From c62b294cf61bcff8e2728a515683dc8e55188e54 Mon Sep 17 00:00:00 2001
From: Apurva <apurvagyan@gmail.com>
Date: Thu, 16 Apr 2026 05:35:27 -0400
Subject: [PATCH 1/7] develop finetuning capabilities for immunogenicity
 classifier

---
 configs/finetune_classifier.yaml              |  12 +
 finetune_classifier.py                        | 214 +++++
 .../data/components/ImmunoMonomerDataset.py   |  27 +-
 .../data/components/MLPDataset.py             |  80 ++
 .../data/components/preprocess_pdb.py         |  77 +-
 .../models/FinetuneClassifierModule.py        | 135 ++++
 .../models/components/MLPClassifier.py        | 132 ++++
 inspect_checkpoint.py                         |  43 +
 scripts/build_finetune_csvs.py                | 139 ++++
 scripts/finetune_classifier.sh                |  16 +
 uv.lock                                       | 743 ------------------
 11 files changed, 853 insertions(+), 765 deletions(-)
 create mode 100644 configs/finetune_classifier.yaml
 create mode 100644 finetune_classifier.py
 create mode 100644 immunofoundation/data/components/MLPDataset.py
 create mode 100644 immunofoundation/models/FinetuneClassifierModule.py
 create mode 100644 immunofoundation/models/components/MLPClassifier.py
 create mode 100644 inspect_checkpoint.py
 create mode 100644 scripts/build_finetune_csvs.py
 create mode 100644 scripts/finetune_classifier.sh

diff --git a/configs/finetune_classifier.yaml b/configs/finetune_classifier.yaml
new file mode 100644
index 0000000..104a56a
--- /dev/null
+++ b/configs/finetune_classifier.yaml
@@ -0,0 +1,12 @@
+# Example finetuning config for ImmunoFoundation classifier
+# Update the values as needed for your experiment
+
+csv: /home/am3826/scratch_pi_sk2433/am3826/finetuning/merged.csv
+out_dir: /home/am3826/scratch_pi_sk2433/am3826/finetuning/run_af2_full
+max_epochs: 50
+batch_size: 8
+num_workers: 8
+matmul_precision: medium
+bio_dim: 32
+hidden_dims: [512, 256, 128, 64, 32]
+class_weights: [0.61755624, 2.62664165]
diff --git a/finetune_classifier.py b/finetune_classifier.py
new file mode 100644
index 0000000..3ca1b1f
--- /dev/null
+++ b/finetune_classifier.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+"""Project-aligned finetuning script.
+
+This reuses the repository's `ImmunoMonomerDataset` and `ImmunoFoundationMonomerModule`.
+It expects the CSV to include a `cif_path` column pointing to mmCIF files and an
+`immunogenicity` column (0/1). It will attempt to load a provided checkpoint into
+the backbone (best-effort) and finetune a classifier head.
+"""
+import os
+from types import SimpleNamespace
+
+import torch
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint, Callback
+import yaml
+
+from immunofoundation.data.components.ImmunoMonomerDataset import ImmunoMonomerDataset, custom_collate_mono
+from immunofoundation.models.ImmunoFoundationMonomerModule import ImmunoFoundationMonomerModule
+from immunofoundation.models.FinetuneClassifierModule import FinetuneClassifierModule
+
+
+def make_data_cfg(csv_path, batch_size=16, train_size=0.8, num_workers=4):
+    # Build a lightweight data_cfg object compatible with ImmunoMonomerDataset
+    mask = SimpleNamespace(mask_rate=0.5, max_distance=8, max_neighbors=12)
+    structure = SimpleNamespace(adj=True, k=15)
+    data_cfg = SimpleNamespace(csv_path=csv_path, train_size=train_size, batch_size=batch_size, num_workers=num_workers, mono=True, mask=mask, structure=structure)
+    return data_cfg
+
+
+def main():
+    # Load config from YAML file
+    config_path = 'configs/finetune_classifier.yaml'  # Update path if needed
+    with open(config_path, 'r') as f:
+        config = yaml.safe_load(f)
+
+    # Use config values
+    csv_path = config['csv']
+    out_dir = config['out_dir']
+    max_epochs = config.get('max_epochs', 50)
+    batch_size = config.get('batch_size', 8)
+    num_workers = config.get('num_workers', 8)
+    matmul_precision = config.get('matmul_precision', 'medium')
+    bio_dim = config.get('bio_dim', 32)
+    hidden_dims = config.get('hidden_dims', [512, 256, 128, 64, 32])
+    class_weights = config.get('class_weights', None)
+
+    os.makedirs(out_dir, exist_ok=True)
+
+    # Optionally set float32 matmul precision to leverage Tensor Cores (perf vs numeric tradeoff)
+    if matmul_precision is not None and matmul_precision != 'none':
+        try:
+            torch.set_float32_matmul_precision(matmul_precision)
+            print(f"Set torch float32 matmul precision -> {matmul_precision}")
+        except Exception as e:
+            print(f"Warning: failed to set float32 matmul precision: {e}")
+
+    data_cfg = make_data_cfg(csv_path, batch_size=batch_size, num_workers=num_workers)
+    train_ds = ImmunoMonomerDataset(data_cfg, is_training=True)
+    val_ds = ImmunoMonomerDataset(data_cfg, is_training=False)
+
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, collate_fn=custom_collate_mono, num_workers=num_workers)
+    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, collate_fn=custom_collate_mono, num_workers=num_workers)
+
+    # Build backbone using defaults similar to training config
+    seq = SimpleNamespace(model_type='esm', freeze_esm=True, aggregate=False, esm_dim=1280, rep_layer=33, esm_variant='esm2_t33_650M_UR50D', out_dim=32, n_heads=8, dim_ffn=256, n_layers=10)
+    struct = SimpleNamespace(model_type='transformer', out_dim=32, n_heads=8, dim_ffn=256, n_layers=4)
+    bio = SimpleNamespace(model_type='mlp', n_bio_prop=93, hidden_dim=64, out_dim=32, n_layers=4)
+    model_cfg = SimpleNamespace(sequence=seq, structure=struct, bio_chem=bio)
+
+    backbone = ImmunoFoundationMonomerModule(model_cfg)
+
+    # Attempt to load checkpoint into backbone (non-fatal)
+    checkpoint_path = config.get('checkpoint', None)
+    if checkpoint_path is not None:
+        try:
+            ckpt = torch.load(checkpoint_path, map_location='cpu')
+            state = ckpt.get('state_dict', ckpt)
+            backbone.load_state_dict(state, strict=False)
+            print('Loaded checkpoint (partial) into backbone')
+        except Exception as e:
+            print('Warning: failed to load checkpoint into backbone:', e)
+
+    # infer num_classes from training dataset labels (if present)
+    sample = None
+    try:
+        sample = train_ds[0]
+    except Exception:
+        pass
+    if sample is not None and 'label' in sample:
+        # compute unique labels in small pass
+        labels = []
+        for i in range(min(len(train_ds), 1000)):
+            try:
+                labels.append(train_ds[i]['label'])
+            except Exception:
+                break
+        num_classes = int(max(labels)) + 1 if len(labels) > 0 else 2
+    else:
+        num_classes = 2
+
+    finetune = FinetuneClassifierModule(
+        backbone,
+        num_classes=num_classes,
+        bio_dim=model_cfg.bio_chem.out_dim,
+        hidden_dims=hidden_dims,
+        class_weights=class_weights
+    )
+
+    checkpoint_cb = ModelCheckpoint(dirpath=out_dir, filename='finetune-{epoch:02d}-{val_loss:.4f}', save_top_k=3, monitor='val/loss', mode='min')
+    # GPU-aware trainer: use a GPU if available
+    accelerator = 'gpu' if torch.cuda.is_available() else 'cpu'
+    devices = 1 if torch.cuda.is_available() else None
+    # callback to print per-epoch metrics (loss/acc) to stdout for easy inspection
+    class PrintMetricsCallback(Callback):
+        def __init__(self, out_dir):
+            super().__init__()
+            self.out_dir = out_dir
+            self.csv_path = os.path.join(self.out_dir, 'metrics.csv')
+            # write header if not exists
+            if not os.path.exists(self.csv_path):
+                with open(self.csv_path, 'w') as fh:
+                    fh.write('epoch,train_loss,train_acc,val_loss,val_acc\n')
+
+        def on_validation_epoch_end(self, trainer, pl_module):
+            metrics = trainer.callback_metrics
+            # gather epoch and metrics with safe extraction
+            epoch = int(trainer.current_epoch) if hasattr(trainer, 'current_epoch') else None
+            def _safe_get(k):
+                v = metrics.get(k, None)
+                if v is None:
+                    return None
+                try:
+                    if hasattr(v, 'item'):
+                        return float(v.item())
+                    elif isinstance(v, (int, float)):
+                        return float(v)
+                    else:
+                        return float(v)
+                except Exception:
+                    return None
+
+            train_loss = _safe_get('train/loss')
+            train_acc = _safe_get('train/acc')
+            val_loss = _safe_get('val/loss')
+            val_acc = _safe_get('val/acc')
+
+            out = {
+                'epoch': epoch,
+                'train/loss': train_loss,
+                'train/acc': train_acc,
+                'val/loss': val_loss,
+                'val/acc': val_acc,
+            }
+            print('Epoch metrics:', out)
+
+            # append to CSV (use empty string for missing)
+            with open(self.csv_path, 'a') as fh:
+                fh.write(f"{epoch},{'' if train_loss is None else train_loss},{'' if train_acc is None else train_acc},{'' if val_loss is None else val_loss},{'' if val_acc is None else val_acc}\n")
+
+    print_cb = PrintMetricsCallback(out_dir=out_dir)
+    # Disable the default sanity validation steps (they can produce an extra validation run
+    # before training starts which confuses single-epoch metrics printing). Set to 0 so
+    # we only see the real validation at the end of each epoch.
+    trainer = Trainer(max_epochs=max_epochs, callbacks=[checkpoint_cb, print_cb], accelerator=accelerator, devices=devices, num_sanity_val_steps=0)
+
+    # Run training (Lightning will log metrics per epoch). The FinetuneClassifierModule
+    # already logs train/loss and train/acc (on_step/on_epoch) and val/loss and val/acc (on_epoch).
+    trainer.fit(finetune, train_loader, val_loader)
+
+    # Optionally run test evaluation on a held-out test split using the best checkpoint
+    run_test = config.get('test', False)
+    test_csv = config.get('test_csv', None)
+    if run_test:
+        # determine test CSV path
+        if test_csv is None:
+            test_csv = os.path.join(out_dir, 'test.csv')
+        if not os.path.exists(test_csv):
+            print(f"Test requested but no test CSV found at {test_csv}. Skipping test run.")
+        else:
+            print('Building test dataloader from', test_csv)
+            test_cfg = make_data_cfg(test_csv, batch_size=batch_size, num_workers=num_workers)
+            test_ds = ImmunoMonomerDataset(test_cfg, is_training=False)
+            test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False, collate_fn=custom_collate_mono, num_workers=num_workers)
+
+            # find best checkpoint saved by the checkpoint callback
+            best_ckpt = checkpoint_cb.best_model_path if getattr(checkpoint_cb, 'best_model_path', None) else None
+            if not best_ckpt or not os.path.exists(best_ckpt):
+                # fallback: pick the most recent finetune-*.ckpt in out_dir
+                import glob
+                ckpts = sorted(glob.glob(os.path.join(out_dir, 'finetune-*.ckpt')), key=os.path.getmtime)
+                best_ckpt = ckpts[-1] if ckpts else None
+
+            if best_ckpt is None:
+                print('No checkpoint found to run test with. Skipping test run.')
+            else:
+                print('Running test with checkpoint:', best_ckpt)
+                # load model from checkpoint (ensures weights match expected backbone signature)
+                test_model = FinetuneClassifierModule.load_from_checkpoint(best_ckpt, backbone=backbone)
+                test_model.eval()
+                # run Lightning test and persist results
+                test_res = trainer.test(test_model, dataloaders=test_loader, ckpt_path=None)
+                try:
+                    import json
+                    out_path = os.path.join(out_dir, 'test_results.json')
+                    with open(out_path, 'w') as fh:
+                        json.dump(test_res, fh, indent=2)
+                    print('Wrote test results to', out_path)
+                except Exception as e:
+                    print('Test finished but failed to write results:', e)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/immunofoundation/data/components/ImmunoMonomerDataset.py b/immunofoundation/data/components/ImmunoMonomerDataset.py
index 291881e..842c4a3 100644
--- a/immunofoundation/data/components/ImmunoMonomerDataset.py
+++ b/immunofoundation/data/components/ImmunoMonomerDataset.py
@@ -54,6 +54,17 @@ def _process_csv_row(self, csv_row):
             final_features['adj'] = kneighbors_graph(coords, n_neighbors = self.data_cfg.structure.k)
         else:
             final_features['adj'] = None
+        # include label if present in CSV (optional for supervised finetuning)
+        # default label column name expected: 'immunogenicity'
+        if 'immunogenicity' in csv_row.index:
+            try:
+                final_features['label'] = int(csv_row['immunogenicity'])
+            except Exception:
+                # try mapping non-integer labels to 0/1
+                if str(csv_row['immunogenicity']).lower() in ('true', '1', 'yes'):
+                    final_features['label'] = 1
+                else:
+                    final_features['label'] = 0
         return final_features
 
     def __getitem__(self, idx):
@@ -143,11 +154,21 @@ def custom_collate_mono(batch_list):
     else:
         adjs = torch.utils.data.default_collate([0]*len(batch_list))
     masks = torch.utils.data.default_collate([pad(torch.tensor(rec['mask']).float(), max_len) for rec in batch_list])
-    sequences = torch.utils.data.default_collate([rec['sequence'] for rec in batch_list])
+    # keep sequences as python list so ESM wrapper can batch-convert them
+    sequences = [rec['sequence'] for rec in batch_list]
 
-    return {
+    batch = {
         "coords": padded_coords,
         "adjs": adjs,
         "sequence": sequences,
         "masks": masks,
-    }
\ No newline at end of file
+    }
+    # optional fields: label and biochem
+    if 'label' in batch_list[0]:
+        labels = torch.tensor([rec['label'] for rec in batch_list], dtype=torch.long)
+        batch['label'] = labels
+    if 'biochem' in batch_list[0]:
+        biochems = torch.utils.data.default_collate([rec['biochem'] for rec in batch_list])
+        batch['biochem'] = biochems
+
+    return batch
\ No newline at end of file
diff --git a/immunofoundation/data/components/MLPDataset.py b/immunofoundation/data/components/MLPDataset.py
new file mode 100644
index 0000000..8a7f34d
--- /dev/null
+++ b/immunofoundation/data/components/MLPDataset.py
@@ -0,0 +1,80 @@
+import os
+from typing import List, Optional
+
+import pandas as pd
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+class MLPDataset(Dataset):
+    """Simple dataset to load CSV rows and return numeric features + label.
+
+    Behavior:
+    - Attempts to infer label column (common names). If `label_col` provided, use it.
+    - Uses `feature_cols` if provided, otherwise selects all numeric columns except label and id-like columns.
+    - Returns dict with 'features' (float32 tensor) and 'label' (long tensor)
+    """
+
+    DEFAULT_LABEL_NAMES = ["label", "labels", "immunogenicity", "is_immunogenic", "y"]
+
+    def __init__(self, csv_path: str, label_col: Optional[str] = None, feature_cols: Optional[List[str]] = None):
+        if not os.path.exists(csv_path):
+            raise FileNotFoundError(f"CSV not found: {csv_path}")
+        self.df = pd.read_csv(csv_path)
+        if label_col is None:
+            label_col = self._infer_label_col()
+        if label_col is None:
+            raise ValueError("Could not infer a label column. Please pass `label_col` explicitly.")
+        self.label_col = label_col
+
+        if feature_cols is None:
+            # choose numeric dtypes except the label
+            numeric_df = self.df.select_dtypes(include=["number"]).copy()
+            if label_col in numeric_df.columns:
+                numeric_df = numeric_df.drop(columns=[label_col])
+            feature_cols = list(numeric_df.columns)
+        if len(feature_cols) == 0:
+            raise ValueError("No numeric feature columns found. Provide `feature_cols` explicitly.")
+
+        self.feature_cols = feature_cols
+        # drop rows with NaNs in selected cols
+        keep_mask = self.df[self.feature_cols + [self.label_col]].notnull().all(axis=1)
+        self.df = self.df.loc[keep_mask].reset_index(drop=True)
+
+        # prepare numpy arrays for speed
+        self.features = self.df[self.feature_cols].astype(np.float32).to_numpy()
+        self.labels = self.df[self.label_col].to_numpy()
+
+        # if labels are not integer, try to map
+        if not np.issubdtype(self.labels.dtype, np.integer):
+            unique = sorted(pd.unique(self.labels))
+            self.label_map = {v: i for i, v in enumerate(unique)}
+            self.labels = np.array([self.label_map[x] for x in self.labels], dtype=np.int64)
+        else:
+            self.label_map = None
+
+    def _infer_label_col(self) -> Optional[str]:
+        for name in self.DEFAULT_LABEL_NAMES:
+            if name in self.df.columns:
+                return name
+        # try common suffixes
+        for col in self.df.columns:
+            if col.lower().startswith("label") or col.lower().endswith("label"):
+                return col
+        return None
+
+    def __len__(self):
+        return len(self.features)
+
+    def __getitem__(self, idx):
+        x = torch.from_numpy(self.features[idx]).float()
+        y = int(self.labels[idx])
+        return {"features": x, "label": torch.tensor(y, dtype=torch.long)}
+
+
+def collate_fn(batch):
+    """Collate function to stack features and labels."""
+    features = torch.stack([b["features"] for b in batch], dim=0)
+    labels = torch.stack([b["label"] for b in batch], dim=0)
+    return {"features": features, "labels": labels}
diff --git a/immunofoundation/data/components/preprocess_pdb.py b/immunofoundation/data/components/preprocess_pdb.py
index 91da607..bd893d0 100644
--- a/immunofoundation/data/components/preprocess_pdb.py
+++ b/immunofoundation/data/components/preprocess_pdb.py
@@ -2,19 +2,57 @@
 from Bio.SeqUtils import seq1
 import gzip
 import numpy as np
+import io
 
-parser = MMCIFParser(QUIET=True)
-    
 
-def extract_ca_and_sequence(pdb_file):
-    parser = MMCIFParser(QUIET=True)
-    if pdb_file.endswith(".gz"):
-        with gzip.open(pdb_file, 'rt') as f:
-            structure = parser.get_structure('protein', f)
+def _read_file_text(path):
+    if path.endswith('.gz'):
+        with gzip.open(path, 'rt') as f:
+            return f.read()
     else:
-        structure = parser.get_structure('protein', pdb_file)
+        with open(path, 'r') as f:
+            return f.read()
+
+
+def extract_ca_and_sequence(pdb_file):
+    """Robustly extract CA coordinates and sequence from mmCIF or PDB files.
+
+    Tries MMCIFParser first (for mmCIF), and falls back to PDBParser when that
+    fails or the file appears to be a PDB file. Accepts gzipped files as well.
+    Returns: (ca_coords_peptide, sequence_peptide, ca_coords_mhc, sequence_mhc)
+    """
+    mmcif_parser = MMCIFParser(QUIET=True)
+    pdb_parser = PDBParser(QUIET=True)
+
+    text = None
+    try:
+        text = _read_file_text(pdb_file)
+    except Exception as e:
+        raise ValueError(f"Could not read structure file {pdb_file}: {e}")
+
+    # Heuristic: mmCIF files usually start with 'data_' directive
+    is_mmcif = text.lstrip().startswith('data_')
+
+    structure = None
+    if is_mmcif:
+        try:
+            # MMCIFParser can accept a file handle
+            fh = io.StringIO(text)
+            structure = mmcif_parser.get_structure('protein', fh)
+        except Exception:
+            structure = None
+
+    if structure is None:
+        # try PDB parser
+        try:
+            fh = io.StringIO(text)
+            structure = pdb_parser.get_structure('protein', fh)
+        except Exception as e:
+            # give a helpful error
+            raise ValueError(f"Failed to parse structure file {pdb_file} as mmCIF or PDB: {e}")
+
     model = structure[0]
-    
+
     ca_coords_peptide = []
     sequence_peptide = []
     if 'A' in model:
@@ -22,30 +60,25 @@ def extract_ca_and_sequence(pdb_file):
             if 'CA' in residue:
                 ca_atom = residue['CA']
                 ca_coords_peptide.append(tuple(ca_atom.get_coord()))
-                
                 try:
                     aa = seq1(residue.get_resname())
                     sequence_peptide.append(aa)
-                except KeyError:
-                    # Handle non-standard residues
+                except Exception:
                     sequence_peptide.append('X')
-    
+
     ca_coords_mhc = []
     sequence_mhc = []
-    
     if 'B' in model:
         for residue in model['B']:
             if 'CA' in residue:
                 ca_atom = residue['CA']
                 ca_coords_mhc.append(tuple(ca_atom.get_coord()))
-                
                 try:
                     aa = seq1(residue.get_resname())
                     sequence_mhc.append(aa)
-                except KeyError:
-                    # Handle non-standard residues
+                except Exception:
                     sequence_mhc.append('X')
-    
+
     sequence_peptide = ''.join(sequence_peptide)
     sequence_mhc = ''.join(sequence_mhc)
 
@@ -55,4 +88,10 @@ def extract_ca_and_sequence(pdb_file):
 def normalize_coords(coords):
     """Normalize coordinates to [-1, 1]."""
     coords = np.array(coords)
-    return 2 * (coords - coords.min()) / (coords.max() - coords.min()) - 1
+    if coords.size == 0:
+        return coords
+    minv = coords.min()
+    maxv = coords.max()
+    if maxv == minv:
+        return coords - minv
+    return 2 * (coords - minv) / (maxv - minv) - 1
diff --git a/immunofoundation/models/FinetuneClassifierModule.py b/immunofoundation/models/FinetuneClassifierModule.py
new file mode 100644
index 0000000..aeb3af7
--- /dev/null
+++ b/immunofoundation/models/FinetuneClassifierModule.py
@@ -0,0 +1,135 @@
+"""
+Finetune classifier wrapper for the ImmunoFoundation backbone.
+
+"""
+
+import torch
+import torch.nn as nn
+from pytorch_lightning import LightningModule
+
+
+class FinetuneClassifierModule(LightningModule):
+    """LightningModule that reuses ImmunoFoundationMonomerModule as an encoder
+    and places a small classifier head on top to predict immunogenicity.
+    """
+
+    def __init__(self, backbone, num_classes: int = 2, bio_dim: int = 0, hidden_dims=None, lr: float = 1e-3, class_weights=None):
+        super().__init__()
+        self.backbone = backbone
+        self.lr = lr
+        self.bio_dim = int(bio_dim)
+
+        # infer dims from backbone config if available
+        seq_dim = getattr(self.backbone.model_cfg.sequence, 'out_dim', None)
+        struct_dim = getattr(self.backbone.model_cfg.structure, 'out_dim', None)
+        if seq_dim is None or struct_dim is None:
+            raise ValueError('Backbone must have sequence.out_dim and structure.out_dim in its model_cfg')
+
+        in_dim = int(seq_dim) + int(struct_dim) + int(bio_dim)
+        # default to a simple 5-layer MLP if not provided
+        if hidden_dims is None:
+            hidden_dims = [512, 256, 128, 64, 32]
+        layers = []
+        prev = in_dim
+        for h in hidden_dims:
+            layers.append(nn.Linear(prev, h))
+            layers.append(nn.ReLU())
+            layers.append(nn.Dropout(0.1))
+            prev = h
+        layers.append(nn.Linear(prev, int(num_classes)))
+        self.classifier = nn.Sequential(*layers)
+
+        # Set up class weights for CrossEntropyLoss if provided
+        if class_weights is not None:
+            weights = torch.tensor(class_weights, dtype=torch.float32)
+            self.criterion = nn.CrossEntropyLoss(weight=weights)
+        else:
+            self.criterion = nn.CrossEntropyLoss()
+
+    def forward(self, batch):
+        # Expect batch to be in the same format used by ImmunoMonomerDataset/custom_collate_mono
+        # backbone.encode returns seq_embeddings, struct_embeddings
+        seq_embeddings, struct_embeddings = self.backbone.encode(batch)
+        # pooling: masked mean over residues if masks present
+        if 'masks' in batch:
+            mask = batch['masks'].unsqueeze(-1)  # (B, L, 1)
+            inv_mask = (1 - mask)
+            seq_pool = (seq_embeddings * inv_mask).sum(dim=1) / inv_mask.sum(dim=1).clamp(min=1)
+            struct_pool = (struct_embeddings * inv_mask).sum(dim=1) / inv_mask.sum(dim=1).clamp(min=1)
+        else:
+            seq_pool = seq_embeddings.mean(dim=1)
+            struct_pool = struct_embeddings.mean(dim=1)
+
+        bio = batch.get('biochem')
+        if bio is None:
+            # pad with zeros to match expected bio_dim
+            if self.bio_dim > 0:
+                bio = torch.zeros(seq_pool.size(0), self.bio_dim, dtype=torch.float32, device=seq_pool.device)
+            else:
+                bio = None
+
+        if bio is None:
+            x = torch.cat([seq_pool, struct_pool], dim=-1)
+        else:
+            # ensure bio is float tensor and on correct device
+            if not torch.is_tensor(bio):
+                bio = torch.tensor(bio, dtype=torch.float32, device=seq_pool.device)
+            else:
+                bio = bio.to(seq_pool.device).float()
+            x = torch.cat([seq_pool, struct_pool, bio], dim=-1)
+
+        # Save for debug printing in training/validation step
+        self._last_classifier_input = x.detach().cpu() if not hasattr(self, '_last_classifier_input') else x.detach().cpu()
+        return self.classifier(x)
+
+    def training_step(self, batch, batch_idx):
+        logits = self(batch)
+        labels = batch.get('label')
+        if labels is None:
+            labels = batch.get('labels')
+        if labels is None:
+            raise ValueError('Batch must contain label for supervised finetuning')
+        if not torch.is_tensor(labels):
+            labels = torch.tensor(labels, dtype=torch.long, device=logits.device)
+        else:
+            labels = labels.to(logits.device)
+        loss = self.criterion(logits, labels)
+        preds = logits.argmax(dim=-1)
+        acc = (preds == labels).float().mean()
+        # # Debug print for first batch of each epoch
+        # if batch_idx == 0:
+        #     print("[DEBUG][TRAIN] Classifier input (first batch):", self._last_classifier_input[:5])
+        #     print("[DEBUG][TRAIN] Predictions:", preds[:10].detach().cpu().numpy())
+        #     print("[DEBUG][TRAIN] Labels:", labels[:10].detach().cpu().numpy())
+        self.log('train/loss', loss, on_step=True, on_epoch=True, prog_bar=False)
+        self.log('train/acc', acc, on_step=True, on_epoch=True, prog_bar=False)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        logits = self(batch)
+        labels = batch.get('label')
+        if labels is None:
+            labels = batch.get('labels')
+        if labels is None:
+            raise ValueError(
+                "Validation batch is missing labels (key 'label' or 'labels'). "
+                "Ensure your merged CSV contains an 'immunogenicity' column and that the validation split includes labels."
+            )
+        if not torch.is_tensor(labels):
+            labels = torch.tensor(labels, dtype=torch.long, device=logits.device)
+        else:
+            labels = labels.to(logits.device)
+        loss = self.criterion(logits, labels)
+        preds = logits.argmax(dim=-1)
+        acc = (preds == labels).float().mean()
+        # Debug print for first batch of each epoch
+        if batch_idx == 0:
+            print("[DEBUG][VAL] Classifier input (first batch):", self._last_classifier_input[:5])
+            print("[DEBUG][VAL] Predictions:", preds[:10].detach().cpu().numpy())
+            print("[DEBUG][VAL] Labels:", labels[:10].detach().cpu().numpy())
+        self.log('val/loss', loss, on_epoch=True)
+        self.log('val/acc', acc, on_epoch=True)
+        return loss
+
+    def configure_optimizers(self):
+        return torch.optim.AdamW(self.parameters(), lr=self.lr)
diff --git a/immunofoundation/models/components/MLPClassifier.py b/immunofoundation/models/components/MLPClassifier.py
new file mode 100644
index 0000000..e8409fb
--- /dev/null
+++ b/immunofoundation/models/components/MLPClassifier.py
@@ -0,0 +1,132 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Dict, Any
+from pytorch_lightning import LightningModule
+
+
+class MLPClassifier(LightningModule):
+    """Simple MLP head for immunogenicity classification.
+
+    Supports optional biochemical encoder (an instance of BiochemicalModel or similar)
+    to be supplied externally. If provided, its outputs are concatenated to raw features.
+    """
+
+    def __init__(self, input_dim: int, num_classes: int, hidden_dims=(128, 64), lr: float = 1e-4, dropout=0.1, backbone: Optional[nn.Module] = None):
+        super().__init__()
+        self.save_hyperparameters()
+        self.input_dim = input_dim
+        self.num_classes = num_classes
+        self.lr = lr
+        self.backbone = backbone
+
+        # compute head input dim
+        head_in = input_dim
+        if backbone is not None:
+            # try to infer backbone output dim if it has attribute cfg.out_dim
+            out_dim = getattr(backbone, 'cfg', None)
+            if out_dim is not None and hasattr(backbone.cfg, 'out_dim'):
+                head_in += int(backbone.cfg.out_dim)
+            else:
+                # fallback: attempt forward with zeros later will adjust
+                head_in += 0
+
+        layers = []
+        prev = head_in
+        for h in hidden_dims:
+            layers.append(nn.Linear(prev, h))
+            layers.append(nn.ReLU())
+            layers.append(nn.Dropout(dropout))
+            prev = h
+        layers.append(nn.Linear(prev, num_classes))
+
+        self.head = nn.Sequential(*layers)
+        self.criterion = nn.CrossEntropyLoss()
+
+    def forward(self, features: torch.Tensor):
+        """features: (B, input_dim)"""
+        x = features
+        if self.backbone is not None:
+            try:
+                with torch.no_grad():
+                    enc = self.backbone(x)
+            except Exception:
+                # if backbone expects different shape, try passing through .forward
+                enc = self.backbone(x)
+            # enc expected (B, dim) or (B, L, dim) -> flatten last dims
+            if enc.dim() > 2:
+                enc = enc.mean(dim=1)
+            x = torch.cat([x, enc], dim=-1)
+
+        logits = self.head(x)
+        return logits
+
+    def training_step(self, batch, batch_idx):
+        x = batch['features']
+        y = batch['labels']
+        logits = self(x)
+        loss = self.criterion(logits, y)
+        preds = torch.argmax(logits, dim=-1)
+        acc = (preds == y).float().mean()
+        self.log('train/loss', loss, on_step=True, on_epoch=True)
+        self.log('train/acc', acc, on_step=True, on_epoch=True)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        x = batch['features']
+        y = batch['labels']
+        logits = self(x)
+        loss = self.criterion(logits, y)
+        preds = torch.argmax(logits, dim=-1)
+        acc = (preds == y).float().mean()
+        self.log('val/loss', loss, on_epoch=True)
+        self.log('val/acc', acc, on_epoch=True)
+        return {'val_loss': loss, 'val_acc': acc}
+
+    def configure_optimizers(self):
+        return torch.optim.AdamW(self.parameters(), lr=self.lr)
+
+    def load_backbone_weights_from_checkpoint(self, ckpt_path: str, prefixes_to_try=('bio', 'biochemical', 'biochem')):
+        """Try to load matching keys from a checkpoint into `self.backbone` if available.
+
+        This is best-effort and will skip keys that don't match.
+        """
+        if self.backbone is None:
+            return
+        try:
+            import torch
+            ckpt = torch.load(ckpt_path, map_location='cpu')
+            # checkpoints saved by Lightning often have 'state_dict' key
+            state = ckpt.get('state_dict', ckpt)
+            # build mapping for backbone keys
+            own_state = self.backbone.state_dict()
+            new_state = {}
+            for k, v in state.items():
+                for p in prefixes_to_try:
+                    if p in k.lower():
+                        # heuristic: strip common prefixes like 'model.' or similar
+                        new_key = k
+                        # if key contains 'backbone' or 'biochem' try to map directly
+                        if new_key in own_state and own_state[new_key].shape == v.shape:
+                            new_state[new_key] = v
+                        else:
+                            # try to remove leading module names until match
+                            parts = new_key.split('.')
+                            for i in range(len(parts)):
+                                candidate = '.'.join(parts[i:])
+                                if candidate in own_state and own_state[candidate].shape == v.shape:
+                                    new_state[candidate] = v
+                                    break
+                        break
+            if len(new_state) == 0:
+                # last resort: try strict=False load of full state
+                try:
+                    self.backbone.load_state_dict(state, strict=False)
+                    return
+                except Exception:
+                    return
+            own_state.update(new_state)
+            self.backbone.load_state_dict(own_state)
+        except Exception:
+            # don't fail training if this cannot be loaded
+            return
diff --git a/inspect_checkpoint.py b/inspect_checkpoint.py
new file mode 100644
index 0000000..e38b9d3
--- /dev/null
+++ b/inspect_checkpoint.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""Utility: print keys and tensor shapes inside a checkpoint file.
+
+Helps determine which submodules can be loaded into a new model.
+"""
+import argparse
+import torch
+
+
+def summarize_state_dict(state_dict, top_n=200):
+    keys = list(state_dict.keys())
+    print(f"Total keys: {len(keys)}")
+    print("First keys (name -> shape):")
+    for k in keys[:top_n]:
+        v = state_dict[k]
+        try:
+            shape = v.shape
+        except Exception:
+            shape = type(v)
+        print(f"  {k} -> {shape}")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('ckpt', help='Path to checkpoint (.ckpt or .pth)')
+    parser.add_argument('--list-only', action='store_true')
+    args = parser.parse_args()
+
+    ckpt = torch.load(args.ckpt, map_location='cpu')
+    if isinstance(ckpt, dict) and 'state_dict' in ckpt:
+        sd = ckpt['state_dict']
+    else:
+        sd = ckpt
+
+    if not isinstance(sd, dict):
+        print('Checkpoint content is not a mapping; top-level type:', type(sd))
+        return
+
+    summarize_state_dict(sd)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/build_finetune_csvs.py b/scripts/build_finetune_csvs.py
new file mode 100644
index 0000000..d565e6e
--- /dev/null
+++ b/scripts/build_finetune_csvs.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+"""Build merged CSVs for finetuning from a PDB/mmCIF directory and a labels CSV.
+
+Produces a merged CSV and optional train/val(/test) splits written to an output directory
+so downstream training scripts can consume them directly.
+
+Usage examples:
+  scripts/build_finetune_csvs.py --pdb-dir /path/to/pdbs --labels-csv labels.csv --out-dir data/finetune
+  scripts/build_finetune_csvs.py --pdb-dir /path/to/pdbs --out-dir data/finetune
+"""
+import argparse
+import os
+import glob
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+
+def stem(path):
+    base = os.path.basename(path)
+    # remove common extensions
+    for ext in ('.cif.gz', '.pdb.gz', '.cif', '.pdb'):
+        if base.endswith(ext):
+            return base[: -len(ext)]
+    return os.path.splitext(base)[0]
+
+
+def build_paths_df(pdb_dir):
+    exts = ('**/*.cif', '**/*.cif.gz', '**/*.pdb', '**/*.pdb.gz')
+    files = []
+    for e in exts:
+        files.extend(glob.glob(os.path.join(pdb_dir, e), recursive=True))
+    files = sorted(files)
+    if len(files) == 0:
+        raise ValueError(f'No structure files found under {pdb_dir}')
+    df = pd.DataFrame({'cif_path': files})
+    df['stem'] = df['cif_path'].apply(stem)
+    return df
+
+
+def try_merge(paths_df, labels_df, merge_on=None):
+    # If user provided explicit merge key, use it
+    if merge_on is not None:
+        if merge_on not in labels_df.columns:
+            raise ValueError(f"merge_on column '{merge_on}' not found in labels CSV")
+        # try direct merge
+        merged = paths_df.merge(labels_df, left_on='stem', right_on=merge_on, how='left')
+        return merged
+
+    # If labels contain `allele` and `mut_pep`, prefer constructing the filename stem as
+    # "{allele}_{mut_pep}" which matches the PDB filenames in your dataset (e.g.
+    # HLA-A*02:01_AAAAQQIQV.pdb).
+    if 'allele' in labels_df.columns and 'mut_pep' in labels_df.columns:
+        labels_df['_allele_mut_stem'] = labels_df['allele'].astype(str).str.strip() + '_' + labels_df['mut_pep'].astype(str).str.strip()
+        merged = paths_df.merge(labels_df, left_on='stem', right_on='_allele_mut_stem', how='left')
+        return merged
+
+    # try to detect a column in labels that overlaps with stems
+    best_col = None
+    best_overlap = 0
+    stems = set(paths_df['stem'].astype(str).unique())
+    for c in labels_df.columns:
+        vals = labels_df[c].astype(str).str.split('.').str[0].str.strip().unique()
+        overlap = len(set(vals).intersection(stems))
+        if overlap > best_overlap:
+            best_overlap = overlap
+            best_col = c
+
+    if best_col is not None and best_overlap > 0:
+        labels_df['_stem_candidate'] = labels_df[best_col].astype(str).str.split('.').str[0].str.strip()
+        merged = paths_df.merge(labels_df, left_on='stem', right_on='_stem_candidate', how='left')
+        return merged
+
+    # fallback: if lengths match, concat by index
+    if len(labels_df) == len(paths_df):
+        merged = pd.concat([paths_df.reset_index(drop=True), labels_df.reset_index(drop=True)], axis=1)
+        return merged
+
+    raise ValueError('Could not automatically merge labels CSV to paths. Provide --merge-on to specify the key.')
+
+
+def split_and_write(merged_df, out_dir, label_col='immunogenicity', train_size=0.8, test_size=0.1, random_state=42):
+    os.makedirs(out_dir, exist_ok=True)
+    merged_path = os.path.join(out_dir, 'merged.csv')
+    merged_df.to_csv(merged_path, index=False)
+    print('Wrote merged CSV ->', merged_path)
+
+    if label_col in merged_df.columns and merged_df[label_col].notnull().any():
+        labels = merged_df[label_col].astype(int)
+        # compute splits
+        if test_size > 0:
+            train_val_idx, test_idx = train_test_split(merged_df.index.tolist(), test_size=test_size, stratify=labels, random_state=random_state)
+            rel_val = (1.0 - train_size) / (1.0 - test_size)
+            train_idx, val_idx = train_test_split(train_val_idx, test_size=rel_val, stratify=labels.iloc[train_val_idx], random_state=random_state)
+        else:
+            train_idx, val_idx = train_test_split(merged_df.index.tolist(), test_size=(1.0 - train_size), stratify=labels, random_state=random_state)
+            test_idx = []
+
+        train_df = merged_df.loc[train_idx].reset_index(drop=True)
+        val_df = merged_df.loc[val_idx].reset_index(drop=True)
+        train_df.to_csv(os.path.join(out_dir, 'train.csv'), index=False)
+        val_df.to_csv(os.path.join(out_dir, 'val.csv'), index=False)
+        print(f'Wrote train ({len(train_df)} rows) and val ({len(val_df)} rows) CSVs to', out_dir)
+        if len(test_idx) > 0:
+            test_df = merged_df.loc[test_idx].reset_index(drop=True)
+            test_df.to_csv(os.path.join(out_dir, 'test.csv'), index=False)
+            print('Wrote test CSV ->', os.path.join(out_dir, 'test.csv'))
+    else:
+        print(f"Label column '{label_col}' not present or empty in merged CSV; only wrote merged paths.")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--pdb-dir', required=True)
+    parser.add_argument('--labels-csv', default=None)
+    parser.add_argument('--merge-on', default=None, help='Column in labels CSV to match to filename stem')
+    parser.add_argument('--out-dir', default='data/finetune', help='Directory to write merged and split CSVs')
+    parser.add_argument('--label-col', default='immunogenicity')
+    parser.add_argument('--train-size', type=float, default=0.8)
+    parser.add_argument('--test-size', type=float, default=0.1)
+    args = parser.parse_args()
+
+    paths_df = build_paths_df(args.pdb_dir)
+    print(f'Found {len(paths_df)} structure files under {args.pdb_dir}')
+
+    if args.labels_csv is None:
+        # just write paths CSV
+        os.makedirs(args.out_dir, exist_ok=True)
+        p = os.path.join(args.out_dir, 'paths.csv')
+        paths_df.to_csv(p, index=False)
+        print('Wrote paths CSV ->', p)
+        return
+
+    labels_df = pd.read_csv(args.labels_csv)
+    merged = try_merge(paths_df, labels_df, merge_on=args.merge_on)
+    split_and_write(merged, args.out_dir, label_col=args.label_col, train_size=args.train_size, test_size=args.test_size)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/finetune_classifier.sh b/scripts/finetune_classifier.sh
new file mode 100644
index 0000000..b735111
--- /dev/null
+++ b/scripts/finetune_classifier.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+#SBATCH --job-name=finetune_if
+#SBATCH --partition=gpu_h200
+#SBATCH --gres=gpu:h200:1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=48G
+#SBATCH --time=6:00:00
+#SBATCH --output=finetune_%j.out
+#SBATCH --error=finetune_%j.err
+
+module load Python/3.12.3-GCCcore-13.3.0
+module load PyTorch/2.7.1-foss-2024a-CUDA-12.6.0
+source /home/am3826/workspace/ImmunoFoundation/.venv/bin/activate
+cd /home/am3826/workspace/ImmunoFoundation
+
+python3 finetune_classifier.py
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From 5250ce0b9d60ff688d82cf74277031533aba6073 Mon Sep 17 00:00:00 2001
From: Apurva <apurvagyan@gmail.com>
Date: Thu, 30 Apr 2026 07:32:02 -0400
Subject: [PATCH 2/7] use full set for testing if interested instead of only
 val split

---
 .../data/components/ImmunoMonomerDataset.py   | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/immunofoundation/data/components/ImmunoMonomerDataset.py b/immunofoundation/data/components/ImmunoMonomerDataset.py
index 842c4a3..1703f8c 100644
--- a/immunofoundation/data/components/ImmunoMonomerDataset.py
+++ b/immunofoundation/data/components/ImmunoMonomerDataset.py
@@ -22,15 +22,20 @@ def __init__(self, data_cfg, is_training):
     def _init_metadata(self):
         pdb_csv = pd.read_csv(self.data_cfg.csv_path)
         self.raw_csv = pdb_csv
-        train_len = int(pdb_csv.shape[0]*self.data_cfg.train_size)-1
-        if self.is_training:
-            pdb_csv = pdb_csv.iloc[:train_len, :]
+        n = pdb_csv.shape[0]
+        train_size = float(getattr(self.data_cfg, 'train_size', 1.0))
+        if train_size >= 1.0 or train_size <= 0.0:
+            # Use all data for both train and val if train_size is 1.0 or 0.0
             self.csv = pdb_csv
-            print (f"Training: {len(self.csv)} samples")
+            print(f"Using all {len(self.csv)} samples (no split)")
         else:
-            pdb_csv = pdb_csv.iloc[train_len:, :]
-            self.csv = pdb_csv
-            print (f"Validation: {len(self.csv)} samples")
+            train_len = int(n * train_size)
+            if self.is_training:
+                self.csv = pdb_csv.iloc[:train_len, :]
+                print(f"Training: {len(self.csv)} samples")
+            else:
+                self.csv = pdb_csv.iloc[train_len:, :]
+                print(f"Validation: {len(self.csv)} samples")
 
     def _process_csv_row(self, csv_row):
         '''

From 3fadc0cb7aba180b29c64511af0624dfc1c8e0b4 Mon Sep 17 00:00:00 2001
From: Apurva <apurvagyan@gmail.com>
Date: Thu, 30 Apr 2026 07:32:43 -0400
Subject: [PATCH 3/7] remove deprecated classifier

---
 .../models/components/MLPClassifier.py        | 132 ------------------
 1 file changed, 132 deletions(-)
 delete mode 100644 immunofoundation/models/components/MLPClassifier.py

diff --git a/immunofoundation/models/components/MLPClassifier.py b/immunofoundation/models/components/MLPClassifier.py
deleted file mode 100644
index e8409fb..0000000
--- a/immunofoundation/models/components/MLPClassifier.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Optional, Dict, Any
-from pytorch_lightning import LightningModule
-
-
-class MLPClassifier(LightningModule):
-    """Simple MLP head for immunogenicity classification.
-
-    Supports optional biochemical encoder (an instance of BiochemicalModel or similar)
-    to be supplied externally. If provided, its outputs are concatenated to raw features.
-    """
-
-    def __init__(self, input_dim: int, num_classes: int, hidden_dims=(128, 64), lr: float = 1e-4, dropout=0.1, backbone: Optional[nn.Module] = None):
-        super().__init__()
-        self.save_hyperparameters()
-        self.input_dim = input_dim
-        self.num_classes = num_classes
-        self.lr = lr
-        self.backbone = backbone
-
-        # compute head input dim
-        head_in = input_dim
-        if backbone is not None:
-            # try to infer backbone output dim if it has attribute cfg.out_dim
-            out_dim = getattr(backbone, 'cfg', None)
-            if out_dim is not None and hasattr(backbone.cfg, 'out_dim'):
-                head_in += int(backbone.cfg.out_dim)
-            else:
-                # fallback: attempt forward with zeros later will adjust
-                head_in += 0
-
-        layers = []
-        prev = head_in
-        for h in hidden_dims:
-            layers.append(nn.Linear(prev, h))
-            layers.append(nn.ReLU())
-            layers.append(nn.Dropout(dropout))
-            prev = h
-        layers.append(nn.Linear(prev, num_classes))
-
-        self.head = nn.Sequential(*layers)
-        self.criterion = nn.CrossEntropyLoss()
-
-    def forward(self, features: torch.Tensor):
-        """features: (B, input_dim)"""
-        x = features
-        if self.backbone is not None:
-            try:
-                with torch.no_grad():
-                    enc = self.backbone(x)
-            except Exception:
-                # if backbone expects different shape, try passing through .forward
-                enc = self.backbone(x)
-            # enc expected (B, dim) or (B, L, dim) -> flatten last dims
-            if enc.dim() > 2:
-                enc = enc.mean(dim=1)
-            x = torch.cat([x, enc], dim=-1)
-
-        logits = self.head(x)
-        return logits
-
-    def training_step(self, batch, batch_idx):
-        x = batch['features']
-        y = batch['labels']
-        logits = self(x)
-        loss = self.criterion(logits, y)
-        preds = torch.argmax(logits, dim=-1)
-        acc = (preds == y).float().mean()
-        self.log('train/loss', loss, on_step=True, on_epoch=True)
-        self.log('train/acc', acc, on_step=True, on_epoch=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        x = batch['features']
-        y = batch['labels']
-        logits = self(x)
-        loss = self.criterion(logits, y)
-        preds = torch.argmax(logits, dim=-1)
-        acc = (preds == y).float().mean()
-        self.log('val/loss', loss, on_epoch=True)
-        self.log('val/acc', acc, on_epoch=True)
-        return {'val_loss': loss, 'val_acc': acc}
-
-    def configure_optimizers(self):
-        return torch.optim.AdamW(self.parameters(), lr=self.lr)
-
-    def load_backbone_weights_from_checkpoint(self, ckpt_path: str, prefixes_to_try=('bio', 'biochemical', 'biochem')):
-        """Try to load matching keys from a checkpoint into `self.backbone` if available.
-
-        This is best-effort and will skip keys that don't match.
-        """
-        if self.backbone is None:
-            return
-        try:
-            import torch
-            ckpt = torch.load(ckpt_path, map_location='cpu')
-            # checkpoints saved by Lightning often have 'state_dict' key
-            state = ckpt.get('state_dict', ckpt)
-            # build mapping for backbone keys
-            own_state = self.backbone.state_dict()
-            new_state = {}
-            for k, v in state.items():
-                for p in prefixes_to_try:
-                    if p in k.lower():
-                        # heuristic: strip common prefixes like 'model.' or similar
-                        new_key = k
-                        # if key contains 'backbone' or 'biochem' try to map directly
-                        if new_key in own_state and own_state[new_key].shape == v.shape:
-                            new_state[new_key] = v
-                        else:
-                            # try to remove leading module names until match
-                            parts = new_key.split('.')
-                            for i in range(len(parts)):
-                                candidate = '.'.join(parts[i:])
-                                if candidate in own_state and own_state[candidate].shape == v.shape:
-                                    new_state[candidate] = v
-                                    break
-                        break
-            if len(new_state) == 0:
-                # last resort: try strict=False load of full state
-                try:
-                    self.backbone.load_state_dict(state, strict=False)
-                    return
-                except Exception:
-                    return
-            own_state.update(new_state)
-            self.backbone.load_state_dict(own_state)
-        except Exception:
-            # don't fail training if this cannot be loaded
-            return

From 2c6beabde2e64ddad70eab61ae5dad7d64fb6d1f Mon Sep 17 00:00:00 2001
From: Apurva <apurvagyan@gmail.com>
Date: Thu, 30 Apr 2026 07:33:26 -0400
Subject: [PATCH 4/7] clean up / revision of finetune classifier module

---
 .../models/FinetuneClassifierModule.py        | 33 +++++++++++--------
 1 file changed, 19 insertions(+), 14 deletions(-)

diff --git a/immunofoundation/models/FinetuneClassifierModule.py b/immunofoundation/models/FinetuneClassifierModule.py
index aeb3af7..0c641fb 100644
--- a/immunofoundation/models/FinetuneClassifierModule.py
+++ b/immunofoundation/models/FinetuneClassifierModule.py
@@ -26,10 +26,12 @@ def __init__(self, backbone, num_classes: int = 2, bio_dim: int = 0, hidden_dims
             raise ValueError('Backbone must have sequence.out_dim and structure.out_dim in its model_cfg')
 
         in_dim = int(seq_dim) + int(struct_dim) + int(bio_dim)
+
         # default to a simple 5-layer MLP if not provided
         if hidden_dims is None:
             hidden_dims = [512, 256, 128, 64, 32]
         layers = []
+
         prev = in_dim
         for h in hidden_dims:
             layers.append(nn.Linear(prev, h))
@@ -47,9 +49,9 @@ def __init__(self, backbone, num_classes: int = 2, bio_dim: int = 0, hidden_dims
             self.criterion = nn.CrossEntropyLoss()
 
     def forward(self, batch):
-        # Expect batch to be in the same format used by ImmunoMonomerDataset/custom_collate_mono
         # backbone.encode returns seq_embeddings, struct_embeddings
         seq_embeddings, struct_embeddings = self.backbone.encode(batch)
+
         # pooling: masked mean over residues if masks present
         if 'masks' in batch:
             mask = batch['masks'].unsqueeze(-1)  # (B, L, 1)
@@ -64,19 +66,18 @@ def forward(self, batch):
         if bio is None:
             # pad with zeros to match expected bio_dim
             if self.bio_dim > 0:
+                print("[WARNING] bio_dim > 0 but 'biochem' features not found in batch. Padding with zeros.")
                 bio = torch.zeros(seq_pool.size(0), self.bio_dim, dtype=torch.float32, device=seq_pool.device)
             else:
                 bio = None
 
-        if bio is None:
-            x = torch.cat([seq_pool, struct_pool], dim=-1)
-        else:
-            # ensure bio is float tensor and on correct device
-            if not torch.is_tensor(bio):
-                bio = torch.tensor(bio, dtype=torch.float32, device=seq_pool.device)
+            # Classifier input: [pooled, bio] if bio_dim > 0 and bio present in batch
+            if self.bio_dim > 0 and batch.get('bio') is not None:
+                print("[WARNING] bio_dim > 0 but 'biochem' features not found in batch. Using 'bio' features instead.")
+                bio = batch['bio']
+                x = torch.cat([seq_pool, struct_pool, bio], dim=-1)
             else:
-                bio = bio.to(seq_pool.device).float()
-            x = torch.cat([seq_pool, struct_pool, bio], dim=-1)
+                x = torch.cat([seq_pool, struct_pool], dim=-1)
 
         # Save for debug printing in training/validation step
         self._last_classifier_input = x.detach().cpu() if not hasattr(self, '_last_classifier_input') else x.detach().cpu()
@@ -96,11 +97,13 @@ def training_step(self, batch, batch_idx):
         loss = self.criterion(logits, labels)
         preds = logits.argmax(dim=-1)
         acc = (preds == labels).float().mean()
+
         # # Debug print for first batch of each epoch
         # if batch_idx == 0:
         #     print("[DEBUG][TRAIN] Classifier input (first batch):", self._last_classifier_input[:5])
         #     print("[DEBUG][TRAIN] Predictions:", preds[:10].detach().cpu().numpy())
         #     print("[DEBUG][TRAIN] Labels:", labels[:10].detach().cpu().numpy())
+        
         self.log('train/loss', loss, on_step=True, on_epoch=True, prog_bar=False)
         self.log('train/acc', acc, on_step=True, on_epoch=True, prog_bar=False)
         return loss
@@ -122,11 +125,13 @@ def validation_step(self, batch, batch_idx):
         loss = self.criterion(logits, labels)
         preds = logits.argmax(dim=-1)
         acc = (preds == labels).float().mean()
-        # Debug print for first batch of each epoch
-        if batch_idx == 0:
-            print("[DEBUG][VAL] Classifier input (first batch):", self._last_classifier_input[:5])
-            print("[DEBUG][VAL] Predictions:", preds[:10].detach().cpu().numpy())
-            print("[DEBUG][VAL] Labels:", labels[:10].detach().cpu().numpy())
+
+        # # Debug print for first batch of each epoch
+        # if batch_idx == 0:
+        #     print("[DEBUG][VAL] Classifier input (first batch):", self._last_classifier_input[:5])
+        #     print("[DEBUG][VAL] Predictions:", preds[:10].detach().cpu().numpy())
+        #     print("[DEBUG][VAL] Labels:", labels[:10].detach().cpu().numpy())
+        
         self.log('val/loss', loss, on_epoch=True)
         self.log('val/acc', acc, on_epoch=True)
         return loss

From cd745f466b40bc6e927979833cb20a5f3eb40718 Mon Sep 17 00:00:00 2001
From: Apurva <apurvagyan@gmail.com>
Date: Thu, 30 Apr 2026 07:34:28 -0400
Subject: [PATCH 5/7] register classifier component in train.py if specified in
 config

---
 train.py | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/train.py b/train.py
index c63d176..2a5b043 100644
--- a/train.py
+++ b/train.py
@@ -32,10 +32,27 @@ def __init__(self, *, cfg: DictConfig):
         self._cfg = cfg
         self._data_cfg = cfg.data
         self._exp_cfg = cfg.experiment
+        # Build backbone (monomer or multimer)
         if self._data_cfg.mono:
-            self._model = ImmunoFoundationMonomerModule(self._cfg.model)
+            backbone = ImmunoFoundationMonomerModule(self._cfg.model)
         else:
-            self._model = ImmunoFoundationMultimerModule(self._cfg.model)
+            backbone = ImmunoFoundationMultimerModule(self._cfg.model)
+
+        # If classifier module specified in config, wrap backbone with FinetuneClassifierModule
+        classifier_cfg = getattr(self._cfg.model, 'classifier', None)
+        if classifier_cfg is not None and getattr(classifier_cfg, 'module', None) == 'FinetuneClassifierModule':
+            from immunofoundation.models.FinetuneClassifierModule import FinetuneClassifierModule
+            self._model = FinetuneClassifierModule(
+                backbone=backbone,
+                num_classes=getattr(classifier_cfg, 'num_classes', 2),
+                bio_dim=getattr(classifier_cfg, 'bio_dim', 0),
+                hidden_dims=getattr(classifier_cfg, 'hidden_dims', [512, 256, 128, 64, 32]),
+                lr=getattr(classifier_cfg, 'lr', 1e-4),
+                class_weights=getattr(classifier_cfg, 'class_weights', None)
+            )
+        else:
+            self._model = backbone
+
         self._datamodule = ImmunoDataModule(self._cfg.data)
  
     def train(self):

From d122c6957aa00ee0c16d4131fdd8297c22b3b4ab Mon Sep 17 00:00:00 2001
From: Apurva <apurvagyan@gmail.com>
Date: Thu, 30 Apr 2026 07:35:07 -0400
Subject: [PATCH 6/7] add af3 use configs for finetuning (or training)

---
 configs/finetune_af3.yaml | 79 +++++++++++++++++++++++++++++++++++++++
 configs/train_af3.yaml    | 77 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 156 insertions(+)
 create mode 100644 configs/finetune_af3.yaml
 create mode 100644 configs/train_af3.yaml

diff --git a/configs/finetune_af3.yaml b/configs/finetune_af3.yaml
new file mode 100644
index 0000000..fcce543
--- /dev/null
+++ b/configs/finetune_af3.yaml
@@ -0,0 +1,79 @@
+data:
+  batch_size: 32
+  max_batch_size: 32
+  num_workers: 0
+  train_size: 0.8
+  mono: false
+  prefetch_factor: 100
+  csv_path: /home/am3826/scratch_pi_sk2433/am3826/iedb_af3/merged_af3.csv
+  mask:
+    mask_rate: 0.5
+    max_distance: 8
+    max_neighbors: 12
+  structure:
+    adj: true
+    graph_type: knn
+    k: 15
+
+model:
+  sequence:
+    model_type: esm
+    freeze_esm: true
+    aggregate: false
+    esm_dim: 1280
+    rep_layer: 33
+    esm_variant: esm2_t33_650M_UR50D
+    out_dim: 32
+    n_heads: 8
+    dim_ffn: 256
+    n_layers: 10
+  structure:
+    model_type: transformer
+    out_dim: 32
+    n_heads: 8
+    dim_ffn: 256
+    n_layers: 4
+  bio_chem:
+    model_type: mlp
+    n_bio_prop: 93 # DO NOT CHANGE THIS
+    hidden_dim: 64
+    out_dim: 32
+    n_layers: 4
+  # FinetuneClassifierModule config
+  classifier:
+    module: FinetuneClassifierModule
+    num_classes: 2
+    bio_dim: 32
+    hidden_dims: [512, 256, 128, 64, 32]
+    lr: 1e-4
+    class_weights: null
+
+optimizer:
+  lr: 1e-4
+  weight_decay: 1e-6
+
+experiment:
+  debug: False
+  num_devices: 2
+  wandb:
+    mode: "disabled"
+    name: "af3_finetune_mlp"
+    project: immunofoundation
+    save_code: false
+    tags: [af3, multimer, finetune, mlp]
+  trainer:
+    min_epochs: 1
+    max_epochs: 200
+    accelerator: gpu
+    log_every_n_steps: 1
+    deterministic: False
+    strategy: ddp
+    check_val_every_n_epoch: 2
+    accumulate_grad_batches: 4
+  checkpointer:
+    dirpath: ckpt/${experiment.wandb.project}/${experiment.wandb.name}
+    save_last: True
+    save_top_k: 3
+    monitor: val/loss
+    mode: min
+    every_n_epochs: 2
diff --git a/configs/train_af3.yaml b/configs/train_af3.yaml
new file mode 100644
index 0000000..c796226
--- /dev/null
+++ b/configs/train_af3.yaml
@@ -0,0 +1,77 @@
+data:
+  batch_size: 32
+  max_batch_size: 32
+  num_workers: 0
+  train_size: 0.8
+  mono: false
+  prefetch_factor: 100
+  csv_path: /home/am3826/scratch_pi_sk2433/am3826/iedb_af3/merged_af3.csv
+  mask:
+    mask_rate: 0.5
+    max_distance: 8
+    max_neighbors: 12
+  structure:
+    adj: true
+    graph_type: knn
+    k: 15
+
+model:
+  sequence:
+    model_type: esm
+    freeze_esm: true
+    aggregate: false
+    esm_dim: 1280
+    rep_layer: 33
+    esm_variant: esm2_t33_650M_UR50D
+    out_dim: 32
+    n_heads: 8
+    dim_ffn: 256
+    n_layers: 10
+  structure:
+    model_type: transformer
+    out_dim: 32
+    n_heads: 8
+    dim_ffn: 256
+    n_layers: 4
+  bio_chem:
+    model_type: mlp
+    n_bio_prop: 93 # DO NOT CHANGE THIS
+    hidden_dim: 64
+    out_dim: 32
+    n_layers: 4
+  
+optimizer:
+  lr: 1e-4
+  weight_decay: 1e-6
+
+experiment:
+  debug: False
+  num_devices: 2
+
+  wandb:
+    name: "af3_phase_1"
+    project: immunofoundation
+    save_code: false
+    tags: [af3, multimer]
+    mode: "online"
+
+  optimizer:
+    lr: 0.0001
+
+  trainer:
+    min_epochs: 1
+    max_epochs: 200
+    accelerator: gpu
+    log_every_n_steps: 1
+    deterministic: False
+    strategy: ddp
+    check_val_every_n_epoch: 2
+    accumulate_grad_batches: 4
+
+  checkpointer:
+    dirpath: ckpt/${experiment.wandb.project}/${experiment.wandb.name}
+    save_last: True
+    save_top_k: 3
+    monitor: val/total_loss
+    mode: min
+    every_n_epochs: 2

From 34620afe6e34fa9db12d3ca9472fc386a8365a62 Mon Sep 17 00:00:00 2001
From: Apurva <apurvagyan@gmail.com>
Date: Thu, 30 Apr 2026 07:35:46 -0400
Subject: [PATCH 7/7] easy access scripts for checking af3 data and finetuning
 on af3 data, with finetune_classifier.yaml for af2 data

---
 scripts/check_af3_data.py |  9 +++++++++
 scripts/finetune_af3.sh   | 19 +++++++++++++++++++
 2 files changed, 28 insertions(+)
 create mode 100644 scripts/check_af3_data.py
 create mode 100644 scripts/finetune_af3.sh

diff --git a/scripts/check_af3_data.py b/scripts/check_af3_data.py
new file mode 100644
index 0000000..8f95e33
--- /dev/null
+++ b/scripts/check_af3_data.py
@@ -0,0 +1,9 @@
+import pandas as pd
+
+af3_csv = "/home/am3826/scratch_pi_sk2433/am3826/iedb_af3/merged_af3.csv"
+df = pd.read_csv(af3_csv)
+display(df.head())
+print(f"Rows: {len(df)}")
+print(f"Columns: {df.columns.tolist()}")
+print(df['immunogenicity'].value_counts())
+print(df.isnull().sum())
diff --git a/scripts/finetune_af3.sh b/scripts/finetune_af3.sh
new file mode 100644
index 0000000..b0232f8
--- /dev/null
+++ b/scripts/finetune_af3.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+#SBATCH --job-name=finetune_af3_mlp
+#SBATCH --output=outputs/finetune_af3_mlp_%j.out
+#SBATCH --error=outputs/finetune_af3_mlp_%j.err
+#SBATCH --partition=pi_sk2433_gpu
+#SBATCH --gres=gpu:2
+#SBATCH --mem=64G
+#SBATCH --cpus-per-task=8
+#SBATCH --time=48:00:00
+#SBATCH --mail-type=END,FAIL
+#SBATCH --mail-user=am3826@columbia.edu
+
+# Activate environment
+source ~/.bashrc
+conda activate immuno
+cd /home/am3826/workspace/ImmunoFoundation
+
+# Run finetuning for MLP classifier on AF3 data
+python train.py experiment=finetune_af3.yaml