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import_data.py
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import_data.py
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'''
This provide the dimension/data/mask to train/test the network.
Once must construct a function similar to "import_dataset_SYNTHETIC":
- DATA FORMAT:
> data: covariates with x_dim dimension.
> label: 0: censoring, 1 ~ K: K competing(single) risk(s)
> time: time-to-event or time-to-censoring
- Based on the data, creat mask1 and mask2 that are required to calculate loss functions.
'''
import numpy as np
import pandas as pd
import random
##### DEFINE USER-FUNCTIONS #####
def f_get_Normalization(X, norm_mode):
num_Patient, num_Feature = np.shape(X)
if norm_mode == 'standard': #zero mean unit variance
for j in range(num_Feature):
if np.std(X[:,j]) != 0:
X[:,j] = (X[:,j] - np.mean(X[:, j]))/np.std(X[:,j])
else:
X[:,j] = (X[:,j] - np.mean(X[:, j]))
elif norm_mode == 'normal': #min-max normalization
for j in range(num_Feature):
X[:,j] = (X[:,j] - np.min(X[:,j]))/(np.max(X[:,j]) - np.min(X[:,j]))
else:
print("INPUT MODE ERROR!")
return X
### MASK FUNCTIONS
'''
fc_mask2 : To calculate LOSS_1 (log-likelihood loss)
fc_mask3 : To calculate LOSS_2 (ranking loss)
'''
def f_get_fc_mask2(time, label, num_Event, num_Category):
'''
mask4 is required to get the log-likelihood loss
mask4 size is [N, num_Event, num_Category]
if not censored : one element = 1 (0 elsewhere)
if censored : fill elements with 1 after the censoring time (for all events)
'''
mask = np.zeros([np.shape(time)[0], num_Event, num_Category]) # for the first loss function
for i in range(np.shape(time)[0]):
if label[i,0] != 0: #not censored
mask[i,int(label[i,0]-1),int(time[i,0])] = 1
else: #label[i,2]==0: censored
mask[i,:,int(time[i,0]+1):] = 1 #fill 1 until from the censoring time (to get 1 - \sum F)
return mask
def f_get_fc_mask3(time, meas_time, num_Category):
'''
mask5 is required calculate the ranking loss (for pair-wise comparision)
mask5 size is [N, num_Category].
- For longitudinal measurements:
1's from the last measurement to the event time (exclusive and inclusive, respectively)
denom is not needed since comparing is done over the same denom
- For single measurement:
1's from start to the event time(inclusive)
'''
mask = np.zeros([np.shape(time)[0], num_Category]) # for the first loss function
if np.shape(meas_time): #lonogitudinal measurements
for i in range(np.shape(time)[0]):
t1 = int(meas_time[i, 0]) # last measurement time
t2 = int(time[i, 0]) # censoring/event time
mask[i,(t1+1):(t2+1)] = 1 #this excludes the last measurement time and includes the event time
else: #single measurement
for i in range(np.shape(time)[0]):
t = int(time[i, 0]) # censoring/event time
mask[i,:(t+1)] = 1 #this excludes the last measurement time and includes the event time
return mask
def import_dataset_SYNTHETIC(norm_mode='standard'):
in_filename = './sample data/SYNTHETIC/synthetic_comprisk.csv'
df = pd.read_csv(in_filename, sep=',')
label = np.asarray(df[['label']])
time = np.asarray(df[['time']])
data = np.asarray(df.iloc[:,4:])
data = f_get_Normalization(data, norm_mode)
num_Category = int(np.max(time) * 1.2) #to have enough time-horizon
num_Event = int(len(np.unique(label)) - 1) #only count the number of events (do not count censoring as an event)
x_dim = np.shape(data)[1]
mask1 = f_get_fc_mask2(time, label, num_Event, num_Category)
mask2 = f_get_fc_mask3(time, -1, num_Category)
DIM = (x_dim)
DATA = (data, time, label)
MASK = (mask1, mask2)
return DIM, DATA, MASK
def import_dataset_METABRIC(norm_mode='standard'):
in_filename1 = './sample data/METABRIC/cleaned_features_final.csv'
in_filename2 = './sample data/METABRIC/label.csv'
df1 = pd.read_csv(in_filename1, sep =',')
df2 = pd.read_csv(in_filename2, sep =',')
data = np.asarray(df1)
data = f_get_Normalization(data, norm_mode)
time = np.asarray(df2[['event_time']])
# time = np.round(time/12.) #unit time = month
label = np.asarray(df2[['label']])
num_Category = int(np.max(time) * 1.2) #to have enough time-horizon
num_Event = int(len(np.unique(label)) - 1) #only count the number of events (do not count censoring as an event)
x_dim = np.shape(data)[1]
mask1 = f_get_fc_mask2(time, label, num_Event, num_Category)
mask2 = f_get_fc_mask3(time, -1, num_Category)
DIM = (x_dim)
DATA = (data, time, label)
MASK = (mask1, mask2)
return DIM, DATA, MASK