dataset-core 0.1.0

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

/// The URL for the Diabetes dataset.
const DIABETES_DATA_URL: &str =
    "https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv";

/// A static string slice containing the name of the Diabetes dataset file.
const DIABETES_FILENAME: &str = "diabetes.csv";

/// The SHA256 hash of the Diabetes dataset file.
const DIABETES_SHA256: &str = "698c203a14aa31941d2251175330c9199f3ccdb31597abbba2a3e35416257a72";

/// The name of the dataset
const DIABETES_DATASET_NAME: &str = "diabetes";

/// A struct representing the Diabetes 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
///
/// This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.
/// The objective is to predict based on diagnostic measurements whether a patient has diabetes.
///
/// Features:
/// - Pregnancies: Number of times pregnant
/// - Glucose: Plasma glucose concentration at 2 hours in an oral glucose tolerance test
/// - BloodPressure: Diastolic blood pressure (mm Hg)
/// - SkinThickness: Triceps skin fold thickness (mm)
/// - Insulin: 2-Hour serum insulin (mu U/ml)
/// - BMI: Body mass index (weight in kg/(height in m)^2)
/// - DiabetesPedigreeFunction: Diabetes pedigree function
/// - Age: Age (years)
///
/// Labels:
/// - Outcome: Class variable (0 or 1)
///
/// See more information at <https://www.kaggle.com/datasets/mathchi/diabetes-data-set/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
/// ```rust
/// use dataset_core::datasets::diabetes::Diabetes;
///
/// let download_dir = "./diabetes"; // the code will create the directory if it doesn't exist
///
/// let dataset = Diabetes::new(download_dir);
/// let features = dataset.features().unwrap();
/// let labels = dataset.labels().unwrap();
///
/// let (features, labels) = dataset.data().unwrap(); // this is also a way to get features and labels
/// // you can use `.to_owned()` to get owned copies of the data
/// let mut features_owned = features.to_owned();
/// let mut labels_owned = labels.to_owned();
///
/// // Example: Modify feature values
/// features_owned[[0, 0]] = 10.0;
/// labels_owned[0] = 1.0;
///
/// assert_eq!(features.shape(), &[768, 8]);
/// assert_eq!(labels.len(), 768);
///
/// // clean up: remove the downloaded files (dispensable)
/// std::fs::remove_dir_all(download_dir).unwrap();
/// ```
#[derive(Debug)]
pub struct Diabetes {
    dataset: Dataset<(Array2<f64>, Array1<f64>)>,
}

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

    /// Acquire and parse the Diabetes dataset.
    fn load_data(dir: &str) -> Result<(Array2<f64>, Array1<f64>), DatasetError> {
        // Prepare the dataset file
        let file_path = acquire_dataset(
            dir,
            DIABETES_FILENAME,
            DIABETES_DATASET_NAME,
            Some(DIABETES_SHA256),
            |temp_path| {
                download_to(DIABETES_DATA_URL, temp_path, None)?;
                Ok(temp_path.join(DIABETES_FILENAME))
            },
        )?;

        // Parse the file
        let file = File::open(&file_path)?;
        let mut rdr = ReaderBuilder::new().has_headers(true).from_reader(file);

        let mut features = Vec::new();
        let mut labels = Vec::new();
        let mut num_features: Option<usize> = None;

        for (idx, result) in rdr.records().enumerate() {
            let record =
                result.map_err(|e| DatasetError::csv_read_error(DIABETES_DATASET_NAME, e))?;
            let line_num = idx + 2; // +1 for 0-indexed, +1 for header

            if num_features.is_none() {
                if record.len() < 2 {
                    return Err(DatasetError::invalid_column_count(
                        DIABETES_DATASET_NAME,
                        2,
                        record.len(),
                        line_num,
                        &format!("{:?}", record),
                    ));
                }
                num_features = Some(record.len() - 1);
            }

            let n_features = num_features.unwrap();
            if record.len() != n_features + 1 {
                return Err(DatasetError::invalid_column_count(
                    DIABETES_DATASET_NAME,
                    n_features + 1,
                    record.len(),
                    line_num,
                    &format!("{:?}", record),
                ));
            }

            for i in 0..n_features {
                features.push(record[i].parse::<f64>().map_err(|e| {
                    let field = format!("feature[{i}]");

                    DatasetError::parse_failed(
                        DIABETES_DATASET_NAME,
                        &field,
                        line_num,
                        &format!("{:?}", record),
                        e,
                    )
                })?);
            }

            labels.push(record[n_features].parse::<f64>().map_err(|e| {
                DatasetError::parse_failed(
                    DIABETES_DATASET_NAME,
                    "label",
                    line_num,
                    &format!("{:?}", record),
                    e,
                )
            })?);
        }

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

        let n_features = num_features.unwrap();
        let features_array = Array2::from_shape_vec((n_samples, n_features), features)
            .map_err(|e| DatasetError::array_shape_error(DIABETES_DATASET_NAME, "features", e))?;
        let labels_array = Array1::from_vec(labels);

        Ok((features_array, labels_array))
    }

    /// 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 `(768, 8)` containing:
    ///     - Pregnancies: Number of times pregnant
    ///     - Glucose: Plasma glucose concentration at 2 hours in an oral glucose tolerance test
    ///     - BloodPressure: Diastolic blood pressure (mm Hg)
    ///     - SkinThickness: Triceps skin fold thickness (mm)
    ///     - Insulin: 2-Hour serum insulin (mu U/ml)
    ///     - BMI: Body mass index (weight in kg/(height in m)^2)
    ///     - DiabetesPedigreeFunction: Diabetes pedigree function
    ///     - Age: Age (years)
    ///
    /// # 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 doesn't match expected dimensions (768 samples, 8 features)
    pub fn features(&self) -> Result<&Array2<f64>, DatasetError> {
        Ok(&self.dataset.load(Self::load_data)?.0)
    }

    /// 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 `(768,)` containing class variable (0 or 1)
    ///
    /// # 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 doesn't match expected dimensions (768 samples)
    pub fn labels(&self) -> Result<&Array1<f64>, DatasetError> {
        Ok(&self.dataset.load(Self::load_data)?.1)
    }

    /// Get both features and labels as references.
    ///
    /// This method triggers lazy loading on first call. Subsequent calls return
    /// the cached data instantly.
    ///
    /// # Returns
    ///
    /// - `&Array2<f64>` - Reference to feature matrix with shape `(768, 8)` containing:
    ///     - Pregnancies: Number of times pregnant
    ///     - Glucose: Plasma glucose concentration at 2 hours in an oral glucose tolerance test
    ///     - BloodPressure: Diastolic blood pressure (mm Hg)
    ///     - SkinThickness: Triceps skin fold thickness (mm)
    ///     - Insulin: 2-Hour serum insulin (mu U/ml)
    ///     - BMI: Body mass index (weight in kg/(height in m)^2)
    ///     - DiabetesPedigreeFunction: Diabetes pedigree function
    ///     - Age: Age (years)
    /// - `&Array1<f64>` - Reference to label vector with shape `(768,)` containing class variable (0 or 1)
    ///
    /// # 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 doesn't match expected dimensions (768 samples, 8 features)
    pub fn data(&self) -> Result<(&Array2<f64>, &Array1<f64>), DatasetError> {
        let data = self.dataset.load(Self::load_data)?;
        Ok((&data.0, &data.1))
    }
}