regression_analysis_comprehensive.rs

//! Comprehensive Regression Analysis Examples
//!
//! This example demonstrates EVERY aspect of the new Regression Analysis system :
//! - `RegressionAnalyzer` with all baseline strategies (Fixed, Rolling Average, Previous Run)
//! - `HistoricalResults` management and `TimestampedResults` creation
//! - Performance trend detection (Improving, Degrading, Stable)  
//! - Statistical significance testing with configurable thresholds
//! - Professional markdown report generation with regression insights
//! - Integration with `PerformanceReport` templates
//! - Real-world scenarios: code optimization, library upgrades, performance monitoring

#![ cfg( feature = "enabled" ) ]
#![ cfg( feature = "markdown_reports" ) ]
#![ allow( clippy ::uninlined_format_args ) ]
#![ allow( clippy ::format_push_string ) ]
#![ allow( clippy ::cast_lossless ) ]
#![ allow( clippy ::cast_possible_truncation ) ]
#![ allow( clippy ::cast_precision_loss ) ]
#![ allow( clippy ::std_instead_of_core ) ]
#![ allow( clippy ::needless_raw_string_hashes ) ]
#![ allow( clippy ::too_many_lines ) ]

use benchkit ::prelude :: *;
use std ::collections ::HashMap;
use std ::time :: { Duration, SystemTime };

/// Create current benchmark results showing performance improvements
fn create_current_results() -> HashMap< String, BenchmarkResult >
{
  let mut results = HashMap ::new();
  
  // Fast sort algorithm - recently optimized, showing improvement
  let fast_sort_times = vec![
  Duration ::from_micros( 85 ), Duration ::from_micros( 88 ), Duration ::from_micros( 82 ),
  Duration ::from_micros( 87 ), Duration ::from_micros( 84 ), Duration ::from_micros( 86 ),
  Duration ::from_micros( 89 ), Duration ::from_micros( 81 ), Duration ::from_micros( 88 ),
  Duration ::from_micros( 85 ), Duration ::from_micros( 87 ), Duration ::from_micros( 83 ),
  Duration ::from_micros( 86 ), Duration ::from_micros( 84 ), Duration ::from_micros( 88 )
 ];
  results.insert( "fast_sort".to_string(), BenchmarkResult ::new( "fast_sort", fast_sort_times ) );
  
  // Hash function - stable performance
  let hash_times = vec![
  Duration ::from_nanos( 150 ), Duration ::from_nanos( 152 ), Duration ::from_nanos( 148 ),
  Duration ::from_nanos( 151 ), Duration ::from_nanos( 149 ), Duration ::from_nanos( 150 ),
  Duration ::from_nanos( 153 ), Duration ::from_nanos( 147 ), Duration ::from_nanos( 151 ),
  Duration ::from_nanos( 150 ), Duration ::from_nanos( 152 ), Duration ::from_nanos( 149 )
 ];
  results.insert( "hash_function".to_string(), BenchmarkResult ::new( "hash_function", hash_times ) );
  
  // Memory allocator - performance regression after system update
  let allocator_times = vec![
  Duration ::from_micros( 320 ), Duration ::from_micros( 335 ), Duration ::from_micros( 315 ),
  Duration ::from_micros( 330 ), Duration ::from_micros( 325 ), Duration ::from_micros( 340 ),
  Duration ::from_micros( 310 ), Duration ::from_micros( 345 ), Duration ::from_micros( 318 ),
  Duration ::from_micros( 332 ), Duration ::from_micros( 327 ), Duration ::from_micros( 338 )
 ];
  results.insert( "memory_allocator".to_string(), BenchmarkResult ::new( "memory_allocator", allocator_times ) );
  
  results
}

/// Create historical baseline data for fixed baseline strategy
fn create_baseline_historical_data() -> HistoricalResults
{
  let mut baseline_data = HashMap ::new();
  
  // Baseline: fast_sort before optimization (slower performance)
  let baseline_fast_sort = vec![
  Duration ::from_micros( 110 ), Duration ::from_micros( 115 ), Duration ::from_micros( 108 ),
  Duration ::from_micros( 112 ), Duration ::from_micros( 117 ), Duration ::from_micros( 111 ),
  Duration ::from_micros( 114 ), Duration ::from_micros( 107 ), Duration ::from_micros( 113 ),
  Duration ::from_micros( 109 ), Duration ::from_micros( 116 ), Duration ::from_micros( 106 )
 ];
  baseline_data.insert( "fast_sort".to_string(), BenchmarkResult ::new( "fast_sort", baseline_fast_sort ) );
  
  // Baseline: hash_function (similar performance)
  let baseline_hash = vec![
  Duration ::from_nanos( 148 ), Duration ::from_nanos( 152 ), Duration ::from_nanos( 146 ),
  Duration ::from_nanos( 150 ), Duration ::from_nanos( 154 ), Duration ::from_nanos( 147 ),
  Duration ::from_nanos( 151 ), Duration ::from_nanos( 149 ), Duration ::from_nanos( 153 ),
  Duration ::from_nanos( 148 ), Duration ::from_nanos( 152 ), Duration ::from_nanos( 150 )
 ];
  baseline_data.insert( "hash_function".to_string(), BenchmarkResult ::new( "hash_function", baseline_hash ) );
  
  // Baseline: memory_allocator before system update (better performance)
  let baseline_allocator = vec![
  Duration ::from_micros( 280 ), Duration ::from_micros( 285 ), Duration ::from_micros( 275 ),
  Duration ::from_micros( 282 ), Duration ::from_micros( 287 ), Duration ::from_micros( 278 ),
  Duration ::from_micros( 284 ), Duration ::from_micros( 276 ), Duration ::from_micros( 283 ),
  Duration ::from_micros( 279 ), Duration ::from_micros( 286 ), Duration ::from_micros( 277 )
 ];
  baseline_data.insert( "memory_allocator".to_string(), BenchmarkResult ::new( "memory_allocator", baseline_allocator ) );
  
  HistoricalResults ::new().with_baseline( baseline_data )
}

/// Create historical runs for rolling average strategy
fn create_rolling_average_historical_data() -> HistoricalResults
{
  let mut historical_runs = Vec ::new();
  
  // Historical run 1 : 2 weeks ago
  let mut run1_results = HashMap ::new();
  let run1_fast_sort = vec![ Duration ::from_micros( 120 ), Duration ::from_micros( 125 ), Duration ::from_micros( 118 ) ];
  let run1_hash = vec![ Duration ::from_nanos( 155 ), Duration ::from_nanos( 160 ), Duration ::from_nanos( 150 ) ];
  let run1_allocator = vec![ Duration ::from_micros( 290 ), Duration ::from_micros( 295 ), Duration ::from_micros( 285 ) ];
  
  run1_results.insert( "fast_sort".to_string(), BenchmarkResult ::new( "fast_sort", run1_fast_sort ) );
  run1_results.insert( "hash_function".to_string(), BenchmarkResult ::new( "hash_function", run1_hash ) );
  run1_results.insert( "memory_allocator".to_string(), BenchmarkResult ::new( "memory_allocator", run1_allocator ) );
  
  historical_runs.push( TimestampedResults ::new(
  SystemTime ::now() - Duration ::from_secs( 1_209_600 ), // 2 weeks ago
  run1_results
 ) );
  
  // Historical run 2 : 1 week ago
  let mut run2_results = HashMap ::new();
  let run2_fast_sort = vec![ Duration ::from_micros( 100 ), Duration ::from_micros( 105 ), Duration ::from_micros( 98 ) ];
  let run2_hash = vec![ Duration ::from_nanos( 150 ), Duration ::from_nanos( 155 ), Duration ::from_nanos( 145 ) ];
  let run2_allocator = vec![ Duration ::from_micros( 285 ), Duration ::from_micros( 290 ), Duration ::from_micros( 280 ) ];
  
  run2_results.insert( "fast_sort".to_string(), BenchmarkResult ::new( "fast_sort", run2_fast_sort ) );
  run2_results.insert( "hash_function".to_string(), BenchmarkResult ::new( "hash_function", run2_hash ) );
  run2_results.insert( "memory_allocator".to_string(), BenchmarkResult ::new( "memory_allocator", run2_allocator ) );
  
  historical_runs.push( TimestampedResults ::new(
  SystemTime ::now() - Duration ::from_secs( 604_800 ), // 1 week ago  
  run2_results
 ) );
  
  // Historical run 3 : 3 days ago
  let mut run3_results = HashMap ::new();
  let run3_fast_sort = vec![ Duration ::from_micros( 95 ), Duration ::from_micros( 98 ), Duration ::from_micros( 92 ) ];
  let run3_hash = vec![ Duration ::from_nanos( 148 ), Duration ::from_nanos( 153 ), Duration ::from_nanos( 147 ) ];
  let run3_allocator = vec![ Duration ::from_micros( 305 ), Duration ::from_micros( 310 ), Duration ::from_micros( 300 ) ];
  
  run3_results.insert( "fast_sort".to_string(), BenchmarkResult ::new( "fast_sort", run3_fast_sort ) );
  run3_results.insert( "hash_function".to_string(), BenchmarkResult ::new( "hash_function", run3_hash ) );
  run3_results.insert( "memory_allocator".to_string(), BenchmarkResult ::new( "memory_allocator", run3_allocator ) );
  
  historical_runs.push( TimestampedResults ::new(
  SystemTime ::now() - Duration ::from_secs( 259_200 ), // 3 days ago
  run3_results
 ) );
  
  HistoricalResults ::new().with_historical_runs( historical_runs )
}

/// Create previous run data for previous run strategy
fn create_previous_run_historical_data() -> HistoricalResults
{
  let mut previous_results = HashMap ::new();
  
  // Previous run: yesterday's results
  let prev_fast_sort = vec![ Duration ::from_micros( 90 ), Duration ::from_micros( 95 ), Duration ::from_micros( 88 ) ];
  let prev_hash = vec![ Duration ::from_nanos( 149 ), Duration ::from_nanos( 154 ), Duration ::from_nanos( 146 ) ];
  let prev_allocator = vec![ Duration ::from_micros( 295 ), Duration ::from_micros( 300 ), Duration ::from_micros( 290 ) ];
  
  previous_results.insert( "fast_sort".to_string(), BenchmarkResult ::new( "fast_sort", prev_fast_sort ) );
  previous_results.insert( "hash_function".to_string(), BenchmarkResult ::new( "hash_function", prev_hash ) );
  previous_results.insert( "memory_allocator".to_string(), BenchmarkResult ::new( "memory_allocator", prev_allocator ) );
  
  let previous_run = TimestampedResults ::new(
  SystemTime ::now() - Duration ::from_secs( 86_400 ), // 1 day ago
  previous_results
 );
  
  HistoricalResults ::new().with_previous_run( previous_run )
}

/// Demonstrate Fixed Baseline Strategy
fn demonstrate_fixed_baseline_strategy()
{
  println!( "🎯 FIXED BASELINE STRATEGY DEMONSTRATION" );
  println!( "=========================================" );
  println!( "Comparing current performance against a fixed baseline measurement." );
  println!( "Use case: Long-term performance tracking against a stable reference point.\n" );
  
  let current_results = create_current_results();
  let historical = create_baseline_historical_data();
  
  // Create analyzer with strict significance threshold
  let analyzer = RegressionAnalyzer ::new()
  .with_baseline_strategy( BaselineStrategy ::FixedBaseline )
  .with_significance_threshold( 0.01 )  // 1% significance level (very strict)
  .with_trend_window( 5 );
  
  let regression_report = analyzer.analyze( &current_results, &historical );
  
  // Display analysis results
  println!( "📊 REGRESSION ANALYSIS RESULTS: " );
  println!( "--------------------------------" );
  
  for operation in [ "fast_sort", "hash_function", "memory_allocator" ]
  {
  if let Some( trend ) = regression_report.get_trend_for( operation )
  {
   let significance = if regression_report.is_statistically_significant( operation )
   {
  "✓ Statistically Significant"
 }
   else
   {
  "- Not Significant"
 };
   
   let trend_emoji = match trend
   {
  PerformanceTrend ::Improving => "🟢 IMPROVING",
  PerformanceTrend ::Degrading => "🔴 DEGRADING", 
  PerformanceTrend ::Stable => "🟡 STABLE",
 };
   
   println!( "  {} - {} ({})", operation, trend_emoji, significance );
 }
 }
  
  // Generate markdown report
  let markdown_report = regression_report.format_markdown();
  println!( "\n📝 GENERATED MARKDOWN REPORT: " );
  println!( "------------------------------" );
  println!( "{}", markdown_report );
  println!( "\n" );
}

/// Demonstrate Rolling Average Strategy  
fn demonstrate_rolling_average_strategy()
{
  println!( "📈 ROLLING AVERAGE STRATEGY DEMONSTRATION" );
  println!( "==========================================" );
  println!( "Comparing current performance against rolling average of recent runs." );
  println!( "Use case: Detecting gradual performance trends over time.\n" );
  
  let current_results = create_current_results();
  let historical = create_rolling_average_historical_data();
  
  // Create analyzer optimized for trend detection
  let analyzer = RegressionAnalyzer ::new()
  .with_baseline_strategy( BaselineStrategy ::RollingAverage )
  .with_significance_threshold( 0.05 )  // 5% significance level (moderate)
  .with_trend_window( 3 );  // Look at last 3 runs for trend analysis
  
  let regression_report = analyzer.analyze( &current_results, &historical );
  
  // Display comprehensive analysis
  println!( "📊 TREND ANALYSIS RESULTS: " );
  println!( "--------------------------" );
  
  for operation in [ "fast_sort", "hash_function", "memory_allocator" ]
  {
  if regression_report.has_historical_data( operation )
  {
   let trend = regression_report.get_trend_for( operation ).unwrap();
   let significance = regression_report.is_statistically_significant( operation );
   
   println!( "  🔍 {} Analysis: ", operation );
   println!( "     Trend: {:?}", trend );
   println!( "     Statistical Significance: {}", if significance { "Yes" } else { "No" } );
   println!( "     Historical Data Points: Available" );
   println!();
 }
 }
  
  // Check overall report status
  if regression_report.has_significant_changes()
  {
  println!( "⚠️  ALERT: Significant performance changes detected!" );
 }
  else
  {
  println!( "✅ STATUS: Performance within normal variation ranges" );
 }
  
  println!( "\n" );
}

/// Demonstrate Previous Run Strategy
fn demonstrate_previous_run_strategy()
{
  println!( "⏮️  PREVIOUS RUN STRATEGY DEMONSTRATION" );
  println!( "=======================================" );
  println!( "Comparing current performance against the immediate previous run." );
  println!( "Use case: Detecting immediate impact of recent changes.\n" );
  
  let current_results = create_current_results();
  let historical = create_previous_run_historical_data();
  
  // Create analyzer for immediate change detection  
  let analyzer = RegressionAnalyzer ::new()
  .with_baseline_strategy( BaselineStrategy ::PreviousRun )
  .with_significance_threshold( 0.10 )  // 10% significance level (lenient)
  .with_trend_window( 2 );  // Only compare current vs previous
  
  let regression_report = analyzer.analyze( &current_results, &historical );
  
  // Display immediate change analysis
  println!( "📊 IMMEDIATE CHANGE ANALYSIS: " );
  println!( "-----------------------------" );
  
  if regression_report.has_previous_run_data()
  {
  for operation in [ "fast_sort", "hash_function", "memory_allocator" ]
  {
   if let Some( trend ) = regression_report.get_trend_for( operation )
   {
  let change_indicator = match trend
  {
   PerformanceTrend ::Improving => "↗️ Performance improved since last run",
   PerformanceTrend ::Degrading => "↘️ Performance degraded since last run",
   PerformanceTrend ::Stable => "➡️ Performance stable since last run",
 };
  
  println!( "  {} - {}", operation, change_indicator );
 }
 }
 }
  else
  {
  println!( "  ❌ No previous run data available for comparison" );
 }
  
  println!( "\n" );
}

/// Demonstrate comprehensive template integration
fn demonstrate_template_integration()
{
  println!( "📋 PERFORMANCE REPORT TEMPLATE INTEGRATION" );
  println!( "===========================================" );
  println!( "Demonstrating full integration with PerformanceReport templates." );
  println!( "Use case: Automated performance documentation with regression insights.\n" );
  
  let current_results = create_current_results();
  let historical = create_rolling_average_historical_data();
  
  // Create comprehensive performance report with regression analysis
  let template = PerformanceReport ::new()
  .title( "Algorithm Performance Analysis with Regression Detection" )
  .add_context( "Comprehensive analysis after code optimization and system updates" )
  .include_statistical_analysis( true )
  .include_regression_analysis( true )
  .with_historical_data( historical )
  .add_custom_section( CustomSection ::new(
   "Optimization Impact Analysis",
   r#"### Key Changes Made

- **fast_sort** : Applied cache-friendly memory access patterns
- **hash_function** : No changes (stable baseline)  
- **memory_allocator** : System update may have introduced overhead

### Expected Outcomes

- fast_sort should show significant improvement
- hash_function should remain stable
- memory_allocator performance needs investigation"#
 ) );
  
  match template.generate( &current_results )
  {
  Ok( report ) =>
  {
   println!( "✅ GENERATED COMPREHENSIVE PERFORMANCE REPORT: " );
   println!( "----------------------------------------------" );
   
   // Display key sections
   let lines: Vec< &str > = report.lines().collect();
   let mut in_regression_section = false;
   let mut regression_lines = Vec ::new();
   
   for line in lines
   {
  if line.contains( "## Regression Analysis" )
  {
   in_regression_section = true;
 }
  else if line.starts_with( "## " ) && in_regression_section
  {
   break;
 }
  
  if in_regression_section
  {
   regression_lines.push( line );
 }
 }
   
   if !regression_lines.is_empty()
   {
  println!( "📊 REGRESSION ANALYSIS SECTION: " );
  for line in regression_lines.iter().take( 15 )  // Show first 15 lines
  {
   println!( "{}", line );
 }
  if regression_lines.len() > 15
  {
   println!( "... ({} more lines)", regression_lines.len() - 15 );
 }
 }
   
   // Report statistics
   let report_size = report.len();
   let line_count = report.matches( '\n' ).count();
   println!( "\n📈 REPORT STATISTICS: " );
   println!( "    Size: {} characters", report_size );
   println!( "    Lines: {} lines", line_count );
   println!( "    Includes: Executive Summary, Performance Results, Statistical Analysis, Regression Analysis, Custom Sections" );
 },
  Err( e ) =>
  {
   println!( "❌ ERROR generating report: {}", e );
 }
 }
  
  println!( "\n" );
}

/// Demonstrate statistical significance tuning
fn demonstrate_significance_tuning()
{
  println!( "🎛️  STATISTICAL SIGNIFICANCE TUNING" );
  println!( "===================================" );
  println!( "Demonstrating how different significance thresholds affect regression detection." );
  println!( "Use case: Calibrating sensitivity for different environments.\n" );
  
  let current_results = create_current_results();
  let historical = create_baseline_historical_data();
  
  let thresholds = vec![ 0.01, 0.05, 0.10, 0.20 ];
  
  for &threshold in &thresholds
  {
  println!( "📊 ANALYSIS WITH {}% SIGNIFICANCE THRESHOLD: ", ( threshold * 100.0 ) as i32 );
  
  let analyzer = RegressionAnalyzer ::new()
   .with_baseline_strategy( BaselineStrategy ::FixedBaseline )
   .with_significance_threshold( threshold );
  
  let regression_report = analyzer.analyze( &current_results, &historical );
  
  let mut significant_count = 0;
  let operations = [ "fast_sort", "hash_function", "memory_allocator" ];
  
  for operation in &operations
  {
   if regression_report.is_statistically_significant( operation )
   {
  significant_count += 1;
 }
 }
  
  println!( "    Significant changes detected: {}/{}", significant_count, operations.len() );
  
  // Show specific results for fast_sort (known improvement)
  if regression_report.is_statistically_significant( "fast_sort" )
  {
   println!( "    fast_sort: ✓ Significant improvement detected" );
 }
  else
  {
   println!( "    fast_sort: - Improvement not statistically significant at this level" );
 }
  
  println!();
 }
  
  println!( "💡 TUNING GUIDANCE: " );
  println!( "   - Strict thresholds (1-5%) : Production environments, critical systems" );
  println!( "   - Moderate thresholds (5-10%) : Development, performance monitoring" );
  println!( "   - Lenient thresholds (10-20%) : Early development, noisy environments\n" );
}

/// Main demonstration function
fn main()
{
  println!( "🚀 BENCHKIT REGRESSION ANALYSIS COMPREHENSIVE DEMO" );
  println!( "====================================================" );
  println!( "This example demonstrates every aspect of the new regression analysis system: \n" );
  
  // Core strategy demonstrations
  demonstrate_fixed_baseline_strategy();
  demonstrate_rolling_average_strategy();
  demonstrate_previous_run_strategy();
  
  // Advanced features
  demonstrate_template_integration();
  demonstrate_significance_tuning();
  
  println!( "✨ SUMMARY OF DEMONSTRATED FEATURES: " );
  println!( "=====================================" );
  println!( "✅ All three baseline strategies (Fixed, Rolling Average, Previous Run)" );
  println!( "✅ Performance trend detection (Improving, Degrading, Stable)" );
  println!( "✅ Statistical significance testing with configurable thresholds" );
  println!( "✅ Historical data management (baseline, runs, previous run)" );
  println!( "✅ Professional markdown report generation" );
  println!( "✅ Full PerformanceReport template integration" );
  println!( "✅ Real-world use cases and configuration guidance" );
  println!( "\n🎯 Ready for production use in performance monitoring workflows!" );
}

#[ cfg( not( feature = "enabled" ) ) ]
fn main()
{
  println!( "This example requires the 'enabled' feature." );
  println!( "Run with: cargo run --example regression_analysis_comprehensive --features enabled" );
}