from svc.utils import linrange, linspace
def tune(**env):
prepareData(env=env)
train(env=env)
forcealignTrn(env=env)
smooth(env=env)
scale(env=env)
res = decodeHldt()
return res['cAcc'], res['uCorr']
params = {
'DATA_REDUCTION': linrange(10,100,10),
}
params = Grid.cartezianGrid(params)
value, tuned_params = params.tune(tune, logger=logger)
params.writeCSV(os.path.join(env['BUILD_DIR'], 'tune_data_reduction.csv'))
env.update(tuned_params)
# I know that all data are always the best
#all()
moveResults()
SCALE_CONCEPT12_RANGE = +-0.6
SCALE_CONCEPT12_STEP = 0.2
for i in range(n_iters):
logger.info("_" * 80)
logger.info('')
logger.info("Setting tuning steps:")
logger.info("=====================")
logger.info(" SCALE_CONCEPT12_STEP: %.2f" % SCALE_CONCEPT12_STEP)
logger.info(" SCALE_PUSHPOP_STEP : %.2f" % SCALE_PUSHPOP_STEP)
logger.info("_" * 80)
logger.info('')
logger.info('')
params = {
'SCALE_PUSHPOP': linrange(SCALE_PUSHPOP, SCALE_PUSHPOP_RANGE, SCALE_PUSHPOP_STEP),
'SCALE_CONCEPT12': linrange(SCALE_CONCEPT12, SCALE_CONCEPT12_RANGE, SCALE_CONCEPT12_STEP),
}
params = Grid.cartezianGrid(params)
value, tuned_params = params.tune(tune_scale, logger=logger)
if i == 0:
fn = 'tune_cued_scale.csv'
else:
fn = 'tune_cued_scale%d.csv' % (i+1, )
params.writeCSV(os.path.join(env['BUILD_DIR'], fn))
SCALE_CONCEPT12 = tuned_params['SCALE_CONCEPT12']
SCALE_CONCEPT12_RANGE = +-SCALE_CONCEPT12_STEP
settings['DATA_REDUCTION'] = 100
def tune(**env):
prepareData(env=env)
train(env=env)
forcealignTrn(env=env)
smooth(env=env)
scale(env=env)
res = decodeHldt()
return res['sActAcc'], res['iF']
# return res['cAcc'], res['uCorr']
params = {
'TRAIN_DATA_REDUCTION': linrange(10, 100, 10),
}
params = Grid.cartezianGrid(params)
value, tuned_params = params.tune(tune, logger=logger)
params.writeCSV(os.path.join(env['BUILD_DIR'], 'tune_data_reduction.csv'))
env.update(tuned_params)
# I know that all data are always the best
#all()
moveResults()
from svc.utils import linrange, linspace
def tune(**env):
settings.update(env)
all(noDcd=True, moveResults=False)
res = decodeHldt()
return res['cAcc'], res['uCorr']
if 'test' not in argv:
params = {
'TRAIN_DATA_REDUCTION': linrange(5,100,5),
}
else:
params = {
'TRAIN_DATA_REDUCTION': [5, 10],
}
params = Grid.cartezianGrid(params)
value, tuned_params = params.tune(tune, logger=logger)
params.writeCSV(os.path.join(env['BUILD_DIR'], 'tune_train_data_reduction.csv'))
settings.update(tuned_params)
all(moveResults=False)