def test_profile():
path = 'results/test/test.pkl'
m = KnnClassifier()
d = Toy()
s = CrossValidation()
e = [Accuracy()]
p = expConfig(dataset=d, setting=s, model=m, metrics=e, resultPath=path)
p.skip_if_file_exist = False
p.run()
assert os.path.exists(path)
def test_results():
path = 'results/test/test.pkl'
m = KnnClassifier()
d = Toy()
s = CrossValidation()
e = [Accuracy()]
p = expConfig(dataset=d,
setting=s,
model=m,
metrics=e,
resultPath=path)
p.skip_if_file_exist = False
p.run()
r = results(root_dir='results/test')
x = r.load()
assert_almost_equals(x[0].metrics[0].values[2], 0.8)
args = parser.parse_args()
# parse parameters
t = args.taskid
g = args.num_gpu
#-------------------------------------------------------------------
# classification models on text for clinical notes
#-------------------------------------------------------------------
if t == 41:
d = ClinicalTS_numeric_combined()
s = HoldOut()
s.num_GPUs = g
e = [Accuracy()]
m = RecurrentNN()
p = expConfig(dataset=d, setting=s, model=m, metrics=e)
p.run()
if t == 42:
d = ClinicalTS_numeric_combined()
s = HoldOut()
s.num_GPUs = g
e = [Accuracy()]
m = FullyConnected()
p = expConfig(dataset=d, setting=s, model=m, metrics=e)
p.run()
#-------------------------------------------------------------------
if t == 43:
d = ClinicalTS_numeric_sequential()
s = HoldOut()
# -*- coding: utf-8 -*- """ Created on Sun Mar 26 17:07:58 2017 @author: YimingZhao """ from modelGlimpseclassifier import Glimpseclassifier from expConfig import expConfig from setting import HoldOut from datasetMNIST import datasetMNIST, datasetMNIST_embed, datasetMNIST3d from metric import Accuracy from Configuration import Configuration_Glimpse AA = Configuration_Glimpse() num = AA.batch_size d = datasetMNIST(n_sample=num) s = HoldOut() e = [Accuracy()] m = Glimpseclassifier(1) path = 'results/test/' + s.name + '/' + d.name + '/' + m.name + '.pkl' p = expConfig(dataset=d, setting=s, model=m, metrics=e, resultPath=path) p.run()