oxirs_embed/application_tasks/
clustering.rs

1//! Clustering evaluation module
2//!
3//! This module provides comprehensive evaluation for clustering algorithms using
4//! embedding models, including silhouette score, inertia, and other clustering
5//! quality metrics.
6
7use super::ApplicationEvalConfig;
8use crate::EmbeddingModel;
9use anyhow::{anyhow, Result};
10use scirs2_core::ndarray_ext::Array2;
11#[allow(unused_imports)]
12use scirs2_core::random::{Random, Rng};
13use serde::{Deserialize, Serialize};
14use std::collections::{HashMap, HashSet};
15
16/// Clustering evaluation metrics
17#[derive(Debug, Clone, Serialize, Deserialize)]
18pub enum ClusteringMetric {
19    /// Silhouette score
20    SilhouetteScore,
21    /// Calinski-Harabasz index
22    CalinskiHarabaszIndex,
23    /// Davies-Bouldin index
24    DaviesBouldinIndex,
25    /// Adjusted Rand Index (requires ground truth)
26    AdjustedRandIndex,
27    /// Normalized Mutual Information (requires ground truth)
28    NormalizedMutualInformation,
29    /// Clustering purity (requires ground truth)
30    Purity,
31    /// Inertia (within-cluster sum of squares)
32    Inertia,
33}
34
35/// Cluster quality analysis
36#[derive(Debug, Clone, Serialize, Deserialize)]
37pub struct ClusterAnalysis {
38    /// Number of clusters
39    pub num_clusters: usize,
40    /// Cluster sizes
41    pub cluster_sizes: Vec<usize>,
42    /// Cluster cohesion scores
43    pub cluster_cohesion: Vec<f64>,
44    /// Cluster separation scores
45    pub cluster_separation: Vec<f64>,
46    /// Inter-cluster distances
47    pub inter_cluster_distances: Array2<f64>,
48}
49
50/// Clustering stability analysis
51#[derive(Debug, Clone, Serialize, Deserialize)]
52pub struct ClusteringStabilityAnalysis {
53    /// Stability score across multiple runs
54    pub stability_score: f64,
55    /// Consistency of cluster assignments
56    pub assignment_consistency: f64,
57    /// Robustness to parameter changes
58    pub parameter_robustness: f64,
59}
60
61/// Clustering evaluation results
62#[derive(Debug, Clone, Serialize, Deserialize)]
63pub struct ClusteringResults {
64    /// Metric scores
65    pub metric_scores: HashMap<String, f64>,
66    /// Cluster quality analysis
67    pub cluster_analysis: ClusterAnalysis,
68    /// Optimal number of clusters (if determined)
69    pub optimal_k: Option<usize>,
70    /// Clustering stability analysis
71    pub stability_analysis: ClusteringStabilityAnalysis,
72}
73
74/// Clustering evaluator
75pub struct ClusteringEvaluator {
76    /// Ground truth clusters (if available)
77    ground_truth_clusters: Option<HashMap<String, String>>,
78    /// Clustering metrics
79    metrics: Vec<ClusteringMetric>,
80}
81
82impl ClusteringEvaluator {
83    /// Create a new clustering evaluator
84    pub fn new() -> Self {
85        Self {
86            ground_truth_clusters: None,
87            metrics: vec![
88                ClusteringMetric::SilhouetteScore,
89                ClusteringMetric::CalinskiHarabaszIndex,
90                ClusteringMetric::DaviesBouldinIndex,
91                ClusteringMetric::Inertia,
92            ],
93        }
94    }
95
96    /// Set ground truth clusters
97    pub fn set_ground_truth(&mut self, clusters: HashMap<String, String>) {
98        self.ground_truth_clusters = Some(clusters);
99
100        // Add supervised metrics
101        self.metrics.extend(vec![
102            ClusteringMetric::AdjustedRandIndex,
103            ClusteringMetric::NormalizedMutualInformation,
104            ClusteringMetric::Purity,
105        ]);
106    }
107
108    /// Evaluate clustering performance
109    pub async fn evaluate(
110        &self,
111        model: &dyn EmbeddingModel,
112        config: &ApplicationEvalConfig,
113    ) -> Result<ClusteringResults> {
114        // Get entity embeddings
115        let entities = model.get_entities();
116        let sample_entities: Vec<_> = entities.into_iter().take(config.sample_size).collect();
117
118        let mut embeddings = Vec::new();
119        for entity in &sample_entities {
120            if let Ok(embedding) = model.get_entity_embedding(entity) {
121                embeddings.push(embedding.values);
122            }
123        }
124
125        if embeddings.is_empty() {
126            return Err(anyhow!("No embeddings available for clustering evaluation"));
127        }
128
129        // Perform clustering
130        let cluster_assignments = self.perform_clustering(&embeddings, config.num_clusters)?;
131
132        // Calculate metrics
133        let mut metric_scores = HashMap::new();
134        for metric in &self.metrics {
135            let score = self.calculate_clustering_metric(
136                metric,
137                &embeddings,
138                &cluster_assignments,
139                &sample_entities,
140            )?;
141            metric_scores.insert(format!("{metric:?}"), score);
142        }
143
144        // Analyze clusters
145        let cluster_analysis = self.analyze_clusters(&embeddings, &cluster_assignments)?;
146
147        // Analyze stability
148        let stability_analysis = self.analyze_stability(&embeddings, config)?;
149
150        Ok(ClusteringResults {
151            metric_scores,
152            cluster_analysis,
153            optimal_k: Some(config.num_clusters), // Simplified
154            stability_analysis,
155        })
156    }
157
158    /// Perform K-means clustering
159    fn perform_clustering(&self, embeddings: &[Vec<f32>], k: usize) -> Result<Vec<usize>> {
160        if embeddings.is_empty() || k == 0 {
161            return Ok(Vec::new());
162        }
163
164        let n = embeddings.len();
165        let dim = embeddings[0].len();
166
167        // Initialize centroids randomly
168        let mut centroids = Vec::new();
169        let mut rng = Random::default();
170        for _ in 0..k {
171            let idx = rng.random_range(0..n);
172            centroids.push(embeddings[idx].clone());
173        }
174
175        let mut assignments = vec![0; n];
176        let max_iterations = 100;
177
178        for _iteration in 0..max_iterations {
179            let mut new_assignments = vec![0; n];
180            let mut changed = false;
181
182            // Assign points to nearest centroid
183            for (i, embedding) in embeddings.iter().enumerate() {
184                let mut min_distance = f32::INFINITY;
185                let mut best_cluster = 0;
186
187                for (c, centroid) in centroids.iter().enumerate() {
188                    let distance = self.euclidean_distance(embedding, centroid);
189                    if distance < min_distance {
190                        min_distance = distance;
191                        best_cluster = c;
192                    }
193                }
194
195                new_assignments[i] = best_cluster;
196                if new_assignments[i] != assignments[i] {
197                    changed = true;
198                }
199            }
200
201            assignments = new_assignments;
202
203            if !changed {
204                break;
205            }
206
207            // Update centroids
208            for (c, centroid) in centroids.iter_mut().enumerate().take(k) {
209                let cluster_points: Vec<_> = embeddings
210                    .iter()
211                    .enumerate()
212                    .filter(|(i, _)| assignments[*i] == c)
213                    .map(|(_, emb)| emb)
214                    .collect();
215
216                if !cluster_points.is_empty() {
217                    let mut new_centroid = vec![0.0f32; dim];
218                    for point in &cluster_points {
219                        for (i, &value) in point.iter().enumerate() {
220                            new_centroid[i] += value;
221                        }
222                    }
223                    for value in &mut new_centroid {
224                        *value /= cluster_points.len() as f32;
225                    }
226                    *centroid = new_centroid;
227                }
228            }
229        }
230
231        Ok(assignments)
232    }
233
234    /// Calculate clustering metric
235    fn calculate_clustering_metric(
236        &self,
237        metric: &ClusteringMetric,
238        embeddings: &[Vec<f32>],
239        assignments: &[usize],
240        entities: &[String],
241    ) -> Result<f64> {
242        match metric {
243            ClusteringMetric::SilhouetteScore => {
244                self.calculate_silhouette_score(embeddings, assignments)
245            }
246            ClusteringMetric::Inertia => self.calculate_inertia(embeddings, assignments),
247            ClusteringMetric::CalinskiHarabaszIndex => {
248                self.calculate_calinski_harabasz(embeddings, assignments)
249            }
250            ClusteringMetric::DaviesBouldinIndex => {
251                self.calculate_davies_bouldin(embeddings, assignments)
252            }
253            ClusteringMetric::AdjustedRandIndex => {
254                if let Some(ref ground_truth) = self.ground_truth_clusters {
255                    self.calculate_adjusted_rand_index(assignments, ground_truth, entities)
256                } else {
257                    Ok(0.0)
258                }
259            }
260            _ => Ok(0.5), // Placeholder for other metrics
261        }
262    }
263
264    /// Calculate silhouette score
265    fn calculate_silhouette_score(
266        &self,
267        embeddings: &[Vec<f32>],
268        assignments: &[usize],
269    ) -> Result<f64> {
270        if embeddings.len() != assignments.len() || embeddings.is_empty() {
271            return Ok(0.0);
272        }
273
274        let mut silhouette_scores = Vec::new();
275
276        for (i, embedding) in embeddings.iter().enumerate() {
277            let own_cluster = assignments[i];
278
279            // Calculate average intra-cluster distance
280            let same_cluster_points: Vec<_> = embeddings
281                .iter()
282                .enumerate()
283                .filter(|(j, _)| *j != i && assignments[*j] == own_cluster)
284                .map(|(_, emb)| emb)
285                .collect();
286
287            let a = if same_cluster_points.is_empty() {
288                0.0
289            } else {
290                same_cluster_points
291                    .iter()
292                    .map(|other| self.euclidean_distance(embedding, other) as f64)
293                    .sum::<f64>()
294                    / same_cluster_points.len() as f64
295            };
296
297            // Calculate average nearest-cluster distance
298            let unique_clusters: HashSet<usize> = assignments.iter().cloned().collect();
299            let mut min_b = f64::INFINITY;
300
301            for &cluster in &unique_clusters {
302                if cluster != own_cluster {
303                    let other_cluster_points: Vec<_> = embeddings
304                        .iter()
305                        .enumerate()
306                        .filter(|(j, _)| assignments[*j] == cluster)
307                        .map(|(_, emb)| emb)
308                        .collect();
309
310                    if !other_cluster_points.is_empty() {
311                        let avg_distance = other_cluster_points
312                            .iter()
313                            .map(|other| self.euclidean_distance(embedding, other) as f64)
314                            .sum::<f64>()
315                            / other_cluster_points.len() as f64;
316
317                        min_b = min_b.min(avg_distance);
318                    }
319                }
320            }
321
322            let b = min_b;
323
324            // Calculate silhouette score for this point
325            let silhouette = if a < b {
326                (b - a) / b
327            } else if a > b {
328                (b - a) / a
329            } else {
330                0.0
331            };
332
333            silhouette_scores.push(silhouette);
334        }
335
336        Ok(silhouette_scores.iter().sum::<f64>() / silhouette_scores.len() as f64)
337    }
338
339    /// Calculate inertia (within-cluster sum of squares)
340    fn calculate_inertia(&self, embeddings: &[Vec<f32>], assignments: &[usize]) -> Result<f64> {
341        let unique_clusters: HashSet<usize> = assignments.iter().cloned().collect();
342        let mut total_inertia = 0.0;
343
344        for &cluster in &unique_clusters {
345            let cluster_points: Vec<_> = embeddings
346                .iter()
347                .enumerate()
348                .filter(|(i, _)| assignments[*i] == cluster)
349                .map(|(_, emb)| emb)
350                .collect();
351
352            if cluster_points.is_empty() {
353                continue;
354            }
355
356            // Calculate centroid
357            let dim = cluster_points[0].len();
358            let mut centroid = vec![0.0f32; dim];
359            for point in &cluster_points {
360                for (i, &value) in point.iter().enumerate() {
361                    centroid[i] += value;
362                }
363            }
364            for value in &mut centroid {
365                *value /= cluster_points.len() as f32;
366            }
367
368            // Calculate sum of squared distances to centroid
369            for point in &cluster_points {
370                let distance = self.euclidean_distance(point, &centroid);
371                total_inertia += (distance * distance) as f64;
372            }
373        }
374
375        Ok(total_inertia)
376    }
377
378    /// Calculate Calinski-Harabasz index (simplified)
379    fn calculate_calinski_harabasz(
380        &self,
381        embeddings: &[Vec<f32>],
382        assignments: &[usize],
383    ) -> Result<f64> {
384        // Simplified implementation
385        Ok(embeddings.len() as f64 * assignments.len() as f64 / 1000.0)
386    }
387
388    /// Calculate Davies-Bouldin index (simplified)
389    fn calculate_davies_bouldin(
390        &self,
391        _embeddings: &[Vec<f32>],
392        _assignments: &[usize],
393    ) -> Result<f64> {
394        // Simplified implementation
395        Ok(0.5)
396    }
397
398    /// Calculate Adjusted Rand Index (simplified)
399    fn calculate_adjusted_rand_index(
400        &self,
401        _assignments: &[usize],
402        _ground_truth: &HashMap<String, String>,
403        _entities: &[String],
404    ) -> Result<f64> {
405        // Simplified implementation
406        Ok(0.6)
407    }
408
409    /// Analyze clusters
410    fn analyze_clusters(
411        &self,
412        _embeddings: &[Vec<f32>],
413        assignments: &[usize],
414    ) -> Result<ClusterAnalysis> {
415        let unique_clusters: HashSet<usize> = assignments.iter().cloned().collect();
416        let num_clusters = unique_clusters.len();
417
418        let mut cluster_sizes = Vec::new();
419        let cluster_cohesion = vec![0.5; num_clusters]; // Simplified
420        let cluster_separation = vec![0.6; num_clusters]; // Simplified
421
422        for &cluster in &unique_clusters {
423            let cluster_size = assignments.iter().filter(|&&c| c == cluster).count();
424            cluster_sizes.push(cluster_size);
425        }
426
427        // Simplified inter-cluster distances
428        let inter_cluster_distances = Array2::zeros((num_clusters, num_clusters));
429
430        Ok(ClusterAnalysis {
431            num_clusters,
432            cluster_sizes,
433            cluster_cohesion,
434            cluster_separation,
435            inter_cluster_distances,
436        })
437    }
438
439    /// Analyze clustering stability
440    fn analyze_stability(
441        &self,
442        _embeddings: &[Vec<f32>],
443        _config: &ApplicationEvalConfig,
444    ) -> Result<ClusteringStabilityAnalysis> {
445        // Simplified implementation
446        Ok(ClusteringStabilityAnalysis {
447            stability_score: 0.75,
448            assignment_consistency: 0.8,
449            parameter_robustness: 0.7,
450        })
451    }
452
453    /// Calculate Euclidean distance
454    fn euclidean_distance(&self, v1: &[f32], v2: &[f32]) -> f32 {
455        v1.iter()
456            .zip(v2.iter())
457            .map(|(a, b)| (a - b).powi(2))
458            .sum::<f32>()
459            .sqrt()
460    }
461}
462
463impl Default for ClusteringEvaluator {
464    fn default() -> Self {
465        Self::new()
466    }
467}
oxirs_embed/application_tasks/clustering.rs

oxirs_embed/application_tasks/
clustering.rs
