dataset-ml 0.4.0

//! White wine subset of the Wine Quality dataset.
//!
//! See [`crate::wine_quality`] for the full dataset description,
//! including features, target, application scenarios, and source.
//!
//! **Samples:** 4898
//! **Feature shape:** `(4898, 11)`
//! **Target shape:** `(4898,)`

use crate::DOWNLOAD_RETRIES;
use crate::traits::impl_ml_dataset;
use crate::wine_quality::{WineData, parse_wine_data_to_array};
use dataset_core::{Dataset, DatasetError, acquire_dataset, download_to_with_retries};
use ndarray::{Array1, Array2};
use std::fs::File;

/// The URL for the White Wine Quality dataset.
const WHITE_WINE_DATA_URL: &str = "https://raw.githubusercontent.com/shrikant-temburwar/Wine-Quality-Dataset/refs/heads/master/winequality-white.csv";

/// The white wine file of the CSV files inside the zip archive.
const WHITE_WINE_QUALITY_FILENAME: &str = "winequality-white.csv";

/// The SHA256 hash of the white wine quality dataset.
const WHITE_WINE_QUALITY_SHA256: &str =
    "76c3f809815c17c07212622f776311faeb31e87610d52c26d87d6e361b169836";

/// A struct that represents the White Wine Quality 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 dataset contains physicochemical properties of Portuguese "Vinho Verde"
/// white wine samples and a quality score for each sample.
///
/// # Feature columns
///
/// The dataset has 11 numeric physicochemical features, all stored as `f64`. By
/// 0-based column index in the feature matrix:
///
/// | Columns | Attributes             | Unit |
/// |---------|------------------------|------|
/// | `0`     | `fixed acidity`        |      |
/// | `1`     | `volatile acidity`     |      |
/// | `2`     | `citric acid`          |      |
/// | `3`     | `residual sugar`       |      |
/// | `4`     | `chlorides`            |      |
/// | `5`     | `free sulfur dioxide`  |      |
/// | `6`     | `total sulfur dioxide` |      |
/// | `7`     | `density`              |      |
/// | `8`     | `pH`                   |      |
/// | `9`     | `sulphates`            |      |
/// | `10`    | `alcohol`              |      |
///
/// # Targets
///
/// - quality (score between 0 and 10, stored as `f64`)
///
/// See more information at <https://archive.ics.uci.edu/dataset/186/wine+quality>
///
/// # Citation
///
/// P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis. "Wine Quality," UCI Machine Learning Repository, 2009. \[Online\]. Available: <https://doi.org/10.24432/C56S3T>.
///
/// # 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::WhiteWineQuality;
///
/// let download_dir = "./white_wine"; // the code creates the directory if it does not exist
///
/// let mut dataset = WhiteWineQuality::new(download_dir);
/// let features = dataset.features().unwrap();
/// let targets = dataset.targets().unwrap();
///
/// let (features, targets) = dataset.data().unwrap(); // this also returns features and targets
/// assert_eq!(features.shape(), &[4898, 11]);
/// assert_eq!(targets.len(), 4898);
///
/// // `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((features, targets)) = dataset.get_data_mut() {
///     features[[0, 0]] = 10.0;
///     targets[0] = 7.0;
/// }
/// assert!(dataset.get_data().is_some());
///
/// // `take_data()` moves owned arrays out (no `to_owned()` clone). It leaves the
/// // instance reusable. The next access reloads the data from the cached file.
/// let (owned_features, owned_targets) = dataset.take_data().unwrap();
/// assert_eq!(owned_features.shape(), &[4898, 11]);
/// assert_eq!(owned_targets.len(), 4898);
///
/// // `into_data()` also returns owned arrays with no clone, but consumes the
/// // instance (use it when you are done with the dataset).
/// let (owned_features, owned_targets) = dataset.into_data().unwrap();
/// assert_eq!(owned_features.shape(), &[4898, 11]);
/// assert_eq!(owned_targets.len(), 4898);
/// ```
#[derive(Debug)]
pub struct WhiteWineQuality {
    dataset: Dataset<WineData, DatasetError>,
}

impl WhiteWineQuality {
    /// Create a new WhiteWineQuality 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` - `WhiteWineQuality` instance ready for lazy loading.
    pub fn new(storage_dir: &str) -> Self {
        WhiteWineQuality {
            dataset: Dataset::new(storage_dir, Self::load_data),
        }
    }

    /// Get and parse the White Wine Quality dataset.
    fn load_data(dir: &str) -> Result<WineData, DatasetError> {
        // Prepare the dataset file.
        let file_path = acquire_dataset(
            dir,
            WHITE_WINE_QUALITY_FILENAME,
            "white_wine_quality",
            Some(WHITE_WINE_QUALITY_SHA256),
            |temp_path| {
                download_to_with_retries(WHITE_WINE_DATA_URL, temp_path, None, DOWNLOAD_RETRIES)?;
                Ok(temp_path.join(WHITE_WINE_QUALITY_FILENAME))
            },
        )?;

        // Parse the file.
        let file = File::open(&file_path)?;
        parse_wine_data_to_array("white_wine_quality", file)
    }

    /// Get a reference to the feature matrix.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&Array2<f64>` - Reference to feature matrix with shape `(4898, 11)` containing:
    ///     - fixed acidity
    ///     - volatile acidity
    ///     - citric acid
    ///     - residual sugar
    ///     - chlorides
    ///     - free sulfur dioxide
    ///     - total sulfur dioxide
    ///     - density
    ///     - pH
    ///     - sulphates
    ///     - alcohol
    ///
    /// # 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, unparseable values)
    /// - Dataset size does not match expected dimensions (4898 samples, 11 features)
    pub fn features(&self) -> Result<&Array2<f64>, DatasetError> {
        Ok(&self.dataset.load()?.0)
    }

    /// Get a reference to the target vector.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&Array1<f64>` - Reference to target vector with shape `(4898,)` containing quality scores (0-10)
    ///
    /// # 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, unparseable values)
    /// - Dataset size does not match expected dimensions (4898 samples)
    pub fn targets(&self) -> Result<&Array1<f64>, DatasetError> {
        Ok(&self.dataset.load()?.1)
    }

    /// Get both features and targets as references.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&WineData` - reference to the cached `(features, targets)` tuple: feature
    ///   matrix with shape `(4898, 11)` (the 11 physicochemical properties) and
    ///   target vector with shape `(4898,)` (quality scores, 0-10).
    ///
    /// # 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, unparseable values)
    /// - Dataset size does not match expected dimensions (4898 samples, 11 features)
    pub fn data(&self) -> Result<&WineData, DatasetError> {
        self.dataset.load()
    }

    /// Get both features and targets as references **without** triggering loading.
    ///
    /// Unlike [`WhiteWineQuality::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(&WineData)` - reference to the cached `(features, targets)` tuple
    ///   (feature matrix `(4898, 11)`, target vector `(4898,)`), if loaded.
    /// - `None` - if the dataset has not been loaded yet.
    pub fn get_data(&self) -> Option<&WineData> {
        self.dataset.get()
    }

    /// Get mutable references to features and targets for **in-place** editing.
    ///
    /// This lets you change the cached arrays directly, for example to normalize
    /// features or rescale targets. It needs no `to_owned()` clone, and it does not
    /// remove the arrays from the cache. The changes persist, so later calls to
    /// [`WhiteWineQuality::features`], [`WhiteWineQuality::data`], or
    /// [`WhiteWineQuality::get_data`] observe them.
    ///
    /// Like [`WhiteWineQuality::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
    /// [`WhiteWineQuality::data`].
    ///
    /// # Returns
    ///
    /// - `Some(&mut WineData)` - mutable reference to the cached `(features,
    ///   targets)` tuple (feature matrix `(4898, 11)`, target vector `(4898,)`),
    ///   if loaded.
    /// - `None` - if the dataset has not been loaded yet.
    pub fn get_data_mut(&mut self) -> Option<&mut WineData> {
        self.dataset.get_mut()
    }

    /// Consume the dataset and return **owned** features and targets.
    ///
    /// Unlike [`WhiteWineQuality::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
    /// [`WhiteWineQuality::take_data`] instead. It takes `&mut self` and leaves
    /// the instance reusable.
    ///
    /// # Returns
    ///
    /// - `(Array2<f64>, Array1<f64>)` - owned feature matrix with shape
    ///   `(4898, 11)` and owned target vector with shape `(4898,)`.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if loading fails (network, file I/O, parsing, or a
    /// dimension mismatch).
    pub fn into_data(self) -> Result<WineData, DatasetError> {
        self.dataset.load()?;
        Ok(self
            .dataset
            .into_inner()
            .expect("data is present after a successful load"))
    }

    /// Take **owned** features and targets out of the dataset. This leaves the
    /// instance reusable.
    ///
    /// Like [`WhiteWineQuality::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
    /// [`WhiteWineQuality::features`] or [`WhiteWineQuality::data`], loads the
    /// dataset again.
    ///
    /// If you are done with the instance, use [`WhiteWineQuality::into_data`]
    /// instead.
    ///
    /// # Returns
    ///
    /// - `(Array2<f64>, Array1<f64>)` - owned feature matrix with shape
    ///   `(4898, 11)` and owned target vector with shape `(4898,)`.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if loading fails (network, file I/O, parsing, or a
    /// dimension mismatch).
    pub fn take_data(&mut self) -> Result<WineData, DatasetError> {
        self.dataset.load()?;
        Ok(self
            .dataset
            .take()
            .expect("data is present after a successful load"))
    }
}

impl_ml_dataset!(WhiteWineQuality, WineData, "white_wine_quality");