def etrims_tree(n_hidden = [1000], coef = [1000.], size=6):
print_time('tree2etrims test size is %d' % size)
print_time('load_etrims')
train_data, train_signal, test_data, test_signal = load_etrims(size=size)
num_function = 100
print_time('train_DecisionTree num function is %d' % num_function)
dt = DecisionTree(num_function=num_function)
dt.fit(train_data, train_signal)
print_time('test_DecisionTree')
score = dt.score(test_data, test_signal)
print_time('score is %f' % score)
print_time('DecisionTree info')
dt.info()
elm_hidden = [(2*size+1)*(2*size+1)*2]
print_time('train_ExtremeDecisionTree elm_hidden is %d, num function is %d' % (elm_hidden[0], num_function))
edt = ExtremeDecisionTree(elm_hidden=elm_hidden, elm_coef=None, num_function=num_function)
edt.fit(train_data, train_signal)
print_time('test_ExtremeDecisionTree')
score = edt.score(test_data, test_signal)
print_time('score is %f' % score)
print_time('test_ExtremeDecisionTree')
score = edt.score(test_data, test_signal)
print_time('score is %f' % score)
print_time('ExtremeDecisionTree info')
edt.info()
print_time('tree2etrims test is finished !')
def mnist_mlelm(n_hidden=[1000]):
print "hidden:", n_hidden
# initialize
train_set, valid_set, test_set = load_mnist()
train_data, train_target = train_set
valid_data, valid_target = valid_set
test_data, test_target = test_set
# size
train_size = 500 # max 50000
valid_size = 10 # max 10000
test_size = 10 # max 10000
train_data, train_target = train_data[:train_size], train_target[:train_size]
valid_data, valid_target = valid_data[:valid_size], valid_target[:valid_size]
test_data, test_target = test_data[:test_size], test_target[:test_size]
# add valid_data/target to train_data/target
"""
train_data = train_data + valid_data
train_target = train_target + valid_target
"""
# model
dt = DecisionTree()
#"""
edt1 = ExtremeDecisionTree(elm_hidden=n_hidden)
edt2 = ExtremeDecisionTree(elm_hidden=n_hidden, elm_coef=[1000., 100., 1000.])
#"""
# fit
#print "fitting ..."
dt.fit(train_data, train_target)
#"""
edt1.fit(train_data, train_target)
edt2.fit(train_data, train_target)
#"""
# test
print "test score is ",
score_dt = dt.score(test_data, test_target)
#"""
score_edt1 = edt1.score(test_data, test_target)
score_edt2 = edt2.score(test_data, test_target)
print score_dt, score_edt1, score_edt2
#"""
#print score_dt
print "dt"
dt.info()
#"""
print "edt1"
edt1.info()
print "edt2"
edt2.info()
