#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ build_v11_longitudinal.py (seed=20260618, 可复现) 把 pseudo_dataset_v10(每人单时间点横截面)扩成 3 期纵向版 pseudo_dataset_v11_longitudinal: - 00_00_visit (基线, 100%) - 01_00_visit (≈+2y, ~60%) - 02_00_visit (≈+4y, ~35%) —— 单调随访保留(到达 k 期则拥有 0..k 期) 设计原则 -------- 1. 仅扩"会随时间变化的队列横截面模块"(EXPAND_TABLES);静态生物学/注解表 (human_genetics / family_history / population 人口学 / curated_phenotypes / sociodemographics / *_tag / 组学原始/bulk 等)一律不动(已由 APFS 克隆保留)。 2. **stage0 行 = v10 基线值逐字保留**(唯一规范化:research_stage 统一成 '00_00_visit', 修掉 blood_tests 的 'undefined' 缺陷);随访行从基线做"个体内自相关漂移"。 3. schema / dtype / 列序完全不变 —— 通过"复制基线帧→只改时间锚点列+连续测量列"保证。 4. 同一 participant 的随访间隔(offset)跨所有模块共享 → 随访日期跨表协调一致。 5. 连续测量列:nunique>15 视为连续 → AR(1) 漂移,按基线 [min,max] 裁剪,保留 dtype; 低基数数值列(≤15,疑似编码/类别)与类别/对象/容器列 → 跨期保持不变。 6. 任意 datetime 列(collection_date/collection_timestamp/cgm_connection_* 等) 按该人随访 offset 整体推进。 7. 重建 events.parquet:覆盖全部 3000 人 ×各自随访期,作为纵向锚点表。 """ import os, sys, shutil import numpy as np import pandas as pd import pyarrow as pa import pyarrow.parquet as pq def write_like(df, src_path, dst_path): """写出 df,但把 v10 中为整数(可空 int64)、而经 pandas 变成 float 的列强制 cast 回 v10 的整数 Arrow 物理类型,保证磁盘 schema 与 v10 严格一致(其余列保持默认推断)。""" target = pq.read_schema(src_path) tname = set(target.names) tbl = pa.Table.from_pandas(df, preserve_index=False) arrays, names = [], [] for name in tbl.column_names: col = tbl.column(name) if name in tname: tgt = target.field(name).type if pa.types.is_integer(tgt) and pa.types.is_floating(col.type): col = col.cast(tgt, safe=False) # float(含NaN/整值) -> 可空 int,NaN->null arrays.append(col); names.append(name) pq.write_table(pa.table(arrays, names=names), dst_path) SEED = 20260618 SRC = "pseudo_dataset_v10" DST = "pseudo_dataset_v11_longitudinal" STAGE_LABELS = ["00_00_visit", "01_00_visit", "02_00_visit"] # 单调保留: max_stage 分布 → stage0=100%, stage1=60%, stage2=35% MAX_STAGE_P = {0: 0.40, 1: 0.25, 2: 0.35} # 随访间隔(天): 期1≈2年, 期2≈再+2年; 个体差异 INTERVAL_MEAN_DAYS = 730 INTERVAL_SD_DAYS = 60 DRIFT_FRAC = 0.15 # 每期个体内漂移 SD = 0.15 × 基线人群 SD(中性,无方向趋势) CONT_NUNIQUE_MIN = 15 # nunique>此值才当连续量做漂移 # 永不改动的列(按列名) STATIC_COLS = { "participant_id", "study_id", "cohort", "timezone", "hmo", "sex", "gender", "genetic_sex", "year_of_birth", "month_of_birth", "array_index", "sample_name", "sample_id", "registration_code", "study_family_member_participant_id", } # 要纵向扩展的表(相对 SRC 的 parquet 路径) EXPAND_TABLES = [ "anthropometrics/anthropometrics.parquet", "blood_pressure/blood_pressure.parquet", "blood_tests/blood_tests.parquet", "body_composition/body_composition.parquet", "bone_density/bone_density.parquet", "carotid_ultrasound/carotid_ultrasound.parquet", "cgm/cgm.parquet", "cgm/iglu.parquet", "cgm/iglu_daily.parquet", "diet_logging/diet_logging.parquet", "ecg/ecg.parquet", "ecg/ecg_qc.parquet", "fundus/fundus.parquet", "fundus/microvasculature.parquet", "gut_microbiome/gut_microbiome.parquet", "hand_grip/hand_grip.parquet", "health_and_medical_history/initial_medical.parquet", "health_and_medical_history/ukbb.parquet", "lifestyle_and_environment/lifestyle_and_environment.parquet", "liver_ultrasound/liver_ultrasound.parquet", "liver_ultrasound/liver_ultrasound_aggregated.parquet", "medical_conditions/medical_conditions.parquet", "medical_procedures/follow_up_medical.parquet", "medical_procedures/initial_medical.parquet", "medical_symptoms/digestive_health.parquet", "medical_symptoms/follow_up_ukbb.parquet", "medical_symptoms/initial_medical.parquet", "nightingale_metabolomics/nightingale_metabolomics.parquet", "oral_microbiome/oral_microbiome.parquet", "oral_microbiome/oral_microbiome_2025_04_UUID.parquet", "rna_seq/rna_seq.parquet", "vascular_health/vascular_health.parquet", ] def build_roster(rng): """每个 participant 一行: max_stage + 期1/期2 的随访 offset(天)。跨表共享。""" pop = pd.read_parquet(os.path.join(SRC, "population/population.parquet"), columns=["participant_id"]) pids = np.sort(pop["participant_id"].unique()) n = len(pids) stages = np.array(list(MAX_STAGE_P.keys())) probs = np.array([MAX_STAGE_P[s] for s in stages], float) probs /= probs.sum() max_stage = rng.choice(stages, size=n, p=probs) iv1 = np.round(rng.normal(INTERVAL_MEAN_DAYS, INTERVAL_SD_DAYS, n)).astype(int) iv2 = iv1 + np.round(rng.normal(INTERVAL_MEAN_DAYS, INTERVAL_SD_DAYS, n)).astype(int) iv1 = np.clip(iv1, 365, None) iv2 = np.clip(iv2, iv1 + 200, None) offset_days = {0: np.zeros(n, int), 1: iv1, 2: iv2} roster = pd.DataFrame({"participant_id": pids, "max_stage": max_stage, "off1": iv1, "off2": iv2}) return roster.set_index("participant_id"), pids, offset_days def classify_columns(df): """返回 (datetime_cols, continuous_cols)。其余列跨期保持不变。""" dt_cols, cont_cols = [], [] for c in df.columns: if c in STATIC_COLS or c == "research_stage": continue if str(c).endswith("_tag") or str(c).endswith("_parquet"): continue # 注解 / 容器路径 dtype = df[c].dtype if pd.api.types.is_datetime64_any_dtype(dtype): dt_cols.append(c) elif pd.api.types.is_numeric_dtype(dtype) and not pd.api.types.is_bool_dtype(dtype): if df[c].nunique(dropna=True) > CONT_NUNIQUE_MIN: cont_cols.append(c) return dt_cols, cont_cols def expand_table(rel, roster, rng): src = os.path.join(SRC, rel) dst = os.path.join(DST, rel) base = pd.read_parquet(src) if "participant_id" not in base.columns or "research_stage" not in base.columns: return rel, "SKIP(no pid/research_stage)", len(base), len(base) if base["participant_id"].duplicated().any(): return rel, "SKIP(already multi-row)", len(base), len(base) n0 = len(base) base = base.sort_values("participant_id").reset_index(drop=True) pid = base["participant_id"].to_numpy() ms = roster.reindex(pid)["max_stage"].to_numpy() off1 = roster.reindex(pid)["off1"].to_numpy() off2 = roster.reindex(pid)["off2"].to_numpy() off_by_stage = {0: np.zeros(len(pid), int), 1: off1, 2: off2} dt_cols, cont_cols = classify_columns(base) # 连续列基线统计(裁剪边界 + 漂移幅度) cont_stats = {} for c in cont_cols: v = pd.to_numeric(base[c], errors="coerce") sd = float(np.nanstd(v.to_numpy())) cont_stats[c] = {"sd": sd if np.isfinite(sd) and sd > 0 else 0.0, "min": float(np.nanmin(v.to_numpy())) if v.notna().any() else np.nan, "max": float(np.nanmax(v.to_numpy())) if v.notna().any() else np.nan, "is_int": pd.api.types.is_integer_dtype(base[c].dtype)} # 漂移用值(float 工作副本),从基线累积 cur = {c: pd.to_numeric(base[c], errors="coerce").astype(float).to_numpy() for c in cont_cols} frames = [] for k, label in enumerate(STAGE_LABELS): f = base.copy() f["research_stage"] = label # 规范化(含修掉 stage0 的 'undefined') # 时间锚点推进 if k > 0: delta = pd.to_timedelta(off_by_stage[k], unit="D") for c in dt_cols: f[c] = base[c] + delta # 连续测量列:个体内 AR(1) 漂移 for c in cont_cols: st = cont_stats[c] if st["sd"] > 0: step = rng.normal(0.0, DRIFT_FRAC * st["sd"], size=len(pid)) cur[c] = np.clip(cur[c] + step, st["min"], st["max"]) vals = cur[c].copy() if st["is_int"]: out = np.where(np.isnan(vals), np.nan, np.round(vals)) f[c] = pd.Series(out, index=f.index).astype(base[c].dtype, errors="ignore") # 保整数 dtype:NaN 安全则转回 try: f[c] = pd.array(np.round(vals), dtype=base[c].dtype) except Exception: f[c] = vals else: f[c] = vals.astype(base[c].dtype, copy=False) # 期成员过滤(单调保留) f = f[ms >= k] frames.append(f) out = pd.concat(frames, ignore_index=True) out = out.sort_values(["participant_id", "research_stage"]).reset_index(drop=True) # dtype 对齐 v10(防漂移引入 object) for c in base.columns: if out[c].dtype != base[c].dtype: try: out[c] = out[c].astype(base[c].dtype) except Exception: pass write_like(out, src, dst) return rel, "OK", n0, len(out) def rebuild_events(roster, rng): pop = pd.read_parquet(os.path.join(SRC, "population/population.parquet")) # 基线日期锚点:优先用 anthropometrics collection_date anth = pd.read_parquet(os.path.join(SRC, "anthropometrics/anthropometrics.parquet"), columns=["participant_id", "collection_date"]) base_date = anth.set_index("participant_id")["collection_date"] ev_old = pd.read_parquet(os.path.join(SRC, "events/events.parquet")) vc_pool = ev_old["visit_center"].dropna().to_numpy() if len(vc_pool) == 0: vc_pool = np.array(["unknown"]) rows = [] for _, r in pop.iterrows(): pid = int(r["participant_id"]) if pid not in roster.index: continue ms = int(roster.loc[pid, "max_stage"]) offs = {0: 0, 1: int(roster.loc[pid, "off1"]), 2: int(roster.loc[pid, "off2"])} bd = base_date.get(pid, pd.Timestamp("2021-06-01")) if pd.isna(bd): bd = pd.Timestamp("2021-06-01") yob = int(r["year_of_birth"]) if pd.notna(r["year_of_birth"]) and int(r["year_of_birth"]) > 1900 else None vcenter = str(rng.choice(vc_pool)) for k in range(ms + 1): d = bd + pd.Timedelta(days=offs[k]) ts = pd.Timestamp(d).tz_localize("UTC") if pd.Timestamp(d).tz is None else pd.Timestamp(d) if yob is not None: age = float(np.clip(d.year - yob + (d.month - 1) / 12.0, 18, 90)) age = round(age, 1) else: age = float("nan") rows.append({ "month_of_birth": r["month_of_birth"], "year_of_birth": r["year_of_birth"], "sex": r["sex"], "research_stage_type": STAGE_LABELS[k], "visit_center": vcenter, "research_stage_timestamp": ts, "research_stage_date": pd.Timestamp(d), "age_at_research_stage": age, "study_id": f"P{pid}_study_id_01", "timezone": "Asia/Jerusalem", "participant_id": pid, "cohort": "10k", "research_stage": STAGE_LABELS[k], "array_index": 0, }) ev = pd.DataFrame(rows, columns=ev_old.columns.tolist()) # dtype 对齐旧 events ev["research_stage_date"] = pd.to_datetime(ev["research_stage_date"]) ev["research_stage_timestamp"] = pd.to_datetime(ev["research_stage_timestamp"], utc=True) ev["age_at_research_stage"] = ev["age_at_research_stage"].astype("float64") for c in ["year_of_birth", "participant_id", "array_index"]: try: ev[c] = ev[c].astype(ev_old[c].dtype) except Exception: pass write_like(ev, os.path.join(SRC, "events/events.parquet"), os.path.join(DST, "events/events.parquet")) return len(ev_old), len(ev), ev["participant_id"].nunique() def main(): if not os.path.isdir(DST): print(f"[FATAL] 目标 {DST} 不存在,请先 APFS 克隆 v10。", file=sys.stderr) sys.exit(1) rng = np.random.default_rng(SEED) roster, pids, _ = build_roster(rng) print(f"== roster: {len(pids)} 人; max_stage 分布 = " f"{roster['max_stage'].value_counts().sort_index().to_dict()}") s1 = int((roster['max_stage'] >= 1).sum()); s2 = int((roster['max_stage'] >= 2).sum()) print(f"== 期成员: stage0={len(pids)}(100%) stage1={s1}({s1/len(pids):.0%}) stage2={s2}({s2/len(pids):.0%})") print("== 扩展模块表:") total_in = total_out = 0 for rel in EXPAND_TABLES: try: r, status, n0, n1 = expand_table(rel, roster, rng) except Exception as e: print(f" [ERR] {rel}: {e}") continue total_in += n0; total_out += n1 print(f" {status:<24} {rel:<55} {n0:>6} -> {n1:>6} 行") print(f"== 扩展合计: {total_in} -> {total_out} 行") o, n, npid = rebuild_events(roster, rng) print(f"== events 重建: {o} -> {n} 行, 覆盖 {npid} 人 (期望 3000)") print("== DONE") if __name__ == "__main__": main()