Best iteration is always 0 about sd-webui-model-mixer

[wkpark]

what's your optimizing method?
you can attach your particial screenshot for example:

• Normally, I select specific model and blocks to optimize,
• Normally, I select Hill Climbing optimizer. I did not test all optimze method. some optimizer will not work as expected or work with specific condition.

[wkpark]

and this is an example hyperactive app (modified slightly. from https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optimization_applications/hyperpara_optimize.py )

"""
This example shows the original purpose of Hyperactive.
You can search for any number of hyperparameters and Hyperactive
will return the best one after the optimization run.

"""

import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_wine
from hyperactive import Hyperactive

data = load_wine()
X, y = data.data, data.target


def model(opt):
    gbr = GradientBoostingClassifier(
        n_estimators=opt["n_estimators"],
        max_depth=opt["max_depth"],
        min_samples_split=opt["min_samples_split"],
        min_samples_leaf=opt["min_samples_leaf"],
        criterion=opt["criterion"],
    )
    scores = cross_val_score(gbr, X, y, cv=4)

    return scores.mean()


search_space = {
    "n_estimators": list(range(10, 150, 5)),
    "max_depth": list(range(2, 12)),
    "min_samples_split": list(range(2, 25)),
    "min_samples_leaf": list(range(1, 25)),
    "criterion": ["friedman_mse", "squared_error"],#, "absolute_error"],
    "subsample": list(np.arange(0.1, 3, 0.1)),
}

if __name__ == '__main__':
    hyper = Hyperactive()
    hyper.add_search(model, search_space, n_iter=40)
    hyper.run()

and its result:

[0] model (Random Search): 100%|───────────────────────────────────────────────────────────────────────| 40/40 [01:17<00:00,  1.94s/it, best_iter=0, best_pos=[17  5 12 18  0 21], best_score=0.9498737373737374]


Results: 'model'
   Best score: 0.9776515151515152
   Best parameter set:
      'n_estimators'      : 130
      'max_depth'         : 3
      'min_samples_split' : 15
      'min_samples_leaf'  : 22
      'criterion'         : friedman_mse
      'subsample'         : 0.7000000000000001
   Best iteration: 0

   Random seed: 416581482

   Evaluation time   : 77.45525693893433 sec    [99.99 %]
   Optimization time : 0.005005359649658203 sec    [0.01 %]
   Iteration time    : 77.46026229858398 sec    [1.94 sec/iter]

you can see "Best iteration: 0" in this case
(I guess this is a mitake/bug of the hyperactive)