aprender-core 0.29.2

Next-generation machine learning library in pure Rust
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//! Integration property tests.

use super::super::*;
pub(crate) use proptest::prelude::*;

pub(super) fn arb_model_type() -> impl Strategy<Value = ModelType> {
    prop_oneof![
        Just(ModelType::LinearRegression),
        Just(ModelType::LogisticRegression),
        Just(ModelType::DecisionTree),
        Just(ModelType::RandomForest),
        Just(ModelType::KMeans),
        Just(ModelType::NaiveBayes),
        Just(ModelType::Custom),
    ]
}

pub(super) fn arb_model_data() -> impl Strategy<Value = Vec<f32>> {
    proptest::collection::vec(
        any::<f32>().prop_filter("finite", |f| f.is_finite()),
        10..500,
    )
}

pub(super) fn arb_large_model_data() -> impl Strategy<Value = Vec<f32>> {
    proptest::collection::vec(
        any::<f32>().prop_filter("finite", |f| f.is_finite()),
        1000..5000,
    )
}

proptest! {
    /// Property: Full metadata stack persists correctly
    #[test]
    fn prop_full_metadata_roundtrip(
        model_name in "[a-zA-Z][a-zA-Z0-9_-]{1,20}",
        description in "[a-zA-Z0-9 ]{1,50}",
        samples in 1usize..100000,
        duration_ms in 1u64..86400000,
        data in arb_model_data()
    ) {
        use tempfile::tempdir;

        #[derive(Debug, serde::Serialize, serde::Deserialize)]
        struct Model { weights: Vec<f32> }

        let model = Model { weights: data.clone() };
        let dir = tempdir().expect("tempdir");
        let path = dir.path().join("full_meta.apr");

        let mut options = SaveOptions::default()
            .with_name(&model_name)
            .with_description(&description);
        options.metadata.training = Some(TrainingInfo {
            samples: Some(samples),
            duration_ms: Some(duration_ms),
            source: Some("test_data".to_string()),
        });

        save(&model, ModelType::Custom, &path, options).expect("save");

        let info = inspect(&path).expect("inspect");
        prop_assert_eq!(info.metadata.model_name.as_deref(), Some(model_name.as_str()));
        prop_assert_eq!(info.metadata.description.as_deref(), Some(description.as_str()));

        let training = info.metadata.training.expect("training");
        prop_assert_eq!(training.samples, Some(samples));
        prop_assert_eq!(training.duration_ms, Some(duration_ms));

        let loaded: Model = load(&path, ModelType::Custom).expect("load");
        prop_assert_eq!(data.len(), loaded.weights.len());
    }

    /// Property: All model types roundtrip correctly
    #[test]
    fn prop_all_model_types_roundtrip(
        model_type in arb_model_type(),
        data in arb_model_data()
    ) {
        use tempfile::tempdir;

        #[derive(Debug, serde::Serialize, serde::Deserialize)]
        struct Model { weights: Vec<f32> }

        let model = Model { weights: data.clone() };
        let dir = tempdir().expect("tempdir");
        let path = dir.path().join("typed.apr");

        save(&model, model_type, &path, SaveOptions::default()).expect("save");

        let info = inspect(&path).expect("inspect");
        prop_assert_eq!(info.model_type, model_type);

        let loaded: Model = load(&path, model_type).expect("load");
        prop_assert_eq!(data.len(), loaded.weights.len());
    }

    /// Property: Large models roundtrip correctly (stress test)
    #[test]
    fn prop_large_model_roundtrip(data in arb_large_model_data()) {
        use tempfile::tempdir;

        #[derive(Debug, serde::Serialize, serde::Deserialize)]
        struct Model { weights: Vec<f32> }

        let model = Model { weights: data.clone() };
        let dir = tempdir().expect("tempdir");
        let path = dir.path().join("large.apr");

        save(&model, ModelType::Custom, &path, SaveOptions::default()).expect("save");

        let loaded: Model = load(&path, ModelType::Custom).expect("load");
        prop_assert_eq!(data.len(), loaded.weights.len());

        // Verify data integrity
        for (i, (orig, loaded_val)) in data.iter().zip(loaded.weights.iter()).enumerate() {
            prop_assert_eq!(
                orig.to_bits(),
                loaded_val.to_bits(),
                "Mismatch at index {}", i
            );
        }
    }

    /// Property: Distillation + License combined
    #[test]
    fn prop_distillation_with_license(data in arb_model_data()) {
        use tempfile::tempdir;

        #[derive(Debug, serde::Serialize, serde::Deserialize)]
        struct Model { weights: Vec<f32> }

        let model = Model { weights: data };
        let dir = tempdir().expect("tempdir");
        let path = dir.path().join("distilled_licensed.apr");

        let distill_info = DistillationInfo {
            method: DistillMethod::Standard,
            teacher: TeacherProvenance {
                hash: "a".repeat(64),
                signature: None,
                model_type: ModelType::Custom,
                param_count: 7_000_000_000,
                ensemble_teachers: None,
            },
            params: DistillationParams {
                temperature: 3.0,
                alpha: 0.7,
                beta: None,
                epochs: 10,
                final_loss: Some(0.42),
            },
            layer_mapping: None,
        };

        let license = LicenseInfo {
            uuid: "12345678-1234-4123-8123-123456789abc".to_string(),
            hash: "b".repeat(64),
            expiry: Some("2025-12-31".to_string()),
            seats: Some(10),
            licensee: Some("Test Corp".to_string()),
            tier: LicenseTier::Enterprise,
        };

        let options = SaveOptions::default()
            .with_distillation_info(distill_info)
            .with_license(license);

        save(&model, ModelType::Custom, &path, options).expect("save");

        let info = inspect(&path).expect("inspect");

        // Both features should be present
        prop_assert!(info.metadata.distillation_info.is_some());
        prop_assert!(info.metadata.license.is_some());
        prop_assert!(info.licensed);

        let restored_distill = info.metadata.distillation_info.expect("distillation");
        prop_assert!(matches!(restored_distill.method, DistillMethod::Standard));

        let restored_license = info.metadata.license.expect("license");
        prop_assert!(matches!(restored_license.tier, LicenseTier::Enterprise));
    }

    /// Property: Multiple saves to same path overwrites correctly
    #[test]
    fn prop_overwrite_preserves_latest(
        data1 in arb_model_data(),
        data2 in arb_model_data()
    ) {
        use tempfile::tempdir;

        #[derive(Debug, serde::Serialize, serde::Deserialize)]
        struct Model { weights: Vec<f32> }

        let dir = tempdir().expect("tempdir");
        let path = dir.path().join("overwrite.apr");

        // Save first model
        let model1 = Model { weights: data1 };
        save(&model1, ModelType::Custom, &path, SaveOptions::default()).expect("save1");

        // Save second model (overwrite)
        let model2 = Model { weights: data2.clone() };
        save(&model2, ModelType::Custom, &path, SaveOptions::default()).expect("save2");

        // Load should return second model
        let loaded: Model = load(&path, ModelType::Custom).expect("load");
        prop_assert_eq!(data2.len(), loaded.weights.len());
    }

    /// Property: File size scales with data size
    #[test]
    fn prop_file_size_scales_with_data(
        small_data in proptest::collection::vec(any::<f32>().prop_filter("finite", |f| f.is_finite()), 10..50),
        large_data in proptest::collection::vec(any::<f32>().prop_filter("finite", |f| f.is_finite()), 500..1000)
    ) {
        use tempfile::tempdir;

        #[derive(Debug, serde::Serialize, serde::Deserialize)]
        struct Model { weights: Vec<f32> }

        let dir = tempdir().expect("tempdir");

        let small_path = dir.path().join("small.apr");
        let model_small = Model { weights: small_data };
        save(&model_small, ModelType::Custom, &small_path, SaveOptions::default()).expect("save small");

        let large_path = dir.path().join("large.apr");
        let model_large = Model { weights: large_data };
        save(&model_large, ModelType::Custom, &large_path, SaveOptions::default()).expect("save large");

        let small_size = std::fs::metadata(&small_path).expect("meta").len();
        let large_size = std::fs::metadata(&large_path).expect("meta").len();

        prop_assert!(large_size > small_size, "Larger data should produce larger file");
    }
}