Added hf_load.py for helper functions of loading the dataset.

hf_load.py

@@ -0,0 +1,171 @@
+"""
+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