pCoMole/peptidomimetics/utility.py

import pandas as pd
import torch
import pdb

samples = '/scratch/pranamlab/tong/pCoMol/peptidomimetics/samples/hyperparameter_ablation/2QSC_rollout_30.csv'
df = pd.read_csv(samples)

toxicities = df['Toxicity'].tolist()
solubilities = df['Solubility'].tolist()
permeabilities = df['Permeability'].tolist()
halflifes = df['Halflife'].tolist()
affinities = df['Affinity'].tolist()
motifs = df['Motif'].tolist()
specificities = df['Specificity'].tolist()

def _augmented_tchebycheff(
    f_vals: torch.Tensor,
    w: torch.Tensor,
) -> torch.Tensor:
    diff = f_vals
    term1 = torch.min(w * diff)
    term2 = 0.5 * torch.sum(w * diff)
    return term1 + term2


weight = torch.tensor([4, 2, 4, 0.2, 4, 2, 1])
original_scores = torch.tensor([0.4621,0.7744,0.2787,2.5357,0.5218,0.3083,0.9954])
print(f"Original Utility: ", _augmented_tchebycheff(original_scores, weight))

# utilities = []
# for i in range(len(df)):
#     f_vals = torch.tensor([toxicities[i], solubilities[i], permeabilities[i], halflifes[i], affinities[i], motifs[i], specificities[i]])
#     utility = _augmented_tchebycheff(f_vals, weight)
#     utilities.append(utility)

# print("Average Utility: ", sum(utilities) / len(utilities))