explainableai 0.1.9

A comprehensive package for Explainable AI and model interpretation

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.

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.