res2 = first_layer_predictor2.fit_transform(
global_f_select.fit_transform(data, stimuli), stimuli)
# Now visualise the predictions.
from viz import get_bars, draw_words, pad, make_collage
bars = get_bars(img_size=(50, 50))
words1 = draw_words(res1, bars)
words2 = draw_words(res2, bars)
words = draw_words(stimuli, bars)
stacked = np.concatenate([words1, words2, words], axis=1)
# pad this slightly in order to be able to distinguish groups
stacked = pad(stacked, [0, 10, 10])
num_x = 8
num_y = 12
start_at = 0
collage = make_collage(stacked[start_at:start_at + (num_x * num_y)].\
reshape(num_x, num_y, stacked.shape[1], stacked.shape[2]))
import pylab as pl
pl.figure()
pl.imshow(collage)
pl.gray()
pl.show()
bars = get_bars(img_size=(50, 50))
words1 = draw_words(res1, bars)
words2 = draw_words(res2, bars)
words = draw_words(stimuli, bars)
stacked = np.concatenate([words1, words2, words], axis=1)
# pad this slightly in order to be able to distinguish groups
stacked = pad(stacked, [0, 10, 10])
num_x = 8
num_y = 12
start_at = 0
collage = make_collage(stacked[start_at:start_at + (num_x * num_y)].\
reshape(num_x, num_y, stacked.shape[1], stacked.shape[2]))
import pylab as pl
pl.figure()
pl.imshow(collage)
pl.gray()
import scoring
pl.figure()
roc1 = scoring.roc(res1, stimuli)
roc2 = scoring.roc(res2, stimuli)
pl.plot(roc1)
pl.plot(roc2)
pl.plot([0, len(stimuli)], [0, 1])