//! Model selection utilities
//!
//! This module provides tools for model selection, including grid search and
//! randomized search for hyperparameter optimization.

use crate::dataframe::DataFrame;
use crate::error::{Error, Result};
use crate::ml::models::{ModelMetrics, SupervisedModel};
use std::collections::HashMap;
use std::marker::PhantomData;

/// A grid of hyperparameters for model selection
#[derive(Debug, Clone)]
pub struct HyperparameterGrid {
    /// Map of parameter names to possible values
    pub params: HashMap<String, Vec<String>>,
}

impl HyperparameterGrid {
    /// Create a new empty hyperparameter grid
    pub fn new() -> Self {
        HyperparameterGrid {
            params: HashMap::new(),
        }
    }

    /// Add a parameter with its possible values
    pub fn add_param<T: ToString>(&mut self, name: &str, values: Vec<T>) -> &mut Self {
        let string_values = values.into_iter().map(|v| v.to_string()).collect();
        self.params.insert(name.to_string(), string_values);
        self
    }

    /// Get all parameter combinations
    ///
    /// # Returns
    /// * Vector of parameter dictionaries, where each dictionary is one combination
    pub fn parameter_combinations(&self) -> Vec<HashMap<String, String>> {
        // Placeholder implementation
        // In a real implementation, this would generate a Cartesian product
        // of all parameter combinations

        let mut combinations = Vec::new();

        // If no parameters, return an empty combination
        if self.params.is_empty() {
            combinations.push(HashMap::new());
            return combinations;
        }

        // For now, just return a single combination with the first value of each parameter
        let mut combination = HashMap::new();
        for (name, values) in &self.params {
            if let Some(value) = values.first() {
                combination.insert(name.clone(), value.clone());
            }
        }

        combinations.push(combination);
        combinations
    }
}

/// Grid search for hyperparameter optimization
///
/// Exhaustively searches all parameter combinations to find the best model.
pub struct GridSearchCV<T: SupervisedModel> {
    /// Base model to tune
    pub base_model: T,
    /// Parameter grid to search
    pub param_grid: HyperparameterGrid,
    /// Scoring metric to optimize
    pub scoring: String,
    /// Number of cross-validation folds
    pub cv: usize,
    /// Whether to use all CPU cores
    pub n_jobs: Option<usize>,
    /// Best parameters found
    pub best_params: Option<HashMap<String, String>>,
    /// Best score found
    pub best_score: Option<f64>,
    /// All results from the search
    pub cv_results: Option<DataFrame>,
}

impl<T: SupervisedModel + Clone> GridSearchCV<T> {
    /// Create a new GridSearchCV instance
    ///
    /// # Arguments
    /// * `base_model` - The model to tune
    /// * `param_grid` - Grid of hyperparameters to search
    /// * `scoring` - Metric to optimize
    /// * `cv` - Number of cross-validation folds
    pub fn new(base_model: T, param_grid: HyperparameterGrid, scoring: &str, cv: usize) -> Self {
        GridSearchCV {
            base_model,
            param_grid,
            scoring: scoring.to_string(),
            cv,
            n_jobs: None,
            best_params: None,
            best_score: None,
            cv_results: None,
        }
    }

    /// Set number of jobs (CPU cores) to use
    pub fn with_n_jobs(mut self, n_jobs: usize) -> Self {
        self.n_jobs = Some(n_jobs);
        self
    }

    /// Fit the model and find the best parameters
    ///
    /// # Arguments
    /// * `data` - Training data
    /// * `target` - Target column name
    pub fn fit(&mut self, data: &DataFrame, target: &str) -> Result<()> {
        // Validate input data
        if !data.has_column(target) {
            return Err(Error::InvalidValue(format!(
                "Target column '{}' not found",
                target
            )));
        }

        if self.cv < 2 {
            return Err(Error::InvalidInput(
                "Number of CV folds must be at least 2".into(),
            ));
        }

        // Get all parameter combinations
        let param_combinations = self.param_grid.parameter_combinations();

        if param_combinations.is_empty() {
            return Err(Error::InvalidInput(
                "No parameter combinations to search".into(),
            ));
        }

        // Placeholder for grid search implementation
        // In a real implementation, this would:
        // 1. For each parameter combination:
        //    a. Create a model with those parameters
        //    b. Perform cross-validation
        //    c. Record scores
        // 2. Find the best parameter combination

        // For now, just use the first combination as the "best"
        let best_params = param_combinations[0].clone();
        let best_score = 0.9; // Placeholder score

        self.best_params = Some(best_params);
        self.best_score = Some(best_score);

        // Create a placeholder for CV results
        let cv_results = DataFrame::new();
        self.cv_results = Some(cv_results);

        Ok(())
    }

    /// Get the best estimator (model with optimal parameters)
    pub fn best_estimator(&self) -> Result<T> {
        if self.best_params.is_none() {
            return Err(Error::InvalidValue("Grid search not fitted".into()));
        }

        // Placeholder - would create a model with the best parameters
        Ok(self.base_model.clone())
    }
}

/// Randomized search for hyperparameter optimization
///
/// Samples random parameter combinations to find a good model.
pub struct RandomizedSearchCV<T: SupervisedModel> {
    /// Base model to tune
    pub base_model: T,
    /// Parameter grid to sample from
    pub param_grid: HyperparameterGrid,
    /// Number of parameter combinations to try
    pub n_iter: usize,
    /// Scoring metric to optimize
    pub scoring: String,
    /// Number of cross-validation folds
    pub cv: usize,
    /// Random seed for reproducibility
    pub random_seed: Option<u64>,
    /// Whether to use all CPU cores
    pub n_jobs: Option<usize>,
    /// Best parameters found
    pub best_params: Option<HashMap<String, String>>,
    /// Best score found
    pub best_score: Option<f64>,
    /// All results from the search
    pub cv_results: Option<DataFrame>,
}

impl<T: SupervisedModel + Clone> RandomizedSearchCV<T> {
    /// Create a new RandomizedSearchCV instance
    ///
    /// # Arguments
    /// * `base_model` - The model to tune
    /// * `param_grid` - Grid of hyperparameters to sample from
    /// * `n_iter` - Number of parameter combinations to try
    /// * `scoring` - Metric to optimize
    /// * `cv` - Number of cross-validation folds
    pub fn new(
        base_model: T,
        param_grid: HyperparameterGrid,
        n_iter: usize,
        scoring: &str,
        cv: usize,
    ) -> Self {
        RandomizedSearchCV {
            base_model,
            param_grid,
            n_iter,
            scoring: scoring.to_string(),
            cv,
            random_seed: None,
            n_jobs: None,
            best_params: None,
            best_score: None,
            cv_results: None,
        }
    }

    /// Set random seed for reproducibility
    pub fn with_random_seed(mut self, seed: u64) -> Self {
        self.random_seed = Some(seed);
        self
    }

    /// Set number of jobs (CPU cores) to use
    pub fn with_n_jobs(mut self, n_jobs: usize) -> Self {
        self.n_jobs = Some(n_jobs);
        self
    }

    /// Fit the model and find the best parameters
    ///
    /// # Arguments
    /// * `data` - Training data
    /// * `target` - Target column name
    pub fn fit(&mut self, data: &DataFrame, target: &str) -> Result<()> {
        // Implementation would be similar to GridSearchCV::fit,
        // but with random sampling of parameter combinations

        // Placeholder implementation
        let best_params = HashMap::new();
        let best_score = 0.9; // Placeholder score

        self.best_params = Some(best_params);
        self.best_score = Some(best_score);

        // Create a placeholder for CV results
        let cv_results = DataFrame::new();
        self.cv_results = Some(cv_results);

        Ok(())
    }

    /// Get the best estimator (model with optimal parameters)
    pub fn best_estimator(&self) -> Result<T> {
        if self.best_params.is_none() {
            return Err(Error::InvalidValue("Randomized search not fitted".into()));
        }

        // Placeholder - would create a model with the best parameters
        Ok(self.base_model.clone())
    }
}
