https://github.com/alcoholrithm/rias
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (4.5%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Alcoholrithm
- Language: Jupyter Notebook
- Default Branch: main
- Size: 3.23 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
How to install
sh
pip install -r requirements.txt
How to use RIAS for a specific dataset and user-defined model
1. Define a DataModule Class that inherit DataModule class.
```python from src.data_utils import DataModule
class MyDataModule(DataModule): pass ```
2. Define a ModelModule Class that inherit BaseModel or use a predefined ModelModule.
```python from src.models import BaseModel
class MyModel(BaseModel): pass ```
or
python
from src.models import XGB
3. Complete the configuration settings for the experiment. Load the base_config and define the missing options. If you use predefined ModelModule, load corresponding config in the src.config.
```python from src.config import base_config as config
config.experiment.random_seed = 0 ... ```
or
```python from src.config import xgb_config as config
config.experiment.random_seed = 0 ... ```
4. Define the evaluation metric for the given dataset.
```python from src.misc.eval_metric import EvalMetric
class MyEvalMetric(EvalMetric): pass ```
5. Run RIAS
```python
Assume we define MyDataModule, MyModel and config
from src.rias import RIAS from src.models import XGB
Prepare the data
datamodule = MyDataModule() data, label, continuouscols, categoricalcols = datamodule.prepare_data()
test_size = 0.2
trainidx, testidx, , _ = traintestsplit(np.arange(len(label)).reshape((-1, 1)), label, testsize=testsize, randomstate=config.experiment.randomseed, stratify=label) trainidx, testidx = trainidx.ravel(), test_idx.ravel()
Xtest, ytest = data.iloc[testidx], label[testidx] data, label = data.iloc[trainidx], label[trainidx]
Run RIAS
rias = RIAS.preparerias(config, MyModel, data, label, continuouscols, categorical_cols, True)
rias.train()
rias.initcalibrator() rias.test(Xtest, y_test, DiabetesEvalMetric()) ```
See the example.ipynb for detail example
Owner
- Login: Alcoholrithm
- Kind: user
- Repositories: 3
- Profile: https://github.com/Alcoholrithm
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Dependencies
- BorutaShap ==1.0.16
- dice-ml ==0.10
- lightgbm ==4.1.0
- lime ==0.2.0.1
- netcal ==1.3.5
- numpy ==1.24.3
- optuna ==3.3.0
- pandas ==1.5.3
- pytorch_tabular ==1.0.2
- scikit-learn ==1.3.1
- shap ==0.42.1
- tensorflow ==2.13.1
- torch ==1.13.1
- torchmetrics ==0.11.4
- tqdm ==4.66.1
- xgboost ==2.0.0