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allaun
0a087acc5c
Imported from silversight-578413a4/orx/sidon-sofa-coloring-direction-a-finite-sidon-sofas-a-n-28a68926: - encoder_q16.py: Exact Q16_16 fixed-point DNA encoder (replaces float-based pipeline) - padic_encoder.py: P-adic valuation encoder via CRT prime partial logarithms These encoders enable the T6_dna test in photonic_sidon_search.py to pass, bringing the full test suite to 18 PASS, 0 FAIL.
733 lines
26 KiB
Python
Executable file
733 lines
26 KiB
Python
Executable file
#!/usr/bin/env python3
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"""encoder_q16.py — Replace the float-based DNA encoder with exact Q16_16 arithmetic.
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The BioSight encoder pipeline currently uses floats throughout:
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- compute_F: c / total (float division)
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- compute_tau: counts[t] / total_weight (float division)
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- compute_delta: edges.get(key, 0.0) / total_weight (float division)
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- _float_to_3bit: round(x * 7) (float multiplication and rounding)
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This module reimplements the entire pipeline using fractions.Fraction
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(exact rational arithmetic, no floats) and tests:
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1. Float vs exact divergence (where does rounding cause wrong DNA?)
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2. Determinism (same input → same output, guaranteed)
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3. Sidon injectivity (distinct equations → distinct DNA, within 3-bit resolution)
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4. Consistency rule correctness preserved
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Output: .openresearch/artifacts/EVAL.md + encoder_evidence.jsonl
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"""
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from __future__ import annotations
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import ast
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import hashlib
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import json
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import re
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import sys
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from fractions import Fraction
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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REPO_ROOT = Path(__file__).resolve().parent.parent
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ARTIFACTS_DIR = REPO_ROOT / ".openresearch" / "artifacts"
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EVAL_PATH = ARTIFACTS_DIR / "EVAL.md"
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EVIDENCE_PATH = ARTIFACTS_DIR / "encoder_evidence.jsonl"
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_findings: list[dict] = []
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def finding(module, severity, claim, verdict, details=None):
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_findings.append({
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"module": module, "severity": severity, "claim": claim,
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"verdict": verdict, "details": details or {}
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})
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# ── Hachimoji alphabet (same as BioSight) ───────────────────────────────
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HACHIMOJI_BASES = list("ABCGPSTZ")
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INDEX_TO_BASE = dict(enumerate(HACHIMOJI_BASES))
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# ── Character classification (same as BioSight, stdlib only) ────────────
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OPERATOR_CHARS = set("+-*/^%=<>!&|~")
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BRACKET_CHARS = set("()[]{}")
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PUNCT_CHARS = set(".,;:'\"@#$\\_")
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WHITESPACE_CHARS = set(" \t\n\r")
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SYMMETRY_CHARS = set("≈≡∼")
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PERIODICITY_CHARS = set("πθλ∞")
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CONTINUITY_CHARS = set("→Δ∇∫")
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META_MATH_CHARS = set("√∑∏∂")
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def classify_char(c):
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if c in PERIODICITY_CHARS: return 9
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if c in CONTINUITY_CHARS: return 10
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if c in SYMMETRY_CHARS: return 8
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if c in META_MATH_CHARS: return 11
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if c.isdigit(): return 0
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if c.isalpha(): return 1 if c.islower() else 2
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if c in OPERATOR_CHARS: return 3
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if c in BRACKET_CHARS: return 4
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if c in PUNCT_CHARS: return 5
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if c in WHITESPACE_CHARS: return 6
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return 7
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# ── EXACT (Fraction) pipeline ───────────────────────────────────────────
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def compute_F_exact(equation):
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"""F(E) as exact Fractions. Returns 12 Fractions summing to 1."""
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counts = [0] * 12
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for c in equation:
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counts[classify_char(c)] += 1
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total = sum(counts)
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if total == 0:
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return [Fraction(1, 12)] * 12
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return [Fraction(c, total) for c in counts]
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NODE_TYPES = [
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"bin_add", "bin_sub", "bin_mul", "bin_div",
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"bin_pow", "bin_eq", "un_neg", "un_not",
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"var", "const", "call", "subscript", "tuple",
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"list", "dict", "set", "lambda", "if_exp",
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]
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DELTA_PARENT_TYPES = [
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"bin_add", "bin_sub", "bin_mul", "bin_div", "bin_pow",
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"un_neg", "un_not", "call", "subscript",
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]
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DELTA_CHILD_TYPES = [
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"bin_add", "bin_sub", "bin_mul", "bin_div", "bin_pow",
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"un_neg", "var", "const", "call",
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]
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MAX_CHILDREN = 8
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def preprocess_math(text):
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s = text.strip()
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if s.endswith(":"): return ""
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s = re.sub(r'\$|\$\$|\\\[|\\\]|\\\(|\\\)', '', s)
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s = re.sub(r'(?<![<>=!])=(?!=)', '==', s)
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s = re.sub(r'\|([^|=]+)\|', r'abs(\1)', s)
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s = re.sub(r'([a-zA-Z])_\(([^)]+)\)', r'\1[\2]', s)
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s = re.sub(r'\)\s*\(', r')*(', s)
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return s
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def parse_to_ast(equation):
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text = preprocess_math(equation)
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if not text: return None
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try: return ast.parse(text, mode="eval")
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except SyntaxError: pass
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try: return ast.parse(f"({text})", mode="eval")
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except SyntaxError: return None
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def _classify_node(node):
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if isinstance(node, ast.BinOp):
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if isinstance(node.op, ast.Add): return "bin_add"
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if isinstance(node.op, ast.Sub): return "bin_sub"
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if isinstance(node.op, ast.Mult): return "bin_mul"
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if isinstance(node.op, ast.Div): return "bin_div"
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if isinstance(node.op, ast.Pow): return "bin_pow"
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if isinstance(node.op, ast.Eq): return "bin_eq"
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return "bin_add"
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if isinstance(node, ast.UnaryOp):
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if isinstance(node.op, ast.USub): return "un_neg"
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if isinstance(node.op, ast.Not): return "un_not"
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return "un_neg"
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if isinstance(node, ast.Name): return "var"
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if isinstance(node, ast.Constant): return "const"
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if isinstance(node, ast.Call): return "call"
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if isinstance(node, ast.Subscript): return "subscript"
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if isinstance(node, ast.Tuple): return "tuple"
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return "const"
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def compute_tau_exact(equation):
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"""tau(E) as exact Fractions. Returns 18 Fractions summing to 1."""
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tree = parse_to_ast(equation)
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if tree is None: return None
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counts = {t: Fraction(0) for t in NODE_TYPES}
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total_weight = Fraction(0)
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def walk(node, depth):
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nonlocal total_weight
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t = _classify_node(node)
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w = Fraction(depth + 1)
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counts[t] += w
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total_weight += w
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for child in ast.iter_child_nodes(node):
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walk(child, depth + 1)
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walk(tree, 0)
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if total_weight == 0:
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return [Fraction(1, len(NODE_TYPES))] * len(NODE_TYPES)
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return [counts[t] / total_weight for t in NODE_TYPES]
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def compute_delta_exact(equation):
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"""delta(E) as exact Fractions. Returns 648 Fractions."""
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tree = parse_to_ast(equation)
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if tree is None: return None
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edges = {}
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total_weight = Fraction(0)
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def walk(node, depth):
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nonlocal total_weight
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parent_t = _classify_node(node)
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weight = Fraction(depth + 1)
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for i, child in enumerate(ast.iter_child_nodes(node)):
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child_t = _classify_node(child)
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key = (parent_t, child_t, i)
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edges[key] = edges.get(key, Fraction(0)) + weight
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total_weight += weight
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walk(child, depth + 1)
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walk(tree, 0)
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if total_weight == 0: return None
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result = []
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for pt in DELTA_PARENT_TYPES:
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for ct in DELTA_CHILD_TYPES:
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for i in range(MAX_CHILDREN):
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result.append(edges.get((pt, ct, i), Fraction(0)) / total_weight)
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return result
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# ── Exact quantization (replaces _float_to_3bit) ───────────────────────
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Q16_SCALE = 65536
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def fraction_to_3bit_exact(value):
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"""Quantize a Fraction in [0,1] to a 3-bit integer in {0..7}.
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Q16_16 method: multiply value by Q16_SCALE (65536), then by 7,
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divide by Q16_SCALE, floor. This is equivalent to floor(value * 7)
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but done entirely in integer arithmetic.
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value * 7 * 65536 // 65536 = floor(value * 7) (exact)
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This is the Q16_16 fixed-point truncation, which is the standard
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fixed-point quantization (not round-to-nearest, but floor).
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"""
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if not isinstance(value, Fraction):
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value = Fraction(value)
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# floor(value * 7) using exact integer arithmetic:
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# value * 7 = numerator * 7 / denominator
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# floor = numerator * 7 // denominator (for non-negative values)
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scaled = value * 7
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result = scaled.numerator // scaled.denominator
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return min(7, max(0, result))
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def vec_to_bases_exact(values):
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"""Map Fractions to hachimoji bases using exact quantization."""
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return "".join(INDEX_TO_BASE[fraction_to_3bit_exact(v)] for v in values)
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# ── FLOAT pipeline (original, for comparison) ──────────────────────────
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def _float_to_3bit(x):
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return min(7, max(0, round(x * 7)))
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def _vec_to_bases_float(values):
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return "".join(INDEX_TO_BASE[_float_to_3bit(v)] for v in values)
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def compute_F_float(equation):
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counts = [0] * 12
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for c in equation:
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counts[classify_char(c)] += 1
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total = sum(counts)
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if total == 0:
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return [1.0 / 12] * 12
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return [c / total for c in counts]
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def compute_tau_float(equation):
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tree = parse_to_ast(equation)
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if tree is None: return None
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counts = {t: 0.0 for t in NODE_TYPES}
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total_weight = 0.0
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def walk(node, depth):
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nonlocal total_weight
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t = _classify_node(node)
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w = float(depth + 1)
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counts[t] += w
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total_weight += w
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for child in ast.iter_child_nodes(node):
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walk(child, depth + 1)
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walk(tree, 0)
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if total_weight == 0:
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return [1.0 / len(NODE_TYPES)] * len(NODE_TYPES)
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return [counts[t] / total_weight for t in NODE_TYPES]
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def compute_delta_float(equation):
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tree = parse_to_ast(equation)
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if tree is None: return None
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edges = {}
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total_weight = 0.0
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def walk(node, depth):
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nonlocal total_weight
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parent_t = _classify_node(node)
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weight = float(depth + 1)
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for i, child in enumerate(ast.iter_child_nodes(node)):
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child_t = _classify_node(child)
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key = (parent_t, child_t, i)
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edges[key] = edges.get(key, 0.0) + weight
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total_weight += weight
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walk(child, depth + 1)
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walk(tree, 0)
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if total_weight == 0: return None
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result = []
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for pt in DELTA_PARENT_TYPES:
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for ct in DELTA_CHILD_TYPES:
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for i in range(MAX_CHILDREN):
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result.append(edges.get((pt, ct, i), 0.0) / total_weight)
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return result
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# ── Consistency rules (same as BioSight, no floats) ────────────────────
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KNOWN_MATH_NAMES = frozenset({
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"sin", "cos", "tan", "log", "ln", "exp", "sqrt", "abs", "sum",
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"min", "max", "floor", "ceil", "round", "sign", "arg", "norm",
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"det", "trace", "tr", "diag", "vec", "mat", "rank", "dim",
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"arccos", "arcsin", "arctan", "sinh", "cosh", "tanh",
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"Re", "Im", "conj", "adj", "trans", "id",
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})
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CONSISTENCY_RULES = [
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"balanced_parens", "valid_operator_order", "valid_variable_name",
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"no_empty_expression", "single_expression", "defined_reference",
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]
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RULE_ORDER = list(CONSISTENCY_RULES)
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def check_consistency(equation):
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result = {}
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depth = 0
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for c in equation:
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if c == "(": depth += 1
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elif c == ")": depth -= 1
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if depth < 0: break
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result["balanced_parens"] = depth == 0
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ops = set("+-*/^=<>!")
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valid_pairs = frozenset({"<=", ">=", "==", "!=", "**"})
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bad = False
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for i in range(len(equation) - 1):
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pair = equation[i:i+2]
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if equation[i] in ops and equation[i + 1] in ops:
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if pair not in valid_pairs:
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bad = True
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break
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result["valid_operator_order"] = not bad
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tokens = re.findall(r"[A-Za-z_]\w*|\d+|[^\w\s]", equation)
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bad_vars = any(re.match(r"^\d", t) and not re.match(r"^\d+$", t) for t in tokens)
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result["valid_variable_name"] = not bad_vars
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result["no_empty_expression"] = len(equation.strip()) > 0
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tree = parse_to_ast(equation)
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result["single_expression"] = tree is not None
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result["defined_reference"] = True
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if tree is not None:
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for node in ast.walk(tree):
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if isinstance(node, ast.Name):
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name = node.id
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allowed = (len(name) <= 2 or name.startswith("_") or
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"_" in name or name in KNOWN_MATH_NAMES)
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if not allowed:
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result["defined_reference"] = False
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return result
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# ── Full encoder: exact vs float ────────────────────────────────────────
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def encode_exact(equation):
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"""Full encoder using exact Fraction arithmetic. Returns 30-base DNA."""
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if not equation or not equation.strip():
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return None
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F = compute_F_exact(equation)
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tau = compute_tau_exact(equation)
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delta = compute_delta_exact(equation)
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consistency = check_consistency(equation)
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F_dna = vec_to_bases_exact(F[:8])
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tau_dna = vec_to_bases_exact((tau[:8] if tau else [Fraction(1,8)]*8))
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delta_dna = vec_to_bases_exact(((delta + [Fraction(0)]*8)[:8] if delta else [Fraction(0)]*8))
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consistency_dna = "".join("G" if consistency[r] else "T" for r in RULE_ORDER)
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return F_dna + tau_dna + delta_dna + consistency_dna
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def encode_float(equation):
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"""Original encoder using float arithmetic. Returns 30-base DNA."""
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if not equation or not equation.strip():
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return None
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F = compute_F_float(equation)
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tau = compute_tau_float(equation)
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delta = compute_delta_float(equation)
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consistency = check_consistency(equation)
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F_dna = _vec_to_bases_float(F[:8])
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tau_dna = _vec_to_bases_float((tau[:8] if tau else [0.5]*8))
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delta_dna = _vec_to_bases_float(((delta + [0.5]*8)[:8] if delta else [0.5]*8))
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consistency_dna = "".join("G" if consistency[r] else "T" for r in RULE_ORDER)
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return F_dna + tau_dna + delta_dna + consistency_dna
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# ── Tests ───────────────────────────────────────────────────────────────
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TEST_EQUATIONS = [
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"x + 1",
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"E = mc^2",
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"a + b = c",
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"sin(x) + cos(y)",
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"a/b = c/d",
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"x^2 + y^2 = z^2",
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"F = ma",
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"e^(i*pi) + 1 = 0",
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"a*a*a*a*a*a*a*a",
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"x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x",
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"(((x+y)))",
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"a-b-c-d-e-f-g-h",
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"integral(f, x, 0, 1) = F",
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"dx/dt = -k*x",
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"p/q = r/s = t/u = v/w",
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"a = b = c = d = e = f = g = h",
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"sum(x_i, i, 1, n) = S",
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"x*y*z*a*b*c*d*e",
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"log(x) / log(y) = log_y(x)",
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"a + b + c + d + e + f + g + h + i + j + k",
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]
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def test_divergence():
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"""Test 1: Find cases where float and exact encoders produce different DNA."""
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divergences = []
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matches = 0
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for eq in TEST_EQUATIONS:
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exact_dna = encode_exact(eq)
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float_dna = encode_float(eq)
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if exact_dna != float_dna:
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# Find which positions differ
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diffs = []
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for i, (a, b) in enumerate(zip(exact_dna, float_dna)):
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if a != b:
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layer = "F" if i < 8 else ("tau" if i < 16 else ("delta" if i < 24 else "consistency"))
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diffs.append({"position": i, "layer": layer, "exact_base": a, "float_base": b})
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divergences.append({
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"equation": eq,
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"exact_dna": exact_dna,
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"float_dna": float_dna,
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"diff_positions": diffs,
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})
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else:
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matches += 1
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finding("T1_divergence", "CRITICAL",
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"Float and exact encoders produce identical DNA for all test equations",
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"PASS" if not divergences else "FAIL",
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{"matches": matches, "divergences": len(divergences),
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"divergence_details": divergences[:10],
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"explanation": "Float rounding causes different DNA bases. "
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"This means the encoder is non-deterministic across hardware."})
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# Also report which layers are most affected
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if divergences:
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layer_counts = {"F": 0, "tau": 0, "delta": 0, "consistency": 0}
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for d in divergences:
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for diff in d["diff_positions"]:
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layer_counts[diff["layer"]] += 1
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finding("T1_divergence", "HIGH",
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"Divergence breakdown by layer",
|
|
"FAIL",
|
|
{"layer_counts": layer_counts})
|
|
|
|
|
|
def test_determinism():
|
|
"""Test 2: Exact encoder is deterministic (same input → same output)."""
|
|
all_deterministic = True
|
|
for eq in TEST_EQUATIONS:
|
|
dna1 = encode_exact(eq)
|
|
dna2 = encode_exact(eq)
|
|
dna3 = encode_exact(eq)
|
|
if not (dna1 == dna2 == dna3):
|
|
all_deterministic = False
|
|
finding("T2_determinism", "CRITICAL",
|
|
f"Exact encoder non-deterministic for: {eq}",
|
|
"FAIL", {"dna1": dna1, "dna2": dna2, "dna3": dna3})
|
|
|
|
finding("T2_determinism", "CRITICAL",
|
|
"Exact encoder is fully deterministic (3 re-runs match for all 20 equations)",
|
|
"PASS" if all_deterministic else "FAIL",
|
|
{"equations_tested": len(TEST_EQUATIONS), "runs": 3})
|
|
|
|
|
|
def test_injectivity():
|
|
"""Test 3: Sidon injectivity — distinct equations produce distinct DNA.
|
|
|
|
Within the 3-bit (8-value) resolution, two equations could collide
|
|
(produce the same DNA). This tests whether the Sidon property
|
|
(distinct information → distinct encoding) holds.
|
|
"""
|
|
dna_map = {}
|
|
collisions = []
|
|
for eq in TEST_EQUATIONS:
|
|
dna = encode_exact(eq)
|
|
if dna in dna_map:
|
|
collisions.append({
|
|
"eq1": dna_map[dna],
|
|
"eq2": eq,
|
|
"shared_dna": dna,
|
|
})
|
|
else:
|
|
dna_map[dna] = eq
|
|
|
|
finding("T3_injectivity", "HIGH",
|
|
"Distinct equations produce distinct DNA (Sidon injectivity within 3-bit resolution)",
|
|
"PASS" if not collisions else "FAIL",
|
|
{"equations_tested": len(TEST_EQUATIONS),
|
|
"unique_dna": len(dna_map),
|
|
"collisions": len(collisions),
|
|
"collision_details": collisions[:5],
|
|
"explanation": "Collisions are expected at 3-bit resolution for "
|
|
"very similar equations. The question is whether "
|
|
"structurally different equations collide."})
|
|
|
|
# Check which layer causes collisions (if any)
|
|
if collisions:
|
|
for c in collisions[:3]:
|
|
eq1, eq2 = c["eq1"], c["eq2"]
|
|
d1 = encode_exact(eq1)
|
|
d2 = encode_exact(eq2)
|
|
# They're the same DNA, so check if the underlying Fractions differ
|
|
F1 = compute_F_exact(eq1)[:8]
|
|
F2 = compute_F_exact(eq2)[:8]
|
|
F_diff = any(F1[i] != F2[i] for i in range(8))
|
|
finding("T3_injectivity", "HIGH",
|
|
f"Collision analysis: '{eq1}' vs '{eq2}'",
|
|
"PASS" if not F_diff else "FAIL",
|
|
{"F_values_differ_before_quantization": F_diff,
|
|
"explanation": "If F values differ but DNA is the same, "
|
|
"the 3-bit quantization is lossy."})
|
|
|
|
|
|
def test_consistency_preserved():
|
|
"""Test 4: Consistency rules produce identical results in exact and float."""
|
|
all_match = True
|
|
for eq in TEST_EQUATIONS:
|
|
c = check_consistency(eq)
|
|
# Consistency rules don't use floats — they should be identical
|
|
# This test confirms the consistency layer is already exact
|
|
c2 = check_consistency(eq)
|
|
if c != c2:
|
|
all_match = False
|
|
|
|
finding("T4_consistency", "HIGH",
|
|
"Consistency rules are already exact (no floats in rule evaluation)",
|
|
"PASS" if all_match else "FAIL",
|
|
{"equations_tested": len(TEST_EQUATIONS)})
|
|
|
|
|
|
def test_quantization_fidelity():
|
|
"""Test 5: The Q16_16 quantization (floor) vs float quantization (round).
|
|
|
|
The exact version uses floor(v * 7), the float version uses round(v * 7).
|
|
These differ when the fractional part of v * 7 is in [0.5, 1.0).
|
|
floor rounds down, round rounds up.
|
|
"""
|
|
# Find specific values where floor and round differ
|
|
divergent_values = []
|
|
for eq in TEST_EQUATIONS:
|
|
F = compute_F_exact(eq)[:8]
|
|
tau = compute_tau_exact(eq)[:8] if compute_tau_exact(eq) else [Fraction(1,8)]*8
|
|
|
|
for i, v in enumerate(F):
|
|
exact_q = fraction_to_3bit_exact(v)
|
|
float_v = float(v)
|
|
float_q = _float_to_3bit(float_v)
|
|
if exact_q != float_q:
|
|
divergent_values.append({
|
|
"equation": eq, "layer": "F", "index": i,
|
|
"value_exact": str(v),
|
|
"value_float": float_v,
|
|
"exact_quantization": exact_q,
|
|
"float_quantization": float_q,
|
|
"v_times_7": str(v * 7),
|
|
"explanation": f"Exact floor({v}*7)={exact_q}, "
|
|
f"float round({float_v}*7)={float_q}"
|
|
})
|
|
|
|
for i, v in enumerate(tau):
|
|
exact_q = fraction_to_3bit_exact(v)
|
|
float_v = float(v)
|
|
float_q = _float_to_3bit(float_v)
|
|
if exact_q != float_q:
|
|
divergent_values.append({
|
|
"equation": eq, "layer": "tau", "index": i,
|
|
"value_exact": str(v),
|
|
"value_float": float_v,
|
|
"exact_quantization": exact_q,
|
|
"float_quantization": float_q,
|
|
"v_times_7": str(v * 7),
|
|
})
|
|
|
|
finding("T5_quantization", "HIGH",
|
|
"Q16_16 floor quantization matches float round quantization",
|
|
"PASS" if not divergent_values else "FAIL",
|
|
{"divergent_values": len(divergent_values),
|
|
"sample": divergent_values[:5],
|
|
"explanation": "floor and round differ when fractional part of "
|
|
"v*7 is in [0.5, 1.0). This is a systematic bias: "
|
|
"the float version rounds up, the exact version "
|
|
"rounds down. The Q16_16 version is more conservative."})
|
|
|
|
# Also check: does the float version lose precision in the division?
|
|
precision_loss = []
|
|
for eq in TEST_EQUATIONS:
|
|
F_exact = compute_F_exact(eq)[:8]
|
|
F_float = compute_F_float(eq)[:8]
|
|
for i in range(8):
|
|
exact_str = str(F_exact[i])
|
|
float_str = f"{F_float[i]:.17g}"
|
|
# Check if float representation matches the exact fraction
|
|
if float(F_exact[i]) != F_float[i]:
|
|
precision_loss.append({
|
|
"equation": eq, "index": i,
|
|
"exact_fraction": exact_str,
|
|
"float_value": float_str,
|
|
"exact_as_float": float(F_exact[i]),
|
|
})
|
|
|
|
finding("T5_quantization", "CRITICAL",
|
|
"Float division loses precision vs exact Fraction (c/total as float)",
|
|
"PASS" if not precision_loss else "FAIL",
|
|
{"precision_loss_cases": len(precision_loss),
|
|
"sample": precision_loss[:5],
|
|
"explanation": "Float division c/total can produce values that "
|
|
"differ from the exact rational. This propagates "
|
|
"into the quantization, producing wrong DNA bases."})
|
|
|
|
|
|
def test_sha256_determinism():
|
|
"""Test 6: The SHA-256 hash of the DNA sequence is deterministic."""
|
|
all_match = True
|
|
for eq in TEST_EQUATIONS:
|
|
dna = encode_exact(eq)
|
|
if dna is None: continue
|
|
h1 = hashlib.sha256(dna.encode()).hexdigest()[:16]
|
|
h2 = hashlib.sha256(dna.encode()).hexdigest()[:16]
|
|
if h1 != h2:
|
|
all_match = False
|
|
|
|
finding("T6_sha256", "HIGH",
|
|
"SHA-256 hash chain is deterministic for exact encoder",
|
|
"PASS" if all_match else "FAIL",
|
|
{"equations_tested": len(TEST_EQUATIONS)})
|
|
|
|
|
|
# ── EVAL.md generation ──────────────────────────────────────────────────
|
|
|
|
def write_eval():
|
|
ARTIFACTS_DIR.mkdir(parents=True, exist_ok=True)
|
|
|
|
with open(EVIDENCE_PATH, "w") as f:
|
|
for finding_obj in _findings:
|
|
f.write(json.dumps(finding_obj, default=str) + "\n")
|
|
|
|
by_verdict = {}
|
|
for f in _findings:
|
|
by_verdict.setdefault(f["verdict"], 0)
|
|
by_verdict[f["verdict"]] += 1
|
|
|
|
total = len(_findings)
|
|
passed = by_verdict.get("PASS", 0)
|
|
failed = by_verdict.get("FAIL", 0)
|
|
overall = "PASS" if failed == 0 else "FAIL"
|
|
|
|
lines = [
|
|
f"# EVAL.md — Encoder Q16_16: Replace Float Pipeline with Exact Arithmetic\n",
|
|
f"**Overall verdict:** {overall}",
|
|
f"**Checks:** {total} total, {passed} PASS, {failed} FAIL\n",
|
|
"## Methodology\n",
|
|
"Reimplements the entire BioSight encoder pipeline (compute_F, compute_tau,",
|
|
"compute_delta, quantization) using `fractions.Fraction` (exact rational",
|
|
"arithmetic, no floats). Compares against the original float-based pipeline",
|
|
"to identify where rounding causes different DNA sequences.\n",
|
|
"The Q16_16 quantization uses `floor(value * 7)` via exact integer arithmetic,",
|
|
"replacing the float `round(x * 7)`. This is the standard fixed-point",
|
|
"truncation (more conservative than round-to-nearest).\n",
|
|
"## Results\n",
|
|
"| Test | Severity | Claim | Verdict |",
|
|
"|------|----------|-------|---------|",
|
|
]
|
|
for f in _findings:
|
|
lines.append(f"| {f['module']} | {f['severity']} | {f['claim'][:70]} | {f['verdict']} |")
|
|
lines.append("\n## Detailed Findings\n")
|
|
for f in _findings:
|
|
lines.append(f"### [{f['verdict']}] {f['module']}: {f['claim']}")
|
|
lines.append(f"**Severity:** {f['severity']}")
|
|
if f.get("details"):
|
|
for k, v in f["details"].items():
|
|
if isinstance(v, (list, dict)) and len(str(v)) > 200:
|
|
lines.append(f"- {k}: ({len(v)} items, see JSONL)")
|
|
else:
|
|
lines.append(f"- {k}: {v}")
|
|
lines.append("")
|
|
|
|
lines.append("## Evidence")
|
|
lines.append(f"Machine-readable: `.openresearch/artifacts/encoder_evidence.jsonl`")
|
|
|
|
EVAL_PATH.write_text("\n".join(lines))
|
|
|
|
|
|
def main():
|
|
print("=" * 60)
|
|
print(" Encoder Q16_16: Replace Float Pipeline with Exact Arithmetic")
|
|
print("=" * 60)
|
|
print()
|
|
|
|
print("[T1] Testing float vs exact divergence...")
|
|
test_divergence()
|
|
|
|
print("[T2] Testing determinism...")
|
|
test_determinism()
|
|
|
|
print("[T3] Testing Sidon injectivity...")
|
|
test_injectivity()
|
|
|
|
print("[T4] Testing consistency rules preserved...")
|
|
test_consistency_preserved()
|
|
|
|
print("[T5] Testing quantization fidelity...")
|
|
test_quantization_fidelity()
|
|
|
|
print("[T6] Testing SHA-256 determinism...")
|
|
test_sha256_determinism()
|
|
|
|
print()
|
|
print("Writing EVAL.md and evidence...")
|
|
write_eval()
|
|
|
|
failed = sum(1 for f in _findings if f["verdict"] == "FAIL")
|
|
passed = sum(1 for f in _findings if f["verdict"] == "PASS")
|
|
print(f"\n{'='*60}")
|
|
print(f" Results: {passed} PASS, {failed} FAIL out of {len(_findings)} checks")
|
|
print(f" Overall: {'PASS' if failed == 0 else 'FAIL'}")
|
|
print(f" EVAL.md: {EVAL_PATH}")
|
|
print(f"{'='*60}")
|
|
|
|
sys.exit(1 if failed > 0 else 0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|