sklears_compose/
automl.rs

1//! `AutoML` integration with neural architecture search
2//!
3//! This module provides automated machine learning capabilities including
4//! neural architecture search, hyperparameter optimization, and automated
5//! feature engineering for pipeline construction.
6
7use scirs2_core::ndarray::{ArrayView1, ArrayView2};
8use scirs2_core::random::rngs::StdRng;
9use scirs2_core::random::{thread_rng, Rng, SeedableRng};
10use sklears_core::{
11    error::{Result as SklResult, SklearsError},
12    traits::Estimator,
13    types::{Float, FloatBounds},
14};
15use std::collections::BTreeMap;
16use std::time::{Duration, Instant};
17
18use crate::{FluentPipelineBuilder, PipelineConfiguration};
19
20/// `AutoML` pipeline optimizer for automated model selection and hyperparameter tuning
21#[derive(Debug)]
22pub struct AutoMLOptimizer {
23    /// Search configuration
24    config: AutoMLConfig,
25    /// Search space for architectures
26    search_space: SearchSpace,
27    /// Optimization history
28    history: OptimizationHistory,
29    /// Random number generator
30    rng: StdRng,
31}
32
33/// `AutoML` configuration
34#[derive(Debug, Clone)]
35pub struct AutoMLConfig {
36    /// Maximum optimization time
37    pub max_time: Duration,
38    /// Maximum number of trials
39    pub max_trials: usize,
40    /// Cross-validation folds
41    pub cv_folds: usize,
42    /// Optimization metric
43    pub metric: OptimizationMetric,
44    /// Search strategy
45    pub strategy: SearchStrategy,
46    /// Population size for genetic algorithms
47    pub population_size: usize,
48    /// Early stopping patience
49    pub early_stopping_patience: Option<usize>,
50    /// Random seed
51    pub random_seed: Option<u64>,
52}
53
54/// Optimization metric
55#[derive(Debug, Clone, PartialEq, Eq)]
56pub enum OptimizationMetric {
57    /// Accuracy for classification
58    Accuracy,
59    /// F1-score for classification
60    F1Score,
61    /// AUC-ROC for classification
62    AUCROC,
63    /// Mean Squared Error for regression
64    MSE,
65    /// Root Mean Squared Error for regression
66    RMSE,
67    /// Mean Absolute Error for regression
68    MAE,
69    /// R-squared for regression
70    R2,
71    /// Custom metric with function
72    Custom(String),
73}
74
75/// Search strategy for optimization
76#[derive(Debug, Clone, PartialEq, Eq)]
77pub enum SearchStrategy {
78    /// Random search
79    Random,
80    /// Grid search
81    Grid,
82    /// Bayesian optimization
83    Bayesian,
84    /// Genetic algorithm
85    Genetic,
86    /// Particle swarm optimization
87    ParticleSwarm,
88    /// Differential evolution
89    DifferentialEvolution,
90    /// Tree-structured Parzen Estimator
91    TPE,
92    /// Hyperband
93    Hyperband,
94}
95
96/// Search space definition for `AutoML`
97#[derive(Debug, Clone)]
98pub struct SearchSpace {
99    /// Algorithm choices
100    pub algorithms: Vec<AlgorithmChoice>,
101    /// Preprocessing options
102    pub preprocessing: Vec<PreprocessingChoice>,
103    /// Feature engineering options
104    pub feature_engineering: Vec<FeatureEngineeringChoice>,
105    /// Hyperparameter ranges
106    pub hyperparameters: BTreeMap<String, ParameterRange>,
107    /// Architecture constraints
108    pub constraints: Vec<ArchitectureConstraint>,
109}
110
111/// Algorithm choice in search space
112#[derive(Debug, Clone)]
113pub struct AlgorithmChoice {
114    /// Algorithm name
115    pub name: String,
116    /// Algorithm type
117    pub algorithm_type: AlgorithmType,
118    /// Hyperparameter ranges specific to this algorithm
119    pub hyperparameters: BTreeMap<String, ParameterRange>,
120    /// Resource requirements
121    pub resource_requirements: ResourceRequirements,
122}
123
124/// Algorithm type enumeration
125#[derive(Debug, Clone, PartialEq, Eq)]
126pub enum AlgorithmType {
127    /// Linear models
128    Linear,
129    /// Tree-based models
130    Tree,
131    /// Ensemble methods
132    Ensemble,
133    /// Neural networks
134    NeuralNetwork,
135    /// Support Vector Machines
136    SVM,
137    /// Nearest neighbors
138    KNN,
139    /// Naive Bayes
140    NaiveBayes,
141    /// Custom algorithm
142    Custom(String),
143}
144
145/// Preprocessing choice
146#[derive(Debug, Clone)]
147pub struct PreprocessingChoice {
148    /// Preprocessing step name
149    pub name: String,
150    /// Step parameters
151    pub parameters: BTreeMap<String, ParameterRange>,
152    /// Optional flag (can be skipped)
153    pub optional: bool,
154}
155
156/// Feature engineering choice
157#[derive(Debug, Clone)]
158pub struct FeatureEngineeringChoice {
159    /// Feature engineering method name
160    pub name: String,
161    /// Method parameters
162    pub parameters: BTreeMap<String, ParameterRange>,
163    /// Optional flag
164    pub optional: bool,
165    /// Computational cost estimate
166    pub cost_estimate: f64,
167}
168
169/// Parameter range for hyperparameter optimization
170#[derive(Debug, Clone)]
171pub enum ParameterRange {
172    /// Continuous range
173    Continuous { min: f64, max: f64, log_scale: bool },
174    /// Discrete integer range
175    Integer { min: i64, max: i64 },
176    /// Categorical choices
177    Categorical(Vec<String>),
178    /// Boolean choice
179    Boolean,
180    /// Fixed value
181    Fixed(ParameterValue),
182}
183
184/// Parameter value
185#[derive(Debug, Clone)]
186pub enum ParameterValue {
187    /// Float
188    Float(f64),
189    /// Int
190    Int(i64),
191    /// String
192    String(String),
193    /// Bool
194    Bool(bool),
195    /// Array
196    Array(Vec<ParameterValue>),
197}
198
199/// Architecture constraint
200#[derive(Debug, Clone)]
201pub enum ArchitectureConstraint {
202    /// Maximum number of layers
203    MaxLayers(usize),
204    /// Maximum number of parameters
205    MaxParameters(usize),
206    /// Maximum memory usage (MB)
207    MaxMemoryMB(usize),
208    /// Maximum training time
209    MaxTrainingTime(Duration),
210    /// Required accuracy threshold
211    MinAccuracy(f64),
212}
213
214/// Resource requirements for an algorithm
215#[derive(Debug, Clone)]
216pub struct ResourceRequirements {
217    /// Memory requirement (MB)
218    pub memory_mb: usize,
219    /// CPU cores needed
220    pub cpu_cores: usize,
221    /// Training time complexity
222    pub time_complexity: TimeComplexity,
223    /// GPU requirement
224    pub requires_gpu: bool,
225}
226
227/// Time complexity classification
228#[derive(Debug, Clone, PartialEq, Eq)]
229pub enum TimeComplexity {
230    /// Constant
231    Constant,
232    /// Logarithmic
233    Logarithmic,
234    /// Linear
235    Linear,
236    /// Linearithmic
237    Linearithmic,
238    /// Quadratic
239    Quadratic,
240    /// Cubic
241    Cubic,
242    /// Exponential
243    Exponential,
244}
245
246/// Optimization history tracking
247#[derive(Debug, Clone)]
248pub struct OptimizationHistory {
249    /// Trial results
250    pub trials: Vec<TrialResult>,
251    /// Best score achieved
252    pub best_score: Option<f64>,
253    /// Best configuration
254    pub best_config: Option<PipelineConfiguration>,
255    /// Optimization start time
256    pub start_time: Option<Instant>,
257    /// Total optimization time
258    pub total_time: Duration,
259}
260
261/// Individual trial result
262#[derive(Debug, Clone)]
263pub struct TrialResult {
264    /// Trial identifier
265    pub trial_id: usize,
266    /// Pipeline configuration tried
267    pub config: PipelineConfiguration,
268    /// Achieved score
269    pub score: f64,
270    /// Training time
271    pub training_time: Duration,
272    /// Validation scores (for CV)
273    pub cv_scores: Vec<f64>,
274    /// Trial timestamp
275    pub timestamp: Instant,
276    /// Trial status
277    pub status: TrialStatus,
278    /// Error message if failed
279    pub error: Option<String>,
280}
281
282/// Trial status
283#[derive(Debug, Clone, PartialEq, Eq)]
284pub enum TrialStatus {
285    /// Trial completed successfully
286    Success,
287    /// Trial failed
288    Failed,
289    /// Trial was stopped early
290    Stopped,
291    /// Trial is running
292    Running,
293    /// Trial is queued
294    Queued,
295}
296
297/// Neural Architecture Search (NAS) component
298#[derive(Debug)]
299pub struct NeuralArchitectureSearch {
300    /// Search space for neural architectures
301    search_space: NeuralSearchSpace,
302    /// Search strategy
303    strategy: NASStrategy,
304    /// Evaluation method
305    evaluator: ArchitectureEvaluator,
306}
307
308/// Neural architecture search space
309#[derive(Debug, Clone)]
310pub struct NeuralSearchSpace {
311    /// Available layer types
312    pub layer_types: Vec<LayerType>,
313    /// Number of layers range
314    pub num_layers: ParameterRange,
315    /// Hidden units range
316    pub hidden_units: ParameterRange,
317    /// Activation functions
318    pub activations: Vec<ActivationFunction>,
319    /// Regularization options
320    pub regularization: Vec<RegularizationOption>,
321    /// Connection patterns
322    pub connections: Vec<ConnectionPattern>,
323}
324
325/// Layer type for neural networks
326#[derive(Debug, Clone, PartialEq, Eq)]
327pub enum LayerType {
328    /// Dense
329    Dense,
330    /// Dropout
331    Dropout,
332    /// BatchNorm
333    BatchNorm,
334    /// Convolution
335    Convolution,
336    /// Pooling
337    Pooling,
338    /// LSTM
339    LSTM,
340    /// GRU
341    GRU,
342    /// Attention
343    Attention,
344    /// Embedding
345    Embedding,
346    /// Custom
347    Custom(String),
348}
349
350/// Activation function options
351#[derive(Debug, Clone, PartialEq, Eq)]
352pub enum ActivationFunction {
353    /// ReLU
354    ReLU,
355    /// LeakyReLU
356    LeakyReLU,
357    /// ELU
358    ELU,
359    /// Swish
360    Swish,
361    /// GELU
362    GELU,
363    /// Tanh
364    Tanh,
365    /// Sigmoid
366    Sigmoid,
367    /// Identity
368    Identity,
369    /// Custom
370    Custom(String),
371}
372
373/// Regularization options
374#[derive(Debug, Clone)]
375pub struct RegularizationOption {
376    /// Regularization type
377    pub reg_type: RegularizationType,
378    /// Strength parameter range
379    pub strength: ParameterRange,
380}
381
382/// Regularization type
383#[derive(Debug, Clone, PartialEq, Eq)]
384pub enum RegularizationType {
385    /// L1
386    L1,
387    /// L2
388    L2,
389    /// Dropout
390    Dropout,
391    /// BatchNorm
392    BatchNorm,
393    /// LayerNorm
394    LayerNorm,
395    /// EarlyStopping
396    EarlyStopping,
397    /// Custom
398    Custom(String),
399}
400
401/// Connection pattern for neural architectures
402#[derive(Debug, Clone, PartialEq, Eq)]
403pub enum ConnectionPattern {
404    /// Sequential
405    Sequential,
406    /// Residual
407    Residual,
408    /// DenseNet
409    DenseNet,
410    /// Highway
411    Highway,
412    /// Custom
413    Custom(String),
414}
415
416/// Neural Architecture Search strategy
417#[derive(Debug, Clone, PartialEq, Eq)]
418pub enum NASStrategy {
419    /// Random search over architectures
420    Random,
421    /// Evolutionary algorithm
422    Evolutionary,
423    /// Reinforcement learning based
424    ReinforcementLearning,
425    /// Differentiable architecture search
426    DARTS,
427    /// Progressive search
428    Progressive,
429    /// One-shot architecture search
430    OneShot,
431}
432
433/// Architecture evaluator for NAS
434#[derive(Debug, Clone)]
435pub struct ArchitectureEvaluator {
436    /// Evaluation strategy
437    pub strategy: EvaluationStrategy,
438    /// Maximum evaluation time per architecture
439    pub max_eval_time: Duration,
440    /// Early stopping criteria
441    pub early_stopping: Option<EarlyStoppingCriteria>,
442}
443
444/// Evaluation strategy for architectures
445#[derive(Debug, Clone, PartialEq, Eq)]
446pub enum EvaluationStrategy {
447    /// Full training
448    FullTraining,
449    /// Early stopping
450    EarlyStopping,
451    /// Weight sharing
452    WeightSharing,
453    /// Performance prediction
454    PerformancePrediction,
455    /// Progressive evaluation
456    Progressive,
457}
458
459/// Early stopping criteria
460#[derive(Debug, Clone)]
461pub struct EarlyStoppingCriteria {
462    /// Metric to monitor
463    pub metric: String,
464    /// Patience (epochs)
465    pub patience: usize,
466    /// Minimum improvement threshold
467    pub min_delta: f64,
468}
469
470impl Default for AutoMLConfig {
471    fn default() -> Self {
472        Self {
473            max_time: Duration::from_secs(3600), // 1 hour
474            max_trials: 100,
475            cv_folds: 5,
476            metric: OptimizationMetric::Accuracy,
477            strategy: SearchStrategy::Random,
478            population_size: 20,
479            early_stopping_patience: Some(10),
480            random_seed: None,
481        }
482    }
483}
484
485impl Default for OptimizationHistory {
486    fn default() -> Self {
487        Self {
488            trials: Vec::new(),
489            best_score: None,
490            best_config: None,
491            start_time: None,
492            total_time: Duration::ZERO,
493        }
494    }
495}
496
497impl AutoMLOptimizer {
498    /// Create a new `AutoML` optimizer
499    pub fn new(config: AutoMLConfig) -> SklResult<Self> {
500        let rng = if let Some(seed) = config.random_seed {
501            StdRng::seed_from_u64(seed)
502        } else {
503            StdRng::from_rng(&mut thread_rng())
504        };
505
506        Ok(Self {
507            config,
508            search_space: SearchSpace::default(),
509            history: OptimizationHistory::default(),
510            rng,
511        })
512    }
513
514    /// Set the search space for optimization
515    #[must_use]
516    pub fn search_space(mut self, search_space: SearchSpace) -> Self {
517        self.search_space = search_space;
518        self
519    }
520
521    /// Run automated optimization
522    pub fn optimize(
523        &mut self,
524        x_train: &ArrayView2<Float>,
525        y_train: &ArrayView1<Float>,
526        x_val: Option<&ArrayView2<Float>>,
527        y_val: Option<&ArrayView1<Float>>,
528    ) -> SklResult<FluentPipelineBuilder> {
529        self.history.start_time = Some(Instant::now());
530        let start_time = Instant::now();
531
532        let mut best_score = f64::NEG_INFINITY;
533        let mut best_config = None;
534        let mut trials_without_improvement = 0;
535
536        for trial_id in 0..self.config.max_trials {
537            // Check time limit
538            if start_time.elapsed() > self.config.max_time {
539                break;
540            }
541
542            // Generate candidate configuration
543            let config = self.generate_candidate_config()?;
544
545            // Evaluate configuration
546            let trial_result =
547                self.evaluate_config(&config, x_train, y_train, x_val, y_val, trial_id)?;
548
549            // Update history
550            self.history.trials.push(trial_result.clone());
551
552            // Check if this is the best configuration
553            if trial_result.score > best_score {
554                best_score = trial_result.score;
555                best_config = Some(config);
556                trials_without_improvement = 0;
557
558                self.history.best_score = Some(best_score);
559                self.history.best_config = best_config.clone();
560            } else {
561                trials_without_improvement += 1;
562            }
563
564            // Early stopping
565            if let Some(patience) = self.config.early_stopping_patience {
566                if trials_without_improvement >= patience {
567                    break;
568                }
569            }
570        }
571
572        self.history.total_time = start_time.elapsed();
573
574        // Return best configuration as FluentPipelineBuilder
575        if let Some(best_config) = best_config {
576            Ok(self.config_to_builder(best_config))
577        } else {
578            Err(SklearsError::InvalidData {
579                reason: "No valid configuration found during optimization".to_string(),
580            })
581        }
582    }
583
584    /// Generate a candidate configuration based on search strategy
585    fn generate_candidate_config(&mut self) -> SklResult<PipelineConfiguration> {
586        match self.config.strategy {
587            SearchStrategy::Random => self.generate_random_config(),
588            SearchStrategy::Genetic => self.generate_genetic_config(),
589            SearchStrategy::Bayesian => self.generate_bayesian_config(),
590            _ => self.generate_random_config(), // Fallback to random
591        }
592    }
593
594    /// Generate random configuration
595    fn generate_random_config(&mut self) -> SklResult<PipelineConfiguration> {
596        // Sample random algorithm
597        let algorithm = &self.search_space.algorithms
598            [self.rng.gen_range(0..self.search_space.algorithms.len())];
599
600        // Sample random preprocessing steps
601        let preprocessing_steps: Vec<_> = self
602            .search_space
603            .preprocessing
604            .iter()
605            .filter(|step| !step.optional || self.rng.gen_bool(0.5))
606            .collect();
607
608        // Sample random feature engineering steps
609        let feature_steps: Vec<_> = self
610            .search_space
611            .feature_engineering
612            .iter()
613            .filter(|step| !step.optional || self.rng.gen_bool(0.3))
614            .collect();
615
616        // Create configuration
617        Ok(PipelineConfiguration::default())
618    }
619
620    /// Generate configuration using genetic algorithm
621    fn generate_genetic_config(&mut self) -> SklResult<PipelineConfiguration> {
622        // Simplified genetic algorithm implementation
623        // In practice, this would maintain a population and perform crossover/mutation
624        self.generate_random_config()
625    }
626
627    /// Generate configuration using Bayesian optimization
628    fn generate_bayesian_config(&mut self) -> SklResult<PipelineConfiguration> {
629        // Simplified Bayesian optimization
630        // In practice, this would use Gaussian processes or other surrogate models
631        self.generate_random_config()
632    }
633
634    /// Evaluate a configuration
635    fn evaluate_config(
636        &mut self,
637        config: &PipelineConfiguration,
638        x_train: &ArrayView2<Float>,
639        y_train: &ArrayView1<Float>,
640        x_val: Option<&ArrayView2<Float>>,
641        y_val: Option<&ArrayView1<Float>>,
642        trial_id: usize,
643    ) -> SklResult<TrialResult> {
644        let start_time = Instant::now();
645
646        // Create pipeline from configuration
647        let pipeline_builder = self.config_to_builder(config.clone());
648
649        // For now, return a mock result
650        // In a real implementation, this would:
651        // 1. Build the pipeline
652        // 2. Perform cross-validation or train/validation split
653        // 3. Calculate the specified metric
654
655        let mock_score = self.rng.gen_range(0.5..1.0);
656        let cv_scores = (0..self.config.cv_folds)
657            .map(|_| self.rng.gen_range(0.4..1.0))
658            .collect();
659
660        Ok(TrialResult {
661            trial_id,
662            config: config.clone(),
663            score: mock_score,
664            training_time: start_time.elapsed(),
665            cv_scores,
666            timestamp: start_time,
667            status: TrialStatus::Success,
668            error: None,
669        })
670    }
671
672    /// Convert configuration to `FluentPipelineBuilder`
673    fn config_to_builder(&self, config: PipelineConfiguration) -> FluentPipelineBuilder {
674        // Create builder with the configuration
675        FluentPipelineBuilder::data_science_preset()
676            .memory(config.memory_config)
677            .caching(config.caching)
678            .validation(config.validation)
679            .debug(config.debug)
680    }
681
682    /// Get optimization results
683    #[must_use]
684    pub fn get_results(&self) -> &OptimizationHistory {
685        &self.history
686    }
687
688    /// Get best trial
689    #[must_use]
690    pub fn get_best_trial(&self) -> Option<&TrialResult> {
691        self.history.trials.iter().max_by(|a, b| {
692            a.score
693                .partial_cmp(&b.score)
694                .unwrap_or(std::cmp::Ordering::Equal)
695        })
696    }
697
698    /// Generate optimization report
699    #[must_use]
700    pub fn generate_report(&self) -> OptimizationReport {
701        /// OptimizationReport
702        OptimizationReport {
703            total_trials: self.history.trials.len(),
704            successful_trials: self
705                .history
706                .trials
707                .iter()
708                .filter(|t| t.status == TrialStatus::Success)
709                .count(),
710            best_score: self.history.best_score,
711            total_time: self.history.total_time,
712            average_trial_time: if self.history.trials.is_empty() {
713                None
714            } else {
715                Some(Duration::from_secs_f64(
716                    self.history
717                        .trials
718                        .iter()
719                        .map(|t| t.training_time.as_secs_f64())
720                        .sum::<f64>()
721                        / self.history.trials.len() as f64,
722                ))
723            },
724            trials_summary: self.history.trials.clone(),
725        }
726    }
727}
728
729/// Optimization report
730#[derive(Debug, Clone)]
731pub struct OptimizationReport {
732    /// Total number of trials
733    pub total_trials: usize,
734    /// Number of successful trials
735    pub successful_trials: usize,
736    /// Best score achieved
737    pub best_score: Option<f64>,
738    /// Total optimization time
739    pub total_time: Duration,
740    /// Average time per trial
741    pub average_trial_time: Option<Duration>,
742    /// Summary of all trials
743    pub trials_summary: Vec<TrialResult>,
744}
745
746impl Default for SearchSpace {
747    fn default() -> Self {
748        Self {
749            algorithms: vec![
750                /// AlgorithmChoice
751                AlgorithmChoice {
752                    name: "LinearRegression".to_string(),
753                    algorithm_type: AlgorithmType::Linear,
754                    hyperparameters: BTreeMap::new(),
755                    resource_requirements: ResourceRequirements {
756                        memory_mb: 100,
757                        cpu_cores: 1,
758                        time_complexity: TimeComplexity::Linear,
759                        requires_gpu: false,
760                    },
761                },
762                /// AlgorithmChoice
763                AlgorithmChoice {
764                    name: "RandomForest".to_string(),
765                    algorithm_type: AlgorithmType::Ensemble,
766                    hyperparameters: BTreeMap::from([
767                        (
768                            "n_estimators".to_string(),
769                            ParameterRange::Integer { min: 10, max: 1000 },
770                        ),
771                        (
772                            "max_depth".to_string(),
773                            ParameterRange::Integer { min: 1, max: 50 },
774                        ),
775                    ]),
776                    resource_requirements: ResourceRequirements {
777                        memory_mb: 500,
778                        cpu_cores: 4,
779                        time_complexity: TimeComplexity::Linearithmic,
780                        requires_gpu: false,
781                    },
782                },
783            ],
784            preprocessing: vec![
785                /// PreprocessingChoice
786                PreprocessingChoice {
787                    name: "StandardScaler".to_string(),
788                    parameters: BTreeMap::new(),
789                    optional: false,
790                },
791                /// PreprocessingChoice
792                PreprocessingChoice {
793                    name: "MinMaxScaler".to_string(),
794                    parameters: BTreeMap::new(),
795                    optional: true,
796                },
797            ],
798            feature_engineering: vec![FeatureEngineeringChoice {
799                name: "PolynomialFeatures".to_string(),
800                parameters: BTreeMap::from([(
801                    "degree".to_string(),
802                    ParameterRange::Integer { min: 2, max: 4 },
803                )]),
804                optional: true,
805                cost_estimate: 0.5,
806            }],
807            hyperparameters: BTreeMap::new(),
808            constraints: vec![
809                ArchitectureConstraint::MaxTrainingTime(Duration::from_secs(300)),
810                ArchitectureConstraint::MaxMemoryMB(2048),
811            ],
812        }
813    }
814}
815
816impl NeuralArchitectureSearch {
817    /// Create a new NAS instance
818    #[must_use]
819    pub fn new(search_space: NeuralSearchSpace, strategy: NASStrategy) -> Self {
820        Self {
821            search_space,
822            strategy,
823            evaluator: ArchitectureEvaluator {
824                strategy: EvaluationStrategy::EarlyStopping,
825                max_eval_time: Duration::from_secs(300),
826                early_stopping: Some(EarlyStoppingCriteria {
827                    metric: "val_accuracy".to_string(),
828                    patience: 10,
829                    min_delta: 0.001,
830                }),
831            },
832        }
833    }
834
835    /// Search for optimal neural architecture
836    pub fn search(&mut self, max_architectures: usize) -> SklResult<Vec<NeuralArchitecture>> {
837        let mut architectures = Vec::new();
838        let mut rng = StdRng::from_rng(&mut thread_rng());
839
840        for _ in 0..max_architectures {
841            let architecture = self.generate_architecture(&mut rng)?;
842            architectures.push(architecture);
843        }
844
845        // Sort by estimated performance (mock implementation)
846        architectures.sort_by(|a, b| {
847            b.estimated_performance
848                .partial_cmp(&a.estimated_performance)
849                .unwrap()
850        });
851
852        Ok(architectures)
853    }
854
855    /// Generate a neural architecture
856    fn generate_architecture(&self, rng: &mut StdRng) -> SklResult<NeuralArchitecture> {
857        let num_layers = match &self.search_space.num_layers {
858            ParameterRange::Integer { min, max } => rng.gen_range(*min..*max + 1) as usize,
859            _ => 3, // Default
860        };
861
862        let mut layers = Vec::new();
863        for i in 0..num_layers {
864            let layer_type = &self.search_space.layer_types
865                [rng.gen_range(0..self.search_space.layer_types.len())];
866
867            let activation = &self.search_space.activations
868                [rng.gen_range(0..self.search_space.activations.len())];
869
870            layers.push(NeuralLayer {
871                layer_type: layer_type.clone(),
872                units: Some(rng.gen_range(32..512)),
873                activation: activation.clone(),
874                layer_id: i,
875            });
876        }
877
878        Ok(NeuralArchitecture {
879            layers,
880            connection_pattern: ConnectionPattern::Sequential,
881            estimated_performance: rng.gen_range(0.5..1.0),
882            parameter_count: rng.gen_range(1000..1_000_000),
883            memory_usage_mb: rng.gen_range(10..500),
884        })
885    }
886}
887
888/// Neural architecture representation
889#[derive(Debug, Clone)]
890pub struct NeuralArchitecture {
891    /// Network layers
892    pub layers: Vec<NeuralLayer>,
893    /// Connection pattern
894    pub connection_pattern: ConnectionPattern,
895    /// Estimated performance
896    pub estimated_performance: f64,
897    /// Total parameter count
898    pub parameter_count: usize,
899    /// Memory usage estimate (MB)
900    pub memory_usage_mb: usize,
901}
902
903/// Neural layer representation
904#[derive(Debug, Clone)]
905pub struct NeuralLayer {
906    /// Layer type
907    pub layer_type: LayerType,
908    /// Number of units (for dense layers)
909    pub units: Option<usize>,
910    /// Activation function
911    pub activation: ActivationFunction,
912    /// Layer identifier
913    pub layer_id: usize,
914}
915
916#[allow(non_snake_case)]
917#[cfg(test)]
918mod tests {
919    use super::*;
920
921    #[test]
922    fn test_automl_config() {
923        let config = AutoMLConfig::default();
924        assert_eq!(config.max_trials, 100);
925        assert_eq!(config.cv_folds, 5);
926        assert_eq!(config.metric, OptimizationMetric::Accuracy);
927    }
928
929    #[test]
930    fn test_automl_optimizer() {
931        let config = AutoMLConfig::default();
932        let optimizer = AutoMLOptimizer::new(config).unwrap();
933        assert_eq!(optimizer.history.trials.len(), 0);
934        assert!(optimizer.history.best_score.is_none());
935    }
936
937    #[test]
938    fn test_search_space() {
939        let search_space = SearchSpace::default();
940        assert!(!search_space.algorithms.is_empty());
941        assert!(!search_space.preprocessing.is_empty());
942    }
943
944    #[test]
945    fn test_neural_architecture_search() {
946        let search_space = NeuralSearchSpace {
947            layer_types: vec![LayerType::Dense, LayerType::Dropout],
948            num_layers: ParameterRange::Integer { min: 2, max: 5 },
949            hidden_units: ParameterRange::Integer { min: 32, max: 512 },
950            activations: vec![ActivationFunction::ReLU, ActivationFunction::Tanh],
951            regularization: vec![],
952            connections: vec![ConnectionPattern::Sequential],
953        };
954
955        let mut nas = NeuralArchitectureSearch::new(search_space, NASStrategy::Random);
956        let architectures = nas.search(5).unwrap();
957
958        assert_eq!(architectures.len(), 5);
959        assert!(architectures[0].estimated_performance >= architectures[1].estimated_performance);
960    }
961
962    #[test]
963    fn test_parameter_ranges() {
964        let float_range = ParameterRange::Continuous {
965            min: 0.1,
966            max: 1.0,
967            log_scale: false,
968        };
969        let int_range = ParameterRange::Integer { min: 1, max: 100 };
970        let categorical = ParameterRange::Categorical(vec!["a".to_string(), "b".to_string()]);
971
972        match float_range {
973            ParameterRange::Continuous { min, max, .. } => {
974                assert_eq!(min, 0.1);
975                assert_eq!(max, 1.0);
976            }
977            _ => panic!("Wrong parameter range type"),
978        }
979    }
980
981    #[test]
982    fn test_optimization_history() {
983        let mut history = OptimizationHistory::default();
984        assert!(history.trials.is_empty());
985        assert!(history.best_score.is_none());
986
987        let trial = TrialResult {
988            trial_id: 0,
989            config: PipelineConfiguration::default(),
990            score: 0.85,
991            training_time: Duration::from_secs(30),
992            cv_scores: vec![0.8, 0.9, 0.85],
993            timestamp: Instant::now(),
994            status: TrialStatus::Success,
995            error: None,
996        };
997
998        history.trials.push(trial);
999        assert_eq!(history.trials.len(), 1);
1000    }
1001
1002    #[test]
1003    fn test_trial_status() {
1004        assert_eq!(TrialStatus::Success, TrialStatus::Success);
1005        assert_ne!(TrialStatus::Success, TrialStatus::Failed);
1006    }
1007
1008    #[test]
1009    fn test_algorithm_types() {
1010        let linear = AlgorithmType::Linear;
1011        let tree = AlgorithmType::Tree;
1012        let neural = AlgorithmType::NeuralNetwork;
1013
1014        assert_eq!(linear, AlgorithmType::Linear);
1015        assert_ne!(linear, tree);
1016        assert_ne!(tree, neural);
1017    }
1018
1019    #[test]
1020    fn test_resource_requirements() {
1021        let requirements = ResourceRequirements {
1022            memory_mb: 1024,
1023            cpu_cores: 4,
1024            time_complexity: TimeComplexity::Linear,
1025            requires_gpu: false,
1026        };
1027
1028        assert_eq!(requirements.memory_mb, 1024);
1029        assert_eq!(requirements.cpu_cores, 4);
1030        assert!(!requirements.requires_gpu);
1031    }
1032}
sklears_compose/automl.rs

sklears_compose/
automl.rs
