lindera-python 3.0.7

//! Training functionality for custom morphological models.
//!
//! This module provides functions for training custom morphological analysis models
//! from annotated corpora.
//!
//! # Examples
//!
//! ```python
//! # Train a model
//! lindera.train(
//!     seed="seed.csv",
//!     corpus="corpus.txt",
//!     char_def="char.def",
//!     unk_def="unk.def",
//!     feature_def="feature.def",
//!     rewrite_def="rewrite.def",
//!     output="model.bin",
//!     lambda_=0.01,
//!     max_iter=100
//! )
//!
//! # Export trained model
//! lindera.export(
//!     model_file="model.bin",
//!     output_dir="/path/to/output"
//! )
//! ```

use std::fs::File;
use std::path::Path;

use pyo3::{exceptions::PyValueError, prelude::*};

use lindera::dictionary::trainer::{Corpus, Model, SerializableModel, Trainer, TrainerConfig};

/// Trains a morphological analysis model from an annotated corpus.
///
/// # Arguments
///
/// * `seed` - Seed lexicon file path (CSV format)
/// * `corpus` - Training corpus file path (annotated text)
/// * `char_def` - Character definition file path (char.def)
/// * `unk_def` - Unknown word definition file path (unk.def)
/// * `feature_def` - Feature definition file path (feature.def)
/// * `rewrite_def` - Rewrite rule definition file path (rewrite.def)
/// * `output` - Output model file path
/// * `lambda_` - L1 regularization (0.0-1.0), default: 0.01
/// * `max_iter` - Maximum number of iterations, default: 100
/// * `max_threads` - Maximum number of threads (None = auto-detect CPU cores)
///
/// # Returns
///
/// * `PyResult<()>` - Returns Ok(()) on success
#[pyfunction]
#[pyo3(signature = (seed, corpus, char_def, unk_def, feature_def, rewrite_def, output, lambda_=0.01, max_iter=100, max_threads=None))]
#[allow(clippy::too_many_arguments)]
pub fn train(
    seed: &str,
    corpus: &str,
    char_def: &str,
    unk_def: &str,
    feature_def: &str,
    rewrite_def: &str,
    output: &str,
    lambda_: f64,
    max_iter: u64,
    max_threads: Option<usize>,
) -> PyResult<()> {
    let seed_path = Path::new(seed);
    let corpus_path = Path::new(corpus);
    let char_def_path = Path::new(char_def);
    let unk_def_path = Path::new(unk_def);
    let feature_def_path = Path::new(feature_def);
    let rewrite_def_path = Path::new(rewrite_def);
    let output_path = Path::new(output);

    // Validate input files
    for (path, name) in [
        (seed_path, "seed"),
        (corpus_path, "corpus"),
        (char_def_path, "char_def"),
        (unk_def_path, "unk_def"),
        (feature_def_path, "feature_def"),
        (rewrite_def_path, "rewrite_def"),
    ] {
        if !path.exists() {
            return Err(PyValueError::new_err(format!(
                "{} file does not exist: {}",
                name,
                path.display()
            )));
        }
    }

    // Load configuration
    let config = TrainerConfig::from_paths(
        seed_path,
        char_def_path,
        unk_def_path,
        feature_def_path,
        rewrite_def_path,
    )
    .map_err(|e| PyValueError::new_err(format!("Failed to load trainer configuration: {e}")))?;

    // Initialize trainer
    let num_threads = max_threads.unwrap_or_else(num_cpus::get);
    let trainer = Trainer::new(config)
        .map_err(|e| PyValueError::new_err(format!("Failed to initialize trainer: {e}")))?
        .regularization_cost(lambda_)
        .max_iter(max_iter)
        .num_threads(num_threads);

    // Load corpus
    let corpus_file = File::open(corpus_path)
        .map_err(|e| PyValueError::new_err(format!("Failed to open corpus file: {e}")))?;
    let corpus = Corpus::from_reader(corpus_file)
        .map_err(|e| PyValueError::new_err(format!("Failed to load corpus: {e}")))?;

    println!("Training with {} examples...", corpus.len());

    // Train model
    let model = trainer
        .train(corpus)
        .map_err(|e| PyValueError::new_err(format!("Training failed: {e}")))?;

    // Save model
    if let Some(parent) = output_path.parent() {
        std::fs::create_dir_all(parent).map_err(|e| {
            PyValueError::new_err(format!("Failed to create output directory: {e}"))
        })?;
    }

    let mut output_file = File::create(output_path)
        .map_err(|e| PyValueError::new_err(format!("Failed to create output file: {e}")))?;

    model
        .write_model(&mut output_file)
        .map_err(|e| PyValueError::new_err(format!("Failed to write model: {e}")))?;

    println!("Model saved to {}", output_path.display());
    Ok(())
}

/// Export dictionary files from trained model
///
/// # Arguments
///
/// * `model` - Trained model file path (.dat format)
/// * `output` - Output directory path (creates lex.csv, matrix.def, unk.def, char.def)
/// * `metadata` - Optional base metadata.json file to update with trained model values
///
/// # Returns
///
/// * `PyResult<()>` - Returns Ok(()) on success
#[pyfunction]
#[pyo3(signature = (model, output, metadata=None))]
pub fn export(model: &str, output: &str, metadata: Option<&str>) -> PyResult<()> {
    let model_path = Path::new(model);
    let output_path = Path::new(output);

    // Validate input files
    if !model_path.exists() {
        return Err(PyValueError::new_err(format!(
            "Model file does not exist: {}",
            model_path.display()
        )));
    }

    // Load trained model
    let model_file = File::open(model_path)
        .map_err(|e| PyValueError::new_err(format!("Failed to open model file: {e}")))?;

    let serializable_model: SerializableModel = Model::read_model(model_file)
        .map_err(|e| PyValueError::new_err(format!("Failed to load model: {e}")))?;

    // Create output directory
    std::fs::create_dir_all(output_path)
        .map_err(|e| PyValueError::new_err(format!("Failed to create output directory: {e}")))?;

    // Export dictionary files
    let lexicon_path = output_path.join("lex.csv");
    let connector_path = output_path.join("matrix.def");
    let unk_path = output_path.join("unk.def");
    let char_def_path = output_path.join("char.def");

    // Write lexicon file
    let mut lexicon_file = File::create(&lexicon_path)
        .map_err(|e| PyValueError::new_err(format!("Failed to create lexicon file: {e}")))?;
    serializable_model
        .write_lexicon(&mut lexicon_file)
        .map_err(|e| PyValueError::new_err(format!("Failed to write lexicon: {e}")))?;

    // Write connection matrix
    let mut connector_file = File::create(&connector_path).map_err(|e| {
        PyValueError::new_err(format!("Failed to create connection matrix file: {e}"))
    })?;
    serializable_model
        .write_connection_costs(&mut connector_file)
        .map_err(|e| PyValueError::new_err(format!("Failed to write connection costs: {e}")))?;

    // Write unknown word definitions
    let mut unk_file = File::create(&unk_path)
        .map_err(|e| PyValueError::new_err(format!("Failed to create unknown word file: {e}")))?;
    serializable_model
        .write_unknown_dictionary(&mut unk_file)
        .map_err(|e| PyValueError::new_err(format!("Failed to write unknown dictionary: {e}")))?;

    // Write character definition file
    let mut char_def_file = File::create(&char_def_path).map_err(|e| {
        PyValueError::new_err(format!("Failed to create character definition file: {e}"))
    })?;

    use std::io::Write;
    writeln!(
        char_def_file,
        "# Character definition file generated from trained model"
    )
    .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "# Format: CATEGORY_NAME invoke group length")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "DEFAULT 0 1 0")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "HIRAGANA 1 1 0")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "KATAKANA 1 1 0")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "KANJI 0 0 2")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "ALPHA 1 1 0")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "NUMERIC 1 1 0")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file)
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;

    writeln!(char_def_file, "# Character mappings")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "0x3041..0x3096 HIRAGANA  # Hiragana")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "0x30A1..0x30F6 KATAKANA  # Katakana")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(
        char_def_file,
        "0x4E00..0x9FAF KANJI     # CJK Unified Ideographs"
    )
    .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "0x0030..0x0039 NUMERIC   # ASCII Digits")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "0x0041..0x005A ALPHA     # ASCII Uppercase")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;
    writeln!(char_def_file, "0x0061..0x007A ALPHA     # ASCII Lowercase")
        .map_err(|e| PyValueError::new_err(format!("Failed to write char.def: {e}")))?;

    let mut files_created = vec![
        lexicon_path.clone(),
        connector_path.clone(),
        unk_path.clone(),
        char_def_path.clone(),
    ];

    // Handle metadata.json update if provided
    if let Some(metadata_str) = metadata {
        let metadata_path = Path::new(metadata_str);
        if !metadata_path.exists() {
            return Err(PyValueError::new_err(format!(
                "Metadata file does not exist: {}",
                metadata_path.display()
            )));
        }

        let output_metadata_path = output_path.join("metadata.json");
        let mut metadata_file = File::create(&output_metadata_path)
            .map_err(|e| PyValueError::new_err(format!("Failed to create metadata file: {e}")))?;

        serializable_model
            .update_metadata_json(metadata_path, &mut metadata_file)
            .map_err(|e| PyValueError::new_err(format!("Failed to update metadata: {e}")))?;

        files_created.push(output_metadata_path);
        println!("Updated metadata.json with trained model values");
    }

    println!("Dictionary files exported to: {}", output_path.display());
    println!("Files created:");
    for file in &files_created {
        println!("  - {}", file.display());
    }

    Ok(())
}