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main_dmpnn.py
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import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import random_split
import numpy as np
from dmpnn import MolSets_DMPNN
from torch_geometric.data import Batch
from torch_geometric.loader import DataLoader
import random
from scipy.stats import spearmanr, pearsonr
from data_utils import graph_set_collate, RevIndexedDataset
hyperpars = {
# Architecture
'hidden_dim': 32,
'emb_dim': 16,
'n_conv_layers': 3,
'after_readout': 'tanh',
# Training
'max_ep': 10000,
'es_patience': 10,
'max_ep_wo_improv': 20,
# Learning rate
'lr': 0.001,
'lrsch_patience': 10,
'lrsch_factor': 0.5,
# Regularization
'weight_decay': 0.0001
}
best_model = None
SEED = 42
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
dataset = RevIndexedDataset('./data/data_list.pkl')
train_data, val_data, test_data = random_split(dataset, (0.6, 0.2, 0.2))
train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
train_loader.collate_fn = graph_set_collate
val_loader = DataLoader(val_data, batch_size=32, shuffle=True)
val_loader.collate_fn = graph_set_collate
# Train model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = MolSets_DMPNN(n_node_features=13, hidden_dim=hyperpars['hidden_dim'], emb_dim=hyperpars['emb_dim'], output_dim=1, n_conv_layers=hyperpars['n_conv_layers'], after_readout=hyperpars['after_readout']).to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=hyperpars['lr'], weight_decay=hyperpars['weight_decay'])
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=hyperpars['lrsch_factor'], patience=hyperpars['lrsch_patience'], verbose=True)
loss_fn = torch.nn.MSELoss()
def train(model, loader, optimizer, criterion):
model.train()
train_loss = 0
for sample in loader:
# "inputs": a batch of graphs sets
inputs, mws, fracs, salt_mols, salt_graphs, targets = sample
sample_size = len(targets)
outs = torch.empty((sample_size, 1)).to(device)
targets = torch.tensor(targets).to(device)
salt_mols = torch.tensor(salt_mols).to(device)
for j in range(sample_size):
graph_set = inputs[j].to(device)
salt_graph = salt_graphs[j].to(device)
frac = torch.tensor(fracs[j]).to(device)
mw = torch.tensor(mws[j]).to(device)
optimizer.zero_grad()
outs[j] = model(graph_set, mw, frac, salt_mols[j], salt_graph)
loss = criterion(outs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
return train_loss / len(train_loader)
def evaluate(model, loader, criterion):
model.eval()
val_loss = 0
with torch.no_grad():
for sample in loader:
inputs, mws, fracs, salt_mols, salt_graphs, targets = sample
sample_size = len(targets)
outs = torch.empty((sample_size, 1)).to(device)
targets = torch.tensor(targets).to(device)
salt_mols = torch.tensor(salt_mols).to(device)
for j in range(sample_size):
graph_set = inputs[j].to(device)
salt_graph = salt_graphs[j].to(device)
frac = torch.tensor(fracs[j]).to(device)
mw = torch.tensor(mws[j]).to(device)
outs[j] = model(graph_set, mw, frac, salt_mols[j], salt_graph)
loss = criterion(outs, targets)
val_loss += loss.item()
return val_loss / len(val_loader)
# Set early stopping criteria
best_val_loss = np.inf
epochs_wo_improv = 0
print(f'Total params: {sum(param.numel() for param in model.parameters())}')
# The training loop
for epoch in range(hyperpars['max_ep']):
train_loss = train(model, train_loader, optimizer, loss_fn)
val_loss = evaluate(model, val_loader, loss_fn)
scheduler.step(val_loss)
# Early stopping check
if epoch > hyperpars['es_patience']:
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = model.state_dict()
epochs_wo_improv = 0
else:
epochs_wo_improv += 1
if epochs_wo_improv >= hyperpars['max_ep_wo_improv']:
print(f'Early stopping at epoch {epoch+1}')
break
print(f'Epoch {epoch+1}: Train Loss={train_loss:.5f}, Val Loss={val_loss:.5f}')
#%% Plots
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
mol_types = pd.read_pickle('./data/data_df_stats.pkl')['mol_type']
model_name = 'DMPNN_{}_h{}_e{}_reg{}_{}'.format(hyperpars['n_conv_layers'], hyperpars['hidden_dim'], hyperpars['emb_dim'], hyperpars['weight_decay'], hyperpars['after_readout'])
targets = []
predicted = []
mol_labels = []
mol_types_list = []
if best_model is not None:
model.load_state_dict(best_model)
torch.save(best_model, 'results/{}.pt'.format(model_name))
model.eval()
with torch.no_grad():
for sample in test_data:
index, inputs, mw, frac, salt_mol, salt_graph, target = sample
inputs = Batch.from_data_list(inputs).to(device)
target = torch.tensor(target).to(device)
frac = torch.tensor(frac).to(device)
salt_mol = torch.tensor(salt_mol).to(device)
mw = torch.tensor(mw).to(device)
salt_graph.to(device)
out = model(inputs, mw, frac, salt_mol, salt_graph)
targets.append(target.cpu().numpy())
predicted.append(out.cpu().numpy())
mol_types_list.append(mol_types[index])
match mol_types[index]:
case 'poly': mol_labels.append(2)
case 'mixed': mol_labels.append(1)
case 'small': mol_labels.append(0)
targets = dataset.get_orig(np.stack(targets).squeeze())
predicted = dataset.get_orig(np.stack(predicted).squeeze())
results = pd.DataFrame({'target': targets, 'predicted': predicted, 'mix_type': mol_types_list})
spearman_r = spearmanr(targets, predicted)
pearson_r = pearsonr(targets, predicted)
print('Spearman r: {}, Pearson r: {}'.format(spearman_r, pearson_r))
sns.scatterplot(data=results, x='target', y='predicted', hue='mix_type')
plt.gca().set_aspect('equal', adjustable='box')
plt.axline([0, 0], [1, 1], color='black')
plt.xlabel('Target - log(S/cm)')
plt.ylabel('Predicted - log(S/cm)')
plt.savefig('results/{}.png'.format(model_name))
results.to_csv('results/{}.csv'.format(model_name), index=False)