from pytorch_pretrained_bert.tokenization import BertTokenizer, WordpieceTokenizer
from pytorch_pretrained_bert.modeling import BertForPreTraining, BertPreTrainedModel, BertModel, BertConfig, BertForMaskedLM, BertForSequenceClassification
from pathlib import Path
import torch
import re
from torch import Tensor
from torch.nn import BCEWithLogitsLoss
from fastai.text import Tokenizer, Vocab
import pandas as pd
import collections
import os
import pdb
from tqdm import tqdm, trange
import sys
import random
import numpy as np
import apex
from sklearn.model_selection import train_test_split
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
from sklearn.metrics import roc_curve, auc
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from pytorch_pretrained_bert.optimization import BertAdam
import logging
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
DATA_PATH=Path('../data/toxic_comments/')
DATA_PATH.mkdir(exist_ok=True)
PATH=Path('../data/toxic_comments/tmp')
PATH.mkdir(exist_ok=True)
CLAS_DATA_PATH=PATH/'class'
CLAS_DATA_PATH.mkdir(exist_ok=True)
model_state_dict = None
# BERT_PRETRAINED_PATH = Path('../trained_model/')
BERT_PRETRAINED_PATH = Path('../../complaints/bert/pretrained-weights/uncased_L-12_H-768_A-12/')
# BERT_PRETRAINED_PATH = Path('../../complaints/bert/pretrained-weights/cased_L-12_H-768_A-12/')
# BERT_PRETRAINED_PATH = Path('../../complaints/bert/pretrained-weights/uncased_L-24_H-1024_A-16/')
# BERT_FINETUNED_WEIGHTS = Path('../trained_model/toxic_comments')
PYTORCH_PRETRAINED_BERT_CACHE = BERT_PRETRAINED_PATH/'cache/'
PYTORCH_PRETRAINED_BERT_CACHE.mkdir(exist_ok=True)
# output_model_file = os.path.join(BERT_FINETUNED_WEIGHTS, "pytorch_model.bin")
# Load a trained model that you have fine-tuned
# model_state_dict = torch.load(output_model_file)
args = {
"train_size": -1,
"val_size": -1,
"full_data_dir": DATA_PATH,
"data_dir": PATH,
"task_name": "toxic_multilabel",
"no_cuda": False,
"bert_model": BERT_PRETRAINED_PATH,
"output_dir": CLAS_DATA_PATH/'output',
"max_seq_length": 512,
"do_train": True,
"do_eval": True,
"do_lower_case": True,
"train_batch_size": 32,
"eval_batch_size": 32,
"learning_rate": 3e-5,
"num_train_epochs": 4.0,
"warmup_proportion": 0.1,
"no_cuda": False,
"local_rank": -1,
"seed": 42,
"gradient_accumulation_steps": 1,
"optimize_on_cpu": False,
"fp16": False,
"loss_scale": 128
}
class BertForMultiLabelSequenceClassification(BertPreTrainedModel):
"""BERT model for classification.
This module is composed of the BERT model with a linear layer on top of
the pooled output.
Params:
`config`: a BertConfig class instance with the configuration to build a new model.
`num_labels`: the number of classes for the classifier. Default = 2.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
with indices selected in [0, ..., num_labels].
Outputs:
if `labels` is not `None`:
Outputs the CrossEntropy classification loss of the output with the labels.
if `labels` is `None`:
Outputs the classification logits of shape [batch_size, num_labels].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
num_labels = 2
model = BertForSequenceClassification(config, num_labels)
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_labels=2):
super(BertForMultiLabelSequenceClassification, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier = torch.nn.Linear(config.hidden_size, num_labels)
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
_, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
if labels is not None:
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1, self.num_labels))
return loss
else:
return logits
def freeze_bert_encoder(self):
for param in self.bert.parameters():
param.requires_grad = False
def unfreeze_bert_encoder(self):
for param in self.bert.parameters():
param.requires_grad = True
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, labels=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
labels: (Optional) [string]. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.labels = labels
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids, label_ids):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_ids = label_ids
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir, data_file_name, size=-1):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
class MultiLabelTextProcessor(DataProcessor):
def __init__(self, data_dir):
self.data_dir = data_dir
self.labels = None
def get_train_examples(self, data_dir, size=-1):
filename = 'train.csv'
logger.info("LOOKING AT {}".format(os.path.join(data_dir, filename)))
if size == -1:
data_df = pd.read_csv(os.path.join(data_dir, filename))
# data_df['comment_text'] = data_df['comment_text'].apply(cleanHtml)
return self._create_examples(data_df, "train")
else:
data_df = pd.read_csv(os.path.join(data_dir, filename))
# data_df['comment_text'] = data_df['comment_text'].apply(cleanHtml)
return self._create_examples(data_df.sample(size), "train")
def get_dev_examples(self, data_dir, size=-1):
"""See base class."""
filename = 'val.csv'
if size == -1:
data_df = pd.read_csv(os.path.join(data_dir, filename))
# data_df['comment_text'] = data_df['comment_text'].apply(cleanHtml)
return self._create_examples(data_df, "dev")
else:
data_df = pd.read_csv(os.path.join(data_dir, filename))
# data_df['comment_text'] = data_df['comment_text'].apply(cleanHtml)
return self._create_examples(data_df.sample(size), "dev")
def get_test_examples(self, data_dir, data_file_name, size=-1):
data_df = pd.read_csv(os.path.join(data_dir, data_file_name))
# data_df['comment_text'] = data_df['comment_text'].apply(cleanHtml)
if size == -1:
return self._create_examples(data_df, "test")
else:
return self._create_examples(data_df.sample(size), "test")
def get_labels(self):
"""See base class."""
if self.labels == None:
self.labels = list(pd.read_csv(os.path.join(self.data_dir, "classes.txt"),header=None)[0].values)
return self.labels
def _create_examples(self, df, set_type, labels_available=True):
"""Creates examples for the training and dev sets."""
examples = []
for (i, row) in enumerate(df.values):
guid = row[0]
text_a = row[1]
if labels_available:
labels = row[2:]
else:
labels = []
examples.append(
InputExample(guid=guid, text_a=text_a, labels=labels))
return examples
def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
label_map = {label : i for i, label in enumerate(label_list)}
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:(max_seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambigiously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
segment_ids = [0] * len(tokens)
if tokens_b:
tokens += tokens_b + ["[SEP]"]
segment_ids += [1] * (len(tokens_b) + 1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
padding = [0] * (max_seq_length - len(input_ids))
input_ids += padding
input_mask += padding
segment_ids += padding
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
labels_ids = []
for label in example.labels:
labels_ids.append(float(label))
# label_id = label_map[example.label]
if ex_index < 0:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("tokens: %s" % " ".join(
[str(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
logger.info("label: %s (id = %s)" % (example.labels, labels_ids))
features.append(
InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_ids=labels_ids))
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True):
"Compute accuracy when `y_pred` and `y_true` are the same size."
if sigmoid: y_pred = y_pred.sigmoid()
# return ((y_pred>thresh)==y_true.byte()).float().mean().item()
return np.mean(((y_pred>thresh)==y_true.byte()).float().cpu().numpy(), axis=1).sum()
def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True):
"Computes the f_beta between `preds` and `targets`"
beta2 = beta ** 2
if sigmoid: y_pred = y_pred.sigmoid()
y_pred = (y_pred>thresh).float()
y_true = y_true.float()
TP = (y_pred*y_true).sum(dim=1)
prec = TP/(y_pred.sum(dim=1)+eps)
rec = TP/(y_true.sum(dim=1)+eps)
res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
return res.mean().item()
def warmup_linear(x, warmup=0.002):
if x < warmup:
return x/warmup
return 1.0 - x
processors = {
"toxic_multilabel": MultiLabelTextProcessor
}
# Setup GPU parameters
if args["local_rank"] == -1 or args["no_cuda"]:
device = torch.device("cuda" if torch.cuda.is_available() and not args["no_cuda"] else "cpu")
n_gpu = torch.cuda.device_count()
# n_gpu = 1
else:
torch.cuda.set_device(args['local_rank'])
device = torch.device("cuda", args['local_rank'])
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args['local_rank'] != -1), args['fp16']))
args['train_batch_size'] = int(args['train_batch_size'] / args['gradient_accumulation_steps'])
random.seed(args['seed'])
np.random.seed(args['seed'])
torch.manual_seed(args['seed'])
if n_gpu > 0:
torch.cuda.manual_seed_all(args['seed'])
task_name = args['task_name'].lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name](args['data_dir'])
label_list = processor.get_labels()
num_labels = len(label_list)
label_list
tokenizer = BertTokenizer.from_pretrained(args['bert_model'], do_lower_case=args['do_lower_case'])
train_examples = None
num_train_steps = None
if args['do_train']:
train_examples = processor.get_train_examples(args['full_data_dir'], size=args['train_size'])
# train_examples = processor.get_train_examples(args['data_dir'], size=args['train_size'])
num_train_steps = int(
len(train_examples) / args['train_batch_size'] / args['gradient_accumulation_steps'] * args['num_train_epochs'])
# Prepare model
def get_model():
# pdb.set_trace()
if model_state_dict:
model = BertForMultiLabelSequenceClassification.from_pretrained(args['bert_model'], num_labels = num_labels, state_dict=model_state_dict)
else:
model = BertForMultiLabelSequenceClassification.from_pretrained(args['bert_model'], num_labels = num_labels)
return model
model = get_model()
if args['fp16']:
model.half()
model.to(device)
if args['local_rank'] != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
from torch.optim.lr_scheduler import _LRScheduler, Optimizer
class CyclicLR(object):
"""Sets the learning rate of each parameter group according to
cyclical learning rate policy (CLR). The policy cycles the learning
rate between two boundaries with a constant frequency, as detailed in
the paper `Cyclical Learning Rates for Training Neural Networks`_.
The distance between the two boundaries can be scaled on a per-iteration
or per-cycle basis.
Cyclical learning rate policy changes the learning rate after every batch.
`batch_step` should be called after a batch has been used for training.
To resume training, save `last_batch_iteration` and use it to instantiate `CycleLR`.
This class has three built-in policies, as put forth in the paper:
"triangular":
A basic triangular cycle w/ no amplitude scaling.
"triangular2":
A basic triangular cycle that scales initial amplitude by half each cycle.
"exp_range":
A cycle that scales initial amplitude by gamma**(cycle iterations) at each
cycle iteration.
This implementation was adapted from the github repo: `bckenstler/CLR`_
Args:
optimizer (Optimizer): Wrapped optimizer.
base_lr (float or list): Initial learning rate which is the
lower boundary in the cycle for eachparam groups.
Default: 0.001
max_lr (float or list): Upper boundaries in the cycle for
each parameter group. Functionally,
it defines the cycle amplitude (max_lr - base_lr).
The lr at any cycle is the sum of base_lr
and some scaling of the amplitude; therefore
max_lr may not actually be reached depending on
scaling function. Default: 0.006
step_size (int): Number of training iterations per
half cycle. Authors suggest setting step_size
2-8 x training iterations in epoch. Default: 2000
mode (str): One of {triangular, triangular2, exp_range}.
Values correspond to policies detailed above.
If scale_fn is not None, this argument is ignored.
Default: 'triangular'
gamma (float): Constant in 'exp_range' scaling function:
gamma**(cycle iterations)
Default: 1.0
scale_fn (function): Custom scaling policy defined by a single
argument lambda function, where
0 <= scale_fn(x) <= 1 for all x >= 0.
mode paramater is ignored
Default: None
scale_mode (str): {'cycle', 'iterations'}.
Defines whether scale_fn is evaluated on
cycle number or cycle iterations (training
iterations since start of cycle).
Default: 'cycle'
last_batch_iteration (int): The index of the last batch. Default: -1
Example:
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> scheduler = torch.optim.CyclicLR(optimizer)
>>> data_loader = torch.utils.data.DataLoader(...)
>>> for epoch in range(10):
>>> for batch in data_loader:
>>> scheduler.batch_step()
>>> train_batch(...)
.. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
.. _bckenstler/CLR: https://github.com/bckenstler/CLR
"""
def __init__(self, optimizer, base_lr=1e-3, max_lr=6e-3,
step_size=2000, mode='triangular', gamma=1.,
scale_fn=None, scale_mode='cycle', last_batch_iteration=-1):
# if not isinstance(optimizer, Optimizer):
# raise TypeError('{} is not an Optimizer'.format(
# type(optimizer).__name__))
self.optimizer = optimizer
if isinstance(base_lr, list) or isinstance(base_lr, tuple):
if len(base_lr) != len(optimizer.param_groups):
raise ValueError("expected {} base_lr, got {}".format(
len(optimizer.param_groups), len(base_lr)))
self.base_lrs = list(base_lr)
else:
self.base_lrs = [base_lr] * len(optimizer.param_groups)
if isinstance(max_lr, list) or isinstance(max_lr, tuple):
if len(max_lr) != len(optimizer.param_groups):
raise ValueError("expected {} max_lr, got {}".format(
len(optimizer.param_groups), len(max_lr)))
self.max_lrs = list(max_lr)
else:
self.max_lrs = [max_lr] * len(optimizer.param_groups)
self.step_size = step_size
if mode not in ['triangular', 'triangular2', 'exp_range'] \
and scale_fn is None:
raise ValueError('mode is invalid and scale_fn is None')
self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = self._triangular_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = self._triangular2_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = self._exp_range_scale_fn
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.batch_step(last_batch_iteration + 1)
self.last_batch_iteration = last_batch_iteration
def batch_step(self, batch_iteration=None):
if batch_iteration is None:
batch_iteration = self.last_batch_iteration + 1
self.last_batch_iteration = batch_iteration
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr
def _triangular_scale_fn(self, x):
return 1.
def _triangular2_scale_fn(self, x):
return 1 / (2. ** (x - 1))
def _exp_range_scale_fn(self, x):
return self.gamma**(x)
def get_lr(self):
step_size = float(self.step_size)
cycle = np.floor(1 + self.last_batch_iteration / (2 * step_size))
x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1)
lrs = []
param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs)
for param_group, base_lr, max_lr in param_lrs:
base_height = (max_lr - base_lr) * np.maximum(0, (1 - x))
if self.scale_mode == 'cycle':
lr = base_lr + base_height * self.scale_fn(cycle)
else:
lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration)
lrs.append(lr)
return lrs
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
t_total = num_train_steps
if args['local_rank'] != -1:
t_total = t_total // torch.distributed.get_world_size()
if args['fp16']:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args['learning_rate'],
bias_correction=False,
max_grad_norm=1.0)
if args['loss_scale'] == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args['loss_scale'])
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args['learning_rate'],
warmup=args['warmup_proportion'],
t_total=t_total)
scheduler = CyclicLR(optimizer, base_lr=2e-5, max_lr=5e-5, step_size=2500, last_batch_iteration=0)
# Eval Fn
eval_examples = processor.get_dev_examples(args['data_dir'], size=args['val_size'])
def eval():
args['output_dir'].mkdir(exist_ok=True)
eval_features = convert_examples_to_features(
eval_examples, label_list, args['max_seq_length'], tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args['eval_batch_size'])
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_ids for f in eval_features], dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args['eval_batch_size'])
all_logits = None
all_labels = None
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
logits = model(input_ids, segment_ids, input_mask)
# logits = logits.detach().cpu().numpy()
# label_ids = label_ids.to('cpu').numpy()
# tmp_eval_accuracy = accuracy(logits, label_ids)
tmp_eval_accuracy = accuracy_thresh(logits, label_ids)
if all_logits is None:
all_logits = logits.detach().cpu().numpy()
else:
all_logits = np.concatenate((all_logits, logits.detach().cpu().numpy()), axis=0)
if all_labels is None:
all_labels = label_ids.detach().cpu().numpy()
else:
all_labels = np.concatenate((all_labels, label_ids.detach().cpu().numpy()), axis=0)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
# ROC-AUC calcualation
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(num_labels):
fpr[i], tpr[i], _ = roc_curve(all_labels[:, i], all_logits[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(all_labels.ravel(), all_logits.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
result = {'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
# 'loss': tr_loss/nb_tr_steps,
'roc_auc': roc_auc }
output_eval_file = os.path.join(args['output_dir'], "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
# writer.write("%s = %s\n" % (key, str(result[key])))
return result
train_features = convert_examples_to_features(
train_examples, label_list, args['max_seq_length'], tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args['train_batch_size'])
logger.info(" Num steps = %d", num_train_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_ids for f in train_features], dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
if args['local_rank'] == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args['train_batch_size'])
from tqdm import tqdm_notebook as tqdm
def fit(num_epocs=args['num_train_epochs']):
global_step = 0
model.train()
for i_ in tqdm(range(int(num_epocs)), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args['gradient_accumulation_steps'] > 1:
loss = loss / args['gradient_accumulation_steps']
if args['fp16']:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args['gradient_accumulation_steps'] == 0:
# scheduler.batch_step()
# modify learning rate with special warm up BERT uses
lr_this_step = args['learning_rate'] * warmup_linear(global_step/t_total, args['warmup_proportion'])
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
logger.info('Loss after epoc {}'.format(tr_loss / nb_tr_steps))
logger.info('Eval after epoc {}'.format(i_+1))
eval()
# Freeze BERT layers for 1 epoch
# model.module.freeze_bert_encoder()
# fit(1)
model.module.unfreeze_bert_encoder()
fit()
# Save a trained model
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, "finetuned_pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
# Load a trained model that you have fine-tuned
model_state_dict = torch.load(output_model_file)
model = BertForMultiLabelSequenceClassification.from_pretrained(args['bert_model'], num_labels = num_labels, state_dict=model_state_dict)
model.to(device)
model
eval()
def predict(model, path, test_filename='test.csv'):
predict_processor = MultiLabelTextProcessor(path)
test_examples = predict_processor.get_test_examples(path, test_filename, size=-1)
# Hold input data for returning it
input_data = [{ 'id': input_example.guid, 'comment_text': input_example.text_a } for input_example in test_examples]
test_features = convert_examples_to_features(
test_examples, label_list, args['max_seq_length'], tokenizer)
logger.info("***** Running prediction *****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args['eval_batch_size'])
all_input_ids = torch.tensor([f.input_ids for f in test_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in test_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in test_features], dtype=torch.long)
test_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids)
# Run prediction for full data
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=args['eval_batch_size'])
all_logits = None
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for step, batch in enumerate(tqdm(test_dataloader, desc="Prediction Iteration")):
input_ids, input_mask, segment_ids = batch
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask)
logits = logits.sigmoid()
if all_logits is None:
all_logits = logits.detach().cpu().numpy()
else:
all_logits = np.concatenate((all_logits, logits.detach().cpu().numpy()), axis=0)
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
return pd.merge(pd.DataFrame(input_data), pd.DataFrame(all_logits, columns=label_list), left_index=True, right_index=True)
result = predict(model, DATA_PATH)
result.shape
cols = ['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
result[cols].to_csv(DATA_PATH/'toxic_kaggle_submission_14_single.csv', index=None)