# this method is slow, because it keeps calling a feature reduction
# method for each bar and each estimator. We will globally reduce the
# features before starting
global_f_select = MultiSelectKBest(f_classif,
pooling_function=np.min,
k=3000)
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)].\
# this method is slow, because it keeps calling a feature reduction
# method for each bar and each estimator. We will globally reduce the
# features before starting
global_f_select = MultiSelectKBest(f_classif,
pooling_function=np.min,
k=3000)
#res2 = first_layer_predictor2.fit_transform(
# global_f_select.fit_transform(data, stimuli), stimuli)
res2 = first_layer_predictor2.fit_transform(data, 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)].\