aprender-core 0.31.2

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
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184

use super::*;

// =========================================================================
// Coverage: TensorCanary - shape mismatch
// =========================================================================

#[test]
fn test_tensor_canary_shape_mismatch() {
    let baseline = TensorCanary {
        name: "t".into(),
        shape: vec![2, 2],
        dtype: "F32".into(),
        mean: 0.0,
        std: 0.1,
        min: -1.0,
        max: 1.0,
        checksum: 0,
    };

    let current = TensorCanary {
        name: "t".into(),
        shape: vec![3, 3], // different shape
        dtype: "F32".into(),
        mean: 0.0,
        std: 0.1,
        min: -1.0,
        max: 1.0,
        checksum: 0,
    };

    match baseline.detect_regression(&current).unwrap() {
        Regression::ShapeMismatch { .. } => {}
        other => panic!("Expected ShapeMismatch, got {:?}", other),
    }
}

// =========================================================================
// Coverage: TensorCanary - checksum mismatch
// =========================================================================

#[test]
fn test_tensor_canary_checksum_mismatch() {
    let baseline = TensorCanary {
        name: "t".into(),
        shape: vec![2],
        dtype: "F32".into(),
        mean: 0.0,
        std: 0.1,
        min: -1.0,
        max: 1.0,
        checksum: 100,
    };

    let current = TensorCanary {
        name: "t".into(),
        shape: vec![2],
        dtype: "F32".into(),
        mean: 0.0,
        std: 0.1,
        min: -1.0,
        max: 1.0,
        checksum: 999, // different checksum
    };

    match baseline.detect_regression(&current).unwrap() {
        Regression::ChecksumMismatch { .. } => {}
        other => panic!("Expected ChecksumMismatch, got {:?}", other),
    }
}

// =========================================================================
// Coverage: CanaryFile new, add_tensor, verify
// =========================================================================

#[test]
fn test_canary_file_new() {
    let canary = CanaryFile::new("test-model");
    assert_eq!(canary.model_name, "test-model");
    assert!(canary.tensors.is_empty());
    assert!(!canary.created_at.is_empty());
}

#[test]
fn test_canary_file_verify_missing_tensor() {
    let mut canary = CanaryFile::new("model");
    canary.add_tensor(TensorCanary {
        name: "missing_tensor".into(),
        shape: vec![2, 2],
        dtype: "F32".into(),
        mean: 0.0,
        std: 0.1,
        min: -1.0,
        max: 1.0,
        checksum: 0,
    });

    // Verify against empty list of current tensors
    let regressions = canary.verify(&[]);
    assert_eq!(regressions.len(), 1);
    assert_eq!(regressions[0].0, "missing_tensor");
}

#[test]
fn test_canary_file_verify_no_regressions() {
    let tensor = TensorCanary {
        name: "t".into(),
        shape: vec![2],
        dtype: "F32".into(),
        mean: 0.5,
        std: 0.1,
        min: 0.0,
        max: 1.0,
        checksum: 42,
    };

    let mut canary = CanaryFile::new("model");
    canary.add_tensor(tensor.clone());

    let regressions = canary.verify(&[tensor]);
    assert!(regressions.is_empty());
}

// =========================================================================
// Coverage: Trend and Regression Debug/Clone
// =========================================================================

#[test]
fn test_trend_variants_debug() {
    let trends = [
        Trend::Improving,
        Trend::Stable,
        Trend::Degrading,
        Trend::Oscillating,
    ];
    for t in &trends {
        let debug = format!("{:?}", t);
        assert!(!debug.is_empty());
    }
}

#[test]
fn test_regression_debug_clone() {
    let r = Regression::MeanDrift {
        expected: 1.0,
        actual: 2.0,
        error: 1.0,
    };
    let debug = format!("{:?}", r);
    assert!(debug.contains("MeanDrift"));
    let cloned = r.clone();
    let debug2 = format!("{:?}", cloned);
    assert_eq!(debug, debug2);
}

// =========================================================================
// Coverage: HanseiReport from_results with failures
// =========================================================================

#[test]
fn test_hansei_report_with_mixed_results() {
    let results = vec![
        (ConversionCategory::GgufToApr, true),
        (ConversionCategory::GgufToApr, false),
        (ConversionCategory::AprToGguf, true),
        (ConversionCategory::SafeTensorsToApr, false),
        (ConversionCategory::SafeTensorsToApr, false),
    ];
    let report = HanseiReport::from_results(&results);
    assert_eq!(report.total_attempts, 5);
    assert_eq!(report.successes, 2);
    assert!(!report.pareto_categories.is_empty());
    assert!(report.success_rate > 0.0 && report.success_rate < 1.0);
}

#[test]
fn test_hansei_report_all_success() {
    let results = vec![
        (ConversionCategory::GgufToApr, true),
        (ConversionCategory::AprToGguf, true),
    ];
    let report = HanseiReport::from_results(&results);
    assert!((report.success_rate - 1.0).abs() < 1e-6);
    assert!(report.pareto_categories.is_empty()); // No failures -> no Pareto
}