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import json
import copy
import torch
import scipy
import numpy as np
import pandas as pd
from tqdm import tqdm
from scipy.special import softmax
from transformers import AutoTokenizer
class CollateFn(object):
def __init__(self, modelname, class_definitions=None, instance_weights=False):
self.instance_weights = instance_weights
use_fast = False if 'deberta' in modelname else True
self.tokenizer = AutoTokenizer.from_pretrained(modelname, use_fast=use_fast)
cited_ids = self.tokenizer.encode('<CITED HERE>', add_special_tokens=False)
self.cited_here_tokens = torch.tensor(cited_ids, dtype=torch.long)
if class_definitions is not None:
self.class_definitions = []
self.class_head_indices = []
for i, defs in enumerate(class_definitions):
self.class_definitions += defs
self.class_head_indices.append(i * torch.ones(len(defs), dtype=torch.long))
self.class_head_indices = torch.cat(self.class_head_indices, dim=0)
self.class_tokens = self.tokenizer(
self.class_definitions,
return_tensors="pt",
max_length=512,
truncation=True,
padding=True
)
def _get_readout_mask(self, tokens):
# cited_here_tokens = torch.tensor([962, 8412, 1530, 1374])
readout_mask = torch.zeros_like(tokens['input_ids'], dtype=torch.bool)
batch_size = tokens['input_ids'].size(0)
l = tokens['input_ids'].size(1)
ctk_l = self.cited_here_tokens.size(0)
for b in range(batch_size):
for i in range(1, l - ctk_l):
if torch.equal(tokens['input_ids'][b, i:i+ctk_l], self.cited_here_tokens):
readout_mask[b, i:i+ctk_l] = True
if not readout_mask[b].any():
# Fallback to CLS if the citation marker isn't matched.
readout_mask[b, 0] = True
return readout_mask
def _tokenize_context(self, context):
tokens = self.tokenizer(
context,
return_tensors="pt",
max_length=512,
truncation=True,
padding=True
)
tokens['readout_mask'] = self._get_readout_mask(
tokens
)
return tokens
def __call__(self, samples):
if self.instance_weights:
text, labels, ds_indices, instance_weights = list(map(list, zip(*samples)))
batched_text = self._tokenize_context(text)
labels = torch.stack(labels)
ds_indices = torch.stack(ds_indices)
instance_weights = torch.stack(instance_weights)
return batched_text, labels, ds_indices, instance_weights
else:
text, labels, ds_indices = list(map(list, zip(*samples)))
batched_text = self._tokenize_context(text)
labels = torch.stack(labels)
ds_indices = torch.stack(ds_indices)
return batched_text, labels, ds_indices, copy.deepcopy(self.class_tokens), self.class_head_indices
class Dataset(object):
def __init__(self, dataframe, class_definitions, lmbd=1.0):
self.class_definitions = class_definitions
self.lmbd = lmbd
self._load_data(dataframe)
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
'''Get datapoint with index'''
return (self.text[idx], self.labels[idx], self.ds_index[idx])
def _load_data(self, annotated_data):
self.labels = torch.LongTensor(annotated_data['label'].tolist())
self.original_labels = torch.LongTensor(annotated_data['label'].tolist())
self.ds_index = torch.zeros_like(self.original_labels)
self.text = annotated_data['context'].tolist()
class MultiHeadDatasets(object):
def __init__(self, datasets, batch_size_factor=2):
self.text = []
self.ds_index = []
self.labels = []
self.class_definitions = []
self.lambdas = []
self.dataset_sizes = [len(d.labels) for d in datasets]
if len(self.dataset_sizes) > 1:
if sum(self.dataset_sizes) / self.dataset_sizes[0] <= batch_size_factor:
self.sample_auxiliary = False
self.adjusted_batch_size_factor = sum(self.dataset_sizes) / self.dataset_sizes[0]
else:
self.sample_auxiliary = True
self.sample_distribution = np.array([d.lmbd for d in datasets[1:]]) / sum([d.lmbd for d in datasets[1:]])
self.adjusted_batch_size_factor = batch_size_factor
else:
self.sample_auxiliary = False
self.adjusted_batch_size_factor = 1
for i, d in enumerate(datasets):
self.text += d.text
self.ds_index.append(i * torch.ones(len(d.text), dtype=torch.long))
self.labels.append(d.labels)
self.class_definitions.append(d.class_definitions)
self.lambdas.append(d.lmbd)
self.labels = torch.cat(self.labels, dim=0)
self.ds_index = torch.cat(self.ds_index, dim=0)
def sample_auxiliary_instace(self):
sampled_dataset_idx = np.random.choice(
np.arange(1, len(self.dataset_sizes)),
p=self.sample_distribution
)
instance_idx = np.random.choice(
self.dataset_sizes[sampled_dataset_idx]
) + sum(self.dataset_sizes[:sampled_dataset_idx])
return instance_idx
def __len__(self):
if self.sample_auxiliary: # if the auxiliary dataset is larger than the main dataset
return self.dataset_sizes[0] * self.adjusted_batch_size_factor
return len(self.labels)
def __getitem__(self, idx):
'''Get datapoint with index'''
if idx < self.dataset_sizes[0] or not self.sample_auxiliary:
return (self.text[idx], self.labels[idx], self.ds_index[idx])
else:
real_idx = self.sample_auxiliary_instace()
return (self.text[real_idx], self.labels[real_idx], self.ds_index[real_idx])
def load_class_definitions(filename):
with open(filename, 'r') as f:
class_definitions = json.load(f)
results = {k:{} for k in class_definitions.keys()}
for k, v in class_definitions.items():
for kk, vv in v.items():
results[k][kk.lower()] = vv
return results
def create_data_channels(filename, class_definition_filename, split=None, lmbd=1.0):
data = pd.read_csv(filename, sep='\t')
data = data.fillna(' ')
print('Number of data instance: {}'.format(data.shape[0]))
# map labels to ids
unique_labels = data['label'].unique().tolist()
label2id = {lb: i for i, lb in enumerate(unique_labels)}
data['label'] = data['label'].apply(
lambda x: label2id[x])
data_train = data[data['split'] == 'train'].reset_index()
data_val = data[data['split'] == 'val'].reset_index()
data_test = data[data['split'] == 'test'].reset_index()
class_definitions = load_class_definitions(class_definition_filename)
dataname = filename.split('/')[-1].split('.')[0]
data_class_definitions = [class_definitions[dataname][lb.lower()] for lb in unique_labels]
train_data = Dataset(data_train, data_class_definitions, lmbd=lmbd)
val_data = Dataset(data_val, data_class_definitions, lmbd=lmbd)
test_data = Dataset(data_test, data_class_definitions, lmbd=lmbd)
return train_data, val_data, test_data, unique_labels
def create_single_data_object(filename, class_definition_filename, split=None, lmbd=1.0):
data = pd.read_csv(filename, sep='\t')
data = data.fillna(' ')
print('Number of data instance: {}'.format(data.shape[0]))
# map labels to ids
unique_labels = data['label'].unique()
label2id = {lb: i for i, lb in enumerate(unique_labels)}
data['label'] = data['label'].apply(
lambda x: label2id[x])
class_definitions = load_class_definitions(class_definition_filename)
dataname = filename.split('/')[-1].split('.')[0]
data_class_definitions = [class_definitions[dataname][lb.lower()] for lb in unique_labels]
if split is None:
return Dataset(data, data_class_definitions, lmbd=lmbd), unique_labels
else:
return Dataset(data[data['split'] == split].reset_index(), data_class_definitions, lmbd=lmbd), unique_labels
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