scirs2_metrics/streaming/advanced/
neural.rs

1//! Neural components for adaptive streaming metrics
2//!
3//! This module provides neural network-based components for parameter optimization,
4//! feature extraction, and adaptive learning in streaming environments.
5
6#![allow(clippy::too_many_arguments)]
7#![allow(dead_code)]
8
9use crate::error::Result;
10use scirs2_core::ndarray::{Array1, Array2};
11use scirs2_core::numeric::Float;
12use scirs2_core::random::Rng;
13use serde::{Deserialize, Serialize};
14use std::time::Duration;
15
16/// Neural network configuration
17#[derive(Debug, Clone, Serialize, Deserialize)]
18pub struct NetworkConfig {
19    /// Hidden layer sizes for parameter optimizer
20    pub optimizer_hidden_layers: Vec<usize>,
21    /// Hidden layer sizes for performance predictor
22    pub predictor_hidden_layers: Vec<usize>,
23    /// Activation function type
24    pub activation: ActivationFunction,
25    /// Dropout rate for regularization
26    pub dropout_rate: f64,
27    /// Batch normalization enabled
28    pub batch_norm: bool,
29    /// Learning rate for neural networks
30    pub learning_rate: f64,
31    /// Weight decay for regularization
32    pub weight_decay: f64,
33}
34
35impl Default for NetworkConfig {
36    fn default() -> Self {
37        Self {
38            optimizer_hidden_layers: vec![128, 64, 32],
39            predictor_hidden_layers: vec![64, 32, 16],
40            activation: ActivationFunction::ReLU,
41            dropout_rate: 0.1,
42            batch_norm: true,
43            learning_rate: 0.001,
44            weight_decay: 0.0001,
45        }
46    }
47}
48
49/// Activation functions for neural networks
50#[derive(Debug, Clone, Serialize, Deserialize)]
51pub enum ActivationFunction {
52    ReLU,
53    LeakyReLU { alpha: f64 },
54    ELU { alpha: f64 },
55    Swish,
56    GELU,
57    Tanh,
58    Sigmoid,
59}
60
61/// Feature extraction configuration
62#[derive(Debug, Clone, Serialize, Deserialize)]
63pub struct FeatureConfig {
64    /// Feature extraction method
65    pub extraction_method: FeatureExtractionMethod,
66    /// Number of features to extract
67    pub num_features: usize,
68    /// Time window for feature extraction
69    pub time_window: Duration,
70    /// Enable automatic feature selection
71    pub auto_feature_selection: bool,
72    /// Feature normalization method
73    pub normalization: FeatureNormalization,
74}
75
76/// Feature extraction methods
77#[derive(Debug, Clone, Serialize, Deserialize)]
78pub enum FeatureExtractionMethod {
79    /// Statistical features (mean, std, skewness, etc.)
80    Statistical,
81    /// Time-series features (trends, seasonality, etc.)
82    TimeSeries,
83    /// Frequency domain features (FFT-based)
84    FrequencyDomain,
85    /// Wavelet-based features
86    Wavelet { wavelet_type: String },
87    /// Neural autoencoder features
88    NeuralAutoencoder { encoding_dim: usize },
89    /// Ensemble of multiple methods
90    Ensemble {
91        methods: Vec<FeatureExtractionMethod>,
92    },
93}
94
95/// Feature normalization methods
96#[derive(Debug, Clone, Serialize, Deserialize)]
97pub enum FeatureNormalization {
98    None,
99    StandardScore,
100    MinMax,
101    Robust,
102    Quantile,
103}
104
105/// Multi-armed bandit for parameter exploration
106#[derive(Debug)]
107pub struct MultiArmedBandit<F: Float + std::fmt::Debug> {
108    /// Available actions (parameter configurations)
109    actions: Vec<ParameterConfiguration<F>>,
110    /// Bandit algorithm
111    algorithm: BanditAlgorithm<F>,
112    /// Action rewards history
113    rewards: Vec<Vec<F>>,
114    /// Action selection counts
115    counts: Vec<usize>,
116    /// Current exploration rate
117    exploration_rate: f64,
118    /// Regret tracking
119    regret_tracker: RegretTracker<F>,
120}
121
122/// Parameter configuration for bandit actions
123#[derive(Debug, Clone)]
124pub struct ParameterConfiguration<F: Float + std::fmt::Debug> {
125    /// Parameter name
126    pub name: String,
127    /// Parameter value
128    pub value: F,
129    /// Parameter bounds
130    pub bounds: (F, F),
131    /// Parameter type
132    pub param_type: ParameterType,
133}
134
135/// Parameter types for optimization
136#[derive(Debug, Clone)]
137pub enum ParameterType {
138    Continuous,
139    Discrete,
140    Integer,
141    Boolean,
142    Categorical(Vec<String>),
143}
144
145/// Bandit algorithms
146#[derive(Debug)]
147pub enum BanditAlgorithm<F: Float> {
148    /// Epsilon-greedy exploration
149    EpsilonGreedy { epsilon: f64 },
150    /// Upper Confidence Bound
151    UCB { confidence_level: f64 },
152    /// Thompson Sampling
153    ThompsonSampling,
154    /// Exponential-weight algorithm
155    Exp3 { learning_rate: F },
156}
157
158/// Regret tracking for bandit performance
159#[derive(Debug, Clone)]
160pub struct RegretTracker<F: Float + std::fmt::Debug> {
161    /// Cumulative regret
162    cumulative_regret: F,
163    /// Instantaneous regret history
164    regret_history: Vec<F>,
165    /// Best possible reward (oracle)
166    best_reward: F,
167    /// Total rounds played
168    total_rounds: usize,
169}
170
171impl<F: Float + std::fmt::Debug> Default for RegretTracker<F> {
172    fn default() -> Self {
173        Self {
174            cumulative_regret: F::zero(),
175            regret_history: Vec::new(),
176            best_reward: F::zero(),
177            total_rounds: 0,
178        }
179    }
180}
181
182impl<F: Float + std::fmt::Debug> RegretTracker<F> {
183    pub fn update(&mut self, reward: F, optimal_reward: F) {
184        let regret = optimal_reward - reward;
185        self.cumulative_regret = self.cumulative_regret + regret;
186        self.regret_history.push(regret);
187        self.best_reward = F::max(self.best_reward, optimal_reward);
188        self.total_rounds += 1;
189    }
190
191    pub fn get_average_regret(&self) -> F {
192        if self.total_rounds > 0 {
193            self.cumulative_regret / F::from(self.total_rounds).expect("Failed to convert to float")
194        } else {
195            F::zero()
196        }
197    }
198}
199
200impl<F: Float + std::fmt::Debug + std::ops::AddAssign + std::iter::Sum> MultiArmedBandit<F> {
201    pub fn new(actions: Vec<ParameterConfiguration<F>>, algorithm: BanditAlgorithm<F>) -> Self {
202        let num_actions = actions.len();
203        Self {
204            actions,
205            algorithm,
206            rewards: vec![Vec::new(); num_actions],
207            counts: vec![0; num_actions],
208            exploration_rate: 0.1,
209            regret_tracker: RegretTracker::default(),
210        }
211    }
212
213    pub fn select_action(&mut self) -> Result<usize> {
214        match &self.algorithm {
215            BanditAlgorithm::EpsilonGreedy { epsilon } => {
216                let mut rng = scirs2_core::random::thread_rng();
217                if rng.random::<f64>() < *epsilon {
218                    // Explore: random action
219                    Ok(rng.random_range(0..self.actions.len()))
220                } else {
221                    // Exploit: best action so far
222                    self.get_best_action()
223                }
224            }
225            BanditAlgorithm::UCB { confidence_level } => self.get_ucb_action(*confidence_level),
226            _ => {
227                // Default to epsilon-greedy
228                self.get_best_action()
229            }
230        }
231    }
232
233    fn get_best_action(&self) -> Result<usize> {
234        let mut best_action = 0;
235        let mut best_average = F::neg_infinity();
236
237        for (i, rewards) in self.rewards.iter().enumerate() {
238            if !rewards.is_empty() {
239                let average = rewards.iter().cloned().sum::<F>()
240                    / F::from(rewards.len()).expect("Operation failed");
241                if average > best_average {
242                    best_average = average;
243                    best_action = i;
244                }
245            }
246        }
247
248        Ok(best_action)
249    }
250
251    fn get_ucb_action(&self, confidence_level: f64) -> Result<usize> {
252        let total_counts: usize = self.counts.iter().sum();
253        let mut best_action = 0;
254        let mut best_ucb = F::neg_infinity();
255
256        for (i, rewards) in self.rewards.iter().enumerate() {
257            let ucb = if rewards.is_empty() {
258                F::infinity() // Unplayed actions have infinite UCB
259            } else {
260                let average = rewards.iter().cloned().sum::<F>()
261                    / F::from(rewards.len()).expect("Operation failed");
262                let confidence_bonus =
263                    F::from(confidence_level * (total_counts as f64 / self.counts[i] as f64).ln())
264                        .expect("Operation failed")
265                        .sqrt();
266                average + confidence_bonus
267            };
268
269            if ucb > best_ucb {
270                best_ucb = ucb;
271                best_action = i;
272            }
273        }
274
275        Ok(best_action)
276    }
277
278    pub fn update_reward(&mut self, action: usize, reward: F) -> Result<()> {
279        if action >= self.actions.len() {
280            return Err(crate::error::MetricsError::InvalidInput(
281                "Invalid action index".to_string(),
282            ));
283        }
284
285        self.rewards[action].push(reward);
286        self.counts[action] += 1;
287
288        // Update regret tracking
289        let optimal_reward = self
290            .rewards
291            .iter()
292            .filter_map(|r| {
293                if r.is_empty() {
294                    None
295                } else {
296                    Some(r.iter().cloned().sum::<F>() / F::from(r.len()).expect("Operation failed"))
297                }
298            })
299            .fold(F::neg_infinity(), F::max);
300
301        self.regret_tracker.update(reward, optimal_reward);
302
303        Ok(())
304    }
305
306    pub fn get_action_statistics(&self) -> Vec<(usize, F, F)> {
307        self.rewards
308            .iter()
309            .enumerate()
310            .map(|(i, rewards)| {
311                if rewards.is_empty() {
312                    (0, F::zero(), F::zero())
313                } else {
314                    let count = rewards.len();
315                    let mean = rewards.iter().cloned().sum::<F>()
316                        / F::from(count).expect("Failed to convert to float");
317                    let variance = rewards.iter().map(|&x| (x - mean) * (x - mean)).sum::<F>()
318                        / F::from(count.max(1)).expect("Operation failed");
319                    (count, mean, variance)
320                }
321            })
322            .collect()
323    }
324}
325
326/// Neural feature extractor
327#[derive(Debug)]
328pub struct NeuralFeatureExtractor<F: Float + std::fmt::Debug> {
329    /// Input dimension
330    input_dim: usize,
331    /// Output dimension
332    output_dim: usize,
333    /// Autoencoder network
334    autoencoder: AutoencoderNetwork<F>,
335    /// Feature selection network
336    feature_selector: FeatureSelectionNetwork<F>,
337    /// Attention mechanism for important features
338    attention: AttentionMechanism<F>,
339}
340
341/// Autoencoder network for feature extraction
342#[derive(Debug, Clone)]
343pub struct AutoencoderNetwork<F: Float + std::fmt::Debug> {
344    /// Encoder layers
345    encoder_layers: Vec<usize>,
346    /// Decoder layers
347    decoder_layers: Vec<usize>,
348    /// Bottleneck dimension
349    encoding_dim: usize,
350    /// Network weights (simplified)
351    weights: Vec<Array2<F>>,
352}
353
354/// Feature selection network
355#[derive(Debug, Clone)]
356pub struct FeatureSelectionNetwork<F: Float + std::fmt::Debug> {
357    /// Selection threshold
358    threshold: F,
359    /// Importance scores
360    importance_scores: Array1<F>,
361    /// Selected feature indices
362    selected_features: Vec<usize>,
363}
364
365/// Attention mechanism for feature importance
366#[derive(Debug, Clone)]
367pub struct AttentionMechanism<F: Float + std::fmt::Debug> {
368    /// Attention type
369    attention_type: AttentionType,
370    /// Attention weights
371    attention_weights: Array1<F>,
372    /// Key dimension
373    key_dim: usize,
374    /// Value dimension
375    value_dim: usize,
376}
377
378/// Types of attention mechanisms
379#[derive(Debug, Clone)]
380pub enum AttentionType {
381    SelfAttention,
382    MultiHead { num_heads: usize },
383    CrossAttention,
384}
385
386/// Adaptive learning rate scheduler
387#[derive(Debug)]
388pub struct AdaptiveLearningScheduler<F: Float + std::fmt::Debug> {
389    /// Initial learning rate
390    initial_lr: F,
391    /// Current learning rate
392    current_lr: F,
393    /// Scheduler type
394    scheduler_type: SchedulerType<F>,
395    /// Performance history for adaptation
396    performance_history: Vec<F>,
397    /// Adaptation parameters
398    adaptation_params: SchedulerAdaptationParams<F>,
399}
400
401impl<F: Float + std::fmt::Debug> AdaptiveLearningScheduler<F> {
402    pub fn new(initial_lr: F, scheduler_type: SchedulerType<F>) -> Self {
403        Self {
404            initial_lr,
405            current_lr: initial_lr,
406            scheduler_type,
407            performance_history: Vec::new(),
408            adaptation_params: SchedulerAdaptationParams::default(),
409        }
410    }
411
412    pub fn update(&mut self, performance: F) -> F {
413        self.performance_history.push(performance);
414
415        // Keep only recent history for memory efficiency
416        if self.performance_history.len() > 100 {
417            self.performance_history.remove(0);
418        }
419
420        match &self.scheduler_type {
421            SchedulerType::StepLR { step_size, gamma } => {
422                if self.performance_history.len().is_multiple_of(*step_size) {
423                    self.current_lr = self.current_lr * *gamma;
424                }
425            }
426            SchedulerType::ReduceLROnPlateau {
427                factor, patience, ..
428            } => {
429                if self.performance_history.len() > *patience {
430                    let recent_performance =
431                        &self.performance_history[self.performance_history.len() - patience..];
432                    let is_plateau = recent_performance.windows(2).all(|w| {
433                        (w[1] - w[0]).abs()
434                            < F::from(0.001).expect("Failed to convert constant to float")
435                    });
436                    if is_plateau {
437                        self.current_lr = self.current_lr * *factor;
438                    }
439                }
440            }
441            _ => {}
442        }
443
444        self.current_lr
445    }
446
447    pub fn get_learning_rate(&self) -> F {
448        self.current_lr
449    }
450
451    pub fn reset(&mut self) {
452        self.current_lr = self.initial_lr;
453        self.performance_history.clear();
454    }
455}
456
457/// Learning rate scheduler types
458#[derive(Debug, Clone)]
459pub enum SchedulerType<F: Float> {
460    /// Constant learning rate
461    Constant,
462    /// Step-wise decay
463    StepLR { step_size: usize, gamma: F },
464    /// Exponential decay
465    ExponentialLR { gamma: F },
466    /// Reduce on plateau
467    ReduceLROnPlateau {
468        factor: F,
469        patience: usize,
470        threshold: F,
471    },
472    /// Cosine annealing
473    CosineAnnealingLR { t_max: usize },
474}
475
476/// Scheduler adaptation parameters
477#[derive(Debug, Clone)]
478pub struct SchedulerAdaptationParams<F: Float + std::fmt::Debug> {
479    /// Minimum learning rate
480    pub min_lr: F,
481    /// Maximum learning rate
482    pub max_lr: F,
483    /// Adaptation sensitivity
484    pub sensitivity: F,
485}
486
487impl<F: Float + std::fmt::Debug> Default for SchedulerAdaptationParams<F> {
488    fn default() -> Self {
489        Self {
490            min_lr: F::from(1e-6).expect("Failed to convert constant to float"),
491            max_lr: F::from(1e-1).expect("Failed to convert constant to float"),
492            sensitivity: F::from(0.1).expect("Failed to convert constant to float"),
493        }
494    }
495}
496
497// Simplified implementations for the other neural components
498impl<F: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand>
499    AutoencoderNetwork<F>
500{
501    pub fn new(input_dim: usize, encoding_dim: usize) -> Self {
502        Self {
503            encoder_layers: vec![input_dim, encoding_dim * 2, encoding_dim],
504            decoder_layers: vec![encoding_dim, encoding_dim * 2, input_dim],
505            encoding_dim,
506            weights: Vec::new(), // Would be properly initialized in real implementation
507        }
508    }
509
510    pub fn encode(&self, _input: &Array1<F>) -> Result<Array1<F>> {
511        // Simplified encoding - would implement actual forward pass
512        Ok(Array1::zeros(self.encoding_dim))
513    }
514}
515
516impl<F: Float + std::fmt::Debug> AttentionMechanism<F> {
517    pub fn new(key_dim: usize, value_dim: usize, attention_type: AttentionType) -> Self {
518        Self {
519            attention_type,
520            attention_weights: Array1::zeros(key_dim),
521            key_dim,
522            value_dim,
523        }
524    }
525}
526
527impl<F: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand>
528    FeatureSelectionNetwork<F>
529{
530    pub fn new(num_features: usize, threshold: F) -> Self {
531        Self {
532            threshold,
533            importance_scores: Array1::zeros(num_features),
534            selected_features: Vec::new(),
535        }
536    }
537
538    pub fn select_features(&mut self, scores: &Array1<F>) -> Vec<usize> {
539        self.importance_scores = scores.clone();
540        self.selected_features = scores
541            .iter()
542            .enumerate()
543            .filter(|(_, &score)| score > self.threshold)
544            .map(|(i, _)| i)
545            .collect();
546        self.selected_features.clone()
547    }
548}
549
550impl<F: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand>
551    NeuralFeatureExtractor<F>
552{
553    pub fn new(input_dim: usize, output_dim: usize) -> Self {
554        Self {
555            input_dim,
556            output_dim,
557            autoencoder: AutoencoderNetwork::new(input_dim, output_dim),
558            feature_selector: FeatureSelectionNetwork::new(
559                input_dim,
560                F::from(0.1).expect("Failed to convert constant to float"),
561            ),
562            attention: AttentionMechanism::new(input_dim, output_dim, AttentionType::SelfAttention),
563        }
564    }
565
566    pub fn extract_features(&self, input: &Array1<F>) -> Result<Array1<F>> {
567        // Simplified feature extraction
568        self.autoencoder.encode(input)
569    }
570}
scirs2_metrics/streaming/advanced/neural.rs

scirs2_metrics/streaming/advanced/
neural.rs
