07bbcbb72cbfcea9ef10087167163138165c5003,plot_poldrack_space_net.py,,,#,6

Before Change

from load_poldrack import load_gain_poldrack
mem = Memory(cachedir="cache", verbose=3)
X, y, _, mask, affine = mem.cache(load_gain_poldrack)(smooth=0)
img_data = np.zeros(list(mask.shape) + [len(X)])
img_data[mask, :] = X.T

// prepare input data for learner
import nibabel
n_samples = img_data.shape[-1]
n_samples_train = n_samples * 8 / 10
mask_img = nibabel.Nifti1Image(mask.astype(np.int), affine)
X_train = nibabel.Nifti1Image(img_data[:, :, :, :n_samples_train], affine)
y_train = y[:n_samples_train]
X_test = nibabel.Nifti1Image(img_data[:, :, :, n_samples_train:], affine)
y_test = y[n_samples_train:]

////// Fit and predict ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
from nilearn.decoding import SpaceNetRegressor

After Change

// prepare input data for learner
n_samples = len(X)
n_samples_train = n_samples * 8 / 10
X_train = X[:n_samples_train]
y_train = y[:n_samples_train]
X_test = X[n_samples_train:]
y_test = y[n_samples_train:]

Instances