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Load SurvHTE-Bench from HuggingFace Hub.
Two interfaces:
1. load_data(dataset_name) → experiment_setups, experiment_repeat_setups
(identical output to the original local load_data())
2. load_splits(dataset_name) → nested dict mirroring prepare_data_split output
results[config_name][scenario_key][rand_idx]["train"|"val"|"test"]
= (X, W, Y, cate_true)
"""
import json
import numpy as np
import pandas as pd
import datasets as hf_datasets
REPO_ID = "snoroozi/SurvHTE-Bench"
SCHEMA = {
"synthetic": dict(
X_cols=None, # resolved dynamically: cols starting with X + digit
W_col="W",
y_cols=["observed_time", "event"],
cate_col=None, # computed as T1 - T0
idx_col="id",
),
"actg_syn": dict(
X_cols=["age","wtkg","hemo","homo","drugs","karnof","oprior","z30","zprior","preanti",
"race","gender","str2","strat","symptom","treat","offtrt",
"cd40","cd420","cd496","r","cd80","cd820"],
W_col="W",
y_cols=["observed_time", "event"],
cate_col="true_cate",
idx_col="idx",
),
"twin": dict(
X_cols=["anemia","cardiac","lung","diabetes","herpes","hydra","hemo","chyper","phyper",
"eclamp","incervix","pre4000","preterm","renal","rh","uterine","othermr",
"gestat","dmage","dmeduc","dmar","nprevist","adequacy",
"dtotord","cigar","drink","wtgain",
"pldel_2","pldel_3","pldel_4","pldel_5","resstatb_2","resstatb_3","resstatb_4",
"mpcb_1","mpcb_2","mpcb_3","mpcb_4","mpcb_5","mpcb_6","mpcb_7","mpcb_8","mpcb_9"],
W_col="W",
y_cols=["observed_time", "event"],
cate_col="true_cate",
idx_col="idx",
),
"actgHC": dict(
X_cols=["gender","race","hemo","homo","drugs","str2","symptom","age","wtkg","karnof","cd40","cd80"],
W_col="trt",
y_cols=None, # 10 versions: t0/e0 .. t9/e9 — caller slices Y[:, r*2:(r+1)*2]
cate_col="cate_base",
idx_col="id",
),
"actgLC": dict(
X_cols=["gender","race","hemo","homo","drugs","str2","symptom","age","wtkg","karnof","cd40","cd80"],
W_col="trt",
y_cols=["observed_time_month", "effect_non_censor"],
cate_col="cate_base",
idx_col="id",
),
}
# Mirrors SPLIT_SIZES in hf_upload.py
SPLIT_SIZES = {
"synthetic": (5000, 2500, 2500),
"actg_syn": (0.50, 0.25, 0.25),
"twin": (0.50, 0.25, 0.25),
"actgHC": (0.50, 0.25, 0.25),
"actgLC": (0.50, 0.25, 0.25),
}
def load_data(
dataset_name: str,
repo_id: str = REPO_ID,
) -> tuple[dict, dict]:
"""Identical output to local load_data()."""
setups_df = hf_datasets.load_dataset(repo_id, name=dataset_name, split="train").to_pandas()
meta_cols = {"setup_key", "scenario", "summary_json", "metadata_json"}
data_cols = [c for c in setups_df.columns if c not in meta_cols]
experiment_setups: dict = {}
for (setup_key, scenario), grp in setups_df.groupby(["setup_key", "scenario"], sort=False):
info = {
"dataset": grp[data_cols].reset_index(drop=True),
"summary": json.loads(grp["summary_json"].iloc[0]),
}
if "metadata_json" in grp.columns and grp["metadata_json"].notna().any():
info["metadata"] = json.loads(grp["metadata_json"].iloc[0])
experiment_setups.setdefault(setup_key, {})[scenario] = info
repeats_df = hf_datasets.load_dataset(repo_id, name=f"{dataset_name}_repeats", split="train").to_pandas()
idx_cols = [c for c in repeats_df.columns if c != "repeat_key"]
if dataset_name in ("actgHC", "actgLC"):
experiment_repeat_setups: dict = {}
for repeat_key, grp in repeats_df.groupby("repeat_key", sort=False):
experiment_repeat_setups[repeat_key] = grp[idx_cols].reset_index(drop=True)
else:
experiment_repeat_setups = repeats_df[idx_cols].reset_index(drop=True)
return experiment_setups, experiment_repeat_setups
def load_splits(
dataset_name: str,
num_repeats: int = 10,
repo_id: str = REPO_ID,
) -> dict:
"""
Returns nested dict mirroring the experiment loop:
results[config_name][scenario_key][rand_idx]["train"|"val"|"test"]
= (X, W, Y, cate_true)
"""
schema = SCHEMA[dataset_name]
W_col = schema["W_col"]
y_cols = schema["y_cols"]
cate_col = schema["cate_col"]
# Load all 30 splits once
raw: dict[tuple, pd.DataFrame] = {}
for split_type in ("train", "val", "test"):
for r in range(num_repeats):
raw[(split_type, r)] = hf_datasets.load_dataset(
repo_id, name=f"{dataset_name}_splits", split=f"{split_type}_{r}"
).to_pandas()
# Discover (config_name, scenario) pairs from train_0
pairs = list(
raw[("train", 0)].groupby(["setup_key", "scenario"], sort=False).groups.keys()
)
# Resolve X columns from train_0
sample_df = raw[("train", 0)]
if schema["X_cols"] is None:
X_cols = [c for c in sample_df.columns if c.startswith("X") and c[1:].isdigit()]
else:
X_cols = schema["X_cols"]
def _extract(df_grp: pd.DataFrame) -> tuple:
X = df_grp[X_cols].to_numpy()
W = df_grp[W_col].to_numpy()
if y_cols is not None:
Y = df_grp[y_cols].to_numpy()
else:
# actgHC: return all t/e cols, caller slices Y[:, r*2:(r+1)*2]
te_cols = [col for i in range(10) for col in (f"t{i}", f"e{i}") if col in df_grp.columns]
Y = df_grp[te_cols].to_numpy()
if cate_col is not None and cate_col in df_grp.columns:
cate_true = df_grp[cate_col].to_numpy()
else:
cate_true = (df_grp["T1"] - df_grp["T0"]).to_numpy()
return (X, W, Y, cate_true)
results: dict = {}
for config_name, scenario_key in pairs:
results.setdefault(config_name, {})[scenario_key] = {r: {} for r in range(num_repeats)}
for split_type in ("train", "val", "test"):
for r in range(num_repeats):
df = raw[(split_type, r)]
grp = df[
(df["setup_key"] == config_name) & (df["scenario"] == scenario_key)
].reset_index(drop=True)
results[config_name][scenario_key][r][split_type] = _extract(grp)
return results |