tft.compute_and_apply_vocabulary( x, default_value=-1, top_k=None, frequency_threshold=None, num_oov_buckets=0, vocab_filename=None, weights=None, labels=None, use_adjusted_mutual_info=False, min_diff_from_avg=0.0, coverage_top_k=None, coverage_frequency_threshold=None, key_fn=None, fingerprint_shuffle=False, name=None )
Generates a vocabulary for
x and maps it to an integer with this vocab.
In case one of the tokens contains the '\n' or '\r' characters or is empty it will be discarded since we are currently writing the vocabularies as text files. This behavior will likely be fixed/improved in the future.
Note that this function will cause a vocabulary to be computed. For large datasets it is highly recommended to either set frequency_threshold or top_k to control the size of the vocabulary, and also the run time of this operation.
SparseTensorof type tf.string or tf.int[8|16|32|64].
default_value: The value to use for out-of-vocabulary values, unless 'num_oov_buckets' is greater than zero.
top_k: Limit the generated vocabulary to the first
top_kelements. If set to None, the full vocabulary is generated.
frequency_threshold: Limit the generated vocabulary only to elements whose absolute frequency is >= to the supplied threshold. If set to None, the full vocabulary is generated. Absolute frequency means the number of occurences of the element in the dataset, as opposed to the proportion of instances that contain that element.
num_oov_buckets: Any lookup of an out-of-vocabulary token will return a bucket ID based on its hash if
num_oov_bucketsis greater than zero. Otherwise it is assigned the
vocab_filename: The file name for the vocabulary file. If None, a name based on the scope name in the context of this graph will be used as the file name. If not None, should be unique within a given preprocessing function. NOTE in order to make your pipelines resilient to implementation details please set
vocab_filenamewhen you are using the vocab_filename on a downstream component.
weights: (Optional) Weights
Tensorfor the vocabulary. It must have the same shape as x.
labels: (Optional) Labels
Tensorfor the vocabulary. It must have dtype int64, have values 0 or 1, and have the same shape as x.
use_adjusted_mutual_info: If true, use adjusted mutual information.
min_diff_from_avg: Mutual information of a feature will be adjusted to zero whenever the difference between count of the feature with any label and its expected count is lower than min_diff_from_average.
coverage_top_k: (Optional), (Experimental) The minimum number of elements per key to be included in the vocabulary.
coverage_frequency_threshold: (Optional), (Experimental) Limit the coverage arm of the vocabulary only to elements whose absolute frequency is >= this threshold for a given key.
key_fn: (Optional), (Experimental) A fn that takes in a single entry of
xand returns the corresponding key for coverage calculation. If this is
None, no coverage arm is added to the vocabulary.
fingerprint_shuffle: (Optional), (Experimental) Whether to sort the vocabularies by fingerprint instead of counts. This is useful for load balancing on the training parameter servers. Shuffle only happens while writing the files, so all the filters above will still take effect.
name: (Optional) A name for this operation.
SparseTensor where each string value is mapped to an
integer. Each unique string value that appears in the vocabulary
is mapped to a different integer and integers are consecutive starting from
zero. String value not in the vocabulary is assigned default_value.
frequency_thresholdis negative. If