A comprehensive package for Explainable AI and model interpretation
Project description
ExplainableAI
ExplainableAI is a Python package that provides tools for creating interpretable machine learning models. It combines various explainable AI techniques with LLM-powered explanations to make model predictions more understandable for both technical and non-technical users.
Features
- Automated Exploratory Data Analysis (EDA)
- Model performance evaluation
- Feature importance calculation
- Feature interaction analysis
- SHAP (SHapley Additive exPlanations) value calculation
- Visualization of model insights
- LLM-powered explanations of model results and individual predictions
- Cross-validation
- Easy-to-use interface for model fitting, analysis, and prediction
Installation
You can install explainableai using pip:
pip install explainableai
Environment Variables
To run this project, you will need to add the following environment variables to your .env file
GEMINI_API_KEY
Usage/Examples
Here's a basic example of how to use ExplainableAI:
from explainableai import XAIWrapper
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
# Load your dataset
df = pd.read_csv('your_dataset.csv')
X = df.drop(columns=['target_column'])
y = df['target_column']
# Perform EDA
XAIWrapper.perform_eda(df)
# Create and initialize your model
model = RandomForestClassifier(n_estimators=100, random_state=42)
# Create XAIWrapper instance
xai = XAIWrapper()
# Fit the model and run XAI analysis
xai.fit(model, X, y, feature_names=X.columns.tolist())
results = xai.analyze()
# Print LLM explanation of results
print(results['llm_explanation'])
# Make a prediction with explanation
new_data = {...} # Dictionary of feature values
prediction, probabilities, explanation = xai.explain_prediction(new_data)
print(f"Prediction: {prediction}")
print(f"Probabilities: {probabilities}")
print(f"Explanation: {explanation}")
Contributing
Contributions are always welcome!
See contributing.md
for ways to get started.
Please adhere to this project's code of conduct
.
Run Locally
Clone the project
git clone https://github.com/ombhojane/explainableai
Go to the project directory
cd explainableai
Install dependencies
pip install -r requirements.txt
Get Started with data.csv dataset or you can have any dataset
python main.py [dataset] [Target_column]
Acknowledgements
- This package uses various open-source libraries including scikit-learn, shap, and matplotlib.
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