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# direct reward backpropagation
import numpy as np
import torch
import wandb
import os
from finetune_utils import loss_wdce
from tqdm import tqdm
import pandas as pd
from plotting import plot_data_with_distribution_seaborn, plot_data
def finetune(args, cfg, policy_model, reward_model, mcts=None, pretrained=None, filename=None, prot_name=None, eps=1e-5):
"""
Finetuning with WDCE loss
"""
base_path = args.base_path
dt = (1 - eps) / args.total_num_steps
if args.no_mcts:
assert pretrained is not None, "pretrained model is required for no mcts"
else:
assert mcts is not None, "mcts is required for mcts"
# set model to train mode
policy_model.train()
torch.set_grad_enabled(True)
optim = torch.optim.AdamW(policy_model.parameters(), lr=args.learning_rate)
# record metrics
batch_losses = []
#batch_rewards = []
# initialize the final seqs and log_rnd of the trajectories that generated those seqs
x_saved, log_rnd_saved, final_rewards_saved = None, None, None
valid_fraction_log = []
affinity_log = []
sol_log = []
hemo_log = []
nf_log = []
permeability_log = []
### End of Fine-Tuning Loop ###
pbar = tqdm(range(args.num_epochs))
for epoch in pbar:
# store metrics
rewards = []
losses = []
policy_model.train()
with torch.no_grad():
if x_saved is None or epoch % args.resample_every_n_step == 0:
# compute final sequences and trajectory log_rnd
if args.no_mcts:
x_final, log_rnd, final_rewards = policy_model.sample_finetuned_with_rnd(args, reward_model, pretrained)
else:
# decides whether to reset tree
if (epoch) % args.reset_every_n_step == 0:
x_final, log_rnd, final_rewards, _, _ = mcts.forward(resetTree=True)
else:
x_final, log_rnd, final_rewards, _, _ = mcts.forward(resetTree=False)
# save for next iteration
x_saved, log_rnd_saved, final_rewards_saved = x_final, log_rnd, final_rewards
else:
x_final, log_rnd, final_rewards = x_saved, log_rnd_saved, final_rewards_saved
# compute wdce loss
loss = loss_wdce(policy_model, log_rnd, x_final, num_replicates=args.wdce_num_replicates, centering=args.centering)
# gradient descent
loss.backward()
# optimizer
if args.grad_clip:
torch.nn.utils.clip_grad_norm_(policy_model.parameters(), args.gradnorm_clip)
optim.step()
optim.zero_grad()
pbar.set_postfix(loss=loss.item())
# sample a eval batch with updated policy to evaluate rewards
x_eval, affinity, sol, hemo, nf, permeability, valid_fraction = policy_model.sample_finetuned(args, reward_model, batch_size=50, dataframe=False)
# append to log
affinity_log.append(affinity)
sol_log.append(sol)
hemo_log.append(hemo)
nf_log.append(nf)
permeability_log.append(permeability)
valid_fraction_log.append(valid_fraction)
batch_losses.append(loss.cpu().detach().numpy())
losses.append(loss.cpu().detach().numpy())
losses = np.array(losses)
if args.no_mcts:
mean_reward_search = final_rewards.mean().item()
min_reward_search = final_rewards.min().item()
max_reward_search = final_rewards.max().item()
median_reward_search = final_rewards.median().item()
else:
mean_reward_search = np.mean(final_rewards)
min_reward_search = np.min(final_rewards)
max_reward_search = np.max(final_rewards)
median_reward_search = np.median(final_rewards)
print("epoch %d"%epoch, "affinity %f"%np.mean(affinity), "sol %f"%np.mean(sol), "hemo %f"%np.mean(hemo), "nf %f"%np.mean(nf), "permeability %f"%np.mean(permeability), "mean loss %f"%np.mean(losses))
wandb.log({"epoch": epoch, "affinity": np.mean(affinity), "sol": np.mean(sol), "hemo": np.mean(hemo), "nf": np.mean(nf), "permeability": np.mean(permeability),
"mean_loss": np.mean(losses),
"mean_reward_search": mean_reward_search, "min_reward_search": min_reward_search,
"max_reward_search": max_reward_search, "median_reward_search": median_reward_search})
if (epoch+1) % args.save_every_n_epochs == 0:
model_path = os.path.join(args.save_path, f'model_{epoch}.ckpt')
torch.save(policy_model.state_dict(), model_path)
print(f"model saved at epoch {epoch}")
### End of Fine-Tuning Loop ###
wandb.finish()
# save logs and plot
plot_path = f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}'
os.makedirs(plot_path, exist_ok=True)
output_log_path = f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/log_{filename}.csv'
save_logs_to_file(valid_fraction_log, affinity_log,
sol_log, hemo_log, nf_log,
permeability_log, output_log_path)
plot_data(valid_fraction_log,
save_path=f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/valid_{filename}.png')
plot_data_with_distribution_seaborn(log1=affinity_log,
save_path=f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/binding_{filename}.png',
label1=f"Average Binding Affinity to {prot_name}",
title=f"Average Binding Affinity to {prot_name} Over Iterations")
plot_data_with_distribution_seaborn(log1=sol_log,
save_path=f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/sol_{filename}.png',
label1="Average Solubility Score",
title="Average Solubility Score Over Iterations")
plot_data_with_distribution_seaborn(log1=hemo_log,
save_path=f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/hemo_{filename}.png',
label1="Average Hemolysis Score",
title="Average Hemolysis Score Over Iterations")
plot_data_with_distribution_seaborn(log1=nf_log,
save_path=f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/nf_{filename}.png',
label1="Average Nonfouling Score",
title="Average Nonfouling Score Over Iterations")
plot_data_with_distribution_seaborn(log1=permeability_log,
save_path=f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/perm_{filename}.png',
label1="Average Permeability Score",
title="Average Permeability Score Over Iterations")
x_eval, affinity, sol, hemo, nf, permeability, valid_fraction, df = policy_model.sample_finetuned(args, reward_model, batch_size=200, dataframe=True)
df.to_csv(f'{base_path}/TR2-D2/tr2d2-pep/results/{args.run_name}/{prot_name}_generation_results.csv', index=False)
return batch_losses
def save_logs_to_file(valid_fraction_log, affinity_log,
sol_log, hemo_log, nf_log,
permeability_log, output_path):
"""
Saves the logs (valid_fraction_log, affinity1_log, and permeability_log) to a CSV file.
Parameters:
valid_fraction_log (list): Log of valid fractions over iterations.
affinity1_log (list): Log of binding affinity over iterations.
permeability_log (list): Log of membrane permeability over iterations.
output_path (str): Path to save the log CSV file.
"""
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# Combine logs into a DataFrame
log_data = {
"Iteration": list(range(1, len(valid_fraction_log) + 1)),
"Valid Fraction": valid_fraction_log,
"Binding Affinity": affinity_log,
"Solubility": sol_log,
"Hemolysis": hemo_log,
"Nonfouling": nf_log,
"Permeability": permeability_log
}
df = pd.DataFrame(log_data)
# Save to CSV
df.to_csv(output_path, index=False) |