tft.bucketize

Returns a bucketized column, with a bucket index assigned to each input.

tft.bucketize(
    x, num_buckets, epsilon=None, weights=None, elementwise=False,
    always_return_num_quantiles=True, name=None
)

x A numeric input Tensor or SparseTensor whose values should be mapped to buckets. For a SparseTensor only non-missing values will be included in the quantiles computation, and the result of bucketize will be a SparseTensor with non-missing values mapped to buckets.
num_buckets Values in the input x are divided into approximately equal-sized buckets, where the number of buckets is num_buckets. By default, the exact number will be available to bucketize. If always_return_num_quantiles is False, the actual number of buckets computed can be less or more than the requested number. Use the generated metadata to find the computed number of buckets.
epsilon (Optional) Error tolerance, typically a small fraction close to zero. If a value is not specified by the caller, a suitable value is computed based on experimental results. For num_buckets less than 100, the value of 0.01 is chosen to handle a dataset of up to ~1 trillion input data values. If num_buckets is larger, then epsilon is set to (1/num_buckets) to enforce a stricter error tolerance, because more buckets will result in smaller range for each bucket, and so we want the boundaries to be less fuzzy. See analyzers.quantiles() for details.
weights (Optional) Weights tensor for the quantiles. Tensor must have the same shape as x.
elementwise (Optional) If true, bucketize each element of the tensor independently.
always_return_num_quantiles (Optional) A bool that determines whether the exact num_buckets should be returned. If False, num_buckets will be treated as a suggestion.
name (Optional) A name for this operation.

A Tensor of the same shape as x, with each element in the returned tensor representing the bucketized value. Bucketized value is in the range [0, actual_num_buckets). Sometimes the actual number of buckets can be different than num_buckets hint, for example in case the number of distinct values is smaller than num_buckets, or in cases where the input values are not uniformly distributed.

ValueError If value of num_buckets is not > 1.