update ks

.gitignore

@@ -22,9 +22,6 @@ dataset/
 
 # Model artifacts and results
 mask-ddpm/example/results/
-example/results/
-!example/results/cont_stats.json
-!example/results/disc_vocab.json
 *.pt
 *.pth
 *.ckpt

docs/README.md

@@ -1,37 +0,0 @@
-# Documentation Index
-
-This folder tracks project decisions, experiments, and evolving ideas.
-
-- `decisions.md`: design/architecture changes and rationales
-- `experiments.md`: experiment runs and results
-- `ideas.md`: future ideas and hypotheses
-- `architecture.md`: system overview and module boundaries
-- `evaluation.md`: evaluation protocol and metric usage
-
-Conventions:
-- Append new entries instead of overwriting old ones.
-- Record exact config file and key overrides when possible.
-- Keep metrics in the order: avg_ks / avg_jsd / avg_lag1_diff.
-
-Tools:
-- `example/diagnose_ks.py` for per-feature KS + CDF plots.
-- `example/run_all.py` for one-command full pipeline (train/export/eval/postprocess/diagnostics).
-- `example/run_all_full.py` legacy full pipeline runner.
-- `example/filtered_metrics.py` for filtered KS after removing collapsed/outlier features.
-- `example/ranked_ks.py` for ranked KS table + cumulative avg_ks if removing top features.
-- `example/evaluate_generated.py` for full-reference metrics (now supports glob over all train*.csv.gz).
-- `example/program_stats.py` for dwell/change/step stats on program-like features.
-- `example/controller_stats.py` for controller saturation/change stats.
-- `example/actuator_stats.py` for spike/dwell stats on actuators.
-- `example/pv_stats.py` for PV quantile/tail stats.
-- `example/aux_stats.py` for aux signal mean/std/lag1 stats.
-- `example/postprocess_types.py` for type-based postprocessing (Type1/2/3/5/6).
-Notes:
-- If `use_quantile_transform` is enabled, run `prepare_data.py` with `full_stats: true` to build quantile tables.
-
-Current status (high level):
-- Two-stage pipeline (GRU trend + diffusion residuals).
-- Quantile transform + post-hoc calibration enabled for continuous features.
-- KS evaluation uses full reference glob and tie-aware KS implementation.
-- Type-based postprocess (empirical resample for Type1/2/3/5/6) used as a KS-lowering baseline.
-- Latest model run (2026-01-27 21:22): avg_ks ~0.405 / avg_jsd ~0.038 / avg_lag1_diff ~0.145.

docs/architecture.md

@@ -1,47 +0,0 @@
-# Architecture Overview
-
-## System Diagram (text)
-```
-            +--------------------+
-            |  Program Generator |
-            | (Type 1 setpoints) |
-            +---------+----------+
-                      |
-                      v
-            +-----------------------+
-            | Controller / Actuator |
-            | (Type 2/3 modules)    |
-            +---------+-------------+
-                      |
-                      v
-        +----------------------------+
-        |  Diffusion (Residuals)     |
-        |  - Continuous PVs          |
-        |  - Discrete mask diffusion |
-        +---------+------------------+
-                  |
-                  v
-     +-----------------------------+
-     | Post-processing             |
-     | - Derived tags (Type 5)     |
-     | - KS-only resample baseline |
-     +-----------------------------+
-```
-
-## Core Components
-- **Stage‑1 Temporal GRU**: learns trend for continuous features.
-- **Diffusion Backbone**: Transformer (default) or GRU; predicts residuals + discrete logits.
-- **Post-hoc Calibration**: optional quantile calibration to align 1D CDFs.
-- **KS-only Baseline**: Type1/2/3/5/6 empirical resampling for rapid KS reduction (diagnostic; may hurt joint realism).
-
-## Feature-Type Split
-1) **Type 1**: Setpoints/demands → program generator  
-2) **Type 2**: Controller outputs → small emulator / conditional head  
-3) **Type 3**: Actuators/valves → spike‑and‑slab / dwell-time model  
-4) **Type 4**: Process PVs (multi‑modal/heavy tail) → diffusion with conditioning  
-5) **Type 5**: Derived tags → deterministic reconstruction (or empirical KS baseline)  
-6) **Type 6**: Auxiliary/vibration → narrow-band AR/SSM or empirical KS baseline
-
-## Data Flow
-- Input CSV → stats/vocab → normalized batches  
-- Trend GRU → residual diffusion → inverse transforms → export  

docs/decisions.md

@@ -1,99 +0,0 @@
-# Design & Decision Log
-
-## 2026-01-26 — Two-stage temporal backbone (GRU) + residual diffusion
-- **Decision**: Add a stage-1 GRU trend model, then train diffusion on residuals.
-- **Why**: Separate temporal consistency from distribution alignment.
-- **Files**:
-  - `example/hybrid_diffusion.py` (added `TemporalGRUGenerator`)
-  - `example/train.py` (two-stage training + residual diffusion)
-  - `example/sample.py`, `example/export_samples.py` (trend + residual synthesis)
-  - `example/config.json` (temporal hyperparameters)
-- **Expected effect**: improve lag-1 consistency; may hurt KS if residual distribution drifts.
-
-## 2026-01-26 — Residual distribution alignment losses
-- **Decision**: Apply distribution losses to residuals (not raw x0).
-- **Why**: Diffusion models residuals; alignment should match residual distribution.
-- **Files**:
-  - `example/train.py` (quantile loss on residuals)
-  - `example/config.json` (quantile weight)
-
-## 2026-01-26 — SNR-weighted loss + residual stats
-- **Decision**: Add SNR-weighted loss and residual mean/std regularization.
-- **Why**: Stabilize diffusion training and improve KS.
-- **Files**:
-  - `example/train.py`
-  - `example/config.json`
-
-## 2026-01-26 — Switchable backbone (GRU vs Transformer)
-- **Decision**: Make the diffusion backbone configurable (`backbone_type`) with a Transformer encoder option.
-- **Why**: Test whether self‑attention reduces temporal vs distribution competition without altering the two‑stage design.
-- **Files**:
-  - `example/hybrid_diffusion.py`
-  - `example/train.py`
-  - `example/sample.py`
-  - `example/export_samples.py`
-  - `example/config.json`
-
-## 2026-01-26 — Per-feature KS diagnostics
-- **Decision**: Add a per-feature KS/CDF diagnostic script to pinpoint KS failures (tails, boundary pile-up, shifts).
-- **Why**: Avoid blind reweighting and find the specific features causing KS to stay high.
-- **Files**:
-  - `example/diagnose_ks.py`
-
-## 2026-01-26 — Quantile transform + sigmoid bounds for continuous features
-- **Decision**: Add optional quantile normalization (TabDDPM-style) and sigmoid-based bounds to reduce KS spikes.
-- **Why**: KS failures are dominated by boundary pile-up and tail mismatch.
-- **Files**:
-  - `example/data_utils.py`
-  - `example/prepare_data.py`
-  - `example/export_samples.py`
-  - `example/config.json`
-
-## 2026-01-27 — Quantile transform without extra standardization
-- **Decision**: When quantile transform is enabled, skip mean/std normalization (quantile output already ~N(0,1)).
-- **Why**: Prevent scale mismatch that pushed values to max bounds and blew up KS.
-- **Files**:
-  - `example/data_utils.py`
-  - `example/export_samples.py`
-
-## 2026-01-27 — Soft bounds + post-scale for boundary pile-up
-- **Decision**: Replace hard sigmoid bounds with soft tanh bounds and allow per-feature post-scaling.
-- **Why**: Many continuous features collapsed to max bound (KS=1.0).
-- **Files**:
-  - `example/export_samples.py`
-  - `example/config.json`
-
-## 2026-01-27 — Post-hoc quantile calibration
-- **Decision**: Add optional post-hoc quantile calibration to align generated 1D CDFs with real data.
-- **Why**: KS remained high with distribution shifts even after boundary fixes.
-- **Files**:
-  - `example/data_utils.py`
-  - `example/export_samples.py`
-  - `example/prepare_data.py`
-  - `example/config.json`
-
-## 2026-01-27 — Full quantile stats in preparation
-- **Decision**: Enable full statistics when quantile transform is active.
-- **Why**: Stabilize quantile tables and reduce CDF mismatch.
-- **Files**:
-  - `example/prepare_data.py`
-  - `example/config.json`
-
-## 2026-01-27 — Filtered KS for diagnostics
-- **Decision**: Add a filtered KS metric that excludes collapsed/outlier features.
-- **Why**: Avoid a handful of features dominating the aggregate KS while still reporting full KS.
-- **Files**:
-  - `example/filtered_metrics.py`
-  - `example/run_all_full.py`
-
-## 2026-01-28 — Tie-aware KS + full-reference aggregation
-- **Decision**: Fix KS computation to handle ties correctly and aggregate all reference files matched by glob.
-- **Why**: Spiky/quantized features were overstating KS; single-file reference was misleading.
-- **Files**:
-  - `example/evaluate_generated.py`
-
-## 2026-01-28 — KS-only postprocess baseline
-- **Decision**: Add an empirical resampling mode for Type1/2/3/5/6 to aggressively reduce KS.
-- **Why**: Provide a diagnostic upper-bound on KS without retraining.
-- **Files**:
-  - `example/postprocess_types.py`

docs/evaluation.md

@@ -1,46 +0,0 @@
-# Evaluation Protocol
-
-## Primary Metrics
-- **avg_ks**: mean KS across continuous features
-- **avg_jsd**: mean JSD across discrete feature marginals
-- **avg_lag1_diff**: lag‑1 correlation mismatch
-
-## Diagnostic Metrics
-- **per‑feature KS**: `example/diagnose_ks.py`
-- **filtered KS**: `example/filtered_metrics.py` (remove collapsed/outlier features)
-- **ranked KS**: `example/ranked_ks.py` (contribution analysis)
-
-## KS Implementation Notes
-- KS is computed with **tie-aware** CDFs (important for discrete/spiky features).
-- Reference data supports **glob input** and aggregates all matching files.
-- Use `--max-rows` to cap reference rows for faster diagnostics.
-
-## Recommended Reporting
-Report both:
-1) **Full metrics** (no filtering)  
-2) **Filtered metrics** (diagnostic only)
-
-Always list which features were filtered.
-If using KS-only postprocess (empirical resampling), note it explicitly because it can weaken joint realism.
-
-## Program‑Generator Metrics (Type 1)
-For setpoints/demands:
-- dwell‑time distribution
-- change‑count per day
-- step‑size distribution
-
-## Controller Metrics (Type 2)
-- saturation ratio near bounds
-- change rate and median step size
-
-## Actuator Metrics (Type 3)
-- top‑k spike mass (top1/top3)
-- unique ratio
-- dwell length
-
-## PV Metrics (Type 4)
-- q05/q50/q95 + tail ratio
-
-## Aux Metrics (Type 6)
-- mean/std
-- lag‑1 correlation

docs/experiments.md

@@ -1,39 +0,0 @@
-# Experiment Log
-
-## Format
-```
-YYYY-MM-DD
-- Config: <config file or key overrides>
-- Result: avg_ks / avg_jsd / avg_lag1_diff
-- Notes
-```
-
-## 2026-01-26
-- Config: `example/config_no_temporal.json` (baseline)
-- Result: 0.6474156 / 0.0576699 / 0.1981700
-- Notes: no temporal stage; better KS, worse lag-1.
-
-## 2026-01-26
-- Config: `example/config_temporal_strong.json` (two-stage)
-- Result: 0.6892453 / 0.0564408 / 0.1568776
-- Notes: lag-1 improves, KS degrades; residual drift remains.
-
-## 2026-01-26
-- Config: `example/config.json` (two-stage residual diffusion; user run on Windows)
-- Result: 0.7131993 / 0.0327603 / 0.2327633
-- Notes: user-reported metrics after temporal stage + residual diffusion.
-
-## 2026-01-26
-- Config: `example/config.json` (two-stage residual diffusion; user run on Windows)
-- Result: 0.7096230 / 0.0331810 / 0.1898416
-- Notes: slight KS improvement, lag-1 improves; still distribution/temporal trade-off.
-
-## 2026-01-27
-- Config: `example/config.json` (quantile transform + calibration, full stats)
-- Result: 0.4046 / 0.0376 / 0.1449
-- Notes: KS and lag-1 improved significantly; JSD regressed vs best discrete run.
-
-## 2026-01-28
-- Config: `example/config.json` + KS-only postprocess (Type1/2/3/5/6 empirical resample)
-- Result: overall_avg_ks 0.2851 (continuous, full-reference KS with tie-aware implementation)
-- Notes: diagnostic baseline; improves KS but not joint realism.

docs/ideas.md

@@ -1,19 +0,0 @@
-# Ideas & Hypotheses
-
-## Transformer as backbone (Plan B)
-- Hypothesis: self-attention may better capture long-range dependencies and reduce conflict between temporal consistency and distribution matching.
-- Risk: higher compute cost, potentially more unstable training.
-- Status: implemented as `backbone_type = "transformer"` in config.
-- Experiment: compare GRU vs Transformer using `run_compare.py`.
-
-## Residual standardization
-- Hypothesis: standardizing residuals before diffusion reduces drift and improves KS.
-
-## Two-stage training with curriculum
-- Hypothesis: train diffusion on residuals only after temporal GRU converges to low error.
-
-## Discrete calibration
-- Hypothesis: post-hoc calibration on discrete marginals can reduce JSD without harming KS.
-
-## Feature-type split modeling
-- Hypothesis: separate generation per feature type (setpoints, controllers, actuators, quantized, derived, aux) yields better overall fidelity.

example/.ipynb_checkpoints/config-checkpoint.json

@@ -1,79 +0,0 @@
-{
-  "data_path": "../../dataset/hai/hai-21.03/train1.csv.gz",
-  "data_glob": "../../dataset/hai/hai-21.03/train*.csv.gz",
-  "split_path": "./feature_split.json",
-  "stats_path": "./results/cont_stats.json",
-  "vocab_path": "./results/disc_vocab.json",
-  "out_dir": "./results",
-  "device": "auto",
-  "timesteps": 600,
-  "batch_size": 12,
-  "seq_len": 96,
-  "epochs": 10,
-  "max_batches": 4000,
-  "lambda": 0.7,
-  "lr": 0.0005,
-  "seed": 1337,
-  "log_every": 10,
-  "ckpt_every": 50,
-  "ema_decay": 0.999,
-  "use_ema": true,
-  "clip_k": 5.0,
-  "grad_clip": 1.0,
-  "use_condition": true,
-  "condition_type": "file_id",
-  "cond_dim": 32,
-  "use_tanh_eps": false,
-  "eps_scale": 1.0,
-  "model_time_dim": 128,
-  "model_hidden_dim": 512,
-  "model_num_layers": 2,
-  "model_dropout": 0.1,
-  "model_ff_mult": 2,
-  "model_pos_dim": 64,
-  "model_use_pos_embed": true,
-  "backbone_type": "transformer",
-  "transformer_num_layers": 3,
-  "transformer_nhead": 4,
-  "transformer_ff_dim": 512,
-  "transformer_dropout": 0.1,
-  "disc_mask_scale": 0.9,
-  "cont_loss_weighting": "inv_std",
-  "cont_loss_eps": 1e-6,
-  "cont_target": "x0",
-  "cont_clamp_x0": 5.0,
-  "use_quantile_transform": true,
-  "quantile_bins": 1001,
-  "cont_bound_mode": "none",
-  "cont_bound_strength": 2.0,
-  "cont_post_calibrate": true,
-  "cont_post_scale": {},
-  "full_stats": true,
-  "type1_features": ["P1_B4002","P2_MSD","P4_HT_LD","P1_B2004","P1_B3004","P1_B4022","P1_B3005"],
-  "type2_features": ["P1_B4005"],
-  "type3_features": ["P1_PCV02Z","P1_PCV01Z","P1_PCV01D","P1_FCV02Z","P1_FCV03D","P1_FCV03Z","P1_LCV01D","P1_LCV01Z"],
-  "type4_features": ["P1_PIT02","P2_SIT02","P1_FT03"],
-  "type5_features": ["P1_FT03Z","P1_FT02Z"],
-  "type6_features": ["P4_HT_PO","P2_24Vdc","P2_HILout"],
-  "shuffle_buffer": 256,
-  "use_temporal_stage1": true,
-  "temporal_backbone": "transformer",
-  "temporal_hidden_dim": 256,
-  "temporal_num_layers": 1,
-  "temporal_dropout": 0.0,
-  "temporal_pos_dim": 64,
-  "temporal_use_pos_embed": true,
-  "temporal_transformer_num_layers": 2,
-  "temporal_transformer_nhead": 4,
-  "temporal_transformer_ff_dim": 256,
-  "temporal_transformer_dropout": 0.1,
-  "temporal_epochs": 3,
-  "temporal_lr": 0.001,
-  "quantile_loss_weight": 0.2,
-  "quantile_points": [0.05, 0.25, 0.5, 0.75, 0.95],
-  "snr_weighted_loss": true,
-  "snr_gamma": 1.0,
-  "residual_stat_weight": 0.05,
-  "sample_batch_size": 4,
-  "sample_seq_len": 96
-}

example/README.md

@@ -72,4 +72,3 @@ python example/run_pipeline.py --device auto
 - The script only samples the first 5000 rows to stay fast.
 - `prepare_data.py` runs without PyTorch, but `train.py` and `sample.py` require it.
 - `train.py` and `sample.py` auto-select GPU if available; otherwise they fall back to CPU.
-- Optional two-stage temporal model (`use_temporal_stage1`) trains a GRU trend backbone first, then diffusion models residuals.

example/actuator_stats.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python3
-"""Stats for actuator/valve-like outputs (Type 3)."""
-
-import argparse
-import csv
-import gzip
-import json
-from pathlib import Path
-from typing import Dict, List
-
-
-def parse_args():
-    base_dir = Path(__file__).resolve().parent
-    parser = argparse.ArgumentParser(description="Actuator/valve stats.")
-    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--reference", default=str(base_dir / "config.json"))
-    parser.add_argument("--features", default="", help="comma-separated list")
-    parser.add_argument("--config", default=str(base_dir / "config.json"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "actuator_stats.json"))
-    parser.add_argument("--max-rows", type=int, default=200000)
-    return parser.parse_args()
-
-
-def resolve_reference_glob(ref_arg: str) -> str:
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json":
-        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
-        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not data_glob:
-            raise SystemExit("reference config has no data_glob/data_path")
-        combined = ref_path.parent / data_glob
-        if "*" in str(combined) or "?" in str(combined):
-            return str(combined)
-        return str(combined.resolve())
-    return str(ref_path)
-
-
-def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
-    vals = {c: [] for c in cols}
-    opener = gzip.open if str(path).endswith(".gz") else open
-    with opener(path, "rt", newline="") as fh:
-        reader = csv.DictReader(fh)
-        for i, row in enumerate(reader):
-            for c in cols:
-                try:
-                    vals[c].append(float(row[c]))
-                except Exception:
-                    pass
-            if max_rows > 0 and i + 1 >= max_rows:
-                break
-    return vals
-
-
-def spike_stats(series: List[float]):
-    if not series:
-        return {
-            "unique_ratio": None,
-            "top1_mass": None,
-            "top3_mass": None,
-            "median_dwell": None,
-        }
-    n = len(series)
-    # discretize by rounding
-    rounded = [round(v, 2) for v in series]
-    counts = {}
-    for v in rounded:
-        counts[v] = counts.get(v, 0) + 1
-    unique_ratio = len(counts) / n
-    top = sorted(counts.values(), reverse=True)
-    top1_mass = top[0] / n if top else None
-    top3_mass = sum(top[:3]) / n if len(top) >= 3 else top1_mass
-
-    # dwell length
-    dwells = []
-    current = rounded[0]
-    dwell = 1
-    for v in rounded[1:]:
-        if v == current:
-            dwell += 1
-        else:
-            dwells.append(dwell)
-            current = v
-            dwell = 1
-    dwells.append(dwell)
-    dwells.sort()
-    median_dwell = dwells[len(dwells) // 2] if dwells else None
-    return {
-        "unique_ratio": unique_ratio,
-        "top1_mass": top1_mass,
-        "top3_mass": top3_mass,
-        "median_dwell": median_dwell,
-    }
-
-
-def main():
-    args = parse_args()
-    features = [f.strip() for f in args.features.split(",") if f.strip()]
-    if not features and Path(args.config).exists():
-        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
-        features = cfg.get("type3_features", []) or []
-    if not features:
-        raise SystemExit("no features specified for actuator_stats")
-
-    gen_vals = read_series(Path(args.generated), features, args.max_rows)
-    ref_glob = resolve_reference_glob(args.reference)
-    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
-    if not ref_paths:
-        raise SystemExit(f"no reference files matched: {ref_glob}")
-    real_vals = {c: [] for c in features}
-    for p in ref_paths:
-        vals = read_series(p, features, args.max_rows)
-        for c in features:
-            real_vals[c].extend(vals[c])
-
-    out = {"features": features, "generated": {}, "reference": {}}
-    for c in features:
-        out["generated"][c] = spike_stats(gen_vals[c])
-        out["reference"][c] = spike_stats(real_vals[c])
-
-    out_path = Path(args.out)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
-    print("wrote", out_path)
-
-
-if __name__ == "__main__":
-    main()

example/aux_stats.py

@@ -1,108 +0,0 @@
-#!/usr/bin/env python3
-"""Stats for auxiliary/vibration signals (Type 6)."""
-
-import argparse
-import csv
-import gzip
-import json
-from pathlib import Path
-from typing import Dict, List
-
-
-def parse_args():
-    base_dir = Path(__file__).resolve().parent
-    parser = argparse.ArgumentParser(description="Aux stats.")
-    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--reference", default=str(base_dir / "config.json"))
-    parser.add_argument("--features", default="", help="comma-separated list")
-    parser.add_argument("--config", default=str(base_dir / "config.json"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "aux_stats.json"))
-    parser.add_argument("--max-rows", type=int, default=200000)
-    return parser.parse_args()
-
-
-def resolve_reference_glob(ref_arg: str) -> str:
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json":
-        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
-        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not data_glob:
-            raise SystemExit("reference config has no data_glob/data_path")
-        combined = ref_path.parent / data_glob
-        if "*" in str(combined) or "?" in str(combined):
-            return str(combined)
-        return str(combined.resolve())
-    return str(ref_path)
-
-
-def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
-    vals = {c: [] for c in cols}
-    opener = gzip.open if str(path).endswith(".gz") else open
-    with opener(path, "rt", newline="") as fh:
-        reader = csv.DictReader(fh)
-        for i, row in enumerate(reader):
-            for c in cols:
-                try:
-                    vals[c].append(float(row[c]))
-                except Exception:
-                    pass
-            if max_rows > 0 and i + 1 >= max_rows:
-                break
-    return vals
-
-
-def mean_std(series: List[float]):
-    if not series:
-        return {"mean": None, "std": None, "lag1": None}
-    n = len(series)
-    mean = sum(series) / n
-    var = sum((x - mean) ** 2 for x in series) / max(n - 1, 1)
-    std = var ** 0.5
-    # lag1 correlation
-    if n < 2:
-        lag1 = None
-    else:
-        x = series[:-1]
-        y = series[1:]
-        mx = sum(x) / len(x)
-        my = sum(y) / len(y)
-        num = sum((a - mx) * (b - my) for a, b in zip(x, y))
-        denx = sum((a - mx) ** 2 for a in x)
-        deny = sum((b - my) ** 2 for b in y)
-        lag1 = num / (denx ** 0.5 * deny ** 0.5) if denx > 0 and deny > 0 else None
-    return {"mean": mean, "std": std, "lag1": lag1}
-
-
-def main():
-    args = parse_args()
-    features = [f.strip() for f in args.features.split(",") if f.strip()]
-    if not features and Path(args.config).exists():
-        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
-        features = cfg.get("type6_features", []) or []
-    if not features:
-        raise SystemExit("no features specified for aux_stats")
-
-    gen_vals = read_series(Path(args.generated), features, args.max_rows)
-    ref_glob = resolve_reference_glob(args.reference)
-    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
-    if not ref_paths:
-        raise SystemExit(f"no reference files matched: {ref_glob}")
-    real_vals = {c: [] for c in features}
-    for p in ref_paths:
-        vals = read_series(p, features, args.max_rows)
-        for c in features:
-            real_vals[c].extend(vals[c])
-
-    out = {"features": features, "generated": {}, "reference": {}}
-    for c in features:
-        out["generated"][c] = mean_std(gen_vals[c])
-        out["reference"][c] = mean_std(real_vals[c])
-
-    out_path = Path(args.out)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
-    print("wrote", out_path)
-
-
-if __name__ == "__main__":
-    main()

example/config.json

@@ -7,8 +7,8 @@
   "out_dir": "./results",
   "device": "auto",
   "timesteps": 600,
-  "batch_size": 12,
-  "seq_len": 96,
+  "batch_size": 128,
+  "seq_len": 128,
   "epochs": 10,
   "max_batches": 4000,
   "lambda": 0.7,
@@ -32,48 +32,14 @@
   "model_ff_mult": 2,
   "model_pos_dim": 64,
   "model_use_pos_embed": true,
-  "backbone_type": "transformer",
-  "transformer_num_layers": 3,
-  "transformer_nhead": 4,
-  "transformer_ff_dim": 512,
-  "transformer_dropout": 0.1,
   "disc_mask_scale": 0.9,
   "cont_loss_weighting": "inv_std",
   "cont_loss_eps": 1e-6,
   "cont_target": "x0",
   "cont_clamp_x0": 5.0,
-  "use_quantile_transform": true,
-  "quantile_bins": 1001,
-  "cont_bound_mode": "none",
-  "cont_bound_strength": 2.0,
-  "cont_post_calibrate": true,
-  "cont_post_scale": {},
-  "full_stats": true,
-  "type1_features": ["P1_B4002","P2_MSD","P4_HT_LD","P1_B2004","P1_B3004","P1_B4022","P1_B3005"],
-  "type2_features": ["P1_B4005"],
-  "type3_features": ["P1_PCV02Z","P1_PCV01Z","P1_PCV01D","P1_FCV02Z","P1_FCV03D","P1_FCV03Z","P1_LCV01D","P1_LCV01Z"],
-  "type4_features": ["P1_PIT02","P2_SIT02","P1_FT03"],
-  "type5_features": ["P1_FT03Z","P1_FT02Z"],
-  "type6_features": ["P4_HT_PO","P2_24Vdc","P2_HILout"],
-  "shuffle_buffer": 256,
-  "use_temporal_stage1": true,
-  "temporal_backbone": "transformer",
-  "temporal_hidden_dim": 256,
-  "temporal_num_layers": 1,
-  "temporal_dropout": 0.0,
-  "temporal_pos_dim": 64,
-  "temporal_use_pos_embed": true,
-  "temporal_transformer_num_layers": 2,
-  "temporal_transformer_nhead": 4,
-  "temporal_transformer_ff_dim": 256,
-  "temporal_transformer_dropout": 0.1,
-  "temporal_epochs": 3,
-  "temporal_lr": 0.001,
-  "quantile_loss_weight": 0.2,
+  "quantile_loss_weight": 0.1,
   "quantile_points": [0.05, 0.25, 0.5, 0.75, 0.95],
-  "snr_weighted_loss": true,
-  "snr_gamma": 1.0,
-  "residual_stat_weight": 0.05,
-  "sample_batch_size": 4,
-  "sample_seq_len": 96
+  "shuffle_buffer": 256,
+  "sample_batch_size": 8,
+  "sample_seq_len": 128
 }

example/config_no_temporal.json

@@ -1,56 +0,0 @@
-{
-  "data_path": "../../dataset/hai/hai-21.03/train1.csv.gz",
-  "data_glob": "../../dataset/hai/hai-21.03/train*.csv.gz",
-  "split_path": "./feature_split.json",
-  "stats_path": "./results/cont_stats.json",
-  "vocab_path": "./results/disc_vocab.json",
-  "out_dir": "./results",
-  "device": "auto",
-  "timesteps": 600,
-  "batch_size": 128,
-  "seq_len": 128,
-  "epochs": 10,
-  "max_batches": 4000,
-  "lambda": 0.7,
-  "lr": 0.0005,
-  "seed": 1337,
-  "log_every": 10,
-  "ckpt_every": 50,
-  "ema_decay": 0.999,
-  "use_ema": true,
-  "clip_k": 5.0,
-  "grad_clip": 1.0,
-  "use_condition": true,
-  "condition_type": "file_id",
-  "cond_dim": 32,
-  "use_tanh_eps": false,
-  "eps_scale": 1.0,
-  "model_time_dim": 128,
-  "model_hidden_dim": 512,
-  "model_num_layers": 2,
-  "model_dropout": 0.1,
-  "model_ff_mult": 2,
-  "model_pos_dim": 64,
-  "model_use_pos_embed": true,
-  "backbone_type": "transformer",
-  "transformer_num_layers": 2,
-  "transformer_nhead": 4,
-  "transformer_ff_dim": 512,
-  "transformer_dropout": 0.1,
-  "disc_mask_scale": 0.9,
-  "cont_loss_weighting": "inv_std",
-  "cont_loss_eps": 1e-6,
-  "cont_target": "x0",
-  "cont_clamp_x0": 5.0,
-  "use_quantile_transform": true,
-  "quantile_bins": 1001,
-  "cont_bound_mode": "none",
-  "cont_bound_strength": 2.0,
-  "cont_post_calibrate": true,
-  "cont_post_scale": {},
-  "full_stats": true,
-  "shuffle_buffer": 1024,
-  "use_temporal_stage1": false,
-  "sample_batch_size": 4,
-  "sample_seq_len": 128
-}

example/config_submission_full.json

@@ -1,81 +0,0 @@
-{
-  "data_path": "../../dataset/hai/hai-21.03/train1.csv.gz",
-  "data_glob": "../../dataset/hai/hai-21.03/train*.csv.gz",
-  "split_path": "./feature_split.json",
-  "stats_path": "./results/cont_stats.json",
-  "vocab_path": "./results/disc_vocab.json",
-  "out_dir": "./results",
-  "device": "auto",
-  "timesteps": 600,
-  "batch_size": 12,
-  "seq_len": 96,
-  "epochs": 10,
-  "max_batches": 4000,
-  "lambda": 0.7,
-  "lr": 0.0005,
-  "seed": 1337,
-  "log_every": 10,
-  "ckpt_every": 50,
-  "ema_decay": 0.999,
-  "use_ema": true,
-  "clip_k": 5.0,
-  "grad_clip": 1.0,
-  "use_condition": true,
-  "condition_type": "file_id",
-  "cond_dim": 32,
-  "use_tanh_eps": false,
-  "eps_scale": 1.0,
-  "model_time_dim": 128,
-  "model_hidden_dim": 512,
-  "model_num_layers": 2,
-  "model_dropout": 0.1,
-  "model_ff_mult": 2,
-  "model_pos_dim": 64,
-  "model_use_pos_embed": true,
-  "backbone_type": "transformer",
-  "transformer_num_layers": 3,
-  "transformer_nhead": 4,
-  "transformer_ff_dim": 512,
-  "transformer_dropout": 0.1,
-  "disc_mask_scale": 0.9,
-  "cont_loss_weighting": "inv_std",
-  "cont_loss_eps": 1e-6,
-  "cont_target": "x0",
-  "cont_clamp_x0": 5.0,
-  "use_quantile_transform": true,
-  "quantile_bins": 1001,
-  "cont_bound_mode": "none",
-  "cont_bound_strength": 2.0,
-  "cont_post_calibrate": true,
-  "cont_post_scale": {},
-  "full_stats": true,
-  "type1_features": ["P1_B4002", "P2_MSD", "P4_HT_LD", "P1_B2004", "P1_B3004", "P1_B4022", "P1_B3005"],
-  "type2_features": ["P1_B4005"],
-  "type3_features": ["P1_PCV02Z", "P1_PCV01Z", "P1_PCV01D", "P1_FCV02Z", "P1_FCV03D", "P1_FCV03Z", "P1_LCV01D", "P1_LCV01Z"],
-  "type4_features": ["P1_PIT02", "P2_SIT02", "P1_FT03"],
-  "type5_features": ["P1_FT03Z", "P1_FT02Z"],
-  "type6_features": ["P4_HT_PO", "P2_24Vdc", "P2_HILout"],
-  "routing_type1_features": ["P1_B4022"],
-  "routing_type5_features": [],
-  "shuffle_buffer": 256,
-  "use_temporal_stage1": true,
-  "temporal_backbone": "transformer",
-  "temporal_hidden_dim": 256,
-  "temporal_num_layers": 1,
-  "temporal_dropout": 0.0,
-  "temporal_pos_dim": 64,
-  "temporal_use_pos_embed": true,
-  "temporal_transformer_num_layers": 2,
-  "temporal_transformer_nhead": 4,
-  "temporal_transformer_ff_dim": 256,
-  "temporal_transformer_dropout": 0.1,
-  "temporal_epochs": 3,
-  "temporal_lr": 0.001,
-  "quantile_loss_weight": 0.2,
-  "quantile_points": [0.05, 0.25, 0.5, 0.75, 0.95],
-  "snr_weighted_loss": true,
-  "snr_gamma": 1.0,
-  "residual_stat_weight": 0.05,
-  "sample_batch_size": 4,
-  "sample_seq_len": 96
-}

example/config_temporal_strong.json

@@ -1,68 +0,0 @@
-{
-  "data_path": "../../dataset/hai/hai-21.03/train1.csv.gz",
-  "data_glob": "../../dataset/hai/hai-21.03/train*.csv.gz",
-  "split_path": "./feature_split.json",
-  "stats_path": "./results/cont_stats.json",
-  "vocab_path": "./results/disc_vocab.json",
-  "out_dir": "./results",
-  "device": "auto",
-  "timesteps": 600,
-  "batch_size": 128,
-  "seq_len": 128,
-  "epochs": 10,
-  "max_batches": 4000,
-  "lambda": 0.7,
-  "lr": 0.0005,
-  "seed": 1337,
-  "log_every": 10,
-  "ckpt_every": 50,
-  "ema_decay": 0.999,
-  "use_ema": true,
-  "clip_k": 5.0,
-  "grad_clip": 1.0,
-  "use_condition": true,
-  "condition_type": "file_id",
-  "cond_dim": 32,
-  "use_tanh_eps": false,
-  "eps_scale": 1.0,
-  "model_time_dim": 128,
-  "model_hidden_dim": 512,
-  "model_num_layers": 2,
-  "model_dropout": 0.1,
-  "model_ff_mult": 2,
-  "model_pos_dim": 64,
-  "model_use_pos_embed": true,
-  "backbone_type": "transformer",
-  "transformer_num_layers": 2,
-  "transformer_nhead": 4,
-  "transformer_ff_dim": 512,
-  "transformer_dropout": 0.1,
-  "disc_mask_scale": 0.9,
-  "cont_loss_weighting": "inv_std",
-  "cont_loss_eps": 1e-6,
-  "cont_target": "x0",
-  "cont_clamp_x0": 5.0,
-  "use_quantile_transform": true,
-  "quantile_bins": 1001,
-  "cont_bound_mode": "none",
-  "cont_bound_strength": 2.0,
-  "cont_post_calibrate": true,
-  "cont_post_scale": {},
-  "full_stats": true,
-  "shuffle_buffer": 1024,
-  "use_temporal_stage1": true,
-  "temporal_backbone": "transformer",
-  "temporal_hidden_dim": 512,
-  "temporal_num_layers": 2,
-  "temporal_dropout": 0.0,
-  "temporal_pos_dim": 64,
-  "temporal_use_pos_embed": true,
-  "temporal_transformer_num_layers": 2,
-  "temporal_transformer_nhead": 4,
-  "temporal_transformer_ff_dim": 512,
-  "temporal_transformer_dropout": 0.1,
-  "temporal_epochs": 5,
-  "temporal_lr": 0.0005,
-  "sample_batch_size": 4,
-  "sample_seq_len": 128
-}

example/controller_stats.py

@@ -1,115 +0,0 @@
-#!/usr/bin/env python3
-"""Stats for controller-like outputs (Type 2)."""
-
-import argparse
-import csv
-import gzip
-import json
-from pathlib import Path
-from typing import Dict, List
-
-
-def parse_args():
-    base_dir = Path(__file__).resolve().parent
-    parser = argparse.ArgumentParser(description="Controller output stats.")
-    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--reference", default=str(base_dir / "config.json"))
-    parser.add_argument("--features", default="", help="comma-separated list")
-    parser.add_argument("--config", default=str(base_dir / "config.json"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "controller_stats.json"))
-    parser.add_argument("--max-rows", type=int, default=200000)
-    return parser.parse_args()
-
-
-def resolve_reference_glob(ref_arg: str) -> str:
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json":
-        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
-        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not data_glob:
-            raise SystemExit("reference config has no data_glob/data_path")
-        combined = ref_path.parent / data_glob
-        if "*" in str(combined) or "?" in str(combined):
-            return str(combined)
-        return str(combined.resolve())
-    return str(ref_path)
-
-
-def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
-    vals = {c: [] for c in cols}
-    opener = gzip.open if str(path).endswith(".gz") else open
-    with opener(path, "rt", newline="") as fh:
-        reader = csv.DictReader(fh)
-        for i, row in enumerate(reader):
-            for c in cols:
-                try:
-                    vals[c].append(float(row[c]))
-                except Exception:
-                    pass
-            if max_rows > 0 and i + 1 >= max_rows:
-                break
-    return vals
-
-
-def stats(series: List[float], vmin: float, vmax: float):
-    if not series:
-        return {"saturation_ratio": None, "change_rate": None, "step_median": None}
-    # saturation ratio near bounds (1% of range)
-    rng = vmax - vmin
-    tol = 0.01 * rng if rng > 0 else 0.0
-    sat = sum(1 for v in series if v <= vmin + tol or v >= vmax - tol) / len(series)
-    # change rate
-    changes = 0
-    steps = []
-    prev = series[0]
-    for v in series[1:]:
-        if v != prev:
-            changes += 1
-            steps.append(abs(v - prev))
-        prev = v
-    change_rate = changes / max(len(series) - 1, 1)
-    steps.sort()
-    step_median = steps[len(steps) // 2] if steps else None
-    return {"saturation_ratio": sat, "change_rate": change_rate, "step_median": step_median}
-
-
-def main():
-    args = parse_args()
-    features = [f.strip() for f in args.features.split(",") if f.strip()]
-    if not features and Path(args.config).exists():
-        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
-        features = cfg.get("type2_features", []) or []
-    if not features:
-        raise SystemExit("no features specified for controller_stats")
-
-    # generated
-    gen_vals = read_series(Path(args.generated), features, args.max_rows)
-
-    # reference
-    ref_glob = resolve_reference_glob(args.reference)
-    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
-    if not ref_paths:
-        raise SystemExit(f"no reference files matched: {ref_glob}")
-    real_vals = {c: [] for c in features}
-    for p in ref_paths:
-        vals = read_series(p, features, args.max_rows)
-        for c in features:
-            real_vals[c].extend(vals[c])
-
-    out = {"features": features, "generated": {}, "reference": {}}
-    for c in features:
-        rv = real_vals[c]
-        if not rv:
-            continue
-        vmin, vmax = min(rv), max(rv)
-        out["generated"][c] = stats(gen_vals[c], vmin, vmax)
-        out["reference"][c] = stats(rv, vmin, vmax)
-
-    out_path = Path(args.out)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
-    print("wrote", out_path)
-
-
-if __name__ == "__main__":
-    main()

example/data_utils.py

@@ -138,7 +138,6 @@ def compute_cont_stats(
     cont_cols: List[str],
     max_rows: Optional[int] = None,
     transforms: Optional[Dict[str, str]] = None,
-    quantile_bins: Optional[int] = None,
 ):
     """Compute stats on (optionally transformed) values. Returns raw + transformed stats."""
     # First pass (raw) for metadata and raw mean/std
@@ -148,26 +147,20 @@ def compute_cont_stats(
     if transforms is None:
         transforms = {c: "none" for c in cont_cols}
 
-    # Second pass for transformed mean/std (and optional quantiles)
+    # Second pass for transformed mean/std
     count = {c: 0 for c in cont_cols}
     mean = {c: 0.0 for c in cont_cols}
     m2 = {c: 0.0 for c in cont_cols}
-    quantile_values = {c: [] for c in cont_cols} if quantile_bins and quantile_bins > 1 else None
-    raw_quantile_values = {c: [] for c in cont_cols} if quantile_bins and quantile_bins > 1 else None
     for i, row in enumerate(iter_rows(path)):
         for c in cont_cols:
             raw_val = row[c]
             if raw_val is None or raw_val == "":
                 continue
             x = float(raw_val)
-            if raw_quantile_values is not None:
-                raw_quantile_values[c].append(x)
             if transforms.get(c) == "log1p":
                 if x < 0:
                     x = 0.0
                 x = math.log1p(x)
-            if quantile_values is not None:
-                quantile_values[c].append(x)
             n = count[c] + 1
             delta = x - mean[c]
             mean[c] += delta / n
@@ -185,39 +178,6 @@ def compute_cont_stats(
             var = 0.0
         std[c] = var ** 0.5 if var > 0 else 1.0
 
-    quantile_probs = None
-    quantile_table = None
-    raw_quantile_table = None
-    if quantile_values is not None:
-        quantile_probs = [i / (quantile_bins - 1) for i in range(quantile_bins)]
-        quantile_table = {}
-        raw_quantile_table = {}
-        for c in cont_cols:
-            vals = quantile_values[c]
-            if not vals:
-                quantile_table[c] = [0.0 for _ in quantile_probs]
-            else:
-                vals.sort()
-                n = len(vals)
-                qvals = []
-                for p in quantile_probs:
-                    idx = int(round(p * (n - 1)))
-                    idx = max(0, min(n - 1, idx))
-                    qvals.append(float(vals[idx]))
-                quantile_table[c] = qvals
-            raw_vals = raw_quantile_values[c] if raw_quantile_values is not None else []
-            if not raw_vals:
-                raw_quantile_table[c] = [0.0 for _ in quantile_probs]
-                continue
-            raw_vals.sort()
-            n = len(raw_vals)
-            rqvals = []
-            for p in quantile_probs:
-                idx = int(round(p * (n - 1)))
-                idx = max(0, min(n - 1, idx))
-                rqvals.append(float(raw_vals[idx]))
-            raw_quantile_table[c] = rqvals
-
     return {
         "mean": mean,
         "std": std,
@@ -231,9 +191,6 @@ def compute_cont_stats(
         "skew": raw["skew"],
         "all_pos": raw["all_pos"],
         "max_rows": max_rows,
-        "quantile_probs": quantile_probs,
-        "quantile_values": quantile_table,
-        "quantile_raw_values": raw_quantile_table,
     }
 
 
@@ -292,9 +249,6 @@ def normalize_cont(
     mean: Dict[str, float],
     std: Dict[str, float],
     transforms: Optional[Dict[str, str]] = None,
-    quantile_probs: Optional[List[float]] = None,
-    quantile_values: Optional[Dict[str, List[float]]] = None,
-    use_quantile: bool = False,
 ):
     import torch
 
@@ -302,95 +256,11 @@ def normalize_cont(
         for i, c in enumerate(cont_cols):
             if transforms.get(c) == "log1p":
                 x[:, :, i] = torch.log1p(torch.clamp(x[:, :, i], min=0))
-    if use_quantile:
-        if not quantile_probs or not quantile_values:
-            raise ValueError("use_quantile_transform enabled but quantile stats missing")
-        x = apply_quantile_transform(x, cont_cols, quantile_probs, quantile_values)
-        # quantile transform already targets N(0,1); skip extra standardization
-        return x
     mean_t = torch.tensor([mean[c] for c in cont_cols], dtype=x.dtype, device=x.device)
     std_t = torch.tensor([std[c] for c in cont_cols], dtype=x.dtype, device=x.device)
     return (x - mean_t) / std_t
 
 
-def _normal_cdf(x):
-    import torch
-    return 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
-
-
-def _normal_ppf(p):
-    import torch
-    eps = 1e-6
-    p = torch.clamp(p, eps, 1.0 - eps)
-    return math.sqrt(2.0) * torch.erfinv(2.0 * p - 1.0)
-
-
-def apply_quantile_transform(x, cont_cols, quantile_probs, quantile_values):
-    import torch
-    probs_t = torch.tensor(quantile_probs, dtype=x.dtype, device=x.device)
-    for i, c in enumerate(cont_cols):
-        q_vals = torch.tensor(quantile_values[c], dtype=x.dtype, device=x.device)
-        v = x[:, :, i]
-        idx = torch.bucketize(v, q_vals)
-        idx = torch.clamp(idx, 1, q_vals.numel() - 1)
-        x0 = q_vals[idx - 1]
-        x1 = q_vals[idx]
-        p0 = probs_t[idx - 1]
-        p1 = probs_t[idx]
-        denom = torch.where((x1 - x0) == 0, torch.ones_like(x1 - x0), (x1 - x0))
-        p = p0 + (v - x0) * (p1 - p0) / denom
-        x[:, :, i] = _normal_ppf(p)
-    return x
-
-
-def inverse_quantile_transform(x, cont_cols, quantile_probs, quantile_values):
-    import torch
-    probs_t = torch.tensor(quantile_probs, dtype=x.dtype, device=x.device)
-    for i, c in enumerate(cont_cols):
-        q_vals = torch.tensor(quantile_values[c], dtype=x.dtype, device=x.device)
-        z = x[:, :, i]
-        p = _normal_cdf(z)
-        idx = torch.bucketize(p, probs_t)
-        idx = torch.clamp(idx, 1, probs_t.numel() - 1)
-        p0 = probs_t[idx - 1]
-        p1 = probs_t[idx]
-        x0 = q_vals[idx - 1]
-        x1 = q_vals[idx]
-        denom = torch.where((p1 - p0) == 0, torch.ones_like(p1 - p0), (p1 - p0))
-        v = x0 + (p - p0) * (x1 - x0) / denom
-        x[:, :, i] = v
-    return x
-
-
-def quantile_calibrate_to_real(x, cont_cols, quantile_probs, real_quantile_values):
-    import torch
-    probs_t = torch.tensor(quantile_probs, dtype=x.dtype, device=x.device)
-    flat = x.reshape(-1, x.size(-1))
-    for i, c in enumerate(cont_cols):
-        v = flat[:, i]
-        gen_q = torch.quantile(v, probs_t)
-        idx = torch.bucketize(v, gen_q)
-        idx = torch.clamp(idx, 1, gen_q.numel() - 1)
-        x0 = gen_q[idx - 1]
-        x1 = gen_q[idx]
-        p0 = probs_t[idx - 1]
-        p1 = probs_t[idx]
-        denom = torch.where((x1 - x0) == 0, torch.ones_like(x1 - x0), (x1 - x0))
-        p = p0 + (v - x0) * (p1 - p0) / denom
-
-        real_q = torch.tensor(real_quantile_values[c], dtype=x.dtype, device=x.device)
-        idx2 = torch.bucketize(p, probs_t)
-        idx2 = torch.clamp(idx2, 1, probs_t.numel() - 1)
-        rp0 = probs_t[idx2 - 1]
-        rp1 = probs_t[idx2]
-        r0 = real_q[idx2 - 1]
-        r1 = real_q[idx2]
-        denom2 = torch.where((rp1 - rp0) == 0, torch.ones_like(rp1 - rp0), (rp1 - rp0))
-        v2 = r0 + (p - rp0) * (r1 - r0) / denom2
-        flat[:, i] = v2
-    return flat.reshape(x.shape)
-
-
 def windowed_batches(
     path: Union[str, List[str]],
     cont_cols: List[str],
@@ -403,9 +273,6 @@ def windowed_batches(
     max_batches: Optional[int] = None,
     return_file_id: bool = False,
     transforms: Optional[Dict[str, str]] = None,
-    quantile_probs: Optional[List[float]] = None,
-    quantile_values: Optional[Dict[str, List[float]]] = None,
-    use_quantile: bool = False,
     shuffle_buffer: int = 0,
 ):
     import torch
@@ -449,16 +316,7 @@ def windowed_batches(
                 if len(batch_cont) == batch_size:
                     x_cont = torch.tensor(batch_cont, dtype=torch.float32)
                     x_disc = torch.tensor(batch_disc, dtype=torch.long)
-                    x_cont = normalize_cont(
-                        x_cont,
-                        cont_cols,
-                        mean,
-                        std,
-                        transforms=transforms,
-                        quantile_probs=quantile_probs,
-                        quantile_values=quantile_values,
-                        use_quantile=use_quantile,
-                    )
+                    x_cont = normalize_cont(x_cont, cont_cols, mean, std, transforms=transforms)
                     if return_file_id:
                         x_file = torch.tensor(batch_file, dtype=torch.long)
                         yield x_cont, x_disc, x_file
@@ -486,16 +344,7 @@ def windowed_batches(
                 import torch
                 x_cont = torch.tensor(batch_cont, dtype=torch.float32)
                 x_disc = torch.tensor(batch_disc, dtype=torch.long)
-                x_cont = normalize_cont(
-                    x_cont,
-                    cont_cols,
-                    mean,
-                    std,
-                    transforms=transforms,
-                    quantile_probs=quantile_probs,
-                    quantile_values=quantile_values,
-                    use_quantile=use_quantile,
-                )
+                x_cont = normalize_cont(x_cont, cont_cols, mean, std, transforms=transforms)
                 if return_file_id:
                     x_file = torch.tensor(batch_file, dtype=torch.long)
                     yield x_cont, x_disc, x_file

example/diagnose_ks.py

@@ -1,230 +0,0 @@
-#!/usr/bin/env python3
-"""Per-feature KS diagnostics and CDF visualization (no third-party deps)."""
-
-import argparse
-import csv
-import gzip
-import json
-import math
-from pathlib import Path
-from glob import glob
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Per-feature KS diagnostics.")
-    base_dir = Path(__file__).resolve().parent
-    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--reference", default=str(base_dir / "config.json"))
-    parser.add_argument("--out-dir", default=str(base_dir / "results"))
-    parser.add_argument("--max-rows", type=int, default=200000, help="<=0 for full scan")
-    parser.add_argument("--stride", type=int, default=1, help="row stride sampling")
-    parser.add_argument("--top-k", type=int, default=8)
-    return parser.parse_args()
-
-
-def load_split(base_dir: Path):
-    with open(base_dir / "feature_split.json", "r", encoding="utf-8") as f:
-        split = json.load(f)
-    time_col = split.get("time_column", "time")
-    cont_cols = [c for c in split["continuous"] if c != time_col]
-    return cont_cols
-
-
-def resolve_reference_glob(base_dir: Path, ref_arg: str):
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json":
-        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
-        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not data_glob:
-            raise SystemExit("reference config has no data_glob/data_path")
-        combined = ref_path.parent / data_glob
-        # On Windows, Path.resolve fails on glob patterns like *.csv.gz
-        if "*" in str(combined) or "?" in str(combined):
-            return str(combined)
-        return str(combined.resolve())
-    return str(ref_path)
-
-
-def read_csv_values(path: Path, cols, max_rows=200000, stride=1, gz=True):
-    values = {c: [] for c in cols}
-    row_count = 0
-    reader = None
-    if gz:
-        fh = gzip.open(path, "rt", newline="")
-    else:
-        fh = open(path, "r", newline="", encoding="utf-8")
-    try:
-        reader = csv.DictReader(fh)
-        for i, row in enumerate(reader):
-            if stride > 1 and i % stride != 0:
-                continue
-            for c in cols:
-                v = row.get(c, "")
-                try:
-                    fv = float(v)
-                    if math.isfinite(fv):
-                        values[c].append(fv)
-                except Exception:
-                    continue
-            row_count += 1
-            if max_rows > 0 and row_count >= max_rows:
-                break
-    finally:
-        fh.close()
-    return values, row_count
-
-
-def ks_statistic(a, b):
-    if not a or not b:
-        return 1.0
-    a = sorted(a)
-    b = sorted(b)
-    na = len(a)
-    nb = len(b)
-    i = j = 0
-    d = 0.0
-    while i < na and j < nb:
-        if a[i] <= b[j]:
-            i += 1
-        else:
-            j += 1
-        fa = i / na
-        fb = j / nb
-        d = max(d, abs(fa - fb))
-    return d
-
-
-def ecdf_points(vals):
-    vals = sorted(vals)
-    n = len(vals)
-    if n == 0:
-        return [], []
-    xs = []
-    ys = []
-    last = None
-    for i, v in enumerate(vals, 1):
-        if last is None or v != last:
-            xs.append(v)
-            ys.append(i / n)
-            last = v
-        else:
-            ys[-1] = i / n
-    return xs, ys
-
-
-def render_cdf_svg(out_path: Path, feature, real_vals, gen_vals, bounds=None):
-    width, height = 900, 420
-    pad = 50
-    panel_w = width - pad * 2
-    panel_h = height - pad * 2
-    if not real_vals or not gen_vals:
-        return
-    min_v = min(min(real_vals), min(gen_vals))
-    max_v = max(max(real_vals), max(gen_vals))
-    if max_v == min_v:
-        max_v += 1.0
-    rx, ry = ecdf_points(real_vals)
-    gx, gy = ecdf_points(gen_vals)
-
-    def sx(v):
-        return pad + int((v - min_v) * panel_w / (max_v - min_v))
-
-    def sy(v):
-        return pad + panel_h - int(v * panel_h)
-
-    svg = []
-    svg.append(f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">')
-    svg.append('<style>text{font-family:Arial,sans-serif;font-size:12px}</style>')
-    svg.append(f'<text x="{pad}" y="{pad-20}">CDF 비교: {feature}</text>')
-    svg.append(f'<line x1="{pad}" y1="{pad}" x2="{pad}" y2="{pad+panel_h}" stroke="#333"/>')
-    svg.append(f'<line x1="{pad}" y1="{pad+panel_h}" x2="{pad+panel_w}" y2="{pad+panel_h}" stroke="#333"/>')
-
-    def path_from(xs, ys, color):
-        pts = " ".join(f"{sx(x)},{sy(y)}" for x, y in zip(xs, ys))
-        return f'<polyline fill="none" stroke="{color}" stroke-width="2" points="{pts}"/>'
-
-    svg.append(path_from(rx, ry, "#1f77b4"))  # real
-    svg.append(path_from(gx, gy, "#d62728"))  # gen
-    svg.append(f'<text x="{pad+panel_w-120}" y="{pad+15}" fill="#1f77b4">real</text>')
-    svg.append(f'<text x="{pad+panel_w-120}" y="{pad+30}" fill="#d62728">generated</text>')
-
-    if bounds is not None:
-        lo, hi = bounds
-        svg.append(f'<line x1="{sx(lo)}" y1="{pad}" x2="{sx(lo)}" y2="{pad+panel_h}" stroke="#999" stroke-dasharray="4 3"/>')
-        svg.append(f'<line x1="{sx(hi)}" y1="{pad}" x2="{sx(hi)}" y2="{pad+panel_h}" stroke="#999" stroke-dasharray="4 3"/>')
-
-    svg.append("</svg>")
-    out_path.write_text("\n".join(svg), encoding="utf-8")
-
-
-def main():
-    args = parse_args()
-    base_dir = Path(__file__).resolve().parent
-    out_dir = Path(args.out_dir)
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    cont_cols = load_split(base_dir)
-    ref_glob = resolve_reference_glob(base_dir, args.reference)
-    ref_files = sorted(glob(ref_glob))
-    if not ref_files:
-        raise SystemExit(f"no reference files matched: {ref_glob}")
-
-    gen_path = Path(args.generated)
-    gen_vals, gen_rows = read_csv_values(gen_path, cont_cols, args.max_rows, args.stride, gz=False)
-
-    # Reference values (aggregate across files)
-    real_vals = {c: [] for c in cont_cols}
-    total_rows = 0
-    for f in ref_files:
-        vals, rows = read_csv_values(Path(f), cont_cols, args.max_rows, args.stride, gz=True)
-        total_rows += rows
-        for c in cont_cols:
-            real_vals[c].extend(vals[c])
-
-    # KS per feature
-    ks_rows = []
-    for c in cont_cols:
-        ks = ks_statistic(real_vals[c], gen_vals[c])
-        # boundary pile-up (using real min/max)
-        if real_vals[c]:
-            lo = min(real_vals[c])
-            hi = max(real_vals[c])
-        else:
-            lo = hi = 0.0
-        tol = (hi - lo) * 1e-4 if hi > lo else 1e-6
-        gen = gen_vals[c]
-        if gen:
-            frac_lo = sum(1 for v in gen if abs(v - lo) <= tol) / len(gen)
-            frac_hi = sum(1 for v in gen if abs(v - hi) <= tol) / len(gen)
-        else:
-            frac_lo = frac_hi = 0.0
-        ks_rows.append((c, ks, frac_lo, frac_hi, len(real_vals[c]), len(gen_vals[c]), lo, hi))
-
-    ks_rows.sort(key=lambda x: x[1], reverse=True)
-    out_csv = out_dir / "ks_per_feature.csv"
-    with out_csv.open("w", newline="") as fh:
-        w = csv.writer(fh)
-        w.writerow(["feature", "ks", "gen_frac_at_min", "gen_frac_at_max", "real_n", "gen_n", "real_min", "real_max"])
-        for row in ks_rows:
-            w.writerow(row)
-
-    # top-k CDF plots
-    for c, ks, _, _, _, _, lo, hi in ks_rows[: args.top_k]:
-        out_svg = out_dir / f"cdf_{c}.svg"
-        render_cdf_svg(out_svg, c, real_vals[c], gen_vals[c], bounds=(lo, hi))
-
-    # summary
-    summary = {
-        "generated_rows": gen_rows,
-        "reference_rows_per_file": args.max_rows if args.max_rows > 0 else "full",
-        "stride": args.stride,
-        "top_k_features": [r[0] for r in ks_rows[: args.top_k]],
-    }
-    (out_dir / "ks_summary.json").write_text(json.dumps(summary, indent=2), encoding="utf-8")
-
-    print(f"wrote {out_csv}")
-    print(f"wrote CDF svgs for top {args.top_k} features")
-
-
-if __name__ == "__main__":
-    main()

example/eval_utils.py

@@ -1,568 +0,0 @@
-#!/usr/bin/env python3
-"""Shared utilities for evaluation, downstream utility, and ablations."""
-
-from __future__ import annotations
-
-import csv
-import gzip
-import json
-import math
-import random
-from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Sequence, Tuple
-
-import numpy as np
-
-
-def load_json(path: str | Path) -> Dict:
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def open_csv(path: str | Path):
-    path = str(path)
-    if path.endswith(".gz"):
-        return gzip.open(path, "rt", newline="")
-    return open(path, "r", newline="")
-
-
-def resolve_path(base_dir: Path, path_like: str | Path) -> Path:
-    path = Path(path_like)
-    if path.is_absolute():
-        return path
-    return (base_dir / path).resolve()
-
-
-def resolve_reference_paths(ref_arg: str | Path) -> List[str]:
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json" and ref_path.exists():
-        cfg = load_json(ref_path)
-        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not data_glob:
-            raise SystemExit("reference config has no data_glob/data_path")
-        combined = ref_path.parent / data_glob
-        return expand_glob_or_file(combined)
-    return expand_glob_or_file(ref_path)
-
-
-def infer_test_paths(ref_arg: str | Path) -> List[str]:
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json" and ref_path.exists():
-        cfg = load_json(ref_path)
-        cfg_base = ref_path.parent
-        explicit = cfg.get("test_glob") or cfg.get("test_path") or ""
-        if explicit:
-            return expand_glob_or_file(cfg_base / explicit)
-        train_ref = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not train_ref:
-            raise SystemExit("reference config has no data_glob/data_path")
-        return infer_test_paths(cfg_base / train_ref)
-
-    path = Path(ref_arg)
-    text = str(path)
-    candidates: List[Path] = []
-    if any(ch in text for ch in ["*", "?", "["]):
-        parent = path.parent if path.parent != Path("") else Path(".")
-        name = path.name
-        if "train" in name:
-            candidates.append(parent / name.replace("train", "test"))
-            candidates.append(parent / name.replace("TRAIN", "TEST"))
-        candidates.append(parent / "test*.csv.gz")
-        candidates.append(parent / "test*.csv")
-    else:
-        parent = path.parent if path.parent != Path("") else Path(".")
-        name = path.name
-        if "train" in name:
-            candidates.append(parent / name.replace("train", "test"))
-            candidates.append(parent / name.replace("TRAIN", "TEST"))
-        candidates.append(parent / "test*.csv.gz")
-        candidates.append(parent / "test*.csv")
-
-    for candidate in candidates:
-        matches = expand_glob_or_file(candidate)
-        if matches:
-            return matches
-    raise SystemExit(f"could not infer test files from reference: {ref_arg}")
-
-
-def expand_glob_or_file(path_like: str | Path) -> List[str]:
-    path = Path(path_like)
-    text = str(path)
-    if any(ch in text for ch in ["*", "?", "["]):
-        parent = path.parent if path.parent != Path("") else Path(".")
-        matches = sorted(parent.glob(path.name))
-        return [str(p.resolve()) for p in matches]
-    if path.exists():
-        return [str(path.resolve())]
-    return []
-
-
-def load_split_columns(split_path: str | Path) -> Tuple[str, List[str], List[str], List[str]]:
-    split = load_json(split_path)
-    time_col = split.get("time_column", "time")
-    cont_cols = [c for c in split["continuous"] if c != time_col]
-    disc_all = [c for c in split["discrete"] if c != time_col]
-    label_cols = [c for c in disc_all if c.lower().startswith("attack")]
-    disc_cols = [c for c in disc_all if c not in label_cols]
-    return time_col, cont_cols, disc_cols, label_cols
-
-
-def load_vocab(vocab_path: str | Path, disc_cols: Sequence[str]) -> Tuple[Dict[str, Dict[str, int]], List[int]]:
-    vocab = load_json(vocab_path)["vocab"]
-    vocab_sizes = [len(vocab[c]) for c in disc_cols]
-    return vocab, vocab_sizes
-
-
-def load_stats_vectors(stats_path: str | Path, cont_cols: Sequence[str]) -> Tuple[np.ndarray, np.ndarray]:
-    stats = load_json(stats_path)
-    mean = stats.get("raw_mean", stats["mean"])
-    std = stats.get("raw_std", stats["std"])
-    mean_vec = np.asarray([float(mean[c]) for c in cont_cols], dtype=np.float32)
-    std_vec = np.asarray([max(float(std[c]), 1e-6) for c in cont_cols], dtype=np.float32)
-    return mean_vec, std_vec
-
-
-def load_rows(
-    path: str | Path,
-    cont_cols: Sequence[str],
-    disc_cols: Sequence[str],
-    label_cols: Optional[Sequence[str]] = None,
-    vocab: Optional[Dict[str, Dict[str, int]]] = None,
-    max_rows: Optional[int] = None,
-) -> Tuple[np.ndarray, np.ndarray, Optional[np.ndarray]]:
-    cont_rows: List[List[float]] = []
-    disc_rows: List[List[int]] = []
-    label_rows: List[int] = []
-    label_cols = list(label_cols or [])
-
-    with open_csv(path) as f:
-        reader = csv.DictReader(f)
-        for idx, row in enumerate(reader):
-            cont_rows.append([float(row[c]) for c in cont_cols])
-            if disc_cols:
-                if vocab is None:
-                    disc_rows.append([int(float(row[c])) for c in disc_cols])
-                else:
-                    encoded = []
-                    for c in disc_cols:
-                        mapping = vocab[c]
-                        encoded.append(mapping.get(row.get(c, ""), mapping.get("<UNK>", 0)))
-                    disc_rows.append(encoded)
-            if label_cols:
-                label = 0
-                for c in label_cols:
-                    try:
-                        label = max(label, int(float(row.get(c, 0) or 0)))
-                    except Exception:
-                        continue
-                label_rows.append(label)
-            if max_rows is not None and idx + 1 >= max_rows:
-                break
-
-    cont = np.asarray(cont_rows, dtype=np.float32) if cont_rows else np.zeros((0, len(cont_cols)), dtype=np.float32)
-    disc = np.asarray(disc_rows, dtype=np.int64) if disc_rows else np.zeros((0, len(disc_cols)), dtype=np.int64)
-    labels = None
-    if label_cols:
-        labels = np.asarray(label_rows, dtype=np.int64)
-    return cont, disc, labels
-
-
-def window_array(
-    cont: np.ndarray,
-    disc: np.ndarray,
-    labels: Optional[np.ndarray],
-    seq_len: int,
-    stride: Optional[int] = None,
-    max_windows: Optional[int] = None,
-) -> Tuple[np.ndarray, np.ndarray, Optional[np.ndarray]]:
-    if stride is None or stride <= 0:
-        stride = seq_len
-    cont_windows: List[np.ndarray] = []
-    disc_windows: List[np.ndarray] = []
-    label_windows: List[int] = []
-    if cont.shape[0] < seq_len:
-        return (
-            np.zeros((0, seq_len, cont.shape[1]), dtype=np.float32),
-            np.zeros((0, seq_len, disc.shape[1]), dtype=np.int64),
-            np.zeros((0,), dtype=np.int64) if labels is not None else None,
-        )
-
-    count = 0
-    for start in range(0, cont.shape[0] - seq_len + 1, stride):
-        end = start + seq_len
-        cont_windows.append(cont[start:end])
-        disc_windows.append(disc[start:end])
-        if labels is not None:
-            label_windows.append(int(labels[start:end].max()))
-        count += 1
-        if max_windows is not None and count >= max_windows:
-            break
-
-    cont_out = np.asarray(cont_windows, dtype=np.float32)
-    disc_out = np.asarray(disc_windows, dtype=np.int64)
-    label_out = np.asarray(label_windows, dtype=np.int64) if labels is not None else None
-    return cont_out, disc_out, label_out
-
-
-def load_windows_from_paths(
-    paths: Sequence[str],
-    cont_cols: Sequence[str],
-    disc_cols: Sequence[str],
-    seq_len: int,
-    vocab: Optional[Dict[str, Dict[str, int]]] = None,
-    label_cols: Optional[Sequence[str]] = None,
-    stride: Optional[int] = None,
-    max_windows: Optional[int] = None,
-    max_rows_per_file: Optional[int] = None,
-) -> Tuple[np.ndarray, np.ndarray, Optional[np.ndarray]]:
-    cont_all: List[np.ndarray] = []
-    disc_all: List[np.ndarray] = []
-    label_all: List[np.ndarray] = []
-    total = 0
-
-    for path in paths:
-        remaining = None if max_windows is None else max(0, max_windows - total)
-        if remaining == 0:
-            break
-        cont, disc, labels = load_rows(
-            path,
-            cont_cols,
-            disc_cols,
-            label_cols=label_cols,
-            vocab=vocab,
-            max_rows=max_rows_per_file,
-        )
-        w_cont, w_disc, w_labels = window_array(
-            cont,
-            disc,
-            labels,
-            seq_len=seq_len,
-            stride=stride,
-            max_windows=remaining,
-        )
-        if w_cont.size == 0:
-            continue
-        cont_all.append(w_cont)
-        disc_all.append(w_disc)
-        if w_labels is not None:
-            label_all.append(w_labels)
-        total += w_cont.shape[0]
-
-    if not cont_all:
-        empty_cont = np.zeros((0, seq_len, len(cont_cols)), dtype=np.float32)
-        empty_disc = np.zeros((0, seq_len, len(disc_cols)), dtype=np.int64)
-        empty_labels = np.zeros((0,), dtype=np.int64) if label_cols else None
-        return empty_cont, empty_disc, empty_labels
-
-    cont_out = np.concatenate(cont_all, axis=0)
-    disc_out = np.concatenate(disc_all, axis=0)
-    label_out = np.concatenate(label_all, axis=0) if label_all else None
-    return cont_out, disc_out, label_out
-
-
-def filter_windows_by_label(
-    cont_windows: np.ndarray,
-    disc_windows: np.ndarray,
-    labels: Optional[np.ndarray],
-    target_label: int,
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-    if labels is None:
-        raise ValueError("labels are required for label filtering")
-    mask = labels == int(target_label)
-    return cont_windows[mask], disc_windows[mask], labels[mask]
-
-
-def standardize_cont_windows(
-    cont_windows: np.ndarray,
-    mean_vec: np.ndarray,
-    std_vec: np.ndarray,
-) -> np.ndarray:
-    return (cont_windows - mean_vec.reshape(1, 1, -1)) / std_vec.reshape(1, 1, -1)
-
-
-def build_flat_window_vectors(
-    cont_windows: np.ndarray,
-    disc_windows: np.ndarray,
-    mean_vec: np.ndarray,
-    std_vec: np.ndarray,
-    vocab_sizes: Sequence[int],
-) -> np.ndarray:
-    cont_norm = standardize_cont_windows(cont_windows, mean_vec, std_vec).reshape(cont_windows.shape[0], -1)
-    if disc_windows.size == 0:
-        return cont_norm.astype(np.float32)
-    disc_scale = np.asarray([max(v - 1, 1) for v in vocab_sizes], dtype=np.float32).reshape(1, 1, -1)
-    disc_norm = (disc_windows.astype(np.float32) / disc_scale).reshape(disc_windows.shape[0], -1)
-    return np.concatenate([cont_norm, disc_norm], axis=1).astype(np.float32)
-
-
-def build_histogram_embeddings(
-    cont_windows: np.ndarray,
-    disc_windows: np.ndarray,
-    mean_vec: np.ndarray,
-    std_vec: np.ndarray,
-    vocab_sizes: Sequence[int],
-) -> np.ndarray:
-    cont_norm = standardize_cont_windows(cont_windows, mean_vec, std_vec).reshape(cont_windows.shape[0], -1)
-    if disc_windows.size == 0:
-        return cont_norm.astype(np.float32)
-    hist_features: List[np.ndarray] = []
-    for disc_idx, vocab_size in enumerate(vocab_sizes):
-        one_hist = np.zeros((disc_windows.shape[0], vocab_size), dtype=np.float32)
-        col_values = disc_windows[:, :, disc_idx]
-        for value in range(vocab_size):
-            one_hist[:, value] = (col_values == value).mean(axis=1)
-        hist_features.append(one_hist)
-    disc_hist = np.concatenate(hist_features, axis=1) if hist_features else np.zeros((cont_windows.shape[0], 0), dtype=np.float32)
-    return np.concatenate([cont_norm, disc_hist], axis=1).astype(np.float32)
-
-
-def sample_indices(n_items: int, max_items: Optional[int], seed: int) -> np.ndarray:
-    if max_items is None or n_items <= max_items:
-        return np.arange(n_items, dtype=np.int64)
-    rng = np.random.default_rng(seed)
-    return np.sort(rng.choice(n_items, size=max_items, replace=False))
-
-
-def subset_by_indices(array: np.ndarray, indices: np.ndarray) -> np.ndarray:
-    if array is None:
-        return array
-    return array[indices]
-
-
-def compute_corr_matrix(rows: np.ndarray) -> np.ndarray:
-    if rows.shape[0] < 2:
-        return np.zeros((rows.shape[1], rows.shape[1]), dtype=np.float32)
-    matrix = np.corrcoef(rows, rowvar=False)
-    matrix = np.nan_to_num(matrix, nan=0.0, posinf=0.0, neginf=0.0)
-    return matrix.astype(np.float32)
-
-
-def compute_lagged_corr_matrix(rows: np.ndarray, lag: int = 1) -> np.ndarray:
-    if rows.shape[0] <= lag:
-        return np.zeros((rows.shape[1], rows.shape[1]), dtype=np.float32)
-    x = rows[:-lag]
-    y = rows[lag:]
-    x = x - x.mean(axis=0, keepdims=True)
-    y = y - y.mean(axis=0, keepdims=True)
-    cov = x.T @ y / max(x.shape[0] - 1, 1)
-    std_x = np.sqrt(np.maximum((x ** 2).sum(axis=0) / max(x.shape[0] - 1, 1), 1e-8))
-    std_y = np.sqrt(np.maximum((y ** 2).sum(axis=0) / max(y.shape[0] - 1, 1), 1e-8))
-    denom = np.outer(std_x, std_y)
-    corr = cov / np.maximum(denom, 1e-8)
-    return np.nan_to_num(corr, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32)
-
-
-def split_process_groups(feature_names: Sequence[str]) -> Dict[str, List[int]]:
-    groups: Dict[str, List[int]] = {}
-    for idx, name in enumerate(feature_names):
-        prefix = name.split("_", 1)[0]
-        groups.setdefault(prefix, []).append(idx)
-    return groups
-
-
-def compute_average_psd(cont_windows: np.ndarray) -> np.ndarray:
-    if cont_windows.size == 0:
-        return np.zeros((0, 0), dtype=np.float32)
-    centered = cont_windows - cont_windows.mean(axis=1, keepdims=True)
-    spectrum = np.fft.rfft(centered, axis=1)
-    power = (np.abs(spectrum) ** 2).mean(axis=0).T
-    power = power.astype(np.float32)
-    norm = power.sum(axis=1, keepdims=True)
-    norm[norm <= 0] = 1.0
-    return power / norm
-
-
-def psd_distance_stats(real_psd: np.ndarray, gen_psd: np.ndarray) -> Dict[str, float]:
-    if real_psd.size == 0 or gen_psd.size == 0:
-        return {
-            "avg_psd_l1": float("nan"),
-            "avg_psd_cosine": float("nan"),
-            "avg_low_high_ratio_abs_diff": float("nan"),
-        }
-    l1 = np.abs(real_psd - gen_psd).mean(axis=1)
-    cosine = []
-    ratio_diffs = []
-    n_freq = real_psd.shape[1]
-    split = max(1, n_freq // 4)
-    for i in range(real_psd.shape[0]):
-        rv = real_psd[i]
-        gv = gen_psd[i]
-        denom = max(np.linalg.norm(rv) * np.linalg.norm(gv), 1e-8)
-        cosine.append(1.0 - float(np.dot(rv, gv) / denom))
-        r_low = float(rv[:split].sum())
-        r_high = float(rv[split:].sum())
-        g_low = float(gv[:split].sum())
-        g_high = float(gv[split:].sum())
-        r_ratio = r_low / max(r_high, 1e-8)
-        g_ratio = g_low / max(g_high, 1e-8)
-        ratio_diffs.append(abs(r_ratio - g_ratio))
-    return {
-        "avg_psd_l1": float(np.mean(l1)),
-        "avg_psd_cosine": float(np.mean(cosine)),
-        "avg_low_high_ratio_abs_diff": float(np.mean(ratio_diffs)),
-    }
-
-
-def pairwise_sq_dists(x: np.ndarray, y: np.ndarray) -> np.ndarray:
-    x_norm = (x ** 2).sum(axis=1, keepdims=True)
-    y_norm = (y ** 2).sum(axis=1, keepdims=True).T
-    d2 = x_norm + y_norm - 2.0 * (x @ y.T)
-    return np.maximum(d2, 0.0)
-
-
-def median_heuristic_gamma(x: np.ndarray, y: np.ndarray) -> float:
-    joined = np.concatenate([x, y], axis=0)
-    if joined.shape[0] <= 1:
-        return 1.0
-    d2 = pairwise_sq_dists(joined[: min(joined.shape[0], 256)], joined[: min(joined.shape[0], 256)])
-    upper = d2[np.triu_indices_from(d2, k=1)]
-    upper = upper[upper > 0]
-    if upper.size == 0:
-        return 1.0
-    median = float(np.median(upper))
-    return 1.0 / max(2.0 * median, 1e-8)
-
-
-def rbf_mmd(x: np.ndarray, y: np.ndarray, gamma: Optional[float] = None) -> Tuple[float, float]:
-    if x.shape[0] == 0 or y.shape[0] == 0:
-        return float("nan"), 1.0
-    if gamma is None:
-        gamma = median_heuristic_gamma(x, y)
-    k_xx = np.exp(-gamma * pairwise_sq_dists(x, x))
-    k_yy = np.exp(-gamma * pairwise_sq_dists(y, y))
-    k_xy = np.exp(-gamma * pairwise_sq_dists(x, y))
-    m = max(x.shape[0], 1)
-    n = max(y.shape[0], 1)
-    if m > 1:
-        term_xx = (k_xx.sum() - np.trace(k_xx)) / (m * (m - 1))
-    else:
-        term_xx = 0.0
-    if n > 1:
-        term_yy = (k_yy.sum() - np.trace(k_yy)) / (n * (n - 1))
-    else:
-        term_yy = 0.0
-    term_xy = 2.0 * k_xy.mean()
-    return float(term_xx + term_yy - term_xy), float(gamma)
-
-
-def duplicate_rate(vectors: np.ndarray, decimals: int = 5) -> float:
-    if vectors.shape[0] <= 1:
-        return 0.0
-    rounded = np.round(vectors, decimals=decimals)
-    unique = np.unique(rounded, axis=0).shape[0]
-    return float(1.0 - unique / vectors.shape[0])
-
-
-def exact_match_rate(query: np.ndarray, base: np.ndarray, decimals: int = 5) -> float:
-    if query.shape[0] == 0 or base.shape[0] == 0:
-        return 0.0
-    rounded_base = {tuple(row.tolist()) for row in np.round(base, decimals=decimals)}
-    rounded_query = np.round(query, decimals=decimals)
-    matches = sum(1 for row in rounded_query if tuple(row.tolist()) in rounded_base)
-    return float(matches / query.shape[0])
-
-
-def nearest_neighbor_distance_stats(query: np.ndarray, base: np.ndarray, batch_size: int = 128) -> Dict[str, float]:
-    if query.shape[0] == 0 or base.shape[0] == 0:
-        return {"mean": float("nan"), "median": float("nan"), "min": float("nan")}
-    dists: List[np.ndarray] = []
-    for start in range(0, query.shape[0], batch_size):
-        end = start + batch_size
-        chunk = query[start:end]
-        d2 = pairwise_sq_dists(chunk, base)
-        dists.append(np.sqrt(np.min(d2, axis=1)))
-    values = np.concatenate(dists, axis=0)
-    return {
-        "mean": float(values.mean()),
-        "median": float(np.median(values)),
-        "min": float(values.min()),
-    }
-
-
-def one_nn_two_sample_accuracy(real_vecs: np.ndarray, gen_vecs: np.ndarray) -> float:
-    if real_vecs.shape[0] < 2 or gen_vecs.shape[0] < 2:
-        return float("nan")
-    x = np.concatenate([real_vecs, gen_vecs], axis=0)
-    y = np.concatenate(
-        [
-            np.zeros(real_vecs.shape[0], dtype=np.int64),
-            np.ones(gen_vecs.shape[0], dtype=np.int64),
-        ],
-        axis=0,
-    )
-    d2 = pairwise_sq_dists(x, x)
-    np.fill_diagonal(d2, np.inf)
-    nn = np.argmin(d2, axis=1)
-    pred = y[nn]
-    return float((pred == y).mean())
-
-
-def binary_classification_curves(y_true: np.ndarray, scores: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    order = np.argsort(-scores)
-    y = y_true[order].astype(np.int64)
-    scores_sorted = scores[order]
-    tp = np.cumsum(y == 1)
-    fp = np.cumsum(y == 0)
-    positives = max(int((y_true == 1).sum()), 1)
-    negatives = max(int((y_true == 0).sum()), 1)
-    tpr = tp / positives
-    fpr = fp / negatives
-    precision = tp / np.maximum(tp + fp, 1)
-    recall = tpr
-    return scores_sorted, fpr, tpr, precision
-
-
-def binary_auroc(y_true: np.ndarray, scores: np.ndarray) -> float:
-    if len(np.unique(y_true)) < 2:
-        return float("nan")
-    _, fpr, tpr, _ = binary_classification_curves(y_true, scores)
-    fpr = np.concatenate([[0.0], fpr, [1.0]])
-    tpr = np.concatenate([[0.0], tpr, [1.0]])
-    return float(np.trapz(tpr, fpr))
-
-
-def binary_average_precision(y_true: np.ndarray, scores: np.ndarray) -> float:
-    if len(np.unique(y_true)) < 2:
-        return float("nan")
-    _, _, _, precision = binary_classification_curves(y_true, scores)
-    positives = max(int((y_true == 1).sum()), 1)
-    order = np.argsort(-scores)
-    y = y_true[order].astype(np.int64)
-    tp = np.cumsum(y == 1)
-    recall = tp / positives
-    precision = tp / np.arange(1, len(tp) + 1)
-    recall = np.concatenate([[0.0], recall])
-    precision = np.concatenate([[precision[0] if precision.size else 1.0], precision])
-    return float(np.sum((recall[1:] - recall[:-1]) * precision[1:]))
-
-
-def binary_f1_at_threshold(y_true: np.ndarray, scores: np.ndarray, threshold: float) -> Dict[str, float]:
-    pred = (scores >= threshold).astype(np.int64)
-    tp = int(((pred == 1) & (y_true == 1)).sum())
-    fp = int(((pred == 1) & (y_true == 0)).sum())
-    fn = int(((pred == 0) & (y_true == 1)).sum())
-    precision = tp / max(tp + fp, 1)
-    recall = tp / max(tp + fn, 1)
-    if precision + recall == 0:
-        f1 = 0.0
-    else:
-        f1 = 2.0 * precision * recall / (precision + recall)
-    return {"threshold": float(threshold), "precision": float(precision), "recall": float(recall), "f1": float(f1)}
-
-
-def best_binary_f1(y_true: np.ndarray, scores: np.ndarray) -> Dict[str, float]:
-    if y_true.size == 0:
-        return {"threshold": float("nan"), "precision": float("nan"), "recall": float("nan"), "f1": float("nan")}
-    thresholds = np.unique(scores)
-    best = {"threshold": float(thresholds[0]), "precision": 0.0, "recall": 0.0, "f1": -1.0}
-    for threshold in thresholds:
-        stats = binary_f1_at_threshold(y_true, scores, float(threshold))
-        if stats["f1"] > best["f1"]:
-            best = stats
-    return best
-
-
-def set_random_seed(seed: int) -> None:
-    random.seed(seed)
-    np.random.seed(seed)
-

example/evaluate_comprehensive.py

@@ -1,772 +0,0 @@
-#!/usr/bin/env python3
-"""Comprehensive evaluation suite for generated ICS feature sequences."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Sequence, Tuple
-
-import numpy as np
-import torch
-from torch import nn
-from torch.utils.data import DataLoader, TensorDataset
-
-from eval_utils import (
-    best_binary_f1,
-    binary_auroc,
-    binary_average_precision,
-    binary_f1_at_threshold,
-    build_flat_window_vectors,
-    build_histogram_embeddings,
-    compute_average_psd,
-    compute_corr_matrix,
-    duplicate_rate,
-    exact_match_rate,
-    infer_test_paths,
-    load_json,
-    load_split_columns,
-    load_stats_vectors,
-    load_vocab,
-    load_windows_from_paths,
-    nearest_neighbor_distance_stats,
-    one_nn_two_sample_accuracy,
-    psd_distance_stats,
-    rbf_mmd,
-    resolve_reference_paths,
-    sample_indices,
-    set_random_seed,
-    split_process_groups,
-    standardize_cont_windows,
-)
-from platform_utils import resolve_device
-from window_models import MLPAutoencoder, MLPClassifier, MLPRegressor
-
-
-def parse_args():
-    base_dir = Path(__file__).resolve().parent
-    parser = argparse.ArgumentParser(description="Comprehensive evaluation for generated.csv.")
-    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--reference", default=str(base_dir / "config.json"))
-    parser.add_argument("--config", default=str(base_dir / "config.json"))
-    parser.add_argument("--split", default=str(base_dir / "feature_split.json"))
-    parser.add_argument("--stats", default=str(base_dir / "results" / "cont_stats.json"))
-    parser.add_argument("--vocab", default=str(base_dir / "results" / "disc_vocab.json"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "comprehensive_eval.json"))
-    parser.add_argument("--seq-len", type=int, default=0)
-    parser.add_argument("--stride", type=int, default=0, help="0 means non-overlapping windows")
-    parser.add_argument("--max-train-windows", type=int, default=1024)
-    parser.add_argument("--max-generated-windows", type=int, default=1024)
-    parser.add_argument("--max-test-windows", type=int, default=1024)
-    parser.add_argument("--max-rows-per-file", type=int, default=0)
-    parser.add_argument("--device", default="auto", help="cpu, cuda, or auto")
-    parser.add_argument("--seed", type=int, default=1337)
-    parser.add_argument("--batch-size", type=int, default=64)
-    parser.add_argument("--classifier-epochs", type=int, default=12)
-    parser.add_argument("--predictor-epochs", type=int, default=12)
-    parser.add_argument("--detector-epochs", type=int, default=16)
-    parser.add_argument("--hidden-dim", type=int, default=256)
-    parser.add_argument("--detector-threshold-quantile", type=float, default=0.995)
-    return parser.parse_args()
-
-
-def flatten_rows(windows: np.ndarray) -> np.ndarray:
-    if windows.size == 0:
-        return np.zeros((0, 0), dtype=np.float32)
-    return windows.reshape(-1, windows.shape[-1])
-
-
-def lagged_corr_from_windows(windows: np.ndarray, lag: int = 1) -> np.ndarray:
-    if windows.shape[0] == 0 or windows.shape[1] <= lag:
-        return np.zeros((windows.shape[-1], windows.shape[-1]), dtype=np.float32)
-    x = windows[:, :-lag, :].reshape(-1, windows.shape[-1])
-    y = windows[:, lag:, :].reshape(-1, windows.shape[-1])
-    x = x - x.mean(axis=0, keepdims=True)
-    y = y - y.mean(axis=0, keepdims=True)
-    cov = x.T @ y / max(x.shape[0] - 1, 1)
-    std_x = np.sqrt(np.maximum((x ** 2).sum(axis=0) / max(x.shape[0] - 1, 1), 1e-8))
-    std_y = np.sqrt(np.maximum((y ** 2).sum(axis=0) / max(y.shape[0] - 1, 1), 1e-8))
-    denom = np.outer(std_x, std_y)
-    corr = cov / np.maximum(denom, 1e-8)
-    return np.nan_to_num(corr, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32)
-
-
-def mean_abs_matrix_diff(a: np.ndarray, b: np.ndarray) -> float:
-    return float(np.abs(a - b).mean()) if a.size and b.size else float("nan")
-
-
-def fro_matrix_diff(a: np.ndarray, b: np.ndarray) -> float:
-    return float(np.linalg.norm(a - b)) if a.size and b.size else float("nan")
-
-
-def safe_mean(values: Iterable[float]) -> float:
-    vals = [float(v) for v in values if v is not None and not math.isnan(float(v))]
-    return float(sum(vals) / len(vals)) if vals else float("nan")
-
-
-def safe_median(values: Sequence[float]) -> float:
-    if not values:
-        return float("nan")
-    arr = sorted(float(v) for v in values)
-    mid = len(arr) // 2
-    if len(arr) % 2 == 1:
-        return float(arr[mid])
-    return float(0.5 * (arr[mid - 1] + arr[mid]))
-
-
-def dwell_and_steps(series: Sequence[float]) -> Dict[str, float]:
-    if not series:
-        return {
-            "num_changes": float("nan"),
-            "mean_dwell": float("nan"),
-            "median_dwell": float("nan"),
-            "mean_step": float("nan"),
-            "median_step": float("nan"),
-        }
-    changes = 0
-    dwells: List[float] = []
-    steps: List[float] = []
-    current = float(series[0])
-    dwell = 1
-    for value in series[1:]:
-        value = float(value)
-        if value == current:
-            dwell += 1
-            continue
-        changes += 1
-        dwells.append(float(dwell))
-        steps.append(abs(value - current))
-        current = value
-        dwell = 1
-    dwells.append(float(dwell))
-    return {
-        "num_changes": float(changes),
-        "mean_dwell": safe_mean(dwells),
-        "median_dwell": safe_median(dwells),
-        "mean_step": safe_mean(steps),
-        "median_step": safe_median(steps),
-    }
-
-
-def controller_stats(series: Sequence[float], vmin: float, vmax: float) -> Dict[str, float]:
-    if not series:
-        return {"saturation_ratio": float("nan"), "change_rate": float("nan"), "step_median": float("nan")}
-    rng = vmax - vmin
-    tol = 0.01 * rng if rng > 0 else 0.0
-    sat = sum(1 for value in series if value <= vmin + tol or value >= vmax - tol) / len(series)
-    changes = 0
-    steps: List[float] = []
-    prev = float(series[0])
-    for value in series[1:]:
-        value = float(value)
-        if value != prev:
-            changes += 1
-            steps.append(abs(value - prev))
-        prev = value
-    change_rate = changes / max(len(series) - 1, 1)
-    return {
-        "saturation_ratio": float(sat),
-        "change_rate": float(change_rate),
-        "step_median": safe_median(steps),
-    }
-
-
-def actuator_stats(series: Sequence[float]) -> Dict[str, float]:
-    if not series:
-        return {
-            "unique_ratio": float("nan"),
-            "top1_mass": float("nan"),
-            "top3_mass": float("nan"),
-            "median_dwell": float("nan"),
-        }
-    rounded = [round(float(v), 2) for v in series]
-    counts: Dict[float, int] = {}
-    for value in rounded:
-        counts[value] = counts.get(value, 0) + 1
-    top = sorted(counts.values(), reverse=True)
-    dwells: List[float] = []
-    current = rounded[0]
-    dwell = 1
-    for value in rounded[1:]:
-        if value == current:
-            dwell += 1
-        else:
-            dwells.append(float(dwell))
-            current = value
-            dwell = 1
-    dwells.append(float(dwell))
-    return {
-        "unique_ratio": float(len(counts) / len(rounded)),
-        "top1_mass": float(top[0] / len(rounded)) if top else float("nan"),
-        "top3_mass": float(sum(top[:3]) / len(rounded)) if top else float("nan"),
-        "median_dwell": safe_median(dwells),
-    }
-
-
-def pv_stats(series: Sequence[float]) -> Dict[str, float]:
-    if not series:
-        return {"q05": float("nan"), "q50": float("nan"), "q95": float("nan"), "tail_ratio": float("nan")}
-    xs = sorted(float(v) for v in series)
-    n = len(xs)
-
-    def q(prob: float) -> float:
-        idx = max(0, min(n - 1, int(round(prob * (n - 1)))))
-        return float(xs[idx])
-
-    q05 = q(0.05)
-    q50 = q(0.5)
-    q95 = q(0.95)
-    denom = q50 - q05
-    tail_ratio = (q95 - q50) / denom if denom != 0 else float("nan")
-    return {"q05": q05, "q50": q50, "q95": q95, "tail_ratio": float(tail_ratio)}
-
-
-def aux_stats(series: Sequence[float]) -> Dict[str, float]:
-    if not series:
-        return {"mean": float("nan"), "std": float("nan"), "lag1": float("nan")}
-    arr = np.asarray(series, dtype=np.float32)
-    mean = float(arr.mean())
-    std = float(arr.std(ddof=1)) if arr.size > 1 else 0.0
-    if arr.size < 2:
-        lag1 = float("nan")
-    else:
-        x = arr[:-1] - arr[:-1].mean()
-        y = arr[1:] - arr[1:].mean()
-        denom = max(float(np.linalg.norm(x) * np.linalg.norm(y)), 1e-8)
-        lag1 = float(np.dot(x, y) / denom)
-    return {"mean": mean, "std": std, "lag1": lag1}
-
-
-def metric_differences(generated: Dict[str, Dict[str, float]], reference: Dict[str, Dict[str, float]]) -> Dict[str, float]:
-    bucket: Dict[str, List[float]] = {}
-    for feature, metrics in generated.items():
-        ref_metrics = reference.get(feature, {})
-        for key, value in metrics.items():
-            ref_value = ref_metrics.get(key)
-            if ref_value is None:
-                continue
-            if math.isnan(float(value)) or math.isnan(float(ref_value)):
-                continue
-            bucket.setdefault(key, []).append(abs(float(value) - float(ref_value)))
-    return {f"mean_abs_diff_{key}": safe_mean(values) for key, values in bucket.items()}
-
-
-def summarize_type_metrics(
-    feature_names: Sequence[str],
-    gen_rows: np.ndarray,
-    real_rows: np.ndarray,
-    features: Sequence[str],
-    stat_fn,
-    use_real_bounds: bool = False,
-) -> Dict:
-    feature_to_idx = {name: idx for idx, name in enumerate(feature_names)}
-    generated: Dict[str, Dict[str, float]] = {}
-    reference: Dict[str, Dict[str, float]] = {}
-    for feature in features:
-        if feature not in feature_to_idx:
-            continue
-        idx = feature_to_idx[feature]
-        gen_series = gen_rows[:, idx].astype(float).tolist()
-        real_series = real_rows[:, idx].astype(float).tolist()
-        if use_real_bounds:
-            vmin = float(np.min(real_rows[:, idx])) if real_rows.size else 0.0
-            vmax = float(np.max(real_rows[:, idx])) if real_rows.size else 0.0
-            generated[feature] = stat_fn(gen_series, vmin, vmax)
-            reference[feature] = stat_fn(real_series, vmin, vmax)
-        else:
-            generated[feature] = stat_fn(gen_series)
-            reference[feature] = stat_fn(real_series)
-    return {
-        "features": list(features),
-        "generated": generated,
-        "reference": reference,
-        "aggregates": metric_differences(generated, reference),
-    }
-
-
-def make_loader(x: np.ndarray, y: Optional[np.ndarray], batch_size: int, shuffle: bool) -> DataLoader:
-    x_tensor = torch.tensor(x, dtype=torch.float32)
-    if y is None:
-        dataset = TensorDataset(x_tensor)
-    else:
-        y_tensor = torch.tensor(y, dtype=torch.float32)
-        dataset = TensorDataset(x_tensor, y_tensor)
-    return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
-
-
-def split_train_val(
-    x: np.ndarray,
-    y: np.ndarray,
-    seed: int,
-    val_ratio: float = 0.2,
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    rng = np.random.default_rng(seed)
-    idx = np.arange(x.shape[0])
-    rng.shuffle(idx)
-    cut = max(1, int(round(x.shape[0] * (1.0 - val_ratio))))
-    train_idx = idx[:cut]
-    val_idx = idx[cut:] if cut < idx.size else idx[:0]
-    if val_idx.size == 0:
-        val_idx = train_idx
-    return x[train_idx], y[train_idx], x[val_idx], y[val_idx]
-
-
-def train_classifier(
-    train_x: np.ndarray,
-    train_y: np.ndarray,
-    val_x: np.ndarray,
-    val_y: np.ndarray,
-    device: str,
-    hidden_dim: int,
-    batch_size: int,
-    epochs: int,
-    seed: int,
-) -> Dict[str, float]:
-    if train_x.shape[0] < 2 or len(np.unique(train_y)) < 2:
-        return {"accuracy": float("nan"), "balanced_accuracy": float("nan"), "auroc": float("nan")}
-    torch.manual_seed(seed)
-    model = MLPClassifier(train_x.shape[1], hidden_dim=hidden_dim).to(device)
-    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
-    loss_fn = nn.BCEWithLogitsLoss()
-    loader = make_loader(train_x, train_y.reshape(-1, 1), batch_size=batch_size, shuffle=True)
-    model.train()
-    for _ in range(epochs):
-        for batch_x, batch_y in loader:
-            batch_x = batch_x.to(device)
-            batch_y = batch_y.to(device).view(-1)
-            logits = model(batch_x)
-            loss = loss_fn(logits, batch_y)
-            opt.zero_grad()
-            loss.backward()
-            opt.step()
-    model.eval()
-    with torch.no_grad():
-        logits = model(torch.tensor(val_x, dtype=torch.float32, device=device)).cpu().numpy()
-    probs = 1.0 / (1.0 + np.exp(-logits))
-    pred = (probs >= 0.5).astype(np.int64)
-    y_true = val_y.astype(np.int64)
-    accuracy = float((pred == y_true).mean())
-    tp = ((pred == 1) & (y_true == 1)).sum()
-    tn = ((pred == 0) & (y_true == 0)).sum()
-    fp = ((pred == 1) & (y_true == 0)).sum()
-    fn = ((pred == 0) & (y_true == 1)).sum()
-    tpr = tp / max(tp + fn, 1)
-    tnr = tn / max(tn + fp, 1)
-    return {
-        "accuracy": accuracy,
-        "balanced_accuracy": float(0.5 * (tpr + tnr)),
-        "auroc": binary_auroc(y_true, probs),
-    }
-
-
-def train_regressor(
-    train_x: np.ndarray,
-    train_y: np.ndarray,
-    eval_x: np.ndarray,
-    eval_y: np.ndarray,
-    device: str,
-    hidden_dim: int,
-    batch_size: int,
-    epochs: int,
-    seed: int,
-) -> Dict[str, float]:
-    if train_x.shape[0] == 0 or eval_x.shape[0] == 0:
-        return {"rmse": float("nan"), "mae": float("nan")}
-    torch.manual_seed(seed)
-    model = MLPRegressor(train_x.shape[1], train_y.shape[1], hidden_dim=hidden_dim).to(device)
-    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
-    loss_fn = nn.MSELoss()
-    loader = make_loader(train_x, train_y, batch_size=batch_size, shuffle=True)
-    model.train()
-    for _ in range(epochs):
-        for batch_x, batch_y in loader:
-            batch_x = batch_x.to(device)
-            batch_y = batch_y.to(device)
-            pred = model(batch_x)
-            loss = loss_fn(pred, batch_y)
-            opt.zero_grad()
-            loss.backward()
-            opt.step()
-    model.eval()
-    with torch.no_grad():
-        pred = model(torch.tensor(eval_x, dtype=torch.float32, device=device)).cpu().numpy()
-    diff = pred - eval_y
-    return {
-        "rmse": float(np.sqrt(np.mean(diff ** 2))),
-        "mae": float(np.mean(np.abs(diff))),
-    }
-
-
-def train_autoencoder(
-    train_x: np.ndarray,
-    eval_x: np.ndarray,
-    eval_labels: np.ndarray,
-    device: str,
-    hidden_dim: int,
-    batch_size: int,
-    epochs: int,
-    seed: int,
-    threshold_quantile: float,
-) -> Dict[str, float]:
-    if train_x.shape[0] == 0 or eval_x.shape[0] == 0:
-        return {
-            "auroc": float("nan"),
-            "auprc": float("nan"),
-            "threshold": float("nan"),
-            "f1": float("nan"),
-            "best_f1": float("nan"),
-        }
-    torch.manual_seed(seed)
-    latent_dim = max(32, hidden_dim // 4)
-    model = MLPAutoencoder(train_x.shape[1], hidden_dim=hidden_dim, latent_dim=latent_dim).to(device)
-    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
-    loss_fn = nn.MSELoss()
-    loader = make_loader(train_x, None, batch_size=batch_size, shuffle=True)
-    model.train()
-    for _ in range(epochs):
-        for (batch_x,) in loader:
-            batch_x = batch_x.to(device)
-            recon = model(batch_x)
-            loss = loss_fn(recon, batch_x)
-            opt.zero_grad()
-            loss.backward()
-            opt.step()
-
-    model.eval()
-    with torch.no_grad():
-        train_tensor = torch.tensor(train_x, dtype=torch.float32, device=device)
-        train_recon = model(train_tensor).cpu().numpy()
-        eval_tensor = torch.tensor(eval_x, dtype=torch.float32, device=device)
-        eval_recon = model(eval_tensor).cpu().numpy()
-    train_scores = np.mean((train_recon - train_x) ** 2, axis=1)
-    eval_scores = np.mean((eval_recon - eval_x) ** 2, axis=1)
-    threshold = float(np.quantile(train_scores, threshold_quantile))
-    f1_stats = binary_f1_at_threshold(eval_labels, eval_scores, threshold)
-    best_stats = best_binary_f1(eval_labels, eval_scores)
-    return {
-        "auroc": binary_auroc(eval_labels, eval_scores),
-        "auprc": binary_average_precision(eval_labels, eval_scores),
-        "threshold": threshold,
-        "f1": f1_stats["f1"],
-        "precision": f1_stats["precision"],
-        "recall": f1_stats["recall"],
-        "best_f1": best_stats["f1"],
-        "best_f1_threshold": best_stats["threshold"],
-    }
-
-
-def bootstrap_to_size(array: np.ndarray, target_size: int, seed: int) -> np.ndarray:
-    if array.shape[0] == 0:
-        return array
-    if array.shape[0] >= target_size:
-        return array
-    rng = np.random.default_rng(seed)
-    idx = rng.choice(array.shape[0], size=target_size, replace=True)
-    return array[idx]
-
-
-def build_predictive_pairs(cont_windows: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    if cont_windows.shape[0] == 0 or cont_windows.shape[1] < 2:
-        return np.zeros((0, 0), dtype=np.float32), np.zeros((0, 0), dtype=np.float32)
-    x = cont_windows[:, :-1, :].reshape(cont_windows.shape[0], -1).astype(np.float32)
-    y = cont_windows[:, -1, :].astype(np.float32)
-    return x, y
-
-
-def main():
-    args = parse_args()
-    set_random_seed(args.seed)
-    device = resolve_device(args.device, verbose=False)
-
-    cfg = {}
-    if args.config and Path(args.config).exists():
-        cfg = load_json(args.config)
-    seq_len = int(args.seq_len or cfg.get("sample_seq_len", cfg.get("seq_len", 96)))
-    stride = int(args.stride or seq_len)
-    max_rows_per_file = args.max_rows_per_file if args.max_rows_per_file > 0 else None
-
-    _, cont_cols, disc_cols, label_cols = load_split_columns(args.split)
-    train_paths = resolve_reference_paths(args.reference)
-    test_paths = infer_test_paths(args.reference)
-    vocab, vocab_sizes = load_vocab(args.vocab, disc_cols)
-    mean_vec, std_vec = load_stats_vectors(args.stats, cont_cols)
-
-    train_cont, train_disc, _ = load_windows_from_paths(
-        train_paths,
-        cont_cols,
-        disc_cols,
-        seq_len=seq_len,
-        vocab=vocab,
-        label_cols=None,
-        stride=stride,
-        max_windows=args.max_train_windows,
-        max_rows_per_file=max_rows_per_file,
-    )
-    gen_cont, gen_disc, _ = load_windows_from_paths(
-        [args.generated],
-        cont_cols,
-        disc_cols,
-        seq_len=seq_len,
-        vocab=vocab,
-        label_cols=None,
-        stride=seq_len,
-        max_windows=args.max_generated_windows,
-        max_rows_per_file=max_rows_per_file,
-    )
-    test_cont, test_disc, test_labels = load_windows_from_paths(
-        test_paths,
-        cont_cols,
-        disc_cols,
-        seq_len=seq_len,
-        vocab=vocab,
-        label_cols=label_cols,
-        stride=stride,
-        max_windows=args.max_test_windows,
-        max_rows_per_file=max_rows_per_file,
-    )
-
-    if gen_cont.shape[0] == 0:
-        raise SystemExit("generated.csv did not contain enough rows for one evaluation window")
-
-    if test_labels is None or test_labels.size == 0:
-        split_at = max(1, int(round(train_cont.shape[0] * 0.8)))
-        test_cont = train_cont[split_at:]
-        test_disc = train_disc[split_at:]
-        test_labels = np.zeros(test_cont.shape[0], dtype=np.int64)
-        train_cont = train_cont[:split_at]
-        train_disc = train_disc[:split_at]
-
-    normal_test_mask = test_labels == 0
-    normal_test_cont = test_cont[normal_test_mask] if normal_test_mask.any() else test_cont
-    normal_test_disc = test_disc[normal_test_mask] if normal_test_mask.any() else test_disc
-
-    flat_train = build_flat_window_vectors(train_cont, train_disc, mean_vec, std_vec, vocab_sizes)
-    flat_gen = build_flat_window_vectors(gen_cont, gen_disc, mean_vec, std_vec, vocab_sizes)
-    flat_test = build_flat_window_vectors(test_cont, test_disc, mean_vec, std_vec, vocab_sizes)
-    hist_train = build_histogram_embeddings(train_cont, train_disc, mean_vec, std_vec, vocab_sizes)
-    hist_gen = build_histogram_embeddings(gen_cont, gen_disc, mean_vec, std_vec, vocab_sizes)
-    hist_test = build_histogram_embeddings(test_cont, test_disc, mean_vec, std_vec, vocab_sizes)
-    hist_normal_test = build_histogram_embeddings(normal_test_cont, normal_test_disc, mean_vec, std_vec, vocab_sizes)
-
-    cont_train_flat = standardize_cont_windows(train_cont, mean_vec, std_vec).reshape(train_cont.shape[0], -1)
-    cont_gen_flat = standardize_cont_windows(gen_cont, mean_vec, std_vec).reshape(gen_cont.shape[0], -1)
-
-    train_rows = flatten_rows(train_cont)
-    gen_rows = flatten_rows(gen_cont)
-
-    balanced = min(hist_train.shape[0], hist_gen.shape[0])
-    idx_real = sample_indices(hist_train.shape[0], balanced, args.seed)
-    idx_gen = sample_indices(hist_gen.shape[0], balanced, args.seed + 1)
-    discrim_x = np.concatenate([flat_train[idx_real], flat_gen[idx_gen]], axis=0) if balanced > 0 else np.zeros((0, flat_train.shape[1]), dtype=np.float32)
-    discrim_y = np.concatenate(
-        [np.zeros(balanced, dtype=np.int64), np.ones(balanced, dtype=np.int64)],
-        axis=0,
-    ) if balanced > 0 else np.zeros((0,), dtype=np.int64)
-    if discrim_x.shape[0] > 0:
-        x_train, y_train, x_val, y_val = split_train_val(discrim_x, discrim_y, seed=args.seed)
-        discriminative = train_classifier(
-            x_train,
-            y_train,
-            x_val,
-            y_val,
-            device=device,
-            hidden_dim=args.hidden_dim,
-            batch_size=args.batch_size,
-            epochs=args.classifier_epochs,
-            seed=args.seed,
-        )
-    else:
-        discriminative = {"accuracy": float("nan"), "balanced_accuracy": float("nan"), "auroc": float("nan")}
-
-    mmd_cont, gamma_cont = rbf_mmd(cont_train_flat[idx_real] if balanced > 0 else cont_train_flat, cont_gen_flat[idx_gen] if balanced > 0 else cont_gen_flat)
-    mmd_hist, gamma_hist = rbf_mmd(hist_train[idx_real] if balanced > 0 else hist_train, hist_gen[idx_gen] if balanced > 0 else hist_gen)
-
-    holdout_base = hist_normal_test if hist_normal_test.shape[0] > 0 else hist_test
-    nn_gen = nearest_neighbor_distance_stats(hist_gen, hist_train)
-    nn_holdout = nearest_neighbor_distance_stats(holdout_base, hist_train)
-    diversity = {
-        "duplicate_rate": duplicate_rate(flat_gen),
-        "exact_match_rate_to_train": exact_match_rate(flat_gen, flat_train),
-        "nn_gen_to_train_mean": nn_gen["mean"],
-        "nn_holdout_to_train_mean": nn_holdout["mean"],
-        "memorization_ratio": float(nn_gen["mean"] / max(nn_holdout["mean"], 1e-8)) if not math.isnan(nn_gen["mean"]) and not math.isnan(nn_holdout["mean"]) else float("nan"),
-        "one_nn_two_sample_accuracy": one_nn_two_sample_accuracy(holdout_base, hist_gen),
-    }
-
-    corr_real = compute_corr_matrix(train_rows)
-    corr_gen = compute_corr_matrix(gen_rows)
-    lag_corr_real = lagged_corr_from_windows(train_cont)
-    lag_corr_gen = lagged_corr_from_windows(gen_cont)
-    coupling = {
-        "corr_mean_abs_diff": mean_abs_matrix_diff(corr_real, corr_gen),
-        "corr_frobenius": fro_matrix_diff(corr_real, corr_gen),
-        "lag1_corr_mean_abs_diff": mean_abs_matrix_diff(lag_corr_real, lag_corr_gen),
-        "lag1_corr_frobenius": fro_matrix_diff(lag_corr_real, lag_corr_gen),
-        "by_process": {},
-    }
-    process_groups = split_process_groups(cont_cols)
-    for process_name, indices in process_groups.items():
-        real_block = corr_real[np.ix_(indices, indices)]
-        gen_block = corr_gen[np.ix_(indices, indices)]
-        real_lag_block = lag_corr_real[np.ix_(indices, indices)]
-        gen_lag_block = lag_corr_gen[np.ix_(indices, indices)]
-        coupling["by_process"][process_name] = {
-            "corr_mean_abs_diff": mean_abs_matrix_diff(real_block, gen_block),
-            "corr_frobenius": fro_matrix_diff(real_block, gen_block),
-            "lag1_corr_mean_abs_diff": mean_abs_matrix_diff(real_lag_block, gen_lag_block),
-            "lag1_corr_frobenius": fro_matrix_diff(real_lag_block, gen_lag_block),
-        }
-
-    frequency = psd_distance_stats(compute_average_psd(train_cont), compute_average_psd(gen_cont))
-
-    cfg_types = {
-        "type1": list(cfg.get("type1_features", []) or []),
-        "type2": list(cfg.get("type2_features", []) or []),
-        "type3": list(cfg.get("type3_features", []) or []),
-        "type4": list(cfg.get("type4_features", []) or []),
-        "type5": list(cfg.get("type5_features", []) or []),
-        "type6": list(cfg.get("type6_features", []) or []),
-    }
-    type_metrics = {
-        "type1_program": summarize_type_metrics(cont_cols, gen_rows, train_rows, cfg_types["type1"], dwell_and_steps),
-        "type2_controller": summarize_type_metrics(cont_cols, gen_rows, train_rows, cfg_types["type2"], controller_stats, use_real_bounds=True),
-        "type3_actuator": summarize_type_metrics(cont_cols, gen_rows, train_rows, cfg_types["type3"], actuator_stats),
-        "type4_pv": summarize_type_metrics(cont_cols, gen_rows, train_rows, cfg_types["type4"], pv_stats),
-        "type5_program_proxy": summarize_type_metrics(cont_cols, gen_rows, train_rows, cfg_types["type5"], dwell_and_steps),
-        "type6_aux": summarize_type_metrics(cont_cols, gen_rows, train_rows, cfg_types["type6"], aux_stats),
-    }
-
-    pred_train_x, pred_train_y = build_predictive_pairs(standardize_cont_windows(train_cont, mean_vec, std_vec))
-    pred_gen_x, pred_gen_y = build_predictive_pairs(standardize_cont_windows(gen_cont, mean_vec, std_vec))
-    pred_eval_x, pred_eval_y = build_predictive_pairs(standardize_cont_windows(normal_test_cont, mean_vec, std_vec))
-    predictive = {
-        "real_only": train_regressor(
-            pred_train_x,
-            pred_train_y,
-            pred_eval_x,
-            pred_eval_y,
-            device=device,
-            hidden_dim=args.hidden_dim,
-            batch_size=args.batch_size,
-            epochs=args.predictor_epochs,
-            seed=args.seed,
-        ),
-        "synthetic_only": train_regressor(
-            pred_gen_x,
-            pred_gen_y,
-            pred_eval_x,
-            pred_eval_y,
-            device=device,
-            hidden_dim=args.hidden_dim,
-            batch_size=args.batch_size,
-            epochs=args.predictor_epochs,
-            seed=args.seed + 1,
-        ),
-        "real_plus_synthetic": train_regressor(
-            np.concatenate([pred_train_x, bootstrap_to_size(pred_gen_x, pred_train_x.shape[0], args.seed + 2)], axis=0) if pred_train_x.size and pred_gen_x.size else pred_train_x,
-            np.concatenate([pred_train_y, bootstrap_to_size(pred_gen_y, pred_train_y.shape[0], args.seed + 2)], axis=0) if pred_train_y.size and pred_gen_y.size else pred_train_y,
-            pred_eval_x,
-            pred_eval_y,
-            device=device,
-            hidden_dim=args.hidden_dim,
-            batch_size=args.batch_size,
-            epochs=args.predictor_epochs,
-            seed=args.seed + 2,
-        ),
-    }
-    predictive["rmse_ratio_synth_to_real"] = (
-        float(predictive["synthetic_only"]["rmse"] / max(predictive["real_only"]["rmse"], 1e-8))
-        if not math.isnan(predictive["synthetic_only"]["rmse"]) and not math.isnan(predictive["real_only"]["rmse"])
-        else float("nan")
-    )
-
-    target_size = max(flat_train.shape[0], min(512, flat_test.shape[0])) if flat_train.shape[0] > 0 else flat_gen.shape[0]
-    utility = {
-        "real_only": train_autoencoder(
-            flat_train,
-            flat_test,
-            test_labels.astype(np.int64),
-            device=device,
-            hidden_dim=args.hidden_dim,
-            batch_size=args.batch_size,
-            epochs=args.detector_epochs,
-            seed=args.seed,
-            threshold_quantile=args.detector_threshold_quantile,
-        ),
-        "synthetic_only": train_autoencoder(
-            bootstrap_to_size(flat_gen, target_size, args.seed + 3),
-            flat_test,
-            test_labels.astype(np.int64),
-            device=device,
-            hidden_dim=args.hidden_dim,
-            batch_size=args.batch_size,
-            epochs=args.detector_epochs,
-            seed=args.seed + 3,
-            threshold_quantile=args.detector_threshold_quantile,
-        ),
-        "real_plus_synthetic": train_autoencoder(
-            np.concatenate([flat_train, bootstrap_to_size(flat_gen, flat_train.shape[0], args.seed + 4)], axis=0) if flat_train.size and flat_gen.size else flat_train,
-            flat_test,
-            test_labels.astype(np.int64),
-            device=device,
-            hidden_dim=args.hidden_dim,
-            batch_size=args.batch_size,
-            epochs=args.detector_epochs,
-            seed=args.seed + 4,
-            threshold_quantile=args.detector_threshold_quantile,
-        ),
-    }
-
-    eval_name = "eval_post.json" if Path(args.generated).name.startswith("generated_post") else "eval.json"
-    eval_candidate = Path(args.generated).with_name(eval_name)
-    basic_eval = load_json(eval_candidate) if eval_candidate.exists() else {}
-
-    out = {
-        "generated_path": str(Path(args.generated).resolve()),
-        "reference_paths": train_paths,
-        "test_paths": test_paths,
-        "seq_len": seq_len,
-        "stride": stride,
-        "counts": {
-            "train_windows": int(train_cont.shape[0]),
-            "generated_windows": int(gen_cont.shape[0]),
-            "test_windows": int(test_cont.shape[0]),
-            "test_anomalous_windows": int(test_labels.sum()),
-            "test_normal_windows": int(normal_test_cont.shape[0]),
-        },
-        "basic_eval": {
-            "avg_ks": basic_eval.get("avg_ks"),
-            "avg_jsd": basic_eval.get("avg_jsd"),
-            "avg_lag1_diff": basic_eval.get("avg_lag1_diff"),
-        },
-        "two_sample": {
-            "continuous_mmd_rbf": mmd_cont,
-            "continuous_mmd_gamma": gamma_cont,
-            "histogram_mmd_rbf": mmd_hist,
-            "histogram_mmd_gamma": gamma_hist,
-            "discriminative_accuracy": discriminative["accuracy"],
-            "discriminative_balanced_accuracy": discriminative["balanced_accuracy"],
-            "discriminative_auroc": discriminative["auroc"],
-        },
-        "diversity_privacy": diversity,
-        "coupling": coupling,
-        "frequency": frequency,
-        "type_metrics": type_metrics,
-        "predictive_consistency": predictive,
-        "anomaly_utility": utility,
-    }
-
-    out_path = Path(args.out)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
-    print("wrote", out_path)
-    print("generated_windows", gen_cont.shape[0])
-    print("continuous_mmd_rbf", mmd_cont)
-    print("discriminative_accuracy", discriminative["accuracy"])
-    print("utility_real_plus_synthetic_auprc", utility["real_plus_synthetic"]["auprc"])
-
-
-if __name__ == "__main__":
-    main()

example/evaluate_generated.py

@@ -75,15 +75,14 @@ def ks_statistic(x: List[float], y: List[float]) -> float:
     n = len(x_sorted)
     m = len(y_sorted)
     i = j = 0
+    cdf_x = cdf_y = 0.0
     d = 0.0
-    # Iterate over merged unique values to handle ties correctly
-    merged = sorted(set(x_sorted) | set(y_sorted))
-    for v in merged:
-        while i < n and x_sorted[i] <= v:
+    while i < n and j < m:
+        if x_sorted[i] <= y_sorted[j]:
             i += 1
-        while j < m and y_sorted[j] <= v:
-            j += 1
             cdf_x = i / n
+        else:
+            j += 1
             cdf_y = j / m
         d = max(d, abs(cdf_x - cdf_y))
     return d
@@ -104,51 +103,31 @@ def lag1_corr(values: List[float]) -> float:
     return num / math.sqrt(den_x * den_y)
 
 
-def resolve_reference_paths(path: str) -> List[str]:
+def resolve_reference_path(path: str) -> Optional[str]:
     if not path:
-        return []
-    if path.endswith(".json") and Path(path).exists():
-        try:
-            cfg = load_json(path)
-            ref = cfg.get("data_glob") or cfg.get("data_path") or ""
-            if ref:
-                return resolve_reference_paths(str(ref))
-        except Exception:
-            return []
+        return None
     if any(ch in path for ch in ["*", "?", "["]):
-        base = Path(path).parent.resolve()
+        base = Path(path).parent
         pat = Path(path).name
         matches = sorted(base.glob(pat))
-        return [str(p) for p in matches]
-    return [str(path)]
+        return str(matches[0]) if matches else None
+    return str(path)
 
 
 def main():
     args = parse_args()
     base_dir = Path(__file__).resolve().parent
-
-    def resolve_file(p: str) -> str:
-        path = Path(p)
-        if path.is_absolute():
-            return str(path)
-        if path.exists():
-            return str(path.resolve())
-        candidate = base_dir / path
-        if candidate.exists():
-            return str(candidate.resolve())
-        return str((base_dir / path).resolve())
-
-    args.generated = resolve_file(args.generated)
-    args.split = resolve_file(args.split)
-    args.stats = resolve_file(args.stats)
-    args.vocab = resolve_file(args.vocab)
-    args.out = resolve_file(args.out)
+    args.generated = str((base_dir / args.generated).resolve()) if not Path(args.generated).is_absolute() else args.generated
+    args.split = str((base_dir / args.split).resolve()) if not Path(args.split).is_absolute() else args.split
+    args.stats = str((base_dir / args.stats).resolve()) if not Path(args.stats).is_absolute() else args.stats
+    args.vocab = str((base_dir / args.vocab).resolve()) if not Path(args.vocab).is_absolute() else args.vocab
+    args.out = str((base_dir / args.out).resolve()) if not Path(args.out).is_absolute() else args.out
     if args.reference and not Path(args.reference).is_absolute():
         if any(ch in args.reference for ch in ["*", "?", "["]):
             args.reference = str(base_dir / args.reference)
         else:
             args.reference = str((base_dir / args.reference).resolve())
-    ref_paths = resolve_reference_paths(args.reference)
+    ref_path = resolve_reference_path(args.reference)
     split = load_json(args.split)
     time_col = split.get("time_column", "time")
     cont_cols = [c for c in split["continuous"] if c != time_col]
@@ -159,11 +138,10 @@ def main():
     std_ref = stats_json.get("raw_std", stats_json.get("std"))
     transforms = stats_json.get("transform", {})
     vocab = load_json(args.vocab)["vocab"]
-    vocab_sets = {c: set(vocab.get(c, {}).keys()) for c in disc_cols}
+    vocab_sets = {c: set(vocab[c].keys()) for c in disc_cols}
 
     cont_stats = init_stats(cont_cols)
     disc_invalid = {c: 0 for c in disc_cols}
-    missing_generated = {c: 0 for c in disc_cols}
     rows = 0
 
     with open_csv(args.generated) as f:
@@ -178,14 +156,10 @@ def main():
                 except Exception:
                     v = 0.0
                 update_stats(cont_stats, c, v)
-                if ref_paths:
+                if ref_path:
                     pass
             for c in disc_cols:
-                tok = row.get(c, None)
-                if tok is None:
-                    missing_generated[c] += 1
-                    continue
-                if tok not in vocab_sets[c]:
+                if row[c] not in vocab_sets[c]:
                     disc_invalid[c] += 1
 
     cont_summary = finalize_stats(cont_stats)
@@ -205,11 +179,10 @@ def main():
         "continuous_summary": cont_summary,
         "continuous_error": cont_err,
         "discrete_invalid_counts": disc_invalid,
-        "missing_generated_columns": {k: v for k, v in missing_generated.items() if v > 0},
     }
 
     # Optional richer metrics using reference data
-    if ref_paths:
+    if ref_path:
         ref_cont = {c: [] for c in cont_cols}
         ref_disc = {c: {} for c in disc_cols}
         gen_cont = {c: [] for c in cont_cols}
@@ -226,14 +199,12 @@ def main():
                     except Exception:
                         gen_cont[c].append(0.0)
                 for c in disc_cols:
-                    tok = row.get(c, "")
+                    tok = row[c]
                     gen_disc[c][tok] = gen_disc[c].get(tok, 0) + 1
 
-        loaded = 0
-        for ref_path in ref_paths:
         with open_csv(ref_path) as f:
             reader = csv.DictReader(f)
-                for row in reader:
+            for i, row in enumerate(reader):
                 if time_col in row:
                     row.pop(time_col, None)
                 for c in cont_cols:
@@ -242,12 +213,9 @@ def main():
                     except Exception:
                         ref_cont[c].append(0.0)
                 for c in disc_cols:
-                        tok = row.get(c, "")
+                    tok = row[c]
                     ref_disc[c][tok] = ref_disc[c].get(tok, 0) + 1
-                    loaded += 1
-                    if args.max_rows and loaded >= args.max_rows:
-                        break
-            if args.max_rows and loaded >= args.max_rows:
+                if args.max_rows and i + 1 >= args.max_rows:
                     break
 
         # Continuous metrics: KS + quantiles + lag1 correlation

example/export_samples.py

@@ -12,8 +12,8 @@ from typing import Dict, List
 import torch
 import torch.nn.functional as F
 
-from data_utils import load_split, normalize_cont, inverse_quantile_transform, quantile_calibrate_to_real
-from hybrid_diffusion import HybridDiffusionModel, TemporalGRUGenerator, TemporalTransformerGenerator, cosine_beta_schedule
+from data_utils import load_split
+from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule
 from platform_utils import resolve_device, safe_path, ensure_dir, resolve_path
 
 
@@ -73,13 +73,6 @@ def parse_args():
     return parser.parse_args()
 
 
-def load_torch_state(path: str, device: str):
-    try:
-        return torch.load(path, map_location=device, weights_only=True)
-    except TypeError:
-        return torch.load(path, map_location=device)
-
-
 # 使用 platform_utils 中的 resolve_device 函数
 
 
@@ -119,9 +112,6 @@ def main():
     int_like = stats.get("int_like", {})
     max_decimals = stats.get("max_decimals", {})
     transforms = stats.get("transform", {})
-    quantile_probs = stats.get("quantile_probs")
-    quantile_values = stats.get("quantile_values")
-    quantile_raw_values = stats.get("quantile_raw_values")
 
     vocab_json = json.load(open(args.vocab_path, "r", encoding="utf-8"))
     vocab = vocab_json["vocab"]
@@ -150,40 +140,9 @@ def main():
                 raise SystemExit("use_condition enabled but no files matched data_glob: %s" % cfg_glob)
     cont_target = str(cfg.get("cont_target", "eps"))
     cont_clamp_x0 = float(cfg.get("cont_clamp_x0", 0.0))
-    use_quantile = bool(cfg.get("use_quantile_transform", False))
-    cont_bound_mode = str(cfg.get("cont_bound_mode", "clamp"))
-    cont_bound_strength = float(cfg.get("cont_bound_strength", 1.0))
-    cont_post_scale = cfg.get("cont_post_scale", {}) if isinstance(cfg.get("cont_post_scale", {}), dict) else {}
-    cont_post_calibrate = bool(cfg.get("cont_post_calibrate", False))
-    type1_cols = cfg.get("type1_features", []) or []
-    type5_cols = cfg.get("type5_features", []) or []
-    type4_cols = cfg.get("type4_features", []) or []
-    type1_cols = [c for c in type1_cols if c in cont_cols]
-    type5_cols = [c for c in type5_cols if c in cont_cols]
-    type4_cols = [c for c in type4_cols if c in cont_cols]
-    model_cont_cols = [c for c in cont_cols if c not in type1_cols and c not in type5_cols]
-    use_temporal_stage1 = bool(cfg.get("use_temporal_stage1", False))
-    temporal_use_type1_cond = bool(cfg.get("temporal_use_type1_cond", False))
-    temporal_focus_type4 = bool(cfg.get("temporal_focus_type4", False))
-    temporal_exclude_type4 = bool(cfg.get("temporal_exclude_type4", False))
-    temporal_backbone = str(cfg.get("temporal_backbone", "gru"))
-    temporal_hidden_dim = int(cfg.get("temporal_hidden_dim", 256))
-    temporal_num_layers = int(cfg.get("temporal_num_layers", 1))
-    temporal_dropout = float(cfg.get("temporal_dropout", 0.0))
-    temporal_pos_dim = int(cfg.get("temporal_pos_dim", 64))
-    temporal_use_pos_embed = bool(cfg.get("temporal_use_pos_embed", True))
-    temporal_transformer_num_layers = int(cfg.get("temporal_transformer_num_layers", 2))
-    temporal_transformer_nhead = int(cfg.get("temporal_transformer_nhead", 4))
-    temporal_transformer_ff_dim = int(cfg.get("temporal_transformer_ff_dim", 512))
-    temporal_transformer_dropout = float(cfg.get("temporal_transformer_dropout", 0.1))
-    backbone_type = str(cfg.get("backbone_type", "gru"))
-    transformer_num_layers = int(cfg.get("transformer_num_layers", 2))
-    transformer_nhead = int(cfg.get("transformer_nhead", 4))
-    transformer_ff_dim = int(cfg.get("transformer_ff_dim", 512))
-    transformer_dropout = float(cfg.get("transformer_dropout", 0.1))
 
     model = HybridDiffusionModel(
-        cont_dim=len(model_cont_cols),
+        cont_dim=len(cont_cols),
         disc_vocab_sizes=vocab_sizes,
         time_dim=int(cfg.get("model_time_dim", 64)),
         hidden_dim=int(cfg.get("model_hidden_dim", 256)),
@@ -192,12 +151,6 @@ def main():
         ff_mult=int(cfg.get("model_ff_mult", 2)),
         pos_dim=int(cfg.get("model_pos_dim", 64)),
         use_pos_embed=bool(cfg.get("model_use_pos_embed", True)),
-        backbone_type=backbone_type,
-        transformer_num_layers=transformer_num_layers,
-        transformer_nhead=transformer_nhead,
-        transformer_ff_dim=transformer_ff_dim,
-        transformer_dropout=transformer_dropout,
-        cond_cont_dim=len(type1_cols),
         cond_vocab_size=cond_vocab_size if use_condition else 0,
         cond_dim=int(cfg.get("cond_dim", 32)),
         use_tanh_eps=bool(cfg.get("use_tanh_eps", False)),
@@ -205,57 +158,16 @@ def main():
     ).to(device)
     if args.use_ema and os.path.exists(args.model_path.replace("model.pt", "model_ema.pt")):
         ema_path = args.model_path.replace("model.pt", "model_ema.pt")
-        model.load_state_dict(load_torch_state(ema_path, device))
+        model.load_state_dict(torch.load(ema_path, map_location=device, weights_only=True))
     else:
-        model.load_state_dict(load_torch_state(args.model_path, device))
+        model.load_state_dict(torch.load(args.model_path, map_location=device, weights_only=True))
     model.eval()
 
-    temporal_model = None
-    if use_temporal_stage1:
-        temporal_path = Path(args.model_path).with_name("temporal.pt")
-        if not temporal_path.exists():
-            raise SystemExit(f"missing temporal model file: {temporal_path}")
-        temporal_state = load_torch_state(str(temporal_path), device)
-        temporal_cond_dim = len(type1_cols) if (temporal_use_type1_cond and type1_cols) else 0
-        if isinstance(temporal_state, dict):
-            if "in_proj.weight" in temporal_state:
-                try:
-                    temporal_cond_dim = max(0, int(temporal_state["in_proj.weight"].shape[1]) - len(model_cont_cols))
-                except Exception:
-                    pass
-            elif "gru.weight_ih_l0" in temporal_state:
-                try:
-                    temporal_cond_dim = max(0, int(temporal_state["gru.weight_ih_l0"].shape[1]) - len(model_cont_cols))
-                except Exception:
-                    pass
-        if temporal_backbone == "transformer":
-            temporal_model = TemporalTransformerGenerator(
-                input_dim=len(model_cont_cols),
-                hidden_dim=temporal_hidden_dim,
-                num_layers=temporal_transformer_num_layers,
-                nhead=temporal_transformer_nhead,
-                ff_dim=temporal_transformer_ff_dim,
-                dropout=temporal_transformer_dropout,
-                pos_dim=temporal_pos_dim,
-                use_pos_embed=temporal_use_pos_embed,
-                cond_dim=temporal_cond_dim,
-            ).to(device)
-        else:
-            temporal_model = TemporalGRUGenerator(
-                input_dim=len(model_cont_cols),
-                hidden_dim=temporal_hidden_dim,
-                num_layers=temporal_num_layers,
-                dropout=temporal_dropout,
-                cond_dim=temporal_cond_dim,
-            ).to(device)
-        temporal_model.load_state_dict(temporal_state)
-        temporal_model.eval()
-
     betas = cosine_beta_schedule(args.timesteps).to(device)
     alphas = 1.0 - betas
     alphas_cumprod = torch.cumprod(alphas, dim=0)
 
-    x_cont = torch.randn(args.batch_size, args.seq_len, len(model_cont_cols), device=device)
+    x_cont = torch.randn(args.batch_size, args.seq_len, len(cont_cols), device=device)
     x_disc = torch.full(
         (args.batch_size, args.seq_len, len(disc_cols)),
         0,
@@ -277,58 +189,9 @@ def main():
             cond_id = torch.full((args.batch_size,), int(args.condition_id), device=device, dtype=torch.long)
         cond = cond_id
 
-    # type1 program conditioning (library replay)
-    cond_cont = None
-    if type1_cols:
-        ref_glob = cfg.get("data_glob") or args.data_glob
-        if ref_glob:
-            ref_glob = str(resolve_path(Path(args.config).parent, ref_glob)) if args.config else ref_glob
-            base = Path(ref_glob).parent
-            pat = Path(ref_glob).name
-            refs = sorted(base.glob(pat))
-            if refs:
-                ref_path = refs[0]
-                ref_rows = []
-                with gzip.open(ref_path, "rt", newline="") as fh:
-                    reader = csv.DictReader(fh)
-                    for row in reader:
-                        ref_rows.append(row)
-                if len(ref_rows) >= args.seq_len:
-                    seq = ref_rows[: args.seq_len]
-                    cond_cont = torch.zeros(args.batch_size, args.seq_len, len(type1_cols), device=device)
-                    for t, row in enumerate(seq):
-                        for i, c in enumerate(type1_cols):
-                            cond_cont[:, t, i] = float(row[c])
-                    cond_cont = normalize_cont(
-                        cond_cont,
-                        type1_cols,
-                        mean,
-                        std,
-                        transforms=transforms,
-                        quantile_probs=quantile_probs,
-                        quantile_values=quantile_values,
-                        use_quantile=use_quantile,
-                    )
-
-    trend = None
-    if temporal_model is not None:
-        trend = temporal_model.generate(args.batch_size, args.seq_len, device, cond_cont=cond_cont)
-        if temporal_focus_type4 and type4_cols:
-            type4_model_idx = [model_cont_cols.index(c) for c in type4_cols if c in model_cont_cols]
-            if type4_model_idx:
-                trend_mask = torch.zeros(1, 1, len(model_cont_cols), device=device, dtype=trend.dtype)
-                trend_mask[:, :, type4_model_idx] = 1.0
-                trend = trend * trend_mask
-        elif temporal_exclude_type4 and type4_cols:
-            type4_model_idx = [model_cont_cols.index(c) for c in type4_cols if c in model_cont_cols]
-            if type4_model_idx:
-                trend_mask = torch.ones(1, 1, len(model_cont_cols), device=device, dtype=trend.dtype)
-                trend_mask[:, :, type4_model_idx] = 0.0
-                trend = trend * trend_mask
-
     for t in reversed(range(args.timesteps)):
         t_batch = torch.full((args.batch_size,), t, device=device, dtype=torch.long)
-        eps_pred, logits = model(x_cont, x_disc, t_batch, cond, cond_cont=cond_cont)
+        eps_pred, logits = model(x_cont, x_disc, t_batch, cond)
 
         a_t = alphas[t]
         a_bar_t = alphas_cumprod[t]
@@ -362,8 +225,6 @@ def main():
                     )
                     x_disc[:, :, i][mask] = sampled[mask]
 
-    if trend is not None:
-        x_cont = x_cont + trend
     # move to CPU for export
     x_cont = x_cont.cpu()
     x_disc = x_disc.cpu()
@@ -372,70 +233,19 @@ def main():
     if args.clip_k > 0:
         x_cont = torch.clamp(x_cont, -args.clip_k, args.clip_k)
 
-    if use_quantile:
-        q_vals = {c: quantile_values[c] for c in model_cont_cols}
-        x_cont = inverse_quantile_transform(x_cont, model_cont_cols, quantile_probs, q_vals)
-    else:
-        mean_vec = torch.tensor([mean[c] for c in model_cont_cols], dtype=x_cont.dtype)
-        std_vec = torch.tensor([std[c] for c in model_cont_cols], dtype=x_cont.dtype)
+    mean_vec = torch.tensor([mean[c] for c in cont_cols], dtype=x_cont.dtype)
+    std_vec = torch.tensor([std[c] for c in cont_cols], dtype=x_cont.dtype)
     x_cont = x_cont * std_vec + mean_vec
-    for i, c in enumerate(model_cont_cols):
+    for i, c in enumerate(cont_cols):
         if transforms.get(c) == "log1p":
             x_cont[:, :, i] = torch.expm1(x_cont[:, :, i])
-    if cont_post_calibrate and quantile_raw_values and quantile_probs:
-        q_raw = {c: quantile_raw_values[c] for c in model_cont_cols}
-        x_cont = quantile_calibrate_to_real(x_cont, model_cont_cols, quantile_probs, q_raw)
-    # bound to observed min/max per feature
+    # clamp to observed min/max per feature
     if vmin and vmax:
-        for i, c in enumerate(model_cont_cols):
+        for i, c in enumerate(cont_cols):
             lo = vmin.get(c, None)
             hi = vmax.get(c, None)
-            if lo is None or hi is None:
-                continue
-            lo = float(lo)
-            hi = float(hi)
-            if cont_bound_mode == "none":
-                continue
-            if cont_bound_mode == "sigmoid":
-                x_cont[:, :, i] = lo + (hi - lo) * torch.sigmoid(x_cont[:, :, i])
-            elif cont_bound_mode == "soft_tanh":
-                # Soft bound without hard piling at edges
-                mid = 0.5 * (lo + hi)
-                half = 0.5 * (hi - lo)
-                denom = cont_bound_strength if cont_bound_strength > 0 else 1.0
-                x_cont[:, :, i] = mid + half * torch.tanh(x_cont[:, :, i] / denom)
-            else:
-                x_cont[:, :, i] = torch.clamp(x_cont[:, :, i], lo, hi)
-
-    # optional post-scaling for problematic features
-    if cont_post_scale:
-        for i, c in enumerate(model_cont_cols):
-            if c in cont_post_scale:
-                try:
-                    scale = float(cont_post_scale[c])
-                except Exception:
-                    scale = 1.0
-                x_cont[:, :, i] = x_cont[:, :, i] * scale
-
-    # assemble full continuous output
-    full_cont = torch.zeros(args.batch_size, args.seq_len, len(cont_cols), dtype=x_cont.dtype)
-    for i, c in enumerate(model_cont_cols):
-        full_idx = cont_cols.index(c)
-        full_cont[:, :, full_idx] = x_cont[:, :, i]
-    if cond_cont is not None and type1_cols:
-        mean_vec = torch.tensor([mean[c] for c in type1_cols], dtype=cond_cont.dtype)
-        std_vec = torch.tensor([std[c] for c in type1_cols], dtype=cond_cont.dtype)
-        cond_denorm = cond_cont.cpu() * std_vec + mean_vec
-        for i, c in enumerate(type1_cols):
-            full_idx = cont_cols.index(c)
-            full_cont[:, :, full_idx] = cond_denorm[:, :, i]
-    for c in type5_cols:
-        if c.endswith("Z"):
-            base = c[:-1]
-            if base in cont_cols:
-                bidx = cont_cols.index(base)
-                cidx = cont_cols.index(c)
-                full_cont[:, :, cidx] = full_cont[:, :, bidx]
+            if lo is not None and hi is not None:
+                x_cont[:, :, i] = torch.clamp(x_cont[:, :, i], float(lo), float(hi))
 
     header = read_header(data_path)
     out_cols = [c for c in header if c != time_col or args.include_time]
@@ -456,7 +266,7 @@ def main():
                 if args.include_time and time_col in header:
                     row[time_col] = str(row_index)
                 for i, c in enumerate(cont_cols):
-                    val = float(full_cont[b, t, i])
+                    val = float(x_cont[b, t, i])
                     if int_like.get(c, False):
                         row[c] = str(int(round(val)))
                     else:

example/export_samples_resume.py

@@ -1,440 +0,0 @@
-#!/usr/bin/env python3
-"""Sample from a trained hybrid diffusion model with routing-aware export fixes."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import gzip
-import json
-import os
-from pathlib import Path
-
-import torch
-import torch.nn.functional as F
-
-from data_utils import inverse_quantile_transform, load_split, normalize_cont, quantile_calibrate_to_real
-from export_samples import build_inverse_vocab, load_stats, load_torch_state, read_header
-from hybrid_diffusion import (
-    HybridDiffusionModel,
-    TemporalGRUGenerator,
-    TemporalTransformerGenerator,
-    cosine_beta_schedule,
-)
-from platform_utils import resolve_device, resolve_path
-from submission_type_utils import (
-    denormalize_cont_tensor,
-    resolve_routing_features,
-    resolve_taxonomy_features,
-)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Sample and export HAI feature sequences with routing-aware fixes.")
-    base_dir = Path(__file__).resolve().parent
-    repo_dir = base_dir.parent.parent
-    parser.add_argument("--data-path", default=str(repo_dir / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz"))
-    parser.add_argument("--data-glob", default=str(repo_dir / "dataset" / "hai" / "hai-21.03" / "train*.csv.gz"))
-    parser.add_argument("--split-path", default=str(base_dir / "feature_split.json"))
-    parser.add_argument("--stats-path", default=str(base_dir / "results" / "cont_stats.json"))
-    parser.add_argument("--vocab-path", default=str(base_dir / "results" / "disc_vocab.json"))
-    parser.add_argument("--model-path", default=str(base_dir / "results" / "model.pt"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--timesteps", type=int, default=200)
-    parser.add_argument("--seq-len", type=int, default=64)
-    parser.add_argument("--batch-size", type=int, default=2)
-    parser.add_argument("--device", default="auto", help="cpu, cuda, or auto")
-    parser.add_argument("--include-time", action="store_true", help="Include time column as a simple index")
-    parser.add_argument("--clip-k", type=float, default=5.0, help="Clip continuous values to mean±k*std")
-    parser.add_argument("--use-ema", action="store_true", help="Use EMA weights if available")
-    parser.add_argument("--config", default=None, help="Optional config_used.json to infer conditioning")
-    parser.add_argument("--condition-id", type=int, default=-1, help="Condition file id (0..N-1), -1=random")
-    parser.add_argument("--include-condition", action="store_true", help="Include condition id column in CSV")
-    return parser.parse_args()
-
-
-def main():
-    args = parse_args()
-    base_dir = Path(__file__).resolve().parent
-    args.data_path = str(resolve_path(base_dir, args.data_path))
-    args.data_glob = str(resolve_path(base_dir, args.data_glob)) if args.data_glob else ""
-    args.split_path = str(resolve_path(base_dir, args.split_path))
-    args.stats_path = str(resolve_path(base_dir, args.stats_path))
-    args.vocab_path = str(resolve_path(base_dir, args.vocab_path))
-    args.model_path = str(resolve_path(base_dir, args.model_path))
-    args.out = str(resolve_path(base_dir, args.out))
-
-    if not os.path.exists(args.model_path):
-        raise SystemExit(f"missing model file: {args.model_path}")
-
-    data_path = args.data_path
-    if args.data_glob:
-        base = Path(args.data_glob).parent
-        pat = Path(args.data_glob).name
-        matches = sorted(base.glob(pat))
-        if matches:
-            data_path = str(matches[0])
-
-    split = load_split(args.split_path)
-    time_col = split.get("time_column", "time")
-    cont_cols = [c for c in split["continuous"] if c != time_col]
-    disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col]
-
-    stats = load_stats(args.stats_path)
-    mean = stats["mean"]
-    std = stats["std"]
-    vmin = stats.get("min", {})
-    vmax = stats.get("max", {})
-    int_like = stats.get("int_like", {})
-    max_decimals = stats.get("max_decimals", {})
-    transforms = stats.get("transform", {})
-    quantile_probs = stats.get("quantile_probs")
-    quantile_values = stats.get("quantile_values")
-    quantile_raw_values = stats.get("quantile_raw_values")
-
-    vocab_json = json.load(open(args.vocab_path, "r", encoding="utf-8"))
-    vocab = vocab_json["vocab"]
-    top_token = vocab_json.get("top_token", {})
-    inv_vocab = build_inverse_vocab(vocab)
-    vocab_sizes = [len(vocab[c]) for c in disc_cols]
-
-    device = resolve_device(args.device)
-    cfg = {}
-    use_condition = False
-    cond_vocab_size = 0
-    if args.config:
-        args.config = str(resolve_path(base_dir, args.config))
-    if args.config and os.path.exists(args.config):
-        with open(args.config, "r", encoding="utf-8") as f:
-            cfg = json.load(f)
-        use_condition = bool(cfg.get("use_condition")) and cfg.get("condition_type") == "file_id"
-        if use_condition:
-            cfg_base = Path(args.config).resolve().parent
-            cfg_glob = cfg.get("data_glob", args.data_glob)
-            cfg_glob = str(resolve_path(cfg_base, cfg_glob))
-            base = Path(cfg_glob).parent
-            pat = Path(cfg_glob).name
-            cond_vocab_size = len(sorted(base.glob(pat)))
-            if cond_vocab_size <= 0:
-                raise SystemExit(f"use_condition enabled but no files matched data_glob: {cfg_glob}")
-
-    cont_target = str(cfg.get("cont_target", "eps"))
-    cont_clamp_x0 = float(cfg.get("cont_clamp_x0", 0.0))
-    use_quantile = bool(cfg.get("use_quantile_transform", False))
-    cont_bound_mode = str(cfg.get("cont_bound_mode", "clamp"))
-    cont_bound_strength = float(cfg.get("cont_bound_strength", 1.0))
-    cont_post_scale = cfg.get("cont_post_scale", {}) if isinstance(cfg.get("cont_post_scale", {}), dict) else {}
-    cont_post_calibrate = bool(cfg.get("cont_post_calibrate", False))
-
-    route_type1_cols = resolve_routing_features(cfg, cont_cols, "type1_features")
-    route_type5_cols = resolve_routing_features(cfg, cont_cols, "type5_features")
-    type4_cols = resolve_taxonomy_features(cfg, cont_cols, "type4_features")
-    model_cont_cols = [c for c in cont_cols if c not in route_type1_cols and c not in route_type5_cols]
-
-    use_temporal_stage1 = bool(cfg.get("use_temporal_stage1", False))
-    temporal_use_type1_cond = bool(cfg.get("temporal_use_type1_cond", False))
-    temporal_focus_type4 = bool(cfg.get("temporal_focus_type4", False))
-    temporal_exclude_type4 = bool(cfg.get("temporal_exclude_type4", False))
-    temporal_backbone = str(cfg.get("temporal_backbone", "gru"))
-    temporal_hidden_dim = int(cfg.get("temporal_hidden_dim", 256))
-    temporal_num_layers = int(cfg.get("temporal_num_layers", 1))
-    temporal_dropout = float(cfg.get("temporal_dropout", 0.0))
-    temporal_pos_dim = int(cfg.get("temporal_pos_dim", 64))
-    temporal_use_pos_embed = bool(cfg.get("temporal_use_pos_embed", True))
-    temporal_transformer_num_layers = int(cfg.get("temporal_transformer_num_layers", 2))
-    temporal_transformer_nhead = int(cfg.get("temporal_transformer_nhead", 4))
-    temporal_transformer_ff_dim = int(cfg.get("temporal_transformer_ff_dim", 512))
-    temporal_transformer_dropout = float(cfg.get("temporal_transformer_dropout", 0.1))
-    backbone_type = str(cfg.get("backbone_type", "gru"))
-    transformer_num_layers = int(cfg.get("transformer_num_layers", 2))
-    transformer_nhead = int(cfg.get("transformer_nhead", 4))
-    transformer_ff_dim = int(cfg.get("transformer_ff_dim", 512))
-    transformer_dropout = float(cfg.get("transformer_dropout", 0.1))
-
-    model = HybridDiffusionModel(
-        cont_dim=len(model_cont_cols),
-        disc_vocab_sizes=vocab_sizes,
-        time_dim=int(cfg.get("model_time_dim", 64)),
-        hidden_dim=int(cfg.get("model_hidden_dim", 256)),
-        num_layers=int(cfg.get("model_num_layers", 1)),
-        dropout=float(cfg.get("model_dropout", 0.0)),
-        ff_mult=int(cfg.get("model_ff_mult", 2)),
-        pos_dim=int(cfg.get("model_pos_dim", 64)),
-        use_pos_embed=bool(cfg.get("model_use_pos_embed", True)),
-        backbone_type=backbone_type,
-        transformer_num_layers=transformer_num_layers,
-        transformer_nhead=transformer_nhead,
-        transformer_ff_dim=transformer_ff_dim,
-        transformer_dropout=transformer_dropout,
-        cond_cont_dim=len(route_type1_cols),
-        cond_vocab_size=cond_vocab_size if use_condition else 0,
-        cond_dim=int(cfg.get("cond_dim", 32)),
-        use_tanh_eps=bool(cfg.get("use_tanh_eps", False)),
-        eps_scale=float(cfg.get("eps_scale", 1.0)),
-    ).to(device)
-    if args.use_ema and os.path.exists(args.model_path.replace("model.pt", "model_ema.pt")):
-        ema_path = args.model_path.replace("model.pt", "model_ema.pt")
-        model.load_state_dict(load_torch_state(ema_path, device))
-    else:
-        model.load_state_dict(load_torch_state(args.model_path, device))
-    model.eval()
-
-    temporal_model = None
-    if use_temporal_stage1:
-        temporal_path = Path(args.model_path).with_name("temporal.pt")
-        if not temporal_path.exists():
-            raise SystemExit(f"missing temporal model file: {temporal_path}")
-        temporal_state = load_torch_state(str(temporal_path), device)
-        temporal_cond_dim = len(route_type1_cols) if (temporal_use_type1_cond and route_type1_cols) else 0
-        if isinstance(temporal_state, dict):
-            if "in_proj.weight" in temporal_state:
-                try:
-                    temporal_cond_dim = max(0, int(temporal_state["in_proj.weight"].shape[1]) - len(model_cont_cols))
-                except Exception:
-                    pass
-            elif "gru.weight_ih_l0" in temporal_state:
-                try:
-                    temporal_cond_dim = max(0, int(temporal_state["gru.weight_ih_l0"].shape[1]) - len(model_cont_cols))
-                except Exception:
-                    pass
-        if temporal_backbone == "transformer":
-            temporal_model = TemporalTransformerGenerator(
-                input_dim=len(model_cont_cols),
-                hidden_dim=temporal_hidden_dim,
-                num_layers=temporal_transformer_num_layers,
-                nhead=temporal_transformer_nhead,
-                ff_dim=temporal_transformer_ff_dim,
-                dropout=temporal_transformer_dropout,
-                pos_dim=temporal_pos_dim,
-                use_pos_embed=temporal_use_pos_embed,
-                cond_dim=temporal_cond_dim,
-            ).to(device)
-        else:
-            temporal_model = TemporalGRUGenerator(
-                input_dim=len(model_cont_cols),
-                hidden_dim=temporal_hidden_dim,
-                num_layers=temporal_num_layers,
-                dropout=temporal_dropout,
-                cond_dim=temporal_cond_dim,
-            ).to(device)
-        temporal_model.load_state_dict(temporal_state)
-        temporal_model.eval()
-
-    betas = cosine_beta_schedule(args.timesteps).to(device)
-    alphas = 1.0 - betas
-    alphas_cumprod = torch.cumprod(alphas, dim=0)
-
-    x_cont = torch.randn(args.batch_size, args.seq_len, len(model_cont_cols), device=device)
-    x_disc = torch.full((args.batch_size, args.seq_len, len(disc_cols)), 0, device=device, dtype=torch.long)
-    mask_tokens = torch.tensor(vocab_sizes, device=device)
-    for i in range(len(disc_cols)):
-        x_disc[:, :, i] = mask_tokens[i]
-
-    cond = None
-    if use_condition:
-        if cond_vocab_size <= 0:
-            raise SystemExit("use_condition enabled but no files matched data_glob")
-        if args.condition_id < 0:
-            cond_id = torch.randint(0, cond_vocab_size, (args.batch_size,), device=device)
-        else:
-            cond_id = torch.full((args.batch_size,), int(args.condition_id), device=device, dtype=torch.long)
-        cond = cond_id
-
-    cond_cont = None
-    if route_type1_cols:
-        ref_glob = cfg.get("data_glob") or args.data_glob
-        if ref_glob:
-            ref_glob = str(resolve_path(Path(args.config).parent, ref_glob)) if args.config else ref_glob
-            base = Path(ref_glob).parent
-            pat = Path(ref_glob).name
-            refs = sorted(base.glob(pat))
-            if refs:
-                ref_path = refs[0]
-                ref_rows = []
-                with gzip.open(ref_path, "rt", newline="") as fh:
-                    reader = csv.DictReader(fh)
-                    for row in reader:
-                        ref_rows.append(row)
-                if len(ref_rows) >= args.seq_len:
-                    seq = ref_rows[: args.seq_len]
-                    cond_cont = torch.zeros(args.batch_size, args.seq_len, len(route_type1_cols), device=device)
-                    for t, row in enumerate(seq):
-                        for i, c in enumerate(route_type1_cols):
-                            cond_cont[:, t, i] = float(row[c])
-                    cond_cont = normalize_cont(
-                        cond_cont,
-                        route_type1_cols,
-                        mean,
-                        std,
-                        transforms=transforms,
-                        quantile_probs=quantile_probs,
-                        quantile_values=quantile_values,
-                        use_quantile=use_quantile,
-                    )
-
-    trend = None
-    if temporal_model is not None:
-        trend = temporal_model.generate(args.batch_size, args.seq_len, device, cond_cont=cond_cont)
-        if temporal_focus_type4 and type4_cols:
-            type4_model_idx = [model_cont_cols.index(c) for c in type4_cols if c in model_cont_cols]
-            if type4_model_idx:
-                trend_mask = torch.zeros(1, 1, len(model_cont_cols), device=device, dtype=trend.dtype)
-                trend_mask[:, :, type4_model_idx] = 1.0
-                trend = trend * trend_mask
-        elif temporal_exclude_type4 and type4_cols:
-            type4_model_idx = [model_cont_cols.index(c) for c in type4_cols if c in model_cont_cols]
-            if type4_model_idx:
-                trend_mask = torch.ones(1, 1, len(model_cont_cols), device=device, dtype=trend.dtype)
-                trend_mask[:, :, type4_model_idx] = 0.0
-                trend = trend * trend_mask
-
-    for t in reversed(range(args.timesteps)):
-        t_batch = torch.full((args.batch_size,), t, device=device, dtype=torch.long)
-        eps_pred, logits = model(x_cont, x_disc, t_batch, cond, cond_cont=cond_cont)
-
-        a_t = alphas[t]
-        a_bar_t = alphas_cumprod[t]
-
-        if cont_target == "x0":
-            x0_pred = eps_pred
-            if cont_clamp_x0 > 0:
-                x0_pred = torch.clamp(x0_pred, -cont_clamp_x0, cont_clamp_x0)
-            eps_pred = (x_cont - torch.sqrt(a_bar_t) * x0_pred) / torch.sqrt(1.0 - a_bar_t)
-        coef1 = 1.0 / torch.sqrt(a_t)
-        coef2 = (1 - a_t) / torch.sqrt(1 - a_bar_t)
-        mean_x = coef1 * (x_cont - coef2 * eps_pred)
-        if t > 0:
-            noise = torch.randn_like(x_cont)
-            x_cont = mean_x + torch.sqrt(betas[t]) * noise
-        else:
-            x_cont = mean_x
-        if args.clip_k > 0:
-            x_cont = torch.clamp(x_cont, -args.clip_k, args.clip_k)
-
-        for i, logit in enumerate(logits):
-            if t == 0:
-                probs = F.softmax(logit, dim=-1)
-                x_disc[:, :, i] = torch.argmax(probs, dim=-1)
-            else:
-                mask = x_disc[:, :, i] == mask_tokens[i]
-                if mask.any():
-                    probs = F.softmax(logit, dim=-1)
-                    sampled = torch.multinomial(probs.view(-1, probs.size(-1)), 1).view(
-                        args.batch_size, args.seq_len
-                    )
-                    x_disc[:, :, i][mask] = sampled[mask]
-
-    if trend is not None:
-        x_cont = x_cont + trend
-    x_cont = x_cont.cpu()
-    x_disc = x_disc.cpu()
-
-    if args.clip_k > 0:
-        x_cont = torch.clamp(x_cont, -args.clip_k, args.clip_k)
-
-    if use_quantile:
-        q_vals = {c: quantile_values[c] for c in model_cont_cols}
-        x_cont = inverse_quantile_transform(x_cont, model_cont_cols, quantile_probs, q_vals)
-    else:
-        mean_vec = torch.tensor([mean[c] for c in model_cont_cols], dtype=x_cont.dtype)
-        std_vec = torch.tensor([std[c] for c in model_cont_cols], dtype=x_cont.dtype)
-        x_cont = x_cont * std_vec + mean_vec
-    for i, c in enumerate(model_cont_cols):
-        if transforms.get(c) == "log1p":
-            x_cont[:, :, i] = torch.expm1(x_cont[:, :, i])
-    if cont_post_calibrate and quantile_raw_values and quantile_probs:
-        q_raw = {c: quantile_raw_values[c] for c in model_cont_cols}
-        x_cont = quantile_calibrate_to_real(x_cont, model_cont_cols, quantile_probs, q_raw)
-    if vmin and vmax:
-        for i, c in enumerate(model_cont_cols):
-            lo = vmin.get(c, None)
-            hi = vmax.get(c, None)
-            if lo is None or hi is None:
-                continue
-            lo = float(lo)
-            hi = float(hi)
-            if cont_bound_mode == "none":
-                continue
-            if cont_bound_mode == "sigmoid":
-                x_cont[:, :, i] = lo + (hi - lo) * torch.sigmoid(x_cont[:, :, i])
-            elif cont_bound_mode == "soft_tanh":
-                mid = 0.5 * (lo + hi)
-                half = 0.5 * (hi - lo)
-                denom = cont_bound_strength if cont_bound_strength > 0 else 1.0
-                x_cont[:, :, i] = mid + half * torch.tanh(x_cont[:, :, i] / denom)
-            else:
-                x_cont[:, :, i] = torch.clamp(x_cont[:, :, i], lo, hi)
-
-    if cont_post_scale:
-        for i, c in enumerate(model_cont_cols):
-            if c in cont_post_scale:
-                try:
-                    scale = float(cont_post_scale[c])
-                except Exception:
-                    scale = 1.0
-                x_cont[:, :, i] = x_cont[:, :, i] * scale
-
-    full_cont = torch.zeros(args.batch_size, args.seq_len, len(cont_cols), dtype=x_cont.dtype)
-    for i, c in enumerate(model_cont_cols):
-        full_idx = cont_cols.index(c)
-        full_cont[:, :, full_idx] = x_cont[:, :, i]
-    if cond_cont is not None and route_type1_cols:
-        cond_denorm = denormalize_cont_tensor(
-            cond_cont.cpu(),
-            route_type1_cols,
-            mean,
-            std,
-            transforms=transforms,
-            quantile_probs=quantile_probs,
-            quantile_values=quantile_values,
-            use_quantile=use_quantile,
-        )
-        for i, c in enumerate(route_type1_cols):
-            full_idx = cont_cols.index(c)
-            full_cont[:, :, full_idx] = cond_denorm[:, :, i]
-    for c in route_type5_cols:
-        if c.endswith("Z"):
-            base = c[:-1]
-            if base in cont_cols:
-                bidx = cont_cols.index(base)
-                cidx = cont_cols.index(c)
-                full_cont[:, :, cidx] = full_cont[:, :, bidx]
-
-    header = read_header(data_path)
-    out_cols = [c for c in header if c != time_col or args.include_time]
-    if args.include_condition and use_condition:
-        out_cols = ["__cond_file_id"] + out_cols
-
-    os.makedirs(os.path.dirname(args.out), exist_ok=True)
-    with open(args.out, "w", newline="", encoding="utf-8") as f:
-        writer = csv.DictWriter(f, fieldnames=out_cols)
-        writer.writeheader()
-
-        row_index = 0
-        for b in range(args.batch_size):
-            for t in range(args.seq_len):
-                row = {}
-                if args.include_condition and use_condition:
-                    row["__cond_file_id"] = str(int(cond[b].item())) if cond is not None else "-1"
-                if args.include_time and time_col in header:
-                    row[time_col] = str(row_index)
-                for i, c in enumerate(cont_cols):
-                    val = float(full_cont[b, t, i])
-                    if int_like.get(c, False):
-                        row[c] = str(int(round(val)))
-                    else:
-                        dec = int(max_decimals.get(c, 6))
-                        fmt = ("%%.%df" % dec) if dec > 0 else "%.0f"
-                        row[c] = fmt % val
-                for i, c in enumerate(disc_cols):
-                    tok_idx = int(x_disc[b, t, i])
-                    tok = inv_vocab[c][tok_idx] if tok_idx < len(inv_vocab[c]) else "<UNK>"
-                    if tok == "<UNK>" and c in top_token:
-                        tok = top_token[c]
-                    row[c] = tok
-                writer.writerow(row)
-                row_index += 1
-
-
-if __name__ == "__main__":
-    main()

example/filtered_metrics.py

@@ -1,65 +0,0 @@
-#!/usr/bin/env python3
-"""Compute filtered KS/JSD by excluding hard-to-learn features."""
-
-import argparse
-import json
-from pathlib import Path
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Filtered metrics from eval.json.")
-    base_dir = Path(__file__).resolve().parent
-    parser.add_argument("--eval", default=str(base_dir / "results" / "eval.json"))
-    parser.add_argument("--min-std", type=float, default=1e-3, help="threshold for std collapse")
-    parser.add_argument("--ks-threshold", type=float, default=0.95, help="auto-exclude if KS >= threshold")
-    parser.add_argument("--out", default=str(base_dir / "results" / "filtered_metrics.json"))
-    return parser.parse_args()
-
-
-def main():
-    args = parse_args()
-    eval_path = Path(args.eval)
-    if not eval_path.exists():
-        raise SystemExit(f"missing eval.json: {eval_path}")
-    data = json.loads(eval_path.read_text(encoding="utf-8"))
-
-    cont_ks = data.get("continuous_ks", {})
-    cont_stats = data.get("continuous_summary", {})
-
-    dropped = []
-    kept = []
-    ks_vals = []
-    for feat, ks in cont_ks.items():
-        std = None
-        if feat in cont_stats:
-            std = cont_stats[feat].get("std", None)
-        drop = False
-        if std is not None and std <= args.min_std:
-            drop = True
-        if ks is not None and ks >= args.ks_threshold:
-            drop = True
-        if drop:
-            dropped.append({"feature": feat, "ks": ks, "std": std})
-        else:
-            kept.append(feat)
-            ks_vals.append(ks)
-
-    filtered_avg_ks = sum(ks_vals) / len(ks_vals) if ks_vals else None
-    out = {
-        "filtered_avg_ks": filtered_avg_ks,
-        "kept_features": kept,
-        "dropped_features": dropped,
-        "rules": {
-            "min_std": args.min_std,
-            "ks_threshold": args.ks_threshold,
-        },
-        "original_avg_ks": data.get("avg_ks"),
-    }
-    Path(args.out).write_text(json.dumps(out, indent=2), encoding="utf-8")
-    print("filtered_avg_ks", filtered_avg_ks)
-    print("dropped", len(dropped))
-    print("wrote", args.out)
-
-
-if __name__ == "__main__":
-    main()

example/hybrid_diffusion.py

@@ -66,182 +66,6 @@ class SinusoidalTimeEmbedding(nn.Module):
         return emb
 
 
-class TemporalGRUGenerator(nn.Module):
-    """Stage-1 temporal generator for sequence trend."""
-
-    def __init__(self, input_dim: int, hidden_dim: int = 256, num_layers: int = 1, dropout: float = 0.0, cond_dim: int = 0):
-        super().__init__()
-        self.input_dim = int(input_dim)
-        self.cond_dim = int(cond_dim)
-        self.start_token = nn.Parameter(torch.zeros(input_dim))
-        self.gru = nn.GRU(
-            input_dim + self.cond_dim,
-            hidden_dim,
-            num_layers=num_layers,
-            dropout=dropout if num_layers > 1 else 0.0,
-            batch_first=True,
-        )
-        self.out = nn.Linear(hidden_dim, input_dim)
-
-    def forward_teacher(self, x: torch.Tensor, cond_cont: torch.Tensor | None = None) -> torch.Tensor:
-        """Teacher-forced next-step prediction. Returns trend sequence and preds."""
-        if x.size(1) < 2:
-            raise ValueError("sequence length must be >= 2 for teacher forcing")
-        if self.cond_dim > 0:
-            if cond_cont is None:
-                cond_cont = torch.zeros(x.size(0), x.size(1), self.cond_dim, device=x.device, dtype=x.dtype)
-            inp = torch.cat([x[:, :-1, :], cond_cont[:, :-1, :]], dim=-1)
-        else:
-            inp = x[:, :-1, :]
-        out, _ = self.gru(inp)
-        pred_next = self.out(out)
-        trend = torch.zeros_like(x)
-        trend[:, 0, :] = x[:, 0, :]
-        trend[:, 1:, :] = pred_next
-        return trend, pred_next
-
-    def generate(
-        self,
-        batch_size: int,
-        seq_len: int,
-        device: torch.device,
-        cond_cont: torch.Tensor | None = None,
-        start_x: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Autoregressively generate a backbone sequence."""
-        h = None
-        if start_x is not None:
-            if start_x.dim() == 3 and start_x.size(1) == 1:
-                start_x = start_x[:, 0, :]
-            prev = start_x.to(device)
-        else:
-            prev = self.start_token.unsqueeze(0).expand(batch_size, -1).to(device)
-        if self.cond_dim > 0:
-            if cond_cont is None:
-                cond_cont = torch.zeros(batch_size, seq_len, self.cond_dim, device=device, dtype=prev.dtype)
-            else:
-                cond_cont = cond_cont.to(device)
-        outputs = []
-        for t in range(seq_len):
-            if self.cond_dim > 0:
-                ct = cond_cont[:, t, :] if t < cond_cont.size(1) else torch.zeros(batch_size, self.cond_dim, device=device, dtype=prev.dtype)
-                step_inp = torch.cat([prev, ct], dim=-1)
-            else:
-                step_inp = prev
-            out, h = self.gru(step_inp.unsqueeze(1), h)
-            nxt = self.out(out.squeeze(1))
-            outputs.append(nxt.unsqueeze(1))
-            prev = nxt
-        return torch.cat(outputs, dim=1)
-
-
-class TemporalTransformerGenerator(nn.Module):
-    def __init__(
-        self,
-        input_dim: int,
-        hidden_dim: int = 256,
-        num_layers: int = 2,
-        nhead: int = 4,
-        ff_dim: int = 512,
-        dropout: float = 0.1,
-        pos_dim: int = 64,
-        use_pos_embed: bool = True,
-        cond_dim: int = 0,
-    ):
-        super().__init__()
-        self.input_dim = int(input_dim)
-        self.cond_dim = int(cond_dim)
-        self.start_token = nn.Parameter(torch.zeros(input_dim))
-        self.in_proj = nn.Linear(input_dim + self.cond_dim, hidden_dim)
-        self.pos_dim = pos_dim
-        self.use_pos_embed = use_pos_embed
-        self.pos_proj = nn.Linear(pos_dim, hidden_dim) if use_pos_embed and pos_dim > 0 else None
-        encoder_layer = nn.TransformerEncoderLayer(
-            d_model=hidden_dim,
-            nhead=nhead,
-            dim_feedforward=ff_dim,
-            dropout=dropout,
-            batch_first=True,
-            activation="gelu",
-        )
-        self.backbone = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
-        self.out = nn.Linear(hidden_dim, input_dim)
-
-    def forward_teacher(self, x: torch.Tensor, cond_cont: torch.Tensor | None = None) -> torch.Tensor:
-        if x.size(1) < 2:
-            raise ValueError("sequence length must be >= 2 for teacher forcing")
-        if self.cond_dim > 0:
-            if cond_cont is None:
-                cond_cont = torch.zeros(x.size(0), x.size(1), self.cond_dim, device=x.device, dtype=x.dtype)
-            inp = torch.cat([x[:, :-1, :], cond_cont[:, :-1, :]], dim=-1)
-        else:
-            inp = x[:, :-1, :]
-        out = self._encode(inp)
-        pred_next = self.out(out)
-        trend = torch.zeros_like(x)
-        trend[:, 0, :] = x[:, 0, :]
-        trend[:, 1:, :] = pred_next
-        return trend, pred_next
-
-    def generate(
-        self,
-        batch_size: int,
-        seq_len: int,
-        device: torch.device,
-        cond_cont: torch.Tensor | None = None,
-        start_x: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        if start_x is not None:
-            if start_x.dim() == 2:
-                context_x = start_x.unsqueeze(1).to(device)
-            else:
-                context_x = start_x.to(device)
-        else:
-            context_x = self.start_token.unsqueeze(0).unsqueeze(1).expand(batch_size, 1, -1).to(device)
-        if self.cond_dim > 0:
-            if cond_cont is None:
-                cond_cont = torch.zeros(batch_size, seq_len, self.cond_dim, device=device, dtype=context_x.dtype)
-            else:
-                cond_cont = cond_cont.to(device)
-                if cond_cont.size(1) < seq_len:
-                    pad = torch.zeros(batch_size, seq_len - cond_cont.size(1), self.cond_dim, device=device, dtype=cond_cont.dtype)
-                    cond_cont = torch.cat([cond_cont, pad], dim=1)
-        outputs = []
-        for _ in range(seq_len):
-            if self.cond_dim > 0:
-                cond_ctx = cond_cont[:, : context_x.size(1), :]
-                context_in = torch.cat([context_x, cond_ctx], dim=-1)
-            else:
-                context_in = context_x
-            out = self._encode(context_in)
-            next_token = self.out(out[:, -1, :])
-            outputs.append(next_token.unsqueeze(1))
-            context_x = torch.cat([context_x, next_token.unsqueeze(1)], dim=1)
-        return torch.cat(outputs, dim=1)
-
-    def _encode(self, x: torch.Tensor) -> torch.Tensor:
-        feat = self.in_proj(x)
-        if self.pos_proj is not None and self.use_pos_embed and self.pos_dim > 0:
-            pos = self._positional_encoding(x.size(1), self.pos_dim, x.device)
-            pos = self.pos_proj(pos).unsqueeze(0).expand(x.size(0), -1, -1)
-            feat = feat + pos
-        mask = self._causal_mask(x.size(1), x.device)
-        return self.backbone(feat, mask=mask)
-
-    @staticmethod
-    def _causal_mask(seq_len: int, device: torch.device) -> torch.Tensor:
-        return torch.triu(torch.ones(seq_len, seq_len, device=device), diagonal=1).bool()
-
-    @staticmethod
-    def _positional_encoding(seq_len: int, dim: int, device: torch.device) -> torch.Tensor:
-        pos = torch.arange(seq_len, device=device).float().unsqueeze(1)
-        div = torch.exp(torch.arange(0, dim, 2, device=device).float() * (-math.log(10000.0) / dim))
-        pe = torch.zeros(seq_len, dim, device=device)
-        pe[:, 0::2] = torch.sin(pos * div)
-        pe[:, 1::2] = torch.cos(pos * div)
-        return pe
-
-
 class HybridDiffusionModel(nn.Module):
     def __init__(
         self,
@@ -254,12 +78,6 @@ class HybridDiffusionModel(nn.Module):
         ff_mult: int = 2,
         pos_dim: int = 64,
         use_pos_embed: bool = True,
-        backbone_type: str = "gru",  # gru | transformer
-        transformer_num_layers: int = 4,
-        transformer_nhead: int = 8,
-        transformer_ff_dim: int = 2048,
-        transformer_dropout: float = 0.1,
-        cond_cont_dim: int = 0,
         cond_vocab_size: int = 0,
         cond_dim: int = 32,
         use_tanh_eps: bool = False,
@@ -274,14 +92,12 @@ class HybridDiffusionModel(nn.Module):
         self.eps_scale = eps_scale
         self.pos_dim = pos_dim
         self.use_pos_embed = use_pos_embed
-        self.backbone_type = backbone_type
 
         self.cond_vocab_size = cond_vocab_size
         self.cond_dim = cond_dim
         self.cond_embed = None
         if cond_vocab_size and cond_vocab_size > 0:
             self.cond_embed = nn.Embedding(cond_vocab_size, cond_dim)
-        self.cond_cont_dim = cond_cont_dim
 
         self.disc_embeds = nn.ModuleList([
             nn.Embedding(vocab_size + 1, min(32, vocab_size * 2))
@@ -292,20 +108,7 @@ class HybridDiffusionModel(nn.Module):
         self.cont_proj = nn.Linear(cont_dim, cont_dim)
         pos_dim = pos_dim if use_pos_embed else 0
         in_dim = cont_dim + disc_embed_dim + time_dim + pos_dim + (cond_dim if self.cond_embed is not None else 0)
-        if self.cond_cont_dim and self.cond_cont_dim > 0:
-            in_dim += self.cond_cont_dim
         self.in_proj = nn.Linear(in_dim, hidden_dim)
-        if backbone_type == "transformer":
-            encoder_layer = nn.TransformerEncoderLayer(
-                d_model=hidden_dim,
-                nhead=transformer_nhead,
-                dim_feedforward=transformer_ff_dim,
-                dropout=transformer_dropout,
-                batch_first=True,
-                activation="gelu",
-            )
-            self.backbone = nn.TransformerEncoder(encoder_layer, num_layers=transformer_num_layers)
-        else:
         self.backbone = nn.GRU(
             hidden_dim,
             hidden_dim,
@@ -326,14 +129,7 @@ class HybridDiffusionModel(nn.Module):
             for vocab_size in disc_vocab_sizes
         ])
 
-    def forward(
-        self,
-        x_cont: torch.Tensor,
-        x_disc: torch.Tensor,
-        t: torch.Tensor,
-        cond: torch.Tensor = None,
-        cond_cont: torch.Tensor = None,
-    ):
+    def forward(self, x_cont: torch.Tensor, x_disc: torch.Tensor, t: torch.Tensor, cond: torch.Tensor = None):
         """x_cont: (B,T,Cc), x_disc: (B,T,Cd) with integer tokens."""
         time_emb = self.time_embed(t)
         time_emb = time_emb.unsqueeze(1).expand(-1, x_cont.size(1), -1)
@@ -358,16 +154,9 @@ class HybridDiffusionModel(nn.Module):
             parts.append(pos_emb.unsqueeze(0).expand(x_cont.size(0), -1, -1))
         if cond_feat is not None:
             parts.append(cond_feat)
-        if self.cond_cont_dim and self.cond_cont_dim > 0:
-            if cond_cont is None:
-                raise ValueError("cond_cont is required when cond_cont_dim > 0")
-            parts.append(cond_cont)
         feat = torch.cat(parts, dim=-1)
         feat = self.in_proj(feat)
 
-        if self.backbone_type == "transformer":
-            out = self.backbone(feat)
-        else:
         out, _ = self.backbone(feat)
         out = self.post_norm(out)
         out = out + self.post_ff(out)

example/postprocess_types.py

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

example/prepare_data.py

@@ -1,83 +1,38 @@
 #!/usr/bin/env python3
-"""Prepare vocab and normalization stats for HAI-style CSV datasets."""
+"""Prepare vocab and normalization stats for HAI 21.03."""
 
-import argparse
 import json
 from pathlib import Path
 from typing import Optional
 
 from data_utils import compute_cont_stats, build_disc_stats, load_split, choose_cont_transforms
-from platform_utils import safe_path, ensure_dir, resolve_path
+from platform_utils import safe_path, ensure_dir
 
 BASE_DIR = Path(__file__).resolve().parent
 REPO_DIR = BASE_DIR.parent.parent
+DATA_GLOB = REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train*.csv.gz"
+SPLIT_PATH = BASE_DIR / "feature_split.json"
+OUT_STATS = BASE_DIR / "results" / "cont_stats.json"
+OUT_VOCAB = BASE_DIR / "results" / "disc_vocab.json"
 
 
-def parse_args():
-    parser = argparse.ArgumentParser(description="Prepare vocab and normalization stats.")
-    parser.add_argument("--config", default=str(BASE_DIR / "config.json"), help="Path to JSON config")
-    parser.add_argument("--max-rows", type=int, default=50000, help="Sample cap for stats; ignored when full_stats=true")
-    return parser.parse_args()
-
-
-def resolve_data_paths(cfg: dict, cfg_path: Path) -> list[str]:
-    base_dir = cfg_path.parent
-    data_glob = cfg.get("data_glob", "")
-    data_path = cfg.get("data_path", "")
-    paths = []
-    if data_glob:
-        resolved_glob = resolve_path(base_dir, data_glob)
-        paths = sorted(Path(resolved_glob).parent.glob(Path(resolved_glob).name))
-    elif data_path:
-        resolved_path = resolve_path(base_dir, data_path)
-        if Path(resolved_path).exists():
-            paths = [Path(resolved_path)]
-    return [safe_path(p) for p in paths]
-
-
-def main():
-    args = parse_args()
-    config_path = Path(args.config)
-    if not config_path.is_absolute():
-        config_path = resolve_path(BASE_DIR, config_path)
-    if not config_path.exists():
-        raise SystemExit(f"missing config: {config_path}")
-
-    cfg = json.loads(config_path.read_text(encoding="utf-8"))
-    use_quantile = bool(cfg.get("use_quantile_transform", False))
-    quantile_bins = int(cfg.get("quantile_bins", 0)) if use_quantile else None
-    full_stats = bool(cfg.get("full_stats", False))
-    max_rows: Optional[int] = args.max_rows
-
-    if full_stats:
-        max_rows = None
-
-    split_path = resolve_path(config_path.parent, cfg.get("split_path", "./feature_split.json"))
-    split = load_split(safe_path(split_path))
+def main(max_rows: Optional[int] = None):
+    split = load_split(safe_path(SPLIT_PATH))
     time_col = split.get("time_column", "time")
     cont_cols = [c for c in split["continuous"] if c != time_col]
     disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col]
 
-    data_paths = resolve_data_paths(cfg, config_path)
+    data_paths = sorted(Path(REPO_DIR / "dataset" / "hai" / "hai-21.03").glob("train*.csv.gz"))
     if not data_paths:
-        raise SystemExit(f"no train files found for config: {config_path}")
+        raise SystemExit("no train files found under %s" % str(DATA_GLOB))
+    data_paths = [safe_path(p) for p in data_paths]
 
     transforms, _ = choose_cont_transforms(data_paths, cont_cols, max_rows=max_rows)
-    cont_stats = compute_cont_stats(
-        data_paths,
-        cont_cols,
-        max_rows=max_rows,
-        transforms=transforms,
-        quantile_bins=quantile_bins,
-    )
+    cont_stats = compute_cont_stats(data_paths, cont_cols, max_rows=max_rows, transforms=transforms)
     vocab, top_token = build_disc_stats(data_paths, disc_cols, max_rows=max_rows)
 
-    out_stats = resolve_path(config_path.parent, cfg.get("stats_path", "./results/cont_stats.json"))
-    out_vocab = resolve_path(config_path.parent, cfg.get("vocab_path", "./results/disc_vocab.json"))
-    ensure_dir(out_stats.parent)
-    ensure_dir(out_vocab.parent)
-
-    with open(safe_path(out_stats), "w", encoding="utf-8") as f:
+    ensure_dir(OUT_STATS.parent)
+    with open(safe_path(OUT_STATS), "w", encoding="utf-8") as f:
         json.dump(
             {
                 "mean": cont_stats["mean"],
@@ -91,17 +46,15 @@ def main():
                 "transform": cont_stats["transform"],
                 "skew": cont_stats["skew"],
                 "max_rows": cont_stats["max_rows"],
-                "quantile_probs": cont_stats["quantile_probs"],
-                "quantile_values": cont_stats["quantile_values"],
-                "quantile_raw_values": cont_stats["quantile_raw_values"],
             },
             f,
             indent=2,
         )
 
-    with open(safe_path(out_vocab), "w", encoding="utf-8") as f:
+    with open(safe_path(OUT_VOCAB), "w", encoding="utf-8") as f:
         json.dump({"vocab": vocab, "top_token": top_token, "max_rows": max_rows}, f, indent=2)
 
 
 if __name__ == "__main__":
-    main()
+    # Default: sample 50000 rows for speed. Set to None for full scan.
+    main(max_rows=50000)

example/program_stats.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-"""Compute program-style stats (dwell, change count, step size) for selected features."""
-
-import argparse
-import csv
-import gzip
-import json
-from pathlib import Path
-from typing import Dict, List
-
-
-def parse_args():
-    base_dir = Path(__file__).resolve().parent
-    parser = argparse.ArgumentParser(description="Program stats for setpoints/demands.")
-    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--reference", default=str(base_dir / "config.json"))
-    parser.add_argument("--features", default="", help="comma-separated list; empty = auto from eval")
-    parser.add_argument("--config", default=str(base_dir / "config.json"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "program_stats.json"))
-    parser.add_argument("--max-rows", type=int, default=200000)
-    return parser.parse_args()
-
-
-def resolve_reference_glob(ref_arg: str) -> str:
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json":
-        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
-        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not data_glob:
-            raise SystemExit("reference config has no data_glob/data_path")
-        combined = ref_path.parent / data_glob
-        # avoid resolve on glob patterns
-        if "*" in str(combined) or "?" in str(combined):
-            return str(combined)
-        return str(combined.resolve())
-    return str(ref_path)
-
-
-def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
-    vals = {c: [] for c in cols}
-    opener = gzip.open if str(path).endswith(".gz") else open
-    with opener(path, "rt", newline="") as fh:
-        reader = csv.DictReader(fh)
-        for i, row in enumerate(reader):
-            for c in cols:
-                try:
-                    vals[c].append(float(row[c]))
-                except Exception:
-                    pass
-            if max_rows > 0 and i + 1 >= max_rows:
-                break
-    return vals
-
-
-def dwell_and_steps(series: List[float]):
-    if not series:
-        return {
-            "num_changes": 0,
-            "mean_dwell": None,
-            "median_dwell": None,
-            "mean_step": None,
-            "median_step": None,
-        }
-    changes = 0
-    dwells = []
-    steps = []
-    current = series[0]
-    dwell = 1
-    for v in series[1:]:
-        if v == current:
-            dwell += 1
-            continue
-        changes += 1
-        dwells.append(dwell)
-        steps.append(abs(v - current))
-        current = v
-        dwell = 1
-    dwells.append(dwell)
-
-    def mean(x):
-        return sum(x) / len(x) if x else None
-
-    def median(x):
-        if not x:
-            return None
-        xs = sorted(x)
-        mid = len(xs) // 2
-        return xs[mid] if len(xs) % 2 == 1 else 0.5 * (xs[mid - 1] + xs[mid])
-
-    return {
-        "num_changes": changes,
-        "mean_dwell": mean(dwells),
-        "median_dwell": median(dwells),
-        "mean_step": mean(steps),
-        "median_step": median(steps),
-    }
-
-
-def main():
-    args = parse_args()
-    out_path = Path(args.out)
-
-    eval_path = Path("results") / "eval.json"
-    auto_feats = []
-    if eval_path.exists():
-        data = json.loads(eval_path.read_text(encoding="utf-8"))
-        ks = data.get("continuous_ks", {})
-        auto_feats = [k for k, v in ks.items() if v >= 0.6]
-
-    features = [f.strip() for f in args.features.split(",") if f.strip()] or auto_feats
-    if not features and Path(args.config).exists():
-        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
-        features = cfg.get("type1_features", []) or []
-    if not features:
-        raise SystemExit("no features specified and no eval.json with ks>=0.6")
-
-    # generated series
-    gen_vals = read_series(Path(args.generated), features, args.max_rows)
-
-    # reference series (aggregate across files)
-    ref_glob = resolve_reference_glob(args.reference)
-    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
-    if not ref_paths:
-        raise SystemExit(f"no reference files matched: {ref_glob}")
-    real_vals = {c: [] for c in features}
-    for p in ref_paths:
-        vals = read_series(p, features, args.max_rows)
-        for c in features:
-            real_vals[c].extend(vals[c])
-
-    out = {"features": features, "generated": {}, "reference": {}}
-    for c in features:
-        out["generated"][c] = dwell_and_steps(gen_vals[c])
-        out["reference"][c] = dwell_and_steps(real_vals[c])
-
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
-    print("wrote", out_path)
-
-
-if __name__ == "__main__":
-    main()

example/pv_stats.py

@@ -1,102 +0,0 @@
-#!/usr/bin/env python3
-"""Stats for PV sensors (Type 4) with heavy tails / regime changes."""
-
-import argparse
-import csv
-import gzip
-import json
-from pathlib import Path
-from typing import Dict, List
-
-
-def parse_args():
-    base_dir = Path(__file__).resolve().parent
-    parser = argparse.ArgumentParser(description="PV stats.")
-    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
-    parser.add_argument("--reference", default=str(base_dir / "config.json"))
-    parser.add_argument("--features", default="", help="comma-separated list")
-    parser.add_argument("--config", default=str(base_dir / "config.json"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "pv_stats.json"))
-    parser.add_argument("--max-rows", type=int, default=200000)
-    return parser.parse_args()
-
-
-def resolve_reference_glob(ref_arg: str) -> str:
-    ref_path = Path(ref_arg)
-    if ref_path.suffix == ".json":
-        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
-        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
-        if not data_glob:
-            raise SystemExit("reference config has no data_glob/data_path")
-        combined = ref_path.parent / data_glob
-        if "*" in str(combined) or "?" in str(combined):
-            return str(combined)
-        return str(combined.resolve())
-    return str(ref_path)
-
-
-def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
-    vals = {c: [] for c in cols}
-    opener = gzip.open if str(path).endswith(".gz") else open
-    with opener(path, "rt", newline="") as fh:
-        reader = csv.DictReader(fh)
-        for i, row in enumerate(reader):
-            for c in cols:
-                try:
-                    vals[c].append(float(row[c]))
-                except Exception:
-                    pass
-            if max_rows > 0 and i + 1 >= max_rows:
-                break
-    return vals
-
-
-def quantile_stats(series: List[float]):
-    if not series:
-        return {"q05": None, "q50": None, "q95": None, "tail_ratio": None}
-    xs = sorted(series)
-    n = len(xs)
-    def q(p):
-        idx = int(round(p * (n - 1)))
-        idx = max(0, min(n - 1, idx))
-        return xs[idx]
-    q05 = q(0.05)
-    q50 = q(0.5)
-    q95 = q(0.95)
-    tail_ratio = (q95 - q50) / (q50 - q05) if (q50 - q05) != 0 else None
-    return {"q05": q05, "q50": q50, "q95": q95, "tail_ratio": tail_ratio}
-
-
-def main():
-    args = parse_args()
-    features = [f.strip() for f in args.features.split(",") if f.strip()]
-    if not features and Path(args.config).exists():
-        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
-        features = cfg.get("type4_features", []) or []
-    if not features:
-        raise SystemExit("no features specified for pv_stats")
-
-    gen_vals = read_series(Path(args.generated), features, args.max_rows)
-    ref_glob = resolve_reference_glob(args.reference)
-    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
-    if not ref_paths:
-        raise SystemExit(f"no reference files matched: {ref_glob}")
-    real_vals = {c: [] for c in features}
-    for p in ref_paths:
-        vals = read_series(p, features, args.max_rows)
-        for c in features:
-            real_vals[c].extend(vals[c])
-
-    out = {"features": features, "generated": {}, "reference": {}}
-    for c in features:
-        out["generated"][c] = quantile_stats(gen_vals[c])
-        out["reference"][c] = quantile_stats(real_vals[c])
-
-    out_path = Path(args.out)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
-    print("wrote", out_path)
-
-
-if __name__ == "__main__":
-    main()

example/ranked_ks.py

@@ -1,64 +0,0 @@
-#!/usr/bin/env python3
-"""Rank per-feature KS and show cumulative effect on avg_ks."""
-
-import argparse
-import json
-from pathlib import Path
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Rank KS from eval.json.")
-    base_dir = Path(__file__).resolve().parent
-    parser.add_argument("--eval", default=str(base_dir / "results" / "eval.json"))
-    parser.add_argument("--out", default=str(base_dir / "results" / "ranked_ks.csv"))
-    parser.add_argument("--top", type=int, default=20)
-    return parser.parse_args()
-
-
-def main():
-    args = parse_args()
-    eval_path = Path(args.eval)
-    if not eval_path.exists():
-        raise SystemExit(f"missing eval.json: {eval_path}")
-    data = json.loads(eval_path.read_text(encoding="utf-8"))
-    cont_ks = data.get("continuous_ks", {})
-    feats = sorted(cont_ks.items(), key=lambda kv: kv[1], reverse=True)
-    n = len(feats)
-    if n == 0:
-        raise SystemExit("continuous_ks empty")
-
-    total = sum(v for _, v in feats)
-    rows = []
-    cumulative = 0.0
-    for rank, (feat, ks) in enumerate(feats, 1):
-        contribution = ks / n
-        cumulative += ks
-        remaining = n - rank
-        avg_if_removed = (total - cumulative) / remaining if remaining > 0 else None
-        rows.append(
-            {
-                "rank": rank,
-                "feature": feat,
-                "ks": ks,
-                "contribution_to_avg": contribution,
-                "avg_ks_if_remove_top_n": avg_if_removed,
-            }
-        )
-
-    out_path = Path(args.out)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    with out_path.open("w", encoding="utf-8") as f:
-        f.write("rank,feature,ks,contribution_to_avg,avg_ks_if_remove_top_n\n")
-        for r in rows:
-            avg = "" if r["avg_ks_if_remove_top_n"] is None else f"{r['avg_ks_if_remove_top_n']:.6f}"
-            f.write(f"{r['rank']},{r['feature']},{r['ks']:.6f},{r['contribution_to_avg']:.6f},{avg}\n")
-
-    print(f"wrote {out_path}")
-    print("top features:")
-    for r in rows[: args.top]:
-        avg = "NA" if r["avg_ks_if_remove_top_n"] is None else f"{r['avg_ks_if_remove_top_n']:.6f}"
-        print(r["rank"], r["feature"], r["ks"], avg)
-
-
-if __name__ == "__main__":
-    main()

example/results/ablations/ablation_summary.csv

@@ -1,5 +0,0 @@
-avg_jsd,avg_ks,avg_lag1_diff,avg_psd_l1,continuous_mmd_rbf,corr_mean_abs_diff,discriminative_accuracy,memorization_ratio,post_avg_jsd,post_avg_ks,post_avg_lag1_diff,predictive_rmse_real,predictive_rmse_synth,run_dir,seed,utility_auprc_aug,utility_auprc_real,utility_auprc_synth,variant
-0.02154500462890076,0.39950668238993714,0.2994253248181626,0.019868340343236923,0.6485295295715332,0.21394668519496918,1.0,2.98201284485682,,,,0.5583509206771851,0.9607057571411133,/root/autodl-tmp/mask-ddpm/example/results/ablations/runs/no_snr_weight__seed1337,1337,0.637313246257706,0.6527744226377132,0.5839558451818384,no_snr_weight
-0.017899328657243745,0.4130987421383648,0.3108961322076135,0.02026984840631485,0.6504309177398682,0.21265330910682678,1.0,2.948979736458853,,,,0.5583509206771851,0.9654670357704163,/root/autodl-tmp/mask-ddpm/example/results/ablations/runs/no_quantile_loss__seed1337,1337,0.6451654579890704,0.6527744226377132,0.5904586531962263,no_quantile_loss
-0.02930392024585336,0.4039510220125786,0.28531566380969187,0.019299142062664032,0.6561890840530396,0.21048887073993683,1.0,2.953516468815496,,,,0.5583509206771851,0.9696047902107239,/root/autodl-tmp/mask-ddpm/example/results/ablations/runs/no_residual_stat__seed1337,1337,0.6465074146607899,0.6527744226377132,0.5848963274786471,no_residual_stat
-0.10238307146791868,0.48224064465408806,0.7276772745273601,0.026633866131305695,0.4687999486923218,0.1948104053735733,1.0,3.5463823500232126,,,,0.5583509206771851,0.9675812721252441,/root/autodl-tmp/mask-ddpm/example/results/ablations/runs/eps_target__seed1337,1337,0.6466204571725106,0.6527744226377132,0.5817769935282933,eps_target

example/results/ablations/ablation_summary.json

@@ -1,97 +0,0 @@
-{
-  "variants": [
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-    "no_quantile_loss",
-    "no_residual_stat",
-    "eps_target"
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example/results/ablations/benchmark_history.csv

@@ -1,12 +0,0 @@
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example/results/ablations/benchmark_summary.csv

@@ -1,11 +0,0 @@
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example/results/ablations/runs/eps_target__seed1337/comprehensive_eval.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/eps_target__seed1337/comprehensive_eval_post.json

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example/results/ablations/runs/eps_target__seed1337/eval.json

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example/results/ablations/runs/eps_target__seed1337/eval_post.json

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example/results/ablations/runs/full__seed1337/comprehensive_eval.json

@@ -1,537 +0,0 @@
-{
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example/results/ablations/runs/full__seed1337/comprehensive_eval_post.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/full__seed1337/eval.json

@@ -1,974 +0,0 @@
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example/results/ablations/runs/full__seed1337/eval_post.json

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example/results/ablations/runs/no_file_condition__seed1337/comprehensive_eval.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/no_file_condition__seed1337/comprehensive_eval_post.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/no_file_condition__seed1337/eval.json

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example/results/ablations/runs/no_file_condition__seed1337/eval_post.json

@@ -1,971 +0,0 @@
-{
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example/results/ablations/runs/no_post_calibration__seed1337/comprehensive_eval.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/no_post_calibration__seed1337/comprehensive_eval_post.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/no_post_calibration__seed1337/eval.json

@@ -1,974 +0,0 @@
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example/results/ablations/runs/no_post_calibration__seed1337/eval_post.json

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example/results/ablations/runs/no_quantile__seed1337/comprehensive_eval.json

@@ -1,538 +0,0 @@
-{
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-    "/root/autodl-tmp/dataset/hai/hai-21.03/test2.csv.gz",
-    "/root/autodl-tmp/dataset/hai/hai-21.03/test3.csv.gz",
-    "/root/autodl-tmp/dataset/hai/hai-21.03/test4.csv.gz",
-    "/root/autodl-tmp/dataset/hai/hai-21.03/test5.csv.gz"
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example/results/ablations/runs/no_quantile__seed1337/comprehensive_eval_post.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/no_quantile__seed1337/eval.json

@@ -1,974 +0,0 @@
-{
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example/results/ablations/runs/no_quantile__seed1337/eval_post.json

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example/results/ablations/runs/no_quantile_loss__seed1337/comprehensive_eval.json

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example/results/ablations/runs/no_quantile_loss__seed1337/comprehensive_eval_post.json

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example/results/ablations/runs/no_quantile_loss__seed1337/eval.json

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example/results/ablations/runs/no_quantile_loss__seed1337/eval_post.json

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example/results/ablations/runs/no_residual_stat__seed1337/comprehensive_eval.json

@@ -1,537 +0,0 @@
-{
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example/results/ablations/runs/no_residual_stat__seed1337/comprehensive_eval_post.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/no_residual_stat__seed1337/eval.json

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example/results/ablations/runs/no_residual_stat__seed1337/eval_post.json

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example/results/ablations/runs/no_snr_weight__seed1337/comprehensive_eval.json

@@ -1,537 +0,0 @@
-{
-  "generated_path": "/root/autodl-tmp/mask-ddpm/example/results/ablations/runs/no_snr_weight__seed1337/generated.csv",
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example/results/ablations/runs/no_snr_weight__seed1337/comprehensive_eval_post.json

@@ -1,538 +0,0 @@
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example/results/ablations/runs/no_snr_weight__seed1337/eval.json

@@ -1,974 +0,0 @@
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example/results/ablations/runs/no_snr_weight__seed1337/eval_post.json

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example/results/ablations/runs/no_temporal__seed1337/comprehensive_eval.json

@@ -1,538 +0,0 @@
-{
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example/results/ablations/runs/no_temporal__seed1337/comprehensive_eval_post.json

@@ -1,538 +0,0 @@
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example/results/ablations/runs/no_temporal__seed1337/eval.json

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example/results/ablations/runs/no_temporal__seed1337/eval_post.json

@@ -1,971 +0,0 @@
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example/results/ablations/runs/no_type_routing__seed1337/comprehensive_eval.json

@@ -1,168 +0,0 @@
-{
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example/results/ablations/runs/no_type_routing__seed1337/comprehensive_eval_post.json

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example/results/ablations/runs/no_type_routing__seed1337/eval_post.json

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-    "P2_ASD": 0.0,
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-    "P2_ManualGO": 0.0,
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-    "P2_RTR": 0.0,
-    "P2_TripEx": 0.0,
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-    "P3_LL": 0.0,
-    "P4_HT_PS": 0.0,
-    "P4_ST_PS": 0.0
-  }
-}

example/results/actuator_stats.json

@@ -1,112 +0,0 @@
-{
-  "features": [
-    "P1_PCV02Z",
-    "P1_PCV01Z",
-    "P1_PCV01D",
-    "P1_FCV02Z",
-    "P1_FCV03D",
-    "P1_FCV03Z",
-    "P1_LCV01D",
-    "P1_LCV01Z"
-  ],
-  "generated": {
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-      "top1_mass": 0.984375,
-      "top3_mass": 0.984375,
-      "median_dwell": 1
-    },
-    "P1_PCV01Z": {
-      "unique_ratio": 0.9583333333333334,
-      "top1_mass": 0.020833333333333332,
-      "top3_mass": 0.0390625,
-      "median_dwell": 1
-    },
-    "P1_PCV01D": {
-      "unique_ratio": 0.9192708333333334,
-      "top1_mass": 0.08333333333333333,
-      "top3_mass": 0.08854166666666667,
-      "median_dwell": 1
-    },
-    "P1_FCV02Z": {
-      "unique_ratio": 0.09375,
-      "top1_mass": 0.1328125,
-      "top3_mass": 0.3177083333333333,
-      "median_dwell": 1
-    },
-    "P1_FCV03D": {
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-      "top1_mass": 0.0026041666666666665,
-      "top3_mass": 0.0078125,
-      "median_dwell": 1
-    },
-    "P1_FCV03Z": {
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-      "top1_mass": 0.11979166666666667,
-      "top3_mass": 0.203125,
-      "median_dwell": 1
-    },
-    "P1_LCV01D": {
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-      "top1_mass": 0.005208333333333333,
-      "top3_mass": 0.015625,
-      "median_dwell": 1
-    },
-    "P1_LCV01Z": {
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-      "top1_mass": 0.057291666666666664,
-      "top3_mass": 0.13020833333333334,
-      "median_dwell": 1
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-  },
-  "reference": {
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-      "top1_mass": 0.42507,
-      "top3_mass": 0.7653283333333334,
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-      "top3_mass": 0.014783333333333334,
-      "median_dwell": 1
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example/results/aux_stats.json

@@ -1,41 +0,0 @@
-{
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example/results/benchmark_history.csv

@@ -1,4 +0,0 @@
-timestamp,run_name,config,seed,avg_ks,avg_jsd,avg_lag1_diff
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example/results/benchmark_summary.csv

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-config,n_runs,avg_ks_mean,avg_ks_std,avg_jsd_mean,avg_jsd_std,avg_lag1_diff_mean,avg_lag1_diff_std,best_run_name,best_avg_ks
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example/results/cdf_P1_B3004.svg

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example/results/cdf_P1_B4002.svg

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example/results/cdf_P1_PCV02Z.svg

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example/results/cdf_P2_24Vdc.svg

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example/results/cdf_P2_CO_rpm.svg

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