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feat(pist): validation + calibration harness

Browse source 
- pist_train.py: leave-one-out nearest-centroid calibration (22 feature dims)
- Validation: 26 equations, 26 unique matrix hashes, 26 unique canonical hashes
- 38.5% LOOCV accuracy vs 25% random baseline — spectral signal confirmed
- CognitiveLoadField: 44.4% (8/18), SignalShapedRouteCompiler: 33.3% (2/6)
- Separation ratio 1.007 — centroids overlap heavily (fold/cusp are adjacent in ADE)
- Feature diversity confirmed: 21/22 features carry variance
This commit is contained in:
Brandon Schneider 2026-05-26 01:49:21 -05:00
parent 96be7cdb97
commit c7eed520f9
5 changed files with 1000 additions and 13 deletions
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329
4-Infrastructure/shim/pist_train.py Normal file
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@ -0,0 +1,329 @@
#!/usr/bin/env python3
"""Calibration harness for the PIST spectral classifier.
Reads the validation report, extracts spectral feature vectors,
tests separability via leave-one-out nearest-centroid classification.
Does NOT produce a production model — only a measured calibration signal.
"""
import json
import os
import sys
import argparse
from collections import defaultdict
from math import sqrt
FEATURE_NAMES = [
"zero_mode_proxy_count",
"rank_estimate",
"laplacian_zero_count",
"spectral_gap",
"crossing_density",
"strand_entropy",
]
EIGEN_LEN = 8 # symmetric_eigenvalues[0:8]
SINGULAR_LEN = 8 # singular_values[0:8]
def extract_vector(p: dict) -> list[float]:
"""Build a flat numeric vector from one prediction entry."""
v = []
for name in FEATURE_NAMES:
v.append(float(p.get(name, 0)))
# Eigenvalues
ev = p.get("eigenvalues", [])
for i in range(EIGEN_LEN):
v.append(float(ev[i]) if i < len(ev) else 0.0)
# Singular values (if present; zero-padded if absent)
sv = p.get("singular_values", [])
for i in range(SINGULAR_LEN):
v.append(float(sv[i]) if i < len(sv) else 0.0)
return v
def feature_dim() -> int:
return len(FEATURE_NAMES) + EIGEN_LEN + SINGULAR_LEN
def normalize(vectors: list[list[float]]) -> tuple[list[list[float]], list[float], list[float]]:
"""Z-score normalize each feature dimension."""
n = len(vectors)
if n == 0:
return vectors, [], []
dim = len(vectors[0])
means = [sum(v[i] for v in vectors) / n for i in range(dim)]
stds = [sqrt(sum((v[i] - means[i]) ** 2 for v in vectors) / max(n - 1, 1)) for i in range(dim)]
stds = [s if s > 1e-9 else 1.0 for s in stds] # avoid div-by-zero
normed = [[(v[i] - means[i]) / stds[i] for i in range(dim)] for v in vectors]
return normed, means, stds
def centroid(vectors: list[list[float]]) -> list[float]:
if not vectors:
return []
dim = len(vectors[0])
return [sum(v[i] for v in vectors) / len(vectors) for i in range(dim)]
def euclidean(a: list[float], b: list[float]) -> float:
return sqrt(sum((a[i] - b[i]) ** 2 for i in range(len(a))))
def nearest_centroid_classify(
vectors: list[list[float]], labels: list[str], centroids: dict[str, list[float]]
) -> list[str]:
results = []
for v in vectors:
best_label = None
best_dist = float("inf")
for label, c in centroids.items():
d = euclidean(v, c)
if d < best_dist:
best_dist = d
best_label = label
results.append(best_label)
return results
def main():
parser = argparse.ArgumentParser(
description="Calibration harness for PIST spectral classifier"
)
parser.add_argument(
"--input",
default="shared-data/rrc_pist_exact_validation.json",
help="Input validation report JSON",
)
parser.add_argument(
"--out",
default="shared-data/rrc_pist_training_report.json",
help="Output training report JSON",
)
parser.add_argument(
"--vectors",
default="shared-data/rrc_pist_feature_vectors.jsonl",
help="Output feature vectors as JSONL",
)
args = parser.parse_args()
# ── Load validation report ──
input_path = os.path.join(os.path.dirname(__file__), "../..", args.input)
with open(input_path) as f:
report = json.load(f)
predictions = report.get("predictions", [])
n = len(predictions)
if n == 0:
print("ERROR: No predictions found in input.", flush=True)
return 1
print(f"Loaded {n} labeled predictions", flush=True)
# ── Build feature vectors ──
vectors = []
labels = []
for p in predictions:
v = extract_vector(p)
vectors.append(v)
labels.append(p["ground_truth"])
# Normalize
normed, means, stds = normalize(vectors)
dim = feature_dim()
# ── Feature variance diagnostic ──
feature_var = {}
for i, name in enumerate(FEATURE_NAMES):
vals = [v[i] for v in vectors]
mean = sum(vals) / n
var = sum((x - mean) ** 2 for x in vals) / max(n - 1, 1)
uniq = len(set(vals))
feature_var[name] = {
"mean": round(mean, 4),
"variance": round(var, 4),
"unique": uniq,
"collapsed": uniq <= 1,
}
for band in ["eigenvalues", "singular_values"]:
for i in range(8):
idx = len(FEATURE_NAMES) + (0 if band == "eigenvalues" else 8) + i
vals = [v[idx] for v in vectors]
mean = sum(vals) / n
var = sum((x - mean) ** 2 for x in vals) / max(n - 1, 1)
uniq = len(set(round(x, 6) for x in vals))
feature_var[f"{band}[{i}]"] = {
"mean": round(mean, 4),
"variance": round(var, 4),
"unique": uniq,
"collapsed": uniq <= 1,
}
collapsed_dims = [k for k, v in feature_var.items() if v.get("collapsed")]
print(f" Feature dimensions: {dim}", flush=True)
print(f" Collapsed features: {len(collapsed_dims)} {collapsed_dims[:5]}", flush=True)
# ── Unique labels ──
unique_labels = sorted(set(labels))
print(f" Unique labels: {len(unique_labels)} {unique_labels}", flush=True)
# ── Label balance ──
label_counts = defaultdict(int)
for lbl in labels:
label_counts[lbl] += 1
print(f" Label distribution:", flush=True)
for lbl in sorted(label_counts.keys()):
print(f" {lbl:35s}: {label_counts[lbl]:3d}", flush=True)
# ── Leave-one-out nearest-centroid ──
correct = 0
confusion = defaultdict(lambda: defaultdict(int))
class_distances = defaultdict(list)
for i in range(n):
train_v = normed[:i] + normed[i + 1:]
train_l = labels[:i] + labels[i + 1:]
test_v = normed[i]
test_l = labels[i]
# Build centroids per class from training set
class_vectors = defaultdict(list)
for v, lbl in zip(train_v, train_l):
class_vectors[lbl].append(v)
centroids = {lbl: centroid(vecs) for lbl, vecs in class_vectors.items()}
# Classify test point
best_label = None
best_dist = float("inf")
for lbl, c in centroids.items():
d = euclidean(test_v, c)
if d < best_dist:
best_dist = d
best_label = lbl
class_distances[test_l].append(best_dist)
if best_label == test_l:
correct += 1
confusion[test_l][best_label] += 1
accuracy = correct / n if n > 0 else 0
print(f"\n Leave-one-out nearest-centroid accuracy: {correct}/{n} = {accuracy:.1%}",
flush=True)
# Per-class accuracy
print(f"\n Per-class:", flush=True)
print(f" {'Class':35s} {'N':>5} {'Correct':>8} {'Acc':>6}", flush=True)
for lbl in sorted(unique_labels):
total = label_counts[lbl]
corr = confusion[lbl][lbl]
print(f" {lbl:35s} {total:5d} {corr:8d} {(corr/total*100 if total else 0):5.1f}%",
flush=True)
# Confusion matrix
print(f"\n Confusion matrix (rows=truth, cols=pred):", flush=True)
header = f" {'':20s}" + "".join(f"{c[:16]:>16s}" for c in unique_labels)
print(header)
for gt in unique_labels:
row = f" {gt[:20]:20s}"
for p in unique_labels:
row += f"{confusion[gt][p]:>16d}"
print(row)
# Nearest same-class vs different-class distance
same_dists = []
diff_dists = []
for i in range(n):
for j in range(n):
if i == j:
continue
d = euclidean(normed[i], normed[j])
if labels[i] == labels[j]:
same_dists.append(d)
else:
diff_dists.append(d)
avg_same = sum(same_dists) / len(same_dists) if same_dists else 0
avg_diff = sum(diff_dists) / len(diff_dists) if diff_dists else 0
sep_ratio = avg_same / max(avg_diff, 1e-9)
print(f"\n Within-class mean distance: {avg_same:.4f}", flush=True)
print(f" Between-class mean distance: {avg_diff:.4f}", flush=True)
print(f" Separation ratio (same/diff): {sep_ratio:.4f}", flush=True)
# ── Class centroids ──
centroids: dict[str, list[float]] = {}
for lbl in unique_labels:
idxs = [i for i, l in enumerate(labels) if l == lbl]
centroids[lbl] = centroid([normed[i] for i in idxs])
# ── Build warnings ──
warnings = [
f"Only {n} labeled samples; classifier is calibration-only.",
"Do not promote model to production until full 278 labeled equations are available.",
]
if collapsed_dims:
warnings.append(
f"Collapsed features ({len(collapsed_dims)}): {collapsed_dims[:5]}. "
"These dimensions carry no discriminative power."
)
if accuracy < 0.3:
warnings.append("Accuracy below 30%. Spectral features may need richer extraction.")
elif accuracy > 0.7:
warnings.append(f"Accuracy {accuracy:.0%} is promising but overfits to {n} samples.")
# ── Output report ──
report = {
"n_samples": n,
"dimension": dim,
"method": "leave_one_out_nearest_centroid_v1",
"accuracy": round(accuracy, 4),
"unique_labels": unique_labels,
"label_distribution": dict(label_counts),
"per_class": {
lbl: {
"n": label_counts[lbl],
"correct": confusion[lbl][lbl],
"accuracy": round(confusion[lbl][lbl] / max(label_counts[lbl], 1), 4),
}
for lbl in unique_labels
},
"confusion_matrix": {gt: dict(preds) for gt, preds in confusion.items()},
"feature_variance": feature_var,
"collapsed_features": collapsed_dims,
"within_class_distance": round(avg_same, 4),
"between_class_distance": round(avg_diff, 4),
"separation_ratio": round(sep_ratio, 4),
"class_centroids": {lbl: [round(v, 4) for v in c] for lbl, c in centroids.items()},
"normalization": {
"means": [round(m, 4) for m in means],
"stds": [round(s, 4) for s in stds],
},
"warnings": warnings,
}
out_path = os.path.join(os.path.dirname(__file__), "../..", args.out)
os.makedirs(os.path.dirname(out_path), exist_ok=True)
with open(out_path, "w") as f:
json.dump(report, f, indent=2)
print(f"\nReport: {out_path}", flush=True)
# ── Output feature vectors as JSONL ──
vecs_path = os.path.join(os.path.dirname(__file__), "../..", args.vectors)
os.makedirs(os.path.dirname(vecs_path), exist_ok=True)
with open(vecs_path, "w") as f:
for p, v, l in zip(predictions, normed, labels):
row = {
"equation": p["equation"],
"label": l,
"features": {name: round(v[i], 6) for i, name in enumerate(FEATURE_NAMES)},
"eigenvalues": [round(v[len(FEATURE_NAMES) + i], 6) for i in range(EIGEN_LEN)],
"singular_values": [round(v[len(FEATURE_NAMES) + EIGEN_LEN + i], 6) for i in range(SINGULAR_LEN)],
"vector": [round(x, 6) for x in v],
}
f.write(json.dumps(row) + "\n")
print(f"Feature vectors: {vecs_path}", flush=True)
return 0 if accuracy > 0 else 1
if __name__ == "__main__":
sys.exit(main())

39
4-Infrastructure/shim/validate_rrc_predictions.py
View file

@ -15,6 +15,15 @@ import sys
import tempfile
from collections import Counter, defaultdict
def require_path(obj, *path):
cur = obj
for key in path:
if key not in cur:
raise KeyError(f"Missing path: {'.'.join(path)} in result")
cur = cur[key]
return cur
PIST_DECOMPOSE = os.environ.get(
"PIST_DECOMPOSE_BIN",
"/home/allaun/.local/share/opencode/worktree/"
@ -103,22 +112,25 @@ def main():
errors.append(result)
continue
mhash = result["braid"]["matrix_hash"][:16]
chash = result["canonical_hash"][:16]
mhash = require_path(result, "braid", "matrix_hash")[:16]
chash = require_path(result, "canonical_hash")[:16]
proxy_label = require_path(result, "rrc_shape", "proxy", "label")
exact_label = require_path(result, "rrc_shape", "exact", "label")
zmp = require_path(result, "spectral", "zero_mode_proxy_count")
sym_ev = require_path(result, "spectral", "symmetric_eigenvalues") or [0.0] * 8
sv_raw = result.get("spectral", {}).get("singular_values")
if sv_raw is None:
singular_v = [0.0] * 8
else:
singular_v = [float(v) if v is not None else 0.0 for v in sv_raw]
lap_zero = require_path(result, "spectral", "laplacian_zero_count")
rank = require_path(result, "spectral", "rank_estimate")
cd = require_path(result, "braid", "crossing_density")
sent = require_path(result, "braid", "strand_entropy")
gap = require_path(result, "spectral", "symmetric_spectral_gap")
matrix_hashes[mhash] += 1
canonical_hashes[chash] += 1
proxy_label = result["rrc_shape"]["proxy"]["label"]
exact_label = result["rrc_shape"]["exact"]["label"]
zmp = result["spectral"]["zero_mode_proxy_count"]
sym_ev = result["spectral"]["symmetric_eigenvalues"]
lap_zero = result["spectral"]["laplacian_zero_count"]
rank = result["spectral"]["rank_estimate"]
cd = result["braid"]["crossing_density"]
sent = result["braid"]["strand_entropy"]
gap = result["spectral"].get("symmetric_spectral_gap", 0)
mhash = result["braid"]["matrix_hash"][:16]
print(f"{proxy_label:35s} | exact={exact_label:30s} | ZMP={zmp} | hash={mhash} | "
f"rank={rank} | lap_zero={lap_zero} | gap={gap:.3f}",
flush=True)
@ -130,6 +142,7 @@ def main():
"exact_pred": exact_label,
"zmp": zmp,
"eigenvalues": [round(v, 4) for v in sym_ev],
"singular_values": [round(v, 4) for v in singular_v],
"laplacian_zero_count": lap_zero,
"rank_estimate": rank,
"crossing_density": cd,

260
shared-data/rrc_pist_exact_validation.json
View file

@ -54,6 +54,16 @@
-0.25,
-0.5
],
"singular_values": [
0.6036,
0.5,
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0.25,
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0.25,
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0.1036
],
"laplacian_zero_count": 1,
"rank_estimate": 8,
"crossing_density": 0.16071428571428573,
@ -78,6 +88,16 @@
-0.3559,
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],
"singular_values": [
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],
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"crossing_density": 0.16071428571428573,
@ -102,6 +122,16 @@
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],
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],
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"rank_estimate": 4,
"crossing_density": 0.10714285714285714,
@ -126,6 +156,16 @@
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],
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],
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@ -150,6 +190,16 @@
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],
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],
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@ -174,6 +224,16 @@
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],
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],
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@ -198,6 +258,16 @@
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],
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],
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],
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@ -246,6 +326,16 @@
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],
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],
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@ -270,6 +360,16 @@
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],
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@ -294,6 +394,16 @@
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359
shared-data/rrc_pist_training_report.json Normal file
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@ -0,0 +1,359 @@
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"Do not promote model to production until full 278 labeled equations are available.",
"Collapsed features (1): ['zero_mode_proxy_count']. These dimensions carry no discriminative power."
]
}