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101 lines
3.2 KiB
Rust
101 lines
3.2 KiB
Rust
use rustframe::compute::models::logreg::LogReg;
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use rustframe::matrix::Matrix;
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/// Two binary classification demos using logistic regression.
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///
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/// Example 1 predicts exam success from hours studied.
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/// Example 2 predicts whether an online shopper will make a purchase.
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fn main() {
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student_passing_example();
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println!("\n-----\n");
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purchase_prediction_example();
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}
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fn student_passing_example() {
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println!("Example 1: exam pass prediction");
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// Hours studied for each student
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let hours = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
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// Label: 0 denotes failure and 1 denotes success
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let passed = vec![0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0];
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let x = Matrix::from_vec(hours.clone(), hours.len(), 1);
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let y = Matrix::from_vec(passed.clone(), passed.len(), 1);
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let mut model = LogReg::new(1);
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model.fit(&x, &y, 0.1, 10000);
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let preds = model.predict(&x);
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println!("Hours -> pred | actual");
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for i in 0..x.rows() {
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println!(
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"{:>2} -> {} | {}",
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hours[i] as i32,
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preds[(i, 0)] as i32,
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passed[i] as i32
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);
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}
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// Probability estimate for a new student
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let new_student = Matrix::from_vec(vec![5.5], 1, 1);
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let p = model.predict_proba(&new_student);
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println!("Probability of passing with 5.5h study: {:.2}", p[(0, 0)]);
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}
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fn purchase_prediction_example() {
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println!("Example 2: purchase likelihood");
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// minutes on site, pages viewed -> made a purchase?
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let raw_x = vec![1.0, 2.0, 3.0, 1.0, 2.0, 4.0, 5.0, 5.0, 3.5, 2.0, 6.0, 6.0];
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let bought = vec![0.0, 0.0, 0.0, 1.0, 0.0, 1.0];
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let x = Matrix::from_rows_vec(raw_x, 6, 2);
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let y = Matrix::from_vec(bought.clone(), bought.len(), 1);
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let mut model = LogReg::new(2);
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model.fit(&x, &y, 0.05, 20000);
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let preds = model.predict(&x);
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println!("time, pages -> pred | actual");
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for i in 0..x.rows() {
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println!(
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"{:>4}m, {:>2} -> {} | {}",
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x[(i, 0)],
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x[(i, 1)] as i32,
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preds[(i, 0)] as i32,
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bought[i] as i32
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);
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}
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let new_visit = Matrix::from_rows_vec(vec![4.0, 4.0], 1, 2);
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let p = model.predict_proba(&new_visit);
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println!("Prob of purchase for 4min/4pages: {:.2}", p[(0, 0)]);
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}
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#[test]
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fn test_student_passing_example() {
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let hours = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
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let passed = vec![0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0];
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let x = Matrix::from_vec(hours.clone(), hours.len(), 1);
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let y = Matrix::from_vec(passed.clone(), passed.len(), 1);
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let mut model = LogReg::new(1);
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model.fit(&x, &y, 0.1, 10000);
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let preds = model.predict(&x);
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for i in 0..y.rows() {
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assert_eq!(preds[(i, 0)], passed[i]);
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}
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}
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#[test]
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fn test_purchase_prediction_example() {
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let raw_x = vec![1.0, 2.0, 3.0, 1.0, 2.0, 4.0, 5.0, 5.0, 3.5, 2.0, 6.0, 6.0];
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let bought = vec![0.0, 0.0, 0.0, 1.0, 0.0, 1.0];
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let x = Matrix::from_rows_vec(raw_x, 6, 2);
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let y = Matrix::from_vec(bought.clone(), bought.len(), 1);
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let mut model = LogReg::new(2);
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model.fit(&x, &y, 0.05, 20000);
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let preds = model.predict(&x);
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for i in 0..y.rows() {
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assert_eq!(preds[(i, 0)], bought[i]);
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}
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} |