ExplainableAI

ExplainableAI is a powerful Python package that combines state-of-the-art machine learning techniques with advanced explainable AI methods and LLM-powered explanations.

Table of Contents

• Features
• Installation
• Quick Start
• Usage Examples
• Environment Variables
• API Reference
• Running Locally
• Contributing
• Acknowledgements
• License

Features

• Automated Exploratory Data Analysis (EDA): Gain quick insights into your dataset.
• Model Performance Evaluation: Comprehensive metrics for model assessment.
• Feature Importance Analysis: Understand which features drive your model's decisions.
• SHAP (SHapley Additive exPlanations) Integration: Deep insights into model behavior.
• Interactive Visualizations: Explore model insights through intuitive charts and graphs.
• LLM-Powered Explanations: Get human-readable explanations for model results and individual predictions.
• Automated Report Generation: Create professional PDF reports with a single command.
• Multi-Model Support: Compare and analyze multiple ML models simultaneously.
• Easy-to-Use Interface: Simple API for model fitting, analysis, and prediction.

Installation

Install ExplainableAI using pip:

pip install explainableai

Quick Start

from explainableai import XAIWrapper
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load sample dataset
X, y = load_iris(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize XAIWrapper
xai = XAIWrapper()

# Fit and analyze model
model = RandomForestClassifier(n_estimators=100, random_state=42)
xai.fit(model, X_train, y_train)
results = xai.analyze(X_test, y_test)

# Print LLM explanation
print(results['llm_explanation'])

# Generate report
xai.generate_report('iris_analysis.pdf')

Usage Examples

Multi-Model Comparison

from explainableai import XAIWrapper
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
import pandas as pd

# Load your dataset
df = pd.read_csv('your_dataset.csv')
X = df.drop(columns=['target_column'])
y = df['target_column']

# Create models
models = {
    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
    'Logistic Regression': LogisticRegression(max_iter=1000),
    'XGBoost': XGBClassifier(n_estimators=100, random_state=42)
}

# Initialize XAIWrapper
xai = XAIWrapper()

# Fit and analyze models
xai.fit(models, X, y)
results = xai.analyze()

# Print LLM explanation of results
print(results['llm_explanation'])

# Generate a comprehensive report
xai.generate_report('multi_model_comparison.pdf')

Explaining Individual Predictions

# ... (after fitting the model)

# 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}")

Environment Variables

To use the LLM-powered explanations, you need to set up the following environment variable:

• GEMINI_API_KEY: Your Google Gemini API key

Add this to your .env file:

GEMINI_API_KEY=your_api_key_here

API Reference

For detailed API documentation, please refer to our API Reference.

Running Locally

To run ExplainableAI locally:

1. Clone the repository:

   git clone https://github.com/ombhojane/explainableai.git
   cd explainableai
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up your environment variables (see Environment Variables).

4. Run the example script:

   python main.py [dataset] [target_column]
   

Contributing

We welcome contributions to ExplainableAI! Please see our Contributing Guidelines for more information on how to get started.

Credits

Explainable AI was created by Om Bhojane. Special thanks to the following contributors for their support.

Acknowledgements

ExplainableAI builds upon several open-source libraries, including:

• scikit-learn
• SHAP
• Matplotlib
• XGBoost

We are grateful to the maintainers and contributors of these projects.

License

ExplainableAI is released under the MIT License.