allaun/Research-Stack
1
0
Fork 0
mirror of https://github.com/allaunthefox/Research-Stack.git synced 2026-07-31 03:05:21 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
Research-Stack/5-Applications/tools-scripts/domain/classify_domains.py

304 lines
12 KiB
Python
Raw Blame History

#!/usr/bin/env python3
"""
classify_domains.py — Add Domain_Type classification to MATH_MODEL_MAP.tsv
Domain taxonomy derived from the Topological Tape Machine specification:
LAYER_A_COMPRESSION — Representation selection, entropy, encoding, compression objectives
LAYER_B_ROUTING — Cognitive load, mixture-of-experts, predictor distribution, decision reweighting
LAYER_C_TOPOLOGY — Metric tensors, geodesics, manifolds, curvature, Christoffel, charts
LAYER_C_BRAID — Braid formation, witness traces, Merkle structures, raycasting, holonomy
LAYER_D_INVARIANTS — Invariant vectors, conservation laws, constraint systems, survival masks
LAYER_E_VERIFICATION — Acceptance predicates, attestation, BEA consensus, validation checks
LAYER_F_CONTROL — Homeostatic control, hysteresis, mode transitions, waveprobe risk, pressure dynamics
LAYER_G_ENERGY — Thermodynamics, Landauer, Carnot, phonon physics, QCL energy, hardware stress
LAYER_H_ALGEBRA — Geometric algebra, chirality, group theory, finite fields, semirings
LAYER_I_ENCODING — Voxel keys, microvoxel seeds, bit-packing, address schemes, quantization
LAYER_J_DYNAMICS — Time evolution, phase transitions, emergence, Langevin, deformation fields
LAYER_K_SIGNAL — DSP, FFT, wave propagation, scattering, Rayleigh, phased arrays
LAYER_L_APPLICATION — FEA, hormone mapping, semi-truck physics, engineering models
Each model is classified into exactly ONE primary domain.
"""
import csv
import sys
# Domain classification rules: (model_number_range_or_name_keywords, domain_type)
# Using model number ranges for bulk assignment, with keyword overrides.
DOMAIN_MAP = {
# LAYER_A: Compression / Representation Selection (models 1-2, 6-10, 33, 44, 54, 71, 74, 102, 126)
"1": "LAYER_A_COMPRESSION",
"2": "LAYER_A_COMPRESSION",
"6": "LAYER_A_COMPRESSION",
"7": "LAYER_A_COMPRESSION",
"8": "LAYER_A_COMPRESSION",
"9": "LAYER_A_COMPRESSION",
"10": "LAYER_A_COMPRESSION",
"33": "LAYER_A_COMPRESSION",
"44": "LAYER_A_COMPRESSION",
"54": "LAYER_A_COMPRESSION",
"71": "LAYER_A_COMPRESSION",
"74": "LAYER_A_COMPRESSION",
"102":"LAYER_A_COMPRESSION",
"126":"LAYER_A_COMPRESSION",
# LAYER_B: Routing / Cognitive Load (models 3-5, 32, 45, 48, 50, 72-73, 75, 95, 98-101, 120-121, 137)
"3": "LAYER_B_ROUTING",
"4": "LAYER_B_ROUTING",
"5": "LAYER_B_ROUTING",
"32": "LAYER_B_ROUTING",
"45": "LAYER_B_ROUTING",
"48": "LAYER_B_ROUTING",
"50": "LAYER_B_ROUTING",
"72": "LAYER_B_ROUTING",
"73": "LAYER_B_ROUTING",
"75": "LAYER_B_ROUTING",
"95": "LAYER_B_ROUTING",
"98": "LAYER_B_ROUTING",
"99": "LAYER_B_ROUTING",
"100":"LAYER_B_ROUTING",
"101":"LAYER_B_ROUTING",
"120":"LAYER_B_ROUTING",
"121":"LAYER_B_ROUTING",
"137":"LAYER_B_ROUTING",
# LAYER_C_TOPOLOGY: Metric tensors, geodesics, manifolds, curvature (models 16-18, 25, 34, 38, 46, 82-89, 96-97, 105-107, 115-117, 119, 135-136)
"16": "LAYER_C_TOPOLOGY",
"17": "LAYER_C_TOPOLOGY",
"18": "LAYER_C_TOPOLOGY",
"25": "LAYER_C_TOPOLOGY",
"34": "LAYER_C_TOPOLOGY",
"38": "LAYER_C_TOPOLOGY",
"46": "LAYER_C_TOPOLOGY",
"82": "LAYER_C_TOPOLOGY",
"83": "LAYER_C_TOPOLOGY",
"84": "LAYER_C_TOPOLOGY",
"85": "LAYER_C_TOPOLOGY",
"86": "LAYER_C_TOPOLOGY",
"87": "LAYER_C_TOPOLOGY",
"88": "LAYER_C_TOPOLOGY",
"89": "LAYER_C_TOPOLOGY",
"96": "LAYER_C_TOPOLOGY",
"97": "LAYER_C_TOPOLOGY",
"105":"LAYER_C_TOPOLOGY",
"106":"LAYER_C_TOPOLOGY",
"107":"LAYER_C_TOPOLOGY",
"115":"LAYER_C_TOPOLOGY",
"116":"LAYER_C_TOPOLOGY",
"117":"LAYER_C_TOPOLOGY",
"119":"LAYER_C_TOPOLOGY",
"135":"LAYER_C_TOPOLOGY",
"136":"LAYER_C_TOPOLOGY",
# LAYER_C_BRAID: Braid formation, witnesses, raycasting, holonomy (models 35-37, 39, 76-78, 110-112, 130)
"35": "LAYER_C_BRAID",
"36": "LAYER_C_BRAID",
"37": "LAYER_C_BRAID",
"39": "LAYER_C_BRAID",
"76": "LAYER_C_BRAID",
"77": "LAYER_C_BRAID",
"78": "LAYER_C_BRAID",
"110":"LAYER_C_BRAID",
"111":"LAYER_C_BRAID",
"112":"LAYER_C_BRAID",
"130":"LAYER_C_BRAID",
# LAYER_D_INVARIANTS: Conservation laws, constraint systems (models 28, 30-31, 43, 56, 61-63, 111, 127-128)
"28": "LAYER_D_INVARIANTS",
"30": "LAYER_D_INVARIANTS",
"31": "LAYER_D_INVARIANTS",
"43": "LAYER_D_INVARIANTS",
"56": "LAYER_D_INVARIANTS",
"61": "LAYER_D_INVARIANTS",
"62": "LAYER_D_INVARIANTS",
"63": "LAYER_D_INVARIANTS",
"127":"LAYER_D_INVARIANTS",
"128":"LAYER_D_INVARIANTS",
# LAYER_E_VERIFICATION: Acceptance, attestation, BEA, validation (models 11, 14-15, 55, 60, 94, 125, 138)
"11": "LAYER_E_VERIFICATION",
"14": "LAYER_E_VERIFICATION",
"15": "LAYER_E_VERIFICATION",
"55": "LAYER_E_VERIFICATION",
"60": "LAYER_E_VERIFICATION",
"94": "LAYER_E_VERIFICATION",
"125":"LAYER_E_VERIFICATION",
"138":"LAYER_E_VERIFICATION",
# LAYER_F_CONTROL: Homeostatic, hysteresis, waveprobe, mode transitions (models 7, 12, 24, 26-29, 49, 88, 90-93, 131-134)
"24": "LAYER_F_CONTROL",
"26": "LAYER_F_CONTROL",
"27": "LAYER_F_CONTROL",
"29": "LAYER_F_CONTROL",
"49": "LAYER_F_CONTROL",
"90": "LAYER_F_CONTROL",
"91": "LAYER_F_CONTROL",
"92": "LAYER_F_CONTROL",
"93": "LAYER_F_CONTROL",
"131":"LAYER_F_CONTROL",
"132":"LAYER_F_CONTROL",
"133":"LAYER_F_CONTROL",
"134":"LAYER_F_CONTROL",
# LAYER_G_ENERGY: Thermodynamics, Landauer, phonon, QCL, hardware stress (models 13, 20-23, 39-42, 47, 51-53, 57-59, 64-70, 108-109, 113-114, 139-140)
"13": "LAYER_G_ENERGY",
"20": "LAYER_G_ENERGY",
"21": "LAYER_G_ENERGY",
"22": "LAYER_G_ENERGY",
"23": "LAYER_G_ENERGY",
"39": "LAYER_G_ENERGY",
"40": "LAYER_G_ENERGY",
"41": "LAYER_G_ENERGY",
"42": "LAYER_G_ENERGY",
"47": "LAYER_G_ENERGY",
"51": "LAYER_G_ENERGY",
"52": "LAYER_G_ENERGY",
"53": "LAYER_G_ENERGY",
"57": "LAYER_G_ENERGY",
"58": "LAYER_G_ENERGY",
"59": "LAYER_G_ENERGY",
"64": "LAYER_G_ENERGY",
"65": "LAYER_G_ENERGY",
"66": "LAYER_G_ENERGY",
"67": "LAYER_G_ENERGY",
"68": "LAYER_G_ENERGY",
"69": "LAYER_G_ENERGY",
"70": "LAYER_G_ENERGY",
"108":"LAYER_G_ENERGY",
"109":"LAYER_G_ENERGY",
"113":"LAYER_G_ENERGY",
"114":"LAYER_G_ENERGY",
"139":"LAYER_G_ENERGY",
"140":"LAYER_G_ENERGY",
# LAYER_H_ALGEBRA: Geometric algebra, chirality, group theory (models 19, 21-23, 43, 117-119)
"19": "LAYER_H_ALGEBRA",
"21": "LAYER_H_ALGEBRA",
"22": "LAYER_H_ALGEBRA",
"23": "LAYER_H_ALGEBRA",
"43": "LAYER_H_ALGEBRA",
"117":"LAYER_H_ALGEBRA",
"118":"LAYER_H_ALGEBRA",
"119":"LAYER_H_ALGEBRA",
# LAYER_I_ENCODING: Voxel keys, microvoxel, bit-packing, address schemes (models 123-124, 129)
"123":"LAYER_I_ENCODING",
"124":"LAYER_I_ENCODING",
"129":"LAYER_I_ENCODING",
# LAYER_J_DYNAMICS: Time evolution, phase transitions, emergence, deformation (models 8-9, 33, 44, 56, 71, 74, 103-104, 131-132)
"103":"LAYER_J_DYNAMICS",
"104":"LAYER_J_DYNAMICS",
# LAYER_K_SIGNAL: DSP, FFT, wave propagation, scattering (models 79-81, 113-114)
"79": "LAYER_K_SIGNAL",
"80": "LAYER_K_SIGNAL",
"81": "LAYER_K_SIGNAL",
# LAYER_L_APPLICATION: FEA, hormone mapping, engineering (models 45, 120-122, 137)
"122":"LAYER_L_APPLICATION",
}
def classify_domain(model_num: str, model_name: str, family: str) -> str:
"""Classify a model into its TTM domain layer."""
# Direct number lookup
if model_num in DOMAIN_MAP:
return DOMAIN_MAP[model_num]
# Keyword fallbacks for unclassified models
name_lower = (model_name + " " + family).lower()
if any(k in name_lower for k in ["compression", "entropy", "shannon", "hutter", "shape", "mi ", "mutual information", "structure yield", "watanabe", "kolmogorov"]):
return "LAYER_A_COMPRESSION"
if any(k in name_lower for k in ["routing", "cognitive", "load", "homeostatic", "pressure", "canal", "reweight", "equilibrium", "logit", "hormone", "half life", "decay rate", "concentration"]):
return "LAYER_B_ROUTING"
if any(k in name_lower for k in ["metric", "geodesic", "manifold", "curvature", "christoffel", "chart", "stereographic", "phi", "phi-weighted", "hyperbolic", "mobius", "non-euclidean", "writhe", "parallel transport", "pga", "geometric algebra", "sine-gordon", "curvature-torsion", "constraint geometry", "universe type scoring", "mean curvature"]):
return "LAYER_C_TOPOLOGY"
if any(k in name_lower for k in ["braid", "raycast", "ray-cast", "mmr", "merkle", "holonomy", "algebraic", "rollup", "diat", "uvmap", "half-mobius closure"]):
return "LAYER_C_BRAID"
if any(k in name_lower for k in ["invariant", "constraint", "conservation", "exact", "narrowing", "precision", "latency table", "sieve", "relation", "proxy"]):
return "LAYER_D_INVARIANTS"
if any(k in name_lower for k in ["verification", "attestation", "bea", "witness", "acceptance", "q-factor", "landauer", "surprise", "regret", "blink", "ternary", "dcvn", "thermal finality"]):
return "LAYER_E_VERIFICATION"
if any(k in name_lower for k in ["control", "hysteresis", "waveprobe", "risk", "heat evolution", "mode transition", "binning", "lut policy", "regret field", "blink cycle", "phase transition", "emergence"]):
return "LAYER_F_CONTROL"
if any(k in name_lower for k in ["thermodynamic", "arrhenius", "black", "coffin-manson", "bit-flip", "qcl", "quantum cascade", "photon", "energy", "phonon", "boltzmann", "carnot", "heat engine", "entropy generation", "rul", "remaining useful", "alcubierre", "dyson", "langevin"]):
return "LAYER_G_ENERGY"
if any(k in name_lower for k in ["chirality", "cl(3,0,1)", "geometric product", "motor", "clifford"]):
return "LAYER_H_ALGEBRA"
if any(k in name_lower for k in ["voxel", "microvoxel", "encoding", "seed", "bit", "pack", "address", "seismic", "topological encoder"]):
return "LAYER_I_ENCODING"
if any(k in name_lower for k in ["deformation", "epoch", "sha256 field", "manifold delta"]):
return "LAYER_J_DYNAMICS"
if any(k in name_lower for k in ["bracket", "braid sb", "cosine similarity", "gradient alignment", "phase accumulation", "dsp", "fft"]):
return "LAYER_K_SIGNAL"
if any(k in name_lower for k in ["fea", "hormone", "semi-truck", "dynamic amplification", "engineering"]):
return "LAYER_L_APPLICATION"
return "UNCLASSIFIED"
def main():
input_path = "6-Documentation/docs/MATH_MODEL_MAP.tsv"
output_path = "6-Documentation/docs/MATH_MODEL_MAP_classified.tsv"
with open(input_path, "r", newline="") as f:
reader = csv.reader(f, delimiter="\t")
header = next(reader)
rows = list(reader)
# Find the comment line (starts with #)
comment_lines = []
data_rows = []
for row in rows:
if row and row[0].startswith("#"):
comment_lines.append(row)
else:
data_rows.append(row)
# Add Domain_Type column to header
header.append("Domain_Type")
# Classify each row
classified = 0
unclassified = 0
domain_counts = {}
for row in data_rows:
model_num = row[0].strip() if row else ""
model_name = row[1].strip() if len(row) > 1 else ""
family = row[2].strip() if len(row) > 2 else ""
domain = classify_domain(model_num, model_name, family)
row.append(domain)
if domain == "UNCLASSIFIED":
unclassified += 1
else:
classified += 1
domain_counts[domain] = domain_counts.get(domain, 0) + 1
# Write output
with open(output_path, "w", newline="") as f:
writer = csv.writer(f, delimiter="\t", lineterminator="\n")
for cl in comment_lines:
writer.writerow(cl)
writer.writerow(header)
for row in data_rows:
writer.writerow(row)
# Summary
print(f"Classified {classified}/{classified + unclassified} models")
if unclassified:
print(f"WARNING: {unclassified} models UNCLASSIFIED")
print()
print("Domain distribution:")
for domain in sorted(domain_counts.keys()):
print(f" {domain:30s} {domain_counts[domain]:3d}")
print(f"\nOutput written to: {output_path}")
if __name__ == "__main__":
main()
Reference in a new issue View git blame Copy permalink