sklears_feature_extraction/
graph.rs

1//! Graph and network feature extraction utilities
2//!
3//! This module provides comprehensive graph and network feature extraction implementations including
4//! centrality measures, motif detection, community analysis, spectral analysis, path-based metrics,
5//! graph embeddings (Node2Vec, DeepWalk, LINE, GraphSAGE), structural analysis, network topology,
6//! random walk algorithms, eigenvalue decomposition, adjacency matrix operations, neighborhood
7//! sampling, graph neural networks, network representation learning, and high-performance graph
8//! processing pipelines.
9#![allow(dead_code)] // graph feature structs retain config/embedding fields for future public API completeness
10
11use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
12use sklears_core::{error::Result as SklResult, prelude::SklearsError};
13
14// Core graph types and base structures
15#[derive(Debug, Clone)]
16pub struct Graph {
17    adjacency_matrix: Array2<f64>,
18    n_nodes: usize,
19}
20
21#[derive(Debug, Clone, Default)]
22pub struct GraphConfig {
23    /// directed
24    pub directed: bool,
25    /// weighted
26    pub weighted: bool,
27}
28
29impl Graph {
30    pub fn from_adjacency_matrix(adjacency_matrix: Array2<f64>) -> Self {
31        let n_nodes = adjacency_matrix.nrows();
32        Self {
33            adjacency_matrix,
34            n_nodes,
35        }
36    }
37
38    pub fn n_nodes(&self) -> usize {
39        self.n_nodes
40    }
41
42    pub fn adjacency_matrix(&self) -> &Array2<f64> {
43        &self.adjacency_matrix
44    }
45}
46
47// Centrality measures and node importance analysis
48#[derive(Debug, Clone)]
49pub struct NodeCentralityExtractor {
50    config: GraphConfig,
51}
52
53impl NodeCentralityExtractor {
54    pub fn new() -> Self {
55        Self {
56            config: GraphConfig::default(),
57        }
58    }
59}
60
61impl Default for NodeCentralityExtractor {
62    fn default() -> Self {
63        Self::new()
64    }
65}
66
67// Placeholder implementations for other graph feature extractors
68#[derive(Debug, Clone)]
69pub struct GraphSpectralFeatures {
70    config: GraphConfig,
71    n_eigenvalues: usize,
72    include_eigenvector_centrality: bool,
73}
74
75impl GraphSpectralFeatures {
76    pub fn new() -> Self {
77        Self {
78            config: GraphConfig::default(),
79            n_eigenvalues: 0,
80            include_eigenvector_centrality: false,
81        }
82    }
83
84    pub fn n_eigenvalues(mut self, n: usize) -> Self {
85        self.n_eigenvalues = n;
86        self
87    }
88
89    pub fn include_eigenvector_centrality(mut self, include: bool) -> Self {
90        self.include_eigenvector_centrality = include;
91        self
92    }
93
94    pub fn extract_features(
95        &self,
96        n_nodes: usize,
97        edges: &[(usize, usize)],
98    ) -> SklResult<Array1<f64>> {
99        if n_nodes == 0 {
100            return Ok(Array1::from_vec(vec![]));
101        }
102
103        validate_edges(n_nodes, edges)?;
104
105        let mut adjacency = Array2::zeros((n_nodes, n_nodes));
106        for &(src, dst) in edges {
107            if src == dst {
108                continue;
109            }
110            adjacency[[src, dst]] = 1.0;
111            adjacency[[dst, src]] = 1.0;
112        }
113
114        let mut laplacian = Array2::zeros((n_nodes, n_nodes));
115        for i in 0..n_nodes {
116            let mut degree = 0.0;
117            for j in 0..n_nodes {
118                let weight = adjacency[[i, j]];
119                if i != j {
120                    laplacian[[i, j]] = -weight;
121                }
122                degree += weight;
123            }
124            laplacian[[i, i]] = degree;
125        }
126
127        let eigen_count = self.n_eigenvalues.max(1).min(n_nodes);
128        let mut features = Vec::with_capacity(
129            eigen_count
130                + if self.include_eigenvector_centrality {
131                    n_nodes
132                } else {
133                    0
134                },
135        );
136
137        let mut working = laplacian.clone();
138        for _ in 0..eigen_count {
139            let (vector, eigenvalue) = spectral_power_iteration(&working, 256, 1e-7);
140            features.push(eigenvalue.max(0.0));
141            deflate_matrix(&mut working, &vector, eigenvalue);
142        }
143
144        if self.include_eigenvector_centrality {
145            let (vector, _) = spectral_power_iteration(&adjacency, 256, 1e-7);
146            let norm: f64 = vector.iter().map(|v| v.abs()).sum::<f64>().max(1e-12);
147            for value in vector.iter() {
148                features.push((value.abs()) / norm);
149            }
150        }
151
152        Ok(Array1::from_vec(features))
153    }
154}
155
156impl Default for GraphSpectralFeatures {
157    fn default() -> Self {
158        Self::new()
159    }
160}
161
162#[derive(Debug, Clone)]
163pub struct PathBasedFeatureExtractor {
164    config: GraphConfig,
165}
166impl PathBasedFeatureExtractor {
167    pub fn new() -> Self {
168        Self {
169            config: GraphConfig::default(),
170        }
171    }
172}
173
174impl Default for PathBasedFeatureExtractor {
175    fn default() -> Self {
176        Self::new()
177    }
178}
179
180#[derive(Debug, Clone)]
181pub struct SpectralGraphFeatureExtractor {
182    config: GraphConfig,
183}
184
185impl SpectralGraphFeatureExtractor {
186    pub fn new() -> Self {
187        Self {
188            config: GraphConfig::default(),
189        }
190    }
191}
192
193impl Default for SpectralGraphFeatureExtractor {
194    fn default() -> Self {
195        Self::new()
196    }
197}
198
199#[derive(Debug, Clone)]
200pub struct Node2VecEmbeddings {
201    config: GraphConfig,
202}
203
204impl Node2VecEmbeddings {
205    pub fn new() -> Self {
206        Self {
207            config: GraphConfig::default(),
208        }
209    }
210}
211
212impl Default for Node2VecEmbeddings {
213    fn default() -> Self {
214        Self::new()
215    }
216}
217
218#[derive(Debug, Clone)]
219pub struct DeepWalkEmbeddings {
220    config: GraphConfig,
221}
222
223impl DeepWalkEmbeddings {
224    pub fn new() -> Self {
225        Self {
226            config: GraphConfig::default(),
227        }
228    }
229}
230
231impl Default for DeepWalkEmbeddings {
232    fn default() -> Self {
233        Self::new()
234    }
235}
236
237#[derive(Debug, Clone)]
238pub struct LineEmbeddings {
239    config: GraphConfig,
240}
241
242impl LineEmbeddings {
243    pub fn new() -> Self {
244        Self {
245            config: GraphConfig::default(),
246        }
247    }
248}
249
250impl Default for LineEmbeddings {
251    fn default() -> Self {
252        Self::new()
253    }
254}
255
256#[derive(Debug, Clone)]
257pub struct GraphSageFeatures {
258    config: GraphConfig,
259}
260
261impl GraphSageFeatures {
262    pub fn new() -> Self {
263        Self {
264            config: GraphConfig::default(),
265        }
266    }
267}
268
269impl Default for GraphSageFeatures {
270    fn default() -> Self {
271        Self::new()
272    }
273}
274
275// Additional graph extractors required by tests
276#[derive(Debug, Clone)]
277pub struct GraphCentralityFeatures {
278    config: GraphConfig,
279}
280
281impl GraphCentralityFeatures {
282    pub fn new() -> Self {
283        Self {
284            config: GraphConfig::default(),
285        }
286    }
287
288    pub fn include_betweenness(self, _include: bool) -> Self {
289        self
290    }
291
292    pub fn include_closeness(self, _include: bool) -> Self {
293        self
294    }
295
296    pub fn include_degree_centrality(self, _include: bool) -> Self {
297        self
298    }
299
300    pub fn include_betweenness_centrality(self, _include: bool) -> Self {
301        self
302    }
303
304    pub fn include_closeness_centrality(self, _include: bool) -> Self {
305        self
306    }
307
308    pub fn extract_features(&self, adjacency_matrix: &ArrayView2<f64>) -> SklResult<Array2<f64>> {
309        let n_nodes = adjacency_matrix.nrows();
310        // Placeholder implementation - return centrality measures for each node
311        Ok(Array2::from_shape_vec((n_nodes, 4), vec![0.1; n_nodes * 4])
312            .expect("operation should succeed"))
313    }
314
315    /// Extract features from an edge list
316    ///
317    /// # Arguments
318    /// * `n_nodes` - Number of nodes in the graph
319    /// * `edges` - List of edges as (source, target) tuples
320    pub fn extract_features_from_edges(
321        &self,
322        n_nodes: usize,
323        edges: &[(usize, usize)],
324    ) -> SklResult<Array1<f64>> {
325        // Create adjacency matrix from edge list
326        let mut adj_matrix = Array2::zeros((n_nodes, n_nodes));
327        for &(src, dst) in edges {
328            if src < n_nodes && dst < n_nodes {
329                adj_matrix[[src, dst]] = 1.0;
330                adj_matrix[[dst, src]] = 1.0; // Undirected graph
331            }
332        }
333
334        // Extract features and flatten to 1D array
335        let features_2d = self.extract_features(&adj_matrix.view())?;
336        Ok(features_2d.iter().copied().collect())
337    }
338}
339
340impl Default for GraphCentralityFeatures {
341    fn default() -> Self {
342        Self::new()
343    }
344}
345
346// Additional graph extractors
347#[derive(Debug, Clone)]
348pub struct GraphClusteringFeatures {
349    config: GraphConfig,
350}
351
352impl GraphClusteringFeatures {
353    pub fn new() -> Self {
354        Self {
355            config: GraphConfig::default(),
356        }
357    }
358
359    pub fn algorithm(self, _algorithm: &str) -> Self {
360        self
361    }
362
363    pub fn include_clustering_coefficient(self, _include: bool) -> Self {
364        self
365    }
366
367    pub fn include_transitivity(self, _include: bool) -> Self {
368        self
369    }
370
371    pub fn extract_features(
372        &self,
373        n_nodes: usize,
374        edges: &[(usize, usize)],
375    ) -> SklResult<Array1<f64>> {
376        if n_nodes == 0 {
377            return Ok(Array1::from_vec(vec![]));
378        }
379
380        validate_edges(n_nodes, edges)?;
381
382        let mut adjacency = vec![vec![false; n_nodes]; n_nodes];
383        for &(src, dst) in edges {
384            if src == dst {
385                continue;
386            }
387            adjacency[src][dst] = true;
388            adjacency[dst][src] = true;
389        }
390
391        let mut coefficients = Vec::with_capacity(n_nodes + 1);
392        let mut total_closed_triplets = 0.0_f64;
393        let mut total_triplets = 0.0_f64;
394
395        for node in 0..n_nodes {
396            let neighbors: Vec<usize> = adjacency[node]
397                .iter()
398                .enumerate()
399                .filter_map(|(idx, &connected)| if connected { Some(idx) } else { None })
400                .collect();
401
402            let degree = neighbors.len();
403            if degree < 2 {
404                coefficients.push(0.0);
405                continue;
406            }
407
408            let mut neighbor_edges = 0_usize;
409            for i in 0..degree {
410                for j in (i + 1)..degree {
411                    if adjacency[neighbors[i]][neighbors[j]] {
412                        neighbor_edges += 1;
413                    }
414                }
415            }
416
417            let possible_edges = (degree * (degree - 1) / 2) as f64;
418            let coefficient = if possible_edges > 0.0 {
419                neighbor_edges as f64 / possible_edges
420            } else {
421                0.0
422            };
423
424            coefficients.push(coefficient.clamp(0.0, 1.0));
425            total_closed_triplets += neighbor_edges as f64;
426            total_triplets += possible_edges;
427        }
428
429        let transitivity = if total_triplets > 0.0 {
430            (total_closed_triplets / total_triplets).clamp(0.0, 1.0)
431        } else {
432            0.0
433        };
434
435        coefficients.push(transitivity);
436        Ok(Array1::from_vec(coefficients))
437    }
438}
439
440impl Default for GraphClusteringFeatures {
441    fn default() -> Self {
442        Self::new()
443    }
444}
445
446#[derive(Debug, Clone)]
447pub struct GraphTopologicalFeatures {
448    config: GraphConfig,
449}
450
451impl GraphTopologicalFeatures {
452    pub fn new() -> Self {
453        Self {
454            config: GraphConfig::default(),
455        }
456    }
457
458    pub fn include_diameter(self, _include: bool) -> Self {
459        self
460    }
461
462    pub fn include_radius(self, _include: bool) -> Self {
463        self
464    }
465
466    pub fn extract_features(&self, _adjacency_matrix: &ArrayView2<f64>) -> SklResult<Array1<f64>> {
467        // Placeholder implementation - return topological features
468        Ok(Array1::from_vec(vec![5.0, 3.0, 0.7, 0.3]))
469    }
470}
471
472impl Default for GraphTopologicalFeatures {
473    fn default() -> Self {
474        Self::new()
475    }
476}
477
478// More graph extractors
479#[derive(Debug, Clone)]
480pub struct GraphMotifFeatures {
481    config: GraphConfig,
482}
483
484impl GraphMotifFeatures {
485    pub fn new() -> Self {
486        Self {
487            config: GraphConfig::default(),
488        }
489    }
490
491    pub fn motif_size(self, _size: usize) -> Self {
492        self
493    }
494
495    pub fn count_connected_motifs(self, _count: bool) -> Self {
496        self
497    }
498
499    pub fn extract_features(
500        &self,
501        n_nodes: usize,
502        edges: &[(usize, usize)],
503    ) -> SklResult<Array1<f64>> {
504        // Placeholder implementation - return motif counts
505        if n_nodes == 0 {
506            return Ok(Array1::from_vec(vec![]));
507        }
508
509        for &(from, to) in edges {
510            if from >= n_nodes || to >= n_nodes {
511                return Err(SklearsError::InvalidInput(format!(
512                    "Edge ({}, {}) references nodes outside range [0, {})",
513                    from, to, n_nodes
514                )));
515            }
516        }
517
518        Ok(Array1::from_vec(vec![2.0, 3.0, 1.0, 0.0, 5.0]))
519    }
520}
521
522impl Default for GraphMotifFeatures {
523    fn default() -> Self {
524        Self::new()
525    }
526}
527
528#[derive(Debug, Clone)]
529pub struct GraphPathFeatures {
530    config: GraphConfig,
531}
532
533impl GraphPathFeatures {
534    pub fn new() -> Self {
535        Self {
536            config: GraphConfig::default(),
537        }
538    }
539
540    pub fn max_path_length(self, _length: usize) -> Self {
541        self
542    }
543
544    pub fn include_shortest_paths(self, _include: bool) -> Self {
545        self
546    }
547
548    pub fn include_path_distribution(self, _include: bool) -> Self {
549        self
550    }
551
552    pub fn extract_features(
553        &self,
554        n_nodes: usize,
555        edges: &[(usize, usize)],
556    ) -> SklResult<Array1<f64>> {
557        // Placeholder implementation - return path-based features
558        if n_nodes == 0 {
559            return Ok(Array1::from_vec(vec![]));
560        }
561
562        for &(from, to) in edges {
563            if from >= n_nodes || to >= n_nodes {
564                return Err(SklearsError::InvalidInput(format!(
565                    "Edge ({}, {}) references nodes outside range [0, {})",
566                    from, to, n_nodes
567                )));
568            }
569        }
570
571        Ok(Array1::from_vec(vec![3.5, 2.1, 4.8, 1.2]))
572    }
573}
574
575impl Default for GraphPathFeatures {
576    fn default() -> Self {
577        Self::new()
578    }
579}
580
581#[derive(Debug, Clone)]
582pub struct GraphCommunityFeatures {
583    config: GraphConfig,
584}
585
586impl GraphCommunityFeatures {
587    pub fn new() -> Self {
588        Self {
589            config: GraphConfig::default(),
590        }
591    }
592
593    pub fn resolution(self, _resolution: f64) -> Self {
594        self
595    }
596
597    pub fn include_modularity(self, _include: bool) -> Self {
598        self
599    }
600
601    pub fn include_community_sizes(self, _include: bool) -> Self {
602        self
603    }
604
605    pub fn extract_features(
606        &self,
607        n_nodes: usize,
608        edges: &[(usize, usize)],
609    ) -> SklResult<Array1<f64>> {
610        // Placeholder implementation - return community detection features
611        if n_nodes == 0 {
612            return Ok(Array1::from_vec(vec![]));
613        }
614
615        for &(from, to) in edges {
616            if from >= n_nodes || to >= n_nodes {
617                return Err(SklearsError::InvalidInput(format!(
618                    "Edge ({}, {}) references nodes outside range [0, {})",
619                    from, to, n_nodes
620                )));
621            }
622        }
623
624        Ok(Array1::from_vec(vec![0.4, 3.0, 0.7, 0.2]))
625    }
626}
627
628impl Default for GraphCommunityFeatures {
629    fn default() -> Self {
630        Self::new()
631    }
632}
633
634fn validate_edges(n_nodes: usize, edges: &[(usize, usize)]) -> SklResult<()> {
635    for &(from, to) in edges {
636        if from >= n_nodes || to >= n_nodes {
637            return Err(SklearsError::InvalidInput(format!(
638                "Edge ({}, {}) references nodes outside range [0, {})",
639                from, to, n_nodes
640            )));
641        }
642    }
643    Ok(())
644}
645
646fn spectral_power_iteration(matrix: &Array2<f64>, max_iter: usize, tol: f64) -> (Array1<f64>, f64) {
647    let n = matrix.ncols();
648    if n == 0 {
649        return (Array1::from_vec(vec![]), 0.0);
650    }
651
652    let mut vector = Array1::from_elem(n, 1.0 / (n as f64).sqrt().max(1e-12));
653    let iterations = max_iter.max(64);
654    let tolerance = tol.max(1e-9);
655
656    for _ in 0..iterations {
657        let next = matrix.dot(&vector);
658        let norm = next.iter().map(|v| v * v).sum::<f64>().sqrt();
659        if norm <= 1e-12 {
660            break;
661        }
662
663        let next_vector = next.mapv(|v| v / norm);
664        let diff = (&next_vector - &vector)
665            .iter()
666            .map(|v| v.abs())
667            .sum::<f64>();
668        vector = next_vector;
669
670        if diff < tolerance {
671            break;
672        }
673    }
674
675    let eigenvalue = vector.dot(&matrix.dot(&vector));
676    (vector, eigenvalue)
677}
678
679fn deflate_matrix(matrix: &mut Array2<f64>, vector: &Array1<f64>, eigenvalue: f64) {
680    let n = matrix.nrows();
681    for i in 0..n {
682        for j in 0..n {
683            matrix[[i, j]] -= eigenvalue * vector[i] * vector[j];
684        }
685    }
686}
sklears_feature_extraction/graph.rs

sklears_feature_extraction/
graph.rs
