Glimpse as Feature Extraction (for Kendall) -- load activity from a glimpse experiment as features for a classifier

load_activity.py

from cPickle import load as load_pickle
from glimpse.experiment import Layer, ExtractFeatures, GetTrainingSet

# load an experiment object from disk, as saved by the 'glab' command.
def load_experiment(path):
    with open(path) as f:
        # load saved experiment. return type is described here:
        # https://github.com/mthomure/glimpse-project/blob/master/glimpse/experiment/experiment.py#L77
        return load_pickle(f)

# summarize data in experiment object for use by a scikit-learn classifier.
def get_classifier_input(exp):
    # get all features as a numpy array. this assumes you want activity from the
    # C2 layer.
    xs = ExtractFeatures(Layer.C2, exp.extractor.activation)
    # get class labels as a numpy array. you may also find the function
    #   glimpse.experiment.GetLabelNames(exp)
    # useful, which returns the class name as a string for each image.
    ys = exp.corpus.labels
    # find out which images were used for training, and which for testing. this
    # is an array of booleans, which is useful for the kind of array indexing
    # described here:
    # http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#boolean-array-indexing
    trn = GetTrainingSet(exp)
    return dict(
        # get features for training set as 2d numpy array
        xs_trn = xs[trn],
        # get features for testing set
        xs_test = xs[~trn],
        # get class labels for training set
        ys_trn = ys[trn],
        # get class labels for testing set
        ys_tst = ys[~trn])

if __name__ == "__main__":
    import sys, pprint
    if len(sys.argv) < 2:
        sys.exit("usage: %s EXPERIMENT.dat" % sys.argv[0])
    path = sys.argv[1]
    exp = load_experiment(path)
    data = get_classifier_input(exp)
    pprint.pprint(data)

session.txt

# here's a simple example exercising the script load-activity.py

# first, compute activity for a set of images.
mthomure:~$ glab --corpus-name easy -p imprint -n 10 --verbose -o out.dat
INFO:root:Reading class sub-directories from: /Users/mthomure/anaconda/lib/python2.7/site-packages/glimpse/corpora/data/easy
INFO:root:Reading images from class directories: ['/Users/mthomure/anaconda/lib/python2.7/site-packages/glimpse/corpora/data/easy/circle',
 '/Users/mthomure/anaconda/lib/python2.7/site-packages/glimpse/corpora/data/easy/cross']
INFO:root:Using pool: MulticorePool
INFO:root:Learning 10 prototypes at 1 sizes from 4 images by imprinting
INFO:root:Learning prototypes took 0.335s
INFO:root:Computing C2 activation maps for 10 images
INFO:root:Computing activation maps took 0.726s
INFO:root:Writing experiment data to file -- out.pkl

# then, extract feature vectors and labels for use in a scikit-learn model.
mthomure:~$ python load_activity.py out.pkl
{'xs_test': array([[ 0.8270908 ,  0.99733424,  0.78693205,  0.84405392,  0.36080733,
         0.88399971,  0.33603266,  0.84947342,  0.94479066,  0.69329476],
       [ 0.81615609,  0.99654359,  0.87037575,  0.81144947,  0.32163975,
         0.90692639,  0.29797998,  0.92359161,  0.95646822,  0.65744346],
       [ 0.43396509,  0.99613065,  0.48352337,  0.62949139,  0.43605357,
         0.81298745,  0.36959648,  0.87253934,  0.94972253,  0.7010594 ],
       [ 0.27677462,  0.9949404 ,  0.25747168,  0.14426161,  0.57402003,
         0.22920944,  0.51329136,  0.34722003,  0.46306002,  0.86341625],
       [ 0.21736699,  0.9950096 ,  0.20971335,  0.14578031,  0.58230257,
         0.2581763 ,  0.46910548,  0.33803439,  0.35121593,  0.95228946],
       [ 0.21323967,  0.99529862,  0.19002655,  0.10273797,  0.4512893 ,
         0.1622162 ,  0.44975236,  0.18094081,  0.25892368,  0.66407734]], dtype=float32),
 'xs_trn': array([[ 1.        ,  0.99805045,  1.        ,  0.6989426 ,  0.40098032,
         1.        ,  0.35889021,  0.8306604 ,  0.88369536,  0.73072773],
       [ 0.63719684,  0.99605894,  0.66267645,  1.        ,  0.32934377,
         0.92108756,  0.32438299,  1.        ,  1.        ,  0.69304198],
       [ 0.20787469,  1.        ,  0.18297496,  0.10189372,  1.        ,
         0.1832864 ,  1.        ,  0.18063352,  0.33744204,  0.71738458],
       [ 0.21831419,  0.99456131,  0.20686066,  0.1402391 ,  0.51418829,
         0.28021285,  0.42769903,  0.37925315,  0.37054563,  1.        ]], dtype=float32),
 'ys_trn': array([0, 0, 1, 1]),
 'ys_tst': array([0, 0, 0, 1, 1, 1])}