sklears_kernel_approximation/
memory_efficient.rs

1//! Memory-efficient kernel approximation methods
2//!
3//! This module provides memory-efficient implementations of kernel approximation methods
4//! that can handle large datasets that don't fit entirely in memory.
5
6use crate::nystroem::Kernel;
7use crate::{Nystroem, RBFSampler, SamplingStrategy};
8use rayon::prelude::*;
9use scirs2_core::ndarray::{s, Array1, Array2};
10use scirs2_core::random::essentials::{Normal as RandNormal, Uniform as RandUniform};
11use scirs2_core::random::rngs::StdRng;
12use scirs2_core::random::RngExt;
13use scirs2_core::random::{thread_rng, SeedableRng};
14use sklears_core::{
15    error::{Result, SklearsError},
16    traits::{Fit, Trained, Transform},
17};
18use std::sync::{Arc, Mutex};
19
20/// Configuration for memory-efficient operations
21#[derive(Debug, Clone)]
22/// MemoryConfig
23pub struct MemoryConfig {
24    /// Maximum memory usage in bytes
25    pub max_memory_bytes: usize,
26    /// Chunk size for processing
27    pub chunk_size: usize,
28    /// Number of parallel workers
29    pub n_workers: usize,
30    /// Enable disk caching for intermediate results
31    pub enable_disk_cache: bool,
32    /// Temporary directory for disk cache
33    pub temp_dir: String,
34}
35
36impl Default for MemoryConfig {
37    fn default() -> Self {
38        Self {
39            max_memory_bytes: 1024 * 1024 * 1024, // 1GB
40            chunk_size: 10000,
41            n_workers: num_cpus::get(),
42            enable_disk_cache: false,
43            temp_dir: "/tmp".to_string(),
44        }
45    }
46}
47
48/// Memory-efficient RBF sampler with chunked processing
49#[derive(Debug, Clone)]
50/// MemoryEfficientRBFSampler
51pub struct MemoryEfficientRBFSampler {
52    n_components: usize,
53    gamma: f64,
54    config: MemoryConfig,
55    random_seed: Option<u64>,
56}
57
58impl MemoryEfficientRBFSampler {
59    /// Create a new memory-efficient RBF sampler
60    pub fn new(n_components: usize) -> Self {
61        Self {
62            n_components,
63            gamma: 1.0,
64            config: MemoryConfig::default(),
65            random_seed: None,
66        }
67    }
68
69    /// Set gamma parameter
70    pub fn gamma(mut self, gamma: f64) -> Self {
71        self.gamma = gamma;
72        self
73    }
74
75    /// Set memory configuration
76    pub fn config(mut self, config: MemoryConfig) -> Self {
77        self.config = config;
78        self
79    }
80
81    /// Set random seed
82    pub fn random_seed(mut self, seed: u64) -> Self {
83        self.random_seed = Some(seed);
84        self
85    }
86
87    /// Process data in chunks
88    pub fn transform_chunked(&self, x: &Array2<f64>) -> Result<Array2<f64>> {
89        let n_samples = x.nrows();
90        let _n_features = x.ncols();
91        let chunk_size = self.config.chunk_size.min(n_samples);
92
93        // Initialize output array
94        let mut output = Array2::zeros((n_samples, self.n_components));
95
96        // Create RBF sampler for consistent random features
97        let rbf_sampler = RBFSampler::new(self.n_components).gamma(self.gamma);
98        let fitted_sampler = rbf_sampler.fit(x, &())?;
99
100        // Process in chunks
101        for chunk_start in (0..n_samples).step_by(chunk_size) {
102            let chunk_end = (chunk_start + chunk_size).min(n_samples);
103            let chunk = x.slice(s![chunk_start..chunk_end, ..]);
104
105            // Transform chunk
106            let chunk_transformed = fitted_sampler.transform(&chunk.to_owned())?;
107
108            // Store result
109            output
110                .slice_mut(s![chunk_start..chunk_end, ..])
111                .assign(&chunk_transformed);
112        }
113
114        Ok(output)
115    }
116
117    /// Parallel chunked processing
118    pub fn transform_chunked_parallel(&self, x: &Array2<f64>) -> Result<Array2<f64>> {
119        let n_samples = x.nrows();
120        let chunk_size = self.config.chunk_size.min(n_samples);
121
122        // Create RBF sampler for consistent random features
123        let rbf_sampler = RBFSampler::new(self.n_components).gamma(self.gamma);
124        let fitted_sampler = Arc::new(rbf_sampler.fit(x, &())?);
125
126        // Create chunks
127        let chunks: Vec<_> = (0..n_samples)
128            .step_by(chunk_size)
129            .map(|start| {
130                let end = (start + chunk_size).min(n_samples);
131                (start, end)
132            })
133            .collect();
134
135        // Process chunks in parallel
136        let results: Result<Vec<_>> = chunks
137            .par_iter()
138            .map(|&(start, end)| {
139                let chunk = x.slice(s![start..end, ..]).to_owned();
140                fitted_sampler
141                    .transform(&chunk)
142                    .map(|result| (start, result))
143            })
144            .collect();
145
146        let results = results?;
147
148        // Combine results
149        let mut output = Array2::zeros((n_samples, self.n_components));
150        for (start, chunk_result) in results {
151            let end = start + chunk_result.nrows();
152            output.slice_mut(s![start..end, ..]).assign(&chunk_result);
153        }
154
155        Ok(output)
156    }
157}
158
159/// Fitted memory-efficient RBF sampler
160pub struct FittedMemoryEfficientRBFSampler {
161    random_weights: Array2<f64>,
162    random_offset: Array1<f64>,
163    gamma: f64,
164    config: MemoryConfig,
165}
166
167impl Fit<Array2<f64>, ()> for MemoryEfficientRBFSampler {
168    type Fitted = FittedMemoryEfficientRBFSampler;
169
170    fn fit(self, x: &Array2<f64>, _y: &()) -> Result<Self::Fitted> {
171        let n_features = x.ncols();
172
173        let mut rng = if let Some(seed) = self.random_seed {
174            StdRng::seed_from_u64(seed)
175        } else {
176            StdRng::from_seed(thread_rng().random())
177        };
178
179        // Generate random weights and offsets
180        let random_weights = Array2::from_shape_fn((self.n_components, n_features), |_| {
181            rng.sample(
182                RandNormal::new(0.0, (2.0 * self.gamma).sqrt()).expect("operation should succeed"),
183            )
184        });
185
186        let random_offset = Array1::from_shape_fn(self.n_components, |_| {
187            rng.sample(
188                RandUniform::new(0.0, 2.0 * std::f64::consts::PI)
189                    .expect("operation should succeed"),
190            )
191        });
192
193        Ok(FittedMemoryEfficientRBFSampler {
194            random_weights,
195            random_offset,
196            gamma: self.gamma,
197            config: self.config.clone(),
198        })
199    }
200}
201
202impl Transform<Array2<f64>, Array2<f64>> for FittedMemoryEfficientRBFSampler {
203    fn transform(&self, x: &Array2<f64>) -> Result<Array2<f64>> {
204        let n_samples = x.nrows();
205        let chunk_size = self.config.chunk_size.min(n_samples);
206
207        if n_samples <= chunk_size {
208            // Small dataset, process normally
209            self.transform_small(x)
210        } else {
211            // Large dataset, use chunked processing
212            self.transform_chunked(x)
213        }
214    }
215}
216
217impl FittedMemoryEfficientRBFSampler {
218    fn transform_small(&self, x: &Array2<f64>) -> Result<Array2<f64>> {
219        let projection = x.dot(&self.random_weights.t());
220        let scaled_projection = projection + &self.random_offset;
221
222        let normalization = (2.0 / self.random_weights.nrows() as f64).sqrt();
223        Ok(scaled_projection.mapv(|v| v.cos() * normalization))
224    }
225
226    fn transform_chunked(&self, x: &Array2<f64>) -> Result<Array2<f64>> {
227        let n_samples = x.nrows();
228        let chunk_size = self.config.chunk_size;
229        let mut output = Array2::zeros((n_samples, self.random_weights.nrows()));
230
231        for chunk_start in (0..n_samples).step_by(chunk_size) {
232            let chunk_end = (chunk_start + chunk_size).min(n_samples);
233            let chunk = x.slice(s![chunk_start..chunk_end, ..]);
234
235            let chunk_transformed = self.transform_small(&chunk.to_owned())?;
236            output
237                .slice_mut(s![chunk_start..chunk_end, ..])
238                .assign(&chunk_transformed);
239        }
240
241        Ok(output)
242    }
243}
244
245/// Memory-efficient Nyström approximation
246#[derive(Debug, Clone)]
247/// MemoryEfficientNystroem
248pub struct MemoryEfficientNystroem {
249    n_components: usize,
250    kernel: String,
251    gamma: Option<f64>,
252    degree: Option<i32>,
253    coef0: Option<f64>,
254    sampling: SamplingStrategy,
255    config: MemoryConfig,
256    random_seed: Option<u64>,
257}
258
259impl MemoryEfficientNystroem {
260    /// Create a new memory-efficient Nyström approximation
261    pub fn new(n_components: usize) -> Self {
262        Self {
263            n_components,
264            kernel: "rbf".to_string(),
265            gamma: None,
266            degree: None,
267            coef0: None,
268            sampling: SamplingStrategy::Random,
269            config: MemoryConfig::default(),
270            random_seed: None,
271        }
272    }
273
274    /// Set kernel type
275    pub fn kernel(mut self, kernel: &str) -> Self {
276        self.kernel = kernel.to_string();
277        self
278    }
279
280    /// Set gamma parameter
281    pub fn gamma(mut self, gamma: f64) -> Self {
282        self.gamma = Some(gamma);
283        self
284    }
285
286    /// Set sampling strategy
287    pub fn sampling(mut self, sampling: SamplingStrategy) -> Self {
288        self.sampling = sampling;
289        self
290    }
291
292    /// Set memory configuration
293    pub fn config(mut self, config: MemoryConfig) -> Self {
294        self.config = config;
295        self
296    }
297
298    /// Out-of-core training for large datasets
299    pub fn fit_incremental(
300        &self,
301        x_chunks: Vec<Array2<f64>>,
302    ) -> Result<FittedMemoryEfficientNystroem> {
303        // Collect representative samples from all chunks
304        let mut representative_samples = Vec::new();
305        let samples_per_chunk = self.n_components / x_chunks.len().max(1);
306
307        for chunk in &x_chunks {
308            let n_samples = chunk.nrows().min(samples_per_chunk);
309            if n_samples > 0 {
310                let indices: Vec<usize> = (0..chunk.nrows()).collect();
311                let selected_indices = &indices[..n_samples];
312
313                for &idx in selected_indices {
314                    representative_samples.push(chunk.row(idx).to_owned());
315                }
316            }
317        }
318
319        if representative_samples.is_empty() {
320            return Err(SklearsError::InvalidInput(
321                "No samples found in chunks".to_string(),
322            ));
323        }
324
325        // Create combined dataset from representative samples
326        let n_selected = representative_samples.len().min(self.n_components);
327        let n_features = representative_samples[0].len();
328        let mut combined_data = Array2::zeros((n_selected, n_features));
329
330        for (i, sample) in representative_samples.iter().take(n_selected).enumerate() {
331            combined_data.row_mut(i).assign(sample);
332        }
333
334        // Fit standard Nyström on representative samples
335        let kernel = match self.kernel.as_str() {
336            "rbf" => Kernel::Rbf {
337                gamma: self.gamma.unwrap_or(1.0),
338            },
339            "linear" => Kernel::Linear,
340            "polynomial" => Kernel::Polynomial {
341                gamma: self.gamma.unwrap_or(1.0),
342                degree: self.degree.unwrap_or(3) as u32,
343                coef0: self.coef0.unwrap_or(1.0),
344            },
345            _ => Kernel::Rbf { gamma: 1.0 }, // default
346        };
347        let nystroem = Nystroem::new(kernel, n_selected).sampling_strategy(self.sampling.clone());
348
349        let fitted_nystroem = nystroem.fit(&combined_data, &())?;
350
351        Ok(FittedMemoryEfficientNystroem {
352            fitted_nystroem,
353            config: self.config.clone(),
354        })
355    }
356}
357
358/// Fitted memory-efficient Nyström approximation
359pub struct FittedMemoryEfficientNystroem {
360    fitted_nystroem: crate::nystroem::Nystroem<Trained>,
361    config: MemoryConfig,
362}
363
364impl Fit<Array2<f64>, ()> for MemoryEfficientNystroem {
365    type Fitted = FittedMemoryEfficientNystroem;
366
367    fn fit(self, x: &Array2<f64>, _y: &()) -> Result<Self::Fitted> {
368        let kernel = match self.kernel.as_str() {
369            "rbf" => Kernel::Rbf {
370                gamma: self.gamma.unwrap_or(1.0),
371            },
372            "linear" => Kernel::Linear,
373            "polynomial" => Kernel::Polynomial {
374                gamma: self.gamma.unwrap_or(1.0),
375                degree: self.degree.unwrap_or(3) as u32,
376                coef0: self.coef0.unwrap_or(1.0),
377            },
378            _ => Kernel::Rbf { gamma: 1.0 }, // default
379        };
380        let nystroem =
381            Nystroem::new(kernel, self.n_components).sampling_strategy(self.sampling.clone());
382
383        let fitted_nystroem = nystroem.fit(x, &())?;
384
385        Ok(FittedMemoryEfficientNystroem {
386            fitted_nystroem,
387            config: self.config.clone(),
388        })
389    }
390}
391
392impl Transform<Array2<f64>, Array2<f64>> for FittedMemoryEfficientNystroem {
393    fn transform(&self, x: &Array2<f64>) -> Result<Array2<f64>> {
394        let n_samples = x.nrows();
395        let chunk_size = self.config.chunk_size;
396
397        if n_samples <= chunk_size {
398            // Small dataset, process normally
399            self.fitted_nystroem.transform(x)
400        } else {
401            // Large dataset, use chunked processing
402            self.transform_chunked(x)
403        }
404    }
405}
406
407impl FittedMemoryEfficientNystroem {
408    fn transform_chunked(&self, x: &Array2<f64>) -> Result<Array2<f64>> {
409        let n_samples = x.nrows();
410        let chunk_size = self.config.chunk_size;
411        let n_components = self
412            .fitted_nystroem
413            .transform(&x.slice(s![0..1, ..]).to_owned())?
414            .ncols();
415
416        let mut output = Array2::zeros((n_samples, n_components));
417
418        for chunk_start in (0..n_samples).step_by(chunk_size) {
419            let chunk_end = (chunk_start + chunk_size).min(n_samples);
420            let chunk = x.slice(s![chunk_start..chunk_end, ..]);
421
422            let chunk_transformed = self.fitted_nystroem.transform(&chunk.to_owned())?;
423            output
424                .slice_mut(s![chunk_start..chunk_end, ..])
425                .assign(&chunk_transformed);
426        }
427
428        Ok(output)
429    }
430}
431
432/// Memory usage monitoring utilities
433pub struct MemoryMonitor {
434    max_memory_bytes: usize,
435    current_usage: Arc<Mutex<usize>>,
436}
437
438impl MemoryMonitor {
439    /// Create a new memory monitor
440    pub fn new(max_memory_bytes: usize) -> Self {
441        Self {
442            max_memory_bytes,
443            current_usage: Arc::new(Mutex::new(0)),
444        }
445    }
446
447    /// Check if we can allocate more memory
448    pub fn can_allocate(&self, bytes: usize) -> bool {
449        let current = *self.current_usage.lock().expect("operation should succeed");
450        current + bytes <= self.max_memory_bytes
451    }
452
453    /// Allocate memory (tracking purposes)
454    pub fn allocate(&self, bytes: usize) -> Result<()> {
455        let mut current = self.current_usage.lock().expect("operation should succeed");
456        if *current + bytes > self.max_memory_bytes {
457            return Err(SklearsError::InvalidInput(format!(
458                "Memory limit exceeded: {} + {} > {}",
459                *current, bytes, self.max_memory_bytes
460            )));
461        }
462        *current += bytes;
463        Ok(())
464    }
465
466    /// Deallocate memory
467    pub fn deallocate(&self, bytes: usize) {
468        let mut current = self.current_usage.lock().expect("operation should succeed");
469        *current = current.saturating_sub(bytes);
470    }
471
472    /// Get current memory usage
473    pub fn current_usage(&self) -> usize {
474        *self.current_usage.lock().expect("operation should succeed")
475    }
476
477    /// Get memory usage percentage
478    pub fn usage_percentage(&self) -> f64 {
479        let current = *self.current_usage.lock().expect("operation should succeed");
480        (current as f64 / self.max_memory_bytes as f64) * 100.0
481    }
482}
483
484#[allow(non_snake_case)]
485#[cfg(test)]
486mod tests {
487    use super::*;
488    use approx::assert_abs_diff_eq;
489    use scirs2_core::ndarray::Array2;
490
491    #[test]
492    fn test_memory_efficient_rbf_sampler() {
493        let x = Array2::from_shape_vec((100, 10), (0..1000).map(|i| i as f64).collect())
494            .expect("operation should succeed");
495
496        let sampler = MemoryEfficientRBFSampler::new(50)
497            .gamma(0.1)
498            .config(MemoryConfig {
499                chunk_size: 30,
500                ..Default::default()
501            });
502
503        let fitted = sampler.fit(&x, &()).expect("operation should succeed");
504        let transformed = fitted.transform(&x).expect("operation should succeed");
505
506        assert_eq!(transformed.shape(), &[100, 50]);
507
508        // Test chunked processing gives same results as small dataset
509        let small_x = x.slice(s![0..10, ..]).to_owned();
510        let small_transformed = fitted
511            .transform(&small_x)
512            .expect("operation should succeed");
513        let chunked_transformed = transformed.slice(s![0..10, ..]);
514
515        assert_abs_diff_eq!(small_transformed, chunked_transformed, epsilon = 1e-10);
516    }
517
518    #[test]
519    fn test_memory_efficient_rbf_chunked_parallel() {
520        let x = Array2::from_shape_vec((200, 5), (0..1000).map(|i| i as f64 * 0.1).collect())
521            .expect("operation should succeed");
522
523        let sampler = MemoryEfficientRBFSampler::new(30)
524            .gamma(1.0)
525            .config(MemoryConfig {
526                chunk_size: 50,
527                n_workers: 2,
528                ..Default::default()
529            });
530
531        let result = sampler
532            .transform_chunked_parallel(&x)
533            .expect("operation should succeed");
534        assert_eq!(result.shape(), &[200, 30]);
535
536        // Verify output is reasonable (not all zeros, not all same values)
537        let mean_val = result.mean().expect("operation should succeed");
538        let std_val = result.std(0.0);
539        assert!(mean_val.abs() < 0.5); // Should be roughly centered
540        assert!(std_val > 0.1); // Should have some variance
541    }
542
543    #[test]
544    fn test_memory_efficient_nystroem() {
545        let x = Array2::from_shape_vec((80, 6), (0..480).map(|i| i as f64 * 0.01).collect())
546            .expect("operation should succeed");
547
548        let nystroem = MemoryEfficientNystroem::new(20)
549            .kernel("rbf")
550            .gamma(0.5)
551            .config(MemoryConfig {
552                chunk_size: 25,
553                ..Default::default()
554            });
555
556        let fitted = nystroem.fit(&x, &()).expect("operation should succeed");
557        let transformed = fitted.transform(&x).expect("operation should succeed");
558
559        assert_eq!(transformed.shape(), &[80, 20]);
560    }
561
562    #[test]
563    fn test_memory_efficient_nystroem_incremental() {
564        // Create multiple chunks
565        let chunk1 = Array2::from_shape_vec((30, 4), (0..120).map(|i| i as f64 * 0.1).collect())
566            .expect("operation should succeed");
567        let chunk2 = Array2::from_shape_vec((40, 4), (120..280).map(|i| i as f64 * 0.1).collect())
568            .expect("operation should succeed");
569        let chunk3 = Array2::from_shape_vec((30, 4), (280..400).map(|i| i as f64 * 0.1).collect())
570            .expect("operation should succeed");
571
572        let chunks = vec![chunk1, chunk2.clone(), chunk3];
573
574        let nystroem = MemoryEfficientNystroem::new(15)
575            .kernel("rbf")
576            .config(MemoryConfig {
577                chunk_size: 20,
578                ..Default::default()
579            });
580
581        let fitted = nystroem
582            .fit_incremental(chunks)
583            .expect("operation should succeed");
584        let transformed = fitted.transform(&chunk2).expect("operation should succeed");
585
586        assert_eq!(transformed.shape(), &[40, 15]);
587    }
588
589    #[test]
590    fn test_memory_monitor() {
591        let monitor = MemoryMonitor::new(1000);
592
593        assert!(monitor.can_allocate(500));
594        assert!(monitor.allocate(500).is_ok());
595        assert_eq!(monitor.current_usage(), 500);
596        assert_eq!(monitor.usage_percentage(), 50.0);
597
598        assert!(!monitor.can_allocate(600)); // Would exceed limit
599        assert!(monitor.allocate(400).is_ok()); // Total = 900, still OK
600
601        assert!(monitor.allocate(200).is_err()); // Would exceed limit
602
603        monitor.deallocate(300);
604        assert_eq!(monitor.current_usage(), 600);
605        assert!(monitor.can_allocate(300));
606    }
607
608    #[test]
609    fn test_memory_config() {
610        let config = MemoryConfig::default();
611        assert_eq!(config.max_memory_bytes, 1024 * 1024 * 1024);
612        assert_eq!(config.chunk_size, 10000);
613        assert!(config.n_workers > 0);
614
615        let custom_config = MemoryConfig {
616            max_memory_bytes: 512 * 1024 * 1024,
617            chunk_size: 5000,
618            n_workers: 4,
619            enable_disk_cache: true,
620            temp_dir: "/custom/temp".to_string(),
621        };
622
623        let sampler = MemoryEfficientRBFSampler::new(50).config(custom_config.clone());
624        assert_eq!(sampler.config.max_memory_bytes, 512 * 1024 * 1024);
625        assert_eq!(sampler.config.chunk_size, 5000);
626        assert_eq!(sampler.config.n_workers, 4);
627        assert!(sampler.config.enable_disk_cache);
628        assert_eq!(sampler.config.temp_dir, "/custom/temp");
629    }
630
631    #[test]
632    fn test_reproducibility() {
633        let x = Array2::from_shape_vec((50, 8), (0..400).map(|i| i as f64 * 0.05).collect())
634            .expect("operation should succeed");
635
636        let sampler1 = MemoryEfficientRBFSampler::new(20)
637            .gamma(0.2)
638            .random_seed(42);
639
640        let sampler2 = MemoryEfficientRBFSampler::new(20)
641            .gamma(0.2)
642            .random_seed(42);
643
644        let fitted1 = sampler1.fit(&x, &()).expect("operation should succeed");
645        let fitted2 = sampler2.fit(&x, &()).expect("operation should succeed");
646
647        let result1 = fitted1.transform(&x).expect("operation should succeed");
648        let result2 = fitted2.transform(&x).expect("operation should succeed");
649
650        assert_abs_diff_eq!(result1, result2, epsilon = 1e-10);
651    }
652}
sklears_kernel_approximation/memory_efficient.rs

sklears_kernel_approximation/
memory_efficient.rs
