def _get_categorical_column(params: dict) -> fc.CategoricalColumn:
if 'vocabulary' in params.keys():
feature = fc.categorical_column_with_vocabulary_list(params['key'],
vocabulary_list=_parse_vocabulary(
params['vocabulary']),
default_value=0)
elif 'bucket_size' in params.keys():
feature = fc.categorical_column_with_hash_bucket(params['key'],
hash_bucket_size=params['bucket_size'])
elif 'file' in params.keys():
feature = fc.categorical_column_with_vocabulary_file(params['key'],
vocabulary_file=params['file'],
default_value=0)
elif 'num_buckets' in params.keys():
feature = fc.categorical_column_with_identity(params['key'],
num_buckets=params['num_buckets'])
elif 'boundaries' in params.keys():
feature = fc.bucketized_column(fc.numeric_column(
params['key']), boundaries=params['boundaries'])
else:
raise Exception("params error")
return feature
def sequence_categorical_column_with_vocabulary_file(
key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0,
default_value=None, dtype=dtypes.string):
"""A sequence of categorical terms where ids use a vocabulary file.
Pass this to `embedding_column` or `indicator_column` to convert sequence
categorical data into dense representation for input to sequence NN, such as
RNN.
Example:
```python
states = sequence_categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
num_oov_buckets=5)
states_embedding = embedding_column(states, dimension=10)
columns = [states_embedding]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
sequence_feature_layer = SequenceFeatures(columns)
sequence_input, sequence_length = sequence_feature_layer(features)
sequence_length_mask = tf.sequence_mask(sequence_length)
rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
rnn_layer = tf.keras.layers.RNN(rnn_cell)
outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)
```
Args:
key: A unique string identifying the input feature.
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.
Returns:
A `SequenceCategoricalColumn`.
Raises:
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.
"""
return fc.SequenceCategoricalColumn(
fc.categorical_column_with_vocabulary_file(
key=key,
vocabulary_file=vocabulary_file,
vocabulary_size=vocabulary_size,
num_oov_buckets=num_oov_buckets,
default_value=default_value,
dtype=dtype))
def sequence_categorical_column_with_vocabulary_file(key,
vocabulary_file,
vocabulary_size=None,
num_oov_buckets=0,
default_value=None,
dtype=dtypes.string):
"""A sequence of categorical terms where ids use a vocabulary file.
Pass this to `embedding_column` or `indicator_column` to convert sequence
categorical data into dense representation for input to sequence NN, such as
RNN.
Example:
```python
states = sequence_categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
num_oov_buckets=5)
states_embedding = embedding_column(states, dimension=10)
columns = [states_embedding]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
sequence_feature_layer = SequenceFeatures(columns)
sequence_input, sequence_length = sequence_feature_layer(features)
sequence_length_mask = tf.sequence_mask(sequence_length)
rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
rnn_layer = tf.keras.layers.RNN(rnn_cell)
outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)
```
Args:
key: A unique string identifying the input feature.
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.
Returns:
A `SequenceCategoricalColumn`.
Raises:
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.
"""
return fc.SequenceCategoricalColumn(
fc.categorical_column_with_vocabulary_file(
key=key,
vocabulary_file=vocabulary_file,
vocabulary_size=vocabulary_size,
num_oov_buckets=num_oov_buckets,
default_value=default_value,
dtype=dtype))
