aprender-core 0.29.1

Next-generation machine learning library in pure Rust
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147

use super::*;

#[test]
fn test_lasso_multivariate_coverage() {
    let x = Matrix::from_vec(4, 2, vec![1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 2.0, 2.0])
        .expect("Valid matrix dimensions for test");
    let y = Vector::from_slice(&[6.0, 8.0, 9.0, 11.0]);

    let mut model = Lasso::new(0.001);
    model.fit(&x, &y).expect("Fit should succeed");

    assert_eq!(model.coefficients().len(), 2);
}

#[test]
fn test_elastic_net_multivariate_coverage() {
    let x = Matrix::from_vec(4, 2, vec![1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 2.0, 2.0])
        .expect("Valid matrix dimensions for test");
    let y = Vector::from_slice(&[6.0, 8.0, 9.0, 11.0]);

    let mut model = ElasticNet::new(0.001, 0.5);
    model.fit(&x, &y).expect("Fit should succeed");

    assert_eq!(model.coefficients().len(), 2);
}

// =========================================================================
// Coverage boost: Load error paths
// =========================================================================

#[test]
fn test_linear_regression_load_nonexistent_file() {
    let result = LinearRegression::load("/tmp/aprender_nonexistent_lr_model.bin");
    assert!(result.is_err());
}

#[test]
fn test_ridge_load_nonexistent_file() {
    let result = Ridge::load("/tmp/aprender_nonexistent_ridge_model.bin");
    assert!(result.is_err());
}

#[test]
fn test_lasso_load_nonexistent_file() {
    let result = Lasso::load("/tmp/aprender_nonexistent_lasso_model.bin");
    assert!(result.is_err());
}

#[test]
fn test_elastic_net_load_nonexistent_file() {
    let result = ElasticNet::load("/tmp/aprender_nonexistent_en_model.bin");
    assert!(result.is_err());
}

#[test]
fn test_linear_regression_load_safetensors_nonexistent() {
    let result = LinearRegression::load_safetensors("/tmp/aprender_nonexistent_lr.safetensors");
    assert!(result.is_err());
}

// =========================================================================
// Coverage boost: ElasticNet l1_ratio clamping
// =========================================================================

#[test]
fn test_elastic_net_l1_ratio_clamped_high() {
    let model = ElasticNet::new(0.1, 2.0); // l1_ratio > 1.0 should be clamped to 1.0
    assert!((model.l1_ratio() - 1.0).abs() < 1e-6);
}

#[test]
fn test_elastic_net_l1_ratio_clamped_low() {
    let model = ElasticNet::new(0.1, -0.5); // l1_ratio < 0.0 should be clamped to 0.0
    assert!(model.l1_ratio().abs() < 1e-6);
}

#[test]
fn test_elastic_net_l1_ratio_boundary_zero() {
    let model = ElasticNet::new(0.1, 0.0);
    assert!(model.l1_ratio().abs() < 1e-6);
}

#[test]
fn test_elastic_net_l1_ratio_boundary_one() {
    let model = ElasticNet::new(0.1, 1.0);
    assert!((model.l1_ratio() - 1.0).abs() < 1e-6);
}

// =========================================================================
// Coverage boost: ElasticNet builder methods
// =========================================================================

#[test]
fn test_elastic_net_with_intercept_false() {
    let x =
        Matrix::from_vec(4, 1, vec![1.0, 2.0, 3.0, 4.0]).expect("Valid matrix dimensions for test");
    let y = Vector::from_slice(&[2.0, 4.0, 6.0, 8.0]);

    let mut model = ElasticNet::new(0.001, 0.5).with_intercept(false);
    model.fit(&x, &y).expect("Fit should succeed");
    assert_eq!(model.coefficients().len(), 1);
}

#[test]
fn test_elastic_net_with_max_iter_debug() {
    let model = ElasticNet::new(0.1, 0.5).with_max_iter(500);
    let _ = format!("{:?}", model); // Exercise Debug impl
}

#[test]
fn test_elastic_net_with_tol_debug() {
    let model = ElasticNet::new(0.1, 0.5).with_tol(1e-6);
    let _ = format!("{:?}", model);
}

// =========================================================================
// Coverage boost: Save/Load roundtrip for various models
// =========================================================================

#[test]
fn test_linear_regression_save_load_roundtrip() {
    let x =
        Matrix::from_vec(4, 1, vec![1.0, 2.0, 3.0, 4.0]).expect("Valid matrix dimensions for test");
    let y = Vector::from_slice(&[2.0, 4.0, 6.0, 8.0]);

    let mut model = LinearRegression::new();
    model.fit(&x, &y).expect("Fit should succeed");

    let dir = std::env::temp_dir().join("aprender_test_lr_save_load");
    std::fs::create_dir_all(&dir).ok();
    let path = dir.join("lr_model.bin");

    model.save(&path).expect("Save should succeed");
    let loaded = LinearRegression::load(&path).expect("Load should succeed");

    let orig_preds = model.predict(&x);
    let loaded_preds = loaded.predict(&x);
    for (a, b) in orig_preds
        .as_slice()
        .iter()
        .zip(loaded_preds.as_slice().iter())
    {
        assert!((a - b).abs() < 1e-6);
    }

    std::fs::remove_dir_all(&dir).ok();
}