qualia_core_db/specialized_libs/machine_learning/
library.rs

1//! `MachineLearningLibrary` facade orchestration impl.
2
3use super::*;
4#[allow(unused_imports)]
5use serde::{Deserialize, Serialize};
6#[allow(unused_imports)]
7use std::collections::HashMap;
8
9impl MachineLearningLibrary {
10    /// Create new machine learning library
11    pub fn new() -> Self {
12        Self {
13            model_manager: ModelManager::new(),
14            inference_engine: InferenceEngine::new(),
15            training_engine: TrainingEngine::new(),
16            optimization_engine: MLOptimizationEngine::new(),
17            performance_monitor: MLPerformanceMonitor::new(),
18            request_count: 0,
19        }
20    }
21
22    /// Initialize the library
23    pub fn initialize(&mut self) -> Result<(), MLError> {
24        // Initialize model manager
25        self.model_manager.initialize()?;
26
27        // Initialize inference engine
28        self.inference_engine.initialize()?;
29
30        // Initialize training engine
31        self.training_engine.initialize()?;
32
33        // Initialize optimization engine
34        self.optimization_engine.initialize()?;
35
36        Ok(())
37    }
38
39    /// Load a model
40    pub fn load_model(
41        &mut self,
42        model_id: String,
43        model_path: &str,
44    ) -> Result<MLOperationResult<Model>, MLError> {
45        let start_time = std::time::Instant::now();
46
47        // Load model
48        let model = self
49            .model_manager
50            .load_model(model_id.clone(), model_path)?;
51
52        let execution_time = start_time.elapsed().as_millis() as u64;
53
54        Ok(MLOperationResult {
55            result: model,
56            execution_time,
57            memory_usage: 0,
58            accuracy: 0.0,
59            resource_utilization: ResourceUtilization::new(),
60        })
61    }
62
63    /// Run inference
64    pub fn run_inference(
65        &mut self,
66        model_id: &str,
67        input_data: &[u8],
68        parameters: InferenceParameters,
69    ) -> Result<MLOperationResult<InferenceResult>, MLError> {
70        let start_time = std::time::Instant::now();
71
72        // Create inference request
73        let request = InferenceRequest {
74            request_id: format!("req_{}", self.request_count),
75            model_id: model_id.to_string(),
76            input_data: input_data.to_vec(),
77            parameters,
78            priority: RequestPriority::Normal,
79            submitted_at: std::time::SystemTime::now()
80                .duration_since(std::time::UNIX_EPOCH)
81                .unwrap()
82                .as_secs(),
83            deadline: None,
84        };
85
86        // Load the model (from cache or storage) and execute the forward pass.
87        let model = self.model_manager.load_model(model_id.to_string(), "")?;
88        let result = self.inference_engine.execute_inference(&request, &model)?;
89        self.request_count += 1;
90
91        let execution_time = start_time.elapsed().as_millis().max(1) as u64;
92
93        let confidence = result.confidence;
94        Ok(MLOperationResult {
95            result,
96            execution_time,
97            memory_usage: 0,
98            accuracy: confidence,
99            resource_utilization: ResourceUtilization::new(),
100        })
101    }
102
103    /// Start training
104    pub fn start_training(
105        &mut self,
106        model_id: &str,
107        training_config: TrainingConfig,
108    ) -> Result<MLOperationResult<TrainingJob>, MLError> {
109        let start_time = std::time::Instant::now();
110
111        // Create training job
112        let job = TrainingJob {
113            job_id: format!(
114                "job_{}",
115                std::time::SystemTime::now()
116                    .duration_since(std::time::UNIX_EPOCH)
117                    .unwrap()
118                    .as_secs()
119            ),
120            model_id: model_id.to_string(),
121            training_config,
122            status: TrainingStatus::Pending,
123            progress: 0.0,
124            metrics: TrainingMetrics::new(),
125        };
126
127        // Start training
128        self.training_engine.start_training_job(&job)?;
129
130        let execution_time = start_time.elapsed().as_millis() as u64;
131
132        Ok(MLOperationResult {
133            result: job,
134            execution_time,
135            memory_usage: 0,
136            accuracy: 0.0,
137            resource_utilization: ResourceUtilization::new(),
138        })
139    }
140
141    /// Optimize model
142    pub fn optimize_model(
143        &mut self,
144        model_id: &str,
145        optimization_algorithm: MLOptimizationAlgorithm,
146    ) -> Result<MLOperationResult<Model>, MLError> {
147        let start_time = std::time::Instant::now();
148
149        // Optimize model
150        let optimized_model = self
151            .optimization_engine
152            .optimize_model(model_id, optimization_algorithm)?;
153
154        let execution_time = start_time.elapsed().as_millis() as u64;
155
156        Ok(MLOperationResult {
157            result: optimized_model,
158            execution_time,
159            memory_usage: 0,
160            accuracy: 0.0,
161            resource_utilization: ResourceUtilization::new(),
162        })
163    }
164
165    /// Get performance statistics
166    pub fn get_performance_stats(&self) -> MLPerformanceMetrics {
167        self.performance_monitor.get_metrics()
168    }
169
170    /// List all models
171    pub fn list_models(&self) -> Vec<String> {
172        self.model_manager.list_models()
173    }
174
175    /// Get model information
176    pub fn get_model_info(&self, model_id: &str) -> Option<ModelMetadata> {
177        self.model_manager.get_model_metadata(model_id)
178    }
179}
qualia_core_db/specialized_libs/machine_learning/library.rs

qualia_core_db/specialized_libs/machine_learning/
library.rs
