inner_train_fMRI_data = train_fMRI_data[inner_train_index, :] inner_test_fMRI_data = train_fMRI_data[inner_test_index, :] inner_train_sMRI_data = train_sMRI_data[inner_train_index, :] inner_test_sMRI_data = train_sMRI_data[inner_test_index, :] inner_train_targets = train_targets[inner_train_index] # correct inner training data #inner_train_data, LS_dict, v_pool = fit_transform_neuroCombat(inner_train_data, train_metadata.iloc[inner_train_index, :], 'Site', continuous_cols=[timepoint + '|total_panss']) inner_train_fMRI_data, model = fit_transform_neuroCombat( inner_train_fMRI_data, train_metadata.iloc[inner_train_index, :], 'Site', continuous_cols=[timepoint + '|total_panss']) # correct testing data inner_test_fMRI_data = apply_neuroCombat_model( inner_test_fMRI_data, train_metadata.iloc[inner_test_index, :], model, 'Site') inner_train_sMRI_data, model = fit_transform_neuroCombat( inner_train_sMRI_data, train_metadata.iloc[inner_train_index, :], 'Site', continuous_cols=[timepoint + '|total_panss']) # correct testing data inner_test_sMRI_data = apply_neuroCombat_model( inner_test_sMRI_data, train_metadata.iloc[inner_test_index, :], model, 'Site') # make inner predictions rgr_lin.fit(inner_train_fMRI_data, inner_train_targets) inner_fMRI_pred = rgr_lin.predict(inner_test_fMRI_data)
train_data = connectivity_data[train_index, :] test_data = connectivity_data[test_index, :] # do supervised site correction? if site_correction == 'comBat_supervised': # correct training data train_data, model = fit_transform_neuroCombat( train_data, metadata.iloc[train_index, :], 'Site', continuous_cols=[timepoint + '|total_panss']) # correct testing dara test_data = apply_neuroCombat_model(test_data, metadata.iloc[test_index, :], model, 'Site') #recreate a stack of matrices for train and test # add extra singleton dimension for channels train_matrices = np.reshape(train_data, (train_size, n_regions, n_regions)) test_matrices = np.reshape(test_data, (test_size, n_regions, n_regions)) train_matrices = train_matrices[:, :, :, np.newaxis] test_matrices = test_matrices[:, :, :, np.newaxis] # create and compile the model model = brainnetCNN_model_2((n_regions, n_regions, 1), n_filters, use_bias, n_outputs=n_PANSS_items) # my custom
train_fMRI_data = fMRI_data[train_index, :] test_fMRI_data = fMRI_data[test_index, :] train_sMRI_data = sMRI_data[train_index, :] test_sMRI_data = sMRI_data[test_index, :] # do supervised site correction? if site_correction == 'comBat_supervised' : # correct training data train_fMRI_data, fMRI_model = fit_transform_neuroCombat(train_fMRI_data, metadata.iloc[train_index, :], 'Site', continuous_cols=[timepoint + '|total_panss']) train_sMRI_data, sMRI_model = fit_transform_neuroCombat(train_sMRI_data, metadata.iloc[train_index, :], 'Site', continuous_cols=[timepoint + '|total_panss']) # correct testing dara test_fMRI_data = apply_neuroCombat_model(test_fMRI_data, metadata.iloc[test_index, :], fMRI_model, 'Site') test_sMRI_data = apply_neuroCombat_model(test_sMRI_data, metadata.iloc[test_index, :], sMRI_model, 'Site') #recreate a stack of matrices for train and test # add extra singleton dimension for channels train_fMRI_matrices = np.reshape(train_fMRI_data, (train_size, n_fMRI_regions, n_fMRI_regions)) test_fMRI_matrices = np.reshape(test_fMRI_data, (test_size, n_fMRI_regions, n_fMRI_regions)) train_fMRI_matrices = train_fMRI_matrices[:, :, :, np.newaxis] test_fMRI_matrices = test_fMRI_matrices[:, :, :, np.newaxis] train_sMRI_matrices = np.reshape(train_sMRI_data, (train_size, n_sMRI_regions, n_sMRI_regions)) test_sMRI_matrices = np.reshape(test_sMRI_data, (test_size, n_sMRI_regions, n_sMRI_regions)) train_sMRI_matrices = train_sMRI_matrices[:, :, :, np.newaxis] test_sMRI_matrices = test_sMRI_matrices[:, :, :, np.newaxis]