StaticQuantizer

Quantization strategy originally used in Stochastic Gradient Trees (SGT)¹.

Firstly, a buffer of size warm_start is stored. The data stored in the buffer is then used to quantize the input feature into n_bins intervals. These intervals will be replicated to every new quantizer. Feature values lying outside of the limits defined by the initial buffer will be mapped to the head or tail of the list of intervals.

Parameters

• n_bins

  Type → int

  Default → 64

  The number of bins (intervals) to divide the input feature.

• warm_start

  Type → int

  Default → 100

  The number of observations used to initialize the quantization intervals.

• buckets

  Type → list | None

  Default → None

  This parameter is only used internally by the quantizer, so it must not be set. Once the intervals are defined, new instances of this quantizer will receive the quantization information via this parameter.

Methods

update

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1. Gouk, H., Pfahringer, B., & Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109). ↩