Construct a Categorical Column that Returns Identity Values
Use this when your inputs are integers in the range [0, num_buckets)
, and
you want to use the input value itself as the categorical ID. Values outside
this range will result in default_value
if specified, otherwise it will
fail.
column_categorical_with_identity(..., num_buckets, default_value = NULL)
... |
Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns. |
num_buckets |
Number of unique values. |
default_value |
If |
Typically, this is used for contiguous ranges of integer indexes, but it
doesn't have to be. This might be inefficient, however, if many of IDs are
unused. Consider column_categorical_with_hash_bucket()
in that case.
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.
A categorical column that returns identity values.
ValueError: if num_buckets
is less than one.
ValueError: if default_value
is not in range [0, num_buckets)
.
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