def main(dirname):
data, predictions, boost_labels, maxent_labels = make_arrays(dirname)
print "Boosting... ",
boost_features = adaboost.boost(predictions, boost_labels)
print "done"
print "Calculating information gain... ",
maxent_features = entropy.choose_features(data, maxent_labels)
print "done"
print
condition_dict = dict((v, k) for k, v in util.conditions.iteritems())
features = [condition_dict[x] for x in boost_features if x in maxent_features]
import pdb
pdb.set_trace()
print "Most predictive feature(s):"
for feature in features:
print feature
def main(dirname):
data, predictions, boost_labels, maxent_labels = make_arrays(dirname)
print "Boosting... ",
boost_features = adaboost.boost(predictions, boost_labels)
print "done"
print "Calculating information gain... ",
maxent_features = entropy.choose_features(data, maxent_labels)
print "done"
print
condition_dict = dict((v, k) for k, v in util.conditions.iteritems())
features = [
condition_dict[x] for x in boost_features if x in maxent_features
]
import pdb
pdb.set_trace()
print "Most predictive feature(s):"
for feature in features:
print feature
# these should be 1 if the data point's rmse is lower (by some arbitrary amount) than the baseline rmse and 0 if not
entropy_labels = np.array([1, 1, 0, 0, 1, 0, 1, 0, 0, 1])
# these should be 1 if the data point's rmse is lower and -1 if not
adaboost_labels = np.array([1, 1, -1, -1, 1, -1, 1, -1, -1, 1])
# this is for maxent
# row represents a data point from an experiment
# each column represents a condition
# element (i, j) is 1 if condition j was true during data point i's experiment and 0 if not
data = np.array([[1, 1, 0, 1, 0], [1, 1, 0, 1, 0], [0, 1, 0, 1, 1],
[0, 0, 0, 1, 1], [1, 1, 0, 1, 0], [0, 0, 0, 1, 1],
[1, 1, 0, 1, 0], [0, 0, 0, 1, 1], [0, 0, 0, 1, 1],
[0, 1, 1, 0, 0]])
# this is for boosting
# element (i, j) is 1 if condition j was true during data point i's experiment and -1 if not
predictions = np.array([[1, 1, -1, 1, -1], [1, 1, -1, 1,
-1], [-1, 1, -1, 1, 1],
[-1, -1, -1, 1, 1], [1, 1, -1, 1, -1],
[-1, -1, -1, 1, 1], [1, 1, -1, 1, -1],
[-1, -1, -1, 1, 1], [-1, -1, -1, 1, 1],
[-1, 1, -1, 1, -1]])
# these give the indices of the top conditions (default is half as many results as there are conditions, rounded up)
# note that maxent doesn't care if the correlation is positive or negative; the top conditions may negatively effect the rmse
entropy_features = entropy.choose_features(data, entropy_labels)
print entropy_features
adaboost_features = adaboost.boost(predictions, adaboost_labels)
print adaboost_features
data = np.array([[1, 1, 0, 1, 0],
[1, 1, 0, 1, 0],
[0, 1, 0, 1, 1],
[0, 0, 0, 1, 1],
[1, 1, 0, 1, 0],
[0, 0, 0, 1, 1],
[1, 1, 0, 1, 0],
[0, 0, 0, 1, 1],
[0, 0, 0, 1, 1],
[0, 1, 1, 0, 0]])
# this is for boosting
# element (i, j) is 1 if condition j was true during data point i's experiment and -1 if not
predictions = np.array([[1, 1, -1, 1, -1],
[1, 1, -1, 1, -1],
[-1, 1, -1, 1, 1],
[-1, -1, -1, 1, 1],
[1, 1, -1, 1, -1],
[-1, -1, -1, 1, 1],
[1, 1, -1, 1, -1],
[-1, -1, -1, 1, 1],
[-1, -1, -1, 1, 1],
[-1, 1, -1, 1, -1]])
# these give the indices of the top conditions (default is half as many results as there are conditions, rounded up)
# note that maxent doesn't care if the correlation is positive or negative; the top conditions may negatively effect the rmse
entropy_features = entropy.choose_features(data, entropy_labels)
print entropy_features
adaboost_features = adaboost.boost(predictions, adaboost_labels)
print adaboost_features