tf.compat.v1.feature_column.categorical_column_with_vocabulary_file

A CategoricalColumn with a vocabulary file. (deprecated)

tf.compat.v1.feature_column.categorical_column_with_vocabulary_file(
    key,
    vocabulary_file,
    vocabulary_size=None,
    num_oov_buckets=0,
    default_value=None,
    dtype=tf.dtypes.string
)

Use this when your inputs are in string or integer format, and you have a vocabulary file that maps each value to an integer ID. By default, out-of-vocabulary values are ignored. Use either (but not both) of num_oov_buckets and default_value to specify how to include out-of-vocabulary values.

For input dictionary features, features[key] is either Tensor or SparseTensor. If Tensor, missing values can be represented by -1 for int and '' for string, which will be dropped by this feature column.

Example with num_oov_buckets: File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state abbreviation. All inputs with values in that file are assigned an ID 0-49, corresponding to its line number. All other values are hashed and assigned an ID 50-54.

import tensorflow as tf
states = tf.feature_column.categorical_column_with_vocabulary_file(
  key='states', vocabulary_file='states.txt', vocabulary_size=5,
  num_oov_buckets=1)
columns = [states]
features = {'states':tf.constant([['california', 'georgia', 'michigan',
'texas', 'new york'], ['new york', 'georgia', 'california', 'michigan',
'texas']])}
linear_prediction = tf.compat.v1.feature_column.linear_model(features,
columns)

Example with default_value: File '/us/states.txt' contains 51 lines - the first line is 'XX', and the other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX' in input, and other values missing from the file, will be assigned ID 0. All others are assigned the corresponding line number 1-50.

import tensorflow as tf
states = tf.feature_column.categorical_column_with_vocabulary_file(
  key='states', vocabulary_file='states.txt', vocabulary_size=6,
  default_value=0)
columns = [states]
features = {'states':tf.constant([['california', 'georgia', 'michigan',
'texas', 'new york'], ['new york', 'georgia', 'california', 'michigan',
'texas']])}
linear_prediction = tf.compat.v1.feature_column.linear_model(features,
columns)

And to make an embedding with either:

import tensorflow as tf
states = tf.feature_column.categorical_column_with_vocabulary_file(
  key='states', vocabulary_file='states.txt', vocabulary_size=5,
  num_oov_buckets=1)
columns = [tf.feature_column.embedding_column(states, 3)]
features = {'states':tf.constant([['california', 'georgia', 'michigan',
'texas', 'new york'], ['new york', 'georgia', 'california', 'michigan',
'texas']])}
input_layer = tf.keras.layers.DenseFeatures(columns)
dense_tensor = input_layer(features)

key A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature Tensor objects, and feature columns.
vocabulary_file The vocabulary file name.
vocabulary_size Number of the elements in the vocabulary. This must be no greater than length of vocabulary_file, if less than length, later values are ignored. If None, it is set to the length of vocabulary_file.
num_oov_buckets Non-negative integer, the number of out-of-vocabulary buckets. All out-of-vocabulary inputs will be assigned IDs in the range [vocabulary_size, vocabulary_size+num_oov_buckets) based on a hash of the input value. A positive num_oov_buckets can not be specified with default_value.
default_value The integer ID value to return for out-of-vocabulary feature values, defaults to -1. This can not be specified with a positive num_oov_buckets.
dtype The type of features. Only string and integer types are supported.

A CategoricalColumn with a vocabulary file.

ValueError vocabulary_file is missing or cannot be opened.
ValueError vocabulary_size is missing or < 1.
ValueError num_oov_buckets is a negative integer.
ValueError num_oov_buckets and default_value are both specified.
ValueError dtype is neither string nor integer.