'group':
'Well+Gene',
'vals':
'eJxNkDtOBDEQRHNO4QOA1d3V33CFEMFoJQJEMppgOQL3D2gPwRCW7Hp+5X2PGvevbeiYcz4PmlVGVURRaR2JtENVulDxkAkKS5FK4Hgau8u43z5k84tgUQYuC7LqqELqSQrJToyCKvoUfvZ9fL69XuWQ6LsqCdaOEtIkC85+fLGCAUdKdPmsNMPG++P754GLorQMi6Eiy6giiCndhMdLSwQXjNMi9JTgPwJfBLCjnXtHG9L0hJe5J5MvAqBh4dmaC8DnL2z8X0FL2lt76algFL3KmpkLQJlqyKDG6nH8AuLPT3I='
},
]
for i, test in enumerate(testdata):
props_file = test['props']
ts_file = test['ts']
nRules = test['nRules']
filter_name = test['filter']
group = test['group']
vals = numpy.array(test['vals'])
logging.info('Loading properties file...')
p.load_file(props_file)
logging.info('Loading training set...')
ts = TrainingSet(p)
ts.Load(ts_file)
data = score(p, ts, nRules, filter_name, group)
nClasses = len(ts.labels)
nKeyCols = len(image_key_columns())
if base64.b64encode(zlib.compress(str(list(data)))) != vals:
logging.error('Test %d failed' % (i))
app.MainLoop()
