def regenerate_data(data_model, pct=80., std=1.):
""" Regenerate test data using specified data generation function
Allowed models: %s """ % data_gen_models
if data_model not in data_gen_models.split(' '):
raise TypeError, 'Unrecognized data model "%s"; must be one of %s' % (data_model, data_gen_models)
import data
reload(data)
eval('data.generate_%s()' % data_model)
data.add_sampling_error(std=std)
data.knockout_uniformly_at_random(pct=pct)
def regenerate_data(data_model, pct=80., std=1.):
""" Regenerate test data using specified data generation function
Allowed models: %s """ % data_gen_models
if data_model not in data_gen_models.split(' '):
raise TypeError, 'Unrecognized data model "%s"; must be one of %s' % (
data_model, data_gen_models)
import data
reload(data)
eval('data.generate_%s()' % data_model)
data.add_sampling_error(std=std)
data.knockout_uniformly_at_random(pct=pct)
import pylab as pl
import data
data.age_range = pl.arange(0, 81, 20)
data.time_range = pl.arange(1980, 2005, 5)
data.regions = pl.randint(5, 15)
time.sleep(pl.rand() * 5.)
t0 = time.time()
data.generate_fe('test_data/%s.csv' %
t0) # included just to get good test coverage
data.generate_smooth_gp_re_a('test_data/%s.csv' % t0,
country_variation=True)
std = 5. * pl.rand(len(pl.csv2rec('test_data/%s.csv' % t0)))
pct = 90.
print data.age_range, data.time_range, data.regions, pl.mean(std), pct
data.add_sampling_error('test_data/%s.csv' % t0,
'test_data/noisy_%s.csv' % t0,
std=std)
data.knockout_uniformly_at_random('test_data/noisy_%s.csv' % t0,
'test_data/missing_noisy_%s.csv' % t0,
pct=pct)
mod_mc = evaluate_model(
'gp_re_a',
'knockout pct=%d, model matches data, has laplace priors, sigma_e = Exp(1)'
% pct, 'test_data/missing_noisy_%s.csv' % t0, 'test_data/%s.csv' % t0)
f = open('dev_log.csv', 'a')
f_csv = csv.writer(f)
f_csv.writerow(results)
f.close()
return mod_mc
if __name__ == '__main__':
import pylab as pl
import data
data.age_range = pl.arange(0, 81, 20)
data.time_range = pl.arange(1980, 2005, 5)
data.regions = pl.randint(5,15)
time.sleep(pl.rand()*5.)
t0 = time.time()
data.generate_fe('test_data/%s.csv'%t0) # included just to get good test coverage
data.generate_smooth_gp_re_a('test_data/%s.csv'%t0, country_variation=True)
std=5.*pl.rand(len(pl.csv2rec('test_data/%s.csv'%t0)))
pct=90.
print data.age_range, data.time_range, data.regions, pl.mean(std), pct
data.add_sampling_error('test_data/%s.csv'%t0, 'test_data/noisy_%s.csv'%t0, std=std)
data.knockout_uniformly_at_random('test_data/noisy_%s.csv'%t0, 'test_data/missing_noisy_%s.csv'%t0, pct=pct)
mod_mc = evaluate_model('gp_re_a', 'knockout pct=%d, model matches data, has laplace priors, sigma_e = Exp(1)' % pct,
'test_data/missing_noisy_%s.csv'%t0, 'test_data/%s.csv'%t0)
