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regression.py
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import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
from tensorflow.keras.constraints import max_norm
import pandas as pd
import numpy as np
from dataset import Graph_Regression_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"
def main(seed):
# tasks = ['caco2', 'logD', 'logS', 'PPB', 'tox']
# os.environ['CUDA_VISIBLE_DEVICES'] = "1"
keras.backend.clear_session()
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
small = {'name': 'Small', 'num_layers': 3, 'num_heads': 4, 'd_model': 128, 'path': 'small_weights','addH':True}
medium = {'name': 'Medium', 'num_layers': 6, 'num_heads': 8, 'd_model': 256, 'path': 'medium_weights','addH':True}
medium2 = {'name': 'Medium', 'num_layers': 6, 'num_heads': 8, 'd_model': 256, 'path': 'medium_weights2',
'addH': True}
large = {'name': 'Large', 'num_layers': 12, 'num_heads': 12, 'd_model': 576, 'path': 'large_weights','addH':True}
medium_without_H = {'name': 'Medium', 'num_layers': 6, 'num_heads': 8, 'd_model': 256, 'path': 'weights_without_H','addH':False}
medium_without_pretrain = {'name': 'Medium', 'num_layers': 6, 'num_heads': 8, 'd_model': 256,'path': 'medium_without_pretraining_weights','addH':True}
arch = medium ## small 3 4 128 medium: 6 6 256 large: 12 8 516
pretraining = True
pretraining_str = 'pretraining' if pretraining else ''
trained_epoch = 10
task = 'PPB'
print(task)
seed = seed
num_layers = arch['num_layers']
num_heads = arch['num_heads']
d_model = arch['d_model']
addH = arch['addH']
dff = d_model * 2
vocab_size = 17
dropout_rate = 0.1
tf.random.set_seed(seed=seed)
graph_dataset = Graph_Regression_Dataset('data/reg/{}.txt'.format(task), smiles_field='SMILES',
label_field='Label',addH=addH).get_data()
train_dataset, test_dataset,val_dataset = graph_dataset.get_data()
value_range = graph_dataset.value_range()
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=0.15)
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'],trained_epoch))
temp.encoder.save_weights(arch['path']+'/bert_weights_encoder{}_{}.h5'.format(arch['name'],trained_epoch))
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'],trained_epoch))
print('load_wieghts')
class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, d_model, total_steps=4000):
super(CustomSchedule, self).__init__()
self.d_model = d_model
self.d_model = tf.cast(self.d_model, tf.float32)
self.total_step = total_steps
self.warmup_steps = total_steps*0.10
def __call__(self, step):
arg1 = step/self.warmup_steps
arg2 = 1-(step-self.warmup_steps)/(self.total_step-self.warmup_steps)
return 10e-5* tf.math.minimum(arg1, arg2)
steps_per_epoch = len(train_dataset)
learning_rate = CustomSchedule(128,100*steps_per_epoch)
optimizer = tf.keras.optimizers.Adam(learning_rate=10e-5)
value_range =
r2 = -10
stopping_monitor = 0
for epoch in range(100):
mse_object = tf.keras.metrics.MeanSquaredError()
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 = tf.reduce_mean(tf.square(y-preds))
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
mse_object.update_state(y,preds)
print('epoch: ',epoch,'loss: {:.4f}'.format(loss.numpy().item()),'mse: {:.4f}'.format(mse_object.result().numpy().item() * (value_range**2)))
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)
r2_new = r2_score(y_true,y_preds)
val_mse = keras.metrics.MSE(y_true, y_preds).numpy() * (value_range**2)
print('val r2: {:.4f}'.format(r2_new), 'val mse:{:.4f}'.format(val_mse))
if r2_new > r2:
r2 = r2_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('regression_weights/{}.h5'.format(task))
else:
stopping_monitor +=1
print('best r2: {:.4f}'.format(r2))
if stopping_monitor>0:
print('stopping_monitor:',stopping_monitor)
if stopping_monitor>20:
break
y_true = []
y_preds = []
model.load_weights('regression_weights/{}.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)
test_r2 = r2_score(y_true, y_preds)
test_mse = keras.metrics.MSE(y_true.reshape(-1), y_preds.reshape(-1)).numpy() * (value_range**2)
print('test r2:{:.4f}'.format(test_r2), 'test mse:{:.4f}'.format(test_mse))
return r2
if __name__ == "__main__":
r2_list = []
for seed in [7,17,27,37,47]:
print(seed)
r2 = main(seed)
r2_list.append(r2)
print(r2_list)