semvec/pypi-packages huggingface dataset:
RIM-interpret 0.0.5
Interpretability metrics for machine learning models
Requires Python: >=3.7
Keywords: interpretability,lime,machine learning,modelling,omics,permutation feature importance,rim,shap

# RIM-interpret

RIM-interpret is a Python package designed to enhance the interpretability of machine learning models.

## Installation

Use pip to install RIM-interpret.

## Usage

RIM-interpret is compatible with most linear and tree-based regression models. In the future, we hope to expand the compatibility to inlcude more regression models and an option for classification tasks.

```python
import RIM_interpret

import sklearn
import pandas as pd
from sklearn import datasets
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split


#Import example dataset and convert to pandas df
data = datasets.load_diabetes()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target
print(df.head())
#Predictors
X=df.drop("target", axis=1)
#Target
y=df["target"]

#Train/test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42)


#Train elastic net regression model
en_model = ElasticNet(alpha = 0.1, l1_ratio = 0.5)
fit_en = en_model.fit(X_train, y_train)
#Create dataframes to test
pfi_df_en = RIM_interpret.get_pfi(fit=fit_en, X_test=X_test, y_test=y_test)
shap_df_en = RIM_interpret.get_shap(fit=fit_en, X_test=X_test, model_type="Linear")
lime_df_en = RIM_interpret.get_lime(fit=fit_en, X_train=X_train, X_test=X_test)
inter_df_en = RIM_interpret.get_inter(fit=fit_en, X_train=X_train, X_test=X_test, y_test=y_test model_type="Linear")

```

## Contributing

Please create a GitHub issue for any bugs or questions (https://github.com/xloffree/RIM-interpret).

## License

[MIT](https://choosealicense.com/licenses/mit/)
PermCCA 0.1.3
A permutation-based CCA analysis toolbox

# A Permutation-Based CCA Analysis Toolbox
**Currently Testing Against the matlab version**

Reference: Winkler AM, Renaud O, Smith SM, Nichols TE. Permutation Inference for Canonical Correlation Analysis. NeuroImage. 2020; 117065

* Returns similar results as the matlab version
* Robust than matlab version thanks to the 'nipals' method in sklearn
* TODO: ADD support for covariates
statified-nies 0.1.3
A hierarchical review app using Streamlit
Requires Python: >=3.9

<a href="https://www.nies.futbol/"><img
src="https://raw.githubusercontent.com/niesfutbol/statified_nies/develop/static/logo_nies.png" align="right" width="256"
/></a>
<a href="hhttps://statifiedfootball.wordpress.com/"><img src="https://raw.githubusercontent.com/niesfutbol/statified_nies/develop/static/logo_statified.png" align="right" width="256" /></a>

# App to show some analyses

## Step to use
### Local app
``` sh
docker-compose up app
```
### Develop Enviroment
``` sh
docker-compose run nies
```