sklears-core 0.1.1

//! Core macro implementation entry points for DSL functionality
//!
//! This module provides the main entry points for the Domain-Specific Language (DSL)
//! macros used in the sklears machine learning framework. It handles macro dispatch
//! and coordinates with parsers and code generators to produce the final generated code.

use crate::dsl_impl::{
    code_generators::{
        generate_feature_engineering_code, generate_hyperparameter_code, generate_pipeline_code,
    },
    parsers::{parse_feature_engineering, parse_hyperparameter_config, parse_ml_pipeline},
};
use proc_macro2::TokenStream;

/// Implementation of the ml_pipeline! macro
///
/// This macro creates efficient ML pipeline code from a high-level configuration.
/// It parses the pipeline configuration DSL and generates optimized Rust code
/// for data processing, model training, and inference.
///
/// # Arguments
/// * `input` - TokenStream containing the pipeline configuration DSL
///
/// # Returns
/// Generated TokenStream with the pipeline implementation
///
/// # Examples
/// ```ignore
/// ml_pipeline! {
///     name: "text_classification_pipeline",
///     input: DataFrame,
///     output: Vec<String>,
///     stages: [
///         preprocess {
///             tokenize,
///             normalize_text,
///             remove_stopwords
///         },
///         model {
///             algorithm: RandomForest,
///             hyperparameters: {
///                 n_trees: 100,
///                 max_depth: 10
///             }
///         },
///         postprocess {
///             confidence_threshold: 0.8,
///             format_output
///         }
///     ]
/// }
/// ```
pub fn ml_pipeline_impl(input: TokenStream) -> TokenStream {
    match parse_ml_pipeline(input) {
        Ok(pipeline) => generate_pipeline_code(pipeline),
        Err(err) => err.to_compile_error(),
    }
}

/// Implementation of the feature_engineering! macro
///
/// This macro generates feature engineering transformations from declarative
/// expressions. It supports complex feature derivations, statistical operations,
/// and data transformations with automatic optimization.
///
/// # Arguments
/// * `input` - TokenStream containing the feature engineering configuration DSL
///
/// # Returns
/// Generated TokenStream with the feature engineering implementation
///
/// # Examples
/// ```ignore
/// feature_engineering! {
///     dataset: my_dataframe,
///     features: [
///         price_per_sqft = price / square_feet,
///         log_income = log(household_income),
///         age_group = categorize(age, [0, 18, 35, 50, 65, 100]),
///         distance_to_center = sqrt((x - center_x)^2 + (y - center_y)^2)
///     ],
///     selection: [
///         "correlation > 0.1",
///         "variance > 0.01",
///         "mutual_info > 0.05"
///     ],
///     validation: [
///         price_per_sqft: "not_null && > 0",
///         log_income: "finite && > 0",
///         age_group: "in_range(0, 4)"
///     ]
/// }
/// ```
pub fn feature_engineering_impl(input: TokenStream) -> TokenStream {
    match parse_feature_engineering(input) {
        Ok(config) => generate_feature_engineering_code(config),
        Err(err) => err.to_compile_error(),
    }
}

/// Implementation of the hyperparameter_config! macro
///
/// This macro creates hyperparameter optimization configurations for machine
/// learning models. It supports various optimization strategies, constraint
/// definitions, and search space specifications.
///
/// # Arguments
/// * `input` - TokenStream containing the hyperparameter configuration DSL
///
/// # Returns
/// Generated TokenStream with the hyperparameter optimization setup
///
/// # Examples
/// ```ignore
/// hyperparameter_config! {
///     model: RandomForestClassifier,
///     parameters: [
///         n_estimators: Integer(range: 10..500, distribution: LogUniform),
///         max_depth: Integer(range: 3..20, distribution: Uniform),
///         min_samples_split: Float(range: 0.01..0.2, distribution: LogUniform),
///         criterion: Categorical(options: ["gini", "entropy"])
///     ],
///     constraints: [
///         n_estimators * max_depth < 10000,  // Computational constraint
///         min_samples_split < 0.1 => max_depth > 5  // Conditional constraint
///     ],
///     optimization: {
///         strategy: BayesianOptimization,
///         n_trials: 100,
///         timeout: 3600,  // seconds
///         early_stopping: {
///             patience: 20,
///             improvement_threshold: 0.001
///         }
///     }
/// }
/// ```
pub fn hyperparameter_config_impl(input: TokenStream) -> TokenStream {
    match parse_hyperparameter_config(input) {
        Ok(config) => generate_hyperparameter_code(config),
        Err(err) => err.to_compile_error(),
    }
}

/// Implementation of the model_evaluation! macro
///
/// This macro generates comprehensive model evaluation code including
/// cross-validation, metric computation, and statistical testing.
///
/// # Arguments
/// * `input` - TokenStream containing the evaluation configuration DSL
///
/// # Returns
/// Generated TokenStream with the evaluation implementation
pub fn model_evaluation_impl(_input: TokenStream) -> TokenStream {
    // TODO: Implement model evaluation macro
    // This is a placeholder for future enhancement
    quote::quote! {
        compile_error!("model_evaluation! macro not yet implemented");
    }
}

/// Implementation of the data_pipeline! macro
///
/// This macro creates efficient data processing pipelines with support for
/// streaming, batch processing, and real-time data transformations.
///
/// # Arguments
/// * `input` - TokenStream containing the data pipeline configuration DSL
///
/// # Returns
/// Generated TokenStream with the data pipeline implementation
pub fn data_pipeline_impl(_input: TokenStream) -> TokenStream {
    // TODO: Implement data pipeline macro
    // This is a placeholder for future enhancement
    quote::quote! {
        compile_error!("data_pipeline! macro not yet implemented");
    }
}

/// Implementation of the experiment_config! macro
///
/// This macro generates experiment tracking and configuration code for
/// machine learning experiments with automatic logging and reproducibility.
///
/// # Arguments
/// * `input` - TokenStream containing the experiment configuration DSL
///
/// # Returns
/// Generated TokenStream with the experiment setup implementation
pub fn experiment_config_impl(_input: TokenStream) -> TokenStream {
    // TODO: Implement experiment config macro
    // This is a placeholder for future enhancement
    quote::quote! {
        compile_error!("experiment_config! macro not yet implemented");
    }
}

/// Utility function to handle macro errors consistently
///
/// Provides standardized error handling and reporting for all DSL macros.
///
/// # Arguments
/// * `error` - The syntax error encountered during parsing
/// * `context` - Additional context about where the error occurred
///
/// # Returns
/// TokenStream containing a compile_error! with formatted error message
pub fn handle_macro_error(error: syn::Error, context: &str) -> TokenStream {
    let error_msg = format!("DSL macro error in {}: {}", context, error);
    quote::quote! {
        compile_error!(#error_msg);
    }
}

/// Macro implementation registry for dynamic dispatch
///
/// Allows for runtime selection of macro implementations based on
/// macro name or other criteria.
pub struct MacroRegistry {
    implementations: std::collections::HashMap<String, fn(TokenStream) -> TokenStream>,
}

impl MacroRegistry {
    /// Create a new macro registry with default implementations
    pub fn new() -> Self {
        let mut registry = Self {
            implementations: std::collections::HashMap::new(),
        };

        // Register core macro implementations
        registry.register("ml_pipeline", ml_pipeline_impl);
        registry.register("feature_engineering", feature_engineering_impl);
        registry.register("hyperparameter_config", hyperparameter_config_impl);
        registry.register("model_evaluation", model_evaluation_impl);
        registry.register("data_pipeline", data_pipeline_impl);
        registry.register("experiment_config", experiment_config_impl);

        registry
    }

    /// Register a new macro implementation
    ///
    /// # Arguments
    /// * `name` - Name of the macro
    /// * `implementation` - Function implementing the macro logic
    pub fn register(&mut self, name: &str, implementation: fn(TokenStream) -> TokenStream) {
        self.implementations
            .insert(name.to_string(), implementation);
    }

    /// Execute a macro by name
    ///
    /// # Arguments
    /// * `name` - Name of the macro to execute
    /// * `input` - Input tokens for the macro
    ///
    /// # Returns
    /// Generated TokenStream or error if macro not found
    pub fn execute(&self, name: &str, input: TokenStream) -> TokenStream {
        if let Some(implementation) = self.implementations.get(name) {
            implementation(input)
        } else {
            let error_msg = format!("Unknown macro: {}", name);
            quote::quote! {
                compile_error!(#error_msg);
            }
        }
    }

    /// List all registered macro names
    pub fn list_macros(&self) -> Vec<String> {
        self.implementations.keys().cloned().collect()
    }
}

impl Default for MacroRegistry {
    fn default() -> Self {
        Self::new()
    }
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;
    use quote::quote;

    #[test]
    fn test_macro_registry_creation() {
        let registry = MacroRegistry::new();
        let macros = registry.list_macros();

        assert!(macros.contains(&"ml_pipeline".to_string()));
        assert!(macros.contains(&"feature_engineering".to_string()));
        assert!(macros.contains(&"hyperparameter_config".to_string()));
    }

    #[test]
    fn test_macro_registry_custom_registration() {
        let mut registry = MacroRegistry::new();

        fn test_macro(_input: TokenStream) -> TokenStream {
            quote! { println!("test macro executed"); }
        }

        registry.register("test_macro", test_macro);
        let macros = registry.list_macros();

        assert!(macros.contains(&"test_macro".to_string()));
    }

    #[test]
    fn test_unknown_macro_execution() {
        let registry = MacroRegistry::new();
        let result = registry.execute("unknown_macro", TokenStream::new());

        // Should return a compile_error
        let result_str = result.to_string();
        assert!(result_str.contains("compile_error"));
        assert!(result_str.contains("Unknown macro"));
    }
}