Spaces:
Runtime error
Runtime error
File size: 9,533 Bytes
9b0ac82 1338ce4 9b0ac82 1338ce4 9b0ac82 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 |
#%%
import pandas as pd
import numpy as np
import toml
import datetime
import torch
import random
import ast
import os
import glob
import nibabel as nib
import json
from tqdm import tqdm
#from .MRI_load import load_mris
from collections import defaultdict
from time import time
from copy import deepcopy
from icecream import ic
#%%
value_mapping = {
'his_SEX': {'female': 0, 'male': 1},
'his_HISPANIC': {'no': 0, 'yes': 1},
'his_NACCNIHR': {'whi': 0, 'blk': 1, 'asi': 2, 'ind': 3, 'haw': 4, 'mul': 5},
}
class CSVDataset:
def __init__(self, dat_file, cnf_file, img_mode=0, arch=None, transforms=None, stripped=None):
''' ... '''
# load data csv
if isinstance(dat_file, str):
print(dat_file)
df = pd.read_csv(dat_file)
else:
df = dat_file
# load configuration file
self.cnf = toml.load(cnf_file)
if 'ID' in df.columns:
self.ids = list(df['ID'])
df.reset_index(drop=True, inplace=True)
# check feature availability in data file
print('Out of {} features in configuration file, '.format(len(self.cnf['feature'])), end='')
tmp = [fea for fea in self.cnf['feature'] if fea not in df.columns]
print('{} are unavailable in data file.'.format(tmp))
# check label availability in data file
print('Out of {} labels in configuration file, '.format(len(self.cnf['label'])), end='')
tmp = [lbl for lbl in self.cnf['label'] if lbl not in df.columns]
print('{} are unavailable in data file.'.format(len(tmp)))
self.cnf['feature'] = {k:v for k,v in self.cnf['feature'].items() if k in df.columns}
self.cnf['label'] = {k:v for k,v in self.cnf['label'].items() if k in df.columns}
# get feature and label names
features = list(self.cnf['feature'].keys())
labels = list(self.cnf['label'].keys())
# omit features that are not present in dat_file
features = [fea for fea in features if fea in df.columns]
shapes = []
# mri
error_features = []
img_fea_to_pop = []
total = 0
total_cohorts = {}
for fea in self.cnf['feature'].keys():
# print('fea: ', fea)
if self.cnf['feature'][fea]['type'] == 'imaging':
print('imaging..')
if img_mode == -1:
# to train non imaging model
img_fea_to_pop.append(fea)
elif img_mode == 0:
print("fea: ", fea)
filenames = df[fea].dropna().to_list()
with open('notexists.txt', 'a') as f:
for fn in filenames:
if fn is None:
continue
if not os.path.exists(fn):
f.write(fn+'\n')
mri_name = fn.split('/')[-1]
total += 1
# load MRI embeddings
elif img_mode == 1:
print("fea: ", fea)
filenames = df[fea].to_list()
# print(len(filenames))
if len(df[~df[fea].isna()]) == 0:
continue
# print(fea)
npy = []
n = 0
for fn in tqdm(filenames):
try:
# print('fn: ', fn)
data = np.load(fn, mmap_mode='r')
if np.isnan(data).any():
npy.append(None)
continue
if data.shape[-1] == 9 or data.shape[-1] == 10:
npy.append(None)
continue
shapes.append(data.shape)
if len(data.shape) < 5:
data = np.expand_dims(data, axis=0)
npy.append(data)
self.cnf['feature'][fea]['shape'] = data.shape
self.cnf['feature'][fea]['img_shape'] = data.shape
# print(data.shape)
n += 1
except:
npy.append(None)
# print(self.cnf['feature'][fea]['shape'])
print(f"{n} MRI embeddings found with shape {self.cnf['feature'][fea]['shape']}")
if n == 0 or len(self.cnf['feature'][fea]['shape']) == 1:
error_features.append(fea)
total += n
print(len(df), len(npy))
df[fea] = npy
# return
elif img_mode == 2:
# load MRIs and use swinunetr model to get the embeddings
print('img_mode is 2')
#embedding_dict = load_mris.get_emb('filename', df, arch=arch, transforms=transforms, stripped=stripped)
mri_embeddings = []
for index, row in df.iterrows():
filename = row['filename']
print(filename)
if filename in embedding_dict:
emb = embedding_dict[filename].flatten()
mri_embeddings.append(emb)
self.cnf['feature'][fea]['shape'] = emb.shape
self.cnf['feature'][fea]['img_shape'] = emb.shape
else:
mri_embeddings.append(None)
print(avail)
df[fea] = mri_embeddings
if 'img_shape' in self.cnf['feature'][fea]:
print(self.cnf['feature'][fea]['img_shape'])
print(f"Total mri embeddings found: {total}")
for fea in img_fea_to_pop:
self.cnf['feature'].pop(fea)
df = df.drop(img_fea_to_pop, axis=1)
features = [fea for fea in features if fea in df.columns]
labels = [lab for lab in labels if lab in df.columns]
# drop columns that are not present in configuration
df = df[features + labels]
# drop rows where ALL features are missing
df_fea = df[features]
df_fea = df_fea.dropna(how='all')
print('Out of {} samples, {} are dropped due to complete feature missing.'.format(len(df), len(df) - len(df_fea)))
df = df[df.index.isin(df_fea.index)]
df.reset_index(drop=True, inplace=True)
# drop rows where ALL labels are missing
df_lbl = df[labels]
df_lbl = df_lbl.dropna(how='all')
print('Out of {} samples, {} are dropped due to complete label missing.'.format(len(df), len(df) - len(df_lbl)))
df = df[df.index.isin(df_lbl.index)]
df.reset_index(drop=True, inplace=True)
print(set(shapes))
print("Error features ")
print(error_features)
# some of the values need to be mapped to the desirable domain
for name in features + labels:
if name in value_mapping:
col = df[name].to_list()
try:
col = [value_mapping[name][s] if not pd.isnull(s) else None for s in col]
except KeyError as err:
print(err, name)
exit()
df[name] = col
# print(features)
df = df.dropna(axis=1, how='all')
features = [fea for fea in features if fea in df.columns]
labels = [lab for lab in labels if lab in df.columns]
# change np.nan to None
df.replace({np.nan: None}, inplace=True)
# done for df
self.df = df
# construct dictionaries for features and labels
self.features, self.labels = [], []
keys = df.columns.values.tolist()
for i in range(len(df)):
vals = df.iloc[i].to_list()
self.features.append(dict(zip(keys[:len(features)], vals[:len(features)])))
self.labels.append(dict(zip(keys[len(features):], vals[len(features):])))
# test: remove if None
for i in range(len(self.features)):
for k, v in list(self.features[i].items()):
if v is None:
self.features[i].pop(k)
# getting label fractions
self.label_fractions = {}
for label in labels:
# self.label_fractions[label] = dict(self.df[label].value_counts() / len(self.df[~self.df[label].isna()]))
self.label_fractions[label] = dict(self.df[label].value_counts() / len(self.df))
def __len__(self):
''' ... '''
return len(self.df)
def __getitem__(self, idx):
''' ... '''
return self.features[idx], self.labels[idx]
@property
def feature_modalities(self):
''' ... '''
return self.cnf['feature']
@property
def label_modalities(self):
''' ... '''
return self.cnf['label']
|