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ad9fbbf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import os
import random
from tqdm import tqdm
from sklearn.metrics import auc, roc_auc_score, precision_recall_curve
from sklearn import metrics
from torch.utils.data import DataLoader
import torch
def Seed_everything(seed=2024):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
if hasattr(torch.backends, 'cudnn'):
torch.backends.cudnn.deterministic = True
def Metric(preds, labels,best_threshold = None):
labels = np.array(labels).reshape(-1)
preds = np.array(preds).reshape(-1)
if best_threshold == None:
best_f1 = 0
best_threshold = 0
for threshold in range(0, 100):
threshold = threshold / 100
binary_pred = [1 if pred >= threshold else 0 for pred in preds]
binary_true = labels
f1 = metrics.f1_score(binary_true, binary_pred)
if f1 > best_f1:
best_f1 = f1
best_threshold = threshold
binary_pred = [1 if pred >= best_threshold else 0 for pred in preds]
binary_true = labels
binary_acc = metrics.accuracy_score(binary_true, binary_pred)
precision = metrics.precision_score(binary_true, binary_pred)
recall = metrics.recall_score(binary_true, binary_pred)
f1 = metrics.f1_score(binary_true, binary_pred)
mcc = metrics.matthews_corrcoef(binary_true, binary_pred)
AUC = roc_auc_score(labels, preds)
precisions, recalls, _ = precision_recall_curve(labels, preds) #######
AUPRC = auc(recalls, precisions)
return AUC, AUPRC, mcc ,binary_acc,precision,recall,f1
def Write_log(logFile, text, isPrint=True):
if isPrint:
print(text)
logFile.write(text)
logFile.write('\n')
return None
class TaskDataset:
def __init__(self, df, protein_data, label_name):
self.df = df
self.protein_data = protein_data
self.label_name = label_name
def __len__(self):
return (self.df.shape[0])
def __getitem__(self, idx):
pdb_id = self.df.loc[idx, 'ID']
protein_X, protein_node_features, protein_masks, labels, adj = self.protein_data[pdb_id]
return {
'PDB_ID': pdb_id,
'PROTEIN_X': protein_X,
'PROTEIN_NODE_FEAT': protein_node_features,
'PROTEIN_MASK': protein_masks,
'LABEL': labels,
'ADJ': adj,
}
def collate_fn(self, batch):
pdb_ids = [item['PDB_ID'] for item in batch]
protein_X = torch.stack([item['PROTEIN_X'] for item in batch], dim=0)
protein_node_features = torch.stack([item['PROTEIN_NODE_FEAT'] for item in batch], dim=0)
protein_masks = torch.stack([item['PROTEIN_MASK'] for item in batch], dim=0)
labels = torch.stack([item['LABEL'] for item in batch], dim=0)
adj = torch.stack([item['ADJ'] for item in batch], dim=0)
return pdb_ids, protein_X, protein_node_features, protein_masks, labels, adj
# main function
def model_test(test, protein_data, model_class, config, weight_dir, output_dir,
logit=False, device=None, num_workers=0):
label_name = ['label'] # some task may have mutiple labels
sequence_name = "sequence"
device = torch.device(device or ('cuda' if torch.cuda.is_available() else 'cpu'))
print("Device:", device)
output_result = os.fspath(output_dir)
output_weight = os.fspath(weight_dir)
os.makedirs(output_result, exist_ok=True)
node_features = config['node_features']
edge_features = config['edge_features']
hidden_dim = config['hidden_dim']
num_encoder_layers = config['num_encoder_layers']
k_neighbors = config['k_neighbors']
augment_eps = config['augment_eps']
dropout = config['dropout']
id_name = config['id_name']
batch_size = config['batch_size']
folds = config['folds']
if test is not None:
log = open(os.path.join(output_result, 'test.log'), 'w', buffering=1)
Write_log(log, str(config) + '\n')
sub = test[[id_name, sequence_name]].copy()
if isinstance(label_name, list):
for l in label_name:
sub[l] = 0.0
sub[l] = sub[l].astype(np.float32)
else:
sub[label_name] = 0.0
test_dataset = TaskDataset(test, protein_data, label_name)
loader_kwargs = dict(
dataset=test_dataset,
batch_size=batch_size,
collate_fn=test_dataset.collate_fn,
shuffle=False,
drop_last=False,
num_workers=num_workers,
)
if num_workers > 0:
loader_kwargs['prefetch_factor'] = 2
test_dataloader = DataLoader(**loader_kwargs)
models = []
for fold in range(folds):
checkpoint = os.path.join(output_weight, 'fold%s.ckpt' % fold)
if not os.path.exists(checkpoint):
print("Missing checkpoint:", checkpoint)
continue
model = model_class(node_features, edge_features, hidden_dim, num_encoder_layers, k_neighbors, augment_eps, dropout)
model.to(device)
state_dict = torch.load(checkpoint, map_location=device, weights_only=True)
model.load_state_dict(state_dict)
model.eval()
models.append(model)
print('model count:', len(models))
if not models:
raise FileNotFoundError(f'No fold*.ckpt files were loaded from {output_weight}')
test_preds = []
test_outputs = []
test_Y = []
all_protein_node_features = []
all_labels = []
with torch.no_grad():
for data in tqdm(test_dataloader):
protein_X, protein_node_features, protein_masks, y, adj = [d.to(device) for d in data[1:]]
all_protein_node_features.append(protein_node_features.detach().cpu().numpy())
all_labels.append(y.detach().cpu().numpy())
if logit:
outputs = [model(protein_X, protein_node_features, protein_masks, adj).sigmoid() for model in models]
else:
outputs = [model(protein_X, protein_node_features, protein_masks) for model in models]
outputs = torch.stack(outputs, 0).mean(0) # 5个模型预测结果求平均,最终shape=(bsize, max_len)
test_outputs.append(outputs.detach().cpu().numpy())
test_seq_y = torch.masked_select(y, protein_masks.bool())
test_seq_preds = torch.masked_select(outputs, protein_masks.bool())
test_preds.append(test_seq_preds.cpu().detach().numpy())
test_Y.append(test_seq_y.cpu().detach().numpy())
test_preds = np.concatenate(test_preds)
test_Y = np.concatenate(test_Y)
test_metric = Metric(test_preds, test_Y)
Write_log(log,'test_auc:%.6f, test_auprc:%.6f, testFYT_mccL:%.6f, test_acc:%.6f, test_pre:%.6f, test_rec:%.6f, test_f1:%.6f' \
% (test_metric[0], test_metric[1], test_metric[2], test_metric[3],
test_metric[4], test_metric[5], test_metric[6]))
test_outputs = np.concatenate(test_outputs) # shape = (num_samples, max_len) or (num_samples, 4 * max_len)
sub['label'] = sub['label'].astype(object)
for i in range(len(sub)):
sub.at[i, 'label'] = test_outputs[i, :len(sub.loc[i, sequence_name])].tolist()
sub.to_csv(os.path.join(output_result, 'result.csv'), index=False)
log.close()
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