update 3-19
This commit is contained in:
rpotter6298
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#!/usr/bin/env python3
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"""v2 port of cnn_logits_rf_cv: CNN logit extraction + RF CV using the v2 PAPILA stack.
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Replaces the hardcoded-config v1 version. Data loading, fold splitting, and
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feature preparation all go through the v2 stack so that --exclude-cols,
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--iop-corr-method, etc. are first-class options.
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Usage:
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python scripts/basic_analysis/cnn_logits_rf_cv_v2.py \
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--eval-mode binary --backbone resnet50 --epochs 40 \
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--exclude-cols Phakic/Pseudophakic Axial_Length \
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--run-name cnn_rf_nocrop_binary
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"""
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from __future__ import annotations
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import argparse
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import random
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import time
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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import sys
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from types import SimpleNamespace
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from typing import Dict, List, Tuple
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import numpy as np
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import pandas as pd
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from PIL import Image
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score, roc_auc_score
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REPO_ROOT = Path(__file__).resolve().parents[2]
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if str(REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(REPO_ROOT))
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from classes.v2.papila_builders import build_papila_data
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from classes.v2.backbones import BACKBONES, load_backbone_weights
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from classes.v2.dataset import ClinicalDataset, _ClinicalView
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from classes.v2.split_manager import PatientFirstSplitManager
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from classes.v2.transforms import build_backbone_transform, build_eval_transform
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# ---------------------------------------------------------------------------
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# Model
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# ---------------------------------------------------------------------------
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class CNNHead(nn.Module):
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def __init__(self, backbone_name: str, num_classes: int) -> None:
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super().__init__()
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spec = BACKBONES[backbone_name]
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backbone = spec.ctor(weights=spec.weights_default)
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if backbone_name.startswith("refuge"):
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load_backbone_weights(backbone_name, backbone)
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out_dim, backbone = spec.strip(backbone)
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self.backbone = backbone
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self.head = nn.Linear(out_dim, num_classes)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.head(self.backbone(x))
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _auc_score(y_true: np.ndarray, probs: np.ndarray, num_classes: int) -> float:
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try:
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if num_classes == 2:
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return float(roc_auc_score(y_true, probs[:, 1]))
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return float(roc_auc_score(y_true, probs, multi_class="ovr", average="macro"))
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except Exception:
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return float("nan")
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def _train_cnn(
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model: nn.Module,
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loader: DataLoader,
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args,
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fold: int,
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device: torch.device,
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) -> None:
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model.train()
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optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
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criterion = nn.CrossEntropyLoss()
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for epoch in range(args.epochs):
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running_loss = total = correct = 0
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for x_img, _meta, y in loader:
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x_img = x_img.to(device)
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y = y.to(device=device, dtype=torch.long)
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optimizer.zero_grad()
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logits = model(x_img)
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loss = criterion(logits, y)
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loss.backward()
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optimizer.step()
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running_loss += float(loss.item()) * int(y.size(0))
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correct += int((logits.argmax(1) == y).sum().item())
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total += int(y.size(0))
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if (epoch + 1) % args.log_every == 0:
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print(
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f" [fold {fold+1}] epoch {epoch+1}/{args.epochs} "
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f"loss={running_loss/max(total,1):.4f} acc={correct/max(total,1):.4f}",
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flush=True,
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)
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def _infer_logits(
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model: nn.Module, loader: DataLoader, device: torch.device
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""Returns (y_true, logits, metadata_vectors)."""
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model.eval()
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y_all, logits_all, md_all = [], [], []
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with torch.no_grad():
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for x_img, x_md, y in loader:
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logits = model(x_img.to(device)).cpu().numpy()
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y_np = y.numpy() if torch.is_tensor(y) else np.asarray(y)
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md_np = x_md.numpy() if torch.is_tensor(x_md) else np.asarray(x_md)
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y_all.append(y_np)
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logits_all.append(logits)
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md_all.append(md_np)
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return (
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np.concatenate(y_all),
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np.concatenate(logits_all),
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np.concatenate(md_all),
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)
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def build_parser() -> argparse.ArgumentParser:
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ap = argparse.ArgumentParser(
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description="CNN logit extraction + RF CV (v2 PAPILA stack)"
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)
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ap.add_argument("--image-dir", default="Papila/FundusImages")
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ap.add_argument("--clinical-dir", default="Papila/ClinicalData")
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ap.add_argument("--label-col", default="Diagnosis")
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ap.add_argument("--cat-cols", nargs="*", default=["Gender", "Phakic/Pseudophakic"])
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ap.add_argument("--exclude-cols", nargs="*", default=[],
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help="Feature columns to drop from the clinical feature matrix.")
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ap.add_argument("--eval-mode", choices=["binary", "multiclass"], default="binary")
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ap.add_argument("--n-splits", type=int, default=5)
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ap.add_argument("--fold-seed", type=int, default=42)
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ap.add_argument("--holdout-per-class", type=int, default=6,
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help="Patients per class reserved for holdout (0 disables).")
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ap.add_argument("--holdout-seed", type=int, default=123)
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ap.add_argument("--backbone", default="resnet50")
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ap.add_argument("--batch-size", type=int, default=8)
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ap.add_argument("--epochs", type=int, default=40)
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ap.add_argument("--lr", type=float, default=1e-4)
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ap.add_argument("--weight-decay", type=float, default=1e-5)
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ap.add_argument("--rf-trees", type=int, default=500)
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ap.add_argument("--rf-max-depth", type=int, default=None)
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ap.add_argument("--rf-min-samples-leaf", type=int, default=1)
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ap.add_argument("--num-workers", type=int, default=0)
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ap.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
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ap.add_argument("--seed", type=int, default=1234)
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ap.add_argument("--log-every", type=int, default=5)
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ap.add_argument("--run-name", default=None)
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ap.add_argument("--output-root", default="analysis_data/basic_analysis/cnn_logits_rf_cv_v2")
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ap.add_argument("--iop-corr-method", choices=["ratio", "ols", "lad", "multi"], default="ratio")
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ap.add_argument("--iop-drop-raw", action="store_true", default=False)
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ap.add_argument("--in-memory-cache", action="store_true", default=True,
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help="Cache all images in RAM before training (default: on).")
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ap.add_argument("--no-in-memory-cache", action="store_false", dest="in_memory_cache",
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help="Disable in-memory image cache.")
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ap.add_argument("--cache-workers", type=int, default=4,
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help="Threads for prebuilding image cache (default: 4).")
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return ap
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def _prebuild_image_cache(df: "pd.DataFrame", data, n_workers: int,
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resize: int = 256) -> dict:
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"""Load, convert to RGB, resize to `resize`px short edge, and cache as uint8 arrays.
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Storing pre-resized images means the per-batch transform only has to do
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CenterCrop + augmentation + ToTensor + Normalize on a small image rather
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than resizing a full-resolution fundus image every step.
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"""
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paths = list({str(data.get_image_path(row)) for _, row in df.iterrows()})
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cache: dict = {}
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print(f"[cache] Preloading {len(paths)} images (resize={resize}px) "
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f"with {n_workers} threads...", flush=True)
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def _load(p: str):
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img = Image.open(p)
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if img.mode != "RGB":
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img = img.convert("RGB")
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w, h = img.size
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scale = resize / min(w, h)
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img = img.resize((round(w * scale), round(h * scale)), Image.BILINEAR)
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return p, np.asarray(img, dtype=np.uint8)
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with ThreadPoolExecutor(max_workers=max(1, n_workers)) as ex:
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for path, arr in ex.map(_load, paths):
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cache[path] = arr
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ex_shape = cache[paths[0]].shape
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print(f"[cache] Done — {len(cache)} images in RAM "
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f"({ex_shape[1]}×{ex_shape[0]} each).", flush=True)
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return cache
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main() -> None:
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args = build_parser().parse_args()
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(args.seed)
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if args.device == "auto":
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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else:
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device = torch.device(args.device)
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print(f"Device: {device}", flush=True)
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run_name = args.run_name or time.strftime("%Y%m%d_%H%M%S")
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out_dir = Path(args.output_root) / run_name
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out_dir.mkdir(parents=True, exist_ok=True)
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# ---------- data ----------
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print("Loading PAPILA data...", flush=True)
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data = build_papila_data(
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image_dir=args.image_dir,
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clinical_dir=args.clinical_dir,
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label_col=args.label_col,
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cat_cols=list(args.cat_cols),
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n_splits=args.n_splits,
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random_seed=args.fold_seed,
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iop_corr_method=args.iop_corr_method,
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iop_drop_raw=args.iop_drop_raw,
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exclude_cols=list(args.exclude_cols or []),
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)
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print(f"Loaded: {len(data.df)} rows feature_dim={data.feature_dim}", flush=True)
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df_mode = data.df.copy()
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if args.eval_mode == "binary":
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df_mode = df_mode[df_mode[args.label_col].isin([0, 1])].reset_index(drop=True)
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num_classes = 2 if args.eval_mode == "binary" else int(df_mode[args.label_col].nunique())
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print(
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f"eval_mode={args.eval_mode} num_classes={num_classes} "
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f"rows={len(df_mode)} patients={df_mode['Patient ID'].nunique()}",
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flush=True,
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)
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# ---------- splits ----------
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split_manager = PatientFirstSplitManager(
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patient_col="Patient ID", label_col=args.label_col
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)
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split_args = SimpleNamespace(
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eval_mode=args.eval_mode,
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holdout_per_class=args.holdout_per_class,
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holdout_seed=args.holdout_seed,
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n_splits=args.n_splits,
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fold_seed=args.fold_seed,
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)
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clinical_ns = SimpleNamespace(df=df_mode, label_col=args.label_col)
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plans = split_manager.build_plans(clinical=clinical_ns, args=split_args, profile=None)
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n_folds = min(args.n_splits, len(plans))
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if plans and plans[0].holdout is not None:
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plans[0].holdout.to_csv(out_dir / "holdout_patients.csv", index=False)
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# ---------- image cache ----------
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image_cache = None
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if args.in_memory_cache:
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image_cache = _prebuild_image_cache(df_mode, data, args.cache_workers)
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# ---------- transforms ----------
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train_tf = build_backbone_transform(args.backbone, augment=True)
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eval_tf = build_eval_transform(args.backbone)
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rows: List[Dict] = []
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holdout_rows: List[Dict] = []
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for fold, split in enumerate(plans[:n_folds]):
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print(f"\n[info] Fold {fold+1}/{n_folds}", flush=True)
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random.seed(args.seed + fold * 100)
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np.random.seed(args.seed + fold * 100)
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torch.manual_seed(args.seed + fold * 100)
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view_train = _ClinicalView(data, split.train)
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view_val = _ClinicalView(data, split.val)
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dl_train = DataLoader(
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ClinicalDataset(view_train, train_tf, image_cache=image_cache),
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batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers,
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)
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dl_val = DataLoader(
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ClinicalDataset(view_val, eval_tf, image_cache=image_cache),
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batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,
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)
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dl_holdout = None
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if split.holdout is not None and not split.holdout.empty:
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dl_holdout = DataLoader(
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ClinicalDataset(_ClinicalView(data, split.holdout), eval_tf,
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image_cache=image_cache),
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batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,
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)
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# Train CNN
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model = CNNHead(args.backbone, num_classes=num_classes).to(device)
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_train_cnn(model, dl_train, args, fold=fold, device=device)
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# Extract logits (re-run train without augmentation for RF features)
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dl_train_eval = DataLoader(
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ClinicalDataset(view_train, eval_tf, image_cache=image_cache),
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batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,
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)
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y_tr, log_tr, md_tr = _infer_logits(model, dl_train_eval, device)
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y_va, log_va, md_va = _infer_logits(model, dl_val, device)
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np.save(out_dir / f"fold{fold}_train_logits.npy", log_tr)
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np.save(out_dir / f"fold{fold}_val_logits.npy", log_va)
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X_tr = np.concatenate([log_tr, md_tr], axis=1)
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X_va = np.concatenate([log_va, md_va], axis=1)
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rf = RandomForestClassifier(
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n_estimators=args.rf_trees,
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max_depth=args.rf_max_depth,
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min_samples_leaf=args.rf_min_samples_leaf,
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class_weight="balanced",
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random_state=args.fold_seed + fold,
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n_jobs=-1,
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)
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rf.fit(X_tr, y_tr)
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p_va = rf.predict_proba(X_va)
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pred_va = np.argmax(p_va, axis=1)
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rows.append({
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"fold": fold,
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"val_acc": float(accuracy_score(y_va, pred_va)),
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"val_auc": _auc_score(y_va, p_va, num_classes),
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"n_val": int(len(y_va)),
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})
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if dl_holdout is not None:
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y_ho, log_ho, md_ho = _infer_logits(model, dl_holdout, device)
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np.save(out_dir / f"fold{fold}_holdout_logits.npy", log_ho)
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X_ho = np.concatenate([log_ho, md_ho], axis=1)
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p_ho = rf.predict_proba(X_ho)
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pred_ho = np.argmax(p_ho, axis=1)
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holdout_rows.append({
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"fold": fold,
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"holdout_acc": float(accuracy_score(y_ho, pred_ho)),
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"holdout_auc": _auc_score(y_ho, p_ho, num_classes),
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"n_holdout": int(len(y_ho)),
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})
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print(
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f"[info] Fold {fold+1} RF: val_acc={rows[-1]['val_acc']:.4f}"
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f" val_auc={rows[-1]['val_auc']:.4f}"
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f" | holdout_acc={holdout_rows[-1]['holdout_acc']:.4f}"
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f" holdout_auc={holdout_rows[-1]['holdout_auc']:.4f}",
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flush=True,
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)
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else:
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print(
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f"[info] Fold {fold+1} RF: val_acc={rows[-1]['val_acc']:.4f}"
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f" val_auc={rows[-1]['val_auc']:.4f}",
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flush=True,
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)
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# ---------- save + summarise ----------
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fold_df = pd.DataFrame(rows)
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fold_df.to_csv(out_dir / "rf_val_metrics.csv", index=False)
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print("\nRF validation metrics:")
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print(fold_df.to_string(index=False, float_format=lambda x: f"{x:.4f}"))
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if holdout_rows:
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ho_df = pd.DataFrame(holdout_rows)
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ho_df.to_csv(out_dir / "rf_holdout_metrics.csv", index=False)
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print("\nRF holdout metrics:")
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print(ho_df.to_string(index=False, float_format=lambda x: f"{x:.4f}"))
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print(
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f"\nMeans: val_acc={fold_df['val_acc'].mean():.4f}"
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f" val_auc={fold_df['val_auc'].mean():.4f}"
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f" holdout_acc={ho_df['holdout_acc'].mean():.4f}"
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f" holdout_auc={ho_df['holdout_auc'].mean():.4f}"
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)
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else:
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print(
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f"\nMeans: val_acc={fold_df['val_acc'].mean():.4f}"
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f" val_auc={fold_df['val_auc'].mean():.4f}"
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)
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print(f"\nSaved outputs to: {out_dir}", flush=True)
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if __name__ == "__main__":
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main()
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