c943318502c17ea9d909e79ce898431282768cb9,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:]

After Change

mem = Memory(cachedir="cache", verbose=3)
data = mem.cache(fetch_mixed_gambles)("data/Jimura_Poldrack_2012_zmaps",
                                      n_subjects=16, make_Xy=True)
X, y, mask_img = data.X, data.y, data.mask_img

// prepare input data for learner
n_samples = len(X)
n_samples_train = n_samples * 8 / 10

Instances