heatmap!

heatmap.py

@@ -0,0 +1,71 @@
+import transformers
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import logging
+import torch
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+
+logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Load the tokenizer and model
+model_name = "ChatterjeeLab/FusOn-pLM"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForMaskedLM.from_pretrained(model_name, trust_remote_code=True)
+model.to(device)
+model.eval()
+
+
+# fix this to take dynamic input
+sequence = 'MCNTNMS'
+all_logits = []
+for i in range(len(sequence)):
+  # add a masked token
+  masked_seq = sequence[:i] + '<mask>' + sequence[i+1:]
+
+  # tokenize masked sequence
+  inputs = tokenizer(masked_seq, return_tensors="pt", padding=True, truncation=True,max_length=2000)
+  inputs = {k: v.to(device) for k, v in inputs.items()}
+
+  # predict logits for the masked token
+
+  with torch.no_grad():
+    logits = model(**inputs).logits
+  mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
+  mask_token_logits = logits[0, mask_token_index, :]
+  top_1_tokens = torch.topk(mask_token_logits, 1, dim=1).indices[0].item()
+  logits_array = mask_token_logits.cpu().numpy()
+
+  # filter out non-amino acid tokens
+  filtered_indices = list(range(4, 23 + 1))
+  filtered_logits = logits_array[:, filtered_indices]
+  all_logits.append(filtered_logits)
+
+token_indices = torch.arange(logits.size(-1))
+tokens = [tokenizer.decode([idx]) for idx in token_indices]
+filtered_tokens = [tokens[i] for i in filtered_indices]
+
+all_logits_array = np.vstack(all_logits)
+normalized_logits_array = (all_logits_array - all_logits_array.min()) / (all_logits_array.max() - all_logits_array.min())
+transposed_logits_array = normalized_logits_array.T
+
+
+
+# Plotting the heatmap
+step = 50
+y_tick_positions = np.arange(0, len(sequence), step)
+y_tick_labels = [str(pos) for pos in y_tick_positions]
+
+plt.figure(figsize=(15, 8))
+sns.heatmap(transposed_logits_array, cmap='plasma', xticklabels=y_tick_labels, yticklabels=filtered_tokens)
+plt.title('Logits for masked per residue tokens')
+plt.ylabel('Token')
+plt.xlabel('Residue Index')
+plt.yticks(rotation=0)
+plt.xticks(y_tick_positions, y_tick_labels, rotation = 0)
+plt.show()
+plt.savefig(f'heatmap_{i}.png', dpi=300, bbox_inches='tight')
+