automl-lite 0.2.0

A simplified automated machine learning package with Neural Architecture Search for non-experts

---

title: AutoML Lite emoji: 🤖 colorFrom: blue colorTo: purple sdk: gradio sdk_version: 4.0.0 app_file: app.py pinned: false license: mit tags:

• automl
• machine-learning
• deep-learning
• neural-architecture-search
• time-series
• classification
• regression
• feature-engineering
• interpretability
• experiment-tracking
• production
• hardware-aware
• multi-objective

---

AutoML Lite 🤖

Automated Machine Learning Made Simple

A lightweight, production-ready automated machine learning library that simplifies the entire ML pipeline from data preprocessing to model deployment.

🎬 Demo

AutoML Lite in Action

Generated HTML Reports

Weights & Biases Integration

🚀 Quick Start

Installation

pip install automl-lite

5-Line ML Pipeline

from automl_lite import AutoMLite
import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Initialize AutoML (zero configuration!)
automl = AutoMLite(time_budget=300)

# Train and get the best model
best_model = automl.fit(data, target_column='target')

# Make predictions
predictions = automl.predict(new_data)

✨ Key Features

🧠 Intelligent Automation

• Auto Feature Engineering: 11.6x feature expansion (20→232 features)
• Smart Model Selection: Tests 15+ algorithms automatically
• Hyperparameter Optimization: Uses Optuna for efficient tuning
• Ensemble Methods: Automatic voting classifiers
• Neural Architecture Search (NAS): Automatically discover optimal neural network architectures

🏭 Production-Ready

• Deep Learning: TensorFlow and PyTorch integration
• Neural Architecture Search: Automated architecture discovery with hardware-aware optimization
• Time Series: ARIMA, Prophet, LSTM forecasting
• Advanced Interpretability: SHAP, LIME, permutation importance
• Experiment Tracking: MLflow, W&B, TensorBoard
• Interactive Dashboards: Real-time monitoring

📊 Comprehensive Reporting

• Interactive HTML Reports: Beautiful visualizations
• Model Performance Analysis: Confusion matrices, ROC curves
• Feature Importance: Detailed analysis and correlations
• Training History: Complete logs and metrics
• NAS Visualizations: Architecture diagrams and Pareto front exploration

🎯 Supported Problem Types

• ✅ Classification (Binary & Multi-class)
• ✅ Regression
• ✅ Time Series Forecasting
• ✅ Deep Learning Tasks

🧠 Neural Architecture Search (NAS)

AutoML Lite includes state-of-the-art Neural Architecture Search capabilities to automatically discover optimal neural network architectures for your specific problem.

Key NAS Features

• Multiple Search Strategies

  • Evolutionary algorithms (genetic search)
  • Reinforcement learning (REINFORCE)
  • Gradient-based (DARTS)

• Hardware-Aware Optimization

  • Mobile deployment constraints
  • Edge device optimization
  • Latency and memory profiling
  • Model size optimization

• Multi-Objective Optimization

  • Balance accuracy, latency, and model size
  • Pareto front exploration
  • Custom objective weights
  • Hard constraint satisfaction

• Transfer Learning

  • Architecture repository
  • Similarity-based retrieval
  • Architecture adaptation
  • Knowledge accumulation

Quick NAS Example

from automl_lite import AutoMLite
from automl_lite.nas import NASConfig

# Configure NAS
config = NASConfig(
    search_strategy='evolutionary',
    time_budget=1800,  # 30 minutes
    enable_hardware_aware=True,
    target_hardware='mobile',
    max_latency_ms=100
)

# Run NAS
automl = AutoMLite(
    enable_deep_learning=True,
    enable_nas=True,
    nas_config=config
)

automl.fit(X_train, y_train)

# Explore results
print(f"Best accuracy: {automl.nas_result.best_accuracy:.3f}")
print(f"Architectures evaluated: {automl.nas_result.total_architectures_evaluated}")

# Get Pareto front for multi-objective optimization
for arch in automl.nas_result.pareto_front:
    print(f"Accuracy: {arch.metadata['accuracy']:.3f}, "
          f"Latency: {arch.metadata['latency']:.1f}ms, "
          f"Size: {arch.metadata['model_size']:.1f}MB")

NAS Use Cases

• Mobile Apps: Find architectures that run efficiently on smartphones
• Edge Devices: Optimize for IoT and embedded systems
• Production Systems: Balance accuracy with inference speed
• Research: Discover novel architectures for specific domains

🔥 Performance Metrics

Production Demo Results

• Training Time: 391.92 seconds for complete pipeline
• Best Model: Random Forest (80.00% accuracy)
• Feature Engineering: 20 → 232 features (11.6x expansion)
• Feature Selection: 132/166 features intelligently selected
• Hyperparameter Optimization: 50 trials with Optuna

🛠️ Advanced Usage

Custom Configuration

config = {
    'time_budget': 600,
    'max_models': 20,
    'cv_folds': 5,
    'feature_engineering': True,
    'ensemble_method': 'voting',
    'interpretability': True
}

automl = AutoMLite(**config)

Time Series Forecasting

automl = AutoMLite(problem_type='time_series')
model = automl.fit(data, target_column='sales', date_column='date')
forecast = automl.predict_future(periods=30)

Deep Learning

automl = AutoMLite(
    enable_deep_learning=True,
    deep_learning_framework='tensorflow'
)
model = automl.fit(data, target_column='target')

Neural Architecture Search (NAS)

from automl_lite.nas import NASConfig

# Basic NAS
automl = AutoMLite(
    enable_deep_learning=True,
    enable_nas=True,
    nas_time_budget=1800  # 30 minutes
)
model = automl.fit(data, target_column='target')

# Hardware-aware NAS for mobile deployment
config = NASConfig(
    search_strategy='evolutionary',
    enable_hardware_aware=True,
    target_hardware='mobile',
    max_latency_ms=100,
    max_memory_mb=50
)
automl = AutoMLite(enable_nas=True, nas_config=config)
model = automl.fit(data, target_column='target')

# Access NAS results
print(f"Best architecture accuracy: {automl.nas_result.best_accuracy:.3f}")
print(f"Pareto front size: {len(automl.nas_result.pareto_front)}")

📈 CLI Interface

# Basic usage
automl-lite train data.csv --target target_column

# With custom config
automl-lite train data.csv --target target_column --config config.yaml

# Generate report
automl-lite report --model model.pkl --output report.html

🎨 Interactive Dashboard

from automl_lite.ui import launch_dashboard
launch_dashboard(automl)

🔍 Model Interpretability

# Get SHAP values
shap_values = automl.explain_model(X_test)

# Feature importance
importance = automl.get_feature_importance()

# Partial dependence plots
automl.plot_partial_dependence('feature_name')

🎯 Use Cases

Perfect For:

• 🏢 Data Scientists - Rapid prototyping
• 🚀 ML Engineers - Production development
• 📊 Analysts - Quick insights
• 🎓 Students - Learning ML concepts
• 🏭 Startups - Fast MVP development

Industries:

• Finance: Credit scoring, fraud detection
• Healthcare: Disease prediction, monitoring
• E-commerce: Segmentation, forecasting
• Marketing: Campaign optimization
• Manufacturing: Predictive maintenance

🔧 Configuration Templates

• Basic: Quick experiments
• Production: Production deployment
• Research: Extensive search
• Customer Churn: Churn prediction
• Fraud Detection: Fraud detection
• House Price: Real estate prediction

📦 Installation Options

From PyPI (Recommended)

pip install automl-lite

With Neural Architecture Search (NAS)

pip install automl-lite[nas]

This installs additional dependencies for NAS:

• networkx - Architecture graph operations
• pygraphviz - Architecture visualization
• pymoo - Multi-objective optimization

From Source

git clone https://github.com/Sherin-SEF-AI/AutoML-Lite.git
cd AutoML-Lite
pip install -e .

# With NAS support
pip install -e ".[nas]"

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📚 Documentation & Resources

• 📖 Full Documentation: GitHub Wiki
• 🎯 API Reference: API Docs
• 🧠 NAS Documentation:
  • NAS API Reference
  • NAS User Guide
• 📝 Examples: Example Notebooks
  • Basic NAS Example
  • Hardware-Aware NAS
  • Multi-Objective NAS
  • Transfer Learning NAS
• 🚀 Quick Start: Installation Guide

💬 Join the Community

• 🌟 Star the Repository: GitHub
• 🐛 Report Issues: Issue Tracker
• 💡 Feature Requests: Discussions
• 📧 Contact: [e-mail]
• 🌐 Website: sherinjosephroy.link
• 💼 LinkedIn: linkedin.com/in/sherin-roy-deepmost

🏆 Why Choose AutoML Lite?

Feature	AutoML Lite	Other Libraries
Setup Time	30 seconds	30+ minutes
Configuration	Zero required	Complex configs
Production Ready	✅ Built-in	❌ Manual setup
Deep Learning	✅ Integrated	❌ Separate setup
Neural Architecture Search	✅ Built-in	❌ Not available
Hardware-Aware NAS	✅ Mobile/Edge support	❌ Not available
Time Series	✅ Native support	❌ Limited
Interpretability	✅ Advanced	❌ Basic
Experiment Tracking	✅ Multi-platform	❌ Limited
Interactive Reports	✅ Beautiful HTML	❌ Basic plots

🎯 Ready to Transform Your ML Workflow?

Stop spending hours on boilerplate code. Start building amazing ML models in minutes!

pip install automl-lite

Try it now and see the difference! 🚀

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Built with ❤️ by the AutoML Lite community

Tags: #python #machinelearning #automl #datascience #ml #ai #automation #productivity #opensource #deeplearning #timeseries #interpretability #experimenttracking #production #deployment #nas #neuralarchitecturesearch #hardwareaware #multiobjective #transferlearning