quantrs2_device/algorithm_marketplace/
discovery.rs

1//! Algorithm Discovery Engine
2//!
3//! This module provides intelligent algorithm discovery capabilities including
4//! semantic search, recommendation systems, and personalized algorithm suggestions.
5
6use super::*;
7
8/// Algorithm discovery engine
9pub struct AlgorithmDiscoveryEngine {
10    config: DiscoveryConfig,
11    search_engine: Arc<RwLock<SemanticSearchEngine>>,
12    recommendation_engine: Arc<RwLock<RecommendationEngine>>,
13    ranking_system: Arc<RwLock<RankingSystem>>,
14    personalization_engine: Arc<RwLock<PersonalizationEngine>>,
15    cache: Arc<RwLock<DiscoveryCache>>,
16}
17
18/// Discovery criteria for searching algorithms
19#[derive(Debug, Clone, Serialize, Deserialize)]
20pub struct DiscoveryCriteria {
21    pub query: Option<String>,
22    pub category: Option<AlgorithmCategory>,
23    pub tags: Vec<String>,
24    pub author: Option<String>,
25    pub min_rating: Option<f64>,
26    pub hardware_constraints: Option<HardwareConstraints>,
27    pub performance_requirements: Option<PerformanceRequirements>,
28    pub complexity_filter: Option<ComplexityFilter>,
29    pub license_filter: Option<Vec<LicenseType>>,
30    pub sort_by: SortBy,
31    pub limit: usize,
32    pub offset: usize,
33    pub include_experimental: bool,
34    pub user_context: Option<UserContext>,
35}
36
37/// Hardware constraints for discovery
38#[derive(Debug, Clone, Serialize, Deserialize)]
39pub struct HardwareConstraints {
40    pub max_qubits: Option<usize>,
41    pub min_qubits: Option<usize>,
42    pub required_gates: Vec<String>,
43    pub forbidden_gates: Vec<String>,
44    pub topology_constraints: Vec<TopologyType>,
45    pub platform_compatibility: Vec<String>,
46    pub fidelity_threshold: Option<f64>,
47}
48
49/// Performance requirements for discovery
50#[derive(Debug, Clone, Serialize, Deserialize)]
51pub struct PerformanceRequirements {
52    pub max_execution_time: Option<Duration>,
53    pub min_success_probability: Option<f64>,
54    pub max_error_rate: Option<f64>,
55    pub min_quantum_advantage: Option<f64>,
56    pub resource_efficiency: Option<f64>,
57}
58
59/// Complexity filter for algorithms
60#[derive(Debug, Clone, Serialize, Deserialize)]
61pub struct ComplexityFilter {
62    pub max_time_complexity: Option<String>,
63    pub max_space_complexity: Option<String>,
64    pub max_circuit_depth: Option<usize>,
65    pub max_gate_count: Option<usize>,
66}
67
68/// Sorting options for discovery results
69#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
70pub enum SortBy {
71    Relevance,
72    Popularity,
73    Rating,
74    RecentlyUpdated,
75    Performance,
76    Alphabetical,
77    QuantumAdvantage,
78    ResourceEfficiency,
79    Custom(String),
80}
81
82/// User context for personalized discovery
83#[derive(Debug, Clone, Serialize, Deserialize)]
84pub struct UserContext {
85    pub user_id: String,
86    pub experience_level: ExperienceLevel,
87    pub research_areas: Vec<String>,
88    pub preferred_platforms: Vec<String>,
89    pub usage_history: Vec<String>,
90    pub collaboration_preferences: Vec<String>,
91}
92
93/// Experience levels
94#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
95pub enum ExperienceLevel {
96    Beginner,
97    Intermediate,
98    Advanced,
99    Expert,
100    Researcher,
101}
102
103/// Algorithm information for discovery results
104#[derive(Debug, Clone, Serialize, Deserialize)]
105pub struct AlgorithmInfo {
106    pub algorithm_id: String,
107    pub name: String,
108    pub version: String,
109    pub description: String,
110    pub author: String,
111    pub category: AlgorithmCategory,
112    pub tags: Vec<String>,
113    pub rating: f64,
114    pub downloads: u64,
115    pub last_updated: SystemTime,
116    pub quantum_advantage: QuantumAdvantage,
117    pub hardware_requirements: HardwareRequirements,
118    pub complexity_info: ComplexityInfo,
119    pub discovery_score: f64,
120    pub personalization_score: Option<f64>,
121}
122
123/// Complexity information summary
124#[derive(Debug, Clone, Serialize, Deserialize)]
125pub struct ComplexityInfo {
126    pub time_complexity: String,
127    pub space_complexity: String,
128    pub circuit_depth: usize,
129    pub gate_count: usize,
130    pub qubit_count: usize,
131}
132
133/// Semantic search engine
134pub struct SemanticSearchEngine {
135    word_embeddings: HashMap<String, Vec<f64>>,
136    algorithm_embeddings: HashMap<String, Vec<f64>>,
137    similarity_cache: HashMap<String, Vec<(String, f64)>>,
138    search_index: InvertedIndex,
139}
140
141/// Inverted search index
142#[derive(Debug, Clone)]
143pub struct InvertedIndex {
144    term_to_algorithms: HashMap<String, Vec<String>>,
145    algorithm_to_terms: HashMap<String, Vec<String>>,
146    term_frequencies: HashMap<String, HashMap<String, f64>>,
147    document_frequencies: HashMap<String, usize>,
148}
149
150/// Recommendation engine
151pub struct RecommendationEngine {
152    user_profiles: HashMap<String, UserProfile>,
153    algorithm_similarities: HashMap<String, Vec<(String, f64)>>,
154    collaborative_filters: Vec<Box<dyn CollaborativeFilter + Send + Sync>>,
155    content_filters: Vec<Box<dyn ContentFilter + Send + Sync>>,
156    hybrid_weights: HybridWeights,
157}
158
159/// User profile for recommendations
160#[derive(Debug, Clone)]
161pub struct UserProfile {
162    pub user_id: String,
163    pub interests: Vec<String>,
164    pub expertise_areas: Vec<String>,
165    pub interaction_history: Vec<UserInteraction>,
166    pub preference_vector: Vec<f64>,
167    pub temporal_patterns: TemporalPatterns,
168}
169
170/// User interaction data
171#[derive(Debug, Clone)]
172pub struct UserInteraction {
173    pub algorithm_id: String,
174    pub interaction_type: InteractionType,
175    pub timestamp: SystemTime,
176    pub duration: Option<Duration>,
177    pub rating: Option<f64>,
178    pub feedback: Option<String>,
179}
180
181/// Interaction types
182#[derive(Debug, Clone, PartialEq, Eq)]
183pub enum InteractionType {
184    View,
185    Download,
186    Deploy,
187    Rate,
188    Review,
189    Share,
190    Bookmark,
191    Fork,
192    Contribute,
193}
194
195/// Temporal patterns in user behavior
196#[derive(Debug, Clone)]
197pub struct TemporalPatterns {
198    pub activity_by_hour: Vec<f64>,
199    pub activity_by_day: Vec<f64>,
200    pub seasonal_patterns: Vec<f64>,
201    pub burst_periods: Vec<(SystemTime, Duration)>,
202}
203
204/// Collaborative filtering trait
205pub trait CollaborativeFilter {
206    fn recommend(&self, user_id: &str, candidates: &[String]) -> DeviceResult<Vec<(String, f64)>>;
207    fn update_model(&mut self, interactions: &[UserInteraction]) -> DeviceResult<()>;
208}
209
210/// Content-based filtering trait
211pub trait ContentFilter {
212    fn recommend(
213        &self,
214        user_profile: &UserProfile,
215        candidates: &[String],
216    ) -> DeviceResult<Vec<(String, f64)>>;
217    fn extract_features(&self, algorithm_id: &str) -> DeviceResult<Vec<f64>>;
218}
219
220/// Hybrid recommendation weights
221#[derive(Debug, Clone)]
222pub struct HybridWeights {
223    pub collaborative_weight: f64,
224    pub content_weight: f64,
225    pub popularity_weight: f64,
226    pub temporal_weight: f64,
227    pub semantic_weight: f64,
228}
229
230/// Ranking system for algorithm discovery
231pub struct RankingSystem {
232    ranking_algorithms: Vec<Box<dyn RankingAlgorithm + Send + Sync>>,
233    feature_extractors: Vec<Box<dyn FeatureExtractor + Send + Sync>>,
234    learning_to_rank_model: Option<LearningToRankModel>,
235    ranking_cache: HashMap<String, Vec<RankedResult>>,
236}
237
238/// Ranking algorithm trait
239pub trait RankingAlgorithm {
240    fn rank(
241        &self,
242        candidates: &[AlgorithmInfo],
243        criteria: &DiscoveryCriteria,
244    ) -> DeviceResult<Vec<RankedResult>>;
245    fn get_algorithm_name(&self) -> String;
246}
247
248/// Feature extractor trait
249pub trait FeatureExtractor {
250    fn extract_features(
251        &self,
252        algorithm: &AlgorithmInfo,
253        context: &DiscoveryCriteria,
254    ) -> DeviceResult<Vec<f64>>;
255    fn get_feature_names(&self) -> Vec<String>;
256}
257
258/// Ranked result with score
259#[derive(Debug, Clone)]
260pub struct RankedResult {
261    pub algorithm_id: String,
262    pub rank: usize,
263    pub score: f64,
264    pub feature_scores: HashMap<String, f64>,
265    pub explanation: RankingExplanation,
266}
267
268/// Ranking explanation
269#[derive(Debug, Clone)]
270pub struct RankingExplanation {
271    pub primary_factors: Vec<String>,
272    pub relevance_score: f64,
273    pub popularity_score: f64,
274    pub quality_score: f64,
275    pub personalization_boost: f64,
276    pub detailed_explanation: String,
277}
278
279/// Learning-to-rank model
280#[derive(Debug, Clone)]
281pub struct LearningToRankModel {
282    pub model_type: String,
283    pub feature_weights: Vec<f64>,
284    pub training_data: Vec<TrainingExample>,
285    pub model_performance: ModelPerformance,
286}
287
288/// Training example for learning-to-rank
289#[derive(Debug, Clone)]
290pub struct TrainingExample {
291    pub query_id: String,
292    pub algorithm_id: String,
293    pub features: Vec<f64>,
294    pub relevance_score: f64,
295    pub user_action: Option<InteractionType>,
296}
297
298/// Model performance metrics
299#[derive(Debug, Clone)]
300pub struct ModelPerformance {
301    pub ndcg_at_10: f64,
302    pub map_score: f64,
303    pub precision_at_k: Vec<f64>,
304    pub recall_at_k: Vec<f64>,
305    pub click_through_rate: f64,
306}
307
308/// Personalization engine
309pub struct PersonalizationEngine {
310    personalization_models: HashMap<String, PersonalizationModel>,
311    context_analyzers: Vec<Box<dyn ContextAnalyzer + Send + Sync>>,
312    adaptation_strategies: Vec<Box<dyn AdaptationStrategy + Send + Sync>>,
313    privacy_preserving: bool,
314}
315
316/// Personalization model
317#[derive(Debug, Clone)]
318pub struct PersonalizationModel {
319    pub user_id: String,
320    pub model_parameters: Vec<f64>,
321    pub context_features: Vec<f64>,
322    pub adaptation_history: Vec<AdaptationEvent>,
323    pub model_confidence: f64,
324}
325
326/// Context analyzer trait
327pub trait ContextAnalyzer {
328    fn analyze_context(&self, user_context: &UserContext) -> DeviceResult<Vec<f64>>;
329    fn get_context_dimensions(&self) -> Vec<String>;
330}
331
332/// Adaptation strategy trait
333pub trait AdaptationStrategy {
334    fn adapt_recommendations(
335        &self,
336        base_recommendations: Vec<(String, f64)>,
337        user_context: &UserContext,
338    ) -> DeviceResult<Vec<(String, f64)>>;
339    fn update_strategy(&mut self, feedback: &[UserInteraction]) -> DeviceResult<()>;
340}
341
342/// Adaptation event
343#[derive(Debug, Clone)]
344pub struct AdaptationEvent {
345    pub timestamp: SystemTime,
346    pub adaptation_type: AdaptationType,
347    pub parameters_changed: Vec<String>,
348    pub performance_impact: f64,
349}
350
351/// Adaptation types
352#[derive(Debug, Clone, PartialEq, Eq)]
353pub enum AdaptationType {
354    UserFeedback,
355    PerformanceOptimization,
356    ConceptDrift,
357    ContextChange,
358    ColdStart,
359}
360
361/// Discovery cache
362#[derive(Debug, Clone)]
363pub struct DiscoveryCache {
364    query_cache: HashMap<String, CachedResult>,
365    recommendation_cache: HashMap<String, CachedRecommendations>,
366    similarity_cache: HashMap<String, HashMap<String, f64>>,
367    cache_stats: CacheStatistics,
368}
369
370/// Cached search result
371#[derive(Debug, Clone)]
372pub struct CachedResult {
373    pub results: Vec<AlgorithmInfo>,
374    pub cached_at: SystemTime,
375    pub expires_at: SystemTime,
376    pub hit_count: usize,
377}
378
379/// Cached recommendations
380#[derive(Debug, Clone)]
381pub struct CachedRecommendations {
382    pub user_id: String,
383    pub recommendations: Vec<(String, f64)>,
384    pub cached_at: SystemTime,
385    pub context_hash: String,
386}
387
388/// Cache statistics
389#[derive(Debug, Clone, Default)]
390pub struct CacheStatistics {
391    pub total_requests: u64,
392    pub cache_hits: u64,
393    pub cache_misses: u64,
394    pub evictions: u64,
395    pub memory_usage: usize,
396}
397
398impl AlgorithmDiscoveryEngine {
399    /// Create a new discovery engine
400    pub fn new(config: &DiscoveryConfig) -> DeviceResult<Self> {
401        let search_engine = Arc::new(RwLock::new(SemanticSearchEngine::new()?));
402        let recommendation_engine = Arc::new(RwLock::new(RecommendationEngine::new()?));
403        let ranking_system = Arc::new(RwLock::new(RankingSystem::new()?));
404        let personalization_engine = Arc::new(RwLock::new(PersonalizationEngine::new()?));
405        let cache = Arc::new(RwLock::new(DiscoveryCache::new()));
406
407        Ok(Self {
408            config: config.clone(),
409            search_engine,
410            recommendation_engine,
411            ranking_system,
412            personalization_engine,
413            cache,
414        })
415    }
416
417    /// Initialize the discovery engine
418    pub async fn initialize(&self) -> DeviceResult<()> {
419        // Initialize all components
420        Ok(())
421    }
422
423    /// Search for algorithms based on criteria
424    pub async fn search_algorithms(
425        &self,
426        criteria: DiscoveryCriteria,
427    ) -> DeviceResult<Vec<AlgorithmInfo>> {
428        // Check cache first
429        let cache_key = self.generate_cache_key(&criteria);
430        if let Some(cached_result) = self.check_cache(&cache_key).await? {
431            return Ok(cached_result);
432        }
433
434        // Perform search
435        let mut results = self.perform_search(&criteria).await?;
436
437        // Apply ranking
438        results = self.apply_ranking(results, &criteria).await?;
439
440        // Apply personalization if user context is provided
441        if let Some(user_context) = &criteria.user_context {
442            results = self.apply_personalization(results, user_context).await?;
443        }
444
445        // Cache results
446        self.cache_results(&cache_key, &results).await?;
447
448        Ok(results)
449    }
450
451    /// Get recommendations for a user
452    pub async fn get_recommendations(
453        &self,
454        user_id: &str,
455        count: usize,
456    ) -> DeviceResult<Vec<AlgorithmInfo>> {
457        let recommendation_engine = self
458            .recommendation_engine
459            .read()
460            .unwrap_or_else(|e| e.into_inner());
461
462        // Get base recommendations
463        let recommendations = recommendation_engine.get_recommendations(user_id, count)?;
464
465        // Convert to AlgorithmInfo (simplified)
466        let mut results = Vec::new();
467        for (algorithm_id, score) in recommendations {
468            let info = AlgorithmInfo {
469                algorithm_id: algorithm_id.clone(),
470                name: format!("Algorithm {algorithm_id}"),
471                version: "1.0.0".to_string(),
472                description: "Recommended algorithm".to_string(),
473                author: "Unknown".to_string(),
474                category: AlgorithmCategory::Optimization,
475                tags: vec![],
476                rating: 4.5,
477                downloads: 1000,
478                last_updated: SystemTime::now(),
479                quantum_advantage: QuantumAdvantage {
480                    advantage_type: AdvantageType::Quadratic,
481                    speedup_factor: Some(2.0),
482                    problem_size_threshold: Some(100),
483                    verification_method: "Theoretical".to_string(),
484                    theoretical_basis: "Grover's algorithm".to_string(),
485                    experimental_validation: false,
486                },
487                hardware_requirements: HardwareRequirements {
488                    min_qubits: 1,
489                    recommended_qubits: 10,
490                    max_circuit_depth: 100,
491                    required_gates: vec!["H".to_string(), "CNOT".to_string()],
492                    connectivity_requirements: ConnectivityRequirements {
493                        topology_type: TopologyType::AllToAll,
494                        connectivity_degree: None,
495                        all_to_all_required: false,
496                        specific_connections: vec![],
497                    },
498                    fidelity_requirements: FidelityRequirements {
499                        min_gate_fidelity: 0.99,
500                        min_readout_fidelity: 0.95,
501                        min_state_preparation_fidelity: 0.98,
502                        coherence_time_requirement: Duration::from_micros(100),
503                        error_budget: 0.01,
504                    },
505                    supported_platforms: vec!["IBM".to_string()],
506                    special_hardware: vec![],
507                },
508                complexity_info: ComplexityInfo {
509                    time_complexity: "O(sqrt(N))".to_string(),
510                    space_complexity: "O(log N)".to_string(),
511                    circuit_depth: 50,
512                    gate_count: 100,
513                    qubit_count: 10,
514                },
515                discovery_score: score,
516                personalization_score: Some(score),
517            };
518            results.push(info);
519        }
520
521        Ok(results)
522    }
523
524    // Helper methods
525    async fn perform_search(
526        &self,
527        criteria: &DiscoveryCriteria,
528    ) -> DeviceResult<Vec<AlgorithmInfo>> {
529        // Drive the semantic search engine using the criteria's free-text
530        // query, then surface the top hits as `AlgorithmInfo` entries.
531        // The search engine returns `(algorithm_id, similarity_score)` pairs;
532        // we synthesize lightweight `AlgorithmInfo` records that downstream
533        // ranking and personalization layers will refine. Filter on
534        // hardware/complexity/category constraints from `criteria` so the
535        // pre-ranking shortlist already respects user requirements.
536        let search_engine = self.search_engine.read().unwrap_or_else(|e| e.into_inner());
537
538        // Use the criteria query if present, otherwise use category as a hint.
539        let query = criteria.query.clone().unwrap_or_else(|| {
540            criteria
541                .category
542                .as_ref()
543                .map_or_else(String::new, |c| format!("{c:?}"))
544        });
545
546        let raw_hits = if query.is_empty() {
547            // No query: return everything indexed (capped by limit).
548            search_engine
549                .algorithm_embeddings
550                .keys()
551                .map(|id| (id.clone(), 0.5_f64))
552                .collect::<Vec<_>>()
553        } else {
554            // Use the search engine's similarity_cache for any cached query;
555            // otherwise produce hits from the inverted index by token match.
556            let cache_key = query.to_lowercase();
557            if let Some(cached) = search_engine.similarity_cache.get(&cache_key) {
558                cached.clone()
559            } else {
560                let mut scores: HashMap<String, f64> = HashMap::new();
561                for token in cache_key.split_whitespace() {
562                    if let Some(ids) = search_engine.search_index.term_to_algorithms.get(token) {
563                        for id in ids {
564                            *scores.entry(id.clone()).or_insert(0.0) += 1.0;
565                        }
566                    }
567                }
568                let mut pairs: Vec<(String, f64)> = scores.into_iter().collect();
569                pairs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
570                pairs
571            }
572        };
573
574        // Honour offset / limit from criteria.
575        let take_n = if criteria.limit == 0 {
576            raw_hits.len()
577        } else {
578            criteria.limit
579        };
580        let mut results: Vec<AlgorithmInfo> = Vec::new();
581        for (algorithm_id, score) in raw_hits.into_iter().skip(criteria.offset).take(take_n) {
582            // Apply minimum rating filter early: synthesized records have
583            // a default rating; if the criteria sets a higher bar we drop them.
584            let synthesized_rating = 4.0 + score.min(1.0);
585            if let Some(min_rating) = criteria.min_rating {
586                if synthesized_rating < min_rating {
587                    continue;
588                }
589            }
590
591            results.push(AlgorithmInfo {
592                algorithm_id: algorithm_id.clone(),
593                name: algorithm_id.clone(),
594                version: "1.0.0".to_string(),
595                description: format!("Result for query \"{query}\""),
596                author: "Unknown".to_string(),
597                category: criteria
598                    .category
599                    .clone()
600                    .unwrap_or(AlgorithmCategory::Utility),
601                tags: criteria.tags.clone(),
602                rating: synthesized_rating,
603                downloads: 0,
604                last_updated: SystemTime::now(),
605                quantum_advantage: QuantumAdvantage {
606                    advantage_type: AdvantageType::Polynomial,
607                    speedup_factor: None,
608                    problem_size_threshold: None,
609                    verification_method: "search-result".to_string(),
610                    theoretical_basis: String::new(),
611                    experimental_validation: false,
612                },
613                hardware_requirements: HardwareRequirements {
614                    min_qubits: 1,
615                    recommended_qubits: 4,
616                    max_circuit_depth: 100,
617                    required_gates: Vec::new(),
618                    connectivity_requirements: ConnectivityRequirements {
619                        topology_type: TopologyType::AllToAll,
620                        connectivity_degree: None,
621                        all_to_all_required: false,
622                        specific_connections: Vec::new(),
623                    },
624                    fidelity_requirements: FidelityRequirements {
625                        min_gate_fidelity: 0.99,
626                        min_readout_fidelity: 0.95,
627                        min_state_preparation_fidelity: 0.98,
628                        coherence_time_requirement: Duration::from_micros(100),
629                        error_budget: 0.01,
630                    },
631                    supported_platforms: Vec::new(),
632                    special_hardware: Vec::new(),
633                },
634                complexity_info: ComplexityInfo {
635                    time_complexity: "Unknown".to_string(),
636                    space_complexity: "Unknown".to_string(),
637                    circuit_depth: 0,
638                    gate_count: 0,
639                    qubit_count: 0,
640                },
641                discovery_score: score,
642                personalization_score: None,
643            });
644        }
645
646        // Hardware-constraint filter (qubit count window).
647        if let Some(hc) = &criteria.hardware_constraints {
648            results.retain(|info| {
649                let q = info.hardware_requirements.recommended_qubits;
650                let min_ok = hc.min_qubits.map_or(true, |min| q >= min);
651                let max_ok = hc.max_qubits.map_or(true, |max| q <= max);
652                min_ok && max_ok
653            });
654        }
655
656        Ok(results)
657    }
658
659    async fn apply_ranking(
660        &self,
661        mut results: Vec<AlgorithmInfo>,
662        criteria: &DiscoveryCriteria,
663    ) -> DeviceResult<Vec<AlgorithmInfo>> {
664        let ranking_system = self
665            .ranking_system
666            .read()
667            .unwrap_or_else(|e| e.into_inner());
668        let ranked_results = ranking_system.rank_algorithms(&results, criteria)?;
669
670        // Sort by rank
671        results.sort_by(|a, b| {
672            let rank_a = ranked_results
673                .iter()
674                .find(|r| r.algorithm_id == a.algorithm_id)
675                .map_or(usize::MAX, |r| r.rank);
676            let rank_b = ranked_results
677                .iter()
678                .find(|r| r.algorithm_id == b.algorithm_id)
679                .map_or(usize::MAX, |r| r.rank);
680            rank_a.cmp(&rank_b)
681        });
682
683        Ok(results)
684    }
685
686    async fn apply_personalization(
687        &self,
688        mut results: Vec<AlgorithmInfo>,
689        user_context: &UserContext,
690    ) -> DeviceResult<Vec<AlgorithmInfo>> {
691        let personalization_engine = self
692            .personalization_engine
693            .read()
694            .unwrap_or_else(|e| e.into_inner());
695        let personalized_scores =
696            personalization_engine.personalize_results(&results, user_context)?;
697
698        // Update personalization scores
699        for (i, score) in personalized_scores.iter().enumerate() {
700            if i < results.len() {
701                results[i].personalization_score = Some(*score);
702            }
703        }
704
705        Ok(results)
706    }
707
708    fn generate_cache_key(&self, criteria: &DiscoveryCriteria) -> String {
709        // Generate a unique cache key based on criteria
710        format!("search:{criteria:?}")
711    }
712
713    async fn check_cache(&self, cache_key: &str) -> DeviceResult<Option<Vec<AlgorithmInfo>>> {
714        let cache = self.cache.read().unwrap_or_else(|e| e.into_inner());
715        if let Some(cached_result) = cache.query_cache.get(cache_key) {
716            if cached_result.expires_at > SystemTime::now() {
717                return Ok(Some(cached_result.results.clone()));
718            }
719        }
720        Ok(None)
721    }
722
723    async fn cache_results(&self, cache_key: &str, results: &[AlgorithmInfo]) -> DeviceResult<()> {
724        let mut cache = self.cache.write().unwrap_or_else(|e| e.into_inner());
725        let cached_result = CachedResult {
726            results: results.to_vec(),
727            cached_at: SystemTime::now(),
728            expires_at: SystemTime::now() + self.config.cache_ttl,
729            hit_count: 0,
730        };
731        cache
732            .query_cache
733            .insert(cache_key.to_string(), cached_result);
734        Ok(())
735    }
736}
737
738// Implementation stubs for sub-components
739impl SemanticSearchEngine {
740    fn new() -> DeviceResult<Self> {
741        Ok(Self {
742            word_embeddings: HashMap::new(),
743            algorithm_embeddings: HashMap::new(),
744            similarity_cache: HashMap::new(),
745            search_index: InvertedIndex::new(),
746        })
747    }
748}
749
750impl InvertedIndex {
751    fn new() -> Self {
752        Self {
753            term_to_algorithms: HashMap::new(),
754            algorithm_to_terms: HashMap::new(),
755            term_frequencies: HashMap::new(),
756            document_frequencies: HashMap::new(),
757        }
758    }
759}
760
761impl RecommendationEngine {
762    fn new() -> DeviceResult<Self> {
763        Ok(Self {
764            user_profiles: HashMap::new(),
765            algorithm_similarities: HashMap::new(),
766            collaborative_filters: vec![],
767            content_filters: vec![],
768            hybrid_weights: HybridWeights {
769                collaborative_weight: 0.4,
770                content_weight: 0.3,
771                popularity_weight: 0.15,
772                temporal_weight: 0.1,
773                semantic_weight: 0.05,
774            },
775        })
776    }
777
778    fn get_recommendations(
779        &self,
780        _user_id: &str,
781        count: usize,
782    ) -> DeviceResult<Vec<(String, f64)>> {
783        // Simplified recommendation logic
784        let mut recommendations = Vec::new();
785        for i in 0..count {
786            recommendations.push((format!("algorithm_{i}"), (i as f64).mul_add(-0.1, 0.8)));
787        }
788        Ok(recommendations)
789    }
790}
791
792impl RankingSystem {
793    fn new() -> DeviceResult<Self> {
794        Ok(Self {
795            ranking_algorithms: vec![],
796            feature_extractors: vec![],
797            learning_to_rank_model: None,
798            ranking_cache: HashMap::new(),
799        })
800    }
801
802    fn rank_algorithms(
803        &self,
804        algorithms: &[AlgorithmInfo],
805        _criteria: &DiscoveryCriteria,
806    ) -> DeviceResult<Vec<RankedResult>> {
807        let mut results = Vec::new();
808        for (i, algorithm) in algorithms.iter().enumerate() {
809            results.push(RankedResult {
810                algorithm_id: algorithm.algorithm_id.clone(),
811                rank: i,
812                score: (i as f64).mul_add(-0.1, 1.0),
813                feature_scores: HashMap::new(),
814                explanation: RankingExplanation {
815                    primary_factors: vec!["relevance".to_string()],
816                    relevance_score: 0.9,
817                    popularity_score: 0.8,
818                    quality_score: 0.85,
819                    personalization_boost: 0.1,
820                    detailed_explanation: "High relevance to query".to_string(),
821                },
822            });
823        }
824        Ok(results)
825    }
826}
827
828impl PersonalizationEngine {
829    fn new() -> DeviceResult<Self> {
830        Ok(Self {
831            personalization_models: HashMap::new(),
832            context_analyzers: vec![],
833            adaptation_strategies: vec![],
834            privacy_preserving: true,
835        })
836    }
837
838    fn personalize_results(
839        &self,
840        results: &[AlgorithmInfo],
841        _user_context: &UserContext,
842    ) -> DeviceResult<Vec<f64>> {
843        // Simplified personalization
844        Ok(results
845            .iter()
846            .enumerate()
847            .map(|(i, _)| (i as f64).mul_add(-0.1, 0.9))
848            .collect())
849    }
850}
851
852impl DiscoveryCache {
853    fn new() -> Self {
854        Self {
855            query_cache: HashMap::new(),
856            recommendation_cache: HashMap::new(),
857            similarity_cache: HashMap::new(),
858            cache_stats: CacheStatistics::default(),
859        }
860    }
861}
quantrs2_device/algorithm_marketplace/discovery.rs

quantrs2_device/algorithm_marketplace/
discovery.rs
