quantrs2_ml/
nlp.rs

1use crate::error::{MLError, Result};
2use crate::qnn::QuantumNeuralNetwork;
3use ndarray::{Array1, Array2};
4use std::collections::HashMap;
5use std::fmt;
6
7/// Type of NLP task
8#[derive(Debug, Clone, Copy, PartialEq)]
9pub enum NLPTaskType {
10    /// Text classification
11    Classification,
12
13    /// Sequence labeling
14    SequenceLabeling,
15
16    /// Machine translation
17    Translation,
18
19    /// Language generation
20    Generation,
21
22    /// Sentiment analysis
23    SentimentAnalysis,
24
25    /// Text summarization
26    Summarization,
27}
28
29/// Strategy for text embedding
30#[derive(Debug, Clone, Copy, PartialEq)]
31pub enum EmbeddingStrategy {
32    /// Bag of words
33    BagOfWords,
34
35    /// Term frequency-inverse document frequency
36    TFIDF,
37
38    /// Word2Vec
39    Word2Vec,
40
41    /// Custom embedding
42    Custom,
43}
44
45impl From<usize> for EmbeddingStrategy {
46    fn from(value: usize) -> Self {
47        match value {
48            0 => EmbeddingStrategy::BagOfWords,
49            1 => EmbeddingStrategy::TFIDF,
50            2 => EmbeddingStrategy::Word2Vec,
51            _ => EmbeddingStrategy::Custom,
52        }
53    }
54}
55
56/// Text preprocessing for NLP
57#[derive(Debug, Clone)]
58pub struct TextPreprocessor {
59    /// Whether to convert to lowercase
60    pub lowercase: bool,
61
62    /// Whether to remove stopwords
63    pub remove_stopwords: bool,
64
65    /// Whether to lemmatize
66    pub lemmatize: bool,
67
68    /// Whether to stem
69    pub stem: bool,
70
71    /// Custom stopwords
72    pub stopwords: Vec<String>,
73}
74
75impl TextPreprocessor {
76    /// Creates a new text preprocessor with default settings
77    pub fn new() -> Self {
78        TextPreprocessor {
79            lowercase: true,
80            remove_stopwords: true,
81            lemmatize: false,
82            stem: false,
83            stopwords: Vec::new(),
84        }
85    }
86
87    /// Sets whether to convert to lowercase
88    pub fn with_lowercase(mut self, lowercase: bool) -> Self {
89        self.lowercase = lowercase;
90        self
91    }
92
93    /// Sets whether to remove stopwords
94    pub fn with_remove_stopwords(mut self, remove_stopwords: bool) -> Self {
95        self.remove_stopwords = remove_stopwords;
96        self
97    }
98
99    /// Sets whether to lemmatize
100    pub fn with_lemmatize(mut self, lemmatize: bool) -> Self {
101        self.lemmatize = lemmatize;
102        self
103    }
104
105    /// Sets whether to stem
106    pub fn with_stem(mut self, stem: bool) -> Self {
107        self.stem = stem;
108        self
109    }
110
111    /// Sets custom stopwords
112    pub fn with_stopwords(mut self, stopwords: Vec<String>) -> Self {
113        self.stopwords = stopwords;
114        self
115    }
116
117    /// Preprocesses text
118    pub fn preprocess(&self, text: &str) -> Result<String> {
119        // This is a dummy implementation
120        // In a real system, this would apply the specified preprocessing steps
121
122        let mut processed = text.to_string();
123
124        if self.lowercase {
125            processed = processed.to_lowercase();
126        }
127
128        if self.remove_stopwords {
129            for stopword in &self.stopwords {
130                processed = processed.replace(stopword, "");
131            }
132        }
133
134        Ok(processed)
135    }
136
137    /// Tokenizes text
138    pub fn tokenize(&self, text: &str) -> Result<Vec<String>> {
139        // This is a dummy implementation
140        // In a real system, this would use a proper tokenizer
141
142        let processed = self.preprocess(text)?;
143        let tokens = processed
144            .split_whitespace()
145            .map(|s| s.to_string())
146            .collect::<Vec<_>>();
147
148        Ok(tokens)
149    }
150}
151
152/// Word embedding for text representation
153#[derive(Debug, Clone)]
154pub struct WordEmbedding {
155    /// Embedding strategy
156    pub strategy: EmbeddingStrategy,
157
158    /// Embedding dimension
159    pub dimension: usize,
160
161    /// Word-to-embedding mapping
162    pub embeddings: HashMap<String, Array1<f64>>,
163
164    /// Vocabulary
165    pub vocabulary: Vec<String>,
166}
167
168impl WordEmbedding {
169    /// Creates a new word embedding
170    pub fn new(strategy: EmbeddingStrategy, dimension: usize) -> Self {
171        WordEmbedding {
172            strategy,
173            dimension,
174            embeddings: HashMap::new(),
175            vocabulary: Vec::new(),
176        }
177    }
178
179    /// Fits the embedding on a corpus
180    pub fn fit(&mut self, corpus: &[&str]) -> Result<()> {
181        // This is a dummy implementation
182        // In a real system, this would build the vocabulary and compute embeddings
183
184        let mut vocabulary = HashMap::new();
185
186        // Build the vocabulary
187        for text in corpus {
188            for word in text.split_whitespace() {
189                let count = vocabulary.entry(word.to_string()).or_insert(0);
190                *count += 1;
191            }
192        }
193
194        // Sort by frequency
195        let mut vocab_items = vocabulary
196            .iter()
197            .map(|(word, count)| (word.clone(), *count))
198            .collect::<Vec<_>>();
199
200        vocab_items.sort_by(|a, b| b.1.cmp(&a.1));
201
202        // Take the top N words
203        self.vocabulary = vocab_items
204            .iter()
205            .map(|(word, _)| word.clone())
206            .take(10000)
207            .collect();
208
209        // Generate random embeddings for each word
210        for word in &self.vocabulary {
211            let embedding = Array1::from_vec(
212                (0..self.dimension)
213                    .map(|_| rand::random::<f64>() * 2.0 - 1.0)
214                    .collect(),
215            );
216
217            self.embeddings.insert(word.clone(), embedding);
218        }
219
220        Ok(())
221    }
222
223    /// Gets the embedding for a word
224    pub fn get_embedding(&self, word: &str) -> Option<&Array1<f64>> {
225        self.embeddings.get(word)
226    }
227
228    /// Gets the embedding for a sentence
229    pub fn embed_text(&self, text: &str) -> Result<Array1<f64>> {
230        // This is a simplified implementation
231        // In a real system, this would properly combine word embeddings
232
233        let words = text.split_whitespace().collect::<Vec<_>>();
234        let mut embedding = Array1::zeros(self.dimension);
235        let mut count = 0;
236
237        for word in words {
238            if let Some(word_embedding) = self.get_embedding(word) {
239                embedding += word_embedding;
240                count += 1;
241            }
242        }
243
244        if count > 0 {
245            embedding /= count as f64;
246        }
247
248        Ok(embedding)
249    }
250}
251
252/// Quantum language model for NLP tasks
253#[derive(Debug, Clone)]
254pub struct QuantumLanguageModel {
255    /// Number of qubits
256    pub num_qubits: usize,
257
258    /// Embedding strategy
259    pub embedding_strategy: EmbeddingStrategy,
260
261    /// Text preprocessor
262    pub preprocessor: TextPreprocessor,
263
264    /// Word embedding
265    pub embedding: WordEmbedding,
266
267    /// Quantum neural network
268    pub qnn: QuantumNeuralNetwork,
269
270    /// Type of NLP task
271    pub task: NLPTaskType,
272
273    /// Class labels (for classification tasks)
274    pub labels: Vec<String>,
275}
276
277impl QuantumLanguageModel {
278    /// Creates a new quantum language model
279    pub fn new(
280        num_qubits: usize,
281        embedding_dimension: usize,
282        strategy: EmbeddingStrategy,
283        task: NLPTaskType,
284        labels: Vec<String>,
285    ) -> Result<Self> {
286        let preprocessor = TextPreprocessor::new();
287        let embedding = WordEmbedding::new(strategy, embedding_dimension);
288
289        // Create a QNN architecture suitable for the task
290        let layers = vec![
291            crate::qnn::QNNLayerType::EncodingLayer {
292                num_features: embedding_dimension,
293            },
294            crate::qnn::QNNLayerType::VariationalLayer {
295                num_params: 2 * num_qubits,
296            },
297            crate::qnn::QNNLayerType::EntanglementLayer {
298                connectivity: "full".to_string(),
299            },
300            crate::qnn::QNNLayerType::VariationalLayer {
301                num_params: 2 * num_qubits,
302            },
303            crate::qnn::QNNLayerType::MeasurementLayer {
304                measurement_basis: "computational".to_string(),
305            },
306        ];
307
308        let output_dim = match task {
309            NLPTaskType::Classification | NLPTaskType::SentimentAnalysis => labels.len(),
310            NLPTaskType::SequenceLabeling => labels.len(),
311            NLPTaskType::Translation => embedding_dimension,
312            NLPTaskType::Generation => embedding_dimension,
313            NLPTaskType::Summarization => embedding_dimension,
314        };
315
316        let qnn = QuantumNeuralNetwork::new(layers, num_qubits, embedding_dimension, output_dim)?;
317
318        Ok(QuantumLanguageModel {
319            num_qubits,
320            embedding_strategy: strategy,
321            preprocessor,
322            embedding,
323            qnn,
324            task,
325            labels,
326        })
327    }
328
329    /// Fits the model on a corpus
330    pub fn fit(&mut self, texts: &[&str], labels: &[usize]) -> Result<()> {
331        // First, fit the embedding on the corpus
332        self.embedding.fit(texts)?;
333
334        // Convert texts to embeddings
335        let mut embeddings = Vec::with_capacity(texts.len());
336
337        for text in texts {
338            let embedding = self.embedding.embed_text(text)?;
339            embeddings.push(embedding);
340        }
341
342        // Convert to ndarray
343        let x_train = Array2::from_shape_vec(
344            (embeddings.len(), self.embedding.dimension),
345            embeddings.iter().flat_map(|e| e.iter().cloned()).collect(),
346        )
347        .map_err(|e| MLError::DataError(format!("Failed to create training data: {}", e)))?;
348
349        // Convert labels to one-hot encoding
350        let y_train = Array1::from_vec(labels.iter().map(|&l| l as f64).collect());
351
352        // Train the QNN
353        self.qnn.train_1d(&x_train, &y_train, 100, 0.01)?;
354
355        Ok(())
356    }
357
358    /// Predicts the label for a text
359    pub fn predict(&self, text: &str) -> Result<(String, f64)> {
360        // Embed the text
361        let embedding = self.embedding.embed_text(text)?;
362
363        // Run the QNN
364        let output = self.qnn.forward(&embedding)?;
365
366        // Find the label with the highest score
367        let mut best_label = 0;
368        let mut best_score = output[0];
369
370        for i in 1..output.len() {
371            if output[i] > best_score {
372                best_score = output[i];
373                best_label = i;
374            }
375        }
376
377        if best_label < self.labels.len() {
378            Ok((self.labels[best_label].clone(), best_score))
379        } else {
380            Err(MLError::MLOperationError(format!(
381                "Invalid prediction index: {}",
382                best_label
383            )))
384        }
385    }
386}
387
388/// Sentiment analyzer using quantum language models
389#[derive(Debug, Clone)]
390pub struct SentimentAnalyzer {
391    /// Quantum language model
392    model: QuantumLanguageModel,
393}
394
395impl SentimentAnalyzer {
396    /// Creates a new sentiment analyzer
397    pub fn new(num_qubits: usize) -> Result<Self> {
398        let model = QuantumLanguageModel::new(
399            num_qubits,
400            32, // embedding dimension
401            EmbeddingStrategy::BagOfWords,
402            NLPTaskType::SentimentAnalysis,
403            vec![
404                "negative".to_string(),
405                "neutral".to_string(),
406                "positive".to_string(),
407            ],
408        )?;
409
410        Ok(SentimentAnalyzer { model })
411    }
412
413    /// Analyzes the sentiment of text
414    pub fn analyze(&self, text: &str) -> Result<(String, f64)> {
415        self.model.predict(text)
416    }
417
418    /// Trains the sentiment analyzer
419    pub fn train(&mut self, texts: &[&str], labels: &[usize]) -> Result<()> {
420        self.model.fit(texts, labels)
421    }
422}
423
424/// Text summarizer using quantum language models
425#[derive(Debug, Clone)]
426pub struct TextSummarizer {
427    /// Quantum language model
428    model: QuantumLanguageModel,
429
430    /// Maximum summary length
431    max_length: usize,
432}
433
434impl TextSummarizer {
435    /// Creates a new text summarizer
436    pub fn new(num_qubits: usize) -> Result<Self> {
437        let model = QuantumLanguageModel::new(
438            num_qubits,
439            64, // embedding dimension
440            EmbeddingStrategy::BagOfWords,
441            NLPTaskType::Summarization,
442            Vec::new(), // No specific labels for summarization
443        )?;
444
445        Ok(TextSummarizer {
446            model,
447            max_length: 100,
448        })
449    }
450
451    /// Sets the maximum summary length
452    pub fn with_max_length(mut self, max_length: usize) -> Self {
453        self.max_length = max_length;
454        self
455    }
456
457    /// Summarizes text
458    pub fn summarize(&self, text: &str) -> Result<String> {
459        // This is a dummy implementation
460        // In a real system, this would use the quantum language model to generate a summary
461
462        let sentences = text.split('.').collect::<Vec<_>>();
463        let num_sentences = sentences.len();
464
465        // Generate a summary by selecting key sentences
466        let num_summary_sentences = (num_sentences / 4).max(1);
467        let selected_indices = vec![0, num_sentences / 2, num_sentences - 1];
468
469        let mut summary = String::new();
470
471        for &index in selected_indices.iter().take(num_summary_sentences) {
472            if index < sentences.len() {
473                summary.push_str(sentences[index]);
474                summary.push('.');
475            }
476        }
477
478        // Truncate to max length if needed
479        if summary.len() > self.max_length {
480            let truncated = summary.chars().take(self.max_length).collect::<String>();
481            let last_space = truncated.rfind(' ').unwrap_or(truncated.len());
482            summary = truncated[..last_space].to_string();
483            summary.push_str("...");
484        }
485
486        Ok(summary)
487    }
488}
489
490impl fmt::Display for NLPTaskType {
491    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
492        match self {
493            NLPTaskType::Classification => write!(f, "Classification"),
494            NLPTaskType::SequenceLabeling => write!(f, "Sequence Labeling"),
495            NLPTaskType::Translation => write!(f, "Translation"),
496            NLPTaskType::Generation => write!(f, "Generation"),
497            NLPTaskType::SentimentAnalysis => write!(f, "Sentiment Analysis"),
498            NLPTaskType::Summarization => write!(f, "Summarization"),
499        }
500    }
501}
502
503impl fmt::Display for EmbeddingStrategy {
504    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
505        match self {
506            EmbeddingStrategy::BagOfWords => write!(f, "Bag of Words"),
507            EmbeddingStrategy::TFIDF => write!(f, "TF-IDF"),
508            EmbeddingStrategy::Word2Vec => write!(f, "Word2Vec"),
509            EmbeddingStrategy::Custom => write!(f, "Custom"),
510        }
511    }
512}
513
514/// Implementation of missing methods for QuantumLanguageModel
515impl QuantumLanguageModel {
516    /// Builds vocabulary from a set of texts
517    pub fn build_vocabulary(&mut self, texts: &[String]) -> Result<usize> {
518        // In a full implementation, this would analyze texts and build vocabulary
519        // For now, just return a dummy vocabulary size
520        let vocab_size = texts
521            .iter()
522            .flat_map(|text| text.split_whitespace())
523            .collect::<std::collections::HashSet<_>>()
524            .len();
525
526        Ok(vocab_size)
527    }
528
529    /// Trains word embeddings
530    pub fn train_embeddings(&mut self, texts: &[String]) -> Result<()> {
531        // Dummy implementation that would train word embeddings
532        // In reality, this would update the embedding matrix based on texts
533        println!(
534            "  Training embeddings for {} texts with strategy: {}",
535            texts.len(),
536            self.embedding_strategy
537        );
538
539        Ok(())
540    }
541
542    /// Trains the language model
543    pub fn train(
544        &mut self,
545        texts: &[String],
546        labels: &[usize],
547        epochs: usize,
548        learning_rate: f64,
549    ) -> Result<()> {
550        // Convert texts to feature vectors using the embedding
551        let num_samples = texts.len();
552        let mut features = Array2::zeros((num_samples, self.embedding.dimension));
553
554        // Create dummy features
555        for (i, text) in texts.iter().enumerate() {
556            // Simple hash-based feature extraction
557            let feature_vec = text
558                .chars()
559                .enumerate()
560                .map(|(j, c)| (c as u32 % 8) as f64 / 8.0 + j as f64 * 0.001)
561                .take(self.embedding.dimension)
562                .collect::<Vec<_>>();
563
564            for (j, &val) in feature_vec
565                .iter()
566                .enumerate()
567                .take(self.embedding.dimension)
568            {
569                if j < features.ncols() {
570                    features[[i, j]] = val;
571                }
572            }
573        }
574
575        // Convert labels to float array
576        let y_train = Array1::from_vec(labels.iter().map(|&l| l as f64).collect());
577
578        // Train the underlying QNN
579        self.qnn
580            .train_1d(&features, &y_train, epochs, learning_rate)?;
581
582        Ok(())
583    }
584
585    /// Classifies a text
586    pub fn classify(&self, text: &str) -> Result<(String, f64)> {
587        // In a real implementation, this would encode the text and run it through the QNN
588
589        // Simple hash-based classification for demonstration
590        let hash = text.chars().map(|c| c as u32).sum::<u32>();
591        let class_idx = (hash % self.labels.len() as u32) as usize;
592        let confidence = 0.7 + 0.3 * (hash % 100) as f64 / 100.0;
593
594        Ok((self.labels[class_idx].clone(), confidence))
595    }
596}
quantrs2_ml/nlp.rs

quantrs2_ml/
nlp.rs
