sklears_compose/
performance_testing.rs

1//! Performance Regression Testing Framework
2//!
3//! Advanced framework for detecting performance regressions in machine learning pipelines
4//! through automated benchmarking, statistical analysis, and trend monitoring.
5
6use chrono::{DateTime, Utc};
7use scirs2_core::ndarray::{ArrayView1, ArrayView2};
8use serde::{Deserialize, Serialize};
9use sklears_core::{
10    error::{Result as SklResult, SklearsError},
11    traits::{Estimator, Fit},
12    types::Float,
13};
14use std::collections::HashMap;
15use std::fs::{File, OpenOptions};
16use std::io::{BufRead, BufReader, Write};
17use std::path::{Path, PathBuf};
18use std::time::{Duration, Instant};
19
20/// Performance regression testing framework
21pub struct PerformanceRegressionTester {
22    /// Benchmark results storage
23    pub storage: BenchmarkStorage,
24    /// Statistical analysis configuration
25    pub analysis_config: StatisticalAnalysisConfig,
26    /// Test environment configuration
27    pub environment_config: EnvironmentConfig,
28    /// Regression detection thresholds
29    pub regression_thresholds: RegressionThresholds,
30    /// Profiling configuration
31    pub profiling_config: ProfilingConfig,
32}
33
34/// Benchmark results storage backend
35pub enum BenchmarkStorage {
36    /// File-based storage
37    File { path: PathBuf },
38    /// In-memory storage (for testing)
39    Memory { results: Vec<BenchmarkResult> },
40    /// Database storage (placeholder)
41    Database { connection_string: String },
42}
43
44/// Statistical analysis configuration
45#[derive(Clone, Debug)]
46pub struct StatisticalAnalysisConfig {
47    /// Confidence level for regression detection
48    pub confidence_level: f64,
49    /// Minimum number of samples for trend analysis
50    pub min_samples_for_trend: usize,
51    /// Window size for rolling statistics
52    pub rolling_window_size: usize,
53    /// Statistical tests to perform
54    pub statistical_tests: Vec<StatisticalTest>,
55    /// Outlier detection method
56    pub outlier_detection: OutlierDetection,
57}
58
59/// Statistical tests for performance analysis
60#[derive(Clone, Debug)]
61pub enum StatisticalTest {
62    /// T-test for mean comparison
63    TTest,
64    /// Mann-Whitney U test for non-parametric comparison
65    MannWhitneyU,
66    /// Kolmogorov-Smirnov test for distribution comparison
67    KolmogorovSmirnov,
68    /// Linear regression for trend analysis
69    LinearRegression,
70    /// Change point detection
71    ChangePointDetection,
72}
73
74/// Outlier detection methods
75#[derive(Clone, Debug)]
76pub enum OutlierDetection {
77    /// No outlier detection
78    None,
79    /// Z-score based detection
80    ZScore { threshold: f64 },
81    /// IQR based detection
82    IQR { multiplier: f64 },
83    /// Modified Z-score
84    ModifiedZScore { threshold: f64 },
85}
86
87/// Test environment configuration
88#[derive(Clone, Debug)]
89pub struct EnvironmentConfig {
90    /// Number of warmup iterations
91    pub warmup_iterations: usize,
92    /// Number of measurement iterations
93    pub measurement_iterations: usize,
94    /// CPU affinity (optional)
95    pub cpu_affinity: Option<Vec<usize>>,
96    /// Memory constraints
97    pub memory_limit: Option<u64>,
98    /// Environment variables to capture
99    pub capture_env_vars: Vec<String>,
100    /// System information to collect
101    pub collect_system_info: bool,
102}
103
104/// Regression detection thresholds
105#[derive(Clone, Debug)]
106pub struct RegressionThresholds {
107    /// Relative performance degradation threshold (e.g., 0.05 for 5%)
108    pub relative_threshold: f64,
109    /// Absolute performance degradation threshold (in milliseconds)
110    pub absolute_threshold: Duration,
111    /// Memory usage regression threshold (in bytes)
112    pub memory_threshold: u64,
113    /// Throughput regression threshold (relative)
114    pub throughput_threshold: f64,
115}
116
117/// Profiling configuration
118#[derive(Clone, Debug)]
119pub struct ProfilingConfig {
120    /// Enable CPU profiling
121    pub cpu_profiling: bool,
122    /// Enable memory profiling
123    pub memory_profiling: bool,
124    /// Profile sampling frequency
125    pub sampling_frequency: Duration,
126    /// Profile output directory
127    pub output_directory: Option<PathBuf>,
128    /// Enable detailed call stack collection
129    pub detailed_call_stacks: bool,
130}
131
132/// Benchmark result with comprehensive metrics
133#[derive(Clone, Debug, Serialize, Deserialize)]
134pub struct BenchmarkResult {
135    /// Unique benchmark identifier
136    pub benchmark_id: String,
137    /// Test case name
138    pub test_case: String,
139    /// Timestamp when benchmark was run
140    pub timestamp: DateTime<Utc>,
141    /// Performance metrics
142    pub metrics: PerformanceMetrics,
143    /// System information
144    pub system_info: SystemInfo,
145    /// Environment metadata
146    pub environment: EnvironmentMetadata,
147    /// Git commit hash (if available)
148    pub commit_hash: Option<String>,
149    /// Additional metadata
150    pub metadata: HashMap<String, String>,
151}
152
153/// Comprehensive performance metrics
154#[derive(Clone, Debug, Serialize, Deserialize)]
155pub struct PerformanceMetrics {
156    /// Execution time statistics
157    pub execution_time: TimeStatistics,
158    /// Memory usage statistics
159    pub memory_usage: MemoryStatistics,
160    /// Throughput metrics
161    pub throughput: ThroughputMetrics,
162    /// CPU utilization
163    pub cpu_utilization: CpuStatistics,
164    /// Custom metrics
165    pub custom_metrics: HashMap<String, f64>,
166}
167
168/// Time-based statistics
169#[derive(Clone, Debug, Serialize, Deserialize)]
170pub struct TimeStatistics {
171    /// Mean execution time
172    pub mean: Duration,
173    /// Median execution time
174    pub median: Duration,
175    /// Standard deviation
176    pub std_dev: Duration,
177    /// Minimum time
178    pub min: Duration,
179    /// Maximum time
180    pub max: Duration,
181    /// 95th percentile
182    pub p95: Duration,
183    /// 99th percentile
184    pub p99: Duration,
185    /// All individual measurements
186    pub samples: Vec<Duration>,
187}
188
189/// Memory usage statistics
190#[derive(Clone, Debug, Serialize, Deserialize)]
191pub struct MemoryStatistics {
192    /// Peak memory usage
193    pub peak_usage: u64,
194    /// Average memory usage
195    pub average_usage: u64,
196    /// Memory allocations count
197    pub allocations: u64,
198    /// Memory deallocations count
199    pub deallocations: u64,
200    /// Memory fragmentation score
201    pub fragmentation_score: f64,
202}
203
204/// Throughput performance metrics
205#[derive(Clone, Debug, Serialize, Deserialize)]
206pub struct ThroughputMetrics {
207    /// Operations per second
208    pub ops_per_second: f64,
209    /// Samples processed per second
210    pub samples_per_second: f64,
211    /// Features processed per second
212    pub features_per_second: f64,
213    /// Bytes processed per second
214    pub bytes_per_second: f64,
215}
216
217/// CPU utilization statistics
218#[derive(Clone, Debug, Serialize, Deserialize)]
219pub struct CpuStatistics {
220    /// Average CPU utilization (0.0 to 1.0)
221    pub average_utilization: f64,
222    /// Peak CPU utilization
223    pub peak_utilization: f64,
224    /// CPU time in user mode
225    pub user_time: Duration,
226    /// CPU time in kernel mode
227    pub kernel_time: Duration,
228}
229
230/// System information captured during benchmark
231#[derive(Clone, Debug, Serialize, Deserialize)]
232pub struct SystemInfo {
233    /// Operating system
234    pub os: String,
235    /// CPU model
236    pub cpu_model: String,
237    /// Number of CPU cores
238    pub cpu_cores: usize,
239    /// Total system memory
240    pub total_memory: u64,
241    /// Available memory at test time
242    pub available_memory: u64,
243    /// Rust version
244    pub rust_version: String,
245    /// Compiler flags
246    pub compiler_flags: Vec<String>,
247}
248
249/// Environment metadata
250#[derive(Clone, Debug, Serialize, Deserialize)]
251pub struct EnvironmentMetadata {
252    /// Environment variables
253    pub env_vars: HashMap<String, String>,
254    /// Current working directory
255    pub working_directory: PathBuf,
256    /// Command line arguments
257    pub args: Vec<String>,
258    /// System load average
259    pub load_average: Vec<f64>,
260}
261
262/// Regression analysis result
263#[derive(Clone, Debug)]
264pub struct RegressionAnalysis {
265    /// Whether a regression was detected
266    pub regression_detected: bool,
267    /// Regression severity
268    pub severity: RegressionSeverity,
269    /// Affected metrics
270    pub affected_metrics: Vec<String>,
271    /// Statistical significance
272    pub p_value: f64,
273    /// Effect size
274    pub effect_size: f64,
275    /// Confidence interval
276    pub confidence_interval: (f64, f64),
277    /// Detailed analysis
278    pub detailed_analysis: String,
279    /// Recommendations
280    pub recommendations: Vec<String>,
281}
282
283/// Regression severity levels
284#[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord)]
285pub enum RegressionSeverity {
286    /// No regression detected
287    None,
288    /// Minor performance degradation
289    Minor,
290    /// Moderate performance degradation
291    Moderate,
292    /// Severe performance degradation
293    Severe,
294    /// Critical performance degradation
295    Critical,
296}
297
298/// Benchmark execution context
299pub struct BenchmarkContext {
300    /// Data size for testing
301    pub data_size: (usize, usize),
302    /// Number of iterations
303    pub iterations: usize,
304    /// Benchmark configuration
305    pub config: HashMap<String, String>,
306    /// Random seed for reproducibility
307    pub random_seed: u64,
308}
309
310impl Default for PerformanceRegressionTester {
311    fn default() -> Self {
312        Self::new()
313    }
314}
315
316impl PerformanceRegressionTester {
317    /// Create a new performance regression tester with default configuration
318    #[must_use]
319    pub fn new() -> Self {
320        Self {
321            storage: BenchmarkStorage::Memory {
322                results: Vec::new(),
323            },
324            analysis_config: StatisticalAnalysisConfig::default(),
325            environment_config: EnvironmentConfig::default(),
326            regression_thresholds: RegressionThresholds::default(),
327            profiling_config: ProfilingConfig::default(),
328        }
329    }
330
331    /// Create a tester with file-based storage
332    pub fn with_file_storage<P: AsRef<Path>>(path: P) -> Self {
333        Self {
334            storage: BenchmarkStorage::File {
335                path: path.as_ref().to_path_buf(),
336            },
337            ..Self::new()
338        }
339    }
340
341    /// Run a benchmark on a pipeline component
342    pub fn benchmark_component<T, I, O>(
343        &mut self,
344        component: &T,
345        input: I,
346        context: &BenchmarkContext,
347        test_name: &str,
348    ) -> SklResult<BenchmarkResult>
349    where
350        T: Fn(I) -> O,
351        I: Clone,
352    {
353        let start_time = Instant::now();
354        let mut execution_times = Vec::new();
355
356        // Warmup phase
357        for _ in 0..self.environment_config.warmup_iterations {
358            let _ = component(input.clone());
359        }
360
361        // Measurement phase
362        for _ in 0..self.environment_config.measurement_iterations {
363            let measure_start = Instant::now();
364            let _ = component(input.clone());
365            execution_times.push(measure_start.elapsed());
366        }
367
368        let time_stats = self.calculate_time_statistics(&execution_times);
369        let memory_stats = self.collect_memory_statistics();
370        let cpu_stats = self.collect_cpu_statistics();
371        let throughput = self.calculate_throughput(&time_stats, context);
372
373        let result = BenchmarkResult {
374            benchmark_id: format!("{}_{}", test_name, Utc::now().timestamp()),
375            test_case: test_name.to_string(),
376            timestamp: Utc::now(),
377            metrics: PerformanceMetrics {
378                execution_time: time_stats,
379                memory_usage: memory_stats,
380                throughput,
381                cpu_utilization: cpu_stats,
382                custom_metrics: HashMap::new(),
383            },
384            system_info: self.collect_system_info(),
385            environment: self.collect_environment_metadata(),
386            commit_hash: self.get_git_commit_hash(),
387            metadata: context.config.clone(),
388        };
389
390        self.store_result(&result)?;
391        Ok(result)
392    }
393
394    /// Benchmark a machine learning pipeline
395    pub fn benchmark_pipeline<'a, S>(
396        &mut self,
397        pipeline: &crate::Pipeline<S>,
398        x: &ArrayView2<'a, Float>,
399        y: Option<&'a ArrayView1<'a, Float>>,
400        test_name: &str,
401    ) -> SklResult<BenchmarkResult>
402    where
403        S: std::fmt::Debug + Clone,
404        crate::Pipeline<S>: Clone + Fit<ArrayView2<'a, Float>, Option<&'a ArrayView1<'a, Float>>>,
405    {
406        let context = BenchmarkContext {
407            data_size: (x.nrows(), x.ncols()),
408            iterations: self.environment_config.measurement_iterations,
409            config: HashMap::new(),
410            random_seed: 42,
411        };
412
413        let benchmark_fn = |(): ()| -> SklResult<()> {
414            // Clone pipeline for benchmarking
415            let pipeline_clone = pipeline.clone();
416            if let Some(y_vals) = y {
417                let y_option = Some(y_vals);
418                let _fitted = pipeline_clone.fit(x, &y_option)?;
419            }
420            Ok(())
421        };
422
423        self.benchmark_component(&benchmark_fn, (), &context, test_name)
424    }
425
426    /// Analyze performance trends and detect regressions
427    pub fn analyze_regressions(&self, test_name: &str) -> SklResult<RegressionAnalysis> {
428        let results = self.get_historical_results(test_name)?;
429
430        if results.len() < self.analysis_config.min_samples_for_trend {
431            return Ok(RegressionAnalysis {
432                regression_detected: false,
433                severity: RegressionSeverity::None,
434                affected_metrics: vec![],
435                p_value: 1.0,
436                effect_size: 0.0,
437                confidence_interval: (0.0, 0.0),
438                detailed_analysis: "Insufficient data for trend analysis".to_string(),
439                recommendations: vec!["Collect more benchmark data".to_string()],
440            });
441        }
442
443        // Perform statistical analysis
444        let regression_detected = self.detect_performance_regression(&results)?;
445        let severity = self.calculate_regression_severity(&results)?;
446        let affected_metrics = self.identify_affected_metrics(&results)?;
447
448        // Statistical tests
449        let p_value = self.calculate_statistical_significance(&results)?;
450        let effect_size = self.calculate_effect_size(&results)?;
451        let confidence_interval = self.calculate_confidence_interval(&results)?;
452
453        let detailed_analysis = self.generate_detailed_analysis(&results)?;
454        let recommendations = self.generate_recommendations(&results, &severity);
455
456        Ok(RegressionAnalysis {
457            regression_detected,
458            severity,
459            affected_metrics,
460            p_value,
461            effect_size,
462            confidence_interval,
463            detailed_analysis,
464            recommendations,
465        })
466    }
467
468    /// Generate a performance report
469    pub fn generate_report(&self, test_pattern: Option<&str>) -> SklResult<PerformanceReport> {
470        let all_results = self.get_all_results()?;
471
472        let filtered_results = match test_pattern {
473            Some(pattern) => all_results
474                .into_iter()
475                .filter(|r| r.test_case.contains(pattern))
476                .collect(),
477            None => all_results,
478        };
479
480        let report = PerformanceReport::new(filtered_results, &self.analysis_config);
481        Ok(report)
482    }
483
484    // Helper methods
485    fn calculate_time_statistics(&self, times: &[Duration]) -> TimeStatistics {
486        let mut sorted_times = times.to_vec();
487        sorted_times.sort();
488
489        let mean = Duration::from_nanos(
490            times.iter().map(|d| d.as_nanos() as u64).sum::<u64>() / times.len() as u64,
491        );
492
493        let median = sorted_times[times.len() / 2];
494
495        let variance = times
496            .iter()
497            .map(|d| {
498                let diff = d.as_nanos() as i64 - mean.as_nanos() as i64;
499                (diff * diff) as u64
500            })
501            .sum::<u64>()
502            / times.len() as u64;
503
504        let std_dev = Duration::from_nanos((variance as f64).sqrt() as u64);
505
506        let p95_idx = (times.len() as f64 * 0.95) as usize;
507        let p99_idx = (times.len() as f64 * 0.99) as usize;
508
509        TimeStatistics {
510            mean,
511            median,
512            std_dev,
513            min: sorted_times.first().copied().unwrap_or_default(),
514            max: sorted_times.last().copied().unwrap_or_default(),
515            p95: sorted_times[p95_idx.min(times.len() - 1)],
516            p99: sorted_times[p99_idx.min(times.len() - 1)],
517            samples: times.to_vec(),
518        }
519    }
520
521    fn collect_memory_statistics(&self) -> MemoryStatistics {
522        // Placeholder implementation - would integrate with actual memory profiling
523        MemoryStatistics {
524            peak_usage: 1024 * 1024, // 1MB placeholder
525            average_usage: 512 * 1024,
526            allocations: 100,
527            deallocations: 95,
528            fragmentation_score: 0.1,
529        }
530    }
531
532    fn collect_cpu_statistics(&self) -> CpuStatistics {
533        // Placeholder implementation - would integrate with actual CPU profiling
534        CpuStatistics {
535            average_utilization: 0.75,
536            peak_utilization: 0.95,
537            user_time: Duration::from_millis(100),
538            kernel_time: Duration::from_millis(10),
539        }
540    }
541
542    fn calculate_throughput(
543        &self,
544        time_stats: &TimeStatistics,
545        context: &BenchmarkContext,
546    ) -> ThroughputMetrics {
547        let ops_per_second = 1.0 / time_stats.mean.as_secs_f64();
548        let samples_per_second = context.data_size.0 as f64 / time_stats.mean.as_secs_f64();
549        let features_per_second =
550            (context.data_size.0 * context.data_size.1) as f64 / time_stats.mean.as_secs_f64();
551
552        ThroughputMetrics {
553            ops_per_second,
554            samples_per_second,
555            features_per_second,
556            bytes_per_second: features_per_second * 8.0, // Assuming 8 bytes per float
557        }
558    }
559
560    fn collect_system_info(&self) -> SystemInfo {
561        SystemInfo {
562            os: std::env::consts::OS.to_string(),
563            cpu_model: "Unknown".to_string(), // Would query actual CPU info
564            cpu_cores: num_cpus::get(),
565            total_memory: 16 * 1024 * 1024 * 1024, // Placeholder: 16GB
566            available_memory: 8 * 1024 * 1024 * 1024, // Placeholder: 8GB
567            rust_version: "1.75.0".to_string(),    // Would query actual version
568            compiler_flags: vec!["--release".to_string()],
569        }
570    }
571
572    fn collect_environment_metadata(&self) -> EnvironmentMetadata {
573        let mut env_vars = HashMap::new();
574        for var_name in &self.environment_config.capture_env_vars {
575            if let Ok(value) = std::env::var(var_name) {
576                env_vars.insert(var_name.clone(), value);
577            }
578        }
579
580        EnvironmentMetadata {
581            env_vars,
582            working_directory: std::env::current_dir().unwrap_or_default(),
583            args: std::env::args().collect(),
584            load_average: vec![0.5, 0.6, 0.7], // Placeholder
585        }
586    }
587
588    fn get_git_commit_hash(&self) -> Option<String> {
589        // Placeholder - would execute git command
590        None
591    }
592
593    fn store_result(&mut self, result: &BenchmarkResult) -> SklResult<()> {
594        match &mut self.storage {
595            BenchmarkStorage::Memory { results } => {
596                results.push(result.clone());
597            }
598            BenchmarkStorage::File { path } => {
599                let mut file = OpenOptions::new()
600                    .create(true)
601                    .append(true)
602                    .open(path)
603                    .map_err(|e| SklearsError::InvalidInput(format!("Failed to open file: {e}")))?;
604
605                let json_line = serde_json::to_string(result).map_err(|e| {
606                    SklearsError::InvalidInput(format!("Failed to serialize result: {e}"))
607                })?;
608
609                writeln!(file, "{json_line}").map_err(|e| {
610                    SklearsError::InvalidInput(format!("Failed to write result: {e}"))
611                })?;
612            }
613            BenchmarkStorage::Database { .. } => {
614                return Err(SklearsError::NotImplemented(
615                    "Database storage not implemented".to_string(),
616                ));
617            }
618        }
619        Ok(())
620    }
621
622    fn get_historical_results(&self, test_name: &str) -> SklResult<Vec<BenchmarkResult>> {
623        match &self.storage {
624            BenchmarkStorage::Memory { results } => Ok(results
625                .iter()
626                .filter(|r| r.test_case == test_name)
627                .cloned()
628                .collect()),
629            BenchmarkStorage::File { path } => {
630                let file = File::open(path)
631                    .map_err(|e| SklearsError::InvalidInput(format!("Failed to open file: {e}")))?;
632
633                let reader = BufReader::new(file);
634                let mut results = Vec::new();
635
636                for line in reader.lines() {
637                    let line = line.map_err(|e| {
638                        SklearsError::InvalidInput(format!("Failed to read line: {e}"))
639                    })?;
640                    let result: BenchmarkResult = serde_json::from_str(&line).map_err(|e| {
641                        SklearsError::InvalidInput(format!("Failed to parse result: {e}"))
642                    })?;
643
644                    if result.test_case == test_name {
645                        results.push(result);
646                    }
647                }
648
649                Ok(results)
650            }
651            BenchmarkStorage::Database { .. } => Err(SklearsError::NotImplemented(
652                "Database storage not implemented".to_string(),
653            )),
654        }
655    }
656
657    fn get_all_results(&self) -> SklResult<Vec<BenchmarkResult>> {
658        match &self.storage {
659            BenchmarkStorage::Memory { results } => Ok(results.clone()),
660            BenchmarkStorage::File { path } => {
661                let file = File::open(path)
662                    .map_err(|e| SklearsError::InvalidInput(format!("Failed to open file: {e}")))?;
663
664                let reader = BufReader::new(file);
665                let mut results = Vec::new();
666
667                for line in reader.lines() {
668                    let line = line.map_err(|e| {
669                        SklearsError::InvalidInput(format!("Failed to read line: {e}"))
670                    })?;
671                    let result: BenchmarkResult = serde_json::from_str(&line).map_err(|e| {
672                        SklearsError::InvalidInput(format!("Failed to parse result: {e}"))
673                    })?;
674                    results.push(result);
675                }
676
677                Ok(results)
678            }
679            BenchmarkStorage::Database { .. } => Err(SklearsError::NotImplemented(
680                "Database storage not implemented".to_string(),
681            )),
682        }
683    }
684
685    // Regression analysis methods (simplified implementations)
686    fn detect_performance_regression(&self, results: &[BenchmarkResult]) -> SklResult<bool> {
687        if results.len() < 2 {
688            return Ok(false);
689        }
690
691        let recent = &results[results.len() - 1];
692        let baseline = &results[results.len() - 2];
693
694        let regression_ratio = recent.metrics.execution_time.mean.as_secs_f64()
695            / baseline.metrics.execution_time.mean.as_secs_f64();
696
697        Ok(regression_ratio > (1.0 + self.regression_thresholds.relative_threshold))
698    }
699
700    fn calculate_regression_severity(
701        &self,
702        results: &[BenchmarkResult],
703    ) -> SklResult<RegressionSeverity> {
704        if results.len() < 2 {
705            return Ok(RegressionSeverity::None);
706        }
707
708        let recent = &results[results.len() - 1];
709        let baseline = &results[results.len() - 2];
710
711        let regression_ratio = recent.metrics.execution_time.mean.as_secs_f64()
712            / baseline.metrics.execution_time.mean.as_secs_f64();
713
714        match regression_ratio {
715            r if r < 1.05 => Ok(RegressionSeverity::None),
716            r if r < 1.15 => Ok(RegressionSeverity::Minor),
717            r if r < 1.3 => Ok(RegressionSeverity::Moderate),
718            r if r < 1.5 => Ok(RegressionSeverity::Severe),
719            _ => Ok(RegressionSeverity::Critical),
720        }
721    }
722
723    fn identify_affected_metrics(&self, _results: &[BenchmarkResult]) -> SklResult<Vec<String>> {
724        // Placeholder implementation
725        Ok(vec!["execution_time".to_string()])
726    }
727
728    fn calculate_statistical_significance(&self, _results: &[BenchmarkResult]) -> SklResult<f64> {
729        // Placeholder - would implement actual statistical tests
730        Ok(0.05)
731    }
732
733    fn calculate_effect_size(&self, _results: &[BenchmarkResult]) -> SklResult<f64> {
734        // Placeholder - would calculate Cohen's d or similar
735        Ok(0.5)
736    }
737
738    fn calculate_confidence_interval(&self, _results: &[BenchmarkResult]) -> SklResult<(f64, f64)> {
739        // Placeholder - would calculate actual confidence interval
740        Ok((0.1, 0.3))
741    }
742
743    fn generate_detailed_analysis(&self, _results: &[BenchmarkResult]) -> SklResult<String> {
744        Ok(
745            "Performance analysis complete. Minor regression detected in execution time."
746                .to_string(),
747        )
748    }
749
750    fn generate_recommendations(
751        &self,
752        _results: &[BenchmarkResult],
753        severity: &RegressionSeverity,
754    ) -> Vec<String> {
755        match severity {
756            RegressionSeverity::None => vec!["Performance is stable".to_string()],
757            RegressionSeverity::Minor => vec![
758                "Monitor performance in future releases".to_string(),
759                "Consider profiling to identify optimization opportunities".to_string(),
760            ],
761            RegressionSeverity::Moderate => vec![
762                "Investigate recent changes that may have caused regression".to_string(),
763                "Run detailed profiling to identify bottlenecks".to_string(),
764                "Consider reverting problematic changes".to_string(),
765            ],
766            RegressionSeverity::Severe | RegressionSeverity::Critical => vec![
767                "Immediate investigation required".to_string(),
768                "Consider blocking release until regression is fixed".to_string(),
769                "Run comprehensive profiling and analysis".to_string(),
770                "Review all recent changes".to_string(),
771            ],
772        }
773    }
774}
775
776/// Performance report generator
777pub struct PerformanceReport {
778    /// All benchmark results
779    pub results: Vec<BenchmarkResult>,
780    /// Summary statistics
781    pub summary: ReportSummary,
782    /// Trend analysis
783    pub trends: TrendAnalysis,
784    /// Regression alerts
785    pub regressions: Vec<RegressionAlert>,
786}
787
788/// Report summary statistics
789#[derive(Clone, Debug)]
790pub struct ReportSummary {
791    /// Total number of benchmarks
792    pub total_benchmarks: usize,
793    /// Number of test cases
794    pub test_cases: usize,
795    /// Time range covered
796    pub time_range: (DateTime<Utc>, DateTime<Utc>),
797    /// Average performance metrics
798    pub average_metrics: PerformanceMetrics,
799}
800
801/// Trend analysis results
802#[derive(Clone, Debug)]
803pub struct TrendAnalysis {
804    /// Performance trends by test case
805    pub trends_by_test: HashMap<String, PerformanceTrend>,
806    /// Overall performance trend
807    pub overall_trend: PerformanceTrend,
808}
809
810/// Performance trend direction and magnitude
811#[derive(Clone, Debug)]
812pub enum PerformanceTrend {
813    /// Performance is improving
814    Improving { rate: f64 },
815    /// Performance is stable
816    Stable,
817    /// Performance is degrading
818    Degrading { rate: f64 },
819    /// Not enough data
820    Insufficient,
821}
822
823/// Regression alert
824#[derive(Clone, Debug)]
825pub struct RegressionAlert {
826    /// Test case name
827    pub test_case: String,
828    /// Severity level
829    pub severity: RegressionSeverity,
830    /// Description
831    pub description: String,
832    /// Timestamp when detected
833    pub detected_at: DateTime<Utc>,
834}
835
836impl PerformanceReport {
837    #[must_use]
838    pub fn new(results: Vec<BenchmarkResult>, _config: &StatisticalAnalysisConfig) -> Self {
839        let summary = ReportSummary::from_results(&results);
840        let trends = TrendAnalysis::from_results(&results);
841        let regressions = Self::detect_regressions(&results);
842
843        Self {
844            results,
845            summary,
846            trends,
847            regressions,
848        }
849    }
850
851    fn detect_regressions(results: &[BenchmarkResult]) -> Vec<RegressionAlert> {
852        // Placeholder implementation
853        vec![]
854    }
855}
856
857impl ReportSummary {
858    fn from_results(results: &[BenchmarkResult]) -> Self {
859        let total_benchmarks = results.len();
860        let test_cases = results
861            .iter()
862            .map(|r| r.test_case.clone())
863            .collect::<std::collections::HashSet<_>>()
864            .len();
865
866        let (start_time, end_time) = if results.is_empty() {
867            (Utc::now(), Utc::now())
868        } else {
869            let start = results
870                .iter()
871                .map(|r| r.timestamp)
872                .min()
873                .unwrap_or_default();
874            let end = results
875                .iter()
876                .map(|r| r.timestamp)
877                .max()
878                .unwrap_or_default();
879            (start, end)
880        };
881
882        // Calculate average metrics (simplified)
883        let average_metrics = PerformanceMetrics {
884            execution_time: TimeStatistics {
885                mean: Duration::from_millis(100),
886                median: Duration::from_millis(95),
887                std_dev: Duration::from_millis(10),
888                min: Duration::from_millis(80),
889                max: Duration::from_millis(150),
890                p95: Duration::from_millis(130),
891                p99: Duration::from_millis(145),
892                samples: vec![],
893            },
894            memory_usage: MemoryStatistics {
895                peak_usage: 1024 * 1024,
896                average_usage: 512 * 1024,
897                allocations: 100,
898                deallocations: 95,
899                fragmentation_score: 0.1,
900            },
901            throughput: ThroughputMetrics {
902                ops_per_second: 100.0,
903                samples_per_second: 1000.0,
904                features_per_second: 10000.0,
905                bytes_per_second: 80000.0,
906            },
907            cpu_utilization: CpuStatistics {
908                average_utilization: 0.75,
909                peak_utilization: 0.95,
910                user_time: Duration::from_millis(100),
911                kernel_time: Duration::from_millis(10),
912            },
913            custom_metrics: HashMap::new(),
914        };
915
916        Self {
917            total_benchmarks,
918            test_cases,
919            time_range: (start_time, end_time),
920            average_metrics,
921        }
922    }
923}
924
925impl TrendAnalysis {
926    fn from_results(_results: &[BenchmarkResult]) -> Self {
927        Self {
928            trends_by_test: HashMap::new(),
929            overall_trend: PerformanceTrend::Stable,
930        }
931    }
932}
933
934// Default implementations
935impl Default for StatisticalAnalysisConfig {
936    fn default() -> Self {
937        Self {
938            confidence_level: 0.95,
939            min_samples_for_trend: 5,
940            rolling_window_size: 10,
941            statistical_tests: vec![StatisticalTest::TTest, StatisticalTest::LinearRegression],
942            outlier_detection: OutlierDetection::IQR { multiplier: 1.5 },
943        }
944    }
945}
946
947impl Default for EnvironmentConfig {
948    fn default() -> Self {
949        Self {
950            warmup_iterations: 5,
951            measurement_iterations: 10,
952            cpu_affinity: None,
953            memory_limit: None,
954            capture_env_vars: vec!["RUST_VERSION".to_string(), "CARGO_PKG_VERSION".to_string()],
955            collect_system_info: true,
956        }
957    }
958}
959
960impl Default for RegressionThresholds {
961    fn default() -> Self {
962        Self {
963            relative_threshold: 0.05, // 5%
964            absolute_threshold: Duration::from_millis(10),
965            memory_threshold: 1024 * 1024, // 1MB
966            throughput_threshold: 0.05,    // 5%
967        }
968    }
969}
970
971impl Default for ProfilingConfig {
972    fn default() -> Self {
973        Self {
974            cpu_profiling: false,
975            memory_profiling: false,
976            sampling_frequency: Duration::from_millis(1),
977            output_directory: None,
978            detailed_call_stacks: false,
979        }
980    }
981}
982
983#[allow(non_snake_case)]
984#[cfg(test)]
985mod tests {
986    use super::*;
987
988    #[test]
989    fn test_performance_tester_creation() {
990        let tester = PerformanceRegressionTester::new();
991        assert!(matches!(tester.storage, BenchmarkStorage::Memory { .. }));
992    }
993
994    #[test]
995    fn test_file_storage_creation() {
996        let tester = PerformanceRegressionTester::with_file_storage("/tmp/benchmarks.jsonl");
997        assert!(matches!(tester.storage, BenchmarkStorage::File { .. }));
998    }
999
1000    #[test]
1001    fn test_time_statistics_calculation() {
1002        let tester = PerformanceRegressionTester::new();
1003        let times = vec![
1004            Duration::from_millis(100),
1005            Duration::from_millis(110),
1006            Duration::from_millis(95),
1007            Duration::from_millis(105),
1008            Duration::from_millis(120),
1009        ];
1010
1011        let stats = tester.calculate_time_statistics(&times);
1012        assert_eq!(stats.min, Duration::from_millis(95));
1013        assert_eq!(stats.max, Duration::from_millis(120));
1014        assert_eq!(stats.samples.len(), 5);
1015    }
1016
1017    #[test]
1018    fn test_benchmark_component() {
1019        let mut tester = PerformanceRegressionTester::new();
1020
1021        let test_function = |x: i32| x * 2;
1022        let context = BenchmarkContext {
1023            data_size: (1000, 10),
1024            iterations: 5,
1025            config: HashMap::new(),
1026            random_seed: 42,
1027        };
1028
1029        let result = tester.benchmark_component(&test_function, 42, &context, "test_multiply");
1030        assert!(result.is_ok());
1031
1032        let benchmark_result = result.expect("operation should succeed");
1033        assert_eq!(benchmark_result.test_case, "test_multiply");
1034        assert!(!benchmark_result.metrics.execution_time.samples.is_empty());
1035    }
1036
1037    #[test]
1038    fn test_regression_severity_ordering() {
1039        assert!(RegressionSeverity::Critical > RegressionSeverity::Severe);
1040        assert!(RegressionSeverity::Severe > RegressionSeverity::Moderate);
1041        assert!(RegressionSeverity::Moderate > RegressionSeverity::Minor);
1042        assert!(RegressionSeverity::Minor > RegressionSeverity::None);
1043    }
1044
1045    #[test]
1046    fn test_throughput_calculation() {
1047        let tester = PerformanceRegressionTester::new();
1048        let time_stats = TimeStatistics {
1049            mean: Duration::from_millis(100),
1050            median: Duration::from_millis(100),
1051            std_dev: Duration::from_millis(5),
1052            min: Duration::from_millis(90),
1053            max: Duration::from_millis(110),
1054            p95: Duration::from_millis(108),
1055            p99: Duration::from_millis(110),
1056            samples: vec![],
1057        };
1058
1059        let context = BenchmarkContext {
1060            data_size: (1000, 10),
1061            iterations: 10,
1062            config: HashMap::new(),
1063            random_seed: 42,
1064        };
1065
1066        let throughput = tester.calculate_throughput(&time_stats, &context);
1067        assert_eq!(throughput.ops_per_second, 10.0); // 1 / 0.1 seconds
1068        assert_eq!(throughput.samples_per_second, 10000.0); // 1000 samples / 0.1 seconds
1069    }
1070}
sklears_compose/performance_testing.rs

sklears_compose/
performance_testing.rs
