dataset-ml 0.2.0

//! Titanic survival dataset.
//!
//! Passenger records from the Kaggle `Titanic: Machine Learning from Disaster`
//! competition, used to predict survival on the RMS Titanic.
//!
//! **Features (11, mixed):**
//! - String features: `Name`, `Sex`, `Ticket`, `Cabin`, `Embarked`
//! - Numeric features: `PassengerId`, `Pclass`, `Age`, `SibSp`, `Parch`, `Fare`
//!
//! **Target:** `Survived` - binary label (`0` = died, `1` = survived)
//!
//! **Samples:** 891
//! **Application:** Binary classification / survival prediction
//!
//! **Source:** Kaggle competition
//! <https://www.kaggle.com/c/titanic/data>

use csv::ReaderBuilder;
use dataset_core::{Dataset, DatasetError, acquire_dataset, download_to};
use ndarray::{Array1, Array2};
use serde::Deserialize;
use std::fs::File;

/// Type alias for Titanic dataset: (string features, numeric features, labels)
type TitanicData = (Array2<String>, Array2<f64>, Array1<f64>);

/// The URL for the Titanic dataset.
const TITANIC_DATA_URL: &str =
    "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv";

/// The name of the Titanic dataset file.
const TITANIC_FILENAME: &str = "titanic.csv";

/// The SHA256 hash of the Titanic dataset file.
const TITANIC_SHA256: &str = "4a437fde05fe5264e1701a7387ac6fb75393772ba38bb2c9c566405af5af4bd7";

/// The name of the dataset
const TITANIC_DATASET_NAME: &str = "titanic";

/// One CSV record of the Titanic dataset, with fields in source column order.
///
/// Numeric columns are `Option<f64>` so that empty fields deserialize to `None`
/// (later mapped to `NaN`); text columns are `String` (empty fields become `""`).
/// Fields are declared in CSV column order and deserialized **positionally**
/// (the loader disables csv's header handling), so this struct is independent of
/// the exact header spelling.
#[derive(Deserialize)]
struct TitanicRecord {
    passenger_id: Option<f64>,
    survived: Option<f64>,
    pclass: Option<f64>,
    name: String,
    sex: String,
    age: Option<f64>,
    sib_sp: Option<f64>,
    parch: Option<f64>,
    ticket: String,
    fare: Option<f64>,
    cabin: String,
    embarked: String,
}

/// A struct representing the Titanic dataset with lazy loading.
///
/// The dataset is not loaded until you call one of the data accessor methods.
/// Once loaded, the data is cached for subsequent accesses.
///
/// # About Dataset
///
/// On April 15, 1912, during her maiden voyage, the widely considered "unsinkable" RMS Titanic sank after colliding with an iceberg.
/// Unfortunately, there weren't enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew.
/// While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
///
/// String features (shape `(891, 5)`), in column order:
/// - `Name`
/// - `Sex`
/// - `Ticket`
/// - `Cabin`
/// - `Embarked`
///
/// Numeric features (shape `(891, 6)`), in column order (may be `NaN` if missing in the source):
/// - `PassengerId`
/// - `Pclass`
/// - `Age`
/// - `SibSp`
/// - `Parch`
/// - `Fare`
///
/// Labels (shape `(891,)`):
/// - `Survived` (`0.0` or `1.0`; `NaN` if missing in source)
///
/// Missing values:
/// - Numeric fields are parsed as `NaN` when missing.
/// - String fields are parsed as empty strings when missing.
///
/// See more information at <https://www.kaggle.com/c/titanic/data>.
///
/// # Thread Safety
///
/// This struct automatically implements `Send` and `Sync` (All fields implement them), making it safe to share across threads.
/// The internal [`Dataset`] ensures thread-safe lazy initialization.
///
/// # Example
/// ```no_run
/// use dataset_ml::titanic::Titanic;
///
/// let download_dir = "./titanic"; // the code will create the directory if it doesn't exist
///
/// let mut dataset = Titanic::new(download_dir);
/// let (string_features, numeric_features) = dataset.features().unwrap();
/// let labels = dataset.labels().unwrap();
///
/// let (string_features, numeric_features, labels) = dataset.data().unwrap(); // this is also a way to get all data
/// assert_eq!(string_features.shape(), &[891, 5]);
/// assert_eq!(numeric_features.shape(), &[891, 6]);
/// assert_eq!(labels.len(), 891);
///
/// // `get_data()` borrows the cached arrays without reloading; `get_data_mut()`
/// // edits them in place — no clone, no reload, the change stays cached. Prefer
/// // this over cloning with `.to_owned()` when you only need to tweak values.
/// if let Some((_strings, numerics, labels)) = dataset.get_data_mut() {
///     numerics[[0, 0]] = 1.0;
///     labels[0] = 1.0;
/// }
/// assert!(dataset.get_data().is_some());
///
/// // `take_data()` moves the owned arrays out (no `to_owned()` clone) and leaves
/// // the instance reusable — the next access reloads from the cached file.
/// let (owned_strings, owned_numerics, owned_labels) = dataset.take_data().unwrap();
/// assert_eq!(owned_strings.shape(), &[891, 5]);
/// assert_eq!(owned_numerics.shape(), &[891, 6]);
/// assert_eq!(owned_labels.len(), 891);
///
/// // `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_strings, owned_numerics, owned_labels) = dataset.into_data().unwrap();
/// assert_eq!(owned_strings.shape(), &[891, 5]);
/// assert_eq!(owned_numerics.shape(), &[891, 6]);
/// assert_eq!(owned_labels.len(), 891);
/// ```
#[derive(Debug)]
pub struct Titanic {
    dataset: Dataset<TitanicData, DatasetError>,
}

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

    /// Acquire and parse the Titanic dataset.
    fn load_data(dir: &str) -> Result<TitanicData, DatasetError> {
        // Prepare the dataset file
        let file_path = acquire_dataset(
            dir,
            TITANIC_FILENAME,
            TITANIC_DATASET_NAME,
            Some(TITANIC_SHA256),
            |temp_path| {
                download_to(TITANIC_DATA_URL, temp_path, None)?;
                Ok(temp_path.join(TITANIC_FILENAME))
            },
        )?;

        // csv deserializes into the struct
        let file = File::open(&file_path)?;
        let mut rdr = ReaderBuilder::new().has_headers(false).from_reader(file);

        let mut string_features = Vec::new();
        let mut numeric_features = Vec::new();
        let mut labels = Vec::new();

        for result in rdr.deserialize::<TitanicRecord>().skip(1) {
            let TitanicRecord {
                passenger_id,
                survived,
                pclass,
                name,
                sex,
                age,
                sib_sp,
                parch,
                ticket,
                fare,
                cabin,
                embarked,
            } = result.map_err(|e| DatasetError::csv_read_error(TITANIC_DATASET_NAME, e))?;

            // Missing numeric fields (`None`) become `NaN`.
            // Label: Survived.
            labels.push(survived.unwrap_or(f64::NAN));

            // Numeric features, in column order:
            // PassengerId, Pclass, Age, SibSp, Parch, Fare.
            numeric_features.extend_from_slice(&[
                passenger_id.unwrap_or(f64::NAN),
                pclass.unwrap_or(f64::NAN),
                age.unwrap_or(f64::NAN),
                sib_sp.unwrap_or(f64::NAN),
                parch.unwrap_or(f64::NAN),
                fare.unwrap_or(f64::NAN),
            ]);

            // String features, in column order: Name, Sex, Ticket, Cabin, Embarked.
            string_features.push(name);
            string_features.push(sex);
            string_features.push(ticket);
            string_features.push(cabin);
            string_features.push(embarked);
        }

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

        // Titanic has a fixed schema of 5 string and 6 numeric features per sample.
        let string_array =
            Array2::from_shape_vec((n_samples, 5), string_features).map_err(|e| {
                DatasetError::array_shape_error(TITANIC_DATASET_NAME, "string_features", e)
            })?;

        let numeric_array =
            Array2::from_shape_vec((n_samples, 6), numeric_features).map_err(|e| {
                DatasetError::array_shape_error(TITANIC_DATASET_NAME, "numeric_features", e)
            })?;

        let labels_array = Array1::from_vec(labels);

        Ok((string_array, numeric_array, labels_array))
    }

    /// Get a reference to both string and numeric feature matrices.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&Array2<String>` - Reference to string feature matrix with shape `(891, 5)` containing:
    ///     - `Name`
    ///     - `Sex`
    ///     - `Ticket`
    ///     - `Cabin`
    ///     - `Embarked`
    ///
    ///   (empty string if missing in source)
    ///
    /// - `&Array2<f64>` - Reference to numeric feature matrix with shape `(891, 6)` containing:
    ///     - `PassengerId`
    ///     - `Pclass`
    ///     - `Age`
    ///     - `SibSp`
    ///     - `Parch`
    ///     - `Fare`
    ///
    ///   (`NaN` if missing in source)
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if:
    /// - Download fails due to network issues
    /// - File I/O operations fail
    /// - Data format is invalid (wrong number of columns, unparseable values)
    /// - Dataset size doesn't match expected dimensions (891 samples)
    pub fn features(&self) -> Result<(&Array2<String>, &Array2<f64>), DatasetError> {
        let data = self.dataset.load()?;
        Ok((&data.0, &data.1))
    }

    /// Get a reference to the label vector.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&Array1<f64>` - Reference to label vector with shape `(891,)` containing `Survived` values (`0.0` or `1.0`, `NaN` if missing in source)
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if:
    /// - Download fails due to network issues
    /// - File I/O operations fail
    /// - Data format is invalid (wrong number of columns, unparseable values)
    /// - Dataset size doesn't match expected dimensions (891 samples)
    pub fn labels(&self) -> Result<&Array1<f64>, DatasetError> {
        Ok(&self.dataset.load()?.2)
    }

    /// Get string features, numeric features and labels as references.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&TitanicData` - reference to the cached `(string features, numeric
    ///   features, labels)` tuple: string feature matrix `(891, 5)` (Name, Sex,
    ///   Ticket, Cabin, Embarked), numeric feature matrix `(891, 6)`
    ///   (PassengerId, Pclass, Age, SibSp, Parch, Fare), and label vector
    ///   `(891,)` (Survived).
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if:
    /// - Download fails due to network issues
    /// - File I/O operations fail
    /// - Data format is invalid (wrong number of columns, unparseable values)
    /// - Dataset size doesn't match expected dimensions (891 samples)
    pub fn data(&self) -> Result<&TitanicData, DatasetError> {
        self.dataset.load()
    }

    /// Get string features, numeric features and labels as references
    /// **without** triggering loading.
    ///
    /// Unlike [`Titanic::data`], which loads the dataset on first call, this never
    /// runs the loader: if the data has not been loaded yet, it returns `None`
    /// instead of downloading and parsing. Use it when you only want the data if
    /// it is already cached and want to avoid paying the download/parse cost
    /// otherwise.
    ///
    /// # Returns
    ///
    /// - `Some(&TitanicData)` - reference to the cached `(string features, numeric
    ///   features, labels)` tuple (`(891, 5)`, `(891, 6)`, `(891,)`), if loaded.
    /// - `None` - if the dataset has not been loaded yet.
    pub fn get_data(&self) -> Option<&TitanicData> {
        self.dataset.get()
    }

    /// Get mutable references to string features, numeric features, and labels
    /// for **in-place** editing.
    ///
    /// This lets you modify the cached arrays directly (e.g. normalize numeric
    /// features, replace missing values) with no `to_owned()` clone and without
    /// removing them from the cache: the changes persist, so later
    /// [`Titanic::features`], [`Titanic::data`], or [`Titanic::get_data`] calls
    /// observe them.
    ///
    /// Like [`Titanic::get_data`], this does **not** trigger loading: it returns
    /// `None` if the dataset has not been loaded. Call a loading accessor (e.g.
    /// [`Titanic::data`]) first if you need to ensure the data is present.
    ///
    /// # Returns
    ///
    /// - `Some(&mut TitanicData)` - mutable reference to the cached `(string
    ///   features, numeric features, labels)` tuple (`(891, 5)`, `(891, 6)`,
    ///   `(891,)`), if loaded.
    /// - `None` - if the dataset has not been loaded yet.
    pub fn get_data_mut(&mut self) -> Option<&mut TitanicData> {
        self.dataset.get_mut()
    }

    /// Consume the dataset and return **owned** string features, numeric features,
    /// and labels.
    ///
    /// Unlike [`Titanic::data`], which borrows the cached data, this moves it out
    /// and returns owned arrays directly — no `to_owned()` clone needed. The dataset
    /// is loaded on first access if it has not been loaded yet.
    ///
    /// This **consumes** `self`, so the instance cannot be used afterwards. If you
    /// want owned data but need to keep using the instance, use
    /// [`Titanic::take_data`] instead — it takes `&mut self` and leaves the instance
    /// reusable.
    ///
    /// # Returns
    ///
    /// - `(Array2<String>, Array2<f64>, Array1<f64>)` - owned string feature matrix
    ///   `(891, 5)`, owned numeric feature matrix `(891, 6)`, and owned label vector
    ///   `(891,)`.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if loading fails (network, file I/O, parsing, or a
    /// dimension mismatch).
    pub fn into_data(self) -> Result<TitanicData, DatasetError> {
        self.dataset.load()?;
        Ok(self
            .dataset
            .into_inner()
            .expect("data is present after a successful load"))
    }

    /// Take **owned** string features, numeric features, and labels out of the
    /// dataset, leaving it reusable.
    ///
    /// Like [`Titanic::into_data`], this returns owned arrays with no `to_owned()`
    /// clone. But instead of consuming the instance, it takes `&mut self` and moves
    /// the cached data out, resetting the instance to its unloaded state: the next
    /// accessor call (e.g. [`Titanic::features`] or [`Titanic::data`]) loads the
    /// dataset again.
    ///
    /// Use [`Titanic::into_data`] instead if you are done with the instance.
    ///
    /// # Returns
    ///
    /// - `(Array2<String>, Array2<f64>, Array1<f64>)` - owned string feature matrix
    ///   `(891, 5)`, owned numeric feature matrix `(891, 6)`, and owned label vector
    ///   `(891,)`.
    ///
    /// # Errors
    ///
    /// Returns `DatasetError` if loading fails (network, file I/O, parsing, or a
    /// dimension mismatch).
    pub fn take_data(&mut self) -> Result<TitanicData, DatasetError> {
        self.dataset.load()?;
        Ok(self
            .dataset
            .take()
            .expect("data is present after a successful load"))
    }
}