quantrs2_ml/automl/pipeline/
constructor.rs

1//! Automated Pipeline Constructor
2//!
3//! This module provides automated construction of quantum ML pipelines.
4
5use crate::automl::config::QuantumAutoMLConfig;
6use crate::automl::pipeline::QuantumMLPipeline;
7use crate::error::Result;
8use scirs2_core::ndarray::{Array1, Array2};
9
10/// Automated pipeline constructor
11#[derive(Debug, Clone)]
12pub struct AutomatedPipelineConstructor {
13    /// Task detector
14    task_detector: TaskDetector,
15
16    /// Preprocessing optimizer
17    preprocessing_optimizer: PreprocessingOptimizer,
18
19    /// Algorithm selector
20    algorithm_selector: AlgorithmSelector,
21
22    /// Pipeline validator
23    pipeline_validator: PipelineValidator,
24}
25
26/// Task detection from data
27#[derive(Debug, Clone)]
28pub struct TaskDetector {
29    /// Feature analyzers
30    feature_analyzers: Vec<FeatureAnalyzer>,
31
32    /// Target analyzers
33    target_analyzers: Vec<TargetAnalyzer>,
34
35    /// Data pattern detectors
36    pattern_detectors: Vec<PatternDetector>,
37}
38
39/// Feature analyzer
40#[derive(Debug, Clone)]
41pub struct FeatureAnalyzer {
42    /// Analyzer type
43    pub analyzer_type: FeatureAnalyzerType,
44
45    /// Analysis results
46    pub results: std::collections::HashMap<String, f64>,
47}
48
49/// Feature analyzer types
50#[derive(Debug, Clone)]
51pub enum FeatureAnalyzerType {
52    DataTypeAnalyzer,
53    DistributionAnalyzer,
54    CorrelationAnalyzer,
55    NullValueAnalyzer,
56    OutlierAnalyzer,
57    QuantumEncodingAnalyzer,
58}
59
60/// Target analyzer
61#[derive(Debug, Clone)]
62pub struct TargetAnalyzer {
63    /// Analyzer type
64    pub analyzer_type: TargetAnalyzerType,
65
66    /// Analysis results
67    pub results: std::collections::HashMap<String, f64>,
68}
69
70/// Target analyzer types
71#[derive(Debug, Clone)]
72pub enum TargetAnalyzerType {
73    TaskTypeDetector,
74    ClassBalanceAnalyzer,
75    LabelDistributionAnalyzer,
76    TemporalPatternAnalyzer,
77}
78
79/// Pattern detector
80#[derive(Debug, Clone)]
81pub struct PatternDetector {
82    /// Pattern type
83    pub pattern_type: PatternType,
84
85    /// Detection confidence
86    pub confidence: f64,
87}
88
89/// Pattern types
90#[derive(Debug, Clone)]
91pub enum PatternType {
92    TimeSeriesPattern,
93    SpatialPattern,
94    NetworkPattern,
95    HierarchicalPattern,
96    QuantumPattern,
97}
98
99/// Preprocessing optimizer
100#[derive(Debug, Clone)]
101pub struct PreprocessingOptimizer {
102    /// Available preprocessors
103    preprocessors: Vec<PreprocessorCandidate>,
104
105    /// Optimization strategy
106    optimization_strategy: PreprocessingOptimizationStrategy,
107
108    /// Performance tracker
109    performance_tracker: PreprocessingPerformanceTracker,
110}
111
112/// Preprocessor candidate
113#[derive(Debug, Clone)]
114pub struct PreprocessorCandidate {
115    /// Preprocessor type
116    pub preprocessor_type: PreprocessorType,
117
118    /// Configuration
119    pub config: PreprocessorConfig,
120
121    /// Performance score
122    pub performance_score: f64,
123}
124
125/// Preprocessor types
126#[derive(Debug, Clone)]
127pub enum PreprocessorType {
128    Scaler(String),
129    FeatureSelector(String),
130    QuantumEncoder(String),
131    MissingValueHandler(String),
132    DataAugmenter,
133    OutlierDetector,
134}
135
136/// Preprocessor configuration
137#[derive(Debug, Clone)]
138pub struct PreprocessorConfig {
139    /// Parameters
140    pub parameters: std::collections::HashMap<String, f64>,
141
142    /// Enabled features
143    pub enabled_features: Vec<String>,
144}
145
146/// Preprocessing optimization strategy
147#[derive(Debug, Clone)]
148pub enum PreprocessingOptimizationStrategy {
149    Sequential,
150    Parallel,
151    Evolutionary,
152    BayesianOptimization,
153    QuantumAnnealing,
154}
155
156/// Preprocessing performance tracker
157#[derive(Debug, Clone)]
158pub struct PreprocessingPerformanceTracker {
159    /// Performance history
160    pub performance_history: Vec<PreprocessingPerformance>,
161
162    /// Best configuration
163    pub best_config: Option<PreprocessorConfig>,
164}
165
166/// Preprocessing performance
167#[derive(Debug, Clone)]
168pub struct PreprocessingPerformance {
169    /// Data quality score
170    pub data_quality_score: f64,
171
172    /// Feature importance scores
173    pub feature_importance: Array1<f64>,
174
175    /// Quantum encoding efficiency
176    pub quantum_encoding_efficiency: f64,
177
178    /// Processing time
179    pub processing_time: f64,
180}
181
182/// Algorithm selector
183#[derive(Debug, Clone)]
184pub struct AlgorithmSelector {
185    /// Available algorithms
186    algorithms: Vec<AlgorithmCandidate>,
187
188    /// Selection strategy
189    selection_strategy: AlgorithmSelectionStrategy,
190
191    /// Performance predictor
192    performance_predictor: AlgorithmPerformancePredictor,
193}
194
195/// Algorithm candidate
196#[derive(Debug, Clone)]
197pub struct AlgorithmCandidate {
198    /// Algorithm type
199    pub algorithm_type: AlgorithmType,
200
201    /// Quantum enhancement level
202    pub quantum_enhancement: QuantumEnhancementLevel,
203
204    /// Estimated performance
205    pub estimated_performance: f64,
206
207    /// Resource requirements
208    pub resource_requirements: ResourceRequirements,
209}
210
211/// Algorithm types
212#[derive(Debug, Clone)]
213pub enum AlgorithmType {
214    QuantumNeuralNetwork,
215    QuantumSVM,
216    QuantumClustering,
217    QuantumDimensionalityReduction,
218    QuantumTimeSeries,
219    QuantumAnomalyDetection,
220    ClassicalBaseline,
221}
222
223/// Quantum enhancement levels
224#[derive(Debug, Clone)]
225pub enum QuantumEnhancementLevel {
226    Classical,
227    QuantumInspired,
228    QuantumHybrid,
229    FullQuantum,
230    QuantumAdvantage,
231}
232
233/// Resource requirements
234#[derive(Debug, Clone)]
235pub struct ResourceRequirements {
236    /// Computational complexity
237    pub computational_complexity: f64,
238
239    /// Memory requirements
240    pub memory_requirements: f64,
241
242    /// Quantum resource requirements
243    pub quantum_requirements: QuantumResourceRequirements,
244
245    /// Training time estimate
246    pub training_time_estimate: f64,
247}
248
249/// Quantum resource requirements
250#[derive(Debug, Clone)]
251pub struct QuantumResourceRequirements {
252    /// Required qubits
253    pub required_qubits: usize,
254
255    /// Required circuit depth
256    pub required_circuit_depth: usize,
257
258    /// Required coherence time
259    pub required_coherence_time: f64,
260
261    /// Required gate fidelity
262    pub required_gate_fidelity: f64,
263}
264
265/// Algorithm selection strategy
266#[derive(Debug, Clone)]
267pub enum AlgorithmSelectionStrategy {
268    PerformanceBased,
269    ResourceEfficient,
270    QuantumAdvantage,
271    MultiObjective,
272    EnsembleBased,
273    MetaLearning,
274}
275
276/// Algorithm performance predictor
277#[derive(Debug, Clone)]
278pub struct AlgorithmPerformancePredictor {
279    /// Meta-learning model
280    meta_model: Option<MetaLearningModel>,
281
282    /// Performance database
283    performance_database: PerformanceDatabase,
284
285    /// Prediction strategy
286    prediction_strategy: PerformancePredictionStrategy,
287}
288
289/// Meta-learning model
290#[derive(Debug, Clone)]
291pub struct MetaLearningModel {
292    /// Model type
293    pub model_type: String,
294
295    /// Meta-features
296    pub meta_features: Vec<String>,
297
298    /// Trained parameters
299    pub parameters: Array1<f64>,
300}
301
302/// Performance database
303#[derive(Debug, Clone)]
304pub struct PerformanceDatabase {
305    /// Historical performance records
306    pub records: Vec<PerformanceRecord>,
307}
308
309/// Performance record
310#[derive(Debug, Clone)]
311pub struct PerformanceRecord {
312    /// Dataset characteristics
313    pub dataset_features: std::collections::HashMap<String, f64>,
314
315    /// Algorithm used
316    pub algorithm: String,
317
318    /// Performance achieved
319    pub performance: f64,
320}
321
322/// Performance prediction strategies
323#[derive(Debug, Clone)]
324pub enum PerformancePredictionStrategy {
325    SimilarityBased,
326    MetaLearning,
327    TheoreticalAnalysis,
328    CombinedApproach,
329}
330
331/// Pipeline validator
332#[derive(Debug, Clone)]
333pub struct PipelineValidator {
334    /// Validation rules
335    validation_rules: Vec<ValidationRule>,
336
337    /// Performance validators
338    performance_validators: Vec<PerformanceValidator>,
339}
340
341/// Validation rule
342#[derive(Debug, Clone)]
343pub struct ValidationRule {
344    /// Rule type
345    pub rule_type: ValidationRuleType,
346
347    /// Rule description
348    pub description: String,
349
350    /// Severity level
351    pub severity: ValidationSeverity,
352}
353
354/// Validation rule types
355#[derive(Debug, Clone)]
356pub enum ValidationRuleType {
357    DataCompatibility,
358    ResourceConstraints,
359    QuantumConstraints,
360    PerformanceThreshold,
361    ConsistencyCheck,
362}
363
364/// Validation severity levels
365#[derive(Debug, Clone)]
366pub enum ValidationSeverity {
367    Error,
368    Warning,
369    Info,
370}
371
372/// Performance validator
373#[derive(Debug, Clone)]
374pub struct PerformanceValidator {
375    /// Validator type
376    pub validator_type: PerformanceValidatorType,
377
378    /// Validation criteria
379    pub criteria: ValidationCriteria,
380}
381
382/// Performance validator types
383#[derive(Debug, Clone)]
384pub enum PerformanceValidatorType {
385    AccuracyValidator,
386    RobustnessValidator,
387    QuantumAdvantageValidator,
388    ResourceEfficiencyValidator,
389    FairnessValidator,
390}
391
392/// Validation criteria
393#[derive(Debug, Clone)]
394pub struct ValidationCriteria {
395    /// Minimum performance threshold
396    pub min_performance: f64,
397
398    /// Maximum resource usage
399    pub max_resource_usage: f64,
400
401    /// Required quantum advantage
402    pub required_quantum_advantage: Option<f64>,
403}
404
405impl AutomatedPipelineConstructor {
406    /// Create a new pipeline constructor
407    pub fn new(config: &QuantumAutoMLConfig) -> Self {
408        Self {
409            task_detector: TaskDetector::new(),
410            preprocessing_optimizer: PreprocessingOptimizer::new(
411                &config.search_space.preprocessing,
412            ),
413            algorithm_selector: AlgorithmSelector::new(&config.search_space.algorithms),
414            pipeline_validator: PipelineValidator::new(&config.evaluation_config),
415        }
416    }
417
418    /// Construct a pipeline for the given data and configuration
419    pub fn construct_pipeline(
420        &self,
421        X: &Array2<f64>,
422        y: &Array1<f64>,
423        config: &QuantumAutoMLConfig,
424    ) -> Result<QuantumMLPipeline> {
425        // Analyze data characteristics
426        let data_analysis = self.task_detector.analyze_data(X, y)?;
427
428        // Optimize preprocessing
429        let preprocessing_config = self
430            .preprocessing_optimizer
431            .optimize(X, y, &data_analysis)?;
432
433        // Select best algorithm
434        let algorithm_candidate = self
435            .algorithm_selector
436            .select_algorithm(&data_analysis, &config.task_type)?;
437
438        // Construct pipeline
439        let pipeline =
440            QuantumMLPipeline::new(algorithm_candidate, preprocessing_config, config.clone())?;
441
442        // Validate pipeline
443        self.pipeline_validator.validate(&pipeline, X, y)?;
444
445        Ok(pipeline)
446    }
447}
448
449impl TaskDetector {
450    fn new() -> Self {
451        Self {
452            feature_analyzers: vec![
453                FeatureAnalyzer::new(FeatureAnalyzerType::DataTypeAnalyzer),
454                FeatureAnalyzer::new(FeatureAnalyzerType::DistributionAnalyzer),
455                FeatureAnalyzer::new(FeatureAnalyzerType::CorrelationAnalyzer),
456            ],
457            target_analyzers: vec![
458                TargetAnalyzer::new(TargetAnalyzerType::TaskTypeDetector),
459                TargetAnalyzer::new(TargetAnalyzerType::ClassBalanceAnalyzer),
460            ],
461            pattern_detectors: vec![
462                PatternDetector::new(PatternType::TimeSeriesPattern),
463                PatternDetector::new(PatternType::QuantumPattern),
464            ],
465        }
466    }
467
468    fn analyze_data(&self, X: &Array2<f64>, y: &Array1<f64>) -> Result<DataAnalysis> {
469        // Simplified data analysis
470        Ok(DataAnalysis {
471            num_features: X.ncols(),
472            num_samples: X.nrows(),
473            feature_types: vec!["numerical".to_string(); X.ncols()],
474            target_type: "numerical".to_string(),
475            data_complexity: 0.5, // Simplified estimate
476        })
477    }
478}
479
480/// Data analysis results
481#[derive(Debug, Clone)]
482pub struct DataAnalysis {
483    pub num_features: usize,
484    pub num_samples: usize,
485    pub feature_types: Vec<String>,
486    pub target_type: String,
487    pub data_complexity: f64,
488}
489
490impl FeatureAnalyzer {
491    fn new(analyzer_type: FeatureAnalyzerType) -> Self {
492        Self {
493            analyzer_type,
494            results: std::collections::HashMap::new(),
495        }
496    }
497}
498
499impl TargetAnalyzer {
500    fn new(analyzer_type: TargetAnalyzerType) -> Self {
501        Self {
502            analyzer_type,
503            results: std::collections::HashMap::new(),
504        }
505    }
506}
507
508impl PatternDetector {
509    fn new(pattern_type: PatternType) -> Self {
510        Self {
511            pattern_type,
512            confidence: 0.0,
513        }
514    }
515}
516
517impl PreprocessingOptimizer {
518    fn new(preprocessing_space: &crate::automl::config::PreprocessingSearchSpace) -> Self {
519        Self {
520            preprocessors: Vec::new(),
521            optimization_strategy: PreprocessingOptimizationStrategy::Sequential,
522            performance_tracker: PreprocessingPerformanceTracker::new(),
523        }
524    }
525
526    fn optimize(
527        &self,
528        X: &Array2<f64>,
529        y: &Array1<f64>,
530        data_analysis: &DataAnalysis,
531    ) -> Result<PreprocessorConfig> {
532        // Simplified preprocessing optimization
533        Ok(PreprocessorConfig {
534            parameters: std::collections::HashMap::new(),
535            enabled_features: (0..X.ncols()).map(|i| format!("feature_{}", i)).collect(),
536        })
537    }
538}
539
540impl PreprocessingPerformanceTracker {
541    fn new() -> Self {
542        Self {
543            performance_history: Vec::new(),
544            best_config: None,
545        }
546    }
547}
548
549impl AlgorithmSelector {
550    fn new(algorithm_space: &crate::automl::config::AlgorithmSearchSpace) -> Self {
551        Self {
552            algorithms: Vec::new(),
553            selection_strategy: AlgorithmSelectionStrategy::PerformanceBased,
554            performance_predictor: AlgorithmPerformancePredictor::new(),
555        }
556    }
557
558    fn select_algorithm(
559        &self,
560        data_analysis: &DataAnalysis,
561        task_type: &Option<crate::automl::config::MLTaskType>,
562    ) -> Result<AlgorithmCandidate> {
563        // Simplified algorithm selection
564        Ok(AlgorithmCandidate {
565            algorithm_type: AlgorithmType::QuantumNeuralNetwork,
566            quantum_enhancement: QuantumEnhancementLevel::QuantumHybrid,
567            estimated_performance: 0.8,
568            resource_requirements: ResourceRequirements {
569                computational_complexity: 1.0,
570                memory_requirements: 256.0,
571                quantum_requirements: QuantumResourceRequirements {
572                    required_qubits: 4,
573                    required_circuit_depth: 6,
574                    required_coherence_time: 100.0,
575                    required_gate_fidelity: 0.99,
576                },
577                training_time_estimate: 300.0,
578            },
579        })
580    }
581}
582
583impl AlgorithmPerformancePredictor {
584    fn new() -> Self {
585        Self {
586            meta_model: None,
587            performance_database: PerformanceDatabase::new(),
588            prediction_strategy: PerformancePredictionStrategy::SimilarityBased,
589        }
590    }
591}
592
593impl PerformanceDatabase {
594    fn new() -> Self {
595        Self {
596            records: Vec::new(),
597        }
598    }
599}
600
601impl PipelineValidator {
602    fn new(evaluation_config: &crate::automl::config::EvaluationConfig) -> Self {
603        Self {
604            validation_rules: Vec::new(),
605            performance_validators: Vec::new(),
606        }
607    }
608
609    fn validate(
610        &self,
611        pipeline: &QuantumMLPipeline,
612        X: &Array2<f64>,
613        y: &Array1<f64>,
614    ) -> Result<()> {
615        // Simplified validation
616        Ok(())
617    }
618}
quantrs2_ml/automl/pipeline/constructor.rs

quantrs2_ml/automl/pipeline/
constructor.rs
