Function one_hot_encode 

pub fn one_hot_encode(
    categorical: &Array2<String>,
    column_names: Option<&[&str]>,
) -> Result<(Array2<f64>, Vec<String>), DatasetError>

One-hot encode a matrix of categorical string features.

The mixed-type loaders (adult, titanic, bank_marketing, abalone, kddcup99, palmer_penguins) and the all-categorical ones (mushroom, car_evaluation) keep their categorical values in ColumnData::String columns. Read each column by name with as_string, then stack the columns into an Array2<String>. No numeric model can consume strings directly. This function expands each column into one indicator column per level it takes. A row gets 1.0 in the column for its own level, and 0.0 everywhere else.

This function sorts levels within a column, so the output layout depends only on the values present. The returned names identify the columns as <column>=<level>, using column_names when supplied, and column_0, column_1, and so on otherwise.

This widens the matrix by however many distinct levels the data holds. That is harmless for mushroom (22 columns become 117). Before running it on kddcup99’s service column (70 levels over millions of rows), check the resulting width.

Parameters

• categorical - The categorical matrix, shape (n_samples, n_features).
• column_names - Optional names for the source columns, used to build the output names. Must have one entry per column when supplied.

Returns

• (Array2<f64>, Vec<String>) - The indicator matrix, shape (n_samples, total_levels), and one name per output column.

Errors

• DatasetError::ValidationError - Returns this when categorical has no rows or no columns.
• DatasetError::LengthMismatch - Returns this when column_names is Some but its length does not match the column count.

Example

use dataset_ml::preprocessing::one_hot_encode;
use ndarray::array;

let categorical = array![
    ["male".to_string(), "S".to_string()],
    ["female".to_string(), "C".to_string()],
    ["male".to_string(), "C".to_string()],
];
let (encoded, names) = one_hot_encode(&categorical, Some(&["sex", "port"])).unwrap();

assert_eq!(names, vec!["sex=female", "sex=male", "port=C", "port=S"]);
assert_eq!(encoded.row(0).to_vec(), vec![0.0, 1.0, 0.0, 1.0]); // male, S