def test_predict_multi():
# Simulate data 100 images worth
sim = Simulator()
sigma = 1
y = [0, 1]
n_reps = 50
output_dir = '.'
dat = sim.create_data(y, sigma, reps=n_reps, output_dir=output_dir)
y = pd.read_csv('y.csv', header=None, index_col=None)
dat = Brain_Data('data.nii.gz', Y=y)
# Predict within given ROIs
# Generate some "rois" (in reality non-contiguous, but also not overlapping)
roi_1 = dat[0].copy()
roi_1.data = np.zeros_like(roi_1.data, dtype=bool)
roi_2 = roi_1.copy()
roi_3 = roi_1.copy()
idx = np.random.choice(range(roi_1.shape()[-1]), size=9999, replace=False)
roi_1.data[idx[:3333]] = 1
roi_2.data[idx[3333:6666]] = 1
roi_3.data[idx[6666:]] = 1
rois = roi_1.append(roi_2).append(roi_3)
# Load in all 50 rois so we can "insert" signal into the first one
# rois = expand_mask(Brain_Data(os.path.join(get_resource_path(), 'k50.nii.gz')))
# roi = rois[0]
from sklearn.datasets import make_classification
X, Y = make_classification(n_samples=100, n_features=rois[0].data.sum(), n_informative=500, n_redundant=5, n_classes=2)
dat.data[:, rois[0].data.astype(bool)] = X
dat.Y = pd.Series(Y)
out = dat.predict_multi(algorithm='svm', cv_dict={'type': 'kfolds', 'n_folds': 3}, method='rois', n_jobs=-1, rois=rois[:3], kernel='linear')
assert len(out) == 3
assert np.sum([elem['weight_map'].data.shape for elem in out]) == rois.data.sum()
# Searchlight
roi_mask = rois[:2].sum()
out = dat.predict_multi(algorithm='svm', cv_dict={'type': 'kfolds', 'n_folds': 3}, method='searchlight', radius=4, verbose=50, n_jobs=-1, process_mask=roi_mask)
assert len(np.nonzero(out.data)[0]) == len(np.nonzero(roi_mask.data)[0])
from nilearn.plotting import plot_glass_brain plot_glass_brain(pdata.mean().to_nifti()) pdata.mean().plot() from nltools.plotting import plotBrain plotBrain(pdata.X) data_subjs=pdata.X["SubjectID"].unique() len(data_subjs) train_subjs=pandas.DataFrame(data_subjs).sample(frac=0.9,random_state=2057) test_subjs= [s for s in data_subjs if s not in train_subjs[0]] pdata.Y=pdata.X["PainLevel"] data_train = pdata[train_subjs[0]] data_test = pdata[test_subjs] #train! stats = data_train.predict(algorithm='ridge',plot=False) #test predicted_pain=data_test.similarity(stats['weight_map'],'dot_product') + stats['intercept']
