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datasets.py
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import pandas as pd
import numpy as np
from torchvision.transforms import Compose
from torch.utils.data import Dataset
import torch
from sklearn.preprocessing import StandardScaler
from tsaug import Resize
DATASETS = ['Mddrest','Abide', 'Ukbb', 'Jpmdd', 'Synth']
def get_dataset_class(dataset_name):
"""Return the dataset class with the given name."""
if dataset_name not in globals():
raise NotImplementedError("Dataset not found: {}".format(dataset_name))
return globals()[dataset_name]
def resize(x_, t):
x_ = np.expand_dims(x_,0)
x_ = Resize(size=t).augment(x_)
x_ = np.squeeze(x_,0)
return x_
class Abstract_Dataset(Dataset):
"""
An Abstract class for all Datasets
"""
def __init__(self):
return
def __read_data__(self):
return
def __getitem__(self, index):
return torch.tensor(self.tc_data[index].astype(np.float32)), torch.tensor(self.labels[index].astype(np.int64))
def __len__(self):
return self.total_subjects
class Ukbb(Abstract_Dataset):
def __init__(
self,
data_len,
pred_len,
data_path,
atlas,
mask_rois,
predict,
label,
ad,
task
):
self.seq_len = data_len - pred_len
self.pred_len = pred_len
self.atlas = atlas
self.mask_rois = mask_rois
self.predict = predict
self.data_info = pd.read_csv(data_path)
self.data_info = self.data_info [self.data_info.ID != 1641691]
self.total_subjects = len(self.data_info)
self.label = label
self.task = task
self.ntime = self.seq_len + self.pred_len
sample_file = self.data_info['tc_file'].iloc[0].replace('ATLAS', self.atlas)
self.nrois = np.load(sample_file).shape[1]
self.tc_data = np.zeros((self.total_subjects, self.ntime, self.nrois),dtype=np.float32)
self.cc_data = np.zeros((self.total_subjects, self.nrois, self.nrois),dtype=np.float32)
self.labels = np.zeros(self.total_subjects, dtype=np.int64)
self.__read_data__()
self.cc_mean = np.mean(self.cc_data, axis=0)
def __read_data__(self):
for i, sub_i in enumerate(range(self.total_subjects)):
tc_file = self.data_info['tc_file'].iloc[i].replace('ATLAS', self.atlas)
cc_file = self.data_info['cc_file'].iloc[i].replace('ATLAS', self.atlas)
tc_vals = np.load(tc_file)[:self.ntime,:]
self.tc_data[i] = tc_vals
cc = np.load(cc_file)
np.fill_diagonal(cc,0)
self.cc_data[i] = cc
self.labels[i] = self.data_info[self.label].iloc[i]
class Mddrest(Abstract_Dataset):
def __init__(
self,
df,
atlas,
ntime):
self.atlas = atlas
self.data_info = df
self.data_info = self.data_info[self.data_info['Ntime'] >= 180]
#bad_sites = ['S5', 'S8', 'S12', 'S14', 'S25', 'S18' ,'S24']
#self.data_info = self.data_info[~self.data_info.Site.isin(bad_sites)]
self.total_subjects = len(self.data_info)
self.label = 'Diagnosis'
self.ntime = ntime
sample_file = self.data_info['tc_atlas'].iloc[0].replace('ATLAS', self.atlas)
self.nrois = np.load(sample_file).shape[1]
self.tc_data = np.zeros((self.total_subjects, self.ntime, self.nrois),dtype=np.float32)
self.labels = np.zeros(self.total_subjects, dtype=np.int64)
self.__read_data__()
def __read_data__(self):
for i, sub_i in enumerate(range(self.total_subjects)):
tc_file = self.data_info['tc_atlas'].iloc[i].replace('ATLAS', self.atlas)
tc_vals = np.load(tc_file)
tc_vals = resize(tc_vals,self.ntime)
self.tc_data[i] = tc_vals
self.labels[i] = self.data_info[self.label].iloc[i]
class Synth(Abstract_Dataset):
def __init__(
self,
df,
atlas,
ntime):
self.atlas = atlas
self.data_info = df
self.data_info = self.data_info[self.data_info['Diagnosis'] == 0]
self.total_subjects = len(self.data_info)
self.ntime = ntime
sample_file = self.data_info['tc_atlas'].iloc[0].replace('ATLAS', self.atlas)
self.nrois = np.load(sample_file).shape[1]
self.tc_data = np.zeros((self.total_subjects, self.ntime, self.nrois),dtype=np.float32)
self.labels = np.zeros(self.total_subjects, dtype=np.int64)
self.__read_data__()
def __read_data__(self):
for i, sub_i in enumerate(range(self.total_subjects)):
label = np.random.choice([0,1])
tc_file = self.data_info['tc_atlas'].iloc[i].replace('ATLAS', self.atlas)
tc_vals = np.load(tc_file)
tc_vals = resize(tc_vals,self.ntime)
if label == 1:
#tc_vals[:,100] = np.random.normal(0, np.std(tc_vals[:,100]), (self.ntime))
tc_vals[:,100] = (1-0.5) * tc_vals[:,10] + 0.5*tc_vals[:,20]
else:
tc_vals[:,100] = (1-0.5) * tc_vals[:,50] + 0.5*tc_vals[:,60]
self.tc_data[i] = tc_vals
self.labels[i] = label
class Jpmdd(Abstract_Dataset):
def __init__(
self,
data_len,
pred_len,
data_path,
atlas,
mask_rois,
predict,
label,
ad,
task
):
self.data_len = data_len
self.seq_len = data_len - pred_len
self.pred_len = pred_len
self.atlas = atlas
self.mask_rois = mask_rois
self.predict = predict
self.data_info = pd.read_csv(data_path)
if ad:
self.data_info = self.data_info[self.data_info[label] == 0]
#bad_sites = ['S5', 'S8', 'S12', 'S14', 'S25', 'S18' ,'S24']
#self.data_info = self.data_info[~self.data_info.Site.isin(bad_sites)]
self.total_subjects = len(self.data_info)
self.label = label
self.task = task
self.ntime = self.seq_len + self.pred_len
sample_file = self.data_info['tc_file'].iloc[0].replace('ATLAS', self.atlas)
self.nrois = np.load(sample_file).shape[1]
self.tc_data = np.zeros((self.total_subjects, self.ntime, self.nrois),dtype=np.float32)
self.cc_data = np.zeros((self.total_subjects, self.nrois, self.nrois),dtype=np.float32)
self.labels = np.zeros(self.total_subjects, dtype=np.int64)
self.__read_data__()
self.cc_mean = np.mean(self.cc_data, axis=0)
def __read_data__(self):
for i, sub_i in enumerate(range(self.total_subjects)):
tc_file = self.data_info['tc_file'].iloc[i].replace('ATLAS', self.atlas)
cc_file = self.data_info['cc_file'].iloc[i].replace('ATLAS', self.atlas)
tc_vals = np.load(tc_file)#[:self.ntime,:]
tc_vals = resize(tc_vals,self.data_len)
self.tc_data[i] = tc_vals
self.cc_data[i] = np.load(cc_file) # - np.identity(self.nrois)
self.labels[i] = self.data_info[self.label].iloc[i]
class Abide(Abstract_Dataset):
def __init__(
self,
df,
atlas,
ntime):
self.atlas = atlas
self.data_info = df
self.data_info = self.data_info[self.data_info['Ntime'] >= 150]
self.total_subjects = len(self.data_info)
self.label = 'Diagnosis'
self.ntime = ntime
sample_file = self.data_info['tc_file'].iloc[0].replace('ATLAS', self.atlas).replace('timecourse.csv', 'tc.npy')
self.nrois = np.load(sample_file).shape[1]
self.tc_data = np.zeros((self.total_subjects, self.ntime, self.nrois),dtype=np.float32)
self.labels = np.zeros(self.total_subjects, dtype=np.int64)
self.__read_data__()
def __read_data__(self):
for i, sub_i in enumerate(range(self.total_subjects)):
tc_file = self.data_info['tc_file'].iloc[i].replace('ATLAS', self.atlas).replace('timecourse.csv', 'tc.npy')
tc_vals = np.load(tc_file)
tc_vals = resize(tc_vals,self.ntime)
self.tc_data[i] = tc_vals
self.labels[i] = self.data_info[self.label].iloc[i]