If any of the keys is HashedCategoricalColumn .
|
ValueError
|
If hash_bucket_size < 1 .
|