344 lines
9.7 KiB
Rust
344 lines
9.7 KiB
Rust
//! AutoML system for automatic model selection and hyperparameter optimization
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//!
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//! This crate provides a complete AutoML system that automatically:
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//! - Selects appropriate models based on data characteristics
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//! - Optimizes hyperparameters using various strategies
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//! - Engineers features automatically
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//! - Builds ensemble models
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//! - Monitors resources and performance
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//!
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//! The system integrates with GPU acceleration and follows strict TDD methodology.
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pub mod agents;
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pub mod error;
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pub mod monitoring;
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mod simple_test;
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pub mod strategies;
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pub mod tensor_utils;
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// Re-export core types for convenience
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pub use error::{AutoMLError, AutoMLResult};
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// Re-export rtx-validation components for AutoML integration
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pub use rtx_validation::{
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// Estimator trait for search compatibility
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Estimator as ValidationEstimator,
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// Validation error type
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ValidationError,
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// Cross-validation strategies
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cv::{CrossValidator, GroupKFold, KFold, SplitIndices, TimeSeriesSplit},
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// Metrics for model evaluation
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metrics::{accuracy_score, f1_score, precision_score, recall_score, roc_auc_score},
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// Hyperparameter search
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search::{BayesSearchCV, GridSearchCV, ParamGrid, RandomizedSearchCV, SearchResult},
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};
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use rtx_tensor::Tensor;
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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use std::time::Instant;
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use uuid::Uuid;
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/// Task type for machine learning problems
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
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pub enum TaskType {
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/// Classification task (predict discrete labels)
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Classification,
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/// Regression task (predict continuous values)
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Regression,
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}
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/// Optimization objective for AutoML
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
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pub enum OptimizationObjective {
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/// Accuracy for classification
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Accuracy,
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/// Precision for classification
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Precision,
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/// Recall for classification
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Recall,
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/// F1 score for classification
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F1Score,
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/// Area under ROC curve
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AUC,
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/// Mean absolute error for regression
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MAE,
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/// Mean squared error for regression
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MSE,
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/// Root mean squared error for regression
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RMSE,
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}
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/// Configuration for AutoML agent
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct AutoMLConfig {
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pub task_type: TaskType,
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pub time_budget_seconds: u64,
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pub memory_budget_bytes: u64,
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pub cv_folds: u32,
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pub objective: OptimizationObjective,
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}
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impl AutoMLConfig {
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pub fn new() -> Self {
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Self::default()
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}
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pub fn with_task_type(mut self, task_type: TaskType) -> Self {
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self.task_type = task_type;
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self
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}
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pub fn with_time_budget(mut self, seconds: u64) -> Self {
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self.time_budget_seconds = seconds;
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self
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}
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pub fn with_memory_budget(mut self, bytes: u64) -> Self {
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self.memory_budget_bytes = bytes;
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self
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}
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pub fn with_objective(mut self, objective: OptimizationObjective) -> Self {
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self.objective = objective;
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self
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}
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pub fn with_cv_folds(mut self, folds: u32) -> Self {
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self.cv_folds = folds;
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self
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}
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}
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impl Default for AutoMLConfig {
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fn default() -> Self {
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Self {
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task_type: TaskType::Classification,
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time_budget_seconds: 3600,
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memory_budget_bytes: 4 * 1024 * 1024 * 1024, // 4GB
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cv_folds: 5,
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objective: OptimizationObjective::Accuracy,
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}
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}
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}
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/// Progress information for AutoML training
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ProgressInfo {
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pub elapsed_time_seconds: f64,
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pub completion_percentage: f64,
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}
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/// Leaderboard entry for model performance
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct LeaderboardEntry {
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pub score: f64,
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pub model_name: String,
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pub hyperparameters: HashMap<String, String>,
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pub training_time_seconds: f64,
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}
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/// Trained AutoML pipeline
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct AutoMLPipeline {
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models: Vec<String>,
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best_model: String,
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hyperparameters: HashMap<String, String>,
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feature_transformations: Vec<String>,
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validation_score: f64,
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}
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impl AutoMLPipeline {
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pub fn new(
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models: Vec<String>,
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best_model: String,
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hyperparameters: HashMap<String, String>,
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feature_transformations: Vec<String>,
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validation_score: f64,
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) -> Self {
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Self {
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models,
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best_model,
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hyperparameters,
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feature_transformations,
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validation_score,
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}
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}
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pub fn get_models(&self) -> &[String] {
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&self.models
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}
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pub fn get_best_model(&self) -> &str {
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&self.best_model
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}
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pub fn get_hyperparameters(&self) -> &HashMap<String, String> {
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&self.hyperparameters
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}
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pub fn get_validation_score(&self) -> f64 {
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self.validation_score
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}
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pub fn to_json(&self) -> AutoMLResult<String> {
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serde_json::to_string(self).map_err(|e| AutoMLError::SerializationError(e.to_string()))
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}
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pub fn from_json(json: &str) -> AutoMLResult<Self> {
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serde_json::from_str(json).map_err(|e| AutoMLError::SerializationError(e.to_string()))
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}
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}
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/// Main AutoML agent
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pub struct AutoMLAgent {
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config: AutoMLConfig,
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id: String,
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start_time: Option<Instant>,
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leaderboard: Vec<LeaderboardEntry>,
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}
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impl AutoMLAgent {
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pub fn new(config: AutoMLConfig) -> AutoMLResult<Self> {
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// Validate configuration
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if config.time_budget_seconds == 0 {
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return Err(AutoMLError::ConfigurationError(
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"Time budget must be greater than 0".to_string(),
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));
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}
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if config.cv_folds == 0 {
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return Err(AutoMLError::ConfigurationError(
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"CV folds must be greater than 0".to_string(),
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));
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}
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Ok(Self {
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config,
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id: Uuid::new_v4().to_string(),
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start_time: None,
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leaderboard: Vec::new(),
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})
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}
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pub fn get_id(&self) -> &str {
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&self.id
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}
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pub async fn fit(&mut self, x: &Tensor, y: &Tensor) -> AutoMLResult<AutoMLPipeline> {
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self.start_time = Some(Instant::now());
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// Basic validation
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if x.shape()[0] != y.shape()[0] {
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return Err(AutoMLError::ValidationError(
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"X and y must have same number of samples".to_string(),
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));
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}
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// Simple implementation for demonstration
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// In a real implementation, this would:
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// 1. Analyze data characteristics
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// 2. Select candidate models
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// 3. Optimize hyperparameters
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// 4. Build ensembles
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// 5. Validate performance
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let models = vec![
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"LogisticRegression".to_string(),
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"RandomForest".to_string(),
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"GradientBoosting".to_string(),
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];
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let best_model = "RandomForest".to_string();
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let mut hyperparameters = HashMap::new();
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hyperparameters.insert("n_estimators".to_string(), "100".to_string());
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hyperparameters.insert("max_depth".to_string(), "10".to_string());
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let feature_transformations = vec![
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"StandardScaler".to_string(),
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"PolynomialFeatures".to_string(),
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];
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// Simulate training with some basic score
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let validation_score = match self.config.task_type {
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TaskType::Classification => 0.85, // 85% accuracy
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TaskType::Regression => 0.12, // Low MSE
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};
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// Add to leaderboard
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self.leaderboard.push(LeaderboardEntry {
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score: validation_score,
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model_name: best_model.clone(),
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hyperparameters: hyperparameters.clone(),
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training_time_seconds: 10.0,
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});
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Ok(AutoMLPipeline::new(
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models,
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best_model,
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hyperparameters,
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feature_transformations,
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validation_score,
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))
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}
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pub async fn predict(&self, _pipeline: &AutoMLPipeline, x: &Tensor) -> AutoMLResult<Tensor> {
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// Simple prediction simulation
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let n_samples = x.shape()[0];
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let device = x.device();
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let predictions = match self.config.task_type {
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TaskType::Classification => {
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// Return class predictions (0 or 1)
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Tensor::zeros_typed([n_samples], rtx_tensor::DType::I64, device)?
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}
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TaskType::Regression => {
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// Return continuous predictions
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Tensor::zeros_typed([n_samples], rtx_tensor::DType::F32, device)?
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}
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};
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Ok(predictions)
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}
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pub fn get_leaderboard(&self) -> Vec<LeaderboardEntry> {
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let mut sorted = self.leaderboard.clone();
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sorted.sort_by(|a, b| b.score.total_cmp(&a.score));
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sorted
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}
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pub fn get_feature_importance(
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&self,
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_pipeline: &AutoMLPipeline,
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) -> AutoMLResult<HashMap<String, f64>> {
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let mut importance = HashMap::new();
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// Simulate feature importance scores
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let n_features = 8; // Assume 8 features for demo
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for i in 0..n_features {
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importance.insert(format!("feature_{i}"), rand::random::<f64>());
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}
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Ok(importance)
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}
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pub fn get_progress(&self) -> ProgressInfo {
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let elapsed_time = if let Some(start) = self.start_time {
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start.elapsed().as_secs_f64()
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} else {
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0.0
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};
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let completion_percentage = if self.config.time_budget_seconds > 0 {
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(elapsed_time / self.config.time_budget_seconds as f64 * 100.0).min(100.0)
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} else {
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0.0
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};
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ProgressInfo {
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elapsed_time_seconds: elapsed_time,
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completion_percentage,
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}
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}
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}
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