eval_acc.py

import pandas as  pd
import nibabel as nb
import numpy as np
import matplotlib.pyplot as plt
import os
import glob
from sklearn.ensemble import RandomForestClassifier
from sktree import ObliqueRandomForestClassifier, PatchObliqueRandomForestClassifier
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import random
import pickle
import scipy.stats as ss

####################################################
df = pd.read_excel('/cis/home/jdey4/data_MRI/subjects_age_sex_data_MRI.xlsx')
#df.head()

#####################################################
df_quality = pd.read_csv('/cis/home/jdey4/data_MRI/QC_catreport.csv')
#df_quality.head()

####################################################
path = '/cis/home/jdey4/data_MRI/'
subjects = os.listdir(path)
X = []
y = []
file_no = 0
count = 0
count_ = 0
IDs = set(df['ID'])
quality_ID = set(df_quality['sub'])
for subject in tqdm(subjects):
    if subject in IDs and subject in quality_ID:
        #print(df[df['ID']==subject]['Sex'])
        IQR = list(df_quality[df_quality['sub']==subject]['Weighted average (IQR)'])[0]
        count += 1
        #print(IQR)
        if IQR is np.nan:
            continue
            
        if IQR[-1] == '%':
            continue
        
        if float(IQR) < 60:
            continue

        count_ += 1
        #print(count, count_)
        gender = list(df[df['ID']==subject]['Sex'])
        sex = int(gender[0]=='FEMALE')
        
        
        current_file = os.path.join(path, subject)
        file_count = 0
        files = glob.glob(current_file+'/mri/*')

        for file in files:
            if 'mwp1' in file:
                try:
                    img = nb.load(file).get_fdata()
                    file_count +=1 
                except:
                    break
                
        for file in files:
            if 'mwp2' in file:
                try:
                    img = np.concatenate((img, nb.load(file).get_fdata()),axis=0)
                    file_count +=1 
                except:
                    break
                

        '''if len(tmp)<2:
            print(subject, ' has less files')'''
           
        if file_count==2:
            X.append(img.reshape(1,-1))
            y.append(sex)

    
X = np.concatenate(X,axis=0)

################################################################
idx = list(range(len(y)))

np.random.seed(0)
np.random.shuffle(idx)
train_samples = int(len(y)*0.8)
test_samples = len(y) - train_samples
train_ids = idx[:train_samples]

predicted_proba_ = []
total_models = 500
for ii in tqdm(range(total_models)):
    with open('morf_models/model'+str(ii)+'_gray.pickle','rb') as f:
        morf = pickle.load(f)
        predicted_proba_.append(
            morf.predict_proba(x_test[:,:113*137*113])
        )
        del morf

    with open('morf_models/model'+str(ii)+'_white.pickle','rb') as f:
        morf = pickle.load(f)
        predicted_proba_.append(
            morf.predict_proba(x_test[:,113*137*113:])
        )
        del morf

predicted_proba = np.mean(predicted_proba_,axis=0)
predicted_label = np.argmax(predicted_proba,axis=1)
print('MORF accuracy', np.mean(predicted_label==y_test))