Fix biased DP quantiles utility bug introduced from recent quantiles refactor. The old quantiles path introduced jitter to handle elements with high mass (e.g., 40 hours per week on adult dataset). This change adds that back into the histogram-based quantiles codepaht, where the jitter occurs directly in the histogram domain rather than on a per-example level, preserving the nice properties of this implementation while hardening it to duplicate values.#90
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…refactor. The old quantiles path introduced jitter to handle elements with high mass (e.g., 40 hours per week on adult dataset). This change adds that back into the histogram-based quantiles codepaht, where the jitter occurs directly in the histogram domain rather than on a per-example level, preserving the nice properties of this implementation while hardening it to duplicate values. There are two separate ways to jitter depending on whether the attribute is int or float. For int, we expand each histogram bin into 4*m bins by randomly assigning each example to one of the equivalent bins (done in bulk via multinomial). For float, we move each example to one of the nearby bins symmetrically (done in bulk via multinomial). PiperOrigin-RevId: 947195276
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Fix biased DP quantiles utility bug introduced from recent quantiles refactor. The old quantiles path introduced jitter to handle elements with high mass (e.g., 40 hours per week on adult dataset). This change adds that back into the histogram-based quantiles codepaht, where the jitter occurs directly in the histogram domain rather than on a per-example level, preserving the nice properties of this implementation while hardening it to duplicate values.
There are two separate ways to jitter depending on whether the attribute is int or float. For int, we expand each histogram bin into 4*m bins by randomly assigning each example to one of the equivalent bins (done in bulk via multinomial). For float, we move each example to one of the nearby bins symmetrically (done in bulk via multinomial).