meghanaraok's picture
Upload 4 files
bf97eb3 verified
raw
history blame
16.7 kB
import csv
import linecache
import pickle
import random
import subprocess
import numpy as np
import redis
import torch
import logging
import ast
from datasets import Dataset
from tqdm import tqdm
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score, roc_auc_score, roc_curve, auc
from torch.utils.data import DataLoader
from transformers import AutoModel, DataCollatorWithPadding, XLNetTokenizer, XLNetTokenizerFast, AutoTokenizer, \
XLNetModel, is_torch_tpu_available
logger = logging.getLogger("lwat")
class MimicIIIDataset(Dataset):
def __init__(self, data):
self.input_ids = data["input_ids"]
self.attention_mask = data["attention_mask"]
self.token_type_ids = data["token_type_ids"]
self.labels = data["targets"]
def __len__(self):
return len(self.input_ids)
def __getitem__(self, item):
return {
"input_ids": torch.tensor(self.input_ids[item], dtype=torch.long),
"attention_mask": torch.tensor(self.attention_mask[item], dtype=torch.float),
"token_type_ids": torch.tensor(self.token_type_ids[item], dtype=torch.long),
"targets": torch.tensor(self.labels[item], dtype=torch.float)
}
class LazyMimicIIIDataset(Dataset):
def __init__(self, filename, task, dataset_type):
print("lazy load from {}".format(filename))
self.filename = filename
self.redis = redis.Redis(unix_socket_path="/tmp/redis.sock")
self.pipe = self.redis.pipeline()
self.num_examples = 0
self.task = task
self.dataset_type = dataset_type
with open(filename, 'r') as f:
for line_num, line in enumerate(f.readlines()):
self.num_examples += 1
example = eval(line)
key = task + '_' + dataset_type + '_' + str(line_num)
input_ids = eval(example[0])
attention_mask = eval(example[1])
token_type_ids = eval(example[2])
labels = eval(example[3])
example_tuple = (input_ids, attention_mask, token_type_ids, labels)
self.pipe.set(key, pickle.dumps(example_tuple))
if line_num % 100 == 0:
self.pipe.execute()
self.pipe.execute()
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
xm.rendezvous(tag="featuresGenerated")
def __len__(self):
return self.num_examples
def __getitem__(self, item):
key = self.task + '_' + self.dataset_type + '_' + str(item)
example = pickle.loads(self.redis.get(key))
return {
"input_ids": torch.tensor(example[0], dtype=torch.long),
"attention_mask": torch.tensor(example[1], dtype=torch.float),
"token_type_ids": torch.tensor(example[2], dtype=torch.long),
"targets": torch.tensor(example[3], dtype=torch.float)
}
class ICDCodeDataset(Dataset):
def __init__(self, data):
self.input_ids = data["input_ids"]
self.attention_mask = data["attention_mask"]
self.token_type_ids = data["token_type_ids"]
def __len__(self):
return len(self.input_ids)
def __getitem__(self, item):
return {
"input_ids": torch.tensor(self.input_ids[item], dtype=torch.long),
"attention_mask": torch.tensor(self.attention_mask[item], dtype=torch.float),
"token_type_ids": torch.tensor(self.token_type_ids[item], dtype=torch.long)
}
def set_random_seed(random_seed):
random.seed(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def tokenize_inputs(text_list, tokenizer, max_seq_len=512):
"""
Tokenizes the input text input into ids. Appends the appropriate special
characters to the end of the text to denote end of sentence. Truncate or pad
the appropriate sequence length.
"""
# tokenize the text, then truncate sequence to the desired length minus 2 for
# the 2 special characters
tokenized_texts = list(map(lambda t: tokenizer.tokenize(t)[:max_seq_len - 2], text_list))
# convert tokenized text into numeric ids for the appropriate LM
input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]
# get token type for token_ids_0
token_type_ids = [tokenizer.create_token_type_ids_from_sequences(x) for x in input_ids]
# append special token to end of sentence: <sep> <cls>
input_ids = [tokenizer.build_inputs_with_special_tokens(x) for x in input_ids]
# attention mask
attention_mask = [[1] * len(x) for x in input_ids]
# padding to max_length
def padding_to_max(sequence, value):
padding_len = max_seq_len - len(sequence)
padding = [value] * padding_len
return sequence + padding
input_ids = [padding_to_max(x, tokenizer.pad_token_id) for x in input_ids]
attention_mask = [padding_to_max(x, 0) for x in attention_mask]
token_type_ids = [padding_to_max(x, tokenizer.pad_token_type_id) for x in token_type_ids]
return input_ids, attention_mask, token_type_ids
def tokenize_dataset(tokenizer, text, labels, max_seq_len):
if (isinstance(tokenizer, XLNetTokenizer) or isinstance(tokenizer, XLNetTokenizerFast)):
data = list(map(lambda t: tokenize_inputs(t, tokenizer, max_seq_len=max_seq_len), text))
input_ids, attention_mask, token_type_ids = zip(*data)
else:
tokenizer.model_max_length = max_seq_len
input_dict = tokenizer(text, padding=True, truncation=True)
input_ids = input_dict["input_ids"]
attention_mask = input_dict["attention_mask"]
token_type_ids = input_dict["token_type_ids"]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"targets": labels
}
def initial_code_title_vectors(label_dict, transformer_model_name, tokenizer_name, code_max_seq_length, code_batch_size,
d_model, device):
logger.info("Generate code title representations from base transformer model")
model = AutoModel.from_pretrained(transformer_model_name)
if isinstance(model, XLNetModel):
model.config.use_mems_eval = False
#
# model.config.use_mems_eval = False
# model.config.reuse_len = 0
code_titles = label_dict["long_title"].fillna("").tolist()
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, padding_side="right")
data = tokenizer(code_titles, padding=True, truncation=True)
code_dataset = ICDCodeDataset(data)
model.to(device)
data_collator = DataCollatorWithPadding(tokenizer, padding="max_length",
max_length=code_max_seq_length)
code_param = {"batch_size": code_batch_size, "collate_fn": data_collator}
code_dataloader = DataLoader(code_dataset, **code_param)
code_dataloader_progress_bar = tqdm(code_dataloader, unit="batches",
desc="Code title representations")
code_dataloader_progress_bar.clear()
# output shape: (num_labels, hidden_size)
initial_code_vectors = torch.zeros(len(code_dataset), d_model)
for i, data in enumerate(code_dataloader_progress_bar):
input_ids = data["input_ids"].to(device, dtype=torch.long)
attention_mask = data["attention_mask"].to(device, dtype=torch.float)
token_type_ids = data["token_type_ids"].to(device, dtype=torch.long)
# output shape: (batch_size, sequence_length, hidden_size)
output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
# Mean pooling. output shape: (batch_size, hidden_size)
mean_last_hidden_state = torch.mean(output[0], 1)
# Max pooling. output shape: (batch_size, hidden_size)
# max_last_hidden_state = torch.max((output[0] * attention_mask.unsqueeze(-1)), 1)[0]
initial_code_vectors[i * input_ids.shape[0]:(i + 1) * input_ids.shape[0], :] = mean_last_hidden_state
code_dataloader_progress_bar.refresh(True)
code_dataloader_progress_bar.clear(True)
code_dataloader_progress_bar.close()
logger.info("Code representations ready for use. Shape {}".format(initial_code_vectors.shape))
return initial_code_vectors
def normalise_labels(labels, n_label):
norm_labels = []
for label in labels:
one_hot_vector_label = [0] * n_label
one_hot_vector_label[label] = 1
norm_labels.append(one_hot_vector_label)
return np.asarray(norm_labels)
def segment_tokenize_inputs(text, tokenizer, max_seq_len, num_chunks):
# input is full text of one document
tokenized_texts = []
tokens = tokenizer.tokenize(text)
start_idx = 0
seq_len = max_seq_len - 2
for i in range(num_chunks):
if start_idx > len(tokens):
tokenized_texts.append([])
continue
tokenized_texts.append(tokens[start_idx:(start_idx + seq_len)])
start_idx += seq_len
# convert tokenized text into numeric ids for the appropriate LM
input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]
# get token type for token_ids_0
token_type_ids = [tokenizer.create_token_type_ids_from_sequences(x) for x in input_ids]
# append special token to end of sentence: <sep> <cls>
input_ids = [tokenizer.build_inputs_with_special_tokens(x) for x in input_ids]
# attention mask
attention_mask = [[1] * len(x) for x in input_ids]
# padding to max_length
def padding_to_max(sequence, value):
padding_len = max_seq_len - len(sequence)
padding = [value] * padding_len
return sequence + padding
input_ids = [padding_to_max(x, tokenizer.pad_token_id) for x in input_ids]
attention_mask = [padding_to_max(x, 0) for x in attention_mask]
token_type_ids = [padding_to_max(x, tokenizer.pad_token_type_id) for x in token_type_ids]
return input_ids, attention_mask, token_type_ids
def segment_tokenize_dataset(tokenizer, text, labels, max_seq_len, num_chunks):
data = list(
map(lambda t: segment_tokenize_inputs(t, tokenizer, max_seq_len, num_chunks), text))
input_ids, attention_mask, token_type_ids = zip(*data)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"targets": labels
}
# The following functions are modified from the relevant codes of https://github.com/aehrc/LAAT
def roc_auc(true_labels, pred_probs, average="macro"):
if pred_probs.shape[0] <= 1:
return
fpr = {}
tpr = {}
if average == "macro":
# get AUC for each label individually
relevant_labels = []
auc_labels = {}
for i in range(true_labels.shape[1]):
# only if there are true positives for this label
if true_labels[:, i].sum() > 0:
fpr[i], tpr[i], _ = roc_curve(true_labels[:, i], pred_probs[:, i])
if len(fpr[i]) > 1 and len(tpr[i]) > 1:
auc_score = auc(fpr[i], tpr[i])
if not np.isnan(auc_score):
auc_labels["auc_%d" % i] = auc_score
relevant_labels.append(i)
# macro-AUC: just average the auc scores
aucs = []
for i in relevant_labels:
aucs.append(auc_labels['auc_%d' % i])
score = np.mean(aucs)
else:
# micro-AUC: just look at each individual prediction
flat_pred = pred_probs.ravel()
fpr["micro"], tpr["micro"], _ = roc_curve(true_labels.ravel(), flat_pred)
score = auc(fpr["micro"], tpr["micro"])
return score
def union_size(x, y, axis):
return np.logical_or(x, y).sum(axis=axis).astype(float)
def intersect_size(x, y, axis):
return np.logical_and(x, y).sum(axis=axis).astype(float)
def macro_accuracy(true_labels, pred_labels):
num = intersect_size(true_labels, pred_labels, 0) / (union_size(true_labels, pred_labels, 0) + 1e-10)
return np.mean(num)
def macro_precision(true_labels, pred_labels):
num = intersect_size(true_labels, pred_labels, 0) / (pred_labels.sum(axis=0) + 1e-10)
return np.mean(num)
def macro_recall(true_labels, pred_labels):
num = intersect_size(true_labels, pred_labels, 0) / (true_labels.sum(axis=0) + 1e-10)
return np.mean(num)
def macro_f1(true_labels, pred_labels):
prec = macro_precision(true_labels, pred_labels)
rec = macro_recall(true_labels, pred_labels)
if prec + rec == 0:
f1 = 0.
else:
f1 = 2 * (prec * rec) / (prec + rec)
return prec, rec, f1
def precision_at_k(true_labels, pred_probs, ks=[1, 5, 8, 10, 15]):
# num true labels in top k predictions / k
sorted_pred = np.argsort(pred_probs)[:, ::-1]
output = []
for k in ks:
topk = sorted_pred[:, :k]
# get precision at k for each example
vals = []
for i, tk in enumerate(topk):
if len(tk) > 0:
num_true_in_top_k = true_labels[i, tk].sum()
denom = len(tk)
vals.append(num_true_in_top_k / float(denom))
output.append(np.mean(vals))
return output
def micro_recall(true_labels, pred_labels):
flat_true = true_labels.ravel()
flat_pred = pred_labels.ravel()
return intersect_size(flat_true, flat_pred, 0) / flat_true.sum(axis=0)
def micro_precision(true_labels, pred_labels):
flat_true = true_labels.ravel()
flat_pred = pred_labels.ravel()
if flat_pred.sum(axis=0) == 0:
return 0.0
return intersect_size(flat_true, flat_pred, 0) / flat_pred.sum(axis=0)
def micro_f1(true_labels, pred_labels):
prec = micro_precision(true_labels, pred_labels)
rec = micro_recall(true_labels, pred_labels)
if prec + rec == 0:
f1 = 0.
else:
f1 = 2 * (prec * rec) / (prec + rec)
return prec, rec, f1
def micro_accuracy(true_labels, pred_labels):
flat_true = true_labels.ravel()
flat_pred = pred_labels.ravel()
return intersect_size(flat_true, flat_pred, 0) / union_size(flat_true, flat_pred, 0)
def calculate_scores(true_labels, logits, average="macro", is_multilabel=True, threshold=0.5):
def sigmoid(x):
return 1 / (1 + np.exp(-x))
pred_probs = sigmoid(logits)
pred_labels = np.rint(pred_probs - threshold + 0.5)
max_size = min(len(true_labels), len(pred_labels))
true_labels = true_labels[: max_size]
pred_labels = pred_labels[: max_size]
pred_probs = pred_probs[: max_size]
p_1 = 0
p_5 = 0
p_8 = 0
p_10 = 0
p_15 = 0
if pred_probs is not None:
if not is_multilabel:
normalised_labels = normalise_labels(true_labels, len(pred_probs[0]))
auc_score = roc_auc(normalised_labels, pred_probs, average=average)
accuracy = accuracy_score(true_labels, pred_labels)
precision = precision_score(true_labels, pred_labels, average=average)
recall = recall_score(true_labels, pred_labels, average=average)
f1 = f1_score(true_labels, pred_labels, average=average)
else:
if average == "macro":
accuracy = macro_accuracy(true_labels, pred_labels) # categorical accuracy
precision, recall, f1 = macro_f1(true_labels, pred_labels)
p_ks = precision_at_k(true_labels, pred_probs, [1, 5, 8, 10, 15])
p_1 = p_ks[0]
p_5 = p_ks[1]
p_8 = p_ks[2]
p_10 = p_ks[3]
p_15 = p_ks[4]
else:
accuracy = micro_accuracy(true_labels, pred_labels)
precision, recall, f1 = micro_f1(true_labels, pred_labels)
auc_score = roc_auc(true_labels, pred_probs, average)
labelwise_f1 = f1_score(true_labels, pred_labels, average=None)
labelwise_f1 = np.array2string(labelwise_f1, separator=',')
else:
auc_score = -1
output = {"{}_precision".format(average): precision, "{}_recall".format(average): recall,
"{}_f1".format(average): f1, "{}_accuracy".format(average): accuracy,
"{}_auc".format(average): auc_score, "{}_P@1".format(average): p_1, "{}_P@5".format(average): p_5,
"{}_P@8".format(average): p_8, "{}_P@10".format(average): p_10, "{}_P@15".format(average): p_15,
"labelwise_f1": labelwise_f1
}
return output