BoostedBuidlingProcessForADG
Implementation of A novel machine learning method for my researches in this field.
This method is only for binary classification tasks up until current version.
Quick Start
install
pip install bbp4adg
import and initialize:
# from exports import BBP, ADG
from bbp4adg import BBP, ADG
adg = BBP(threshold=delta)
start training
# Or adg = ADG(threshold=delta) for original adg
model, perf =adg.fit(X,y)
print('arguments count:', len(model.arguments))
print(model.arguments)
print('relations count:', len(model.relations))
print(model.relations)
print("accuracy:", perf)
evaluate on test set
print("test accuracy:", adg.score(X_test,y_test))
ToDos
- more comments
- doc tests
- more semantics than grounded
How to contribute
Issues and PRs are welcomed.
Please read the contributing document.
Metadata
Release files for bbp4adg 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| bbp4adg-0.1.0.tar.gz | 9.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bbp4adg-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 19.7 kB
Release files / bbp4adg-0.1.0.tar.gz
| Download URL | bbp4adg-0.1.0.tar.gz |
|---|---|
| Size | 9.1 kB |
| Tags | Source |
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Release files / bbp4adg-0.1.0-py3-none-any.whl
| Download URL | bbp4adg-0.1.0-py3-none-any.whl |
|---|---|
| Size | 10.6 kB |
| Tags | Python 3 |
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