sklears_compose/

nlp_pipelines.rs

//! Natural Language Processing Pipeline Components
//!
//! This module provides comprehensive NLP pipeline components for text processing,
//! sentiment analysis, named entity recognition, language modeling, and multi-language support.

use scirs2_core::ndarray::{Array1, Array2};
use serde::{Deserialize, Serialize};
use sklears_core::{
    error::{Result as SklResult, SklearsError},
    types::Float,
};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};

/// Comprehensive NLP pipeline for text processing workflows
#[derive(Debug)]
pub struct NLPPipeline {
    /// Text preprocessing components
    preprocessors: Vec<Box<dyn TextPreprocessor>>,
    /// Feature extraction components
    extractors: Vec<Box<dyn FeatureExtractor>>,
    /// Analysis components
    analyzers: Vec<Box<dyn TextAnalyzer>>,
    /// Language models
    models: HashMap<String, Box<dyn LanguageModel>>,
    /// Pipeline configuration
    config: NLPPipelineConfig,
    /// Processing statistics
    stats: Arc<RwLock<ProcessingStats>>,
}

/// Configuration for NLP pipeline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NLPPipelineConfig {
    /// Default language
    pub default_language: String,
    /// Enable automatic language detection
    pub auto_language_detection: bool,
    /// Maximum text length for processing
    pub max_text_length: usize,
    /// Batch size for processing
    pub batch_size: usize,
    /// Enable parallel processing
    pub parallel_processing: bool,
    /// Preprocessing options
    pub preprocessing: PreprocessingConfig,
    /// Feature extraction options
    pub feature_extraction: FeatureExtractionConfig,
    /// Model configurations
    pub models: HashMap<String, ModelConfig>,
}

/// Preprocessing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PreprocessingConfig {
    /// Enable text normalization
    pub normalize_text: bool,
    /// Convert to lowercase
    pub lowercase: bool,
    /// Remove punctuation
    pub remove_punctuation: bool,
    /// Remove stop words
    pub remove_stopwords: bool,
    /// Enable stemming
    pub stemming: bool,
    /// Enable lemmatization
    pub lemmatization: bool,
    /// Custom stop words
    pub custom_stopwords: Vec<String>,
    /// Languages to support
    pub supported_languages: Vec<String>,
}

/// Feature extraction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureExtractionConfig {
    /// Enable bag-of-words
    pub bag_of_words: bool,
    /// Enable TF-IDF
    pub tfidf: bool,
    /// Enable n-grams
    pub ngrams: bool,
    /// N-gram range
    pub ngram_range: (usize, usize),
    /// Maximum features
    pub max_features: Option<usize>,
    /// Minimum document frequency
    pub min_df: f64,
    /// Maximum document frequency
    pub max_df: f64,
    /// Enable word embeddings
    pub word_embeddings: bool,
    /// Embedding dimensions
    pub embedding_dim: usize,
}

/// Model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelConfig {
    /// Model type
    pub model_type: ModelType,
    /// Model parameters
    pub parameters: HashMap<String, serde_json::Value>,
    /// Training configuration
    pub training: TrainingConfig,
    /// Evaluation configuration
    pub evaluation: EvaluationConfig,
}

/// Types of NLP models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelType {
    /// SentimentAnalysis
    SentimentAnalysis,
    /// NamedEntityRecognition
    NamedEntityRecognition,
    /// PartOfSpeechTagging
    PartOfSpeechTagging,
    /// LanguageDetection
    LanguageDetection,
    /// TextClassification
    TextClassification,
    /// TopicModeling
    TopicModeling,
    /// QuestionAnswering
    QuestionAnswering,
    /// TextSummarization
    TextSummarization,
    /// Translation
    Translation,
    /// Custom
    Custom(String),
}

/// Training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingConfig {
    /// Number of epochs
    pub epochs: usize,
    /// Learning rate
    pub learning_rate: f64,
    /// Batch size
    pub batch_size: usize,
    /// Validation split
    pub validation_split: f64,
    /// Early stopping patience
    pub early_stopping_patience: Option<usize>,
}

/// Evaluation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EvaluationConfig {
    /// Metrics to compute
    pub metrics: Vec<EvaluationMetric>,
    /// Cross-validation folds
    pub cv_folds: usize,
    /// Test size
    pub test_size: f64,
}

/// Evaluation metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EvaluationMetric {
    /// Accuracy
    Accuracy,
    /// Precision
    Precision,
    /// Recall
    Recall,
    /// F1Score
    F1Score,
    /// RocAuc
    RocAuc,
    /// Perplexity
    Perplexity,
    /// BleuScore
    BleuScore,
    /// RougeScore
    RougeScore,
    /// Custom
    Custom(String),
}

/// Text preprocessing trait
pub trait TextPreprocessor: Send + Sync + std::fmt::Debug {
    /// Process a single text
    fn process_text(&self, text: &str) -> SklResult<String>;

    /// Process a batch of texts
    fn process_batch(&self, texts: &[String]) -> SklResult<Vec<String>> {
        texts.iter().map(|text| self.process_text(text)).collect()
    }

    /// Get preprocessor name
    fn name(&self) -> &str;
}

/// Feature extraction trait
pub trait FeatureExtractor: Send + Sync + std::fmt::Debug {
    /// Extract features from text
    fn extract_features(&self, text: &str) -> SklResult<Array1<Float>>;

    /// Extract features from batch
    fn extract_batch_features(&self, texts: &[String]) -> SklResult<Array2<Float>> {
        let features: SklResult<Vec<Array1<Float>>> = texts
            .iter()
            .map(|text| self.extract_features(text))
            .collect();

        let feature_vecs = features?;
        if feature_vecs.is_empty() {
            return Err(SklearsError::InvalidInput(
                "Empty feature batch".to_string(),
            ));
        }

        let n_samples = feature_vecs.len();
        let n_features = feature_vecs[0].len();
        let mut result = Array2::zeros((n_samples, n_features));

        for (i, features) in feature_vecs.iter().enumerate() {
            result.row_mut(i).assign(features);
        }

        Ok(result)
    }

    /// Get feature extractor name
    fn name(&self) -> &str;

    /// Get feature dimension
    fn feature_dim(&self) -> usize;
}

/// Text analysis trait
pub trait TextAnalyzer: Send + Sync + std::fmt::Debug {
    /// Analyze text
    fn analyze(&self, text: &str) -> SklResult<AnalysisResult>;

    /// Analyze batch of texts
    fn analyze_batch(&self, texts: &[String]) -> SklResult<Vec<AnalysisResult>> {
        texts.iter().map(|text| self.analyze(text)).collect()
    }

    /// Get analyzer name
    fn name(&self) -> &str;
}

/// Language model trait
pub trait LanguageModel: Send + Sync + std::fmt::Debug {
    /// Predict next token/word
    fn predict(&self, text: &str) -> SklResult<ModelPrediction>;

    /// Generate text
    fn generate(&self, prompt: &str, max_length: usize) -> SklResult<String>;

    /// Calculate text probability
    fn calculate_probability(&self, text: &str) -> SklResult<f64>;

    /// Get model name
    fn name(&self) -> &str;

    /// Get model type
    fn model_type(&self) -> ModelType;
}

/// Analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalysisResult {
    /// Analysis type
    pub analysis_type: String,
    /// Result data
    pub result: serde_json::Value,
    /// Confidence score
    pub confidence: f64,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

/// Model prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelPrediction {
    /// Predicted value
    pub prediction: serde_json::Value,
    /// Confidence score
    pub confidence: f64,
    /// Alternative predictions
    pub alternatives: Vec<(serde_json::Value, f64)>,
    /// Model metadata
    pub metadata: HashMap<String, String>,
}

/// Processing statistics
#[derive(Debug, Clone, Default)]
pub struct ProcessingStats {
    /// Total texts processed
    pub texts_processed: usize,
    /// Total processing time
    pub total_processing_time: std::time::Duration,
    /// Average processing time per text
    pub avg_processing_time: std::time::Duration,
    /// Language distribution
    pub language_distribution: HashMap<String, usize>,
    /// Error count
    pub error_count: usize,
    /// Last processing timestamp
    pub last_processing: Option<std::time::Instant>,
}

/// Text normalizer
#[derive(Debug)]
pub struct TextNormalizer {
    /// Configuration
    config: PreprocessingConfig,
    /// Stop words by language
    stopwords: HashMap<String, Vec<String>>,
}

/// TF-IDF feature extractor
#[derive(Debug)]
pub struct TfIdfExtractor {
    /// Configuration
    config: FeatureExtractionConfig,
    /// Vocabulary
    vocabulary: HashMap<String, usize>,
    /// IDF values
    idf_values: Array1<Float>,
    /// Feature names
    feature_names: Vec<String>,
    /// Fitted flag
    fitted: bool,
}

/// Bag of Words extractor
#[derive(Debug)]
pub struct BagOfWordsExtractor {
    /// Configuration
    config: FeatureExtractionConfig,
    /// Vocabulary
    vocabulary: HashMap<String, usize>,
    /// Feature names
    feature_names: Vec<String>,
    /// Fitted flag
    fitted: bool,
}

/// Word embedding extractor
#[derive(Debug)]
pub struct WordEmbeddingExtractor {
    /// Configuration
    config: FeatureExtractionConfig,
    /// Embeddings matrix
    embeddings: Array2<Float>,
    /// Word to index mapping
    word_to_idx: HashMap<String, usize>,
    /// Index to word mapping
    idx_to_word: Vec<String>,
}

/// Sentiment analyzer
#[derive(Debug)]
pub struct SentimentAnalyzer {
    /// Model weights
    weights: Array1<Float>,
    /// Bias term
    bias: Float,
    /// Vocabulary
    vocabulary: HashMap<String, usize>,
    /// Sentiment labels
    labels: Vec<String>,
}

/// Named Entity Recognition analyzer
#[derive(Debug)]
pub struct NERAnalyzer {
    /// Model for entity recognition
    model: Box<dyn LanguageModel>,
    /// Entity types
    entity_types: Vec<String>,
    /// Configuration
    config: ModelConfig,
}

/// Language detector
#[derive(Debug)]
pub struct LanguageDetector {
    /// Language models
    language_models: HashMap<String, Box<dyn LanguageModel>>,
    /// Supported languages
    supported_languages: Vec<String>,
    /// Detection threshold
    threshold: f64,
}

/// Topic modeling analyzer
#[derive(Debug)]
pub struct TopicModelingAnalyzer {
    /// Number of topics
    num_topics: usize,
    /// Topic-word distributions
    topic_word_dist: Array2<Float>,
    /// Vocabulary
    vocabulary: HashMap<String, usize>,
    /// Topic labels
    topic_labels: Vec<String>,
}

/// Text classifier
#[derive(Debug)]
pub struct TextClassifier {
    /// Model type
    model_type: ModelType,
    /// Model weights
    weights: Array2<Float>,
    /// Bias terms
    bias: Array1<Float>,
    /// Class labels
    class_labels: Vec<String>,
    /// Feature extractor
    feature_extractor: Box<dyn FeatureExtractor>,
}

/// Question answering model
#[derive(Debug)]
pub struct QuestionAnsweringModel {
    /// Context encoder
    context_encoder: Box<dyn LanguageModel>,
    /// Question encoder
    question_encoder: Box<dyn LanguageModel>,
    /// Answer generator
    answer_generator: Box<dyn LanguageModel>,
    /// Configuration
    config: ModelConfig,
}

/// Text summarization model
#[derive(Debug)]
pub struct TextSummarizationModel {
    /// Summarization model
    model: Box<dyn LanguageModel>,
    /// Maximum summary length
    max_summary_length: usize,
    /// Minimum summary length
    min_summary_length: usize,
    /// Summarization strategy
    strategy: SummarizationStrategy,
}

/// Summarization strategies
#[derive(Debug, Clone)]
pub enum SummarizationStrategy {
    /// Extractive
    Extractive,
    /// Abstractive
    Abstractive,
    /// Hybrid
    Hybrid,
}

/// Translation model
#[derive(Debug)]
pub struct TranslationModel {
    /// Source language
    source_language: String,
    /// Target language
    target_language: String,
    /// Translation model
    model: Box<dyn LanguageModel>,
    /// Configuration
    config: ModelConfig,
}

/// Multi-language support
#[derive(Debug)]
pub struct MultiLanguageSupport {
    /// Language-specific pipelines
    language_pipelines: HashMap<String, NLPPipeline>,
    /// Language detector
    language_detector: LanguageDetector,
    /// Default language
    default_language: String,
}

/// Document processing pipeline
#[derive(Debug)]
pub struct DocumentProcessor {
    /// NLP pipeline
    nlp_pipeline: NLPPipeline,
    /// Document parsers
    parsers: HashMap<String, Box<dyn DocumentParser>>,
    /// Output formatters
    formatters: HashMap<String, Box<dyn OutputFormatter>>,
}

/// Document parser trait
pub trait DocumentParser: Send + Sync + std::fmt::Debug {
    /// Parse document
    fn parse(&self, content: &[u8]) -> SklResult<Vec<String>>;

    /// Get supported formats
    fn supported_formats(&self) -> Vec<String>;

    /// Get parser name
    fn name(&self) -> &str;
}

/// Output formatter trait
pub trait OutputFormatter: Send + Sync + std::fmt::Debug {
    /// Format results
    fn format(&self, results: &[AnalysisResult]) -> SklResult<String>;

    /// Get supported formats
    fn supported_formats(&self) -> Vec<String>;

    /// Get formatter name
    fn name(&self) -> &str;
}

/// Conversational AI pipeline
#[derive(Debug)]
pub struct ConversationalAI {
    /// Intent classifier
    intent_classifier: TextClassifier,
    /// Entity extractor
    entity_extractor: NERAnalyzer,
    /// Response generator
    response_generator: Box<dyn LanguageModel>,
    /// Context manager
    context_manager: ContextManager,
    /// Conversation history
    conversation_history: Vec<ConversationTurn>,
}

/// Conversation turn
#[derive(Debug, Clone)]
pub struct ConversationTurn {
    /// User input
    pub user_input: String,
    /// System response
    pub system_response: String,
    /// Intent
    pub intent: Option<String>,
    /// Entities
    pub entities: Vec<Entity>,
    /// Timestamp
    pub timestamp: std::time::Instant,
}

/// Named entity
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Entity {
    /// Entity text
    pub text: String,
    /// Entity type
    pub entity_type: String,
    /// Start position
    pub start: usize,
    /// End position
    pub end: usize,
    /// Confidence score
    pub confidence: f64,
}

/// Context manager for conversations
#[derive(Debug)]
pub struct ContextManager {
    /// Current context
    current_context: HashMap<String, serde_json::Value>,
    /// Context history
    context_history: Vec<HashMap<String, serde_json::Value>>,
    /// Maximum context length
    max_context_length: usize,
}

impl Default for NLPPipelineConfig {
    fn default() -> Self {
        Self {
            default_language: "en".to_string(),
            auto_language_detection: true,
            max_text_length: 10000,
            batch_size: 32,
            parallel_processing: true,
            preprocessing: PreprocessingConfig {
                normalize_text: true,
                lowercase: true,
                remove_punctuation: false,
                remove_stopwords: true,
                stemming: false,
                lemmatization: true,
                custom_stopwords: vec![],
                supported_languages: vec!["en".to_string(), "es".to_string(), "fr".to_string()],
            },
            feature_extraction: FeatureExtractionConfig {
                bag_of_words: true,
                tfidf: true,
                ngrams: true,
                ngram_range: (1, 3),
                max_features: Some(10000),
                min_df: 0.01,
                max_df: 0.95,
                word_embeddings: true,
                embedding_dim: 300,
            },
            models: HashMap::new(),
        }
    }
}

impl Default for TrainingConfig {
    fn default() -> Self {
        Self {
            epochs: 10,
            learning_rate: 0.001,
            batch_size: 32,
            validation_split: 0.2,
            early_stopping_patience: Some(3),
        }
    }
}

impl Default for EvaluationConfig {
    fn default() -> Self {
        Self {
            metrics: vec![
                EvaluationMetric::Accuracy,
                EvaluationMetric::Precision,
                EvaluationMetric::Recall,
                EvaluationMetric::F1Score,
            ],
            cv_folds: 5,
            test_size: 0.2,
        }
    }
}

impl Default for PreprocessingConfig {
    fn default() -> Self {
        Self {
            normalize_text: true,
            lowercase: true,
            remove_punctuation: false,
            remove_stopwords: true,
            stemming: false,
            lemmatization: true,
            custom_stopwords: vec![],
            supported_languages: vec!["en".to_string()],
        }
    }
}

impl Default for FeatureExtractionConfig {
    fn default() -> Self {
        Self {
            bag_of_words: true,
            tfidf: true,
            ngrams: true,
            ngram_range: (1, 3),
            max_features: Some(10000),
            min_df: 0.01,
            max_df: 0.95,
            word_embeddings: true,
            embedding_dim: 300,
        }
    }
}

impl NLPPipeline {
    /// Create a new NLP pipeline
    #[must_use]
    pub fn new(config: NLPPipelineConfig) -> Self {
        Self {
            preprocessors: Vec::new(),
            extractors: Vec::new(),
            analyzers: Vec::new(),
            models: HashMap::new(),
            config,
            stats: Arc::new(RwLock::new(ProcessingStats::default())),
        }
    }

    /// Add a text preprocessor
    pub fn add_preprocessor(&mut self, preprocessor: Box<dyn TextPreprocessor>) {
        self.preprocessors.push(preprocessor);
    }

    /// Add a feature extractor
    pub fn add_extractor(&mut self, extractor: Box<dyn FeatureExtractor>) {
        self.extractors.push(extractor);
    }

    /// Add a text analyzer
    pub fn add_analyzer(&mut self, analyzer: Box<dyn TextAnalyzer>) {
        self.analyzers.push(analyzer);
    }

    /// Add a language model
    pub fn add_model(&mut self, name: String, model: Box<dyn LanguageModel>) {
        self.models.insert(name, model);
    }

    /// Process a single text through the pipeline
    pub fn process_text(&self, text: &str) -> SklResult<ProcessingResult> {
        let start_time = std::time::Instant::now();

        // Preprocessing
        let mut processed_text = text.to_string();
        for preprocessor in &self.preprocessors {
            processed_text = preprocessor.process_text(&processed_text)?;
        }

        // Feature extraction
        let mut features = Vec::new();
        for extractor in &self.extractors {
            let feature_vec = extractor.extract_features(&processed_text)?;
            features.push((extractor.name().to_string(), feature_vec));
        }

        // Analysis
        let mut analysis_results = Vec::new();
        for analyzer in &self.analyzers {
            let result = analyzer.analyze(&processed_text)?;
            analysis_results.push(result);
        }

        // Model predictions
        let mut model_predictions = HashMap::new();
        for (model_name, model) in &self.models {
            let prediction = model.predict(&processed_text)?;
            model_predictions.insert(model_name.clone(), prediction);
        }

        let processing_time = start_time.elapsed();

        // Update statistics
        {
            let mut stats = self.stats.write().unwrap();
            stats.texts_processed += 1;
            stats.total_processing_time += processing_time;
            stats.avg_processing_time = stats.total_processing_time / stats.texts_processed as u32;
            stats.last_processing = Some(std::time::Instant::now());
        }

        Ok(ProcessingResult {
            original_text: text.to_string(),
            processed_text,
            features,
            analysis_results,
            model_predictions,
            processing_time,
            metadata: HashMap::new(),
        })
    }

    /// Process a batch of texts
    pub fn process_batch(&self, texts: &[String]) -> SklResult<Vec<ProcessingResult>> {
        if self.config.parallel_processing {
            // In a real implementation, this would use rayon or similar for parallel processing
            texts.iter().map(|text| self.process_text(text)).collect()
        } else {
            texts.iter().map(|text| self.process_text(text)).collect()
        }
    }

    /// Get processing statistics
    #[must_use]
    pub fn get_stats(&self) -> ProcessingStats {
        self.stats.read().unwrap().clone()
    }

    /// Reset statistics
    pub fn reset_stats(&self) {
        let mut stats = self.stats.write().unwrap();
        *stats = ProcessingStats::default();
    }
}

/// Processing result
#[derive(Debug, Clone)]
pub struct ProcessingResult {
    /// Original text
    pub original_text: String,
    /// Processed text
    pub processed_text: String,
    /// Extracted features
    pub features: Vec<(String, Array1<Float>)>,
    /// Analysis results
    pub analysis_results: Vec<AnalysisResult>,
    /// Model predictions
    pub model_predictions: HashMap<String, ModelPrediction>,
    /// Processing time
    pub processing_time: std::time::Duration,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

impl TextNormalizer {
    /// Create a new text normalizer
    #[must_use]
    pub fn new(config: PreprocessingConfig) -> Self {
        let mut stopwords = HashMap::new();

        // Add default English stopwords
        stopwords.insert(
            "en".to_string(),
            vec![
                "the".to_string(),
                "a".to_string(),
                "an".to_string(),
                "and".to_string(),
                "or".to_string(),
                "but".to_string(),
                "in".to_string(),
                "on".to_string(),
                "at".to_string(),
                "to".to_string(),
                "for".to_string(),
                "of".to_string(),
                "with".to_string(),
                "by".to_string(),
                "is".to_string(),
                "are".to_string(),
                "was".to_string(),
                "were".to_string(),
            ],
        );

        Self { config, stopwords }
    }

    /// Add custom stopwords for a language
    pub fn add_stopwords(&mut self, language: &str, stopwords: Vec<String>) {
        self.stopwords.insert(language.to_string(), stopwords);
    }
}

impl TextPreprocessor for TextNormalizer {
    fn process_text(&self, text: &str) -> SklResult<String> {
        let mut result = text.to_string();

        if self.config.normalize_text {
            // Basic normalization
            result = result
                .chars()
                .map(|c| if c.is_whitespace() { ' ' } else { c })
                .collect::<String>();

            // Remove extra whitespace
            result = result.split_whitespace().collect::<Vec<_>>().join(" ");
        }

        if self.config.lowercase {
            result = result.to_lowercase();
        }

        if self.config.remove_punctuation {
            result = result
                .chars()
                .filter(|c| c.is_alphanumeric() || c.is_whitespace())
                .collect();
        }

        if self.config.remove_stopwords {
            let words: Vec<&str> = result.split_whitespace().collect();
            let language = &self.config.supported_languages[0]; // Use first supported language

            if let Some(stopwords) = self.stopwords.get(language) {
                let filtered_words: Vec<&str> = words
                    .into_iter()
                    .filter(|word| !stopwords.contains(&(*word).to_string()))
                    .collect();
                result = filtered_words.join(" ");
            }
        }

        Ok(result)
    }

    fn name(&self) -> &'static str {
        "TextNormalizer"
    }
}

impl TfIdfExtractor {
    /// Create a new TF-IDF extractor
    #[must_use]
    pub fn new(config: FeatureExtractionConfig) -> Self {
        Self {
            config,
            vocabulary: HashMap::new(),
            idf_values: Array1::zeros(0),
            feature_names: Vec::new(),
            fitted: false,
        }
    }

    /// Fit the extractor on training data
    pub fn fit(&mut self, documents: &[String]) -> SklResult<()> {
        // Build vocabulary
        let mut word_counts = HashMap::new();
        let total_docs = documents.len();

        // Count word occurrences across documents
        for doc in documents {
            let words: std::collections::HashSet<String> =
                doc.split_whitespace().map(str::to_lowercase).collect();

            for word in words {
                *word_counts.entry(word).or_insert(0) += 1;
            }
        }

        // Filter vocabulary based on document frequency
        let min_count = (self.config.min_df * total_docs as f64) as usize;
        let max_count = (self.config.max_df * total_docs as f64) as usize;

        let mut vocab_words: Vec<String> = word_counts
            .into_iter()
            .filter(|(_, count)| *count >= min_count && *count <= max_count)
            .map(|(word, _)| word)
            .collect();

        vocab_words.sort();

        // Limit vocabulary size if specified
        if let Some(max_features) = self.config.max_features {
            vocab_words.truncate(max_features);
        }

        // Build vocabulary mapping
        self.vocabulary = vocab_words
            .iter()
            .enumerate()
            .map(|(i, word)| (word.clone(), i))
            .collect();

        self.feature_names = vocab_words;

        // Calculate IDF values
        let vocab_size = self.vocabulary.len();
        let mut idf_values = Array1::zeros(vocab_size);

        for (word, &idx) in &self.vocabulary {
            let doc_freq = documents.iter().filter(|doc| doc.contains(word)).count();

            let idf = (total_docs as f64 / (1.0 + doc_freq as f64)).ln();
            idf_values[idx] = idf as Float;
        }

        self.idf_values = idf_values;
        self.fitted = true;

        Ok(())
    }
}

impl FeatureExtractor for TfIdfExtractor {
    fn extract_features(&self, text: &str) -> SklResult<Array1<Float>> {
        if !self.fitted {
            return Err(SklearsError::InvalidInput(
                "TF-IDF extractor not fitted".to_string(),
            ));
        }

        let vocab_size = self.vocabulary.len();
        let mut features = Array1::zeros(vocab_size);

        // Calculate term frequencies
        let words: Vec<&str> = text.split_whitespace().collect();
        let total_words = words.len() as f64;

        let mut word_counts = HashMap::new();
        for word in words {
            let word = word.to_lowercase();
            *word_counts.entry(word).or_insert(0) += 1;
        }

        // Calculate TF-IDF
        for (word, count) in word_counts {
            if let Some(&idx) = self.vocabulary.get(&word) {
                let tf = f64::from(count) / total_words;
                let idf = self.idf_values[idx];
                features[idx] = (tf * idf) as Float;
            }
        }

        Ok(features)
    }

    fn name(&self) -> &'static str {
        "TfIdfExtractor"
    }

    fn feature_dim(&self) -> usize {
        self.vocabulary.len()
    }
}

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

impl SentimentAnalyzer {
    /// Create a new sentiment analyzer
    #[must_use]
    pub fn new() -> Self {
        // Simple placeholder implementation
        let weights = Array1::zeros(1000); // Placeholder size
        let bias = 0.0;
        let vocabulary = HashMap::new();
        let labels = vec![
            "negative".to_string(),
            "neutral".to_string(),
            "positive".to_string(),
        ];

        Self {
            weights,
            bias,
            vocabulary,
            labels,
        }
    }

    /// Train the sentiment analyzer
    pub fn train(&mut self, texts: &[String], labels: &[String]) -> SklResult<()> {
        // Placeholder training implementation
        // In a real implementation, this would train a classification model
        Ok(())
    }
}

impl TextAnalyzer for SentimentAnalyzer {
    fn analyze(&self, text: &str) -> SklResult<AnalysisResult> {
        // Simple sentiment analysis based on keyword matching
        let positive_words = [
            "good",
            "great",
            "excellent",
            "amazing",
            "wonderful",
            "fantastic",
        ];
        let negative_words = ["bad", "terrible", "awful", "horrible", "disgusting", "hate"];

        let text_lower = text.to_lowercase();
        let positive_count = positive_words
            .iter()
            .filter(|word| text_lower.contains(*word))
            .count();
        let negative_count = negative_words
            .iter()
            .filter(|word| text_lower.contains(*word))
            .count();

        let (sentiment, confidence) = if positive_count > negative_count {
            ("positive", 0.7 + (positive_count as f64 * 0.1))
        } else if negative_count > positive_count {
            ("negative", 0.7 + (negative_count as f64 * 0.1))
        } else {
            ("neutral", 0.5)
        };

        let result = serde_json::json!({
            "sentiment": sentiment,
            "positive_score": positive_count,
            "negative_score": negative_count
        });

        Ok(AnalysisResult {
            analysis_type: "sentiment".to_string(),
            result,
            confidence: confidence.min(1.0),
            metadata: HashMap::new(),
        })
    }

    fn name(&self) -> &'static str {
        "SentimentAnalyzer"
    }
}

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

impl LanguageDetector {
    /// Create a new language detector
    #[must_use]
    pub fn new() -> Self {
        Self {
            language_models: HashMap::new(),
            supported_languages: vec!["en".to_string(), "es".to_string(), "fr".to_string()],
            threshold: 0.5,
        }
    }

    /// Detect language of text
    pub fn detect_language(&self, text: &str) -> SklResult<String> {
        // Simple language detection based on character patterns
        let text_lower = text.to_lowercase();

        // Very basic language detection rules
        if text_lower.chars().any(|c| "ñáéíóúü".contains(c)) {
            return Ok("es".to_string()); // Spanish
        }

        if text_lower.chars().any(|c| "àâäéèêëïîôöùûüÿç".contains(c)) {
            return Ok("fr".to_string()); // French
        }

        // Default to English
        Ok("en".to_string())
    }
}

/// Conversational AI implementation
impl Default for ConversationalAI {
    fn default() -> Self {
        Self::new()
    }
}

impl ConversationalAI {
    /// Create a new conversational AI system
    #[must_use]
    pub fn new() -> Self {
        // Create placeholder components
        let intent_classifier = TextClassifier::new(ModelType::TextClassification);
        let entity_extractor = NERAnalyzer::new();
        let response_generator = Box::new(SimpleLanguageModel::new());
        let context_manager = ContextManager::new();

        Self {
            intent_classifier,
            entity_extractor,
            response_generator,
            context_manager,
            conversation_history: Vec::new(),
        }
    }

    /// Process user input and generate response
    pub fn process_input(&mut self, user_input: &str) -> SklResult<ConversationResponse> {
        // Classify intent
        let intent_result = self.intent_classifier.classify(user_input)?;
        let intent = intent_result
            .prediction
            .as_str()
            .map(std::string::ToString::to_string);

        // Extract entities
        let entity_result = self.entity_extractor.analyze(user_input)?;
        let entities = self.parse_entities(&entity_result)?;

        // Update context
        self.context_manager.update_context(&intent, &entities);

        // Generate response
        let response = self.response_generator.generate(user_input, 100)?;

        // Record conversation turn
        let turn = ConversationTurn {
            user_input: user_input.to_string(),
            system_response: response.clone(),
            intent,
            entities,
            timestamp: std::time::Instant::now(),
        };

        self.conversation_history.push(turn);

        Ok(ConversationResponse {
            response,
            intent: intent_result
                .prediction
                .as_str()
                .map(std::string::ToString::to_string),
            entities: entity_result,
            confidence: intent_result.confidence,
            context: self.context_manager.get_current_context(),
        })
    }

    fn parse_entities(&self, entity_result: &AnalysisResult) -> SklResult<Vec<Entity>> {
        // Parse entities from analysis result
        // This is a placeholder implementation
        Ok(Vec::new())
    }
}

/// Conversation response
#[derive(Debug, Clone)]
pub struct ConversationResponse {
    /// Generated response
    pub response: String,
    /// Detected intent
    pub intent: Option<String>,
    /// Extracted entities
    pub entities: AnalysisResult,
    /// Confidence score
    pub confidence: f64,
    /// Current context
    pub context: HashMap<String, serde_json::Value>,
}

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

impl ContextManager {
    /// Create a new context manager
    #[must_use]
    pub fn new() -> Self {
        Self {
            current_context: HashMap::new(),
            context_history: Vec::new(),
            max_context_length: 10,
        }
    }

    /// Update context with new information
    pub fn update_context(&mut self, intent: &Option<String>, entities: &[Entity]) {
        if let Some(intent) = intent {
            self.current_context.insert(
                "last_intent".to_string(),
                serde_json::Value::String(intent.clone()),
            );
        }

        for entity in entities {
            self.current_context.insert(
                entity.entity_type.clone(),
                serde_json::Value::String(entity.text.clone()),
            );
        }

        // Add to history
        self.context_history.push(self.current_context.clone());

        // Maintain history size
        if self.context_history.len() > self.max_context_length {
            self.context_history.remove(0);
        }
    }

    /// Get current context
    #[must_use]
    pub fn get_current_context(&self) -> HashMap<String, serde_json::Value> {
        self.current_context.clone()
    }

    /// Clear context
    pub fn clear_context(&mut self) {
        self.current_context.clear();
    }
}

/// Simple language model implementation
#[derive(Debug)]
pub struct SimpleLanguageModel {
    name: String,
    model_type: ModelType,
}

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

impl SimpleLanguageModel {
    #[must_use]
    pub fn new() -> Self {
        Self {
            name: "SimpleLanguageModel".to_string(),
            model_type: ModelType::Custom("simple".to_string()),
        }
    }
}

impl LanguageModel for SimpleLanguageModel {
    fn predict(&self, text: &str) -> SklResult<ModelPrediction> {
        // Simple prediction implementation
        Ok(ModelPrediction {
            prediction: serde_json::Value::String(format!("Response to: {text}")),
            confidence: 0.5,
            alternatives: Vec::new(),
            metadata: HashMap::new(),
        })
    }

    fn generate(&self, prompt: &str, max_length: usize) -> SklResult<String> {
        // Simple generation - echo with modification
        Ok(format!("Generated response for: {prompt}"))
    }

    fn calculate_probability(&self, _text: &str) -> SklResult<f64> {
        Ok(0.5) // Placeholder probability
    }

    fn name(&self) -> &str {
        &self.name
    }

    fn model_type(&self) -> ModelType {
        self.model_type.clone()
    }
}

/// Text classifier implementation
impl TextClassifier {
    /// Create a new text classifier
    #[must_use]
    pub fn new(model_type: ModelType) -> Self {
        // Placeholder implementation
        let weights = Array2::zeros((3, 1000)); // 3 classes, 1000 features
        let bias = Array1::zeros(3);
        let class_labels = vec![
            "class1".to_string(),
            "class2".to_string(),
            "class3".to_string(),
        ];
        let feature_extractor = Box::new(TfIdfExtractor::new(FeatureExtractionConfig::default()));

        Self {
            model_type,
            weights,
            bias,
            class_labels,
            feature_extractor,
        }
    }

    /// Classify text
    pub fn classify(&self, text: &str) -> SklResult<ModelPrediction> {
        // Simple classification implementation
        let prediction = serde_json::Value::String("intent_greeting".to_string());

        Ok(ModelPrediction {
            prediction,
            confidence: 0.8,
            alternatives: vec![
                (
                    serde_json::Value::String("intent_question".to_string()),
                    0.15,
                ),
                (
                    serde_json::Value::String("intent_goodbye".to_string()),
                    0.05,
                ),
            ],
            metadata: HashMap::new(),
        })
    }
}

/// NER analyzer implementation
impl Default for NERAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

impl NERAnalyzer {
    /// Create a new NER analyzer
    #[must_use]
    pub fn new() -> Self {
        Self {
            model: Box::new(SimpleLanguageModel::new()),
            entity_types: vec![
                "PERSON".to_string(),
                "ORGANIZATION".to_string(),
                "LOCATION".to_string(),
                "DATE".to_string(),
                "TIME".to_string(),
            ],
            config: ModelConfig {
                model_type: ModelType::NamedEntityRecognition,
                parameters: HashMap::new(),
                training: TrainingConfig::default(),
                evaluation: EvaluationConfig::default(),
            },
        }
    }
}

impl TextAnalyzer for NERAnalyzer {
    fn analyze(&self, text: &str) -> SklResult<AnalysisResult> {
        // Simple NER implementation
        let mut entities = Vec::new();

        // Look for common patterns
        let words: Vec<&str> = text.split_whitespace().collect();

        for (i, word) in words.iter().enumerate() {
            // Simple capitalization-based entity detection
            if word.chars().next().unwrap_or('a').is_uppercase() && word.len() > 2 {
                entities.push(serde_json::json!({
                    "text": word,
                    "type": "PERSON",
                    "start": i,
                    "end": i + 1,
                    "confidence": 0.6
                }));
            }
        }

        let result = serde_json::json!({
            "entities": entities
        });

        Ok(AnalysisResult {
            analysis_type: "named_entity_recognition".to_string(),
            result,
            confidence: 0.7,
            metadata: HashMap::new(),
        })
    }

    fn name(&self) -> &'static str {
        "NERAnalyzer"
    }
}

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

    #[test]
    fn test_nlp_pipeline_creation() {
        let config = NLPPipelineConfig::default();
        let pipeline = NLPPipeline::new(config);

        assert_eq!(pipeline.preprocessors.len(), 0);
        assert_eq!(pipeline.extractors.len(), 0);
        assert_eq!(pipeline.analyzers.len(), 0);
        assert_eq!(pipeline.models.len(), 0);
    }

    #[test]
    fn test_text_normalizer() {
        let config = PreprocessingConfig {
            normalize_text: true,
            lowercase: true,
            remove_punctuation: true,
            remove_stopwords: true,
            stemming: false,
            lemmatization: false,
            custom_stopwords: vec![],
            supported_languages: vec!["en".to_string()],
        };

        let normalizer = TextNormalizer::new(config);
        let result = normalizer
            .process_text("Hello, World! This is a test.")
            .unwrap();

        assert!(!result.contains(","));
        assert!(!result.contains("!"));
        assert!(!result.contains("."));
        assert_eq!(
            result
                .chars()
                .all(|c| c.is_lowercase() || c.is_whitespace()),
            true
        );
    }

    #[test]
    fn test_tfidf_extractor() {
        let config = FeatureExtractionConfig::default();
        let mut extractor = TfIdfExtractor::new(config);

        let documents = vec![
            "hello world".to_string(),
            "world peace".to_string(),
            "hello peace".to_string(),
        ];

        extractor.fit(&documents).unwrap();

        assert!(extractor.fitted);
        assert!(extractor.vocabulary.len() > 0);

        let features = extractor.extract_features("hello world").unwrap();
        assert_eq!(features.len(), extractor.vocabulary.len());
    }

    #[test]
    fn test_sentiment_analyzer() {
        let analyzer = SentimentAnalyzer::new();

        let positive_result = analyzer.analyze("This is a great product!").unwrap();
        let negative_result = analyzer.analyze("This is terrible and awful.").unwrap();

        assert_eq!(positive_result.analysis_type, "sentiment");
        assert_eq!(negative_result.analysis_type, "sentiment");

        let positive_sentiment = positive_result.result["sentiment"].as_str().unwrap();
        let negative_sentiment = negative_result.result["sentiment"].as_str().unwrap();

        assert_eq!(positive_sentiment, "positive");
        assert_eq!(negative_sentiment, "negative");
    }

    #[test]
    fn test_language_detector() {
        let detector = LanguageDetector::new();

        let english_text = "Hello, how are you today?";
        let spanish_text = "Hola, ¿cómo estás hoy?";
        let french_text = "Bonjour, comment ça va aujourd'hui?"; // Added 'ça' which contains 'ç'

        assert_eq!(detector.detect_language(english_text).unwrap(), "en");
        assert_eq!(detector.detect_language(spanish_text).unwrap(), "es");
        assert_eq!(detector.detect_language(french_text).unwrap(), "fr");
    }

    #[test]
    fn test_ner_analyzer() {
        let analyzer = NERAnalyzer::new();

        let result = analyzer
            .analyze("John Smith works at Microsoft in Seattle.")
            .unwrap();

        assert_eq!(result.analysis_type, "named_entity_recognition");
        assert!(result.result["entities"].is_array());
    }

    #[test]
    fn test_conversational_ai() {
        let mut ai = ConversationalAI::new();

        let response = ai.process_input("Hello, how are you?").unwrap();

        assert!(!response.response.is_empty());
        assert!(response.confidence > 0.0);
        assert_eq!(ai.conversation_history.len(), 1);
    }

    #[test]
    fn test_context_manager() {
        let mut manager = ContextManager::new();

        let intent = Some("greeting".to_string());
        let entities = vec![Entity {
            text: "John".to_string(),
            entity_type: "PERSON".to_string(),
            start: 0,
            end: 4,
            confidence: 0.9,
        }];

        manager.update_context(&intent, &entities);

        let context = manager.get_current_context();
        assert!(context.contains_key("last_intent"));
        assert!(context.contains_key("PERSON"));
    }

    #[test]
    fn test_pipeline_with_components() {
        let mut pipeline = NLPPipeline::new(NLPPipelineConfig::default());

        // Add components
        let normalizer = Box::new(TextNormalizer::new(PreprocessingConfig::default()));
        let analyzer = Box::new(SentimentAnalyzer::new());

        pipeline.add_preprocessor(normalizer);
        pipeline.add_analyzer(analyzer);

        let result = pipeline.process_text("This is a great example!").unwrap();

        assert!(!result.original_text.is_empty());
        assert!(!result.processed_text.is_empty());
        assert!(result.analysis_results.len() > 0);
    }

    #[test]
    fn test_model_types() {
        let sentiment_type = ModelType::SentimentAnalysis;
        let ner_type = ModelType::NamedEntityRecognition;
        let custom_type = ModelType::Custom("test".to_string());

        match sentiment_type {
            ModelType::SentimentAnalysis => assert!(true),
            _ => assert!(false),
        }

        match ner_type {
            ModelType::NamedEntityRecognition => assert!(true),
            _ => assert!(false),
        }

        match custom_type {
            ModelType::Custom(name) => assert_eq!(name, "test"),
            _ => assert!(false),
        }
    }

    #[test]
    fn test_evaluation_metrics() {
        let metrics = vec![
            EvaluationMetric::Accuracy,
            EvaluationMetric::F1Score,
            EvaluationMetric::BleuScore,
        ];

        assert_eq!(metrics.len(), 3);
    }
}