began work on v3

v3/classes/utils.py

@@ -0,0 +1,47 @@
+"""General-purpose utilities for the V2 hypertower pipeline."""
+from __future__ import annotations
+
+import random as pyrandom
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import torch
+
+
+def seed_everything(seed: int) -> None:
+    pyrandom.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+def choose_device(device_arg: str | None) -> torch.device:
+    if device_arg and device_arg != "auto":
+        return torch.device(device_arg)
+    return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def _drop_mixed_label_patients(df: pd.DataFrame, *, patient_col: str, label_col: str):
+    """Remove patients whose rows carry conflicting labels. Returns (clean_df, mixed_pids)."""
+    per_patient = (
+        df.groupby(patient_col)[label_col]
+        .agg(lambda s: set(pd.to_numeric(s, errors="coerce").dropna().astype(int).tolist()))
+    )
+    mixed = [pid for pid, labels in per_patient.items() if len(labels) > 1]
+    if not mixed:
+        return df, []
+    return df[~df[patient_col].isin(mixed)].reset_index(drop=True), mixed
+
+
+def _relabel_mixed_patients_to_max(df: pd.DataFrame, *, patient_col: str, label_col: str):
+    """Set all rows for each patient to that patient's max observed label."""
+    out = df.copy()
+    labels = pd.to_numeric(out[label_col], errors="coerce")
+    patient_max = labels.groupby(out[patient_col]).transform("max")
+    changed_rows = int((labels != patient_max).fillna(False).sum())
+    out[label_col] = patient_max.astype(int)
+    per_patient_unique = out.groupby(patient_col)[label_col].nunique(dropna=True)
+    still_mixed = per_patient_unique[per_patient_unique > 1].index.tolist()
+    return out.reset_index(drop=True), changed_rows, still_mixed