### Train a custom classifier model Source: https://docs.rs/bayespam/latest/src/bayespam/lib.rs.html Create a new classifier instance and train it with specific spam and ham examples. ```rust use bayespam::classifier::Classifier; fn main() -> Result<(), std::io::Error> { // Create a new classifier with an empty model let mut classifier = Classifier::new(); // Train the classifier with a new spam example let spam = "Don't forget our special promotion: -30% on men shoes, only today!"; classifier.train_spam(spam); // Train the classifier with a new ham example let ham = "Hi Bob, don't forget our meeting today at 4pm."; classifier.train_ham(ham); // Identify a typical spam message let spam = "Lose up to 19% weight. Special promotion on our new weightloss."; let is_spam = classifier.identify(spam); assert!(is_spam); // Identify a typical ham message let ham = "Hi Bob, can you send me your machine learning homework?"; let is_spam = classifier.identify(ham); assert!(!is_spam); Ok(()) } ``` -------------------------------- ### Train a custom spam classifier model Source: https://docs.rs/bayespam/latest/bayespam Train a new classifier model from scratch by providing spam and ham examples. This allows for custom classification based on your data. ```rust use bayespam::classifier::Classifier; fn main() -> Result<(), std::io::Error> { // Create a new classifier with an empty model let mut classifier = Classifier::new(); // Train the classifier with a new spam example let spam = "Don't forget our special promotion: -30% on men shoes, only today!"; classifier.train_spam(spam); // Train the classifier with a new ham example let ham = "Hi Bob, don't forget our meeting today at 4pm."; classifier.train_ham(ham); // Identify a typical spam message let spam = "Lose up to 19% weight. Special promotion on our new weightloss."; let is_spam = classifier.identify(spam); assert!(is_spam); // Identify a typical ham message let ham = "Hi Bob, can you send me your machine learning homework?"; let is_spam = classifier.identify(ham); assert!(!is_spam); Ok(()) } ``` -------------------------------- ### Classifier Initialization and Loading Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html Provides methods for creating a new classifier or loading a pre-trained model from a file. ```APIDOC ## Classifier Initialization and Loading ### Description Methods for initializing a new Bayesian spam classifier or loading a pre-trained model from a file. ### Methods #### `new()` - **Description**: Builds a new classifier with an empty model. - **Returns**: A new `Classifier` instance. #### `new_from_pre_trained(file: &mut File)` - **Description**: Builds a new classifier with a pre-trained model loaded from the specified file. - **Parameters**: - **file** (*File*) - Required - A mutable reference to a `File` object containing the pre-trained model. - **Returns**: `Result` - Ok containing the loaded `Classifier` or an `io::Error` if loading fails. ``` -------------------------------- ### Add dependency to Cargo.toml Source: https://docs.rs/bayespam/latest/src/bayespam/lib.rs.html Include the bayespam crate in your project manifest. ```ini [dependencies] bayespam = "1.2.0" ``` -------------------------------- ### Add bayespam to Cargo.toml Source: https://docs.rs/bayespam/latest/bayespam Add this to your Cargo.toml manifest to include the bayespam crate in your project. ```toml [dependencies] bayespam = "1.2.0" ``` -------------------------------- ### Classifier Unit Tests Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html Test suite covering new classifier training, Unicode support, and pre-trained model loading. ```rust #[cfg(test)] mod tests { use super::*; #[test] fn test_new() { // Create a new classifier with an empty model let mut classifier = Classifier::new(); // Train the classifier with a new spam example let spam = "Don't forget our special promotion: -30% on men shoes, only today!"; classifier.train_spam(spam); // Train the classifier with a new ham example let ham = "Hi Bob, don't forget our meeting today at 4pm."; classifier.train_ham(ham); // Identify a typical spam message let spam = "Lose up to 19% weight. Special promotion on our new weightloss."; let is_spam = classifier.identify(spam); assert!(is_spam); // Identify a typical ham message let ham = "Hi Bob, can you send me your machine learning homework?"; let is_spam = classifier.identify(ham); assert!(!is_spam); } #[test] fn test_new_unicode() { // Create a new classifier with an empty model let mut classifier = Classifier::new(); // Train the classifier with a new spam example let spam = "Bon plan pour Nöel: profitez de -50% sur le 2ème article."; classifier.train_spam(spam); // Train the classifier with a new ham example let ham = "Vous êtes tous cordialement invités à notre repas de Noël."; classifier.train_ham(ham); // Identify a typical spam message let spam = "Préparez les fêtes de Nöel: 1 article offert!"; let is_spam = classifier.identify(spam); assert!(is_spam); // Identify a typical ham message let ham = "Pourras-tu être des nôtres pour le repas de Noël?"; let is_spam = classifier.identify(ham); assert!(!is_spam); } #[test] fn test_new_from_pre_trained() -> Result<(), io::Error> { // Identify a typical spam message let spam = "Lose up to 19% weight. Special promotion on our new weightloss."; let is_spam = identify(spam)?; assert!(is_spam); // Identify a typical ham message let ham = "Hi Bob, can you send me your machine learning homework?"; let is_spam = identify(ham)?; assert!(!is_spam); Ok(()) } } ``` -------------------------------- ### Classifier Methods Source: https://docs.rs/bayespam/latest/bayespam/classifier/struct.Classifier.html Methods for initializing, training, and utilizing the Bayesian spam classifier. ```APIDOC ## Classifier Methods ### Description Methods to manage the lifecycle and classification logic of the Classifier. ### Methods - **new()**: Build a new classifier with an empty model. - **new_from_pre_trained(file: &mut File)**: Build a new classifier with a pre-trained model loaded from a file. - **save(&self, file: &mut File, pretty: bool)**: Save the classifier to a file as JSON. - **train_spam(&mut self, msg: &str)**: Train the classifier with a spam message. - **train_ham(&mut self, msg: &str)**: Train the classifier with a ham message. - **score(&self, msg: &str) -> f32**: Compute the spam score of a message. - **identify(&self, msg: &str) -> bool**: Identify whether a message is spam or not. ``` -------------------------------- ### Identify messages using a pre-trained model Source: https://docs.rs/bayespam/latest/src/bayespam/lib.rs.html Use a model.json file in the package root to classify messages as spam or ham. ```rust use bayespam::classifier; fn main() -> Result<(), std::io::Error> { // Identify a typical spam message let spam = "Lose up to 19% weight. Special promotion on our new weightloss."; let is_spam = classifier::identify(spam)?; assert!(is_spam); // Identify a typical ham message let ham = "Hi Bob, can you send me your machine learning homework?"; let is_spam = classifier::identify(ham)?; assert!(!is_spam); Ok(()) } ``` -------------------------------- ### Identify messages using a pre-trained model Source: https://docs.rs/bayespam/latest/bayespam Use a pre-trained model (model.json) to score and identify messages. Ensure the model.json file is in your package root. ```rust use bayespam::classifier; fn main() -> Result<(), std::io::Error> { // Identify a typical spam message let spam = "Lose up to 19% weight. Special promotion on our new weightloss."; let is_spam = classifier::identify(spam)?; assert!(is_spam); // Identify a typical ham message let ham = "Hi Bob, can you send me your machine learning homework?"; let is_spam = classifier::identify(ham)?; assert!(!is_spam); Ok(()) } ``` -------------------------------- ### Pre-trained Model Scoring and Identification Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html Utility functions for scoring and identifying spam using a pre-trained model loaded from a file. ```rust /// Compute the spam score of `msg`, based on a pre-trained model. /// The higher the score, the stronger the liklihood that `msg` is a spam is. pub fn score(msg: &str) -> Result { let mut file = File::open(DEFAULT_FILE_PATH)?; Classifier::new_from_pre_trained(&mut file).map(|classifier| classifier.score(msg)) } /// Identify whether `msg` is a spam or not, based on a pre-trained model. pub fn identify(msg: &str) -> Result { let score = score(msg)?; let is_spam = score > SPAM_PROB_THRESHOLD; Ok(is_spam) } ``` -------------------------------- ### Bayesian Spam Classifier Implementation Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html The core implementation of the Classifier struct, including serialization logic, training methods, and probability calculation. ```rust 1use std::collections::HashMap; 2use std::fs::File; 3use std::io; 4 5use serde::{Deserialize, Serialize}; 6use serde_json::{from_reader, to_writer, to_writer_pretty}; 7use unicode_segmentation::UnicodeSegmentation; 8 9const DEFAULT_FILE_PATH: &str = "model.json"; 10const INITIAL_RATING: f32 = 0.5; 11const SPAM_PROB_THRESHOLD: f32 = 0.8; 12 13#[derive(Debug, Default, Serialize, Deserialize)] 14struct Counter { 15 ham: u32, 16 spam: u32, 17} 18 19/// A bayesian spam classifier. 20#[derive(Default, Debug, Deserialize, Serialize)] 21#[serde(from = "ClassifierSerialized")] 22pub struct Classifier { 23 token_table: HashMap, 24 #[serde(skip)] 25 spam_total_count: u32, 26 #[serde(skip)] 27 ham_total_count: u32, 28} 29 30/// The classifier model as it is serialized to disk. 31/// 32/// Does not include the `spam_total_count` and `ham_total_count` fields which 33/// can be recomputed from `token_table`. 34#[derive(Deserialize, Serialize)] 35struct ClassifierSerialized { 36 token_table: HashMap, 37} 38 39impl std::convert::From for Classifier { 40 fn from(c: ClassifierSerialized) -> Self { 41 let spam_total_count = c.token_table.values().map(|x| x.spam).sum(); 42 let ham_total_count = c.token_table.values().map(|x| x.ham).sum(); 43 Self { 44 token_table: c.token_table, 45 spam_total_count, 46 ham_total_count, 47 } 48 } 49} 50 51impl Classifier { 52 /// Build a new classifier with an empty model. 53 pub fn new() -> Self { 54 Default::default() 55 } 56 57 /// Build a new classifier with a pre-trained model loaded from `file`. 58 pub fn new_from_pre_trained(file: &mut File) -> Result { 59 let pre_trained_model = from_reader(file)?; 60 Ok(pre_trained_model) 61 } 62 63 /// Save the classifier to `file` as JSON. 64 /// The JSON will be pretty printed if `pretty` is `true`. 65 pub fn save(&self, file: &mut File, pretty: bool) -> Result<(), io::Error> { 66 if pretty { 67 to_writer_pretty(file, &self)?; 68 } else { 69 to_writer(file, &self)?; 70 } 71 Ok(()) 72 } 73 74 /// Split `msg` into a list of words. 75 fn load_word_list(msg: &str) -> Vec { 76 let word_list = msg.unicode_words().collect::>(); 77 word_list.iter().map(|word| word.to_string()).collect() 78 } 79 80 /// Train the classifier with a spam `msg`. 81 pub fn train_spam(&mut self, msg: &str) { 82 for word in Self::load_word_list(msg) { 83 let counter = self.token_table.entry(word).or_default(); 84 counter.spam += 1; 85 self.spam_total_count += 1; 86 } 87 } 88 89 /// Train the classifier with a ham `msg`. 90 pub fn train_ham(&mut self, msg: &str) { 91 for word in Self::load_word_list(msg) { 92 let counter = self.token_table.entry(word).or_default(); 93 counter.ham += 1; 94 self.ham_total_count += 1; 95 } 96 } 97 98 /// Return the total number of spam in token table. 99 fn spam_total_count(&self) -> u32 { 100 self.spam_total_count 101 } 102 103 /// Return the total number of ham in token table. 104 fn ham_total_count(&self) -> u32 { 105 self.ham_total_count 106 } 107 108 /// Compute the probability of each word of `msg` to be part of a spam. 109 fn rate_words(&self, msg: &str) -> Vec { 110 Self::load_word_list(msg) 111 .into_iter() 112 .map(|word| { 113 // If word was previously added in the model 114 if let Some(counter) = self.token_table.get(&word) { 115 // If the word has only been part of spam messages, 116 // assign it a probability of 0.99 to be part of a spam 117 if counter.spam > 0 && counter.ham == 0 { 118 return 0.99; 119 // If the word has only been part of ham messages, 120 // assign it a probability of 0.01 to be part of a spam 121 } else if counter.spam == 0 && counter.ham > 0 { 122 return 0.01; 123 // If the word has been part of both spam and ham messages, 124 // calculate the probability to be part of a spam 125 } else if self.spam_total_count() > 0 && self.ham_total_count() > 0 { 126 let ham_prob = (counter.ham as f32) / (self.ham_total_count() as f32); 127 let spam_prob = (counter.spam as f32) / (self.spam_total_count() as f32); 128 return (spam_prob / (ham_prob + spam_prob)).max(0.01); 129 } 130 } 131 // If word was never added to the model, ``` -------------------------------- ### Classifier Training and Identification Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html This section covers the core functionalities of the Classifier struct, including training with spam and ham messages, and identifying messages based on the trained model. ```APIDOC ## Classifier Methods ### `new()` Creates a new classifier with an empty model. ### `train_spam(msg: &str)` Trains the classifier with a given spam message. ### `train_ham(msg: &str)` Trains the classifier with a given ham (non-spam) message. ### `score(msg: &str) -> f32` Computes the spam score of a message. A higher score indicates a stronger likelihood of the message being spam. ### `identify(msg: &str) -> bool` Identifies whether a message is spam or not based on its score. Returns `true` if the message is identified as spam, `false` otherwise. ### Example Usage: ```rust let mut classifier = Classifier::new(); classifier.train_spam("Buy now and get 50% off!"); classifier.train_ham("Meeting scheduled for tomorrow."); let is_spam = classifier.identify("Special offer: limited time discount!"); assert!(is_spam); ``` ``` -------------------------------- ### Pre-trained Model Functions Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html These functions allow you to use a pre-trained spam classification model without needing to train it yourself. They load the model from a default file path. ```APIDOC ## Pre-trained Model Functions ### `score(msg: &str) -> Result` Computes the spam score of a message using a pre-trained model. The model is loaded from a default file path. - **Returns**: A `Result` containing the spam score (`f32`) on success, or an `io::Error` if the model file cannot be opened or processed. ### `identify(msg: &str) -> Result` Identifies whether a message is spam or not using a pre-trained model. This function first calculates the spam score and then compares it against a predefined spam probability threshold. - **Returns**: A `Result` containing a boolean (`true` if spam, `false` if ham) on success, or an `io::Error` if the score calculation fails. ### Example Usage: ```rust match identify("Claim your prize now!") { Ok(is_spam) => { if is_spam { println!("Message is likely spam."); } else { println!("Message is likely ham."); } } Err(e) => { eprintln!("Error identifying message: {}", e); } } ``` ``` -------------------------------- ### Classifier Training Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html Methods for training the classifier with spam and ham messages. ```APIDOC ## Classifier Training ### Description Methods for training the Bayesian spam classifier with examples of spam and ham messages. ### Methods #### `train_spam(msg: &str)` - **Description**: Trains the classifier with a given message, marking it as spam. - **Parameters**: - **msg** (*&str*) - Required - The message content to train as spam. #### `train_ham(msg: &str)` - **Description**: Trains the classifier with a given message, marking it as ham. - **Parameters**: - **msg** (*&str*) - Required - The message content to train as ham. ``` -------------------------------- ### Classifier Saving Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html Method for saving the current state of the classifier to a file. ```APIDOC ## Classifier Saving ### Description Saves the current state of the Bayesian spam classifier to a specified file. ### Method #### `save(file: &mut File, pretty: bool)` - **Description**: Saves the classifier model to the given file. - **Parameters**: - **file** (*File*) - Required - A mutable reference to a `File` object where the model will be saved. - **pretty** (*bool*) - Required - If `true`, the JSON output will be pretty-printed; otherwise, it will be compact. - **Returns**: `Result<(), io::Error>` - Ok if saving is successful, or an `io::Error` if an error occurs during saving. ``` -------------------------------- ### identify Source: https://docs.rs/bayespam/latest/bayespam/classifier/fn.identify.html Classifies a given message string as spam or not spam. ```APIDOC ## identify ### Description Identify whether a message is spam or not, based on a pre-trained model. ### Method Function Call ### Parameters #### Arguments - **msg** (&str) - Required - The message content to be classified. ### Response - **Result** - Returns Ok(true) if the message is identified as spam, Ok(false) if not, or an Error if classification fails. ``` -------------------------------- ### Classifier Struct Source: https://docs.rs/bayespam/latest/bayespam/classifier/index.html The core structure used for Bayesian spam classification. ```APIDOC ## Struct: Classifier ### Description A bayesian spam classifier used to process messages against a pre-trained model. ``` -------------------------------- ### Compute spam score with bayespam Source: https://docs.rs/bayespam/latest/bayespam/classifier/fn.score.html Calculates the spam likelihood of a message string. Returns a Result containing an f32 score or an Error. ```rust pub fn score(msg: &str) -> Result ``` -------------------------------- ### Compute Spam Score and Identify Spam Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html Methods for calculating the probability of a message being spam and determining its classification status. ```rust /// Compute the spam score of `msg`. /// The higher the score, the stronger the liklihood that `msg` is a spam is. pub fn score(&self, msg: &str) -> f32 { // Compute the probability of each word to be part of a spam let ratings = self.rate_words(msg); let ratings = match ratings.len() { // If there are no ratings, return a score of 0 0 => return 0.0, // If there are more than 20 ratings, keep only the 10 first // and 10 last ratings to calculate a score x if x > 20 => { let length = ratings.len(); let mut ratings = ratings; ratings.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap()); [&ratings[..10], &ratings[length - 10..]].concat() } // In all other cases, keep ratings to calculate a score _ => ratings, }; // Combine individual ratings let product: f32 = ratings.iter().product(); let alt_product: f32 = ratings.iter().map(|x| 1.0 - x).product(); product / (product + alt_product) } /// Identify whether `msg` is a spam or not. pub fn identify(&self, msg: &str) -> bool { self.score(msg) > SPAM_PROB_THRESHOLD } } ``` -------------------------------- ### Classifier struct definition Source: https://docs.rs/bayespam/latest/bayespam/classifier/struct.Classifier.html The primary structure used for Bayesian spam classification. ```rust pub struct Classifier { /* private fields */ } ``` -------------------------------- ### Message Classification Source: https://docs.rs/bayespam/latest/src/bayespam/classifier.rs.html Methods for classifying messages and calculating spam probabilities. ```APIDOC ## Message Classification ### Description Provides functionality to classify messages and determine the probability of them being spam. ### Methods #### `rate_message(msg: &str)` - **Description**: Calculates the probability of a given message being spam. - **Parameters**: - **msg** (*&str*) - Required - The message content to classify. - **Returns**: `f32` - The probability (between 0.0 and 1.0) that the message is spam. ``` -------------------------------- ### identify Function Source: https://docs.rs/bayespam/latest/bayespam/classifier/index.html Function to determine if a message is spam. ```APIDOC ## Function: identify ### Description Identify whether `msg` is a spam or not, based on a pre-trained model. ``` -------------------------------- ### score Function Source: https://docs.rs/bayespam/latest/bayespam/classifier/index.html Function to calculate the spam probability score. ```APIDOC ## Function: score ### Description Compute the spam score of `msg`, based on a pre-trained model. The higher the score, the stronger the likelihood that `msg` is a spam. ``` -------------------------------- ### score function Source: https://docs.rs/bayespam/latest/bayespam/classifier/fn.score.html Computes the spam score of a message based on a pre-trained model. ```APIDOC ## Function: score ### Description Compute the spam score of a message, based on a pre-trained model. The higher the score, the stronger the likelihood that the message is spam. ### Signature `pub fn score(msg: &str) -> Result` ### Parameters - **msg** (str) - Required - The message content to be evaluated. ### Response - **Result** - Returns a floating-point score representing the spam probability or an Error if the evaluation fails. ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.