mirror of
https://github.com/allaunthefox/SilverSight.git
synced 2026-07-31 01:25:21 +00:00
allaun
4156acf4e8
Sweeps N=2000,5000,10000 × p=2..10 × K=1000,10000 × 5 seeds = 240 runs. DAG visualization for pipeline inspection. Results ingest to bmcte.experiment_runs on neon-64gb. GPU Ryser for p≥9 via PyTorch CUDA. Build: 2987 jobs, 0 errors
646 lines
22 KiB
Python
646 lines
22 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
bmcte_extension_v1.py — Extend λ(p)/H(p) invariance to higher photon numbers.
|
||
|
||
Sweeps N × p × K × seeds grid, measures entropy, variance, runtime.
|
||
Uses PyTorch CUDA for p ≥ 9 (Ryser permanent on GPU).
|
||
|
||
Usage:
|
||
python3 bmcte_extension_v1.py # full sweep
|
||
python3 bmcte_extension_v1.py --dry-run # show DAG only
|
||
python3 bmcte_extension_v1.py --p-max 8 # CPU only
|
||
python3 bmcte_extension_v1.py --N 2000 --p 2 4 6 # single N
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import argparse
|
||
import hashlib
|
||
import json
|
||
import math
|
||
import os
|
||
import sys
|
||
import time
|
||
from dataclasses import asdict, dataclass, field
|
||
from datetime import datetime, timezone
|
||
from pathlib import Path
|
||
from typing import Optional
|
||
|
||
import numpy as np
|
||
|
||
# PyTorch for GPU Ryser
|
||
try:
|
||
import torch
|
||
HAS_TORCH = True
|
||
HAS_CUDA = torch.cuda.is_available()
|
||
except ImportError:
|
||
HAS_TORCH = False
|
||
HAS_CUDA = False
|
||
|
||
RECEIPT_PATH = Path(__file__).resolve().parent / "extension_v1_receipt.json"
|
||
|
||
# DB connection (optional)
|
||
try:
|
||
sys.path.insert(0, str(Path("/home/allaun/Research Stack/4-Infrastructure/shim")))
|
||
import psycopg2
|
||
import psycopg2.extras
|
||
from rds_connect import connect_rds
|
||
HAS_DB = True
|
||
except ImportError:
|
||
HAS_DB = False
|
||
connect_rds = None
|
||
|
||
|
||
# =========================================================================
|
||
# DB Ingestion
|
||
# =========================================================================
|
||
|
||
def ingest_results(results: list[dict], experiment_id: str = "bmcte_extension_v1") -> int:
|
||
"""Write experiment results to bmcte.experiment_runs table.
|
||
|
||
Returns number of rows inserted.
|
||
"""
|
||
if not HAS_DB:
|
||
print(" DB: psycopg2 not available, skipping ingestion")
|
||
return 0
|
||
|
||
conn = connect_rds()
|
||
cur = conn.cursor()
|
||
|
||
# Ensure schema/table exist
|
||
cur.execute("""
|
||
CREATE SCHEMA IF NOT EXISTS bmcte;
|
||
CREATE TABLE IF NOT EXISTS bmcte.experiment_runs (
|
||
id SERIAL PRIMARY KEY,
|
||
experiment_id TEXT NOT NULL,
|
||
run_hash TEXT NOT NULL UNIQUE,
|
||
N INTEGER NOT NULL,
|
||
p INTEGER NOT NULL,
|
||
K INTEGER NOT NULL,
|
||
seed INTEGER NOT NULL,
|
||
lambda_theory DOUBLE PRECISION,
|
||
entropy_measured DOUBLE PRECISION,
|
||
nonzero_modes INTEGER,
|
||
weight_mean DOUBLE PRECISION,
|
||
weight_variance DOUBLE PRECISION,
|
||
weight_cv DOUBLE PRECISION,
|
||
ryser_overflow BOOLEAN DEFAULT FALSE,
|
||
used_shots INTEGER,
|
||
runtime_ms DOUBLE PRECISION,
|
||
used_gpu BOOLEAN DEFAULT FALSE,
|
||
hardware JSONB,
|
||
created_at TIMESTAMPTZ DEFAULT NOW(),
|
||
receipt_hash TEXT
|
||
);
|
||
""")
|
||
conn.commit()
|
||
|
||
inserted = 0
|
||
for r in results:
|
||
if "error" in r:
|
||
continue
|
||
|
||
run_hash = hashlib.sha256(
|
||
f"{r['N']}:{r['p']}:{r['K']}:{r['seed']}".encode()
|
||
).hexdigest()[:16]
|
||
|
||
try:
|
||
cur.execute("""
|
||
INSERT INTO bmcte.experiment_runs
|
||
(experiment_id, run_hash, N, p, K, seed,
|
||
lambda_theory, entropy_measured, nonzero_modes,
|
||
weight_mean, weight_variance, weight_cv,
|
||
ryser_overflow, used_shots, runtime_ms, used_gpu, hardware)
|
||
VALUES (%(experiment_id)s, %(run_hash)s, %(N)s, %(p)s, %(K)s, %(seed)s,
|
||
%(lambda_theory)s, %(entropy_measured)s, %(nonzero_modes)s,
|
||
%(weight_mean)s, %(weight_variance)s, %(weight_cv)s,
|
||
%(ryser_overflow)s, %(used_shots)s, %(runtime_ms)s, %(used_gpu)s,
|
||
%(hardware)s::jsonb)
|
||
ON CONFLICT (run_hash) DO NOTHING
|
||
""", {
|
||
"experiment_id": experiment_id,
|
||
"run_hash": run_hash,
|
||
"N": r["N"], "p": r["p"], "K": r["K"], "seed": r["seed"],
|
||
"lambda_theory": r.get("lambda_theory"),
|
||
"entropy_measured": r.get("entropy_measured"),
|
||
"nonzero_modes": r.get("nonzero_modes"),
|
||
"weight_mean": r.get("weight_mean"),
|
||
"weight_variance": r.get("weight_variance"),
|
||
"weight_cv": r.get("weight_cv"),
|
||
"ryser_overflow": r.get("ryser_overflow", False),
|
||
"used_shots": r.get("used_shots"),
|
||
"runtime_ms": r.get("runtime_ms"),
|
||
"used_gpu": r.get("used_gpu", False),
|
||
"hardware": json.dumps({
|
||
"cpu": os.cpu_count(),
|
||
"gpu": torch.cuda.get_device_name(0) if HAS_CUDA else None,
|
||
}),
|
||
})
|
||
inserted += 1
|
||
except Exception as exc:
|
||
print(f" DB insert error: {exc}")
|
||
|
||
conn.commit()
|
||
cur.close()
|
||
conn.close()
|
||
return inserted
|
||
|
||
|
||
# =========================================================================
|
||
# DAG Definition
|
||
# =========================================================================
|
||
|
||
@dataclass
|
||
class DAGNode:
|
||
"""One node in the experiment DAG."""
|
||
id: str
|
||
label: str
|
||
inputs: list[str] = field(default_factory=list)
|
||
outputs: list[str] = field(default_factory=list)
|
||
status: str = "pending" # pending | running | done | skipped
|
||
duration_ms: float = 0.0
|
||
metadata: dict = field(default_factory=dict)
|
||
|
||
|
||
class ExperimentDAG:
|
||
"""Directed acyclic graph for experiment inspection."""
|
||
|
||
def __init__(self):
|
||
self.nodes: dict[str, DAGNode] = {}
|
||
self.edges: list[tuple[str, str]] = []
|
||
|
||
def add(self, node_id: str, label: str, inputs: list[str] = None,
|
||
outputs: list[str] = None, **meta) -> DAGNode:
|
||
node = DAGNode(
|
||
id=node_id, label=label,
|
||
inputs=inputs or [], outputs=outputs or [],
|
||
metadata=meta,
|
||
)
|
||
self.nodes[node_id] = node
|
||
for inp in (inputs or []):
|
||
self.edges.append((inp, node_id))
|
||
return node
|
||
|
||
def mark(self, node_id: str, status: str, duration_ms: float = 0.0):
|
||
if node_id in self.nodes:
|
||
self.nodes[node_id].status = status
|
||
self.nodes[node_id].duration_ms = duration_ms
|
||
|
||
def to_mermaid(self) -> str:
|
||
"""Render DAG as Mermaid flowchart."""
|
||
lines = ["graph TD"]
|
||
for node in self.nodes.values():
|
||
status_icon = {
|
||
"pending": "⏳", "running": "🔄", "done": "✅", "skipped": "⏭️"
|
||
}.get(node.status, "❓")
|
||
safe_label = node.label.replace('"', "'")
|
||
lines.append(f' {node.id}["{status_icon} {safe_label}"]')
|
||
for src, dst in self.edges:
|
||
lines.append(f" {src} --> {dst}")
|
||
return "\n".join(lines)
|
||
|
||
def to_ascii(self) -> str:
|
||
"""Render DAG as ASCII tree."""
|
||
lines = []
|
||
# Find root nodes (no inputs)
|
||
roots = [n for n in self.nodes.values() if not n.inputs]
|
||
visited = set()
|
||
|
||
def render(node: DAGNode, indent: int = 0):
|
||
if node.id in visited:
|
||
return
|
||
visited.add(node.id)
|
||
status_icon = {
|
||
"pending": "[ ]", "running": "[>]", "done": "[x]", "skipped": "[-]"
|
||
}.get(node.status, "[?]")
|
||
prefix = " " * indent
|
||
dur = f" ({node.duration_ms:.0f}ms)" if node.duration_ms > 0 else ""
|
||
lines.append(f"{prefix}{status_icon} {node.label}{dur}")
|
||
# Find children
|
||
children = [self.nodes[dst] for src, dst in self.edges if src == node.id]
|
||
for child in children:
|
||
render(child, indent + 1)
|
||
|
||
for root in roots:
|
||
render(root)
|
||
return "\n".join(lines)
|
||
|
||
def summary(self) -> dict:
|
||
return {
|
||
"total_nodes": len(self.nodes),
|
||
"done": sum(1 for n in self.nodes.values() if n.status == "done"),
|
||
"pending": sum(1 for n in self.nodes.values() if n.status == "pending"),
|
||
"total_ms": sum(n.duration_ms for n in self.nodes.values()),
|
||
}
|
||
|
||
|
||
# =========================================================================
|
||
# Core: Ryser Permanent
|
||
# =========================================================================
|
||
|
||
def ryser_permanent_cpu(M: np.ndarray) -> complex:
|
||
"""Ryser permanent via bitmask subset enumeration. CPU (NumPy)."""
|
||
p = M.shape[0]
|
||
if p == 0:
|
||
return 1.0 + 0.0j
|
||
if p == 1:
|
||
return complex(M[0, 0])
|
||
|
||
total = 0.0 + 0.0j
|
||
for mask in range(1, 1 << p):
|
||
sign = (-1) ** (p - bin(mask).count("1"))
|
||
row_sums = np.zeros(p, dtype=np.complex128)
|
||
for j in range(p):
|
||
if mask & (1 << j):
|
||
row_sums += M[:, j]
|
||
total += sign * np.prod(row_sums)
|
||
return total
|
||
|
||
|
||
def ryser_permanent_gpu(M: torch.Tensor) -> torch.Tensor:
|
||
"""Ryser permanent via bitmask enumeration. GPU (PyTorch CUDA)."""
|
||
p = M.shape[0]
|
||
if p == 0:
|
||
return torch.tensor(1.0, dtype=torch.complex64, device=M.device)
|
||
if p == 1:
|
||
return M[0, 0]
|
||
|
||
device = M.device
|
||
total = torch.tensor(0.0, dtype=torch.complex64, device=device)
|
||
|
||
# Enumerate all non-empty subsets as bitmasks
|
||
for mask in range(1, 1 << p):
|
||
sign = (-1) ** (p - bin(mask).count("1"))
|
||
# Build column mask
|
||
cols = [j for j in range(p) if mask & (1 << j)]
|
||
row_sums = M[:, cols].sum(dim=1) # (p,)
|
||
total = total + sign * row_sums.prod()
|
||
|
||
return total
|
||
|
||
|
||
# =========================================================================
|
||
# Core: Monte Carlo Estimator
|
||
# =========================================================================
|
||
|
||
def categorical_sample(probs: np.ndarray, rng: np.random.RandomState) -> int:
|
||
"""Sample from categorical distribution."""
|
||
cdf = np.cumsum(probs)
|
||
r = rng.random() * cdf[-1]
|
||
return int(np.searchsorted(cdf, r))
|
||
|
||
|
||
def run_single(
|
||
N: int, p: int, K: int, seed: int,
|
||
use_gpu: bool = False,
|
||
) -> dict:
|
||
"""One Monte Carlo run: sample K configurations, compute permanents."""
|
||
|
||
rng = np.random.RandomState(seed)
|
||
start = time.time()
|
||
|
||
# Build random unitary (Haar-ish via QR)
|
||
A = rng.randn(N, N) + 1j * rng.randn(N, N)
|
||
Q, R = np.linalg.qr(A)
|
||
U = Q @ np.diag(np.exp(1j * np.angle(np.diag(R))))
|
||
|
||
# Column probability distributions
|
||
col_probs = [np.abs(U[:, j]) ** 2 for j in range(p)]
|
||
|
||
# Monte Carlo sampling
|
||
mode_counts = np.zeros(N, dtype=np.float64)
|
||
weights = []
|
||
ryser_overflow = False
|
||
used_shots = 0
|
||
|
||
for _ in range(K):
|
||
# Sample output modes
|
||
S = [categorical_sample(col_probs[j], rng) for j in range(p)]
|
||
|
||
# Gather p×p submatrix
|
||
M = U[np.array(S), :p]
|
||
|
||
# Compute permanent
|
||
if use_gpu and HAS_CUDA:
|
||
M_gpu = torch.tensor(M, dtype=torch.complex64, device="cuda")
|
||
perm = ryser_permanent_gpu(M_gpu).cpu().item()
|
||
else:
|
||
perm = ryser_permanent_cpu(M)
|
||
|
||
w = abs(perm) ** 2
|
||
if math.isnan(w) or math.isinf(w):
|
||
ryser_overflow = True
|
||
continue
|
||
|
||
weights.append(w)
|
||
for s in S:
|
||
mode_counts[s] += w
|
||
used_shots += 1
|
||
|
||
elapsed = time.time() - start
|
||
|
||
# Compute metrics
|
||
total_prob = float(np.sum(mode_counts))
|
||
if total_prob > 0:
|
||
mode_probs = mode_counts / total_prob
|
||
else:
|
||
mode_probs = np.ones(N) / N
|
||
|
||
# Shannon entropy
|
||
eps = 1e-15
|
||
p_vec = np.clip(mode_probs, eps, 1.0)
|
||
entropy = float(-np.sum(p_vec * np.log2(p_vec)))
|
||
|
||
# λ(p) theory
|
||
lambda_theory = math.exp(-p * p / N)
|
||
|
||
# Weight statistics
|
||
weights = np.array(weights) if weights else np.array([0.0])
|
||
weight_mean = float(np.mean(weights))
|
||
weight_var = float(np.var(weights))
|
||
weight_cv = float(np.std(weights) / max(np.mean(weights), eps))
|
||
|
||
nonzero_modes = int(np.sum(mode_probs > 1e-15))
|
||
|
||
return {
|
||
"N": N, "p": p, "K": K, "seed": seed,
|
||
"lambda_theory": round(lambda_theory, 6),
|
||
"entropy_measured": round(entropy, 6),
|
||
"nonzero_modes": nonzero_modes,
|
||
"weight_mean": round(weight_mean, 6),
|
||
"weight_variance": round(weight_var, 6),
|
||
"weight_cv": round(weight_cv, 6),
|
||
"ryser_overflow": ryser_overflow,
|
||
"used_shots": used_shots,
|
||
"runtime_ms": round(elapsed * 1000, 1),
|
||
"used_gpu": use_gpu,
|
||
}
|
||
|
||
|
||
# =========================================================================
|
||
# Experiment Grid
|
||
# =========================================================================
|
||
|
||
def build_grid(
|
||
N_values: list[int],
|
||
p_values: list[int],
|
||
K_values: list[int],
|
||
n_seeds: int,
|
||
p_gpu_threshold: int = 9,
|
||
) -> list[dict]:
|
||
"""Build all (N, p, K, seed) combinations."""
|
||
grid = []
|
||
for N in N_values:
|
||
for p in p_values:
|
||
if p >= N:
|
||
continue # skip impossible configs
|
||
for K in K_values:
|
||
for seed in range(n_seeds):
|
||
use_gpu = HAS_CUDA and p >= p_gpu_threshold
|
||
grid.append({
|
||
"N": N, "p": p, "K": K, "seed": seed,
|
||
"use_gpu": use_gpu,
|
||
})
|
||
return grid
|
||
|
||
|
||
def analyze_results(results: list[dict]) -> dict:
|
||
"""Aggregate results by (N, p) pair."""
|
||
from collections import defaultdict
|
||
groups = defaultdict(list)
|
||
for r in results:
|
||
groups[(r["N"], r["p"])].append(r)
|
||
|
||
analysis = []
|
||
for (N, p), runs in sorted(groups.items()):
|
||
entropies = [r["entropy_measured"] for r in runs]
|
||
runtimes = [r["runtime_ms"] for r in runs]
|
||
cvs = [r["weight_cv"] for r in runs]
|
||
nonzero = [r["nonzero_modes"] for r in runs]
|
||
overflows = sum(1 for r in runs if r["ryser_overflow"])
|
||
|
||
analysis.append({
|
||
"N": N, "p": p,
|
||
"lambda_theory": runs[0]["lambda_theory"],
|
||
"entropy_mean": round(float(np.mean(entropies)), 6),
|
||
"entropy_std": round(float(np.std(entropies)), 6),
|
||
"entropy_min": round(float(np.min(entropies)), 6),
|
||
"entropy_max": round(float(np.max(entropies)), 6),
|
||
"nonzero_modes_mean": round(float(np.mean(nonzero)), 1),
|
||
"weight_cv_mean": round(float(np.mean(cvs)), 6),
|
||
"runtime_ms_mean": round(float(np.mean(runtimes)), 1),
|
||
"runtime_ms_std": round(float(np.std(runtimes)), 1),
|
||
"overflow_count": overflows,
|
||
"n_runs": len(runs),
|
||
})
|
||
|
||
return {"grid_points": len(analysis), "analysis": analysis}
|
||
|
||
|
||
# =========================================================================
|
||
# Main
|
||
# =========================================================================
|
||
|
||
def main() -> int:
|
||
parser = argparse.ArgumentParser(description="BMCTE Extension Experiment v1")
|
||
parser.add_argument("--N", type=int, nargs="*", default=[2000, 5000, 10000],
|
||
help="Mode counts")
|
||
parser.add_argument("--p", type=int, nargs="*", default=[2, 3, 4, 5, 6, 7, 8, 10],
|
||
help="Photon numbers")
|
||
parser.add_argument("--K", type=int, nargs="*", default=[1000, 10000],
|
||
help="Sample counts")
|
||
parser.add_argument("--seeds", type=int, default=5,
|
||
help="Independent seeds per config")
|
||
parser.add_argument("--p-gpu-threshold", type=int, default=9,
|
||
help="Photon count at which to use GPU")
|
||
parser.add_argument("--dry-run", action="store_true",
|
||
help="Show DAG only, don't execute")
|
||
parser.add_argument("--output", type=str, default=str(RECEIPT_PATH),
|
||
help="Output receipt path")
|
||
parser.add_argument("--no-db", action="store_true",
|
||
help="Skip database ingestion")
|
||
|
||
args = parser.parse_args()
|
||
|
||
# Build DAG
|
||
dag = ExperimentDAG()
|
||
|
||
# Input node
|
||
dag.add("input", "Input Grid", outputs=["grid"])
|
||
dag.nodes["input"].metadata = {
|
||
"N": args.N, "p": args.p, "K": args.K, "seeds": args.seeds,
|
||
"cuda": HAS_CUDA, "torch": HAS_TORCH,
|
||
}
|
||
|
||
# Grid expansion
|
||
grid = build_grid(args.N, args.p, args.K, args.seeds, args.p_gpu_threshold)
|
||
dag.add("grid", f"Expand Grid ({len(grid)} runs)", inputs=["input"], outputs=["collect"])
|
||
|
||
# Group by (N, p) for DAG visualization
|
||
from collections import Counter
|
||
np_counts = Counter((r["N"], r["p"]) for r in grid)
|
||
run_ids = []
|
||
for (N, p), count in sorted(np_counts.items()):
|
||
node_id = f"run_N{N}_p{p}"
|
||
run_ids.append(node_id)
|
||
use_gpu = HAS_CUDA and p >= args.p_gpu_threshold
|
||
device = "GPU" if use_gpu else "CPU"
|
||
dag.add(node_id, f"N={N}, p={p} ({count} runs, {device})",
|
||
inputs=["grid"], outputs=["collect"])
|
||
|
||
# Collect
|
||
dag.add("collect", "Collect Results", inputs=run_ids, outputs=["analyze"])
|
||
|
||
# Analyze
|
||
dag.add("analyze", "Analyze (aggregate by N,p)", inputs=["collect"], outputs=["receipt"])
|
||
|
||
# Receipt
|
||
dag.add("receipt", "Write Receipt JSON", inputs=["analyze"], outputs=["db"])
|
||
|
||
# DB ingestion
|
||
db_label = "Ingest to PostgreSQL" if HAS_DB and not args.no_db else "DB (skipped)"
|
||
dag.add("db", db_label, inputs=["receipt"])
|
||
|
||
# Print DAG
|
||
print("=" * 70)
|
||
print("BMCTE Extension Experiment v1 — DAG")
|
||
print("=" * 70)
|
||
print()
|
||
print(dag.to_ascii())
|
||
print()
|
||
print(f"Total runs: {len(grid)}")
|
||
print(f"CUDA available: {HAS_CUDA}")
|
||
if HAS_CUDA:
|
||
print(f"GPU: {torch.cuda.get_device_name(0)}")
|
||
print(f"GPU threshold: p >= {args.p_gpu_threshold}")
|
||
print()
|
||
|
||
if args.dry_run:
|
||
print("Dry run — exiting without execution.")
|
||
# Write DAG mermaid to file
|
||
dag_path = Path(args.output).with_suffix(".dag.mmd")
|
||
dag_path.write_text(dag.to_mermaid())
|
||
print(f"Mermaid DAG: {dag_path}")
|
||
return 0
|
||
|
||
# Execute grid
|
||
dag.mark("input", "done")
|
||
dag.mark("grid", "done")
|
||
|
||
results = []
|
||
total_start = time.time()
|
||
|
||
for i, run_cfg in enumerate(grid):
|
||
N, p, K, seed = run_cfg["N"], run_cfg["p"], run_cfg["K"], run_cfg["seed"]
|
||
use_gpu = run_cfg["use_gpu"]
|
||
node_id = f"run_N{N}_p{p}"
|
||
|
||
if i == 0 or (N, p) != (grid[i-1]["N"], grid[i-1]["p"]):
|
||
dag.mark(node_id, "running")
|
||
|
||
try:
|
||
result = run_single(N, p, K, seed, use_gpu=use_gpu)
|
||
results.append(result)
|
||
|
||
if result["ryser_overflow"]:
|
||
print(f" [{i+1}/{len(grid)}] N={N:>5} p={p:>2} K={K:>5} seed={seed} "
|
||
f"OVERFLOW runtime={result['runtime_ms']:.0f}ms")
|
||
else:
|
||
print(f" [{i+1}/{len(grid)}] N={N:>5} p={p:>2} K={K:>5} seed={seed} "
|
||
f"H={result['entropy_measured']:.3f} "
|
||
f"λ={result['lambda_theory']:.4f} "
|
||
f"runtime={result['runtime_ms']:.0f}ms")
|
||
|
||
except Exception as exc:
|
||
print(f" [{i+1}/{len(grid)}] N={N:>5} p={p:>2} K={K:>5} seed={seed} "
|
||
f"ERROR: {exc}")
|
||
results.append({
|
||
"N": N, "p": p, "K": K, "seed": seed,
|
||
"error": str(exc)[:200],
|
||
})
|
||
|
||
# Mark node done when all seeds for this (N,p) are complete
|
||
if i + 1 >= len(grid) or (grid[i+1]["N"], grid[i+1]["p"]) != (N, p):
|
||
dag.mark(node_id, "done")
|
||
|
||
total_elapsed = time.time() - total_start
|
||
dag.mark("collect", "done", total_elapsed * 1000)
|
||
|
||
# Analyze
|
||
t0 = time.time()
|
||
analysis = analyze_results(results)
|
||
dag.mark("analyze", "done", (time.time() - t0) * 1000)
|
||
|
||
# Print analysis table
|
||
print()
|
||
print("=" * 70)
|
||
print("RESULTS")
|
||
print("=" * 70)
|
||
print(f"{'N':>6} {'p':>3} {'λ_theory':>10} {'H_mean':>8} {'H_std':>7} "
|
||
f"{'nonzero':>8} {'CV_mean':>8} {'runtime':>9} {'overflow':>8}")
|
||
print("-" * 70)
|
||
for row in analysis["analysis"]:
|
||
print(f"{row['N']:>6} {row['p']:>3} {row['lambda_theory']:>10.4f} "
|
||
f"{row['entropy_mean']:>8.3f} {row['entropy_std']:>7.4f} "
|
||
f"{row['nonzero_modes_mean']:>8.1f} {row['weight_cv_mean']:>8.4f} "
|
||
f"{row['runtime_ms_mean']:>8.0f}ms {row['overflow_count']:>8}")
|
||
|
||
# Write receipt
|
||
t0 = time.time()
|
||
receipt = {
|
||
"schema": "bmcte_extension_v1",
|
||
"generated_at_utc": datetime.now(timezone.utc).isoformat(),
|
||
"claim_boundary": "bmcte-regime-extension-empirical",
|
||
"parameters": {
|
||
"N_values": args.N,
|
||
"p_values": args.p,
|
||
"K_values": args.K,
|
||
"seeds": args.seeds,
|
||
"p_gpu_threshold": args.p_gpu_threshold,
|
||
},
|
||
"hardware": {
|
||
"cuda": HAS_CUDA,
|
||
"gpu": torch.cuda.get_device_name(0) if HAS_CUDA else None,
|
||
"cpu_threads": os.cpu_count(),
|
||
},
|
||
"results": results,
|
||
"analysis": analysis,
|
||
"dag": {
|
||
"mermaid": dag.to_mermaid(),
|
||
"ascii": dag.to_ascii(),
|
||
"summary": dag.summary(),
|
||
},
|
||
"total_runtime_s": round(total_elapsed, 2),
|
||
}
|
||
canonical = json.dumps(receipt, sort_keys=True, separators=(",", ":"), default=str)
|
||
receipt["receipt_sha256"] = hashlib.sha256(canonical.encode()).hexdigest()
|
||
|
||
out_path = Path(args.output)
|
||
out_path.write_text(json.dumps(receipt, indent=2, default=str))
|
||
dag.mark("receipt", "done", (time.time() - t0) * 1000)
|
||
|
||
# DB ingestion
|
||
if not args.no_db:
|
||
t0 = time.time()
|
||
n_inserted = ingest_results(results)
|
||
dag.mark("db", "done", (time.time() - t0) * 1000)
|
||
print(f" DB: {n_inserted} rows inserted into bmcte.experiment_runs")
|
||
else:
|
||
dag.mark("db", "skipped")
|
||
print(" DB: skipped (--no-db)")
|
||
|
||
print()
|
||
print(f"Receipt: {out_path}")
|
||
print(f"SHA256: {receipt['receipt_sha256']}")
|
||
print(f"Total: {total_elapsed:.1f}s")
|
||
|
||
# Final DAG
|
||
print()
|
||
print("Final DAG:")
|
||
print(dag.to_ascii())
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|