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Init module for TF.Transform.
Modules
coders module: Module level imports for tensorflow_transform.coders.
experimental module: Module level imports for tensorflow_transform.experimental.
Classes
class TFTransformOutput: A wrapper around the output of the tf.Transform.
class TransformFeaturesLayer: A Keras layer for applying a tf.Transform output to input layers.
Functions
annotate_asset(...): Creates mapping between user-defined keys and SavedModel assets.
apply_buckets(...): Returns a bucketized column, with a bucket index assigned to each input.
apply_buckets_with_interpolation(...): Interpolates within the provided buckets and then normalizes to 0 to 1.
apply_pyfunc(...): Applies a python function to some Tensors.
apply_vocabulary(...): Maps x to a vocabulary specified by the deferred tensor.
bag_of_words(...): Computes a bag of "words" based on the specified ngram configuration.
bucketize(...): Returns a bucketized column, with a bucket index assigned to each input.
bucketize_per_key(...): Returns a bucketized column, with a bucket index assigned to each input.
compute_and_apply_vocabulary(...): Generates a vocabulary for x and maps it to an integer with this vocab.
count_per_key(...): Computes the count of each element of a Tensor.
covariance(...): Computes the covariance matrix over the whole dataset.
deduplicate_tensor_per_row(...): Deduplicates each row (0-th dimension) of the provided tensor.
estimated_probability_density(...): Computes an approximate probability density at each x, given the bins.
get_analyze_input_columns(...): Return columns that are required inputs of AnalyzeDataset.
get_num_buckets_for_transformed_feature(...): Provides the number of buckets for a transformed feature if annotated.
get_transform_input_columns(...): Return columns that are required inputs of TransformDataset.
hash_strings(...): Hash strings into buckets.
histogram(...): Computes a histogram over x, given the bin boundaries or bin count.
make_and_track_object(...): Keeps track of the object created by invoking trackable_factory_callable.
max(...): Computes the maximum of the values of a Tensor over the whole dataset.
mean(...): Computes the mean of the values of a Tensor over the whole dataset.
min(...): Computes the minimum of the values of a Tensor over the whole dataset.
ngrams(...): Create a SparseTensor of n-grams.
pca(...): Computes PCA on the dataset using biased covariance.
quantiles(...): Computes the quantile boundaries of a Tensor over the whole dataset.
scale_by_min_max(...): Scale a numerical column into the range [output_min, output_max].
scale_by_min_max_per_key(...): Scale a numerical column into a predefined range on a per-key basis.
scale_to_0_1(...): Returns a column which is the input column scaled to have range [0,1].
scale_to_0_1_per_key(...): Returns a column which is the input column scaled to have range [0,1].
scale_to_gaussian(...): Returns an (approximately) normal column with mean to 0 and variance 1.
scale_to_z_score(...): Returns a standardized column with mean 0 and variance 1.
scale_to_z_score_per_key(...): Returns a standardized column with mean 0 and variance 1, grouped per key.
segment_indices(...): Returns a Tensor of indices within each segment.
size(...): Computes the total size of instances in a Tensor over the whole dataset.
sparse_tensor_left_align(...): Re-arranges a tf.SparseTensor and returns a left-aligned version of it.
sparse_tensor_to_dense_with_shape(...): Converts a SparseTensor into a dense tensor and sets its shape.
sum(...): Computes the sum of the values of a Tensor over the whole dataset.
tfidf(...): Maps the terms in x to their term frequency * inverse document frequency.
tukey_h_params(...): Computes the h parameters of the values of a Tensor over the dataset.
tukey_location(...): Computes the location of the values of a Tensor over the whole dataset.
tukey_scale(...): Computes the scale of the values of a Tensor over the whole dataset.
var(...): Computes the variance of the values of a Tensor over the whole dataset.
vocabulary(...): Computes the unique values of a Tensor over the whole dataset.
word_count(...): Find the token count of each document/row.
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| version |
'1.7.0'
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