#![allow(clippy::ptr_arg)]
//! # Series Encoder
//! Encode a series of categorical features as a one-hot numeric array.

use crate::error::Failed;
use crate::linalg::BaseVector;
use crate::math::num::RealNumber;
use std::collections::HashMap;
use std::hash::Hash;

/// Make a one-hot encoded vector from a categorical variable
///
/// Example:
/// ```
/// use smartcore::preprocessing::series_encoder::make_one_hot;
/// let one_hot: Vec<f64> = make_one_hot(2, 3);
/// assert_eq!(one_hot, vec![0.0, 0.0, 1.0]);
/// ```
pub fn make_one_hot<T: RealNumber, V: BaseVector<T>>(
    category_idx: usize,
    num_categories: usize,
) -> V {
    let pos = T::from_f64(1f64).unwrap();
    let mut z = V::zeros(num_categories);
    z.set(category_idx, pos);
    z
}

/// Turn a collection of Hashable objects into a one-hot vectors.
/// This struct encodes single class per exmample
///
/// You can fit_to_iter a category enumeration by passing an iterator of categories.
/// category numbers will be assigned in the order they are encountered
///
/// Example:
/// ```
/// use std::collections::HashMap;
/// use smartcore::preprocessing::series_encoder::SeriesOneHotEncoder;
///
/// let fake_categories: Vec<usize> = vec![1, 2, 3, 4, 5, 3, 5, 3, 1, 2, 4];
/// let it = fake_categories.iter().map(|&a| a);
/// let enc = SeriesOneHotEncoder::<usize>::fit_to_iter(it);
/// let oh_vec: Vec<f64> = enc.transform_one(&1).unwrap();
/// // notice that 1 is actually a zero-th positional category
/// assert_eq!(oh_vec, vec![1.0, 0.0, 0.0, 0.0, 0.0]);
/// ```
///
/// You can also pass a predefined category enumeration such as a hashmap `HashMap<CategoryType, usize>` or a vector `Vec<CategoryType>`
///
///
/// ```
/// use std::collections::HashMap;
/// use smartcore::preprocessing::series_encoder::SeriesOneHotEncoder;
///
/// let category_map: HashMap<&str, usize> =
/// vec![("cat", 2), ("background",0), ("dog", 1)]
/// .into_iter()
/// .collect();
/// let category_vec = vec!["background", "dog", "cat"];
///
/// let enc_lv = SeriesOneHotEncoder::<&str>::from_positional_category_vec(category_vec);
/// let enc_lm = SeriesOneHotEncoder::<&str>::from_category_map(category_map);
///
/// // ["background", "dog", "cat"]
/// println!("{:?}", enc_lv.get_categories());
/// assert_eq!(enc_lv.transform_one::<f64>(&"dog"), enc_lm.transform_one::<f64>(&"dog"))
/// ```
#[derive(Debug, Clone)]
pub struct SeriesOneHotEncoder<CategoryType> {
    category_map: HashMap<CategoryType, usize>,
    categories: Vec<CategoryType>,
    /// Number of categories for categorical variable
    pub num_categories: usize,
}

impl<'a, CategoryType: 'a + Hash + Eq + Clone> SeriesOneHotEncoder<CategoryType> {
    /// Fit an encoder to a lable list
    pub fn fit_to_iter(categories: impl Iterator<Item = CategoryType>) -> Self {
        let mut category_map: HashMap<CategoryType, usize> = HashMap::new();
        let mut category_num = 0usize;
        let mut unique_lables: Vec<CategoryType> = Vec::new();

        for l in categories {
            if !category_map.contains_key(&l) {
                category_map.insert(l.clone(), category_num);
                unique_lables.push(l.clone());
                category_num += 1;
            }
        }
        Self {
            category_map,
            num_categories: category_num,
            categories: unique_lables,
        }
    }

    /// Build an encoder from a predefined (category -> class number) map
    pub fn from_category_map(category_map: HashMap<CategoryType, usize>) -> Self {
        let mut _unique_cat: Vec<(CategoryType, usize)> =
            category_map.iter().map(|(k, v)| (k.clone(), *v)).collect();
        _unique_cat.sort_by(|a, b| a.1.cmp(&b.1));
        let categories: Vec<CategoryType> = _unique_cat.into_iter().map(|a| a.0).collect();
        Self {
            num_categories: categories.len(),
            categories,
            category_map,
        }
    }

    /// Build an encoder from a predefined positional category-class num vector
    pub fn from_positional_category_vec(categories: Vec<CategoryType>) -> Self {
        let category_map: HashMap<CategoryType, usize> = categories
            .iter()
            .enumerate()
            .map(|(v, k)| (k.clone(), v))
            .collect();
        Self {
            num_categories: categories.len(),
            category_map,
            categories,
        }
    }

    /// Take an iterator as a series to transform
    pub fn transform_iter<U: RealNumber>(
        &self,
        cat_it: impl Iterator<Item = CategoryType>,
    ) -> Vec<Option<Vec<U>>> {
        cat_it.map(|l| self.transform_one(&l)).collect()
    }

    /// Transform a slice of category types into one-hot vectors
    /// None is returned if unknown category is encountered
    pub fn transfrom_series<U: RealNumber>(
        &self,
        categories: &'a [CategoryType],
    ) -> Vec<Option<Vec<U>>> {
        let v = categories.iter().cloned();
        self.transform_iter(v)
    }

    /// Transform a single category type into a one-hot vector
    pub fn transform_one<U: RealNumber>(&self, category: &CategoryType) -> Option<Vec<U>> {
        match self.category_map.get(category) {
            None => None,
            Some(&idx) => Some(make_one_hot(idx, self.num_categories)),
        }
    }

    /// Get categories ordered by encoder's category enumeration
    pub fn get_categories(&self) -> &Vec<CategoryType> {
        &self.categories
    }

    /// Invert one-hot vector, back to the category
    pub fn invert_one<U: RealNumber>(&self, one_hot: Vec<U>) -> Result<CategoryType, Failed> {
        let pos = U::from_f64(1f64).unwrap();

        let s: Vec<usize> = one_hot
            .into_iter()
            .enumerate()
            .filter_map(|(idx, v)| if v == pos { Some(idx) } else { None })
            .collect();

        if s.len() == 1 {
            let idx = s[0];
            return Ok(self.categories[idx].clone());
        }
        let pos_entries = format!(
            "Expected a single positive entry, {} entires found",
            s.len()
        );
        Err(Failed::transform(&pos_entries[..]))
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn from_categories() {
        let fake_categories: Vec<usize> = vec![1, 2, 3, 4, 5, 3, 5, 3, 1, 2, 4];
        let it = fake_categories.iter().map(|&a| a);
        let enc = SeriesOneHotEncoder::<usize>::fit_to_iter(it);
        let oh_vec: Vec<f64> = match enc.transform_one(&1) {
            None => panic!("Wrong categories"),
            Some(v) => v,
        };
        let res: Vec<f64> = vec![1f64, 0f64, 0f64, 0f64, 0f64];
        assert_eq!(oh_vec, res);
    }

    fn build_fake_str_enc<'a>() -> SeriesOneHotEncoder<&'a str> {
        let fake_category_pos = vec!["background", "dog", "cat"];
        let enc = SeriesOneHotEncoder::<&str>::from_positional_category_vec(fake_category_pos);
        enc
    }

    #[test]
    fn category_map_and_vec() {
        let category_map: HashMap<&str, usize> = vec![("background", 0), ("dog", 1), ("cat", 2)]
            .into_iter()
            .collect();
        let enc = SeriesOneHotEncoder::<&str>::from_category_map(category_map);
        let oh_vec: Vec<f64> = match enc.transform_one(&"dog") {
            None => panic!("Wrong categories"),
            Some(v) => v,
        };
        let res: Vec<f64> = vec![0f64, 1f64, 0f64];
        assert_eq!(oh_vec, res);
    }

    #[test]
    fn positional_categories_vec() {
        let enc = build_fake_str_enc();
        let oh_vec: Vec<f64> = match enc.transform_one(&"dog") {
            None => panic!("Wrong categories"),
            Some(v) => v,
        };
        let res: Vec<f64> = vec![0.0, 1.0, 0.0];
        assert_eq!(oh_vec, res);
    }

    #[test]
    fn invert_label_test() {
        let enc = build_fake_str_enc();
        let res: Vec<f64> = vec![0.0, 1.0, 0.0];
        let lab = enc.invert_one(res).unwrap();
        assert_eq!(lab, "dog");
        if let Err(e) = enc.invert_one(vec![0.0, 0.0, 0.0]) {
            let pos_entries = format!("Expected a single positive entry, 0 entires found");
            assert_eq!(e, Failed::transform(&pos_entries[..]));
        };
    }

    #[test]
    fn test_many_categorys() {
        let enc = build_fake_str_enc();
        let res: Vec<Option<Vec<f64>>> =
            enc.transfrom_series(&["dog", "cat", "fish", "background"]);
        let v = vec![
            Some(vec![0.0, 1.0, 0.0]),
            Some(vec![0.0, 0.0, 1.0]),
            None,
            Some(vec![1.0, 0.0, 0.0]),
        ];
        assert_eq!(res, v)
    }
}