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simulation_script.py
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simulation_script.py
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import numpy as np
import csv
import pandas as pd
########################################################### Helper functions################################################################
#In: The original raw data
#Out: The maximum and minimum for every variable
def getMinMaxValues(original_data):
max_values = []
min_values = []
for column in original_data:
max_values.append(original_data[column].max())
min_values.append(original_data[column].min())
return max_values,min_values
#In: An integer
#out: the number of decimal places of Integer
def num_after_point(x):
s = str(x)
if not '.' in s:
return 0
if(s[::-1].find('.') == 1):
return 0
if (s[::-1].find('.') == 5): ## for mean_gm
return 6
if (s[::-1].find('.') > 6): #for subtypes
return 3
return s[::-1].find('.')
#In: the simulated data
#Out: the simulated data after it has been clipped and matched for percision
def cleanUP(category,arr_value):
#####Index Code###
#value_iter - Variable
#0 - age_scan
#1 - gender
#2 - mean_gm
#3 - tiv
#4 - ADAS13
#5 - ADNI_MEM
#6 - ADNI_EF
#7 - BNTTOTAL
#8 - CLOCKSCOR
#9 - sub1
#10 -sub2
#11 -sub3
#12 -sub4
#13 -sub5
#14 -sub6
#15 -sub7
iterr = 0
max_values,min_values = getMinMaxValues(df_csv)
#iterate through each subject for one of the 9 variables
for subID in category:
value_iter = 0
#Iterate through each variables within one subject
for value in subID:
#clip using max and min
category[iterr][value_iter] = np.clip(category[iterr][value_iter], min_values[value_iter], max_values[value_iter], out=None)
#Add threshold for the gender. If greater than 0.5 make 1 and if less than 0.5 make 0.
if (value_iter == 1):
category[iterr][value_iter] = np.where(subID[value_iter]> 0.5,1,0)
else:
#match the percision for all variables to the original data
category[iterr][value_iter] = np.around(value,num_after_point(df_csv.loc[arr_value][value_iter]))
value_iter += 1
iterr += 1
return category
#In: the original raw data
# MCI values are changed to pMCI and sMCI
def splitMCI(df_csv):
mci_iter = 0
for index, row in df_csv.iterrows():
if (row["DX"] == "MCI" and row["conv_2_ad"] == 1):
df_csv.loc[mci_iter,"DX"] = "pMCI"
elif(row["DX"] == "MCI" and row["conv_2_ad"] == 0):
df_csv.loc[mci_iter,"DX"] = "sMCI"
mci_iter += 1
return df_csv
########################################################################################################################################
#load data
csv_data = pd.read_csv('adni_vcog_reduced_20180919.csv')
df_csv = pd.DataFrame(csv_data)
df_csv.head()
#change MCI under 'DX' to pMCI or sMCI
df_csv = splitMCI(df_csv.copy())
#Drop subjects who have missing values in any of these variables
df_csv.dropna(subset=['age_scan','gender','mean_gm','tiv', 'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR','sub1','sub2','sub3','sub4','sub5','sub6','sub7','DX','flag_status','conv_2_ad','dataset'],
inplace=True)
#Create a subset of the variables
df_csv = df_csv[['age_scan','gender','mean_gm','tiv', 'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR','sub1','sub2','sub3','sub4','sub5','sub6','sub7','DX','flag_status','conv_2_ad','dataset']]
mean_dataframe = pd.DataFrame()
gaussian_dataframe = pd.DataFrame()
covariance_dictionary = {}
correlation_dictionary = {}
#################################################################################################################
Clinical = ['CN','pMCI','sMCI','Dementia']
Subclass = ['Negative','Non-HPS+','HPS+']
check = 0
for diagnosis in Clinical:
for status in Subclass:
temp_df = pd.DataFrame()
temp_mean_df = pd.DataFrame()
temp_covariance = pd.DataFrame()
#These arrays used to fill the columns in csv file
conversion_Array = []
clinical_Array = []
subclass_Array = []
dataset_Array = []
for i,row in df_csv.iterrows():
DX = row['DX']
flag_status = row['flag_status']
conv_2_ad = row['conv_2_ad']
dataset = row['dataset']
if(DX == diagnosis and status == flag_status):
temp_df = temp_df.append(row)
clinical_Array.append(DX)
subclass_Array.append(status)
conversion_Array.append(conv_2_ad)
dataset_Array.append(dataset)
#Determine the mean
temp_mean_df = temp_df.mean()
temp_mean_df['DX'] = diagnosis
temp_mean_df['flag_status'] = status
mean_dataframe = mean_dataframe.append(temp_mean_df, ignore_index = True)
#####
category = diagnosis +','+status
temp_df = temp_df[['age_scan','gender','mean_gm','tiv', 'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR','sub1','sub2','sub3','sub4','sub5','sub6','sub7']]
#Determine the covariance
temp_covariance = np.cov(temp_df.T)
temp_covariance_df = pd.DataFrame(temp_covariance, columns = ['age_scan','gender','mean_gm','tiv', 'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR','sub1','sub2','sub3','sub4','sub5','sub6','sub7'] )
#temp_covariance_df.rename({1:'age_scan',2:'gender',3:'mean_gm',4:'tiv',5:'ADAS13',6:'ADNI_MEM',7:'ADNI_EF',8:'BNTTOTAL',9:'CLOCKSCOR',10:'sub1',11:'sub2',12:'sub3',13:'sub4',14:'sub5',15:'sub6',16:'sub7'}, axis='index')
covariance_dictionary[category] = temp_covariance_df
#Determine the Correlation Matrix
temp_correlation = np.corrcoef(temp_df.T)
temp_correlation_df = pd.DataFrame(temp_correlation, columns = ['age_scan','gender','mean_gm','tiv', 'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR','sub1','sub2','sub3','sub4','sub5','sub6','sub7'])
correlation_dictionary[category] = temp_correlation_df
#Determine the gaussian distribution
temp_gaussian = np.random.multivariate_normal(temp_df.mean(),temp_covariance_df,temp_df.shape[0])
arbitrary_value = 75 # an arbitray subject's values is used to match percision
temp_gaussian = cleanUP(temp_gaussian,arbitrary_value)
temp_gaussian_df = pd.DataFrame(temp_gaussian, columns = ['age_scan','gender','mean_gm','tiv', 'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR','sub1','sub2','sub3','sub4','sub5','sub6','sub7'])
temp_gaussian_df.insert(temp_gaussian_df.shape[1],'DX',clinical_Array)
temp_gaussian_df.insert(temp_gaussian_df.shape[1],'flag_status',subclass_Array)
temp_gaussian_df.insert(temp_gaussian_df.shape[1],'conv_2_ad',conversion_Array)
temp_gaussian_df.insert(temp_gaussian_df.shape[1],'dataset',dataset_Array)
gaussian_dataframe = gaussian_dataframe.append(temp_gaussian_df, ignore_index = True)
##Create CSV file for simulated data
gaussian_dataframe.to_csv('simulated_data_v2.csv', sep='\t', encoding='utf-8')