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ica_concat_fmri.py
Akrem Sellami authored
ica_concat_fmri.py 7.74 KiB
"""""""""""""""
ica code
"""""""""""""""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# from keras.models import load_model
# import numpy as np
# import keras
# from keras.layers import Input, Dense, concatenate
# from keras.models import Model
# from keras import backend as k
# from keras import optimizers
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
plt.switch_backend('agg')
import sys as os
import os
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA, FastICA
from sklearn.pipeline import Pipeline
from sklearn.metrics import mean_squared_error
from math import sqrt
# loading multimodal fMRI data
def load_data(sub, view):
# Import Task fMRI data
if view == 1:
view_tfmri = np.load(os.path.join(path, "tfmri/{}/gii_matrix_fsaverage5.npy".format(sub)))
return view_tfmri
# Import Resting-State fMRI data
if view == 2:
view_rsfmri = np.load(os.path.join(path, "rsfmri/{}/correlation_matrix_fsaverage5.npy".format(sub)))
return view_rsfmri
# Import concatenated fMRI data
if view ==3:
view_rsfmri = np.load(os.path.join(path, "rsfmri/{}/correlation_matrix_fsaverage5.npy".format(sub)))
view_tfmri = np.load(os.path.join(path, "tfmri/{}/gii_matrix_fsaverage5.npy".format(sub)))
fmri_data =np.concatenate([view_tfmri, view_rsfmri], axis=1)
return fmri_data
# normalization to range [-1, 1]
def normalization(data):
normalized_data= 2* (data - np.min(data)) / (np.max(data) - np.min(data)) - 1
return normalized_data
# Path
path = "/home/asellami/data_fsaverage5"
missing_data=[36]
index_subjects=np.arange(3,43)
index_subjects = np.delete(index_subjects, np.argwhere(index_subjects == missing_data))
view=3
if view == 1:
v = 'tfmri'
elif view == 2:
v = 'rsfmri'
else:
v = 'concat'
# MSE
mse_train = []
mse_test = []
# RMSE
rmse_train = []
rmse_test = []
#
# Standard deviation MSE
std_mse_train = []
std_mse_test = []
# Standard deviation RMSE
std_rmse_train = []
std_rmse_test = []
ar = np.arange(2,101)
#dimensions = ar[ar%2==0]
dimensions=[2, 6, 10, 16, 20, 26, 30, 36, 40, 46, 50, 56, 60, 66, 70, 76, 80, 86, 90, 96, 100]
for dim in dimensions:
# Cross Validation
kf = KFold(n_splits=10)
print(kf.get_n_splits(index_subjects))
print("number of splits:", kf)
print("number of features:", dimensions)
cvscores_mse_test = []
cvscores_rmse_test = []
cvscores_mse_train = []
cvscores_rmse_train = []
fold=0
for train_index, test_index in kf.split(index_subjects):
fold+=1
print(f"Fold #{fold}")
print("TRAIN:", index_subjects[train_index], "TEST:", index_subjects[test_index])
# load training and testing data
print('Load training data... (view {})'.format(view))
train_data = np.concatenate([load_data(sub, view) for sub in index_subjects[train_index]])
print("Shape of the training data:", train_data.shape)
print('Load testdata... (view {})'.format(view))
test_data = np.concatenate([load_data(sub, view) for sub in index_subjects[test_index]])
print("Shape of the test data:", test_data.shape)
# Data normalization to range [-1, 1]
# print("Data normalization to range [-1, 1]")
scaler = MinMaxScaler()
normalized_train_data = scaler.fit_transform(train_data)
normalized_test_data = scaler.fit_transform(test_data)
# intialize ica
ica = FastICA(n_components=dim)
# fit ica on training set
ica.fit(normalized_train_data)
# Apply the mapping (transform) to both the training set and the test set
X_train_ica = ica.transform(normalized_train_data)
X_test_ica = ica.transform(normalized_test_data)
print("Original shape: ", normalized_train_data.shape)
print("Transformed shape:", X_train_ica.shape)
# Reconstruction of training data
print("Reconstruction of training data... ")
X_train_new = ica.inverse_transform(X_train_ica)
print("Reconstructed matrix shape:", X_train_new.shape)
mse = mean_squared_error(normalized_train_data, X_train_new)
print('Reconstruction MSE : ', mse)
cvscores_mse_train.append(mse)
rms = sqrt(mse)
print('Reconstruction RMSE : ', rms)
cvscores_rmse_train.append(rms)
# Reconstruction of test data
print("Reconstruction of test data... ")
X_test_new = ica.inverse_transform(X_test_ica)
print("Reconstructed matrix shape:", X_test_new.shape)
mse = mean_squared_error(normalized_test_data, X_test_new)
cvscores_mse_test.append(mse)
print('Reconstruction MSE : ', mse)
rms = sqrt(mse)
print('Reconstruction RMSE : ', rms)
cvscores_rmse_test.append(rms)
# Apply dimensionality reduction
directory = '../../../regression/ica/{}/{}/fold_{}/'.format(v, dim, fold)
if not os.path.exists(directory):
os.makedirs(directory)
for sub in index_subjects:
subject=load_data(sub, view)
normalized_subject = scaler.fit_transform(subject)
transformed_subject = ica.transform(normalized_subject)
file = directory + "X_{}.npy".format(sub)
np.save(file, transformed_subject)
print('Shape of Latent representation:', transformed_subject.shape)
print('Transpose of latent representation', transformed_subject.T.shape)
print("shape of vector mse train", np.array([cvscores_mse_train]).shape)
print(cvscores_mse_train)
np.save('cvscores_mse_train_ica_dim_{}.npy'.format(dim), np.array([cvscores_mse_train]))
print("shape of vector mse test", np.array([cvscores_mse_test]).shape)
print(cvscores_mse_test)
np.save( 'cvscores_mse_test_ica_dim_{}.npy'.format(dim), np.array([cvscores_mse_train]))
print("shape of vector rmse train", np.array([cvscores_rmse_train]).shape)
print(cvscores_rmse_train)
np.save( 'cvscores_mse_train_ica_dim_{}.npy'.format(dim), np.array([cvscores_rmse_train]))
print("shape of vector rmse test", np.array([cvscores_rmse_test]).shape)
print(cvscores_rmse_test)
np.save( 'rmse_test_ica_dim_{}.npy'.format(dim), np.array([cvscores_rmse_test]))
print("%.2f%% (+/- %.5f%%)" % (np.mean(cvscores_mse_test), np.std(cvscores_mse_test)))
mse_train.append(np.mean(cvscores_mse_train))
mse_test.append(np.mean(cvscores_mse_test))
rmse_train.append(np.mean(cvscores_rmse_train))
rmse_test.append(np.mean(cvscores_rmse_test))
std_mse_train.append(np.std(cvscores_mse_train))
std_mse_test.append(np.std(cvscores_mse_test))
std_rmse_train.append(np.std(cvscores_rmse_train))
std_rmse_test.append(np.std(cvscores_rmse_test))
np.save( 'mse_test_mean_ica.npy', np.array([mse_test]))
np.save( 'rmse_test_mean_ica.npy', np.array([rmse_test]))
np.save( 'std_mse_mean_ica.npy', np.array([std_mse_test]))
np.save( 'std_rmse_mean_ica.npy', np.array([std_rmse_test]))
# plotting the mse train
# setting x and y axis range
#plt.xlim(1, 120)
plt.plot(dimensions, mse_train, label="mse_train")
plt.plot(dimensions, mse_test, label="mse_test")
plt.xlabel('Encoding dimension')
plt.ylabel('Reconstruction error (MSE)')
# showing legend
plt.legend()
plt.savefig('reconstruction_error_mse_ica_tfmri.pdf')
plt.savefig('reconstruction_error_mse_ica_tfmri.png')
plt.close()
# plotting the rmse train
# setting x and y axis range
#plt.xlim(1, 120)
plt.plot(dimensions, rmse_train, label="rmse_train")
plt.plot(dimensions, rmse_test, label="rmse_test")
plt.xlabel('Encoding dimension')
plt.ylabel('Reconstruction error (RMSE)')
# showing legend
plt.legend()
plt.savefig('reconstruction_error_rmse_ica_tfmri.pdf')
plt.savefig('reconstruction_error_rmse_ica_tfmri.png')
plt.close()