https://github.com/aiez/ezr2

ezr2 — explainable multi-objective optimization in one file, zero dependencies, pure Python stdlib. Build a large pool of candidate rows, recursively split it by far-point projection (a descendant of SWAY), label only a few dozen informative rows, then sort the rest. A regression or classification tree explains which input ranges lead to the best goals, and a branch-delta reads as an action. Runs on a CSV, or a live model (override labelled()).

# sibling data gist supplies the CSVs (no data lives in here)
git clone https://github.com/aiez/optimiz                 # optimization data
git clone https://github.com/aiez/ezr2 && cd ezr2
python3 test_ezr2.py tree                         # build + show a tree
python3 test_ezr2.py all                          # run every self-test
make test                                         # same, via konfig

Sections: NAME | SYNOPSIS | DESCRIPTION | DATA | OPTIONS | FINDINGS | SEE ALSO | LICENSE | AUTHOR

Files: ezr2.py | test_ezr2.py | dtlz.py | Growing.py | pctl.py | ezr2.md | Random.md | Growing.md | Makefile | pyproject.toml | LICENSE.md

NAME

ezr2 - explainable multi-objective optimization via active
       learning and a dual-mode (regression|classification) tree

SYNOPSIS

python3 test_ezr2.py [--key=val ...] <test>
python3 test_ezr2.py -h | all
python3 dtlz.py [--model=dtlz1..dtlz7] [--M=int] [--N=int]

Sibling gists (one parent dir; no naked paths):
  ezr2/     this repo (ezr2.py library + test_ezr2.py dispatch)
  optimiz/  optimization CSVs   (https://github.com/aiez/optimiz)
  konfig/   shared Makefile + dotfiles (make help|sh|vi|...)

DESCRIPTION

Summarizes a CSV into Num/Sym columns in constant space; scores
each row by distance-to-heaven over its goals; spends a small
label budget via SWAY-style far-point projection; then grows a
tree that minimizes a size-weighted variance (sd for numeric
goals, entropy for symbolic) so the SAME code yields regression
or classification trees. A leaf is a real cluster; the delta
between two leaves' branch tests is a feasible intervention.

Two modes: a static CSV, or a live model -- override the
`labelled()` seam so goals are computed on demand (see dtlz.py,
which drives ezr2 over the DTLZ1-7 benchmarks).

Tests live in test_ezr2.py (`from ezr2 import *; main(globals())`);
the library ezr2.py has no tests inside it.

DATA

First row names the columns; a name's last char sets its role,
its first char its type:
  [A-Z]*    numeric        (e.g. "Clndrs")
  [a-z]*    symbolic       (e.g. "origin")
  *+        maximize goal  (e.g. "Mpg+")
  *-        minimize goal  (e.g. "Lbs-")
  *!        class label    (e.g. "sick!")
  *X        ignored        (e.g. "HpX")
  *~        protected x-column

E.g. Clndrs,Volume,HpX,Model,origin,Lbs-,Acc+,Mpg+ -> numeric
inputs Clndrs/Volume/Model, symbolic input origin, ignored HpX,
goals minimize Lbs, maximize Acc/Mpg.

OPTIONS

--file   data file             = ../optimiz/misc_auto93.csv
--seed   random seed           = 1
--leaf   tree min leaf rows    = 3
--maxd   tree max depth        = 8
--grow   add labels/round      = 4
--budget labeling cap          = 50
--cap    max rows kept         = 1024
--check  rows labelled by tree = 5    (at-k trust knob)
--keepf  keep frac             = 0.66
--round  decimals shown        = 3
--landscape  active | random   = active

FINDINGS

Active beats random ~3:1 where landscapes separate, ties at the
sparsity ceiling; the edge lives in the hard tail, not the mean
(Random.md). `grow` is the dominant knob and SWAY's (keepf=0.5,
grow=2) is the weakest setting in its own space -- growing a
little faster gains ~11 win points across a smooth, low-
sensitivity plateau (Growing.md).

SEE ALSO

ezr2.md       the tour -- a build-order textbook of the code
dtlz.py       drive ezr2 with an external model (DTLZ1-7)
https://github.com/aiez/optimiz   sibling data gist (CSVs; never bundled here)
https://github.com/aiez/ezr       the original ezr (v1)

LICENSE

MIT (c) 2026 Tim Menzies <timm@ieee.org>.

AUTHOR

Tim Menzies, http://timm.fyi

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