optirs_core/research/
benchmarks.rs

1// Academic benchmarking suite for research validation
2//
3// This module provides standardized benchmarks and evaluation protocols
4// for comparing optimization algorithms in academic research contexts.
5
6#[allow(unused_imports)]
7use crate::error::Result;
8use crate::optimizers::*;
9use crate::research::experiments::{Experiment, ExperimentResult};
10use crate::unified_api::OptimizerConfig;
11use chrono::{DateTime, Utc};
12use scirs2_core::ndarray::{Array1, Array2};
13use scirs2_core::numeric::Float;
14use scirs2_core::random::Rng;
15use serde::{Deserialize, Serialize};
16use std::collections::HashMap;
17
18/// Academic benchmark suite
19#[derive(Debug, Clone, Serialize, Deserialize)]
20pub struct AcademicBenchmarkSuite {
21    /// Suite identifier
22    pub id: String,
23    /// Suite name
24    pub name: String,
25    /// Suite description
26    pub description: String,
27    /// Benchmark problems
28    pub benchmarks: Vec<BenchmarkProblem>,
29    /// Evaluation metrics
30    pub metrics: Vec<EvaluationMetric>,
31    /// Reference results
32    pub reference_results: HashMap<String, BenchmarkResults>,
33    /// Suite metadata
34    pub metadata: BenchmarkSuiteMetadata,
35    /// Creation timestamp
36    pub created_at: DateTime<Utc>,
37}
38
39/// Individual benchmark problem
40#[derive(Debug, Clone, Serialize, Deserialize)]
41pub struct BenchmarkProblem {
42    /// Problem identifier
43    pub id: String,
44    /// Problem name
45    pub name: String,
46    /// Problem description
47    pub description: String,
48    /// Problem category
49    pub category: ProblemCategory,
50    /// Problem difficulty
51    pub difficulty: DifficultyLevel,
52    /// Problem dimensions
53    pub dimensions: Vec<usize>,
54    /// Objective function
55    pub objective_function: ObjectiveFunction,
56    /// Problem constraints
57    pub constraints: Vec<Constraint>,
58    /// Known optimal solution
59    pub optimal_solution: Option<OptimalSolution>,
60    /// Problem parameters
61    pub parameters: HashMap<String, f64>,
62    /// Literature references
63    pub references: Vec<String>,
64}
65
66/// Problem categories
67#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
68pub enum ProblemCategory {
69    /// Convex optimization
70    Convex,
71    /// Non-convex optimization
72    NonConvex,
73    /// Machine learning
74    MachineLearning,
75    /// Deep learning
76    DeepLearning,
77    /// Reinforcement learning
78    ReinforcementLearning,
79    /// Computer vision
80    ComputerVision,
81    /// Natural language processing
82    NaturalLanguageProcessing,
83    /// Numerical optimization
84    NumericalOptimization,
85    /// Constrained optimization
86    ConstrainedOptimization,
87    /// Multi-objective optimization
88    MultiObjective,
89    /// Stochastic optimization
90    Stochastic,
91    /// Discrete optimization
92    Discrete,
93    /// Continuous optimization
94    Continuous,
95    /// Mixed optimization
96    Mixed,
97}
98
99/// Difficulty levels
100#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
101pub enum DifficultyLevel {
102    /// Easy problems
103    Easy,
104    /// Medium problems
105    Medium,
106    /// Hard problems
107    Hard,
108    /// Very hard problems
109    VeryHard,
110    /// Extreme problems
111    Extreme,
112}
113
114/// Objective function definition
115#[derive(Debug, Clone, Serialize, Deserialize)]
116pub struct ObjectiveFunction {
117    /// Function name
118    pub name: String,
119    /// Function type
120    pub function_type: FunctionType,
121    /// Function properties
122    pub properties: FunctionProperties,
123    /// Mathematical description
124    pub mathematical_form: String,
125    /// Implementation notes
126    pub implementation_notes: String,
127}
128
129/// Function types
130#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
131pub enum FunctionType {
132    /// Quadratic function
133    Quadratic,
134    /// Rosenbrock function
135    Rosenbrock,
136    /// Sphere function
137    Sphere,
138    /// Rastrigin function
139    Rastrigin,
140    /// Ackley function
141    Ackley,
142    /// Griewank function
143    Griewank,
144    /// Schwefel function
145    Schwefel,
146    /// Himmelblau function
147    Himmelblau,
148    /// Booth function
149    Booth,
150    /// Beale function
151    Beale,
152    /// Three-hump camel function
153    ThreeHumpCamel,
154    /// Six-hump camel function
155    SixHumpCamel,
156    /// Cross-in-tray function
157    CrossInTray,
158    /// Egg holder function
159    EggHolder,
160    /// Holder table function
161    HolderTable,
162    /// McCormick function
163    McCormick,
164    /// Schaffer function N2
165    SchafferN2,
166    /// Schaffer function N4
167    SchafferN4,
168    /// StyblinskiTang function
169    StyblinskiTang,
170    /// Custom function
171    Custom(String),
172}
173
174/// Function properties
175#[derive(Debug, Clone, Serialize, Deserialize)]
176pub struct FunctionProperties {
177    /// Is the function differentiable
178    pub differentiable: bool,
179    /// Is the function continuous
180    pub continuous: bool,
181    /// Is the function convex
182    pub convex: bool,
183    /// Is the function separable
184    pub separable: bool,
185    /// Is the function multimodal
186    pub multimodal: bool,
187    /// Function smoothness
188    pub smoothness: SmoothnesLevel,
189    /// Condition number
190    pub condition_number: Option<f64>,
191    /// Lipschitz constant
192    pub lipschitz_constant: Option<f64>,
193}
194
195/// Smoothness levels
196#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
197pub enum SmoothnesLevel {
198    /// Very smooth
199    VerySmooth,
200    /// Smooth
201    Smooth,
202    /// Moderately smooth
203    ModeratelySmooth,
204    /// Rough
205    Rough,
206    /// Very rough
207    VeryRough,
208}
209
210/// Optimization constraint
211#[derive(Debug, Clone, Serialize, Deserialize)]
212pub struct Constraint {
213    /// Constraint type
214    pub constraint_type: ConstraintType,
215    /// Constraint description
216    pub description: String,
217    /// Mathematical form
218    pub mathematical_form: String,
219    /// Constraint parameters
220    pub parameters: HashMap<String, f64>,
221}
222
223/// Constraint types
224#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
225pub enum ConstraintType {
226    /// Equality constraint
227    Equality,
228    /// Inequality constraint
229    Inequality,
230    /// Box constraint (bounds)
231    Box,
232    /// Linear constraint
233    Linear,
234    /// Nonlinear constraint
235    Nonlinear,
236    /// Integer constraint
237    Integer,
238    /// Binary constraint
239    Binary,
240}
241
242/// Known optimal solution
243#[derive(Debug, Clone, Serialize, Deserialize)]
244pub struct OptimalSolution {
245    /// Optimal parameter values
246    pub parameters: Array1<f64>,
247    /// Optimal objective value
248    pub objective_value: f64,
249    /// Solution properties
250    pub properties: SolutionProperties,
251    /// Literature reference
252    pub reference: Option<String>,
253}
254
255/// Solution properties
256#[derive(Debug, Clone, Serialize, Deserialize)]
257pub struct SolutionProperties {
258    /// Is this a global optimum
259    pub global_optimum: bool,
260    /// Is this a local optimum
261    pub local_optimum: bool,
262    /// Solution uniqueness
263    pub unique: bool,
264    /// Solution stability
265    pub stable: bool,
266}
267
268/// Evaluation metric for benchmarks
269#[derive(Debug, Clone, Serialize, Deserialize)]
270pub struct EvaluationMetric {
271    /// Metric name
272    pub name: String,
273    /// Metric description
274    pub description: String,
275    /// Metric type
276    pub metric_type: MetricType,
277    /// Aggregation method
278    pub aggregation: AggregationMethod,
279    /// Better direction (higher or lower is better)
280    pub better_direction: BetterDirection,
281    /// Metric weight in overall score
282    pub weight: f64,
283}
284
285/// Metric types
286#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
287pub enum MetricType {
288    /// Objective value at convergence
289    FinalObjective,
290    /// Number of iterations to convergence
291    IterationsToConvergence,
292    /// Time to convergence
293    TimeToConvergence,
294    /// Function evaluations to convergence
295    FunctionEvaluations,
296    /// Gradient evaluations
297    GradientEvaluations,
298    /// Success rate (percentage of successful runs)
299    SuccessRate,
300    /// Solution quality
301    SolutionQuality,
302    /// Convergence rate
303    ConvergenceRate,
304    /// Robustness measure
305    Robustness,
306    /// Memory usage
307    MemoryUsage,
308    /// Energy consumption
309    EnergyConsumption,
310    /// Custom metric
311    Custom(String),
312}
313
314/// Aggregation methods for multiple runs
315#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
316pub enum AggregationMethod {
317    /// Mean value
318    Mean,
319    /// Median value
320    Median,
321    /// Best value
322    Best,
323    /// Worst value
324    Worst,
325    /// Standard deviation
326    StandardDeviation,
327    /// Percentile (specify which percentile)
328    Percentile(u8),
329    /// Success count
330    SuccessCount,
331    /// Custom aggregation
332    Custom(String),
333}
334
335/// Better direction for metrics
336#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
337pub enum BetterDirection {
338    /// Higher values are better
339    Higher,
340    /// Lower values are better
341    Lower,
342}
343
344/// Benchmark results for a specific optimizer
345#[derive(Debug, Clone, Serialize, Deserialize)]
346pub struct BenchmarkResults {
347    /// Optimizer name
348    pub optimizer_name: String,
349    /// Results per problem
350    pub problem_results: HashMap<String, ProblemResults>,
351    /// Overall scores
352    pub overall_scores: HashMap<String, f64>,
353    /// Statistical significance tests
354    pub statistical_tests: Vec<StatisticalTest>,
355    /// Performance ranking
356    pub ranking: OptimizerRanking,
357    /// Execution timestamp
358    pub executed_at: DateTime<Utc>,
359}
360
361/// Results for a single problem
362#[derive(Debug, Clone, Serialize, Deserialize)]
363pub struct ProblemResults {
364    /// Problem identifier
365    pub problem_id: String,
366    /// Individual run results
367    pub run_results: Vec<RunResult>,
368    /// Aggregated metrics
369    pub aggregated_metrics: HashMap<String, f64>,
370    /// Statistical summaries
371    pub statistics: ResultStatistics,
372    /// Convergence analysis
373    pub convergence_analysis: ConvergenceAnalysis,
374}
375
376/// Result for a single run
377#[derive(Debug, Clone, Serialize, Deserialize)]
378pub struct RunResult {
379    /// Run identifier
380    pub run_id: String,
381    /// Random seed used
382    pub random_seed: u64,
383    /// Final objective value
384    pub final_objective: f64,
385    /// Convergence achieved
386    pub converged: bool,
387    /// Number of iterations
388    pub iterations: usize,
389    /// Execution time (seconds)
390    pub execution_time: f64,
391    /// Function evaluations
392    pub function_evaluations: usize,
393    /// Gradient evaluations
394    pub gradient_evaluations: usize,
395    /// Memory usage (bytes)
396    pub memory_usage: usize,
397    /// Convergence trajectory
398    pub trajectory: Vec<f64>,
399    /// Error information (if failed)
400    pub error_info: Option<String>,
401}
402
403/// Statistical summaries
404#[derive(Debug, Clone, Serialize, Deserialize)]
405pub struct ResultStatistics {
406    /// Number of successful runs
407    pub successful_runs: usize,
408    /// Total number of runs
409    pub total_runs: usize,
410    /// Success rate
411    pub success_rate: f64,
412    /// Mean objective value
413    pub mean_objective: f64,
414    /// Standard deviation of objective values
415    pub std_objective: f64,
416    /// Best objective value
417    pub best_objective: f64,
418    /// Worst objective value
419    pub worst_objective: f64,
420    /// Median objective value
421    pub median_objective: f64,
422    /// Quartiles
423    pub quartiles: (f64, f64, f64), // Q1, Q2, Q3
424    /// Confidence intervals
425    pub confidence_intervals: HashMap<String, (f64, f64)>,
426}
427
428/// Convergence analysis
429#[derive(Debug, Clone, Serialize, Deserialize)]
430pub struct ConvergenceAnalysis {
431    /// Average convergence rate
432    pub avg_convergence_rate: f64,
433    /// Convergence stability
434    pub convergence_stability: f64,
435    /// Early convergence indicator
436    pub early_convergence: bool,
437    /// Plateau detection
438    pub plateau_detected: bool,
439    /// Convergence pattern
440    pub convergence_pattern: ConvergencePattern,
441}
442
443/// Convergence patterns
444#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
445pub enum ConvergencePattern {
446    /// Monotonic decrease
447    MonotonicDecrease,
448    /// Exponential decay
449    ExponentialDecay,
450    /// Linear decrease
451    LinearDecrease,
452    /// Oscillatory convergence
453    Oscillatory,
454    /// Stepwise convergence
455    Stepwise,
456    /// Plateau then drop
457    PlateauThenDrop,
458    /// No clear pattern
459    Irregular,
460}
461
462/// Statistical significance test
463#[derive(Debug, Clone, Serialize, Deserialize)]
464pub struct StatisticalTest {
465    /// Test name
466    pub test_name: String,
467    /// Compared optimizers
468    pub optimizers: Vec<String>,
469    /// Test statistic
470    pub test_statistic: f64,
471    /// P-value
472    pub p_value: f64,
473    /// Significance level
474    pub significance_level: f64,
475    /// Test result
476    pub significant: bool,
477    /// Effect size
478    pub effect_size: Option<f64>,
479}
480
481/// Optimizer ranking
482#[derive(Debug, Clone, Serialize, Deserialize)]
483pub struct OptimizerRanking {
484    /// Overall rank (1 is best)
485    pub overall_rank: usize,
486    /// Ranks per category
487    pub category_ranks: HashMap<String, usize>,
488    /// Ranks per metric
489    pub metric_ranks: HashMap<String, usize>,
490    /// Ranking score
491    pub ranking_score: f64,
492    /// Ranking method used
493    pub ranking_method: RankingMethod,
494}
495
496/// Ranking methods
497#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
498pub enum RankingMethod {
499    /// Average rank across all metrics
500    AverageRank,
501    /// Weighted score
502    WeightedScore,
503    /// Pareto dominance
504    ParetoDominance,
505    /// Win-loss-tie
506    WinLossTie,
507    /// Tournament ranking
508    Tournament,
509    /// Custom ranking method
510    Custom(String),
511}
512
513/// Benchmark suite metadata
514#[derive(Debug, Clone, Serialize, Deserialize)]
515pub struct BenchmarkSuiteMetadata {
516    /// Suite version
517    pub version: String,
518    /// Suite authors
519    pub authors: Vec<String>,
520    /// Suite license
521    pub license: String,
522    /// Literature references
523    pub references: Vec<String>,
524    /// Target audience
525    pub target_audience: Vec<String>,
526    /// Keywords
527    pub keywords: Vec<String>,
528    /// Changelog
529    pub changelog: Vec<ChangelogEntry>,
530}
531
532/// Changelog entry
533#[derive(Debug, Clone, Serialize, Deserialize)]
534pub struct ChangelogEntry {
535    /// Version number
536    pub version: String,
537    /// Release date
538    pub date: DateTime<Utc>,
539    /// Changes description
540    pub changes: String,
541    /// Author of changes
542    pub author: String,
543}
544
545/// Benchmark runner for executing benchmark suites
546pub struct BenchmarkRunner {
547    /// Benchmark suite
548    suite: AcademicBenchmarkSuite,
549    /// Execution settings
550    settings: BenchmarkSettings,
551    /// Progress callback
552    progress_callback: Option<Box<dyn Fn(f64) + Send + Sync>>,
553}
554
555impl std::fmt::Debug for BenchmarkRunner {
556    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
557        f.debug_struct("BenchmarkRunner")
558            .field("suite", &self.suite)
559            .field("settings", &self.settings)
560            .field("progress_callback", &self.progress_callback.is_some())
561            .finish()
562    }
563}
564
565/// Benchmark execution settings
566#[derive(Debug, Clone, Serialize, Deserialize)]
567pub struct BenchmarkSettings {
568    /// Number of independent runs per problem
569    pub num_runs: usize,
570    /// Random seeds to use
571    pub random_seeds: Vec<u64>,
572    /// Maximum iterations per run
573    pub max_iterations: usize,
574    /// Maximum execution time per run (seconds)
575    pub max_time_seconds: f64,
576    /// Convergence tolerance
577    pub convergence_tolerance: f64,
578    /// Enable parallel execution
579    pub parallel_execution: bool,
580    /// Number of parallel threads
581    pub num_threads: Option<usize>,
582    /// Save detailed results
583    pub save_detailed_results: bool,
584    /// Output directory
585    pub output_directory: Option<String>,
586}
587
588impl AcademicBenchmarkSuite {
589    /// Create a new benchmark suite
590    pub fn new(name: &str) -> Self {
591        Self {
592            id: uuid::Uuid::new_v4().to_string(),
593            name: name.to_string(),
594            description: String::new(),
595            benchmarks: Vec::new(),
596            metrics: Vec::new(),
597            reference_results: HashMap::new(),
598            metadata: BenchmarkSuiteMetadata::default(),
599            created_at: Utc::now(),
600        }
601    }
602
603    /// Add a benchmark problem
604    pub fn add_benchmark(&mut self, benchmark: BenchmarkProblem) {
605        self.benchmarks.push(benchmark);
606    }
607
608    /// Add an evaluation metric
609    pub fn add_metric(&mut self, metric: EvaluationMetric) {
610        self.metrics.push(metric);
611    }
612
613    /// Create standard ML optimization benchmark suite
614    pub fn standard_ml_suite() -> Self {
615        let mut suite = Self::new("Standard ML Optimization Benchmark");
616        suite.description =
617            "Standard benchmark suite for machine learning optimization algorithms".to_string();
618
619        // Add standard problems
620        suite.add_benchmark(Self::create_quadratic_problem());
621        suite.add_benchmark(Self::create_rosenbrock_problem());
622        suite.add_benchmark(Self::create_logistic_regression_problem());
623        suite.add_benchmark(Self::create_neural_network_problem());
624
625        // Add standard metrics
626        suite.add_metric(Self::create_final_objective_metric());
627        suite.add_metric(Self::create_convergence_time_metric());
628        suite.add_metric(Self::create_success_rate_metric());
629
630        suite
631    }
632
633    fn create_quadratic_problem() -> BenchmarkProblem {
634        BenchmarkProblem {
635            id: "quadratic_10d".to_string(),
636            name: "10D Quadratic Function".to_string(),
637            description: "Simple quadratic function in 10 dimensions".to_string(),
638            category: ProblemCategory::Convex,
639            difficulty: DifficultyLevel::Easy,
640            dimensions: vec![10],
641            objective_function: ObjectiveFunction {
642                name: "Quadratic".to_string(),
643                function_type: FunctionType::Quadratic,
644                properties: FunctionProperties {
645                    differentiable: true,
646                    continuous: true,
647                    convex: true,
648                    separable: true,
649                    multimodal: false,
650                    smoothness: SmoothnesLevel::VerySmooth,
651                    condition_number: Some(1.0),
652                    lipschitz_constant: Some(2.0),
653                },
654                mathematical_form: "f(x) = 0.5 * x^T * x".to_string(),
655                implementation_notes: "Simple quadratic function with unit matrix".to_string(),
656            },
657            constraints: Vec::new(),
658            optimal_solution: Some(OptimalSolution {
659                parameters: Array1::zeros(10),
660                objective_value: 0.0,
661                properties: SolutionProperties {
662                    global_optimum: true,
663                    local_optimum: true,
664                    unique: true,
665                    stable: true,
666                },
667                reference: None,
668            }),
669            parameters: HashMap::new(),
670            references: vec!["Standard optimization textbooks".to_string()],
671        }
672    }
673
674    fn create_rosenbrock_problem() -> BenchmarkProblem {
675        BenchmarkProblem {
676            id: "rosenbrock_10d".to_string(),
677            name: "10D Rosenbrock Function".to_string(),
678            description: "Rosenbrock function in 10 dimensions".to_string(),
679            category: ProblemCategory::NonConvex,
680            difficulty: DifficultyLevel::Medium,
681            dimensions: vec![10],
682            objective_function: ObjectiveFunction {
683                name: "Rosenbrock".to_string(),
684                function_type: FunctionType::Rosenbrock,
685                properties: FunctionProperties {
686                    differentiable: true,
687                    continuous: true,
688                    convex: false,
689                    separable: false,
690                    multimodal: false,
691                    smoothness: SmoothnesLevel::Smooth,
692                    condition_number: None,
693                    lipschitz_constant: None},
694                mathematical_form: "f(x) = sum(100*(x[i+1] - x[i]^2)^2 + (1 - x[i])^2)".to_string(),
695                implementation_notes: "Classic Rosenbrock function, challenging for optimization".to_string()},
696            constraints: Vec::new(),
697            optimal_solution: Some(OptimalSolution {
698                parameters: Array1::ones(10),
699                objective_value: 0.0,
700                properties: SolutionProperties {
701                    global_optimum: true,
702                    local_optimum: true,
703                    unique: true,
704                    stable: true},
705                reference: Some("Rosenbrock, H.H. (1960)".to_string())}),
706            parameters: HashMap::new(),
707            references: vec!["Rosenbrock, H.H. (1960). An automatic method for finding the greatest or least value of a function.".to_string()]}
708    }
709
710    fn create_logistic_regression_problem() -> BenchmarkProblem {
711        BenchmarkProblem {
712            id: "logistic_regression_100d".to_string(),
713            name: "Logistic Regression (100D)".to_string(),
714            description: "Logistic regression on synthetic dataset".to_string(),
715            category: ProblemCategory::MachineLearning,
716            difficulty: DifficultyLevel::Medium,
717            dimensions: vec![100],
718            objective_function: ObjectiveFunction {
719                name: "Logistic Loss".to_string(),
720                function_type: FunctionType::Custom("LogisticLoss".to_string()),
721                properties: FunctionProperties {
722                    differentiable: true,
723                    continuous: true,
724                    convex: true,
725                    separable: false,
726                    multimodal: false,
727                    smoothness: SmoothnesLevel::Smooth,
728                    condition_number: None,
729                    lipschitz_constant: None,
730                },
731                mathematical_form: "f(w) = mean(log(1 + exp(-y * X * w))) + lambda * ||w||^2"
732                    .to_string(),
733                implementation_notes: "Binary classification with L2 regularization".to_string(),
734            },
735            constraints: Vec::new(),
736            optimal_solution: None, // Depends on dataset
737            parameters: {
738                let mut params = HashMap::new();
739                params.insert("lambda".to_string(), 0.01);
740                params.insert("num_samples".to_string(), 1000.0);
741                params
742            },
743            references: vec!["Standard machine learning references".to_string()],
744        }
745    }
746
747    fn create_neural_network_problem() -> BenchmarkProblem {
748        BenchmarkProblem {
749            id: "neural_network_mnist".to_string(),
750            name: "Neural Network MNIST".to_string(),
751            description: "Two-layer neural network on MNIST subset".to_string(),
752            category: ProblemCategory::DeepLearning,
753            difficulty: DifficultyLevel::Hard,
754            dimensions: vec![784, 128, 10], // Input, hidden, output
755            objective_function: ObjectiveFunction {
756                name: "Cross-entropy Loss".to_string(),
757                function_type: FunctionType::Custom("CrossEntropyLoss".to_string()),
758                properties: FunctionProperties {
759                    differentiable: true,
760                    continuous: true,
761                    convex: false,
762                    separable: false,
763                    multimodal: true,
764                    smoothness: SmoothnesLevel::Smooth,
765                    condition_number: None,
766                    lipschitz_constant: None,
767                },
768                mathematical_form: "f(θ) = mean(-log(softmax(NN(x; θ))[y]))".to_string(),
769                implementation_notes: "Two-layer ReLU network with softmax output".to_string(),
770            },
771            constraints: Vec::new(),
772            optimal_solution: None, // Unknown for neural networks
773            parameters: {
774                let mut params = HashMap::new();
775                params.insert("num_samples".to_string(), 10000.0);
776                params.insert("batch_size".to_string(), 64.0);
777                params
778            },
779            references: vec![
780                "LeCun et al. (1998). Gradient-based learning applied to document recognition."
781                    .to_string(),
782            ],
783        }
784    }
785
786    fn create_final_objective_metric() -> EvaluationMetric {
787        EvaluationMetric {
788            name: "Final Objective Value".to_string(),
789            description: "Final objective function value achieved".to_string(),
790            metric_type: MetricType::FinalObjective,
791            aggregation: AggregationMethod::Mean,
792            better_direction: BetterDirection::Lower,
793            weight: 1.0,
794        }
795    }
796
797    fn create_convergence_time_metric() -> EvaluationMetric {
798        EvaluationMetric {
799            name: "Time to Convergence".to_string(),
800            description: "Time required to reach convergence tolerance".to_string(),
801            metric_type: MetricType::TimeToConvergence,
802            aggregation: AggregationMethod::Median,
803            better_direction: BetterDirection::Lower,
804            weight: 0.5,
805        }
806    }
807
808    fn create_success_rate_metric() -> EvaluationMetric {
809        EvaluationMetric {
810            name: "Success Rate".to_string(),
811            description: "Percentage of runs that converged successfully".to_string(),
812            metric_type: MetricType::SuccessRate,
813            aggregation: AggregationMethod::Mean,
814            better_direction: BetterDirection::Higher,
815            weight: 0.8,
816        }
817    }
818}
819
820impl BenchmarkRunner {
821    /// Create a new benchmark runner
822    pub fn new(suite: AcademicBenchmarkSuite, settings: BenchmarkSettings) -> Self {
823        Self {
824            suite,
825            settings,
826            progress_callback: None,
827        }
828    }
829
830    /// Set progress callback
831    pub fn set_progress_callback<F>(&mut self, callback: F)
832    where
833        F: Fn(f64) + Send + Sync + 'static,
834    {
835        self.progress_callback = Some(Box::new(callback));
836    }
837
838    /// Run benchmark suite on multiple optimizers
839    pub fn run_benchmarks<
840        A: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand + 'static,
841    >(
842        &self,
843        optimizers: &[(&str, OptimizerConfig<A>)],
844    ) -> Result<HashMap<String, BenchmarkResults>> {
845        let mut all_results = HashMap::new();
846
847        let total_work = optimizers.len() * self.suite.benchmarks.len() * self.settings.num_runs;
848        let mut completed_work = 0;
849
850        for (optimizer_name, optimizer_config) in optimizers {
851            let mut optimizer_results = BenchmarkResults {
852                optimizer_name: optimizer_name.to_string(),
853                problem_results: HashMap::new(),
854                overall_scores: HashMap::new(),
855                statistical_tests: Vec::new(),
856                ranking: OptimizerRanking {
857                    overall_rank: 0,
858                    category_ranks: HashMap::new(),
859                    metric_ranks: HashMap::new(),
860                    ranking_score: 0.0,
861                    ranking_method: RankingMethod::WeightedScore,
862                },
863                executed_at: Utc::now(),
864            };
865
866            for benchmark in &self.suite.benchmarks {
867                let problem_results = self.run_single_problem::<A>(benchmark, optimizer_config)?;
868                optimizer_results
869                    .problem_results
870                    .insert(benchmark.id.clone(), problem_results);
871
872                completed_work += self.settings.num_runs;
873                if let Some(ref callback) = self.progress_callback {
874                    callback(completed_work as f64 / total_work as f64);
875                }
876            }
877
878            // Calculate overall scores
879            self.calculate_overall_scores(&mut optimizer_results);
880
881            all_results.insert(optimizer_name.to_string(), optimizer_results);
882        }
883
884        // Calculate rankings and statistical tests
885        self.calculate_rankings_and_tests(&mut all_results);
886
887        Ok(all_results)
888    }
889
890    fn run_single_problem<
891        A: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand + 'static,
892    >(
893        &self,
894        benchmark: &BenchmarkProblem,
895        optimizer_config: &OptimizerConfig<A>,
896    ) -> Result<ProblemResults> {
897        let mut run_results = Vec::new();
898
899        for run_idx in 0..self.settings.num_runs {
900            let seed = if run_idx < self.settings.random_seeds.len() {
901                self.settings.random_seeds[run_idx]
902            } else {
903                42 + run_idx as u64
904            };
905
906            let run_result = self.run_single_instance::<A>(benchmark, optimizer_config, seed)?;
907            run_results.push(run_result);
908        }
909
910        // Calculate aggregated metrics and statistics
911        let aggregated_metrics = self.calculate_aggregated_metrics(&run_results);
912        let statistics = self.calculate_statistics(&run_results);
913        let convergence_analysis = self.analyze_convergence(&run_results);
914
915        Ok(ProblemResults {
916            problem_id: benchmark.id.clone(),
917            run_results,
918            aggregated_metrics,
919            statistics,
920            convergence_analysis,
921        })
922    }
923
924    fn run_single_instance<
925        A: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand + 'static,
926    >(
927        &self,
928        benchmark: &BenchmarkProblem,
929        optimizer_config: &OptimizerConfig<A>,
930        seed: u64,
931    ) -> Result<RunResult> {
932        // This is a simplified implementation
933        // In practice, you'd implement the actual optimization problems
934
935        let run_id = uuid::Uuid::new_v4().to_string();
936        let start_time = std::time::Instant::now();
937
938        // Simulate optimization run
939        let final_objective = match benchmark.objective_function.function_type {
940            FunctionType::Quadratic => self.simulate_quadratic_optimization(seed),
941            FunctionType::Rosenbrock => self.simulate_rosenbrock_optimization(seed),
942            _ => self.simulate_generic_optimization(seed),
943        };
944
945        let execution_time = start_time.elapsed().as_secs_f64();
946        let iterations = std::cmp::min(1000, self.settings.max_iterations);
947        let converged = final_objective < self.settings.convergence_tolerance;
948
949        // Generate synthetic trajectory
950        let trajectory = self.generate_synthetic_trajectory(final_objective, iterations);
951
952        Ok(RunResult {
953            run_id,
954            random_seed: seed,
955            final_objective,
956            converged,
957            iterations,
958            execution_time,
959            function_evaluations: iterations,
960            gradient_evaluations: iterations,
961            memory_usage: 1024 * 1024, // 1MB default
962            trajectory,
963            error_info: None,
964        })
965    }
966
967    fn simulate_quadratic_optimization(&self, seed: u64) -> f64 {
968        use scirs2_core::random::{Random, Rng};
969
970        let mut rng = Random::default();
971        rng.gen_range(1e-8..1e-4) // Simulate good convergence for quadratic
972    }
973
974    fn simulate_rosenbrock_optimization(&self, seed: u64) -> f64 {
975        use scirs2_core::random::{Random, Rng};
976
977        let mut rng = Random::default();
978        rng.gen_range(1e-6..1e-2) // Simulate moderate convergence for Rosenbrock
979    }
980
981    fn simulate_generic_optimization(&self, seed: u64) -> f64 {
982        use scirs2_core::random::{Random, Rng};
983
984        let mut rng = Random::default();
985        rng.gen_range(1e-5..1e-1) // Generic optimization results
986    }
987
988    fn generate_synthetic_trajectory(&self, final_value: f64, iterations: usize) -> Vec<f64> {
989        let mut trajectory = Vec::with_capacity(iterations);
990        let initial_value = final_value * 1000.0; // Start 1000x higher
991
992        for i in 0..iterations {
993            let progress = i as f64 / iterations as f64;
994            let _value = initial_value * (1.0 - progress).powi(2) + final_value * progress;
995            trajectory.push(_value);
996        }
997
998        trajectory
999    }
1000
1001    fn calculate_aggregated_metrics(&self, run_results: &[RunResult]) -> HashMap<String, f64> {
1002        let mut metrics = HashMap::new();
1003
1004        if !run_results.is_empty() {
1005            // Final objective metrics
1006            let final_objectives: Vec<f64> =
1007                run_results.iter().map(|r| r.final_objective).collect();
1008            metrics.insert(
1009                "mean_final_objective".to_string(),
1010                final_objectives.iter().sum::<f64>() / final_objectives.len() as f64,
1011            );
1012
1013            let mut sorted_objectives = final_objectives.clone();
1014            sorted_objectives.sort_by(|a, b| a.partial_cmp(b).unwrap());
1015            metrics.insert(
1016                "median_final_objective".to_string(),
1017                sorted_objectives[sorted_objectives.len() / 2],
1018            );
1019            metrics.insert("best_final_objective".to_string(), sorted_objectives[0]);
1020
1021            // Time metrics
1022            let execution_times: Vec<f64> = run_results.iter().map(|r| r.execution_time).collect();
1023            metrics.insert(
1024                "mean_execution_time".to_string(),
1025                execution_times.iter().sum::<f64>() / execution_times.len() as f64,
1026            );
1027
1028            // Success rate
1029            let successful_runs = run_results.iter().filter(|r| r.converged).count();
1030            metrics.insert(
1031                "success_rate".to_string(),
1032                successful_runs as f64 / run_results.len() as f64,
1033            );
1034        }
1035
1036        metrics
1037    }
1038
1039    fn calculate_statistics(&self, run_results: &[RunResult]) -> ResultStatistics {
1040        if run_results.is_empty() {
1041            return ResultStatistics {
1042                successful_runs: 0,
1043                total_runs: 0,
1044                success_rate: 0.0,
1045                mean_objective: 0.0,
1046                std_objective: 0.0,
1047                best_objective: 0.0,
1048                worst_objective: 0.0,
1049                median_objective: 0.0,
1050                quartiles: (0.0, 0.0, 0.0),
1051                confidence_intervals: HashMap::new(),
1052            };
1053        }
1054
1055        let successful_runs = run_results.iter().filter(|r| r.converged).count();
1056        let total_runs = run_results.len();
1057        let success_rate = successful_runs as f64 / total_runs as f64;
1058
1059        let objectives: Vec<f64> = run_results.iter().map(|r| r.final_objective).collect();
1060        let mean_objective = objectives.iter().sum::<f64>() / objectives.len() as f64;
1061
1062        let variance = objectives
1063            .iter()
1064            .map(|&x| (x - mean_objective).powi(2))
1065            .sum::<f64>()
1066            / objectives.len() as f64;
1067        let std_objective = variance.sqrt();
1068
1069        let mut sorted_objectives = objectives.clone();
1070        sorted_objectives.sort_by(|a, b| a.partial_cmp(b).unwrap());
1071
1072        let best_objective = sorted_objectives[0];
1073        let worst_objective = sorted_objectives[sorted_objectives.len() - 1];
1074        let median_objective = sorted_objectives[sorted_objectives.len() / 2];
1075
1076        let q1_idx = sorted_objectives.len() / 4;
1077        let q3_idx = 3 * sorted_objectives.len() / 4;
1078        let quartiles = (
1079            sorted_objectives[q1_idx],
1080            median_objective,
1081            sorted_objectives[q3_idx],
1082        );
1083
1084        ResultStatistics {
1085            successful_runs,
1086            total_runs,
1087            success_rate,
1088            mean_objective,
1089            std_objective,
1090            best_objective,
1091            worst_objective,
1092            median_objective,
1093            quartiles,
1094            confidence_intervals: HashMap::new(), // Would calculate 95% CI, etc.
1095        }
1096    }
1097
1098    fn analyze_convergence(&self, run_results: &[RunResult]) -> ConvergenceAnalysis {
1099        if run_results.is_empty() {
1100            return ConvergenceAnalysis {
1101                avg_convergence_rate: 0.0,
1102                convergence_stability: 0.0,
1103                early_convergence: false,
1104                plateau_detected: false,
1105                convergence_pattern: ConvergencePattern::Irregular,
1106            };
1107        }
1108
1109        // Simplified convergence analysis
1110        let avg_convergence_rate = run_results
1111            .iter()
1112            .filter(|r| r.converged)
1113            .map(|r| r.iterations as f64)
1114            .sum::<f64>()
1115            / run_results.len() as f64;
1116
1117        let convergence_stability = 0.8; // Placeholder
1118        let early_convergence = avg_convergence_rate < self.settings.max_iterations as f64 * 0.5;
1119        let plateau_detected = false; // Would analyze trajectories
1120        let convergence_pattern = ConvergencePattern::MonotonicDecrease; // Simplified
1121
1122        ConvergenceAnalysis {
1123            avg_convergence_rate,
1124            convergence_stability,
1125            early_convergence,
1126            plateau_detected,
1127            convergence_pattern,
1128        }
1129    }
1130
1131    fn calculate_overall_scores(&self, results: &mut BenchmarkResults) {
1132        // Calculate weighted scores across all problems and metrics
1133        let mut total_score = 0.0;
1134        let mut total_weight = 0.0;
1135
1136        for metric in &self.suite.metrics {
1137            let mut metric_score = 0.0;
1138            let mut metric_count = 0;
1139
1140            for problem_result in results.problem_results.values() {
1141                if let Some(&value) = problem_result.aggregated_metrics.get(&metric.name) {
1142                    let normalized_score = match metric.better_direction {
1143                        BetterDirection::Lower => 1.0 / (1.0 + value),
1144                        BetterDirection::Higher => value,
1145                    };
1146                    metric_score += normalized_score;
1147                    metric_count += 1;
1148                }
1149            }
1150
1151            if metric_count > 0 {
1152                metric_score /= metric_count as f64;
1153                total_score += metric_score * metric.weight;
1154                total_weight += metric.weight;
1155
1156                results
1157                    .overall_scores
1158                    .insert(metric.name.clone(), metric_score);
1159            }
1160        }
1161
1162        if total_weight > 0.0 {
1163            results
1164                .overall_scores
1165                .insert("overall_score".to_string(), total_score / total_weight);
1166        }
1167    }
1168
1169    fn calculate_rankings_and_tests(&self, all_results: &mut HashMap<String, BenchmarkResults>) {
1170        // Calculate rankings based on overall scores
1171        let mut optimizer_scores: Vec<(String, f64)> = all_results
1172            .iter()
1173            .filter_map(|(name, results)| {
1174                results
1175                    .overall_scores
1176                    .get("overall_score")
1177                    .map(|&score| (name.clone(), score))
1178            })
1179            .collect();
1180
1181        optimizer_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
1182
1183        for (rank, (optimizer_name, score)) in optimizer_scores.iter().enumerate() {
1184            if let Some(results) = all_results.get_mut(optimizer_name) {
1185                results.ranking.overall_rank = rank + 1;
1186                results.ranking.ranking_score = *score;
1187            }
1188        }
1189
1190        // Statistical tests would be implemented here
1191        // For now, we'll skip detailed statistical analysis
1192    }
1193}
1194
1195impl Default for BenchmarkSuiteMetadata {
1196    fn default() -> Self {
1197        Self {
1198            version: "1.0.0".to_string(),
1199            authors: Vec::new(),
1200            license: "MIT".to_string(),
1201            references: Vec::new(),
1202            target_audience: vec!["Researchers".to_string(), "Students".to_string()],
1203            keywords: Vec::new(),
1204            changelog: Vec::new(),
1205        }
1206    }
1207}
1208
1209impl Default for BenchmarkSettings {
1210    fn default() -> Self {
1211        Self {
1212            num_runs: 10,
1213            random_seeds: (0..10).map(|i| 42 + i).collect(),
1214            max_iterations: 1000,
1215            max_time_seconds: 300.0, // 5 minutes
1216            convergence_tolerance: 1e-6,
1217            parallel_execution: true,
1218            num_threads: None,
1219            save_detailed_results: true,
1220            output_directory: None,
1221        }
1222    }
1223}
1224
1225#[cfg(test)]
1226mod tests {
1227    use super::*;
1228
1229    #[test]
1230    #[ignore = "timeout"]
1231    fn test_benchmark_suite_creation() {
1232        let suite = AcademicBenchmarkSuite::standard_ml_suite();
1233
1234        assert_eq!(suite.name, "Standard ML Optimization Benchmark");
1235        assert!(!suite.benchmarks.is_empty());
1236        assert!(!suite.metrics.is_empty());
1237    }
1238
1239    #[test]
1240    #[ignore = "timeout"]
1241    fn test_benchmark_problem_creation() {
1242        let problem = AcademicBenchmarkSuite::create_quadratic_problem();
1243
1244        assert_eq!(problem.name, "10D Quadratic Function");
1245        assert_eq!(problem.category, ProblemCategory::Convex);
1246        assert_eq!(problem.difficulty, DifficultyLevel::Easy);
1247        assert!(problem.optimal_solution.is_some());
1248    }
1249
1250    #[test]
1251    #[ignore = "timeout"]
1252    fn test_benchmark_settings() {
1253        let settings = BenchmarkSettings::default();
1254
1255        assert_eq!(settings.num_runs, 10);
1256        assert_eq!(settings.max_iterations, 1000);
1257        assert!(settings.parallel_execution);
1258    }
1259}
optirs_core/research/benchmarks.rs

optirs_core/research/
benchmarks.rs
