Create compute_class_weights.py

dataset/compute_class_weights.py

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+import pandas as pd
+import ast
+from sklearn.model_selection import train_test_split
+import numpy as np
+import torch.nn.functional as F
+import torch
+from torch.utils.data import Dataset
+from datasets import Dataset as HFDataset, DatasetDict
+from transformers import AutoTokenizer
+import pdb
+
+def main():
+
+    data = pd.read_csv('dataset_drop_500.csv')
+
+    print(len(data))
+
+    binding_sites = data['mutTarget_motifs'].tolist()
+    targets = data['Target'].tolist()
+
+    # No need for padding the first position of binding sites for class weight calculations
+    binding_sites = [ast.literal_eval(binding_site) for binding_site in binding_sites]
+    binding_sites = [len(binding_site) for binding_site in binding_sites]
+    targets = [len(seq) for seq in targets]
+    pdb.set_trace()
+
+    train_val_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
+    train_data, val_data = train_test_split(train_val_data, test_size=0.25, random_state=42)
+
+    train_index = train_data.index.to_numpy()
+    print(len(train_index))
+    return
+
+    train_binding_dataset = [binding_sites[i] for i in train_index]
+    train_targets = [targets[i] for i in train_index]
+
+    num_binding_sites = sum(train_binding_dataset)
+    num_total = sum(train_targets)
+    num_non_binding_sites = num_total - num_binding_sites
+    weight_for_binding = num_total / (2 * num_binding_sites)
+    weight_for_non_binding = num_total / (2 * num_non_binding_sites)
+
+    print(weight_for_binding, weight_for_non_binding)
+
+
+if __name__ == "__main__":
+    main()