Pinebioml

description:
1. Tuner:
a. Formalize tuner tuning range.
b. Adding tuner.detail() to access optuna tuning range.
c. optuna tuning range changes.
2. update README.md.

description:
1. remove pacmap from requirment. (C++ 14 build tool dependancy)

description:
1. Document update
2. Add reference: now you can call tuner.reference() to list major reference of that tuner.
3. Add optuna optimize history plot


bug:
1. feature bagging:
- contradicts to log domain in selection method (the negative output of pca)
- the variance of feature will change during bagging (variance matters)
2. Lasso logistic regression in grid search need to to modified. In temporary using binary search.
3. confusion matrix no title

On Going:
0. A reliable tutorial. (including mac) / Document reference
1. Pine monitor, progress bar and report. experiment setting visualization.

ToDo:
0. threshold tuner (or like jadbio)
1. search space dict
2. report and diagnose, Pine pipeline graph.
3. using pretty, beautiful, good-looking, precise packages:
a. pca
b. The only OPLS da reliable(compare to others), alive, python implement
https://github.com/Omicometrics/pypls?tab=readme-ov-file
c. https://www.omicsanalyst.ca/docs/Gallery.xhtml
4. barutoSHAP (baruto, shap, barutoshap)
5. fairness learning (mljar, fairlearn)
6. cv std
n. multi objective optimize for optuna
n. add parameter dict(json or yaml-like)
n. interactive interface or GUI (maybe nicegui/ plotly)

description:
1. RF tuner search range change: cpp :[1e-3, 1e-1] -> [1e-5, 1e-1]. the larger dataset needs smaller cpp.
2. Pine do not retune the model while doing cv evaluation.
3. pca_plot standardization add elpsilon to prevent zero std.
4. c4.5 selection add add elpsilon to prevent zero std.
5. tuner name for print.
6. add argument: return=True to function fit in sklearn estimator wrapper.
7. modified the scorer sparser for roc_auc's multiple response method
8. wrapper's argument name was wrong.

description:
1. task support:
\ data_overview | selection | tuner | summary | Pine
binary | v v v v v
multi | v v v v v
regression | v v v v v

2. selection revised.
3. Pine scorer revised: polymorphism for regression and classification.
4. Pine do_stage revised: polymorphism for regression and classification
5. plot functions revised: sturctural adjustment.
6. tuner metric sparser reviesd: kargs supported now. Nearly full support sklearn scoerers

bug:
1. feature bagging:
- contradicts to log domain in selection method (the negative output of pca)
- the variance of feature will change during bagging (variance matters)
2. Lasso logistic regression in grid search need to to modified. In temporary using binary search.

fixed:
1. C++ 14.0 issue can be solved by: https://blog.csdn.net/Lc_001/article/details/129195335
2. IO didn't reset repeatued index.

On Going:
0. A reliable tutorial. (including mac) / Document reference
1. Pine monitor, progress bar and report. experiment setting visualization.

ToDo:
0. threshold tuner
1. report and diagnose, Pine pipeline graph.
2. using pretty, beautiful, good-looking, precise packages:
a. pca
b. The only OPLS da reliable(compare to others), alive, python implement
https://github.com/Omicometrics/pypls?tab=readme-ov-file
3. barutoSHAP
n. add parameter dict(json or yaml-like)
n. interactive interface or GUI (maybe nicegui/ plotly)

description:
1. Tutorial
2. task support:
\ data_overview | selection | tuner | summary | Pine
binary | v v v v v
multi | v v v v
regression | v v v

3. tuner metrics easy mode.


fixed:
1. RF too slow: In some reason(still unknown) the sklearn rf using "thread" mode as joblib.parallel_backend. That leads to single processing. Fixed by manually set joblib.parallel_backend to loky.
2. DT random_state
3. Lasso in essemble selector returns all zero score.