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multi_view_representation_learning/pca_rsfmri.py

+
+"""""""""""""""
+       PCA  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
+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=2
+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 pca
+        pca = PCA(n_components=dim)
+
+        # fit PCA on training set
+        pca.fit(normalized_train_data)
+
+        # Apply the mapping (transform) to both the training set and the test set
+        X_train_pca = pca.transform(normalized_train_data)
+        X_test_pca = pca.transform(normalized_test_data)
+        print("Original shape:   ", normalized_train_data.shape)
+        print("Transformed shape:", X_train_pca.shape)
+
+        # Reconstruction of training data
+        print("Reconstruction of training data... ")
+        X_train_new = pca.inverse_transform(X_train_pca)
+        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 = pca.inverse_transform(X_test_pca)
+        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/pca/{}/{}/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 = pca.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_pca_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_pca_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_pca_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_pca_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_pca.npy', np.array([mse_test]))
+np.save( 'rmse_test_mean_pca.npy', np.array([rmse_test]))
+np.save( 'std_mse_mean_pca.npy', np.array([std_mse_test]))
+np.save( 'std_rmse_mean_pca.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_pca_tfmri.pdf')
+plt.savefig('reconstruction_error_mse_pca_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_pca_tfmri.pdf')
+plt.savefig('reconstruction_error_rmse_pca_tfmri.png')
+plt.close()
+