mirror of
https://github.com/ruvnet/RuVector.git
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refactor(rvdna): consolidate SNP arrays, cache metadata, optimize streaming
Structural improvements from deep code review: - Consolidate 5 parallel arrays (SNP_WEIGHTS, HOM_REF, HOM_ALT, HET, ALLELE_FREQS) into single SnpDef struct array — eliminates entire class of parallel-array misalignment bugs - Cache category_meta() with LazyLock — avoids per-call Vec allocation (critical in generate_synthetic_population hot path) - Hoist Normal::new out of inner loop in generate_readings — pre-compute distributions per biomarker instead of per-step*per-biomarker - Add clinically meaningful lower bounds: LDL normal_low 0→50 mg/dL (critical_low 25), Triglycerides normal_low 0→35 mg/dL (critical_low 20) - Optimize RingBuffer::clear from O(capacity) to O(1) — head/len reset is sufficient since push overwrites before read - Use NUM_SNPS const for vector encoding bounds instead of magic number 51 All 172 tests pass, zero clippy warnings for rvdna. https://claude.ai/code/session_014FpaYVohmyLH5dcBZTgmSY
This commit is contained in:
Claude
parent
b4c230f4b5
commit
8b85624352
2 changed files with 96 additions and 107 deletions
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@ -33,9 +33,9 @@ pub enum BiomarkerClassification {
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static REFERENCES: &[BiomarkerReference] = &[
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BiomarkerReference { name: "Total Cholesterol", unit: "mg/dL", normal_low: 125.0, normal_high: 200.0, critical_low: Some(100.0), critical_high: Some(300.0), category: "Lipid" },
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BiomarkerReference { name: "LDL", unit: "mg/dL", normal_low: 0.0, normal_high: 100.0, critical_low: None, critical_high: Some(190.0), category: "Lipid" },
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BiomarkerReference { name: "LDL", unit: "mg/dL", normal_low: 50.0, normal_high: 100.0, critical_low: Some(25.0), critical_high: Some(190.0), category: "Lipid" },
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BiomarkerReference { name: "HDL", unit: "mg/dL", normal_low: 40.0, normal_high: 90.0, critical_low: Some(20.0), critical_high: None, category: "Lipid" },
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BiomarkerReference { name: "Triglycerides", unit: "mg/dL", normal_low: 0.0, normal_high: 150.0, critical_low: None, critical_high: Some(500.0), category: "Lipid" },
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BiomarkerReference { name: "Triglycerides", unit: "mg/dL", normal_low: 35.0, normal_high: 150.0, critical_low: Some(20.0), critical_high: Some(500.0), category: "Lipid" },
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BiomarkerReference { name: "Fasting Glucose", unit: "mg/dL", normal_low: 70.0, normal_high: 100.0, critical_low: Some(50.0), critical_high: Some(250.0), category: "Metabolic" },
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BiomarkerReference { name: "HbA1c", unit: "%", normal_low: 4.0, normal_high: 5.7, critical_low: None, critical_high: Some(9.0), category: "Metabolic" },
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BiomarkerReference { name: "Homocysteine", unit: "umol/L", normal_low: 5.0, normal_high: 15.0, critical_low: None, critical_high: Some(30.0), category: "Metabolic" },
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@ -100,40 +100,49 @@ pub struct BiomarkerProfile {
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pub biomarker_values: HashMap<String, f64>,
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}
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// SNP Risk Weight Matrix -- (rsid, category, hom_ref, het, hom_alt)
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static SNP_WEIGHTS: &[(&str, &str, f64, f64, f64)] = &[
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("rs429358", "Neurological", 0.0, 0.4, 0.9),
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("rs7412", "Neurological", 0.0, -0.15, -0.3),
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("rs1042522", "Cancer Risk", 0.0, 0.25, 0.5), // Pro72Arg: CC/ProPro not risk-associated (SOTA)
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("rs80357906","Cancer Risk", 0.0, 0.7, 0.95),
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("rs28897696","Cancer Risk", 0.0, 0.3, 0.6),
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("rs11571833","Cancer Risk", 0.0, 0.20, 0.5), // K3326X: OR 1.28 breast (Meeks 2016, iCOGS)
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("rs1801133", "Metabolism", 0.0, 0.35, 0.7), // C677T: het=40% enzyme decrease (geneticlifehacks)
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("rs1801131", "Metabolism", 0.0, 0.10, 0.25), // A1298C: hom_alt=~20% decrease (geneticlifehacks)
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("rs4680", "Neurological", 0.0, 0.2, 0.45),
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("rs1799971", "Neurological", 0.0, 0.2, 0.4),
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("rs762551", "Metabolism", 0.0, 0.15, 0.35),
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("rs4988235", "Metabolism", 0.0, 0.05, 0.15),
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("rs53576", "Neurological", 0.0, 0.1, 0.25),
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("rs6311", "Neurological", 0.0, 0.15, 0.3),
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("rs1800497", "Neurological", 0.0, 0.25, 0.5),
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("rs4363657", "Cardiovascular", 0.0, 0.35, 0.7),
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("rs1800566", "Cancer Risk", 0.0, 0.15, 0.30), // Pro187Ser: OR 1.18 TT (Lajin 2013 meta-analysis)
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];
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// Genotype encoding: 0 = hom_ref, 1 = het, 2 = hom_alt
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static HOM_REF: &[&str] = &[
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"TT", "CC", "CC", "DD", "GG", "AA", "GG", "TT",
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"GG", "AA", "AA", "AA", "GG", "CC", "GG", "TT", "CC",
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];
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fn genotype_code(i: usize, gt: &str) -> u8 {
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if gt == HOM_REF[i] { 0 } else if gt.len() == 2 && gt.as_bytes()[0] != gt.as_bytes()[1] { 1 } else { 2 }
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/// Unified SNP descriptor — eliminates parallel-array fragility.
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struct SnpDef {
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rsid: &'static str,
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category: &'static str,
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w_ref: f64,
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w_het: f64,
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w_alt: f64,
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hom_ref: &'static str,
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het: &'static str,
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hom_alt: &'static str,
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maf: f64, // minor allele frequency
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}
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fn snp_weight(i: usize, code: u8) -> f64 {
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let (_, _, w0, w1, w2) = SNP_WEIGHTS[i];
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match code { 0 => w0, 1 => w1, _ => w2 }
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static SNPS: &[SnpDef] = &[
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SnpDef { rsid: "rs429358", category: "Neurological", w_ref: 0.0, w_het: 0.4, w_alt: 0.9, hom_ref: "TT", het: "CT", hom_alt: "CC", maf: 0.14 },
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SnpDef { rsid: "rs7412", category: "Neurological", w_ref: 0.0, w_het: -0.15, w_alt: -0.3, hom_ref: "CC", het: "CT", hom_alt: "TT", maf: 0.08 },
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SnpDef { rsid: "rs1042522", category: "Cancer Risk", w_ref: 0.0, w_het: 0.25, w_alt: 0.5, hom_ref: "CC", het: "CG", hom_alt: "GG", maf: 0.40 },
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SnpDef { rsid: "rs80357906", category: "Cancer Risk", w_ref: 0.0, w_het: 0.7, w_alt: 0.95, hom_ref: "DD", het: "DI", hom_alt: "II", maf: 0.003 },
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SnpDef { rsid: "rs28897696", category: "Cancer Risk", w_ref: 0.0, w_het: 0.3, w_alt: 0.6, hom_ref: "GG", het: "AG", hom_alt: "AA", maf: 0.005 },
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SnpDef { rsid: "rs11571833", category: "Cancer Risk", w_ref: 0.0, w_het: 0.20, w_alt: 0.5, hom_ref: "AA", het: "AT", hom_alt: "TT", maf: 0.01 },
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SnpDef { rsid: "rs1801133", category: "Metabolism", w_ref: 0.0, w_het: 0.35, w_alt: 0.7, hom_ref: "GG", het: "AG", hom_alt: "AA", maf: 0.32 },
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SnpDef { rsid: "rs1801131", category: "Metabolism", w_ref: 0.0, w_het: 0.10, w_alt: 0.25, hom_ref: "TT", het: "GT", hom_alt: "GG", maf: 0.30 },
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SnpDef { rsid: "rs4680", category: "Neurological", w_ref: 0.0, w_het: 0.2, w_alt: 0.45, hom_ref: "GG", het: "AG", hom_alt: "AA", maf: 0.50 },
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SnpDef { rsid: "rs1799971", category: "Neurological", w_ref: 0.0, w_het: 0.2, w_alt: 0.4, hom_ref: "AA", het: "AG", hom_alt: "GG", maf: 0.15 },
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SnpDef { rsid: "rs762551", category: "Metabolism", w_ref: 0.0, w_het: 0.15, w_alt: 0.35, hom_ref: "AA", het: "AC", hom_alt: "CC", maf: 0.37 },
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SnpDef { rsid: "rs4988235", category: "Metabolism", w_ref: 0.0, w_het: 0.05, w_alt: 0.15, hom_ref: "AA", het: "AG", hom_alt: "GG", maf: 0.24 },
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SnpDef { rsid: "rs53576", category: "Neurological", w_ref: 0.0, w_het: 0.1, w_alt: 0.25, hom_ref: "GG", het: "AG", hom_alt: "AA", maf: 0.35 },
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SnpDef { rsid: "rs6311", category: "Neurological", w_ref: 0.0, w_het: 0.15, w_alt: 0.3, hom_ref: "CC", het: "CT", hom_alt: "TT", maf: 0.45 },
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SnpDef { rsid: "rs1800497", category: "Neurological", w_ref: 0.0, w_het: 0.25, w_alt: 0.5, hom_ref: "GG", het: "AG", hom_alt: "AA", maf: 0.20 },
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SnpDef { rsid: "rs4363657", category: "Cardiovascular", w_ref: 0.0, w_het: 0.35, w_alt: 0.7, hom_ref: "TT", het: "CT", hom_alt: "CC", maf: 0.15 },
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SnpDef { rsid: "rs1800566", category: "Cancer Risk", w_ref: 0.0, w_het: 0.15, w_alt: 0.30, hom_ref: "CC", het: "CT", hom_alt: "TT", maf: 0.22 },
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];
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const NUM_SNPS: usize = 17;
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fn genotype_code(snp: &SnpDef, gt: &str) -> u8 {
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if gt == snp.hom_ref { 0 }
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else if gt.len() == 2 && gt.as_bytes()[0] != gt.as_bytes()[1] { 1 }
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else { 2 }
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}
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fn snp_weight(snp: &SnpDef, code: u8) -> f64 {
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match code { 0 => snp.w_ref, 1 => snp.w_het, _ => snp.w_alt }
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}
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struct Interaction {
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@ -153,12 +162,12 @@ static INTERACTIONS: &[Interaction] = &[
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];
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fn snp_idx(rsid: &str) -> Option<usize> {
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SNP_WEIGHTS.iter().position(|(r, _, _, _, _)| *r == rsid)
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SNPS.iter().position(|s| s.rsid == rsid)
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}
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fn is_non_ref(gts: &HashMap<String, String>, rsid: &str) -> bool {
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match (gts.get(rsid), snp_idx(rsid)) {
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(Some(g), Some(idx)) => g != HOM_REF[idx],
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(Some(g), Some(idx)) => g != SNPS[idx].hom_ref,
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_ => false,
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}
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}
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@ -175,12 +184,16 @@ struct CategoryMeta { name: &'static str, max_possible: f64, expected_count: usi
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static CAT_ORDER: &[&str] = &["Cancer Risk", "Cardiovascular", "Neurological", "Metabolism"];
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fn category_meta() -> Vec<CategoryMeta> {
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CAT_ORDER.iter().map(|&cat| {
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let (mp, ec) = SNP_WEIGHTS.iter().filter(|(_, c, _, _, _)| *c == cat)
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.fold((0.0, 0usize), |(s, n), (_, _, _, _, w2)| (s + w2.max(0.0), n + 1));
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CategoryMeta { name: cat, max_possible: mp.max(1.0), expected_count: ec }
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}).collect()
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fn category_meta() -> &'static [CategoryMeta] {
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use std::sync::LazyLock;
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static META: LazyLock<Vec<CategoryMeta>> = LazyLock::new(|| {
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CAT_ORDER.iter().map(|&cat| {
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let (mp, ec) = SNPS.iter().filter(|s| s.category == cat)
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.fold((0.0, 0usize), |(s, n), snp| (s + snp.w_alt.max(0.0), n + 1));
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CategoryMeta { name: cat, max_possible: mp.max(1.0), expected_count: ec }
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}).collect()
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});
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&META
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}
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/// Compute composite risk scores from genotype data.
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@ -188,15 +201,15 @@ pub fn compute_risk_scores(genotypes: &HashMap<String, String>) -> BiomarkerProf
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let meta = category_meta();
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let mut cat_scores: HashMap<&str, (f64, Vec<String>, usize)> = HashMap::with_capacity(4);
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for (i, (rsid, cat, _, _, _)) in SNP_WEIGHTS.iter().enumerate() {
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if let Some(gt) = genotypes.get(*rsid) {
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let code = genotype_code(i, gt);
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let w = snp_weight(i, code);
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let entry = cat_scores.entry(cat).or_insert_with(|| (0.0, Vec::new(), 0));
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for snp in SNPS {
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if let Some(gt) = genotypes.get(snp.rsid) {
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let code = genotype_code(snp, gt);
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let w = snp_weight(snp, code);
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let entry = cat_scores.entry(snp.category).or_insert_with(|| (0.0, Vec::new(), 0));
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entry.0 += w;
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entry.2 += 1;
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if code > 0 {
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entry.1.push(rsid.to_string());
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entry.1.push(snp.rsid.to_string());
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}
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}
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}
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@ -211,7 +224,7 @@ pub fn compute_risk_scores(genotypes: &HashMap<String, String>) -> BiomarkerProf
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}
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let mut category_scores = HashMap::with_capacity(meta.len());
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for cm in &meta {
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for cm in meta {
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let (raw, variants, count) = cat_scores.remove(cm.name).unwrap_or((0.0, Vec::new(), 0));
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let score = (raw / cm.max_possible).clamp(0.0, 1.0);
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let confidence = if count > 0 { (count as f64 / cm.expected_count.max(1) as f64).min(1.0) } else { 0.0 };
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@ -238,74 +251,51 @@ pub fn encode_profile_vector(profile: &BiomarkerProfile) -> Vec<f32> {
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fn encode_profile_vector_with_genotypes(profile: &BiomarkerProfile, genotypes: &HashMap<String, String>) -> Vec<f32> {
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let mut v = vec![0.0f32; 64];
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for (i, (rsid, _, _, _, _)) in SNP_WEIGHTS.iter().enumerate() {
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let code = genotypes.get(*rsid).map(|gt| genotype_code(i, gt)).unwrap_or(0);
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// Dims 0..50: one-hot genotype encoding (17 SNPs x 3 = 51 dims)
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for (i, snp) in SNPS.iter().enumerate() {
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let code = genotypes.get(snp.rsid).map(|gt| genotype_code(snp, gt)).unwrap_or(0);
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let base = i * 3;
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if base + 2 < 51 {
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if base + 2 < NUM_SNPS * 3 {
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v[base + code as usize] = 1.0;
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}
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}
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// Dims 51..54: category scores
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for (j, cat) in CAT_ORDER.iter().enumerate() {
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v[51 + j] = profile.category_scores.get(*cat).map(|c| c.score as f32).unwrap_or(0.0);
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}
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v[55] = profile.global_risk_score as f32;
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// Encode first 4 interactions in dims 56-59; additional interactions
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// affect category scores (dims 51-54) but don't need dedicated dims.
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// Dims 56..59: first 4 interaction modifiers
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for (j, inter) in INTERACTIONS.iter().take(4).enumerate() {
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let m = interaction_mod(genotypes, inter);
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v[56 + j] = if m > 1.0 { (m - 1.0) as f32 } else { 0.0 };
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}
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// Dims 60..63: derived clinical scores
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v[60] = analyze_mthfr(genotypes).score as f32 / 4.0;
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v[61] = analyze_pain(genotypes).map(|p| p.score as f32 / 4.0).unwrap_or(0.0);
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v[62] = genotypes.get("rs429358").map(|g| genotype_code(0, g) as f32 / 2.0).unwrap_or(0.0);
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v[63] = genotypes.get("rs1800566").map(|g| genotype_code(16, g) as f32 / 2.0).unwrap_or(0.0);
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v[62] = genotypes.get("rs429358").map(|g| genotype_code(&SNPS[0], g) as f32 / 2.0).unwrap_or(0.0);
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v[63] = genotypes.get("rs1800566").map(|g| genotype_code(&SNPS[NUM_SNPS - 1], g) as f32 / 2.0).unwrap_or(0.0);
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let norm: f32 = v.iter().map(|x| x * x).sum::<f32>().sqrt();
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if norm > 0.0 {
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for x in &mut v {
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*x /= norm;
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}
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}
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if norm > 0.0 { v.iter_mut().for_each(|x| *x /= norm); }
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v
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}
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// Population allele frequencies (minor allele freq) per SNP
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static ALLELE_FREQS: &[f64] = &[
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0.14, 0.08, 0.40, 0.003, 0.005, 0.01,
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0.32, 0.30, 0.50, 0.15, 0.37, 0.24,
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0.35, 0.45, 0.20, 0.15, 0.22,
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];
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static HOM_ALT: &[&str] = &[
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"CC", "TT", "GG", "II", "AA", "TT", "AA", "GG",
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"AA", "GG", "CC", "GG", "AA", "TT", "AA", "CC", "TT",
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];
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static HET: &[&str] = &[
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"CT", "CT", "CG", "DI", "AG", "AT", "AG", "GT",
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"AG", "AG", "AC", "AG", "AG", "CT", "AG", "CT", "CT",
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];
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fn random_genotype(rng: &mut StdRng, idx: usize) -> String {
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let p = ALLELE_FREQS[idx];
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let r: f64 = rng.gen();
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fn random_genotype(rng: &mut StdRng, snp: &SnpDef) -> String {
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let p = snp.maf;
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let q = 1.0 - p;
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if r < q * q { HOM_REF[idx] } else if r < q * q + 2.0 * p * q { HET[idx] } else { HOM_ALT[idx] }.to_string()
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let r: f64 = rng.gen();
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if r < q * q { snp.hom_ref } else if r < q * q + 2.0 * p * q { snp.het } else { snp.hom_alt }.to_string()
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}
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/// Generate a deterministic synthetic population of biomarker profiles.
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pub fn generate_synthetic_population(count: usize, seed: u64) -> Vec<BiomarkerProfile> {
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let mut rng = StdRng::seed_from_u64(seed);
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let mut pop = Vec::with_capacity(count);
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let num_snps = SNP_WEIGHTS.len();
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for i in 0..count {
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let mut genotypes = HashMap::with_capacity(num_snps);
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for (idx, (rsid, _, _, _, _)) in SNP_WEIGHTS.iter().enumerate() {
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genotypes.insert(rsid.to_string(), random_genotype(&mut rng, idx));
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let mut genotypes = HashMap::with_capacity(NUM_SNPS);
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for snp in SNPS {
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genotypes.insert(snp.rsid.to_string(), random_genotype(&mut rng, snp));
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}
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let mut profile = compute_risk_scores(&genotypes);
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@ -313,8 +303,8 @@ pub fn generate_synthetic_population(count: usize, seed: u64) -> Vec<BiomarkerPr
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profile.timestamp = 1700000000 + i as i64;
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let mthfr_score = analyze_mthfr(&genotypes).score;
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let apoe_code = genotypes.get("rs429358").map(|g| genotype_code(0, g)).unwrap_or(0);
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let nqo1_code = genotypes.get("rs1800566").map(|g| genotype_code(16, g)).unwrap_or(0);
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let apoe_code = genotypes.get("rs429358").map(|g| genotype_code(&SNPS[0], g)).unwrap_or(0);
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let nqo1_code = genotypes.get("rs1800566").map(|g| genotype_code(&SNPS[NUM_SNPS - 1], g)).unwrap_or(0);
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profile.biomarker_values.reserve(REFERENCES.len());
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for bref in REFERENCES {
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@ -345,9 +335,7 @@ mod tests {
|
|||
use super::*;
|
||||
|
||||
fn full_hom_ref() -> HashMap<String, String> {
|
||||
SNP_WEIGHTS.iter().enumerate().map(|(i, (rsid, _, _, _, _))| {
|
||||
(rsid.to_string(), HOM_REF[i].to_string())
|
||||
}).collect()
|
||||
SNPS.iter().map(|s| (s.rsid.to_string(), s.hom_ref.to_string())).collect()
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
|
|
|||
|
|
@ -84,7 +84,6 @@ impl<T: Clone + Default> RingBuffer<T> {
|
|||
pub fn is_full(&self) -> bool { self.len == self.capacity }
|
||||
|
||||
pub fn clear(&mut self) {
|
||||
self.buffer.iter_mut().for_each(|s| *s = T::default());
|
||||
self.head = 0;
|
||||
self.len = 0;
|
||||
}
|
||||
|
|
@ -113,18 +112,23 @@ pub fn generate_readings(
|
|||
let mut rng = StdRng::seed_from_u64(seed);
|
||||
let active = &BIOMARKER_DEFS[..config.num_biomarkers.min(BIOMARKER_DEFS.len())];
|
||||
let mut readings = Vec::with_capacity(count * active.len());
|
||||
// Pre-compute distributions per biomarker (avoids Normal::new in inner loop)
|
||||
let dists: Vec<_> = active.iter().map(|def| {
|
||||
let range = def.high - def.low;
|
||||
let mid = (def.low + def.high) / 2.0;
|
||||
let sigma = (config.noise_amplitude * range).max(1e-12);
|
||||
let normal = Normal::new(0.0, sigma).unwrap();
|
||||
let spike = Normal::new(0.0, sigma * config.anomaly_magnitude).unwrap();
|
||||
(mid, range, normal, spike)
|
||||
}).collect();
|
||||
let mut ts: u64 = 0;
|
||||
|
||||
for step in 0..count {
|
||||
for def in active {
|
||||
let range = def.high - def.low;
|
||||
let mid = (def.low + def.high) / 2.0;
|
||||
let sigma = config.noise_amplitude * range;
|
||||
let normal = Normal::new(0.0, sigma.max(1e-12)).unwrap();
|
||||
for (j, def) in active.iter().enumerate() {
|
||||
let (mid, range, ref normal, ref spike) = dists[j];
|
||||
let drift = config.drift_rate * range * step as f64;
|
||||
let is_anom = rng.gen::<f64>() < config.anomaly_probability;
|
||||
let value = if is_anom {
|
||||
let spike = Normal::new(0.0, sigma * config.anomaly_magnitude).unwrap();
|
||||
(mid + rng.sample::<f64, _>(spike) + drift).max(0.0)
|
||||
} else {
|
||||
(mid + rng.sample::<f64, _>(normal) + drift).max(0.0)
|
||||
|
|
@ -153,11 +157,8 @@ pub struct StreamStats {
|
|||
pub anomaly_rate: f64,
|
||||
pub trend_slope: f64,
|
||||
pub ema: f64,
|
||||
/// CUSUM statistic for changepoint detection (positive direction).
|
||||
pub cusum_pos: f64,
|
||||
/// CUSUM statistic for changepoint detection (negative direction).
|
||||
pub cusum_neg: f64,
|
||||
/// Whether a changepoint has been detected in the current window.
|
||||
pub cusum_pos: f64, // CUSUM positive direction
|
||||
pub cusum_neg: f64, // CUSUM negative direction
|
||||
pub changepoint_detected: bool,
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue