app.py

# app.py: Entry point of the mlu application.

import numpy as np
import pandas as pd
from modules.data_transformation import handle_missing_values, normalize, encode_categorical
from modules.array_manipulation import filter, aggregate, summary
from modules.chain import Chain
from modules.ethical_ai_utils import bias_detection, fairness_metrics
from modules.anomaly_detection_utils import isolation_forest, local_outlier_factor
from modules.reinforcement_learning_utils import Environment, train_model

def demonstrate_data_transformation():
    try:
        print("\n--- Data Transformation Examples ---")
        data = np.array([1, 2, np.nan, 4, 5])
        print("Original data:", data)
        data = handle_missing_values(data)
        print("After handling missing values (mean):", data)
        data = normalize(data)
        print("After normalization:", data)
    except Exception as e:
        print(f"An error occurred during data transformation demonstration: {e}")

def demonstrate_array_manipulation():
    try:
        print("\n--- Array Manipulation Examples ---")
        array = np.array([1, -2, 3, -4, 5])
        print("Original array:", array)
        filtered_array = filter(array, lambda x: x > 0)
        print("After filtering (positive values):", filtered_array)
        aggregate_sum = aggregate(array, 'sum')
        print("Aggregation (sum):", aggregate_sum)
        summary_result = summary(array)
        print("Summary:")
        for key, value in summary_result.items():
            print(f"{key}: {value}")
    except Exception as e:
        print(f"An error occurred during array manipulation demonstration: {e}")

def demonstrate_chaining():
    try:
        print("\n--- Chaining Mechanism Example ---")
        data = np.array([-2, -1, 0, 1, 2])
        print("Original data:", data)
        result = Chain(data).filter(lambda x: x > 0).map(lambda x: x * 2).summary().value()
        print("Chained operations result:", result)
    except Exception as e:
        print(f"An error occurred during chaining demonstration: {e}")

def demonstrate_ethical_ai():
    try:
        print("\n--- Ethical AI Demonstration ---")
        data = pd.DataFrame({'Age': [23, 25, 46, 30, 22], 'Income': [50000, 60000, 80000, 120000, 45000], 'Gender': [1, 0, 1, 0, 1]})
        target = 'Gender'
        print("Bias Detection Report:")
        print(bias_detection(data, target))
        predictions = np.array([1, 0, 1, 0, 1])
        ground_truth = np.array([1, 1, 0, 0, 1])
        groups = np.array([0, 1, 0, 1, 0])
        print("Fairness Metrics:")
        fairness_report = fairness_metrics(predictions, ground_truth, groups)
        for metric, value in fairness_report.items():
            print(f"{metric}: {value}")
    except Exception as e:
        print(f"An error occurred during Ethical AI demonstration: {e}")

def demonstrate_reinforcement_learning():
    try:
        print("\n--- Reinforcement Learning Demonstration ---")
        class SimpleEnvironment(Environment):
            def _get_initial_state(self):
                return np.random.rand(4)  # Example state
                
            def _apply_action(self, action):
                newState = np.random.rand(4)  # Example new state
                reward = np.random.rand()  # Example reward
                is_done = np.random.choice([True, False])  # Example termination condition
                return newState, reward, is_done
        
        class SimpleModel:
            def predict(self, state):
                return np.random.choice([0, 1])  # Example action
            
            def update(self, state, action, reward, newState, done):
                pass  # Placeholder for model update logic
        
        environment = SimpleEnvironment()
        model = SimpleModel()
        train_model(environment, model, episodes=5)
    except Exception as e:
        print(f"An error occurred during Reinforcement Learning demonstration: {e}")

def demonstrate_anomaly_detection():
    try:
        print("\n--- Anomaly Detection Demonstration ---")
        data = [[-1.1], [0.2], [101.1], [0.3], [0.5], [-100.1], [0.4], [0.6]]
        print("Isolation Forest Anomalies:")
        isolation_indices = isolation_forest(data)
        print(f"Anomalies Indices: {isolation_indices}")
        print("Local Outlier Factor Anomalies:")
        lof_indices = local_outlier_factor(data)
        print(f"Anomalies Indices: {lof_indices}")
    except Exception as e:
        print(f"An error occurred during Anomaly Detection demonstration: {e}")

def main():
    try:
        print("Welcome to the mlu application.")
        demonstrate_data_transformation()
        demonstrate_array_manipulation()
        demonstrate_chaining()
        demonstrate_ethical_ai()
        demonstrate_reinforcement_learning()
        demonstrate_anomaly_detection()
    except Exception as e:
        print(f"An error occurred in main: {e}")

if __name__ == "__main__":
    main()