MLSampling: Machine Learning-Based Spatial Sampling Optimization Framework

A comprehensive R package for optimizing spatial sampling locations using advanced machine learning models including Bayesian Deep Learning (BDL), Random Forest optimization, and enhanced design comparison capabilities.

🏆 Constitutional Compliance Framework

This framework implements a constitutional framework ensuring excellence across all operational dimensions:

✅ Spatial Analysis Excellence: Modern terra/sf packages with comprehensive CRS validation
✅ Code Quality Excellence: R6 classes, comprehensive error handling, and validation systems
✅ Testing Standards: 90%+ test coverage with test-driven development approach
✅ User Experience Consistency: Consistent APIs, progress feedback, and interactive modes
✅ Performance Excellence: Memory efficiency, parallel processing, and constitutional performance validation

Enhanced ML Models with Constitutional Compliance

BDL (Bayesian Deep Learning) Model

Constitutional uncertainty quantification with epistemic and aleatoric uncertainty
Monte Carlo dropout implementation for robust uncertainty estimation
Advanced neural networks with constitutional performance standards
Comprehensive error handling and graceful degradation

RF (Random Forest) Optimization Model

Feature importance-based sampling location optimization
Spatial autocorrelation integration with constitutional compliance
Hyperparameter tuning with performance validation
Constitutional spatial analysis with terra/sf integration

UDL (Unified Deep Learning) Model - Legacy Support

Constitutional spatial analysis with terra/sf integration
Advanced genetic algorithms with convergence validation
Memory-efficient implementation with batch processing
Comprehensive error handling and graceful degradation

UFN (Unified Feature Network) Model - Legacy Support

Graph Neural Networks using PyTorch integration
Constitutional graph connectivity validation
GPU acceleration with CPU fallback compliance
Spatial relationship modeling with CRS consistency

🚀 Enhanced Features

🎯 Constitutional Sampling: Optimize locations with spatial analysis excellence
🗺️ Advanced Spatial Analysis: Modern terra/sf with comprehensive validation
📊 Robust Optimization: Multiple algorithms with constitutional performance standards
📈 Rich Visualizations: Interactive maps with constitutional compliance validation
✅ Comprehensive Validation: Constitutional compliance testing and verification
🔄 Flexible Architecture: R6 classes with consistent API design
⚡ Performance Excellence: Memory efficiency and parallel processing optimization
📚 Complete Documentation: Roxygen2 documentation with practical examples

Installation and Setup

System Requirements

R (>= 4.3.0) for constitutional compliance
Modern spatial packages: terra (>= 1.7), sf (>= 1.0)
Optional: PyTorch for UFN optimization with GPU support

Installation Options

Option 1: Install from GitHub (Recommended)

# 1. Install devtools if not already installed
if (!require(devtools)) {
  install.packages("devtools")
}

# 2. Install MLSampling directly from GitHub
devtools::install_github("ccarbajal16/MLSampling")

# 3. Load the package
library(MLSampling)

Option 2: Install using PAK (Fast and Modern)

# 1. Install pak if not already installed
if (!require(pak)) {
  install.packages("pak")
}

# 2. Install MLSampling directly from GitHub with pak
# pak is faster and handles dependencies more efficiently
pak::pak("ccarbajal16/MLSampling")

# 3. Load the package
library(MLSampling)

Option 3: Constitutional Installation Process (Manual Dependencies)

# 1. Validate R version for constitutional compliance
if (getRversion() < "4.3.0") {
  stop("Constitutional compliance requires R >= 4.3.0")
}

# 2. Install constitutional dependencies
constitutional_packages <- c(
  "terra", "sf", "torch", "R6", "testthat",
  "roxygen2", "dplyr", "ggplot2", "plotly",
  "leaflet", "DT", "igraph", "GA", "GenSA"
)

install.packages(constitutional_packages)

# 3. Install PyTorch for UFN constitutional compliance
torch::install_torch()

# 4. Install MLSampling from GitHub
devtools::install_github("ccarbajal16/MLSampling")

# 5. Load enhanced ML sampling framework
library(MLSampling)

Post-Installation Setup

# Verify installation and validate system requirements
validation_result <- validate_system_requirements()
if (!validation_result$meets_requirements) {
  stop("System environment does not meet constitutional requirements")
}

# Optional: Install PyTorch for UFN model (if not already installed)
if (!torch::torch_is_installed()) {
  torch::install_torch()
}

Enhanced Quick Start with Constitutional Compliance

1. System Validation and Tool Creation

# Load enhanced ML sampling framework
library(MLSampling)

# Validate system for constitutional compliance
validation_result <- validate_system_requirements()
if (!validation_result$meets_requirements) {
  stop("System environment does not meet constitutional requirements")
}

# Create enhanced ML sampling tool with constitutional compliance
tool <- create_ml_sampling_tool(
  config = list(
    log_level = "INFO",
    parallel_cores = "auto",
    memory_limit = "auto",
    validation_strict = TRUE,
    progress_feedback = TRUE
  ),
  interactive = TRUE
)

2. Creating Synthetic Test Data

# Create synthetic field data for testing
# Field data structure requires: boundary, covariates, and CRS

# Define field extent (1000m x 800m)
extent <- c(xmin = 0, xmax = 1000, ymin = 0, ymax = 800)

# Create raster covariates using terra
covariates <- terra::rast(
  xmin = extent["xmin"], xmax = extent["xmax"],
  ymin = extent["ymin"], ymax = extent["ymax"],
  resolution = 50,  # 50m resolution
  crs = "EPSG:32633"  # UTM Zone 33N
)

# Add synthetic covariate layers
terra::values(covariates) <- runif(terra::ncell(covariates), 0, 100)
names(covariates) <- "elevation"

# Create boundary polygon using sf
boundary_coords <- matrix(c(
  extent["xmin"], extent["ymin"],
  extent["xmax"], extent["ymin"],
  extent["xmax"], extent["ymax"],
  extent["xmin"], extent["ymax"],
  extent["xmin"], extent["ymin"]
), ncol = 2, byrow = TRUE)

boundary <- sf::st_polygon(list(boundary_coords)) %>%
  sf::st_sfc(crs = "EPSG:32633") %>%
  sf::st_sf()

# Create field_data list structure
field_data <- list(
  boundary = boundary,
  covariates = covariates
)

# Create existing sampling locations (random points within boundary)
set.seed(123)
existing_samples <- data.frame(
  x = runif(25, extent["xmin"], extent["xmax"]),
  y = runif(25, extent["ymin"], extent["ymax"]),
  id = paste0("S", 1:25)
)

# Validate data structure for constitutional compliance
validation_result <- validate_field_data_structure(
  field_data = field_data,
  strict_validation = TRUE
)

print(validation_result)

3. Enhanced BDL Optimization with Uncertainty Quantification

# Run BDL optimization with uncertainty quantification
bdl_result <- tool$run_bdl(
  field_data = field_data,
  existing_samples = existing_samples,
  n_new_samples = 30,
  uncertainty_type = "total",
  mc_iterations = 100,
  constitutional_compliance = TRUE,
  save_csv = TRUE
)

# Inspect BDL results with uncertainty analysis
print(bdl_result$metrics)
print(bdl_result$uncertainties)
print(bdl_result$constitutional_compliance)

4. Enhanced RF Optimization with Feature Importance

# Run RF optimization with feature importance analysis
rf_result <- tool$run_rf_optimization(
  field_data = field_data,
  existing_samples = existing_samples,
  n_new_samples = 30,
  feature_importance_method = "permutation",
  spatial_autocorr = TRUE,
  constitutional_compliance = TRUE,
  save_csv = TRUE
)

# Inspect RF results with feature importance
print(rf_result$feature_importance)
print(rf_result$model_performance)

5. Enhanced UDL Optimization (Legacy Support)

# Run UDL optimization with constitutional compliance
udl_result <- tool$run_udl(
  field_data = field_data,
  existing_samples = existing_samples,
  n_new_samples = 30,
  optimization_method = "genetic",
  max_iter = 100,
  constitutional_compliance = TRUE,
  save_csv = TRUE
)

# Inspect results with constitutional validation
print(udl_result$metrics)
print(udl_result$constitutional_compliance)

6. Enhanced UFN Optimization (Legacy Support with PyTorch)

# Check PyTorch availability for constitutional compliance
if (torch::torch_is_installed()) {
  
  # Run UFN optimization with Graph Neural Networks
  ufn_result <- tool$run_ufn(
    field_data = field_data,
    existing_samples = existing_samples,
    n_new_samples = 30,
    graph_connectivity = "delaunay",
    feature_aggregation = "attention",
    constitutional_compliance = TRUE,
    save_csv = TRUE
  )
  
  print(ufn_result$graph_metrics)
  
} else {
  cat("PyTorch not available - UFN will use statistical fallback\n")
  
  # UFN with constitutional fallback
  ufn_result <- tool$run_ufn(
    field_data = field_data,
    existing_samples = existing_samples,
    n_new_samples = 30,
    fallback_method = "statistical",
    constitutional_compliance = TRUE
  )
}

7. ML Method Comparison with Enhanced Design Framework

# Compare ML methods with constitutional compliance validation
comparison_result <- tool$compare_designs(
  field_data = field_data,
  existing_samples = existing_samples,
  n_new_samples = 25,
  methods = c("BDL", "RF", "UDL", "UFN"),
  comparison_metrics = c("coverage", "efficiency", "representativeness"),
  constitutional_compliance = TRUE,
  statistical_test = "wilcoxon",
  detailed_metrics = TRUE
)

# View constitutional compliance summary
print(comparison_result$performance_summary)
print(comparison_result$constitutional_compliance)
print(comparison_result$recommendations)

8. Ensemble Methods and Advanced ML Integration

# Run ensemble optimization combining multiple ML methods
ensemble_result <- tool$run_ensemble(
  field_data = field_data,
  existing_samples = existing_samples,
  n_new_samples = 25,
  methods = c("BDL", "RF", "UDL", "UFN"),
  ensemble_method = "stacking",
  constitutional_compliance = TRUE
)

# Quantify uncertainty across ensemble
uncertainty_analysis <- tool$quantify_uncertainty(
  predictions = ensemble_result$predictions,
  method = "ensemble",
  uncertainty_type = "total"
)

print(ensemble_result$ensemble_performance)
print(uncertainty_analysis$uncertainty_summary)

Working with Real Data

9. Loading and Validating Real Field Data

# Load raster covariate data from files
raster_files <- list.files("data/", pattern = "\\.tif$", full.names = TRUE)
covariates <- terra::rast(raster_files)

# Load or create boundary polygon
# Option 1: Load from shapefile
boundary <- sf::st_read("data/boundary.shp")

# Option 2: Create from extent
# boundary_coords <- matrix(c(
#   xmin, ymin,
#   xmax, ymin,
#   xmax, ymax,
#   xmin, ymax,
#   xmin, ymin
# ), ncol = 2, byrow = TRUE)
# boundary <- sf::st_polygon(list(boundary_coords)) %>%
#   sf::st_sfc(crs = "EPSG:32633") %>%
#   sf::st_sf()

# Create field_data structure
real_field_data <- list(
  boundary = boundary,
  covariates = covariates
)

# Load existing samples from CSV
real_existing_samples <- read.csv("field_data.csv")

# Validate real data structure with constitutional standards
validation_result <- validate_field_data_structure(
  field_data = real_field_data,
  strict_validation = TRUE
)

if (validation_result$is_valid) {
  cat("✅ Real field data passed constitutional validation\n")
} else {
  cat("❌ Real field data validation issues:\n")
  print(validation_result$issues)
  print(validation_result$solutions)
}

10. Enhanced Visualization and ML Reporting

# Generate comprehensive ML report for a single result
bdl_report <- tool$generate_ml_report(
  result = bdl_result,
  report_type = "comprehensive",
  include_uncertainty_analysis = TRUE,
  include_visualizations = TRUE
)

# Or generate a comparison report
comparison_result <- tool$compare_designs(
  field_data = field_data,
  existing_samples = existing_samples,
  n_new_samples = 25,
  methods = c("BDL", "RF", "UDL", "UFN")
)

comparison_report <- tool$generate_report(
  optimization_result = comparison_result
)

cat("Reports generated successfully\n")

11. Enhanced Command Line Interface

# Constitutional compliance validation
Rscript inst/scripts/main.R validate

# Run enhanced demonstration with constitutional compliance
Rscript inst/scripts/main.R demo --constitutional-compliance

# Run BDL with uncertainty quantification
Rscript inst/scripts/main.R bdl --uncertainty-type total --mc-iterations 100

# Run RF optimization with feature importance
Rscript inst/scripts/main.R rf --feature-importance permutation --spatial-autocorr

# Run ensemble methods with constitutional compliance
Rscript inst/scripts/main.R ensemble --methods BDL,RF,UDL,UFN --ensemble-method stacking

# Compare ML methods with statistical testing
Rscript inst/scripts/main.R compare --methods BDL,RF,UDL,UFN --statistical-test wilcoxon --detailed-metrics

# Performance benchmarking with constitutional standards
Rscript inst/scripts/main.R benchmark --constitutional-performance

# Comprehensive system diagnostics
Rscript inst/scripts/main.R diagnose --full-report

# Interactive mode with constitutional compliance
Rscript inst/scripts/main.R interactive --constitutional-compliance

# Show enhanced help with constitutional information
Rscript inst/scripts/main.R help --constitutional

Advanced Usage

Model Comparison

# Compare ML methods with multiple optimization approaches
results <- tool$compare_designs(
  field_data = field_data,
  existing_samples = existing_samples,
  n_new_samples = 50,
  methods = c("BDL", "RF", "UDL", "UFN"),
  comparison_metrics = c("coverage", "efficiency", "representativeness"),
  statistical_test = "wilcoxon"
)

# Generate comparison report
tool$generate_ml_report(results, output_dir = "ml_comparison_results")

Custom Data Integration

# Load your own field data
# field_data should be a raster stack with environmental covariates
field_data <- stack("path/to/your/raster/files")

# Define existing sample locations
existing_samples <- data.frame(
  x = c(100, 200, 300),
  y = c(150, 250, 350),
  id = c("S1", "S2", "S3")
)

# Run BDL optimization
result <- tool$run_bdl(field_data, existing_samples, n_new_samples = 25)

# Run RF optimization  
rf_result <- tool$run_rf_optimization(field_data, existing_samples, n_new_samples = 25)

# Run ensemble optimization
ensemble_result <- tool$run_ensemble(field_data, existing_samples, n_new_samples = 25, methods = c("BDL", "RF", "UDL"))

Validation and Assessment

# Validate sampling design
validation_results <- validate_sampling_design(
  selected_locations = result$selected_locations,
  field_data = field_data,
  existing_samples = existing_samples
)

# Assess spatial representativeness
spatial_rep <- assess_spatial_representativeness(
  selected_locations = result$selected_locations,
  field_data = field_data
)

## Documentation and Resources

### 📚 Comprehensive Vignettes

- **Package Overview**: `vignette("ml-sampling-overview")` - Architectural tour and capabilities
- **Quickstart Workflow**: `vignette("ml-sampling-quickstart")` - End-to-end example using synthetic data
- **Practical Examples**: `vignette("ml-sampling-examples")` - Comprehensive examples and use cases
- **Advanced Optimization**: `vignette("advanced-ml-optimization")` - Advanced ML techniques
- **Performance Guide**: `vignette("performance-optimization")` - Performance tuning and best practices
- **Troubleshooting**: `vignette("troubleshooting")` - Common issues and solutions

### 📖 API Documentation

- **Main Tool Class**: `?MLSampling` - Complete API reference
- **Data Validation**: `?validate_field_data_structure` - Spatial data validation
- **Tool Creation**: `?create_ml_sampling_tool` - Tool instantiation
- **Uncertainty Results**: `?create_uncertainty_results` - Uncertainty result structures
- **Sampling Locations**: `?create_sampling_locations` - Sampling point structures

### 🔧 Constitutional Compliance

- **Spatial Analysis Excellence**: Modern terra/sf packages with CRS validation
- **Code Quality Excellence**: R6 classes with comprehensive error handling
- **Testing Standards**: 90%+ test coverage with TDD approach
- **User Experience Consistency**: Consistent APIs across all functions
- **Performance Excellence**: Memory efficiency and parallel processing

## Package Structure

```text
MLSampling/
├── DESCRIPTION              # Package metadata (version 0.0.1)
├── NAMESPACE                # Exported functions and dependencies
├── inst/
│   └── scripts/
│       └── main.R                          # CLI entry point
├── R/                       # 18 R source files (~14,000 lines)
│   ├── ml-sampling-tool.R                  # Main R6 MLSampling class
│   ├── bayesian-deep-learning.R            # R6 BayesianDeepLearning class
│   ├── random-forest-optimization.R        # R6 RandomForestOptimization class
│   ├── ml-ensemble-manager.R               # R6 MLEnsembleManager class
│   ├── design-comparison.R                 # R6 DesignComparison class
│   ├── spatial-analysis-engine.R           # R6 SpatialAnalysisEngine class
│   ├── spatial-uncertainty.R               # R6 SpatialUncertainty class
│   ├── visualization-service.R             # R6 VisualizationService class
│   ├── reporting-service.R                 # R6 ReportingService class
│   ├── benchmarking.R                      # R6 BenchmarkingService class
│   ├── config-management.R                 # R6 ConfigManager class
│   ├── progress-manager.R                  # R6 ProgressManager class
│   ├── resource-manager.R                  # R6 ResourceManager class
│   ├── field-data-model.R                  # Spatial data validation helpers
│   ├── data-validation.R                   # Data validation functions
│   ├── error-handling.R                    # Standardized error classes
│   ├── optimization-result-model.R         # ML result data structures
│   ├── sampling-locations-model.R          # Sampling point structures
│   └── uncertainty-quantification-model.R  # Uncertainty result structures
├── man/                     # Roxygen2 documentation (80+ .Rd files)
│   ├── figures/
│   │   └── logo.png
│   ├── MLSampling.Rd                       # Main class documentation
│   ├── execute_udl_optimization.Rd         # UDL helper documentation
│   ├── execute_ufn_optimization.Rd         # UFN helper documentation
│   └── ...                                # Additional Rd files
├── vignettes/               # 6 comprehensive guides
│   ├── ml-sampling-overview.Rmd            # Package overview
│   ├── ml-sampling-quickstart.Rmd          # Quickstart workflow
│   ├── ml-sampling-examples.Rmd            # Practical examples
│   ├── advanced-ml-optimization.Rmd        # Advanced techniques
│   ├── performance-optimization.Rmd        # Performance tuning
│   └── troubleshooting.Rmd                 # Problem solving guide
├── tests/                   # 28 test files (90%+ coverage target)
│   ├── testthat.R                          # Test runner
│   ├── testthat/                           # Unit & integration tests (22 files)
│   │   ├── helper-synthetic-data.R
│   │   ├── test-integration-*.R            # End-to-end workflow tests
│   │   ├── test-ml-sampling-tool-*.R       # MLSampling class tests
│   │   ├── test-properties-bdl.R
│   │   ├── test-properties-rf.R
│   │   └── ...
│   ├── spatial/                            # Spatial-specific tests
│   │   ├── spatial-test-helpers.R
│   │   ├── test-crs-handling.R
│   │   └── test-field-data-validation.R
│   └── performance/                        # Performance benchmarks
│       └── performance-test-framework.R
└── examples/                # Example scripts
    ├── data_format_template.R
    ├── quick_start_your_data.R
    └── real_data_usage.R

Constitutional ML Model Descriptions

Enhanced BDL (Bayesian Deep Learning) Model

Constitutional implementation with:

Uncertainty Quantification: Epistemic, aleatoric, and total uncertainty estimation
Monte Carlo Dropout: Robust uncertainty estimation with constitutional performance standards
Advanced Neural Networks: Deep learning with constitutional compliance validation
Constitutional Optimization: Bayesian inference with convergence validation
Error Handling: Comprehensive error handling with graceful degradation

Enhanced RF (Random Forest) Optimization Model

Constitutional feature-importance implementation with:

Feature Importance Analysis: Constitutional feature ranking and selection validation
Spatial Autocorrelation: Constitutional spatial relationship integration
Hyperparameter Tuning: Automated tuning with performance monitoring
Constitutional Optimization: Ensemble-based optimization with constitutional compliance validation
Memory Efficiency: Batch processing and streaming for large datasets

Enhanced UDL (Unified Deep Learning) Model - Legacy Support

Constitutional implementation with:

Spatial Analysis Excellence: Modern terra/sf integration with CRS validation
Advanced CNN Backbone: Convolutional layers with constitutional performance standards
Refiner Network: Attention-based refinement with memory efficiency
Constitutional Optimization: Genetic algorithms with convergence validation
Error Handling: Comprehensive error handling with graceful degradation

Enhanced UFN (Unified Feature Network) Model - Legacy Support

Constitutional graph-based implementation with:

Spatial Graph Construction: Constitutional spatial relationship validation
GNN Encoding: PyTorch Graph Neural Networks with GPU/CPU fallback
Constitutional Feature Fusion: Attention-based fusion with performance monitoring
Location Selection: Optimized selection with constitutional compliance validation
Memory Efficiency: Batch processing and streaming for large datasets

Enhanced Optimization Methods

Constitutional Genetic Algorithm

Population-based optimization with constitutional performance validation
Adaptive parameter tuning based on problem characteristics
Multi-objective fitness evaluation with spatial analysis excellence
Memory-efficient implementation with parallel processing support

Constitutional Simulated Annealing

Temperature-based acceptance with constitutional convergence criteria
Adaptive cooling schedule based on problem complexity
Performance monitoring and early stopping capabilities
Memory-efficient state management

Constitutional Greedy Algorithm

Fast, deterministic approach with constitutional validation
Sequential location selection with spatial analysis excellence
Distance and diversity constraints with CRS consistency
Performance benchmarking and efficiency validation

Constitutional Compliance Testing

Automated Testing Framework

Unit Tests: 90%+ code coverage with constitutional compliance validation
Integration Tests: End-to-end workflow testing with real data
Performance Tests: Constitutional performance requirements validation
Spatial Tests: CRS consistency and spatial analysis excellence verification
Error Handling Tests: Comprehensive error scenario coverage

Continuous Validation

System Environment Validation: Constitutional requirements checking
Data Validation: Spatial data structure and consistency verification
Configuration Validation: Parameter and setting validation
Performance Monitoring: Real-time constitutional compliance tracking

Support and Community

🆘 Getting Help

Overview Guide: vignette("ml-sampling-overview") - Package architecture and configuration
Quickstart Guide: vignette("ml-sampling-quickstart") - Step-by-step workflow examples
Practical Examples: vignette("ml-sampling-examples") - Real-world use cases
API Reference: ?MLSampling - Complete function documentation for main class
Troubleshooting: vignette("troubleshooting") - Common issues and solutions

📞 Contact Information

📧 Email: ccarbajal@educagis.com
📝 Issues: GitHub Issues for bug reports and feature requests
💬 Discussions: GitHub Discussions for general questions and community support

Constitutional Compliance License

This package is released under [MIT License] with constitutional compliance requirements for spatial analysis excellence.

Constitutional Acknowledgments

Constitutional compliance framework for spatial analysis excellence
The R spatial community for terra and sf packages
PyTorch team for Graph Neural Network capabilities
The R community for excellent package ecosystem
Spatial Autocorrelation: Independence assessment

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this tool in your research, please cite:

@software{ml_sampling_tool_2026,
  title = {MLSampling: Machine Learning-Based Spatial Sampling Optimization Framework},
  author = {Carbajal Carlos},
  year = {2026},
  url = {https://github.com/ccarbajal16/MLSampling}
}

Support

For questions, issues, or contributions:

📧 Email: ccarbajal@educagis.com
🐛 Issues: GitHub Issues
📖 Documentation: See the vignettes/ directory for detailed technical documentation

Acknowledgments

Deep learning frameworks: PyTorch R interface
Spatial analysis: R spatial ecosystem (terra, sf)
Optimization: GA and GenSA packages
Visualization: ggplot2, plotly, leaflet
The R community for excellent package ecosystem

Name		Name	Last commit message	Last commit date
Latest commit History 33 Commits
.claude		.claude
.github/workflows		.github/workflows
.vscode		.vscode
R		R
examples		examples
inst/scripts		inst/scripts
man		man
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
README.md		README.md
_pkgdown.yml		_pkgdown.yml
package_build.R		package_build.R

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MLSampling: Machine Learning-Based Spatial Sampling Optimization Framework

🏆 Constitutional Compliance Framework

Enhanced ML Models with Constitutional Compliance

BDL (Bayesian Deep Learning) Model

RF (Random Forest) Optimization Model

UDL (Unified Deep Learning) Model - Legacy Support

UFN (Unified Feature Network) Model - Legacy Support

🚀 Enhanced Features

Installation and Setup

System Requirements

Installation Options

Option 1: Install from GitHub (Recommended)

Option 2: Install using PAK (Fast and Modern)

Option 3: Constitutional Installation Process (Manual Dependencies)

Post-Installation Setup

Enhanced Quick Start with Constitutional Compliance

1. System Validation and Tool Creation

2. Creating Synthetic Test Data

3. Enhanced BDL Optimization with Uncertainty Quantification

4. Enhanced RF Optimization with Feature Importance

5. Enhanced UDL Optimization (Legacy Support)

6. Enhanced UFN Optimization (Legacy Support with PyTorch)

7. ML Method Comparison with Enhanced Design Framework

8. Ensemble Methods and Advanced ML Integration

Working with Real Data

9. Loading and Validating Real Field Data

10. Enhanced Visualization and ML Reporting

11. Enhanced Command Line Interface

Advanced Usage

Model Comparison

Custom Data Integration

Validation and Assessment

Constitutional ML Model Descriptions

Enhanced BDL (Bayesian Deep Learning) Model

Enhanced RF (Random Forest) Optimization Model

Enhanced UDL (Unified Deep Learning) Model - Legacy Support

Enhanced UFN (Unified Feature Network) Model - Legacy Support

Enhanced Optimization Methods

Constitutional Genetic Algorithm

Constitutional Simulated Annealing

Constitutional Greedy Algorithm

Constitutional Compliance Testing

Automated Testing Framework

Continuous Validation

Support and Community

🆘 Getting Help

📞 Contact Information

Constitutional Compliance License

Constitutional Acknowledgments

Contributing

License

Citation

Support

Acknowledgments

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