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test_eval.py
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test_eval.py
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import numpy as np
from transformers import BertModel, BertTokenizer
import torch
from datasets import Dataset
from transformers import DataCollatorWithPadding
from transformers import TrainingArguments
import pandas as pd
from datasets import load_metric
import random
from torch import nn
from transformers import Trainer
import os
from transformers import AutoConfig, EarlyStoppingCallback
from transformers.models.bert import BertPreTrainedModel
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.trainer_utils import IntervalStrategy
import time
from sklearn import preprocessing
import re
import io
import argparse
parser = argparse.ArgumentParser(description='Prot_EC Inference on user sequences')
parser.add_argument('--input_file', type=str, default='test_eval_data.txt',help='The input file name')
parser.add_argument('--output_file', type=str, default='output.txt',help='The output file name')
args = parser.parse_args()
input_file = args.input_file
output_file = args.output_file
mode = 0
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# Parsing data from input file:
input_file_path = './' + input_file
with io.open(input_file_path, 'rt') as f:
lines = f.read()
lines = lines.splitlines()
ID = []
seq_raw = []
seq_pattern = r'seq:(.+)'
ID_pattern = r'ID:(.+)'
for line in lines:
if re.findall(ID_pattern,line):
ID.append(re.findall(ID_pattern,line)[0].replace(' ',''))
elif re.findall(seq_pattern,line):
seq_raw.append(re.findall(seq_pattern,line)[0].replace(' ',''))
########################################################################################################################
########################################################################################################################
# EC1_Random_test #
########################################################################################################################
########################################################################################################################
########################################################### Data Preparation: ##########################################
# Initializing label Encoder:
le_1 = preprocessing.LabelEncoder()
le_1.classes_ = np.load('./le_1.npy')
le_2 = preprocessing.LabelEncoder()
le_2.classes_ = np.load('./le_2.npy')
le_3 = preprocessing.LabelEncoder()
le_3.classes_ = np.load('./le_3.npy')
le_4 = preprocessing.LabelEncoder()
le_4.classes_ = np.load('./le_4.npy')
# Preprocessing sequences:
seq = seq_raw
seq_clean = []
text = []
for i in range(0,len(seq)):
comment = ' '.join(seq[i])
text.append(comment)
dict_test = {'text':text}
########################################################################################################################
########################################################################################################################
############################################### Building Model: ########################################################
class MyBertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = BertModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
if config.intermediate_hidden_size != 0:
self.intermediate_classifier = nn.Linear(config.hidden_size, config.intermediate_hidden_size)
self.classifier = nn.Linear(config.intermediate_hidden_size, config.num_labels)
else:
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.config.use_pooler == 1:
pooled_output = outputs[1]
elif self.config.use_mean == 1:
# print(attention_mask.shape)
# token_embeddings.sum(axis=1) / attention_mask.sum(axis=-1).unsqueeze(-1)
pooled_output = torch.sum(outputs[0] * attention_mask.unsqueeze(-1), dim=1) / torch.sum(attention_mask, dim=1).unsqueeze(-1)
# print(pooled_output.shape)
# pooled_output = torch.mean(outputs[0], dim=1)
else:
pooled_output = outputs[0][:, 0]
pooled_output = self.dropout(pooled_output)
if config.intermediate_hidden_size != 0:
intermediate_output = self.intermediate_classifier(pooled_output)
logits = self.classifier(intermediate_output)
else:
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
# loss_fct = BCEWithLogitsLoss()
loss_fct = CrossEntropyLoss()
# print(list(logits.size()))
# print(list(labels.size()))
# print(logits.dtype)
# print(labels.dtype)
labels = labels.type(torch.float32)
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def tokenize_function(example):
return tokenizer(example["text"], add_special_tokens=True, truncation=True, max_length=1024)
def compute_metrics(eval_preds):
# print("PROBLEM!!!")
metric = load_metric("accuracy")
# sdfdf
# metric = load_metric("accuracy")
logits, labels = eval_preds
predictions = np.argmax(logits, axis=-1)
labels_real = np.argmax(labels, axis=1)
return metric.compute(predictions=predictions, references=labels_real)
model_type = "Rostlab/prot_bert_bfd"
# model_type = "Rostlab/prot_bert"
# model_type = "bert-base-cased"
# model_type = "bert-base-uncased"
# model_type = "distilbert-base-uncased"
# do_lower_case = True
do_lower_case = False
tokenizer = BertTokenizer.from_pretrained(model_type, do_lower_case=do_lower_case)
# tokenizer.add_tokens(["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24"])
# model = BertForSequenceClassification.from_pretrained("Rostlab/prot_bert_bfd")
config = AutoConfig.from_pretrained(model_type)
subject = 'EC_num'
config.classifier_dropout = 0
config.hidden_dropout_prob = 0
# config.hidden_size = 1024
mode_hidden_map = {0:0, 1:32, 2:128, 3:1024}
# config.intermediate_hidden_size = 1024
# config.intermediate_hidden_size = 128
# config.intermediate_hidden_size = 32
# config.intermediate_hidden_size = 0
config.intermediate_hidden_size = mode_hidden_map[mode]
########################################################################################################################
##################################################### Training: ##########################################################
# num_epochs = 1
# num_epochs = 10
num_epochs = 10
# num_epochs = 100
config.use_pooler = 0
config.use_mean = 1
# label_num = 7
# Enabling multilabel classification:
label_num = len(le_1.classes_)-1
config.num_labels = label_num
config.problem_type = "multi_label_classification"
# freeze_positional = 0
# freeze_non_positional = 0
# freeze_attention = 0
# freeze_layer_norm = 0
# freeze_pooler = 0
# freeze_positional = 1
# freeze_non_positional = 1
# freeze_attention = 1
# freeze_layer_norm = 1
# freeze_pooler = 1
freeze_positional = 0
freeze_non_positional = 1
freeze_attention = 1
freeze_layer_norm = 0
freeze_pooler = 0
transfer = 0
random_init = 0
if random_init:
transfer = 0
if subject == 'secreted':
transfer = 0
early_stopping = 1
patience = 10
if early_stopping:
create_validation_split = 1
else:
create_validation_split = 0
ten_fold = 0
# monitor_value = ''
# initial_lr = 5e-6
# initial_lr = 5e-5
initial_lr = 5e-4
if subject == 'EC_num':
batch_size = 1
balanced_loss = 0
# batch_size = 4
losses_all = []
fold_range = [0]
if ten_fold:
fold_range = range(10)
for fold in fold_range:
model = None
dataset = None
if transfer == 0:
if random_init:
model = MyBertForSequenceClassification(config=config)
else:
model = MyBertForSequenceClassification.from_pretrained(model_type, config=config)
if transfer == 1:
results_df = pd.read_csv('results/training_results.csv')
results_df = results_df[results_df['subject'] == 'secreted']
results_df = results_df[results_df['hidden_layer_size'] == config.intermediate_hidden_size]
results_df = results_df[results_df['usemean'] == config.use_mean]
results_df = results_df[results_df['usepooler'] == config.use_pooler]
secreted_model_dir = results_df.iloc[0]['save_dir']
model = MyBertForSequenceClassification.from_pretrained(secreted_model_dir, config=config)
# Loading Checkpoint:
# model = MyBertForSequenceClassification.from_pretrained("./models/0706-172025/checkpoint-4350")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# model.to(device)
# dataset = load_dataset('text', data_files={'train': 'data/uniparc_peptides_spaced.txt'}, cache_dir='data/processed_datasets')
# dataset = load_dataset('csv', data_files={'train': 'old/input/amp.csv'},
# # cache_dir='data/processed_datasets',
# delimiter=',',
# # task="text-classification"
# )
# CSV file loader, 2 columns, 'text' and 'labels'. Text is spaced capital sequences.
if subject == 'EC_num':
# dataset = Dataset.from_pandas(df)
# dataset_train = Dataset.from_dict(dict_train, split='train')
dataset_test = Dataset.from_dict(dict_test, split='test')
# dataset_test2 = Dataset.from_dict(dict_test2, split='test')
# dataset = load_dataset('dict')
# dataset = load_dataset('csv', data_files={'train': 'df_test_temp.csv',
# 'test': 'df_test_temp.csv'},
# cache_dir='data/processed_datasets',
# delimiter=',',
# # task="text-classification"
# )
# reload = False
# if not reload:
# # Find the indices for train, validation, and test splits
# random.seed(42)
# data_len = len(dataset['train'])
# randomlist = list(range(data_len))
# random.shuffle(randomlist)
# valid_begin = int(fold * 0.1 * data_len)
# valid_end = int((fold + 1) * 0.1 * data_len)
# test_begin = valid_end
# test_end = int((fold + 2) * 0.1 * data_len)
# if fold == 9:
# test_begin = 0
# test_end = int(0.1 * data_len)
# test_indices = randomlist[test_begin:test_end]
# valid_indices = randomlist[valid_begin:valid_end]
#
# # Create the validation split
# dataset['validation'] = dataset['train'].select(valid_indices)
# if ten_fold:
# # In ten fold cross validation, isolate the test split
# dataset['test'] = dataset['train'].select(test_indices)
# if create_validation_split:
# train_indices = [ind for ind in randomlist if ind not in test_indices + valid_indices]
# dataset['train'] = dataset['train'].select(train_indices)
# else:
# # If no validation split is needed (for no early stopping), do not isolate it from the train split
# train_indices = [ind for ind in randomlist if ind not in test_indices]
# dataset['train'] = dataset['train'].select(train_indices)
# else:
# if create_validation_split:
# train_indices = [ind for ind in randomlist if ind not in valid_indices]
# dataset['train'] = dataset['train'].select(train_indices)
# del randomlist
# tokenized_datasets_train = dataset_train.map(tokenize_function, batched=True)
tokenized_datasets_test = dataset_test.map(tokenize_function, batched=True)
# all_data = tokenize_function(dataset_test)
# tokenized_datasets_test = copy.deepcopy(dataset_test)
# tokenized_datasets_test = tokenized_datasets_test.add_column('input_ids', all_data[0])
# tokenized_datasets_test = tokenized_datasets_test.add_column('token_type_ids', all_data[2])
# tokenized_datasets_test = tokenized_datasets_test.add_column('attention_mask', all_data[1])
# all_data = tokenize_function(dataset_train)
# tokenized_datasets_train = copy.deepcopy(dataset_train)
# tokenized_datasets_train = tokenized_datasets_train.add_column('input_ids', all_data[0])
# tokenized_datasets_train = tokenized_datasets_train.add_column('token_type_ids', all_data[2])
# tokenized_datasets_train = tokenized_datasets_train.add_column('attention_mask', all_data[1])
# tokenized_datasets_test2 = dataset_test2.map(tokenize_function, batched=True)
# tokenized_datasets_protein = dataset_protein.map(tokenize_function, batched=True)
# tokenized_datasets_train.set_format("torch", columns=['input_ids'])
tokenized_datasets_test.set_format("torch", columns=['input_ids'])
# tokenized_datasets_test2.set_format("torch", columns=['input_ids'])
# tokenized_datasets.save_to_disk('data/processed_datasets/peptides')
# tokenized_datasets.save_to_disk('data/processed_datasets/'+subject+str(fold))
# tokenized_datasets.save_to_disk('data/processed_datasets/'+ subject)
# else:
# # tokenized_datasets = load_from_disk('data/processed_datasets/peptides')
# tokenized_datasets = load_from_disk('data/processed_datasets/'+subject+str(fold))
# # tokenized_datasets = load_from_disk('data/processed_datasets/secreted')
# tokenized_datasets_train = tokenized_datasets_train.remove_columns('text')
tokenized_datasets_test = tokenized_datasets_test.remove_columns('text')
# tokenized_datasets_test2 = tokenized_datasets_test2.remove_columns('text')
# for c in tokenized_datasets.column_names['train']:
# if c in ['keyword', 'length']:
# tokenized_datasets = tokenized_datasets.remove_columns(c)
# tokenized_datasets_train.set_format("torch")
tokenized_datasets_test.set_format("torch")
# tokenized_datasets_test2.set_format("torch")
# tokenized_datasets_protein.set_format("torch", columns=['input_ids'])
# print(tokenized_datasets['train'][1])
# tokenized_datasets = s.map(tokenize_function, batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer)
timestr = time.strftime("%m%d-%H%M%S")
save_dir = 'models_temp/' + timestr + '/'
while os.path.isdir(save_dir):
timestr = timestr.split('-')[0] + '-' + timestr.split('-')[1][:4] + str(int(timestr.split('-')[1][4:] + random.randint(1,60)))
save_dir = 'models_temp/' + timestr + '/'
os.makedirs(save_dir, exist_ok=True)
model.resize_token_embeddings(len(tokenizer))
training_args = TrainingArguments(num_train_epochs=num_epochs,output_dir=save_dir,
per_device_train_batch_size=batch_size,
learning_rate=initial_lr,
load_best_model_at_end=True,
evaluation_strategy=IntervalStrategy.EPOCH,
metric_for_best_model='eval_accuracy',
save_total_limit=patience+1,
save_strategy = IntervalStrategy.EPOCH,
# # prediction_loss_only=True,
gradient_accumulation_steps=int(32/batch_size), eval_accumulation_steps=int(32/batch_size),
# fp16=True, fp16_full_eval=True,
per_device_eval_batch_size=batch_size,
# # debug="underflow_overflow"
)
param_names = []
for name, param in model.named_parameters():
param_names.append(name)
# positional_embedding_params = ['bert.embeddings.word_embeddings.weight', 'bert.embeddings.position_embeddings.weight',
# 'bert.embeddings.token_type_embeddings.weight', 'bert.embeddings.LayerNorm.weight',
# 'bert.embeddings.LayerNorm.bias']
positional_embedding_params = ['bert.embeddings.position_embeddings.weight']
non_positional_embedding_params = ['bert.embeddings.word_embeddings.weight', 'bert.embeddings.token_type_embeddings.weight']
pooler_params = ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']
classifier_params = ['intermediate_classifier.weight', 'intermediate_classifier.bias', 'classifier.weight', 'classifier.bias']
layer_norm_params = []
attention_params = []
for l in param_names:
if 'LayerNorm' in l:
layer_norm_params.append(l)
elif l not in positional_embedding_params+non_positional_embedding_params+pooler_params+classifier_params:
attention_params.append(l)
print(len(positional_embedding_params+non_positional_embedding_params+layer_norm_params+attention_params+pooler_params+classifier_params), len(param_names))
unfrozen_params = []
unfrozen_params += classifier_params
if freeze_positional == 0:
unfrozen_params += positional_embedding_params
if freeze_non_positional == 0:
unfrozen_params += non_positional_embedding_params
if freeze_layer_norm == 0:
unfrozen_params += layer_norm_params
if freeze_pooler == 0:
unfrozen_params += pooler_params
if freeze_attention == 0:
unfrozen_params += attention_params
frozen_counter = 0
grad_counter = 0
for name, param in model.named_parameters():
if name in unfrozen_params:
param.requires_grad = True
grad_counter += len(param.flatten())
else:
param.requires_grad = False
frozen_counter += len(param.flatten())
print('Frozen parameters:', frozen_counter, grad_counter, grad_counter+frozen_counter, grad_counter*100/(grad_counter+frozen_counter))
callbacks = []
if early_stopping == 1:
callbacks = [EarlyStoppingCallback(early_stopping_patience=patience)]
config = AutoConfig.from_pretrained("./models_/models_random_EC1_EX_tune/0904-153510")
model = MyBertForSequenceClassification.from_pretrained("./models_/models_random_EC1_EX_tune/0904-153510", config=config)
trainer = None
trainer = Trainer(
model,
training_args,
# train_dataset=tokenized_datasets_train,
eval_dataset=tokenized_datasets_test,
compute_metrics=compute_metrics,
callbacks=callbacks,
data_collator=data_collator,
tokenizer = tokenizer
)
# Load checkpoint model:
# checkpoint = torch.load("./models_random_EC1_EX/0805-135007")
# trainer.train()
# dirs = [x[0] for x in os.walk(save_dir)]
# for d in dirs:
# if 'checkpoint' in d:
# shutil.rmtree(d, ignore_errors='True')
# trainer.save_model()
# print(trainer.evaluate())
# Loading saved model:
# config = AutoConfig.from_pretrained("./models_random_EC1_EX/0805-135007")
# model = MyBertForSequenceClassification.from_pretrained("./models_random_EC1_EX/0805-135007", config=config)
all_predictions_eval_EC1 = trainer.predict(test_dataset=tokenized_datasets_test)
# all_labels_eval_EC1 = np.argmax(np.array(all_predictions_eval_EC1[1]), axis=-1)
# all_logits_eval_EC1 = all_predictions_eval_EC1[0]
all_predictions_eval_EC1 = np.argmax(np.array(all_predictions_eval_EC1[0]), axis=-1)
pred_labels_EC1 = list(le_1.inverse_transform(all_predictions_eval_EC1))
pred_EC1 = list(all_predictions_eval_EC1)
########################################################################################################################
########################################################################################################################
# EC2_Random_test #
########################################################################################################################
########################################################################################################################
########################################################### Data Preparation: ##########################################
text = []
for i in range(0,len(seq_raw)):
comment = ' '.join(seq[i])
comment = str(pred_EC1[i]) + " " + comment
text.append(comment)
dict_test = {'text':text}
########################################################################################################################
############################################### Building Model: ########################################################
class MyBertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = BertModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
if config.intermediate_hidden_size != 0:
self.intermediate_classifier = nn.Linear(config.hidden_size, config.intermediate_hidden_size)
self.classifier = nn.Linear(config.intermediate_hidden_size, config.num_labels)
else:
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.config.use_pooler == 1:
pooled_output = outputs[1]
elif self.config.use_mean == 1:
# print(attention_mask.shape)
# token_embeddings.sum(axis=1) / attention_mask.sum(axis=-1).unsqueeze(-1)
pooled_output = torch.sum(outputs[0] * attention_mask.unsqueeze(-1), dim=1) / torch.sum(attention_mask, dim=1).unsqueeze(-1)
# print(pooled_output.shape)
# pooled_output = torch.mean(outputs[0], dim=1)
else:
pooled_output = outputs[0][:, 0]
pooled_output = self.dropout(pooled_output)
if config.intermediate_hidden_size != 0:
intermediate_output = self.intermediate_classifier(pooled_output)
logits = self.classifier(intermediate_output)
else:
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
# loss_fct = BCEWithLogitsLoss()
loss_fct = CrossEntropyLoss()
# print(list(logits.size()))
# print(list(labels.size()))
# print(logits.dtype)
# print(labels.dtype)
labels = labels.type(torch.float32)
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def tokenize_function(example):
return tokenizer(example["text"], add_special_tokens=True, truncation=True, max_length=1024)
def compute_metrics(eval_preds):
# print("PROBLEM!!!")
metric = load_metric("accuracy")
# sdfdf
# metric = load_metric("accuracy")
logits, labels = eval_preds
predictions = np.argmax(logits, axis=-1)
labels_real = np.argmax(labels, axis=1)
return metric.compute(predictions=predictions, references=labels_real)
model_type = "Rostlab/prot_bert_bfd"
# model_type = "Rostlab/prot_bert"
# model_type = "bert-base-cased"
# model_type = "bert-base-uncased"
# model_type = "distilbert-base-uncased"
# do_lower_case = True
do_lower_case = False
tokenizer = BertTokenizer.from_pretrained(model_type, do_lower_case=do_lower_case)
# elif (a==str(0) or a==str(1) or a==str(2) or a==str(3) or a==str(4) or a==str(5) or a==str(6)):
# tokenizer.add_tokens(["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23"])
tokenizer.add_tokens(["0","1","2","3","4","5","6"])
# model = BertForSequenceClassification.from_pretrained("Rostlab/prot_bert_bfd")
config = AutoConfig.from_pretrained(model_type)
subject = 'EC_num'
config.classifier_dropout = 0
config.hidden_dropout_prob = 0
# config.hidden_size = 1024
mode_hidden_map = {0:0, 1:32, 2:128, 3:1024}
# config.intermediate_hidden_size = 1024
# config.intermediate_hidden_size = 128
# config.intermediate_hidden_size = 32
# config.intermediate_hidden_size = 0
config.intermediate_hidden_size = mode_hidden_map[mode]
########################################################################################################################
##################################################### Training: ##########################################################
# num_epochs = 1
# num_epochs = 10
num_epochs = 10
# num_epochs = 100
config.use_pooler = 0
config.use_mean = 1
# Enabling multilabel classification:
label_num = len(le_2.classes_)-1
config.num_labels = label_num
config.problem_type = "multi_label_classification"
# freeze_positional = 0
# freeze_non_positional = 0
# freeze_attention = 0
# freeze_layer_norm = 0
# freeze_pooler = 0
# freeze_positional = 1
# freeze_non_positional = 1
# freeze_attention = 1
# freeze_layer_norm = 1
# freeze_pooler = 1
freeze_positional = 0
freeze_non_positional = 1
freeze_attention = 1
freeze_layer_norm = 0
freeze_pooler = 0
transfer = 0
random_init = 0
if random_init:
transfer = 0
if subject == 'secreted':
transfer = 0
early_stopping = 1
patience = 10
if early_stopping:
create_validation_split = 1
else:
create_validation_split = 0
ten_fold = 0
# monitor_value = ''
# initial_lr = 5e-6
# initial_lr = 5e-5
initial_lr = 5e-4
if subject == 'EC_num':
batch_size = 1
balanced_loss = 0
# batch_size = 4
losses_all = []
fold_range = [0]
if ten_fold:
fold_range = range(10)
for fold in fold_range:
model = None
dataset = None
if transfer == 0:
if random_init:
model = MyBertForSequenceClassification(config=config)
else:
model = MyBertForSequenceClassification.from_pretrained(model_type, config=config)
if transfer == 1:
results_df = pd.read_csv('results/training_results.csv')
results_df = results_df[results_df['subject'] == 'secreted']
results_df = results_df[results_df['hidden_layer_size'] == config.intermediate_hidden_size]
results_df = results_df[results_df['usemean'] == config.use_mean]
results_df = results_df[results_df['usepooler'] == config.use_pooler]
secreted_model_dir = results_df.iloc[0]['save_dir']
model = MyBertForSequenceClassification.from_pretrained(secreted_model_dir, config=config)
# Loading Checkpoint:
# model = MyBertForSequenceClassification.from_pretrained("./models/0706-172025/checkpoint-4350")
# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# model.to(device)
# dataset = load_dataset('text', data_files={'train': 'data/uniparc_peptides_spaced.txt'}, cache_dir='data/processed_datasets')
# dataset = load_dataset('csv', data_files={'train': 'old/input/amp.csv'},
# # cache_dir='data/processed_datasets',
# delimiter=',',
# # task="text-classification"
# )
# CSV file loader, 2 columns, 'text' and 'labels'. Text is spaced capital sequences.
if subject == 'EC_num':
# dataset = Dataset.from_pandas(df)
dataset_test = Dataset.from_dict(dict_test, split='test')
# dataset_test2 = Dataset.from_dict(dict_test2, split='test')
# dataset = load_dataset('dict')
# dataset = load_dataset('csv', data_files={'train': 'df_test_temp.csv',
# 'test': 'df_test_temp.csv'},
# cache_dir='data/processed_datasets',
# delimiter=',',
# # task="text-classification"
# )
# reload = False
# if not reload:
# # Find the indices for train, validation, and test splits
# random.seed(42)
# data_len = len(dataset['train'])
# randomlist = list(range(data_len))
# random.shuffle(randomlist)
# valid_begin = int(fold * 0.1 * data_len)
# valid_end = int((fold + 1) * 0.1 * data_len)
# test_begin = valid_end
# test_end = int((fold + 2) * 0.1 * data_len)
# if fold == 9:
# test_begin = 0
# test_end = int(0.1 * data_len)
# test_indices = randomlist[test_begin:test_end]
# valid_indices = randomlist[valid_begin:valid_end]
#
# # Create the validation split
# dataset['validation'] = dataset['train'].select(valid_indices)
# if ten_fold:
# # In ten fold cross validation, isolate the test split
# dataset['test'] = dataset['train'].select(test_indices)
# if create_validation_split:
# train_indices = [ind for ind in randomlist if ind not in test_indices + valid_indices]
# dataset['train'] = dataset['train'].select(train_indices)
# else:
# # If no validation split is needed (for no early stopping), do not isolate it from the train split
# train_indices = [ind for ind in randomlist if ind not in test_indices]
# dataset['train'] = dataset['train'].select(train_indices)
# else:
# if create_validation_split:
# train_indices = [ind for ind in randomlist if ind not in valid_indices]
# dataset['train'] = dataset['train'].select(train_indices)
# del randomlist
tokenized_datasets_test = dataset_test.map(tokenize_function, batched=True)
# tokenized_datasets_test2 = dataset_test2.map(tokenize_function, batched=True)
# tokenized_datasets_protein = dataset_protein.map(tokenize_function, batched=True)
tokenized_datasets_test.set_format("torch", columns=['input_ids'])
# tokenized_datasets_test2.set_format("torch", columns=['input_ids'])
# tokenized_datasets.save_to_disk('data/processed_datasets/peptides')
# tokenized_datasets.save_to_disk('data/processed_datasets/'+subject+str(fold))
# tokenized_datasets.save_to_disk('data/processed_datasets/'+ subject)
# else:
# # tokenized_datasets = load_from_disk('data/processed_datasets/peptides')
# tokenized_datasets = load_from_disk('data/processed_datasets/'+subject+str(fold))
# # tokenized_datasets = load_from_disk('data/processed_datasets/secreted')
tokenized_datasets_test = tokenized_datasets_test.remove_columns('text')
# tokenized_datasets_test2 = tokenized_datasets_test2.remove_columns('text')
# for c in tokenized_datasets.column_names['train']:
# if c in ['keyword', 'length']:
# tokenized_datasets = tokenized_datasets.remove_columns(c)
tokenized_datasets_test.set_format("torch")
# tokenized_datasets_test2.set_format("torch")
# tokenized_datasets_protein.set_format("torch", columns=['input_ids'])
# print(tokenized_datasets['train'][1])
# tokenized_datasets = s.map(tokenize_function, batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer)
timestr = time.strftime("%m%d-%H%M%S")
save_dir = 'models/' + timestr + '/'
while os.path.isdir(save_dir):
timestr = timestr.split('-')[0] + '-' + timestr.split('-')[1][:4] + str(int(timestr.split('-')[1][4:] + random.randint(1,60)))
save_dir = 'models/' + timestr + '/'
os.makedirs(save_dir, exist_ok=True)
model.resize_token_embeddings(len(tokenizer))
training_args = TrainingArguments(num_train_epochs=num_epochs,output_dir=save_dir,
per_device_train_batch_size=batch_size,
learning_rate=initial_lr,
load_best_model_at_end=True,
evaluation_strategy=IntervalStrategy.EPOCH,
metric_for_best_model='eval_accuracy',
save_total_limit=patience+1,
save_strategy = IntervalStrategy.EPOCH,
# # prediction_loss_only=True,
gradient_accumulation_steps=int(32/batch_size), eval_accumulation_steps=int(32/batch_size),
# fp16=True, fp16_full_eval=True,
per_device_eval_batch_size=batch_size,
# # debug="underflow_overflow"
)
param_names = []
for name, param in model.named_parameters():
param_names.append(name)
# positional_embedding_params = ['bert.embeddings.word_embeddings.weight', 'bert.embeddings.position_embeddings.weight',
# 'bert.embeddings.token_type_embeddings.weight', 'bert.embeddings.LayerNorm.weight',
# 'bert.embeddings.LayerNorm.bias']
positional_embedding_params = ['bert.embeddings.position_embeddings.weight']
non_positional_embedding_params = ['bert.embeddings.word_embeddings.weight', 'bert.embeddings.token_type_embeddings.weight']
pooler_params = ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']
classifier_params = ['intermediate_classifier.weight', 'intermediate_classifier.bias', 'classifier.weight', 'classifier.bias']
layer_norm_params = []
attention_params = []
for l in param_names:
if 'LayerNorm' in l:
layer_norm_params.append(l)
elif l not in positional_embedding_params+non_positional_embedding_params+pooler_params+classifier_params:
attention_params.append(l)
print(len(positional_embedding_params+non_positional_embedding_params+layer_norm_params+attention_params+pooler_params+classifier_params), len(param_names))
unfrozen_params = []
unfrozen_params += classifier_params
if freeze_positional == 0:
unfrozen_params += positional_embedding_params
if freeze_non_positional == 0:
unfrozen_params += non_positional_embedding_params
if freeze_layer_norm == 0:
unfrozen_params += layer_norm_params
if freeze_pooler == 0:
unfrozen_params += pooler_params
if freeze_attention == 0:
unfrozen_params += attention_params
frozen_counter = 0
grad_counter = 0
for name, param in model.named_parameters():
if name in unfrozen_params:
param.requires_grad = True
grad_counter += len(param.flatten())
else:
param.requires_grad = False
frozen_counter += len(param.flatten())
print('Frozen parameters:', frozen_counter, grad_counter, grad_counter+frozen_counter, grad_counter*100/(grad_counter+frozen_counter))
callbacks = []
if early_stopping == 1:
callbacks = [EarlyStoppingCallback(early_stopping_patience=patience)]
config = AutoConfig.from_pretrained("./models_/models_random_EC2_EX_tune/0908-184427")
model = MyBertForSequenceClassification.from_pretrained("./models_/models_random_EC2_EX_tune/0908-184427", config=config)
trainer = None
trainer = Trainer(
model,
training_args,
# train_dataset=tokenized_datasets_train,
eval_dataset=tokenized_datasets_test,
compute_metrics=compute_metrics,
callbacks=callbacks,
data_collator=data_collator,
tokenizer = tokenizer
)
all_predictions_eval_EC2 = trainer.predict(test_dataset=tokenized_datasets_test)
# all_labels_eval_EC2 = np.argmax(np.array(all_predictions_eval_EC2[1]), axis=-1)
# all_logits_eval_EC2 = all_predictions_eval_EC2[0]
all_predictions_eval_EC2 = np.argmax(np.array(all_predictions_eval_EC2[0]), axis=-1)
pred_labels_EC2 = list(le_2.inverse_transform(all_predictions_eval_EC2))
pred_EC2 = list(all_predictions_eval_EC2)
########################################################################################################################
########################################################################################################################
# EC3_Random_test #
########################################################################################################################
########################################################################################################################
########################################################### Data Preparation: ##########################################
text = []
for i in range(0,len(seq_raw)):
comment = ' '.join(seq[i])
comment = str(pred_EC1[i]) + " " + str(pred_EC2[i]) + " " + comment