column_categorical_with_hash_bucket: Represents Sparse Feature where IDs are set by Hashing

In tfestimators: Interface to 'TensorFlow' Estimators

Represents Sparse Feature where IDs are set by Hashing

Description

Use this when your sparse features are in string or integer format, and you want to distribute your inputs into a finite number of buckets by hashing. output_id = Hash(input_feature_string) % bucket_size For input dictionary features, ⁠features$key$⁠ is either tensor or sparse tensor object. If it's tensor object, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

Usage

column_categorical_with_hash_bucket(..., hash_bucket_size, dtype = tf$string)

Arguments

...	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
hash_bucket_size	An int > 1. The number of buckets.
dtype	The type of features. Only string and integer types are supported.

Value

A ⁠_HashedCategoricalColumn⁠.

Raises

• ValueError: hash_bucket_size is not greater than 1.

• ValueError: dtype is neither string nor integer.

See Also

Other feature column constructors: column_bucketized(), column_categorical_weighted(), column_categorical_with_identity(), column_categorical_with_vocabulary_file(), column_categorical_with_vocabulary_list(), column_crossed(), column_embedding(), column_numeric(), input_layer()

tfestimators documentation built on Aug. 19, 2025, 1:15 a.m.