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test_cluster_all.py
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import numpy as np
from sklearn.model_selection import train_test_split
from transformers import DistilBertModel, BertModel, BertTokenizer, BertForMaskedLM, BertForPreTraining, \
BertForSequenceClassification, AutoModelForSequenceClassification
import torch
from datasets import load_dataset, Dataset
from transformers import DataCollatorWithPadding, DataCollatorForLanguageModeling
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 BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.trainer_utils import IntervalStrategy
import shutil
import time
from sklearn import preprocessing
import pickle
from collections import Counter
from sklearn.metrics import confusion_matrix, mean_squared_error, classification_report
from sklearn.metrics import f1_score, matthews_corrcoef, accuracy_score, roc_auc_score, precision_score, recall_score, average_precision_score
def delete_multiple_element(list_object, indices):
indices = sorted(indices, reverse=True)
for idx in indices:
if idx < len(list_object):
list_object.pop(idx)
mode = 0
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
########################################################################################################################
########################################################################################################################
# EC1_Random_test #
########################################################################################################################
########################################################################################################################
########################################################### Data Preparation: ##########################################
# Loading dataset from csv file:
df_train = pd.read_csv('./clustered_train_df')
df_dev = pd.read_csv('./clustered_dev_df')
df_test = pd.read_csv('./clustered_test_df')
# Preprocessing training labels: output-labels_OHE
EC_total1 = list(df_train['EC_num'].values)
seq = list(df_train['sequence'].values)
seq_clean = []
EC_labels = []
EC1_labels = []
EC2_labels = []
EC3_labels = []
EC4_labels = []
for i in range(0,len(EC_total1)):
EC_labels_temp = []
if EC_total1[i] != '[]':
EC_total_struc = EC_total1[i].replace('[', '').replace(']', '').replace(' ', '').replace('\'', '').split(',')
# Mining first EC value:
EC1 = EC_total_struc[0].split('.')[0]
EC1_labels.append(EC1)
EC_labels_temp.append(EC1)
# Mining second EC value:
EC2 = 'x'
for a in EC_total_struc:
# EC2 = a.split('.')[1]
if a.split('.')[1] != '-':
EC2 = a.split('.')[1]
break
EC2_labels.append(EC2)
EC_labels_temp.append(EC2)
# Mining third EC value:
EC3 = 'x'
for a in EC_total_struc:
# EC2 = a.split('.')[1]
if a.split('.')[2] != '-':
EC3 = a.split('.')[2]
break
EC3_labels.append(EC3)
EC_labels_temp.append(EC3)
# Mining fourth EC value:
EC4 = 'x'
for a in EC_total_struc:
# EC2 = a.split('.')[1]
if a.split('.')[3] != '-':
EC4 = a.split('.')[3]
break
EC4_labels.append(EC4)
EC_labels_temp.append(EC4)
EC_labels.append(EC_labels_temp)
else:
EC1_labels.append('x')
EC2_labels.append('x')
EC3_labels.append('x')
EC4_labels.append('x')
EC_labels.append([])
le_1 = preprocessing.LabelEncoder()
le_1.fit(EC1_labels)
EC1_labels_encoded = le_1.transform(EC1_labels)
# le_2 = preprocessing.LabelEncoder()
# le_2.fit(EC2_labels)
# EC2_labels_encoded = le_2.transform(EC2_labels)
# le_3 = preprocessing.LabelEncoder()
# le_3.fit(EC3_labels)
# EC3_labels_encoded = le_3.transform(EC3_labels)
# le_2.inverse_transform()
"""
Total samples: 438522
Total blank EC_num: 229699
Blank EC1: 229699
Blank EC2: 231657
Blank EC3: 237544
Blank EC4: 260220
"""
# count=0
# for a in EC4_labels:
# if a == 'x':
# count+=1
text = []
labels = []
check = []
for i in range(0,len(EC1_labels_encoded)):
if EC1_labels[i] != 'x':
comment = ' '.join(seq[i])
# comment = str(EC2_labels_encoded[i]) + " " + comment
text.append(comment)
labels.append(EC1_labels_encoded[i])
text_3 = text
labels_3 = labels
####################################################
labels_OHE = []
label_num = len(list(set(labels_3)))
for i in range(0, len(labels_3)):
l_arr = np.zeros((label_num), int)
l_arr[labels_3[i]] = int(1)
labels_OHE.append(l_arr)
counts = np.zeros((label_num), int)
for i in range(0, len(labels_3)):
counts[labels_3[i]] += 1
text_train = text_3
labels_train = labels_OHE
# Preprocessing dev labels: output-labels_OHE
EC_total1 = list(df_test['EC_num'].values)
seq = list(df_test['sequence'].values)
seq_clean = []
EC_labels = []
EC1_labels = []
EC2_labels = []
EC3_labels = []
EC4_labels = []
for i in range(0,len(EC_total1)):
EC_labels_temp = []
if EC_total1[i] != '[]':
EC_total_struc = EC_total1[i].replace('[', '').replace(']', '').replace(' ', '').replace('\'', '').split(',')
# Mining first EC value:
EC1 = EC_total_struc[0].split('.')[0]
EC1_labels.append(EC1)
EC_labels_temp.append(EC1)
# Mining second EC value:
EC2 = 'x'
for a in EC_total_struc:
# EC2 = a.split('.')[1]
if a.split('.')[1] != '-':
EC2 = a.split('.')[1]
break
EC2_labels.append(EC2)
EC_labels_temp.append(EC2)
# Mining third EC value:
EC3 = 'x'
for a in EC_total_struc:
# EC2 = a.split('.')[1]
if a.split('.')[2] != '-':
EC3 = a.split('.')[2]
break
EC3_labels.append(EC3)
EC_labels_temp.append(EC3)
# Mining fourth EC value:
EC4 = 'x'
for a in EC_total_struc:
# EC2 = a.split('.')[1]
if a.split('.')[3] != '-':
EC4 = a.split('.')[3]
break
EC4_labels.append(EC4)
EC_labels_temp.append(EC4)
EC_labels.append(EC_labels_temp)
else:
EC1_labels.append('x')
EC2_labels.append('x')
EC3_labels.append('x')
EC4_labels.append('x')
EC_labels.append([])
# Calculating x structure for successive EC numbers:
index_hasEC1_notEC2 = []
index_hasEC1_notEC3 = []
index_hasEC2_notEC3 = []
index_hasEC1_notEC4 = []
index_hasEC2_notEC4 = []
index_hasEC3_notEC4 = []
index1 = index2 = index3= index4 = 0
for i in range(0,len(EC1_labels)):
if EC1_labels[i] != 'x':
if EC2_labels[i] == 'x':
index_hasEC1_notEC2.append(index1)
if EC3_labels[i] == 'x':
index_hasEC1_notEC3.append(index1)
if EC4_labels[i] == 'x':
index_hasEC1_notEC4.append(index1)
index1+=1
if EC2_labels[i] != 'x':
if EC3_labels[i] == 'x':
index_hasEC2_notEC3.append(index2)
if EC4_labels[i] == 'x':
index_hasEC2_notEC4.append(index2)
index2+=1
if EC3_labels[i] != 'x':
if EC4_labels[i] == 'x':
index_hasEC3_notEC4.append(index3)
index3+=1
# le_1 = preprocessing.LabelEncoder()
# le_1.fit(EC1_labels)
EC1_labels_encoded = le_1.transform(EC1_labels)
# le_2 = preprocessing.LabelEncoder()
# le_2.fit(EC2_labels)
# EC2_labels_encoded = le_2.transform(EC2_labels) ###Fix needed for EC2 classification
# le_3 = preprocessing.LabelEncoder()
# le_3.fit(EC3_labels)
# EC3_labels_encoded = le_3.transform(EC3_labels)
# le_2.inverse_transform()
"""
Total samples: 438522
Total blank EC_num: 229699
Blank EC1: 229699
Blank EC2: 231657
Blank EC3: 237544
Blank EC4: 260220
"""
count=0
for a in EC1_labels:
if a == 'x':
count+=1
text = []
labels = []
check = []
for i in range(0,len(EC1_labels_encoded)):
if EC1_labels[i] != 'x':
comment = ' '.join(seq[i])
# comment = str(EC2_labels_encoded[i]) + " " + comment
text.append(comment)
labels.append(EC1_labels_encoded[i])
text_3 = text
labels_3 = labels
# sum=0
# EC1_temp = []
# for i in range(0,len(EC1_labels)):
# if EC1_labels[i] != 'x':
# EC1_temp.append(EC1_labels[i])
#
# for i in range(0,len(EC1_temp)):
# if EC1_temp[i] == pred_orig[i]:
# sum+=1
####################################################
labels_OHE = []
label_num = len(list(set(labels_3)))
for i in range(0, len(labels_3)):
l_arr = np.zeros((label_num), int)
l_arr[labels_3[i]] = int(1)
labels_OHE.append(l_arr)
counts = np.zeros((label_num), int)
for i in range(0, len(labels_3)):
counts[labels_3[i]] += 1
text_dev = text_3
labels_dev = labels_OHE
# text_train, text_test, labels_train, labels_test = train_test_split(text_3, labels_OHE, stratify=labels_3, test_size=0.1)
# # text_test1, text_test2, labels_test1, labels_test2 = train_test_split(text_test, labels_test, test_size=0.1)
dict_train = {'text':text_train,'labels':labels_train}
dict_test = {'text':text_dev,'labels':labels_dev}
########################################################################################################################
########################################################################################################################
############################################### 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","24"])
##########################################################################################################################
# import torch
# from transformers import BertTokenizer, BertModel
#
# tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
# model = BertModel.from_pretrained("bert-base-cased")
#
# print(len(tokenizer)) # 28996
# tokenizer.add_tokens(["NEW_TOKEN1"])
# print(len(tokenizer)) # 28997
#
# model.resize_token_embeddings(len(tokenizer))
# # The new vector is added at the end of the embedding matrix
#
# print(len(model.embeddings.word_embeddings.weight[-1, :]))
# # Randomly generated matrix
#
# model.embeddings.word_embeddings.weight[-1, :] = torch.zeros([model.config.hidden_size])
#
# print(len(model.embeddings.word_embeddings.weight[-1, :]))
# # outputs a vector of zeros of shape [768]
########################################################################################################################
# model = BertForSequenceClassification.from_pretrained("Rostlab/prot_bert_bfd")
config = AutoConfig.from_pretrained(model_type)
# subject = 'secreted'
# subject = 'iamppred'
# subject = 'ampscanner'
# subject = 'iamp2l'
# subject = 'ampep'
# subject = 'secproct'
# subject = 'hemolythic'
# subject = 'hlppredfuse'
# subject = 'rnnamp'
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:
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 == 'iamp2l' or subject == 'iamppred':
batch_size = 32
if subject == 'secreted' or subject == 'ampscanner' or subject == 'hemolythic' or subject == 'hemolythic_2021' or subject == 'hlppredfuse' or subject == 'rnnamp':
batch_size = 16
if subject == 'ampep' or subject == 'secproct':
batch_size = 8
if subject == 'secproct':
batch_size = 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 == 'iamp2l':
dataset = load_dataset('csv', data_files={'train': 'data/iamp2l/iamp2l_raw.csv', 'test': 'data/iamp2l/independent/independent.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif subject == 'iamppred':
dataset = load_dataset('csv', data_files={'train': 'data/iamppred/old/train.csv', 'test': 'data/iamppred/old/test.csv'},
cache_dir='data/processed_datasets',
delimiter='\t',
# task="text-classification"
)
elif subject == 'secreted':
dataset = load_dataset('csv', data_files={'train': 'data/swissprot/secreted_all_cleaned_all_train.csv', 'test': 'data/swissprot/secreted_all_cleaned_all_test.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif subject == 'ampscanner':
dataset = load_dataset('csv', data_files={'train': 'data/ampscanner/ampscanner_train.csv',
'test': 'data/ampscanner/ampscanner_test.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif subject == 'ampep':
dataset = load_dataset('csv', data_files={'train': 'data/ampep/ampep.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
)
elif subject == 'secproct':
dataset = load_dataset('csv', data_files={'train': 'data/secproct/blood_train.csv',
'test': 'data/secproct/blood_test.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif subject == 'hemolythic':
dataset = load_dataset('csv', data_files={'train': 'data/hemolythic/hemolythic_train.csv',
'test': 'data/hemolythic/hemolythic_test.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif subject == 'hemolythic_2021':
dataset = load_dataset('csv', data_files={'train': 'data/hemolythic_2021/hemolythic_2021_train.csv',
'test': 'data/hemolythic_2021/hemolythic_2021_test.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif subject == 'hlppredfuse':
dataset = load_dataset('csv', data_files={'train': 'data/hlppredfuse/hlppredfuse_train.csv',
'test': 'data/hlppredfuse/hlppredfuse_test.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif subject == 'rnnamp':
dataset = load_dataset('csv', data_files={'train': 'data/rnnamp/rnnamp_train.csv',
'test': 'data/rnnamp/rnnamp_test.csv'},
cache_dir='data/processed_datasets',
delimiter=',',
# task="text-classification"
)
elif 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_clustered_EC1_EX_tune/0904-153350")
model = MyBertForSequenceClassification.from_pretrained("./models_clustered_EC1_EX_tune/0904-153350", 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_train_EC1 = trainer.predict(test_dataset=tokenized_datasets_train)
all_labels_train_EC1 = np.argmax(np.array(all_predictions_train_EC1[1]), axis=-1)
all_logits_train_EC1 = all_predictions_train_EC1[0]
all_predictions_train_EC1 = np.argmax(np.array(all_predictions_train_EC1[0]), axis=-1)
accuracy_train_EC1 = (np.sum(all_predictions_train_EC1 == all_labels_train_EC1))/len(all_labels_train_EC1)
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)
accuracy_eval_EC1 = (np.sum(all_predictions_eval_EC1 == all_labels_eval_EC1)) / len(all_labels_eval_EC1)
accuracy_matrics_Clustered_EC1_EX = [all_predictions_train_EC1, all_labels_train_EC1, all_logits_train_EC1, accuracy_train_EC1 ,all_predictions_eval_EC1, all_labels_eval_EC1, all_logits_eval_EC1, accuracy_eval_EC1]
with open('./Accuracy_matrics_clustered_EC1_EX_tune.pkl', 'wb') as f:
pickle.dump(accuracy_matrics_Clustered_EC1_EX, f)
# Loading pickle file:
# file = open("./Accuracy_matrics_random_EC1_EX.pkl", 'rb')
# [all_predictions_train_EC1, all_labels_train_EC1, all_logits_train_EC1, accuracy_train_EC1 ,all_predictions_eval_EC1, all_labels_eval_EC1, all_logits_eval_EC1, accuracy_eval_EC1] = pickle.load(file)
# all_predictions_eval2 = trainer.predict(test_dataset=tokenized_datasets_test2)
# all_labels_eval2 = np.array(all_predictions_eval2[1])
# all_logits_eval2 = all_predictions_eval2[0]
# all_predictions_eval2 = np.argmax(np.array(all_predictions_eval2[0]), axis=-1)
# all_predictions_test = trainer.predict(test_dataset=tokenized_datasets['test'])
# all_labels_test = np.array(all_predictions_test[1])
# all_logits_test = all_predictions_test[0]
# all_predictions_test = np.argmax(np.array(all_predictions_test[0]), axis=-1)
# prediction_df = pd.DataFrame(data=[[all_labels_train, all_predictions_train, all_logits_train,
# all_labels_eval, all_predictions_eval, all_logits_eval,
# all_labels_test, all_predictions_test, all_logits_test]], columns=[
# 'train_labels', 'train_predictions', 'train_logits',
# 'eval_labels', 'eval_predictions', 'eval_logits',
# 'test_labels', 'test_predictions', 'test_logits',
# ])
# prediction_df.to_csv(save_dir+'predictions.csv', index=False)
# print(save_dir)
# print("Eval Accuracies: \n")
# print('Acc:', accuracy_score(all_labels_eval, all_predictions_eval),
# ', Recall:', recall_score(all_labels_eval, all_predictions_eval, average="weighted"), ', Precision:', precision_score(all_labels_eval, all_predictions_eval, average="weighted"),
# ', ROC:', roc_auc_score(all_labels_eval, all_logits_eval[:,1]), ', MCC:', matthews_corrcoef(all_labels_eval, all_predictions_eval),
# ', AP:', average_precision_score(all_labels_eval, all_logits_eval[:,1]),
# ', F1:', f1_score(all_labels_eval, all_predictions_eval, average="weighted"))
#
# print("Training Accuracies: \n")
# print('Acc:', accuracy_score(all_labels_train, all_predictions_train),
# ', Recall:', recall_score(all_labels_train, all_predictions_train), ', Precision:', precision_score(all_labels_train, all_predictions_train),
# ', ROC:', roc_auc_score(all_labels_train, all_logits_train[:,1]), ', MCC:', matthews_corrcoef(all_labels_train, all_predictions_train),
# ', AP:', average_precision_score(all_labels_train, all_logits_train[:,1]),
# ', F1:', f1_score(all_labels_train, all_predictions_train, average='weighted'))
# print('Acc:', accuracy_score(all_labels_test, all_predictions_test),
# ', Recall:', recall_score(all_labels_test, all_predictions_test), ', Precision:', precision_score(all_labels_test, all_predictions_test),
# ', ROC:', roc_auc_score(all_labels_test, all_logits_test[:,1]), ', MCC:', matthews_corrcoef(all_labels_test, all_predictions_test),
# ', AP:', average_precision_score(all_labels_test, all_logits_test[:,1]),
# ', F1:', f1_score(all_labels_test, all_predictions_test))
#
# print(round(accuracy_score(all_labels_test, all_predictions_test), 4),
# round(recall_score(all_labels_test, all_predictions_test), 4), round(precision_score(all_labels_test, all_predictions_test), 4),
# round(roc_auc_score(all_labels_test, all_logits_test[:,1]), 4), round(matthews_corrcoef(all_labels_test, all_predictions_test), 4))
# print('F1:', f1_score(all_labels_test, all_predictions_test), ', Acc:', accuracy_score(all_labels_test, all_predictions_test), ', MCC:',
# matthews_corrcoef(all_labels_test, all_predictions_test), ', ROC:', roc_auc_score(all_labels_test, all_logits_test[:,1]),
# ', Precision:', precision_score(all_labels_test, all_predictions_test), ', Recall:',
# recall_score(all_labels_test, all_predictions_test), ', AP:', average_precision_score(all_labels_test, all_logits_test[:,1]))
# df = pd.DataFrame(data=[[f1_score(all_labels_eval, all_predictions_eval), accuracy_score(all_labels_eval, all_predictions_eval),
# matthews_corrcoef(all_labels_eval, all_predictions_eval), roc_auc_score(all_labels_eval, all_logits_eval[:,1]),
# precision_score(all_labels_eval, all_predictions_eval),
# recall_score(all_labels_eval, all_predictions_eval), average_precision_score(all_labels_eval, all_logits_eval[:,1]),
# f1_score(all_labels_test, all_predictions_test), accuracy_score(all_labels_test, all_predictions_test),
# matthews_corrcoef(all_labels_test, all_predictions_test), roc_auc_score(all_labels_test, all_logits_test[:,1]),
# precision_score(all_labels_test, all_predictions_test),
# recall_score(all_labels_test, all_predictions_test), average_precision_score(all_labels_test, all_logits_test[:,1]),
# subject, config.use_pooler, config.use_mean, config.intermediate_hidden_size, batch_size,
# freeze_positional, patience, freeze_non_positional, freeze_attention,
# freeze_layer_norm, freeze_pooler, initial_lr, transfer, num_epochs, save_dir, early_stopping, create_validation_split, fold, ten_fold]], columns=[
# 'f1_eval', 'accuracy_eval', 'mcc_eval', 'roc_auc_eval', 'precision_eval', 'recall_eval', 'average_precision_score_eval',
# 'f1_test', 'accuracy_test', 'mcc_test', 'roc_auc_test', 'precision_test', 'recall_test', 'average_precision_score_test',
# 'subject', 'usepooler', 'usemean', 'hidden_layer_size', 'batchsize', 'frozenpositional', 'patience',
# 'frozennonpositional', 'frozenattention', 'frozenlayernorm', 'frozenpooler', 'lr', 'transfer', 'num_epochs', 'save_dir',
# 'early_stopping', 'create_validation_split', 'fold', 'ten_fold'])
# if not os.path.isfile('results/training_results.csv'):
# df.to_csv('results/training_results.csv', index=False)
# else: # else it exists so append without writing the header
# df.to_csv('results/training_results.csv', index=False, mode='a', header=False)
# fresh_model = MyBertForSequenceClassification.from_pretrained("Rostlab/prot_bert_bfd", config=config)
# new_model = trainer.model
# new_model.to(device='cpu')
# new_model_state_dict = new_model.state_dict()
# for name, param in fresh_model.named_parameters():
# # if 'attn' in name or 'mlp' in name:
# new_param = new_model_state_dict[name]
# if torch.abs(param.data - new_param.data).sum() > 1e-8:
# print(f'{name} was modified')
# else:
# print(f'{name} is unchanged')
# results_df = pd.read_csv('results/training_results.csv')
# results_df = results_df[results_df['subject'] == 'ampep']
# results_df = results_df[results_df['hidden_layer_size'] == 1024]
#
# all_predictions_test = []
# all_logits_test = []
# all_labels_test = []
# for dir in results_df['save_dir']:
# pred_df = pd.read_csv(dir+'predictions.csv')
# all_predictions_test.extend(np.array(pred_df['test_predictions']))
# all_logits_test.extend(np.array(pred_df['test_logits']))