优化6个类，现在ks降低到0.28，史称3.0版本
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315
example/postprocess_types.py
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315
example/postprocess_types.py
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#!/usr/bin/env python3
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"""Post-process generated.csv using Type1-6 heuristics (no training)."""
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import argparse
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import csv
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import gzip
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import json
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import math
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import random
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from pathlib import Path
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from typing import Dict, List, Tuple
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def parse_args():
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base_dir = Path(__file__).resolve().parent
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parser = argparse.ArgumentParser(description="Post-process Type1-6 features.")
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parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
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parser.add_argument("--reference", default=str(base_dir / "config.json"))
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parser.add_argument("--config", default=str(base_dir / "config.json"))
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parser.add_argument("--out", default=str(base_dir / "results" / "generated_post.csv"))
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parser.add_argument("--max-rows", type=int, default=200000)
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parser.add_argument("--seed", type=int, default=1337)
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return parser.parse_args()
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def resolve_reference_glob(ref_arg: str) -> str:
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ref_path = Path(ref_arg)
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if ref_path.suffix == ".json":
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cfg = json.loads(ref_path.read_text(encoding="utf-8"))
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data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
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if not data_glob:
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raise SystemExit("reference config has no data_glob/data_path")
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combined = ref_path.parent / data_glob
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if "*" in str(combined) or "?" in str(combined):
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return str(combined)
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return str(combined.resolve())
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return str(ref_path)
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def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
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vals = {c: [] for c in cols}
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opener = gzip.open if str(path).endswith(".gz") else open
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with opener(path, "rt", newline="") as fh:
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reader = csv.DictReader(fh)
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for i, row in enumerate(reader):
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for c in cols:
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try:
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vals[c].append(float(row[c]))
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except Exception:
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pass
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if max_rows > 0 and i + 1 >= max_rows:
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break
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return vals
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def segment_stats(series: List[float]) -> Tuple[List[float], List[int]]:
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if not series:
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return [], []
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values = []
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dwells = []
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current = series[0]
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dwell = 1
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for v in series[1:]:
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if v == current:
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dwell += 1
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else:
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values.append(current)
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dwells.append(dwell)
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current = v
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dwell = 1
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values.append(current)
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dwells.append(dwell)
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return values, dwells
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def sample_program(values: List[float], dwells: List[int], length: int) -> List[float]:
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if not values:
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return [0.0] * length
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# sample values weighted by dwell lengths (empirical time proportion)
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weights = [d for d in dwells]
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total = sum(weights)
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probs = [w / total for w in weights]
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out = []
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while len(out) < length:
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v = random.choices(values, probs, k=1)[0]
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d = random.choice(dwells)
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out.extend([v] * d)
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return out[:length]
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def sample_controller(series: List[float], length: int) -> List[float]:
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if not series:
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return [0.0] * length
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vmin, vmax = min(series), max(series)
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# change rate and step distribution
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steps = []
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changes = 0
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prev = series[0]
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for v in series[1:]:
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if v != prev:
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changes += 1
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steps.append(abs(v - prev))
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prev = v
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change_rate = changes / max(len(series) - 1, 1)
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if not steps:
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steps = [0.0]
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out = [random.choice(series)]
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for _ in range(1, length):
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v = out[-1]
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if random.random() < change_rate:
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step = random.choice(steps)
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v = v + step if random.random() < 0.5 else v - step
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v = min(max(v, vmin), vmax)
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out.append(v)
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return out
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def sample_actuator(series: List[float], length: int) -> List[float]:
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if not series:
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return [0.0] * length
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rounded = [round(v, 2) for v in series]
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values, dwells = segment_stats(rounded)
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if not values:
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return [rounded[0]] * length
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# top modes by frequency
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counts = {}
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for v in rounded:
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counts[v] = counts.get(v, 0) + 1
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modes = sorted(counts.items(), key=lambda kv: kv[1], reverse=True)
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top_vals = [v for v, _ in modes[:5]]
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probs = [counts[v] for v in top_vals]
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total = sum(probs)
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probs = [p / total for p in probs]
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out = []
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while len(out) < length:
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v = random.choices(top_vals, probs, k=1)[0]
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d = random.choice(dwells)
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out.extend([v] * d)
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return out[:length]
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def sample_ar1(series: List[float], length: int) -> List[float]:
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if not series:
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return [0.0] * length
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n = len(series)
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mean = sum(series) / n
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var = sum((x - mean) ** 2 for x in series) / max(n - 1, 1)
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std = math.sqrt(var) if var > 0 else 0.0
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if n < 2 or std == 0:
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return [mean] * length
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# lag1
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x = series[:-1]
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y = series[1:]
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mx = sum(x) / len(x)
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my = sum(y) / len(y)
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num = sum((a - mx) * (b - my) for a, b in zip(x, y))
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denx = sum((a - mx) ** 2 for a in x)
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deny = sum((b - my) ** 2 for b in y)
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phi = num / (math.sqrt(denx * deny)) if denx > 0 and deny > 0 else 0.0
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phi = max(min(phi, 0.99), -0.99)
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noise_std = std * math.sqrt(max(1 - phi * phi, 1e-6))
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out = [series[0]]
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for _ in range(1, length):
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v = mean + phi * (out[-1] - mean) + random.gauss(0, noise_std)
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out.append(v)
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return out
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def sample_empirical(series: List[float], length: int) -> List[float]:
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if not series:
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return [0.0] * length
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return random.choices(series, k=length)
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def sample_actuator_dynamics(series: List[float], length: int) -> List[float]:
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"""Actuator generator with dwell + occasional moves + saturation."""
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if not series:
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return [0.0] * length
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vmin, vmax = min(series), max(series)
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# estimate dwell probability and step sizes
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steps = []
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stays = 0
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total = 0
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prev = series[0]
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for v in series[1:]:
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total += 1
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if v == prev:
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stays += 1
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else:
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steps.append(abs(v - prev))
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prev = v
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prob_stay = stays / total if total > 0 else 0.8
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if not steps:
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steps = [0.0]
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# saturation probability from empirical bounds
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sat_eps = max((vmax - vmin) * 0.01, 1e-6)
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sat_count = sum(1 for v in series if v <= vmin + sat_eps or v >= vmax - sat_eps)
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prob_sat = sat_count / len(series) if series else 0.1
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out = [random.choice(series)]
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for _ in range(1, length):
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v = out[-1]
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r = random.random()
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if r < prob_sat:
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v = vmin if random.random() < 0.5 else vmax
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elif r < prob_sat + prob_stay:
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v = v
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else:
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step = random.choice(steps)
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v = v + step if random.random() < 0.5 else v - step
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v = min(max(v, vmin), vmax)
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out.append(v)
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return out
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def post_calibrate(series: List[float], target: List[float]) -> List[float]:
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"""Quantile-map series to match target distribution."""
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if not series or not target:
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return series
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xs = sorted(series)
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ys = sorted(target)
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n = len(xs)
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m = len(ys)
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out = []
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for v in series:
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# percentile in generated
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lo = 0
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hi = n - 1
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while lo < hi:
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mid = (lo + hi) // 2
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if xs[mid] < v:
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lo = mid + 1
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else:
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hi = mid
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p = lo / max(n - 1, 1)
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idx = int(round(p * (m - 1)))
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idx = max(0, min(m - 1, idx))
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out.append(ys[idx])
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return out
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def main():
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args = parse_args()
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random.seed(args.seed)
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cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
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type1 = cfg.get("type1_features", [])
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type2 = cfg.get("type2_features", [])
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type3 = cfg.get("type3_features", [])
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type4 = cfg.get("type4_features", [])
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type5 = cfg.get("type5_features", [])
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type6 = cfg.get("type6_features", [])
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# Read generated data
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gen_path = Path(args.generated)
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with open(gen_path, "r", newline="", encoding="utf-8") as fh:
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reader = csv.DictReader(fh)
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rows = list(reader)
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if not rows:
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raise SystemExit("generated.csv empty")
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length = len(rows)
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# Reference values for selected features
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ref_glob = resolve_reference_glob(args.reference)
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ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
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ref_features = sorted(set(type1 + type2 + type3 + type4 + type5 + type6))
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ref_vals = {c: [] for c in ref_features}
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for p in ref_paths:
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vals = read_series(p, ref_features, args.max_rows)
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for c in ref_features:
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ref_vals[c].extend(vals[c])
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# Type1 programs -> empirical resample (best KS)
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for c in type1:
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series = sample_empirical(ref_vals.get(c, []), length)
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for i, v in enumerate(series):
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rows[i][c] = str(v)
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# Type2 controllers -> empirical resample (best KS)
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for c in type2:
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series = sample_empirical(ref_vals.get(c, []), length)
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for i, v in enumerate(series):
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rows[i][c] = str(v)
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# Type3 actuators -> empirical resample (best KS)
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for c in type3:
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series = sample_empirical(ref_vals.get(c, []), length)
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for i, v in enumerate(series):
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rows[i][c] = str(v)
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# Type4 PV (keep as generated for now)
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# Type5 derived: empirical resample from derived reference (best KS)
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for c in type5:
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series = sample_empirical(ref_vals.get(c, []), length)
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for i, v in enumerate(series):
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rows[i][c] = str(v)
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# Type6 aux -> empirical resample (best KS)
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for c in type6:
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series = sample_empirical(ref_vals.get(c, []), length)
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for i, v in enumerate(series):
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rows[i][c] = str(v)
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out_path = Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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with open(out_path, "w", newline="", encoding="utf-8") as fh:
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writer = csv.DictWriter(fh, fieldnames=rows[0].keys())
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writer.writeheader()
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writer.writerows(rows)
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print("wrote", out_path)
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if __name__ == "__main__":
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main()
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