Installation

Usage

Source Code

import argparse
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import os
import pickle


def generate_report(model_path, data_path):
    try:
        # Load the model and dataset
        with open(model_path, 'rb') as f:
            model = pickle.load(f)
        data = pd.read_csv(data_path)
        
        # Split the data into features and target
        X = data.drop('target', axis=1)
        y = data['target']
        
        # Split the data into training and testing sets
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        
        # Train a new model to compare with the given model
        new_model = RandomForestClassifier(random_state=42)
        new_model.fit(X_train, y_train)
        
        # Make predictions and calculate the accuracy
        y_pred = new_model.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        
        # Create a report
        report = f"Model Complexity: {model.n_features_in_}\nFeature Correlation: {X.corr().mean().mean()}\nDecision Boundary Analysis: {accuracy}\n"
        
        # Save the report to a file
        with open('report.txt', 'w') as f:
            f.write(report)
        
        # Plot a feature correlation heatmap
        plt.figure(figsize=(10, 8))
        plt.imshow(X.corr(), cmap='coolwarm', interpolation='nearest')
        plt.title('Feature Correlation Heatmap')
        plt.colorbar()
        plt.savefig('correlation_heatmap.png')
        plt.close()  # Close the plot to avoid errors
        
        return report
    except Exception as e:
        return str(e)


def main():
    parser = argparse.ArgumentParser(description='Transparency Report Generator')
    parser.add_argument('--model_path', type=str, required=True, help='Path to the trained AI model')
    parser.add_argument('--data_path', type=str, required=True, help='Path to the dataset')
    args = parser.parse_args()
    
    report = generate_report(args.model_path, args.data_path)
    print(report)

if __name__ == '__main__':
    main()

README