dataset-ml 0.4.0

//! SMS Spam Collection dataset.
//!
//! This dataset is a set of SMS messages tagged as legitimate (`ham`) or spam.
//! Researchers collected it for SMS spam research. This is the crate's first
//! **text** dataset. The "features" are the raw message strings. There is no
//! numeric or categorical feature matrix. Vectorize the text yourself
//! (bag-of-words, TF-IDF, embeddings, and so on). The document accessor is
//! [`SmsSpam::texts`] (it returns an `Array1<String>` of raw messages), not
//! `features()`.
//!
//! **Documents:** `Array1<String>` of 5,574 raw SMS message bodies
//!
//! **Target:** `label`, one of `ham` or `spam`
//!
//! **Samples:** 5,574 (4,827 ham, 747 spam)
//! **Application:** Binary text classification / spam detection
//!
//! **Source:** UCI Machine Learning Repository
//! <https://doi.org/10.24432/C5CC84>

use crate::DOWNLOAD_RETRIES;
use crate::traits::impl_ml_dataset;
use csv::ReaderBuilder;
use dataset_core::{Dataset, DatasetError, acquire_dataset, download_to_with_retries, unzip};
use ndarray::Array1;
use std::fs::File;

/// Type alias for the SMS Spam dataset: (message texts, labels).
type SmsSpamData = (Array1<String>, Array1<&'static str>);

/// The URL for the SMS Spam Collection dataset (a ZIP archive).
const SMS_SPAM_DATA_URL: &str =
    "https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip";

/// The name of the downloaded ZIP archive (inside the temp dir).
const SMS_SPAM_ZIP_FILENAME: &str = "smsspamcollection.zip";

/// The name of the data file inside the ZIP archive.
const SMS_SPAM_SOURCE_FILENAME: &str = "SMSSpamCollection";

/// The name of the cached SMS Spam dataset file.
const SMS_SPAM_FILENAME: &str = "sms_spam.csv";

/// The SHA256 hash of the cached SMS Spam dataset file (the extracted
/// `SMSSpamCollection` file's bytes).
const SMS_SPAM_SHA256: &str = "7d039a24a6083ed9ef0f806ebad56bbb976e3aeb8de05669173bfdc4996c239d";

/// The name of the dataset.
const SMS_SPAM_DATASET_NAME: &str = "sms_spam";

/// Number of samples.
const N_SAMPLES: usize = 5_574;

/// Number of columns per record (1 label + 1 message text).
const N_COLUMNS: usize = 2;

/// Source column index of the label.
const LABEL_COLUMN: usize = 0;

/// Source column index of the message text.
const TEXT_COLUMN: usize = 1;

/// A struct that represents the SMS Spam Collection dataset with lazy loading.
///
/// The dataset loads only when you call a data accessor method. After the first
/// load, the dataset caches the data for later accesses.
///
/// # About Dataset
///
/// The SMS Spam Collection is a set of SMS messages. Researchers collected the
/// messages for SMS spam research. It contains 5,574 English messages, each
/// tagged as `ham` (legitimate) or `spam`. The messages come from several
/// sources, including the Grumbletext website, the NUS SMS Corpus, and a PhD
/// thesis collection. It is a standard benchmark for text classification.
///
/// # Documents
///
/// Unlike the tabular loaders, there is no feature matrix. Each sample is a raw
/// message string. [`SmsSpam::texts`] returns a `(5574,)` `Array1<String>` of the
/// message bodies. Vectorize the messages yourself (bag-of-words, TF-IDF,
/// embeddings, and so on) before you use them as model input.
///
/// # Labels
///
/// - `label` (shape `(5574,)`): the `Array1<&'static str>` is one of `"ham"`
///   (legitimate) or `"spam"`.
///
/// See more information at <https://archive.ics.uci.edu/dataset/228/sms+spam+collection>.
///
/// # Citation
///
/// Almeida, T. & Hidalgo, J. (2011). SMS Spam Collection \[Dataset\]. UCI Machine
/// Learning Repository. <https://doi.org/10.24432/C5CC84>
///
/// # Thread Safety
///
/// This struct implements `Send` and `Sync` automatically, because all fields
/// implement them. This makes the struct safe to share across threads. The
/// internal [`Dataset`] makes lazy initialization thread-safe.
///
/// # Example
/// ```no_run
/// use dataset_ml::sms_spam::SmsSpam;
///
/// let download_dir = "./sms_spam"; // the code creates the directory if it does not exist
///
/// let mut dataset = SmsSpam::new(download_dir);
/// let texts = dataset.texts().unwrap();
/// let labels = dataset.labels().unwrap();
///
/// let (texts, labels) = dataset.data().unwrap(); // this also returns texts and labels
/// assert_eq!(texts.len(), 5574);
/// assert_eq!(labels.len(), 5574);
///
/// // `get_data()` borrows the cached arrays without reloading. `get_data_mut()`
/// // edits the arrays in place. This needs no clone and no reload. The change
/// // stays cached. Prefer this method over `.to_owned()` when you only need to
/// // change values.
/// if let Some((texts, labels)) = dataset.get_data_mut() {
///     texts[0] = "hello world".to_string();
///     labels[0] = "spam";
/// }
/// assert!(dataset.get_data().is_some());
///
/// // `take_data()` moves the owned arrays out (no `to_owned()` clone). It leaves
/// // the instance reusable. The next access reloads the data from the cached file.
/// let (owned_texts, owned_labels) = dataset.take_data().unwrap();
/// assert_eq!(owned_texts.len(), 5574);
/// assert_eq!(owned_labels.len(), 5574);
///
/// // `into_data()` also returns the owned arrays with no clone, but consumes the
/// // instance (use it when you are done with the dataset).
/// let (owned_texts, owned_labels) = dataset.into_data().unwrap();
/// assert_eq!(owned_texts.len(), 5574);
/// assert_eq!(owned_labels.len(), 5574);
/// ```
#[derive(Debug)]
pub struct SmsSpam {
    dataset: Dataset<SmsSpamData, DatasetError>,
}

impl SmsSpam {
    /// Create a new SmsSpam instance without loading data.
    ///
    /// The dataset loads lazily, on your first call to a data accessor method.
    /// This is a lightweight operation that only stores the storage directory.
    ///
    /// # Parameters
    ///
    /// - `storage_dir` - Directory where the dataset is stored.
    ///
    /// # Returns
    ///
    /// - `Self` - `SmsSpam` instance ready for lazy loading.
    pub fn new(storage_dir: &str) -> Self {
        SmsSpam {
            dataset: Dataset::new(storage_dir, Self::load_data),
        }
    }

    /// Get and parse the SMS Spam dataset.
    fn load_data(dir: &str) -> Result<SmsSpamData, DatasetError> {
        // Prepare the dataset file. Download the ZIP, extract it, and use the
        // `SMSSpamCollection` file. The code caches this file under `sms_spam.csv`.
        let file_path = acquire_dataset(
            dir,
            SMS_SPAM_FILENAME,
            SMS_SPAM_DATASET_NAME,
            Some(SMS_SPAM_SHA256),
            |temp_path| {
                download_to_with_retries(
                    SMS_SPAM_DATA_URL,
                    temp_path,
                    Some(SMS_SPAM_ZIP_FILENAME),
                    DOWNLOAD_RETRIES,
                )?;
                unzip(&temp_path.join(SMS_SPAM_ZIP_FILENAME), temp_path)?;
                Ok(temp_path.join(SMS_SPAM_SOURCE_FILENAME))
            },
        )?;

        // The source is tab-separated with no header: `label<TAB>message`. The
        // messages are free text and can contain `"`, `,`, and other punctuation.
        // The code disables quote processing, so it splits each record only on tabs.
        let file = File::open(&file_path)?;
        let mut rdr = ReaderBuilder::new()
            .delimiter(b'\t')
            .has_headers(false)
            .quoting(false)
            .from_reader(file);

        let mut texts: Vec<String> = Vec::with_capacity(N_SAMPLES);
        let mut labels: Vec<&'static str> = Vec::with_capacity(N_SAMPLES);

        for (idx, result) in rdr.records().enumerate() {
            let record =
                result.map_err(|e| DatasetError::csv_read_error(SMS_SPAM_DATASET_NAME, e))?;
            let line_num = idx + 1; // headerless file, lines are 1-indexed

            // Skip blank lines, such as a trailing newline.
            if record.iter().all(|f| f.is_empty()) {
                continue;
            }

            if record.len() != N_COLUMNS {
                return Err(DatasetError::invalid_column_count(
                    SMS_SPAM_DATASET_NAME,
                    N_COLUMNS,
                    record.len(),
                    line_num,
                ));
            }

            // Label. The code maps the source token to a readable `&'static str`.
            let label = match &record[LABEL_COLUMN] {
                "ham" => "ham",
                "spam" => "spam",
                other => {
                    return Err(DatasetError::invalid_value(
                        SMS_SPAM_DATASET_NAME,
                        "label",
                        other,
                        line_num,
                    ));
                }
            };
            labels.push(label);

            // Message text. The code stores it unchanged.
            texts.push(record[TEXT_COLUMN].to_string());
        }

        let n_samples = labels.len();
        if n_samples == 0 {
            return Err(DatasetError::empty_dataset(SMS_SPAM_DATASET_NAME));
        }

        let texts_array = Array1::from_vec(texts);
        let labels_array = Array1::from_vec(labels);

        Ok((texts_array, labels_array))
    }

    /// Get a reference to the message-text vector.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// This method is the SMS Spam equivalent of the tabular loaders' `features()`.
    /// The data is text, so the "features" are the raw message strings. This
    /// method returns a 1-D `Array1<String>`, not a 2-D feature matrix.
    ///
    /// # Returns
    ///
    /// - `&Array1<String>` - Reference to the message-text vector with shape
    ///   `(5574,)`, each entry a raw SMS message body.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if:
    /// - Download fails due to network issues
    /// - File extraction or I/O operations fail
    /// - Data format is invalid (wrong number of columns, or invalid labels)
    /// - Dataset size does not match expected dimensions (5,574 samples)
    pub fn texts(&self) -> Result<&Array1<String>, DatasetError> {
        Ok(&self.dataset.load()?.0)
    }

    /// Get a reference to the labels vector.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&Array1<&'static str>` - Reference to labels vector with shape `(5574,)` containing `"ham"` or `"spam"`
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if:
    /// - Download fails due to network issues
    /// - File extraction or I/O operations fail
    /// - Data format is invalid (wrong number of columns, or invalid labels)
    /// - Dataset size does not match expected dimensions (5,574 samples)
    pub fn labels(&self) -> Result<&Array1<&'static str>, DatasetError> {
        Ok(&self.dataset.load()?.1)
    }

    /// Get both message texts and labels as references.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&SmsSpamData` - reference to the cached `(texts, labels)` tuple: the
    ///   message-text vector `(5574,)` and the label vector `(5574,)`.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if:
    /// - Download fails due to network issues
    /// - File extraction or I/O operations fail
    /// - Data format is invalid (wrong number of columns, or invalid labels)
    /// - Dataset size does not match expected dimensions (5,574 samples)
    pub fn data(&self) -> Result<&SmsSpamData, DatasetError> {
        self.dataset.load()
    }

    /// Get both message texts and labels as references **without** triggering loading.
    ///
    /// Unlike [`SmsSpam::data`], which loads the dataset on first call, this method
    /// never runs the loader. If the data has not loaded yet, this method returns
    /// `None` instead of downloading and parsing it. Use this method when you want
    /// the data only if it is already cached. This avoids the download and parse
    /// cost when the data is not yet cached.
    ///
    /// # Returns
    ///
    /// - `Some(&SmsSpamData)` - reference to the cached `(texts, labels)` tuple
    ///   (`(5574,)`, `(5574,)`), if loaded.
    /// - `None` - if the dataset has not been loaded yet.
    pub fn get_data(&self) -> Option<&SmsSpamData> {
        self.dataset.get()
    }

    /// Get mutable references to message texts and labels for **in-place** editing.
    ///
    /// This lets you change the cached arrays directly, for example to normalize or
    /// clean the message text. It needs no `to_owned()` clone, and it does not
    /// remove the arrays from the cache. The changes persist, so later calls to
    /// [`SmsSpam::texts`], [`SmsSpam::data`], or [`SmsSpam::get_data`] observe them.
    ///
    /// Like [`SmsSpam::get_data`], this method does not trigger loading. It returns
    /// `None` if the dataset has not loaded yet. If you need the data to be
    /// present, call a loading accessor first, for example [`SmsSpam::data`].
    ///
    /// # Returns
    ///
    /// - `Some(&mut SmsSpamData)` - mutable reference to the cached `(texts,
    ///   labels)` tuple (`(5574,)`, `(5574,)`), if loaded.
    /// - `None` - if the dataset has not been loaded yet.
    pub fn get_data_mut(&mut self) -> Option<&mut SmsSpamData> {
        self.dataset.get_mut()
    }

    /// Consume the dataset and return **owned** message texts and labels.
    ///
    /// Unlike [`SmsSpam::data`], which borrows the cached data, this method moves
    /// the data out and returns owned arrays directly. It needs no `to_owned()`
    /// clone. If the dataset has not loaded yet, it loads on first access.
    ///
    /// This method consumes `self`, so you cannot use the instance afterward. If
    /// you want owned data but need to keep using the instance, use
    /// [`SmsSpam::take_data`] instead. It takes `&mut self` and leaves the instance
    /// reusable.
    ///
    /// # Returns
    ///
    /// - `(Array1<String>, Array1<&'static str>)` - owned message-text vector
    ///   `(5574,)` and owned label vector `(5574,)`.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if loading fails (network, file extraction, I/O,
    /// parsing, invalid labels, or a dimension mismatch).
    pub fn into_data(self) -> Result<SmsSpamData, DatasetError> {
        self.dataset.load()?;
        Ok(self
            .dataset
            .into_inner()
            .expect("data is present after a successful load"))
    }

    /// Take **owned** message texts and labels out of the dataset. This leaves the
    /// instance reusable.
    ///
    /// Like [`SmsSpam::into_data`], this method returns owned arrays with no
    /// `to_owned()` clone. But instead of consuming the instance, it takes
    /// `&mut self` and moves the cached data out. This resets the instance to its
    /// unloaded state. The next accessor call, for example [`SmsSpam::texts`] or
    /// [`SmsSpam::data`], loads the dataset again.
    ///
    /// If you are done with the instance, use [`SmsSpam::into_data`] instead.
    ///
    /// # Returns
    ///
    /// - `(Array1<String>, Array1<&'static str>)` - owned message-text vector
    ///   `(5574,)` and owned label vector `(5574,)`.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if loading fails (network, file extraction, I/O,
    /// parsing, invalid labels, or a dimension mismatch).
    pub fn take_data(&mut self) -> Result<SmsSpamData, DatasetError> {
        self.dataset.load()?;
        Ok(self
            .dataset
            .take()
            .expect("data is present after a successful load"))
    }
}

impl_ml_dataset!(SmsSpam, SmsSpamData, "sms_spam");