SENSE: Systematic Enhancement for Neural Selection and Evolution

Introduction

SENSE is a sophisticated AI framework that merges various machine learning techniques into a single, highly adaptive system. It's crafted for scenarios where data evolves, and models need to adapt autonomously over time.

Key Features

• Evolutionary Algorithms: Employs genetic algorithms for model optimization, ensuring models evolve with changing data landscapes.
• Reinforcement Learning (SARSA Agent): Enhances decision-making with an epsilon-greedy strategy for action selection.
• Online Learning: Utilizes LSTM for learning from sequential data, allowing for real-time adaptation.
• Anomaly Detection: Uses autoencoders to identify outliers, ensuring data quality and model relevance.
• System Resource Monitoring: Dynamically adjusts computational load based on system resource usage.
• Data Drift and Model Degradation Handling: Automatically detects and mitigates issues related to data drift or model performance degradation.

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Quick Start Guide

1. Prerequisites

Ensure you have the following installed:

• Python 3.6 or newer
• TensorFlow 2.x
• Additional libraries: numpy, scipy, pandas, requests, transformers, psutil

2. Installation

Run the following commands:
git clone https://github.com/pwnzersaurus/SENSE.git
cd SENSE
pip install -r requirements.txt

3. Running SENSE

For local data:
python sense.py --data_source path/to/your/data.csv --target_column your_target_column

For URL-based data:
python sense.py --data_source http://example.com/data.csv --target_column your_target_column

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Usage Examples

Basic Data Analysis

Use SENSE's built-in functions to analyze your data:

from sense import SENSE_Evolver

Load and preprocess data

data = SENSE_Evolver.load_data('path/to/your/data.csv', 'target_column')
train_X, val_X, train_y, val_y = SENSE_Evolver.preprocess_data(data)

Initialize SENSE

sense_system = SENSE_Evolver(
state_size=10,
action_size=4,
input_dim=train_X.shape[1],
output_dim=1
)

Run SENSE for a few generations

sense_system.evolve_population(
sense_system.create_population(),
(val_X, val_y),
train_X
)

Advanced Scenario: Handling Data Drift

This example demonstrates how SENSE adapts to data drift:

from sense import SENSE_Evolver

Assuming 'new_data' and 'old_data' are your datasets

sense_system = SENSE_Evolver(
state_size=10,
action_size=4,
input_dim=new_data.shape[1],
output_dim=1
)

Check for drift

if sense_system.check_data_drift(new_data, old_data):
population = sense_system.create_population() # Reset models due to drift
else:
population = sense_system.evolve_population(
sense_system.create_population(),
(val_X, val_y),
new_data
)

Continue with model evolution or training

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Contributing to SENSE

Contributions are welcomed! Please refer to our Contribution Guidelines for how to get involved.

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License

SENSE is released under the MIT License. For more details, see the License file.

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Detailed Documentation

Check the WIKI

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Note

• Ensure your data source file or URL is accessible and contains the correct format (CSV with headers).
• Adjust parameters like state_size, action_size, etc., based on your specific problem domain.
• The system is designed to run autonomously but can be further customized for specific use cases through the command-line arguments or programmatically.