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classification-mfgb-hs-r0-pretrained-nr_ahr.py
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import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
import pandas as pd
import numpy as np
from dataset import M2VGraph_Classification_Dataset
from sklearn.metrics import r2_score, roc_auc_score
import os
from model import PredictModel, BertModel
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
keras.backend.clear_session()
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
def main(seed):
task = 'nr-ahr'
print(task)
medium_balanced = {'name': 'Medium', 'num_layers': 6, 'num_heads': 8, 'd_model': 256, 'path': 'weights_mfgb_hs_balanced', 'addH': True}
arch = medium_balanced ## small 3 4 128 medium: 6 6 256 large: 12 8 516
pretraining = True
pretraining_str = 'pretraining' if pretraining else ''
# trained_epoch = 12
num_layers = arch['num_layers']
num_heads = arch['num_heads']
d_model = arch['d_model']
addH = arch['addH']
dff = d_model * 2
vocab_size = 717
dropout_rate = 0.1
seed = seed
np.random.seed(seed=seed)
tf.random.set_seed(seed=seed)
train_dataset, test_dataset , val_dataset = M2VGraph_Classification_Dataset('data/clf/{}.csv'.format(task), smiles_field='SMILES',
label_field='Label',addH=arch['addH']).get_data()
x, adjoin_matrix, y = next(iter(train_dataset.take(1)))
seq = tf.cast(tf.math.equal(x, 0), tf.float32)
mask = seq[:, tf.newaxis, tf.newaxis, :]
model = PredictModel(num_layers=num_layers, d_model=d_model, dff=dff, num_heads=num_heads, vocab_size=vocab_size,
dense_dropout=0.5)
if pretraining:
temp = BertModel(num_layers=num_layers, d_model=d_model, dff=dff, num_heads=num_heads, vocab_size=vocab_size)
pred = temp(x, mask=mask, training=True, adjoin_matrix=adjoin_matrix)
temp.load_weights(arch['path']+'/bert_weights_{}.h5'.format(arch['name']))
temp.encoder.save_weights(arch['path']+'/bert_weights_encoder_{}_{}_{}.h5'.format(arch['name'], task, seed))
del temp
pred = model(x,mask=mask,training=True,adjoin_matrix=adjoin_matrix)
model.encoder.load_weights(arch['path']+'/bert_weights_encoder_{}_{}_{}.h5'.format(arch['name'], task, seed))
print('load_wieghts')
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5)
auc= -10
stopping_monitor = 0
# loss_train_writer = tf.summary.create_file_writer('./logs/train_log/nohloss-{}-{}'.format(str(seed), task))
# acc_train_writer = tf.summary.create_file_writer('./logs/train_log/nohacc-{}-{}'.format(str(seed), task))
#
# auc_val_writer = tf.summary.create_file_writer('./logs/val_log/nohauc-{}-{}'.format(str(seed), task))
# acc_val_writer = tf.summary.create_file_writer('./logs/val_log/nohacc-{}-{}'.format(str(seed), task))
#
# auc_test_writer = tf.summary.create_file_writer('./logs/test_log/nohauc-{}-{}'.format(str(seed), task))
# acc_test_writer = tf.summary.create_file_writer('./logs/test_log/nohacc-{}-{}'.format(str(seed), task))
for epoch in range(256):
accuracy_object = tf.keras.metrics.BinaryAccuracy()
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
for x,adjoin_matrix,y in train_dataset:
with tf.GradientTape() as tape:
seq = tf.cast(tf.math.equal(x, 0), tf.float32)
mask = seq[:, tf.newaxis, tf.newaxis, :]
preds = model(x,mask=mask,training=True,adjoin_matrix=adjoin_matrix)
loss = loss_object(y,preds)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
accuracy_object.update_state(y,preds)
# with acc_train_writer.as_default():
# tf.summary.scalar(name="train_acc", data=accuracy_object.result().numpy().item(), step=epoch)
# with loss_train_writer.as_default():
# tf.summary.scalar(name="train_loss", data=loss.numpy().item(), step=epoch)
print('epoch: ',epoch,'loss: {:.4f}'.format(loss.numpy().item()),'accuracy: {:.4f}'.format(accuracy_object.result().numpy().item()))
y_true = []
y_preds = []
for x, adjoin_matrix, y in val_dataset:
seq = tf.cast(tf.math.equal(x, 0), tf.float32)
mask = seq[:, tf.newaxis, tf.newaxis, :]
preds = model(x,mask=mask,adjoin_matrix=adjoin_matrix,training=False)
y_true.append(y.numpy())
y_preds.append(preds.numpy())
y_true = np.concatenate(y_true,axis=0).reshape(-1)
y_preds = np.concatenate(y_preds,axis=0).reshape(-1)
y_preds = tf.sigmoid(y_preds).numpy()
auc_new = roc_auc_score(y_true,y_preds)
val_accuracy = keras.metrics.binary_accuracy(y_true.reshape(-1), y_preds.reshape(-1)).numpy()
# with auc_val_writer.as_default():
# tf.summary.scalar(name="val_auc", data=auc_new, step=epoch)
# with acc_val_writer.as_default():
# tf.summary.scalar(name="val_acc", data=val_accuracy, step=epoch)
print('val auc:{:.4f}'.format(auc_new), 'val accuracy:{:.4f}'.format(val_accuracy))
if auc_new > auc:
auc = auc_new
stopping_monitor = 0
# np.save('{}/{}{}{}{}{}'.format(arch['path'], task, seed, arch['name'], trained_epoch, trained_epoch, pretraining_str),
# [y_true, y_preds])
model.save_weights('weights_mfgb_hs_balanced/{}_{}.h5'.format(task,seed))
print('save model weights')
else:
stopping_monitor += 1
print('best val auc: {:.4f}'.format(auc))
if stopping_monitor > 0:
print('stopping_monitor:',stopping_monitor)
if stopping_monitor >= 100:
break
y_true = []
y_preds = []
model.load_weights('weights_mfgb_hs_balanced/{}_{}.h5'.format(task, seed))
for x, adjoin_matrix, y in test_dataset:
seq = tf.cast(tf.math.equal(x, 0), tf.float32)
mask = seq[:, tf.newaxis, tf.newaxis, :]
preds = model(x, mask=mask, adjoin_matrix=adjoin_matrix, training=False)
y_true.append(y.numpy())
y_preds.append(preds.numpy())
y_true = np.concatenate(y_true, axis=0).reshape(-1)
y_preds = np.concatenate(y_preds, axis=0).reshape(-1)
y_preds = tf.sigmoid(y_preds).numpy()
test_auc = roc_auc_score(y_true, y_preds)
test_accuracy = keras.metrics.binary_accuracy(y_true.reshape(-1), y_preds.reshape(-1)).numpy()
# with auc_test_writer.as_default():
# tf.summary.scalar(name="test_auc", data=test_auc, step=epoch)
# with acc_test_writer.as_default():
# tf.summary.scalar(name="test_acc", data=test_accuracy, step=epoch)
print('test auc:{:.4f}'.format(test_auc), 'test accuracy:{:.4f}'.format(test_accuracy))
return test_auc
if __name__ == '__main__':
auc_list = []
for seed in [7, 17, 27, 37, 47, 57, 67, 77, 87, 97]:
print(seed)
auc = main(seed)
auc_list.append(auc)
print(auc_list)