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physionet.py
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physionet.py
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###########################
# Latent ODEs for Irregularly-Sampled Time Series
# Authors: Yulia Rubanova and Ricky Chen
###########################
import os
import matplotlib
if os.path.exists("/Users/yulia"):
matplotlib.use('TkAgg')
else:
matplotlib.use('Agg')
import matplotlib.pyplot
import matplotlib.pyplot as plt
import lib.utils as utils
import numpy as np
import tarfile
import torch
from torch.utils.data import DataLoader
from torchvision.datasets.utils import download_url
from lib.utils import get_device
# Adapted from: https://github.com/rtqichen/time-series-datasets
# get minimum and maximum for each feature across the whole dataset
def get_data_min_max(records):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
data_min, data_max = None, None
inf = torch.Tensor([float("Inf")])[0].to(device)
for b, (record_id, tt, vals, mask, labels) in enumerate(records):
n_features = vals.size(-1)
batch_min = []
batch_max = []
for i in range(n_features):
non_missing_vals = vals[:,i][mask[:,i] == 1]
if len(non_missing_vals) == 0:
batch_min.append(inf)
batch_max.append(-inf)
else:
batch_min.append(torch.min(non_missing_vals))
batch_max.append(torch.max(non_missing_vals))
batch_min = torch.stack(batch_min)
batch_max = torch.stack(batch_max)
if (data_min is None) and (data_max is None):
data_min = batch_min
data_max = batch_max
else:
data_min = torch.min(data_min, batch_min)
data_max = torch.max(data_max, batch_max)
return data_min, data_max
class PhysioNet(object):
urls = [
'https://physionet.org/files/challenge-2012/1.0.0/set-a.tar.gz?download',
'https://physionet.org/files/challenge-2012/1.0.0/set-b.tar.gz?download',
]
outcome_urls = ['https://physionet.org/files/challenge-2012/1.0.0/Outcomes-a.txt']
params = [
'Age', 'Gender', 'Height', 'ICUType', 'Weight', 'Albumin', 'ALP', 'ALT', 'AST', 'Bilirubin', 'BUN',
'Cholesterol', 'Creatinine', 'DiasABP', 'FiO2', 'GCS', 'Glucose', 'HCO3', 'HCT', 'HR', 'K', 'Lactate', 'Mg',
'MAP', 'MechVent', 'Na', 'NIDiasABP', 'NIMAP', 'NISysABP', 'PaCO2', 'PaO2', 'pH', 'Platelets', 'RespRate',
'SaO2', 'SysABP', 'Temp', 'TroponinI', 'TroponinT', 'Urine', 'WBC'
]
params_dict = {k: i for i, k in enumerate(params)}
labels = [ "SAPS-I", "SOFA", "Length_of_stay", "Survival", "In-hospital_death" ]
labels_dict = {k: i for i, k in enumerate(labels)}
def __init__(self, root, train=True, download=False,
quantization = 0.1, n_samples = None, device = torch.device("cpu")):
self.root = root
self.train = train
self.reduce = "average"
self.quantization = quantization
if download:
self.download()
if not self._check_exists():
raise RuntimeError('Dataset not found. You can use download=True to download it')
if self.train:
data_file = self.training_file
else:
data_file = self.test_file
if device == torch.device("cpu"):
self.data = torch.load(os.path.join(self.processed_folder, data_file), map_location='cpu')
self.labels = torch.load(os.path.join(self.processed_folder, self.label_file), map_location='cpu')
else:
self.data = torch.load(os.path.join(self.processed_folder, data_file))
self.labels = torch.load(os.path.join(self.processed_folder, self.label_file))
if n_samples is not None:
self.data = self.data[:n_samples]
self.labels = self.labels[:n_samples]
def download(self):
if self._check_exists():
return
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
os.makedirs(self.raw_folder, exist_ok=True)
os.makedirs(self.processed_folder, exist_ok=True)
# Download outcome data
for url in self.outcome_urls:
filename = url.rpartition('/')[2]
download_url(url, self.raw_folder, filename, None)
txtfile = os.path.join(self.raw_folder, filename)
with open(txtfile) as f:
lines = f.readlines()
outcomes = {}
for l in lines[1:]:
l = l.rstrip().split(',')
record_id, labels = l[0], np.array(l[1:]).astype(float)
outcomes[record_id] = torch.Tensor(labels).to(self.device)
torch.save(
labels,
os.path.join(self.processed_folder, filename.split('.')[0] + '.pt')
)
for url in self.urls:
filename = url.rpartition('/')[2]
download_url(url, self.raw_folder, filename, None)
tar = tarfile.open(os.path.join(self.raw_folder, filename), "r:gz")
tar.extractall(self.raw_folder)
tar.close()
print('Processing {}...'.format(filename))
dirname = os.path.join(self.raw_folder, filename.split('.')[0])
patients = []
total = 0
for txtfile in os.listdir(dirname):
record_id = txtfile.split('.')[0]
with open(os.path.join(dirname, txtfile)) as f:
lines = f.readlines()
prev_time = 0
tt = [0.]
vals = [torch.zeros(len(self.params)).to(self.device)]
mask = [torch.zeros(len(self.params)).to(self.device)]
nobs = [torch.zeros(len(self.params))]
for l in lines[1:]:
total += 1
time, param, val = l.split(',')
# Time in hours
time = float(time.split(':')[0]) + float(time.split(':')[1]) / 60.
# round up the time stamps (up to 6 min by default)
# used for speed -- we actually don't need to quantize it in Latent ODE
time = round(time / self.quantization) * self.quantization
if time != prev_time:
tt.append(time)
vals.append(torch.zeros(len(self.params)).to(self.device))
mask.append(torch.zeros(len(self.params)).to(self.device))
nobs.append(torch.zeros(len(self.params)).to(self.device))
prev_time = time
if param in self.params_dict:
#vals[-1][self.params_dict[param]] = float(val)
n_observations = nobs[-1][self.params_dict[param]]
if self.reduce == 'average' and n_observations > 0:
prev_val = vals[-1][self.params_dict[param]]
new_val = (prev_val * n_observations + float(val)) / (n_observations + 1)
vals[-1][self.params_dict[param]] = new_val
else:
vals[-1][self.params_dict[param]] = float(val)
mask[-1][self.params_dict[param]] = 1
nobs[-1][self.params_dict[param]] += 1
else:
assert param == 'RecordID', 'Read unexpected param {}'.format(param)
tt = torch.tensor(tt).to(self.device)
vals = torch.stack(vals)
mask = torch.stack(mask)
labels = None
if record_id in outcomes:
# Only training set has labels
labels = outcomes[record_id]
# Out of 5 label types provided for Physionet, take only the last one -- mortality
labels = labels[4]
patients.append((record_id, tt, vals, mask, labels))
torch.save(
patients,
os.path.join(self.processed_folder,
filename.split('.')[0] + "_" + str(self.quantization) + '.pt')
)
print('Done!')
def _check_exists(self):
for url in self.urls:
filename = url.rpartition('/')[2]
if not os.path.exists(
os.path.join(self.processed_folder,
filename.split('.')[0] + "_" + str(self.quantization) + '.pt')
):
return False
return True
@property
def raw_folder(self):
return os.path.join(self.root, self.__class__.__name__, 'raw')
@property
def processed_folder(self):
return os.path.join(self.root, self.__class__.__name__, 'processed')
@property
def training_file(self):
return 'set-a_{}.pt'.format(self.quantization)
@property
def test_file(self):
return 'set-b_{}.pt'.format(self.quantization)
@property
def label_file(self):
return 'Outcomes-a.pt'
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
def get_label(self, record_id):
return self.labels[record_id]
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Split: {}\n'.format('train' if self.train is True else 'test')
fmt_str += ' Root Location: {}\n'.format(self.root)
fmt_str += ' Quantization: {}\n'.format(self.quantization)
fmt_str += ' Reduce: {}\n'.format(self.reduce)
return fmt_str
def visualize(self, timesteps, data, mask, plot_name):
width = 15
height = 15
non_zero_attributes = (torch.sum(mask,0) > 2).numpy()
non_zero_idx = [i for i in range(len(non_zero_attributes)) if non_zero_attributes[i] == 1.]
n_non_zero = sum(non_zero_attributes)
mask = mask[:, non_zero_idx]
data = data[:, non_zero_idx]
params_non_zero = [self.params[i] for i in non_zero_idx]
params_dict = {k: i for i, k in enumerate(params_non_zero)}
n_col = 3
n_row = n_non_zero // n_col + (n_non_zero % n_col > 0)
fig, ax_list = plt.subplots(n_row, n_col, figsize=(width, height), facecolor='white')
#for i in range(len(self.params)):
for i in range(n_non_zero):
param = params_non_zero[i]
param_id = params_dict[param]
tp_mask = mask[:,param_id].long()
tp_cur_param = timesteps[tp_mask == 1.]
data_cur_param = data[tp_mask == 1., param_id]
ax_list[i // n_col, i % n_col].plot(tp_cur_param.numpy(), data_cur_param.numpy(), marker='o')
ax_list[i // n_col, i % n_col].set_title(param)
fig.tight_layout()
fig.savefig(plot_name)
plt.close(fig)
def variable_time_collate_fn(batch, args, device = torch.device("cpu"), data_type = "train",
data_min = None, data_max = None):
"""
Expects a batch of time series data in the form of (record_id, tt, vals, mask, labels) where
- record_id is a patient id
- tt is a 1-dimensional tensor containing T time values of observations.
- vals is a (T, D) tensor containing observed values for D variables.
- mask is a (T, D) tensor containing 1 where values were observed and 0 otherwise.
- labels is a list of labels for the current patient, if labels are available. Otherwise None.
Returns:
combined_tt: The union of all time observations.
combined_vals: (M, T, D) tensor containing the observed values.
combined_mask: (M, T, D) tensor containing 1 where values were observed and 0 otherwise.
"""
D = batch[0][2].shape[1]
combined_tt, inverse_indices = torch.unique(torch.cat([ex[1] for ex in batch]), sorted=True, return_inverse=True)
combined_tt = combined_tt.to(device)
offset = 0
combined_vals = torch.zeros([len(batch), len(combined_tt), D]).to(device)
combined_mask = torch.zeros([len(batch), len(combined_tt), D]).to(device)
combined_labels = None
N_labels = 1
combined_labels = torch.zeros(len(batch), N_labels) + torch.tensor(float('nan'))
combined_labels = combined_labels.to(device = device)
for b, (record_id, tt, vals, mask, labels) in enumerate(batch):
tt = tt.to(device)
vals = vals.to(device)
mask = mask.to(device)
if labels is not None:
labels = labels.to(device)
indices = inverse_indices[offset:offset + len(tt)]
offset += len(tt)
combined_vals[b, indices] = vals
combined_mask[b, indices] = mask
if labels is not None:
combined_labels[b] = labels
combined_vals, _, _ = utils.normalize_masked_data(combined_vals, combined_mask,
att_min = data_min, att_max = data_max)
if torch.max(combined_tt) != 0.:
combined_tt = combined_tt / torch.max(combined_tt)
data_dict = {
"data": combined_vals,
"time_steps": combined_tt,
"mask": combined_mask,
"labels": combined_labels}
data_dict = utils.split_and_subsample_batch(data_dict, args, data_type = data_type)
return data_dict
if __name__ == '__main__':
torch.manual_seed(1991)
dataset = PhysioNet('data/physionet', train=False, download=True)
dataloader = DataLoader(dataset, batch_size=10, shuffle=True, collate_fn=variable_time_collate_fn)
print(dataloader.__iter__().next())