One hot encoder

src/lib.rs

@@ -91,6 +91,8 @@ pub mod naive_bayes;
 /// Supervised neighbors-based learning methods
 pub mod neighbors;
 pub(crate) mod optimization;
+/// Preprocessing utilities
+pub mod preprocessing;
 /// Support Vector Machines
 pub mod svm;
 /// Supervised tree-based learning methods

src/preprocessing/categorical.rs

@@ -0,0 +1,329 @@
+//! # One-hot Encoding For [RealNumber](../../math/num/trait.RealNumber.html) Matricies
+//! Transform a data [Matrix](../../linalg/trait.BaseMatrix.html) by replacing all categorical variables with their one-hot equivalents
+//!
+//! Internally OneHotEncoder treats every categorical column as a series and transforms it using [CategoryMapper](../series_encoder/struct.CategoryMapper.html)
+//!
+//! ### Usage Example
+//! ```
+//! use smartcore::linalg::naive::dense_matrix::DenseMatrix;
+//! use smartcore::preprocessing::categorical::{OneHotEncoder, OneHotEncoderParams};
+//! let data = DenseMatrix::from_2d_array(&[
+//!         &[1.5, 1.0, 1.5, 3.0],
+//!         &[1.5, 2.0, 1.5, 4.0],
+//!         &[1.5, 1.0, 1.5, 5.0],
+//!         &[1.5, 2.0, 1.5, 6.0],
+//!   ]);
+//! let encoder_params = OneHotEncoderParams::from_cat_idx(&[1, 3]);
+//! // Infer number of categories from data and return a reusable encoder
+//! let encoder = OneHotEncoder::fit(&data, encoder_params).unwrap();
+//! // Transform categorical to one-hot encoded (can transform similar)
+//! let oh_data = encoder.transform(&data).unwrap();
+//! // Produces the following:
+//! //    &[1.5, 1.0, 0.0, 1.5, 1.0, 0.0, 0.0, 0.0]
+//! //    &[1.5, 0.0, 1.0, 1.5, 0.0, 1.0, 0.0, 0.0]
+//! //    &[1.5, 1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0]
+//! //    &[1.5, 0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0]
+//! ```
+use std::iter;
+
+use crate::error::Failed;
+use crate::linalg::Matrix;
+
+use crate::preprocessing::data_traits::{CategoricalFloat, Categorizable};
+use crate::preprocessing::series_encoder::CategoryMapper;
+
+/// OneHotEncoder Parameters
+#[derive(Debug, Clone)]
+pub struct OneHotEncoderParams {
+    /// Column number that contain categorical variable
+    pub col_idx_categorical: Option<Vec<usize>>,
+    /// (Currently not implemented) Try and infer which of the matrix columns are categorical variables
+    infer_categorical: bool,
+}
+
+impl OneHotEncoderParams {
+    /// Generate parameters from categorical variable column numbers
+    pub fn from_cat_idx(categorical_params: &[usize]) -> Self {
+        Self {
+            col_idx_categorical: Some(categorical_params.to_vec()),
+            infer_categorical: false,
+        }
+    }
+}
+
+/// Calculate the offset to parameters to due introduction of one-hot encoding
+fn find_new_idxs(num_params: usize, cat_sizes: &[usize], cat_idxs: &[usize]) -> Vec<usize> {
+    // This functions uses iterators and returns a vector.
+    // In case we get a huge amount of paramenters this might be a problem
+    // todo: Change this such that it will return an iterator
+
+    let cat_idx = cat_idxs.iter().copied().chain((num_params..).take(1));
+
+    // Offset is constant between two categorical values, here we calculate the number of steps
+    // that remain constant
+    let repeats = cat_idx.scan(0, |a, v| {
+        let im = v + 1 - *a;
+        *a = v;
+        Some(im)
+    });
+
+    // Calculate the offset to parameter idx due to newly intorduced one-hot vectors
+    let offset_ = cat_sizes.iter().scan(0, |a, &v| {
+        *a = *a + v - 1;
+        Some(*a)
+    });
+    let offset = (0..1).chain(offset_);
+
+    let new_param_idxs: Vec<usize> = (0..num_params)
+        .zip(
+            repeats
+                .zip(offset)
+                .map(|(r, o)| iter::repeat(o).take(r))
+                .flatten(),
+        )
+        .map(|(idx, ofst)| idx + ofst)
+        .collect();
+    new_param_idxs
+}
+
+fn validate_col_is_categorical<T: Categorizable>(data: &[T]) -> bool {
+    for v in data {
+        if !v.is_valid() {
+            return false;
+        }
+    }
+    true
+}
+
+/// Encode Categorical variavbles of data matrix to one-hot
+#[derive(Debug, Clone)]
+pub struct OneHotEncoder {
+    category_mappers: Vec<CategoryMapper<CategoricalFloat>>,
+    col_idx_categorical: Vec<usize>,
+}
+
+impl OneHotEncoder {
+    /// Create an encoder instance with categories infered from data matrix
+    pub fn fit<T, M>(data: &M, params: OneHotEncoderParams) -> Result<OneHotEncoder, Failed>
+    where
+        T: Categorizable,
+        M: Matrix<T>,
+    {
+        match (params.col_idx_categorical, params.infer_categorical) {
+            (None, false) => Err(Failed::fit(
+                "Must pass categorical series ids or infer flag",
+            )),
+
+            (Some(_idxs), true) => Err(Failed::fit(
+                "Ambigous parameters, got both infer and categroy ids",
+            )),
+
+            (Some(mut idxs), false) => {
+                // make sure categories have same order as data columns
+                idxs.sort_unstable();
+
+                let (nrows, _) = data.shape();
+
+                // col buffer to avoid allocations
+                let mut col_buf: Vec<T> = iter::repeat(T::zero()).take(nrows).collect();
+
+                let mut res: Vec<CategoryMapper<CategoricalFloat>> = Vec::with_capacity(idxs.len());
+
+                for &idx in &idxs {
+                    data.copy_col_as_vec(idx, &mut col_buf);
+                    if !validate_col_is_categorical(&col_buf) {
+                        let msg = format!(
+                            "Column {} of data matrix containts non categorizable (integer) values",
+                            idx
+                        );
+                        return Err(Failed::fit(&msg[..]));
+                    }
+                    let hashable_col = col_buf.iter().map(|v| v.to_category());
+                    res.push(CategoryMapper::fit_to_iter(hashable_col));
+                }
+
+                Ok(Self {
+                    category_mappers: res,
+                    col_idx_categorical: idxs,
+                })
+            }
+
+            (None, true) => {
+                todo!("Auto-Inference for Categorical Variables not yet implemented")
+            }
+        }
+    }
+
+    /// Transform categorical variables to one-hot encoded and return a new matrix
+    pub fn transform<T, M>(&self, x: &M) -> Result<M, Failed>
+    where
+        T: Categorizable,
+        M: Matrix<T>,
+    {
+        let (nrows, p) = x.shape();
+        let additional_params: Vec<usize> = self
+            .category_mappers
+            .iter()
+            .map(|enc| enc.num_categories())
+            .collect();
+
+        // Eac category of size v adds v-1 params
+        let expandws_p: usize = p + additional_params.iter().fold(0, |cs, &v| cs + v - 1);
+
+        let new_col_idx = find_new_idxs(p, &additional_params[..], &self.col_idx_categorical[..]);
+        let mut res = M::zeros(nrows, expandws_p);
+
+        for (pidx, &old_cidx) in self.col_idx_categorical.iter().enumerate() {
+            let cidx = new_col_idx[old_cidx];
+            let col_iter = (0..nrows).map(|r| x.get(r, old_cidx).to_category());
+            let sencoder = &self.category_mappers[pidx];
+            let oh_series = col_iter.map(|c| sencoder.get_one_hot::<T, Vec<T>>(&c));
+
+            for (row, oh_vec) in oh_series.enumerate() {
+                match oh_vec {
+                    None => {
+                        // Since we support T types, bad value in a series causes in to be invalid
+                        let msg = format!("At least one value in column {} doesn't conform to category definition", old_cidx);
+                        return Err(Failed::transform(&msg[..]));
+                    }
+                    Some(v) => {
+                        // copy one hot vectors to their place in the data matrix;
+                        for (col_ofst, &val) in v.iter().enumerate() {
+                            res.set(row, cidx + col_ofst, val);
+                        }
+                    }
+                }
+            }
+        }
+
+        // copy old data in x to their new location while skipping catergorical vars (already treated)
+        let mut skip_idx_iter = self.col_idx_categorical.iter();
+        let mut cur_skip = skip_idx_iter.next();
+
+        for (old_p, &new_p) in new_col_idx.iter().enumerate() {
+            // if found treated varible, skip it
+            if let Some(&v) = cur_skip {
+                if v == old_p {
+                    cur_skip = skip_idx_iter.next();
+                    continue;
+                }
+            }
+
+            for r in 0..nrows {
+                let val = x.get(r, old_p);
+                res.set(r, new_p, val);
+            }
+        }
+
+        Ok(res)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::linalg::naive::dense_matrix::DenseMatrix;
+    use crate::preprocessing::series_encoder::CategoryMapper;
+
+    #[test]
+    fn adjust_idxs() {
+        assert_eq!(find_new_idxs(0, &[], &[]), Vec::<usize>::new());
+        // [0,1,2] -> [0, 1, 1, 1, 2]
+        assert_eq!(find_new_idxs(3, &[3], &[1]), vec![0, 1, 4]);
+    }
+
+    fn build_cat_first_and_last() -> (DenseMatrix<f64>, DenseMatrix<f64>) {
+        let orig = DenseMatrix::from_2d_array(&[
+            &[1.0, 1.5, 3.0],
+            &[2.0, 1.5, 4.0],
+            &[1.0, 1.5, 5.0],
+            &[2.0, 1.5, 6.0],
+        ]);
+
+        let oh_enc = DenseMatrix::from_2d_array(&[
+            &[1.0, 0.0, 1.5, 1.0, 0.0, 0.0, 0.0],
+            &[0.0, 1.0, 1.5, 0.0, 1.0, 0.0, 0.0],
+            &[1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0],
+            &[0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0],
+        ]);
+
+        (orig, oh_enc)
+    }
+
+    fn build_fake_matrix() -> (DenseMatrix<f64>, DenseMatrix<f64>) {
+        // Categorical first and last
+        let orig = DenseMatrix::from_2d_array(&[
+            &[1.5, 1.0, 1.5, 3.0],
+            &[1.5, 2.0, 1.5, 4.0],
+            &[1.5, 1.0, 1.5, 5.0],
+            &[1.5, 2.0, 1.5, 6.0],
+        ]);
+
+        let oh_enc = DenseMatrix::from_2d_array(&[
+            &[1.5, 1.0, 0.0, 1.5, 1.0, 0.0, 0.0, 0.0],
+            &[1.5, 0.0, 1.0, 1.5, 0.0, 1.0, 0.0, 0.0],
+            &[1.5, 1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0],
+            &[1.5, 0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0],
+        ]);
+
+        (orig, oh_enc)
+    }
+
+    #[test]
+    fn hash_encode_f64_series() {
+        let series = vec![3.0, 1.0, 2.0, 1.0];
+        let hashable_series: Vec<CategoricalFloat> =
+            series.iter().map(|v| v.to_category()).collect();
+        let enc = CategoryMapper::from_positional_category_vec(hashable_series);
+        let inv = enc.invert_one_hot(vec![0.0, 0.0, 1.0]);
+        let orig_val: f64 = inv.unwrap().into();
+        assert_eq!(orig_val, 2.0);
+    }
+    #[test]
+    fn test_fit() {
+        let (x, _) = build_fake_matrix();
+        let params = OneHotEncoderParams::from_cat_idx(&[1, 3]);
+        let oh_enc = OneHotEncoder::fit(&x, params).unwrap();
+        assert_eq!(oh_enc.category_mappers.len(), 2);
+
+        let num_cat: Vec<usize> = oh_enc
+            .category_mappers
+            .iter()
+            .map(|a| a.num_categories())
+            .collect();
+        assert_eq!(num_cat, vec![2, 4]);
+    }
+
+    #[test]
+    fn matrix_transform_test() {
+        let (x, expected_x) = build_fake_matrix();
+        let params = OneHotEncoderParams::from_cat_idx(&[1, 3]);
+        let oh_enc = OneHotEncoder::fit(&x, params).unwrap();
+        let nm = oh_enc.transform(&x).unwrap();
+        assert_eq!(nm, expected_x);
+
+        let (x, expected_x) = build_cat_first_and_last();
+        let params = OneHotEncoderParams::from_cat_idx(&[0, 2]);
+        let oh_enc = OneHotEncoder::fit(&x, params).unwrap();
+        let nm = oh_enc.transform(&x).unwrap();
+        assert_eq!(nm, expected_x);
+    }
+
+    #[test]
+    fn fail_on_bad_category() {
+        let m = DenseMatrix::from_2d_array(&[
+            &[1.0, 1.5, 3.0],
+            &[2.0, 1.5, 4.0],
+            &[1.0, 1.5, 5.0],
+            &[2.0, 1.5, 6.0],
+        ]);
+
+        let params = OneHotEncoderParams::from_cat_idx(&[1]);
+        match OneHotEncoder::fit(&m, params) {
+            Err(_) => {
+                assert!(true);
+            }
+            _ => assert!(false),
+        }
+    }
+}

src/preprocessing/data_traits.rs

@@ -0,0 +1,43 @@
+//! Traits to indicate that float variables can be viewed as categorical
+//! This module assumes
+
+use crate::math::num::RealNumber;
+
+pub type CategoricalFloat = u16;
+
+// pub struct CategoricalFloat(u16);
+const ERROR_MARGIN: f64 = 0.001;
+
+pub trait Categorizable: RealNumber {
+    type A;
+
+    fn to_category(self) -> CategoricalFloat;
+
+    fn is_valid(self) -> bool;
+}
+
+impl Categorizable for f32 {
+    type A = CategoricalFloat;
+
+    fn to_category(self) -> CategoricalFloat {
+        self as CategoricalFloat
+    }
+
+    fn is_valid(self) -> bool {
+        let a = self.to_category();
+        (a as f32 - self).abs() < (ERROR_MARGIN as f32)
+    }
+}
+
+impl Categorizable for f64 {
+    type A = CategoricalFloat;
+
+    fn to_category(self) -> CategoricalFloat {
+        self as CategoricalFloat
+    }
+
+    fn is_valid(self) -> bool {
+        let a = self.to_category();
+        (a as f64 - self).abs() < ERROR_MARGIN
+    }
+}

src/preprocessing/mod.rs

@@ -0,0 +1,5 @@
+/// Transform a data matrix by replaceing all categorical variables with their one-hot vector equivalents
+pub mod categorical;
+mod data_traits;
+/// Encode a series (column, array) of categorical variables as one-hot vectors
+pub mod series_encoder;