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test_graph_encode_norm_single.py
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test_graph_encode_norm_single.py
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import cPickle as pickle
import sys
import numpy as np
import torch
import torch.cuda
from rdkit import Chem
from sklearn import metrics
from sklearn.model_selection import train_test_split
from torch import nn
from torch import optim
from torch.utils.data import DataLoader
from models.normed_encoded_basic_model import BasicModel
from models.graph_model_wrapper import GraphWrapper
from mol_graph import *
from mol_graph import GraphEncoder
from pre_process.data_loader import GraphDataSet, collate_2d_graphs, collate_2d_tensors
from pre_process.load_dataset import load_classification_dataset
from mpnn_functions.encoders.bond_autoencoder import BondAutoEncoder
from mpnn_functions.encoders.atom_autoencoder import AtomAutoEncoder
import tqdm
def count_model_params(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
return sum([np.prod(p.size()) for p in model_parameters])
def save_model(model, model_name, model_att, model_metrics):
# type: (nn.Module, dict) -> None
torch.save(model.state_dict(), 'basic_model_' + str(model_name) + '.state_dict')
with open('basic_model_attributes.pickle', 'wb') as out_file:
pickle.dump(model_att, out_file)
with open('basic_model_' + str(model_name) + '_stats.pickle', 'wb') as out_file:
pickle.dump(model_metrics, out_file)
def test_model(model, dataset):
model.eval()
labels = []
true_labels = []
with torch.no_grad():
for batch in tqdm.tqdm(dataset):
labels = labels + model(batch).max(dim=-1)[1].cpu().data.numpy().tolist()
true_labels = true_labels + batch['labels'].cpu().data.numpy().tolist()
return (
metrics.accuracy_score(true_labels, labels),
metrics.precision_score(true_labels, labels, average='binary'),
metrics.recall_score(true_labels, labels, average='binary')
)
seed = 317
torch.manual_seed(seed)
data_file = sys.argv[1]
mgf = MolGraphFactory(Mol2DGraph.TYPE, AtomFeatures(), BondFeatures())
try:
file_data = np.load(data_file+'.npz')
data = file_data['data']
for graph in data:
graph.mask = np.ones(graph.afm.shape[0], dtype=np.float32).reshape(graph.afm.shape[0], 1)
graph.afm = graph.afm.astype(np.float32)
graph.bfm = graph.bfm.astype(np.float32)
graph.adj = graph.adj.astype(np.float32)
graph.label = long(graph.label)
no_labels = int(file_data['no_labels'])
all_labels = file_data['all_labels']
file_data.close()
except IOError:
data, no_labels, all_labels = load_classification_dataset(data_file+'.csv',
'InChI', Chem.MolFromInchi, mgf, 'target')
graph_encoder = GraphEncoder()
with open('basic_model_graph_encoder.pickle', 'wb') as out:
pickle.dump(graph_encoder, out)
np.savez_compressed(data_file, data=data, no_labels=no_labels, all_labels=all_labels)
model_attributes = {
'afm': 8,
'bfm': 2,
'mfm': 2*8,
'adj': data[0].adj.shape[-1],
'out': 4*8,
'classification_output': 2
}
ae = AtomAutoEncoder()
# ae.load_state_dict(torch.load('./atom_autoencoder.state_dict', map_location=lambda storage, loc: storage))
be = BondAutoEncoder()
# be.load_state_dict(torch.load('./bond_autoencoder.state_dict', map_location=lambda storage, loc: storage))
model = nn.Sequential(
GraphWrapper(BasicModel(model_attributes['afm'], model_attributes['bfm'], model_attributes['mfm'],
model_attributes['adj'], model_attributes['out'], atom_encoder=ae.encoder,
bond_encoder=be.encoder)),
nn.BatchNorm1d(model_attributes['out']),
nn.Linear(model_attributes['out'], model_attributes['classification_output'])
)
selected_label = 243
for graph in data:
graph.label = long(selected_label == graph.label)
all_labels = (selected_label == all_labels)
model.float()
model.apply(BasicModel.init_weights)
ae.load_state_dict(torch.load('./atom_autoencoder.state_dict', map_location=lambda storage, loc: storage))
be.load_state_dict(torch.load('./bond_autoencoder.state_dict', map_location=lambda storage, loc: storage))
# model[0].graph_model.ae = ae.encoder
# model[0].graph_model.be = be.encoder
print "Model has: {} parameters".format(count_model_params(model))
if torch.cuda.is_available():
model.cuda()
train, test, train_labels, test_labels = train_test_split(data, all_labels, test_size=0.1,
random_state=seed, stratify=all_labels)
del data
del all_labels
del test_labels
train, val, t_labels, v_labels = train_test_split(train, train_labels, test_size=0.1, random_state=seed, stratify=train_labels)
del train_labels
train = GraphDataSet(train)
val = GraphDataSet(val)
test = GraphDataSet(test)
train = DataLoader(train, 16, shuffle=True, collate_fn=collate_2d_graphs)
val = DataLoader(val, 16, shuffle=True, collate_fn=collate_2d_graphs)
test = DataLoader(test, 16, shuffle=True, collate_fn=collate_2d_graphs)
# weight = float(sum(t_labels)) / len(t_labels)
# weight = torch.Tensor([weight, 1-weight])
# criterion = nn.CrossEntropyLoss(weight=weight)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-3)
model.train()
losses = []
epoch_losses = []
break_con = False
for epoch in tqdm.trange(500):
model.train()
epoch_loss = 0
for batch in tqdm.tqdm(train):
model.zero_grad()
loss = criterion(model(batch), batch['labels'])
losses.append(loss.item())
epoch_loss += loss.item()
loss.backward()
optimizer.step()
acc, pre, rec = test_model(model, val)
f1 = 2 * (pre * rec) / (pre + rec)
tqdm.tqdm.write(
"epoch {} training loss: {}, validation acc: {}, pre: {}, rec: {}, F1: {}".format(epoch, epoch_loss, acc,
pre, rec, f1))
if not np.isnan(f1) and f1 > 0.8:
save_model(model, str(selected_label)+'_epoch_'+str(epoch), model_attributes, {'acc': acc, 'pre': pre, 'rec': rec, 'f1': f1})
# epoch_losses.append(epoch_loss)
# if 0 == (epoch+1) % 50:
# print "epoch: {}, loss: {}".format(epoch, epoch_loss)
# break_con = loss.item() < 0.02
# if break_con:
# break
acc, pre, rec = test_model(model, test)
f1 = 2 * (pre * rec) / (pre + rec)
tqdm.tqdm.write(
"Testing acc: {}, pre: {}, rec: {}, F1: {}".format(epoch, epoch_loss, acc, pre, rec, f1))
# save_model(model, model_attributes)
# model.eval()
# labels = []
# true_labels = []
# for batch in val:
# labels = labels + model(batch).max(dim=-1)[1].cpu().data.numpy().tolist()
# true_labels = true_labels + batch['labels'].cpu().data.numpy().tolist()
#
# print "accuracy: {}, precision: {}, recall: {}".format(
# metrics.accuracy_score(true_labels, labels),
# metrics.precision_score(true_labels, labels, average='micro'),
# metrics.recall_score(true_labels, labels, average='micro')
# )