causal-hub 0.0.4

use std::io::{Read, Write};

use csv::{ReaderBuilder, WriterBuilder};
use ndarray::prelude::*;

use crate::{
    datasets::Dataset,
    io::CsvIO,
    models::Labelled,
    types::{Labels, Set},
};

/// A type alias for a gaussian variable.
pub type GaussType = f64;
/// A type alias for a gaussian sample.
pub type GaussSample = Array1<GaussType>;

/// A struct representing a gaussian dataset.
#[derive(Clone, Debug)]
pub struct GaussTable {
    labels: Labels,
    values: Array2<GaussType>,
}

impl Labelled for GaussTable {
    #[inline]
    fn labels(&self) -> &Labels {
        &self.labels
    }
}

impl GaussTable {
    /// Creates a new gaussian dataset.
    ///
    /// # Arguments
    ///
    /// * `labels` - The labels of the variables.
    /// * `values` - The values of the variables.
    ///
    /// # Panics
    ///
    /// * Panics if the number of columns in `values` does not match the number of `labels`.
    ///
    /// # Results
    ///
    /// A new gaussian dataset instance.
    ///
    pub fn new(mut labels: Labels, mut values: Array2<GaussType>) -> Self {
        // Assert that the number of labels matches the number of columns in values.
        assert_eq!(
            labels.len(),
            values.ncols(),
            "Number of labels must match number of columns in values."
        );

        // Sort labels and values accordingly.
        if !labels.is_sorted() {
            // Allocate indices to sort labels.
            let mut indices: Vec<usize> = (0..labels.len()).collect();
            // Sort the indices by labels.
            indices.sort_by_key(|&i| &labels[i]);
            // Sort the labels.
            labels.sort();
            // Allocate new values.
            let mut new_values = values.clone();
            // Sort the new values according to the sorted indices.
            indices.into_iter().enumerate().for_each(|(i, j)| {
                new_values.column_mut(i).assign(&values.column(j));
            });
            // Update values.
            values = new_values;
        }
        // Assert values are finite.
        assert!(
            values.iter().all(|&x| x.is_finite()),
            "Values must have finite values."
        );

        Self { labels, values }
    }
}

impl Dataset for GaussTable {
    type Values = Array2<GaussType>;

    #[inline]
    fn values(&self) -> &Self::Values {
        &self.values
    }

    #[inline]
    fn sample_size(&self) -> f64 {
        self.values.nrows() as f64
    }

    fn select(&self, x: &Set<usize>) -> Self {
        // Assert that the indices are valid.
        x.iter().for_each(|&i| {
            assert!(
                i < self.values.ncols(),
                "Index out of bounds in variables selection: \n\
                \t expected:    index < |columns| , \n\
                \t found:       index == {} and |columns| == {} .",
                i,
                self.values.ncols()
            );
        });

        // Select the labels.
        let labels: Labels = x
            .iter()
            .map(|&i| self.labels.get_index(i).unwrap())
            .cloned()
            .collect();

        // Select the values.
        let mut new_values = Array2::zeros((self.values.nrows(), x.len()));
        // Copy the selected columns.
        x.iter().enumerate().for_each(|(j, &i)| {
            new_values.column_mut(j).assign(&self.values.column(i));
        });
        // Update the values.
        let values = new_values;

        // Return the new dataset.
        Self::new(labels, values)
    }
}

impl CsvIO for GaussTable {
    fn from_csv_reader<R: Read>(reader: R) -> Self {
        // Create a CSV reader from the string.
        let mut reader = ReaderBuilder::new().has_headers(true).from_reader(reader);

        // Assert that the reader has headers.
        assert!(reader.has_headers(), "Reader must have headers.");

        // Read the headers.
        let labels: Labels = reader
            .headers()
            .expect("Failed to read the headers.")
            .into_iter()
            .map(|x| x.to_owned())
            .collect();

        // Read the records.
        let values: Array1<_> = reader
            .into_records()
            .enumerate()
            .flat_map(|(i, row)| {
                // Get the record row.
                let row = row.unwrap_or_else(|_| panic!("Malformed record on line {}.", i + 1));
                // Get the record values and convert to indices.
                let row: Vec<_> = row
                    .into_iter()
                    .enumerate()
                    .map(|(i, x)| {
                        // Assert no missing values.
                        assert!(!x.is_empty(), "Missing value on line {}.", i + 1);
                        // Cast the value.
                        x.parse::<GaussType>().unwrap()
                    })
                    .collect();
                // Collect the values.
                row
            })
            .collect();

        // Get the number of rows and columns.
        let ncols = labels.len();
        let nrows = values.len() / ncols;
        // Reshape the values to the correct shape.
        let values = values
            .into_shape_with_order((nrows, ncols))
            .expect("Failed to rearrange values to the correct shape.");

        // Construct the dataset.
        Self::new(labels, values)
    }

    fn to_csv_writer<W: Write>(&self, writer: W) {
        // Create the CSV writer.
        let mut writer = WriterBuilder::new().has_headers(true).from_writer(writer);

        // Write the headers.
        writer
            .write_record(self.labels.iter())
            .expect("Failed to write CSV headers.");

        // Write the records.
        self.values.rows().into_iter().for_each(|row| {
            // Map the row values to strings.
            let record = row.iter().map(|x| x.to_string());
            // Write the record.
            writer
                .write_record(record)
                .expect("Failed to write CSV record.");
        });
    }
}