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))