'''
Fit model with best parameters
'''
# fit model, get predictions
if (len(data_year) == 0): # no data model
[M, C] = gpr.gpmodel_nodata(pyear=prior_year,
pmort=prior_mort,
scale=best_scale,
predictionyears=predictionyears,
sim=1000,
amp2x=best_amp2x,
mse=mse)
else: # data model
[M, C] = gpr.gpmodel(cc, rr, data_year, data_mort, data_var, data_category,
prior_year, prior_mort, mse, best_scale, best_amp2x,
predictionyears)
## find mean and standard error, drawing from M and C
draws = 1000
mort_draws = np.zeros((draws, len(predictionyears)))
gpr_seeds = [x + 123456 for x in range(1, 1001)]
for draw in range(draws):
np.random.seed(gpr_seeds[draw])
mort_draws[draw, :] = Realization(M, C)(predictionyears)
# collapse across draws
# note: space transformations need to be performed at the draw level
logit_est = gpr.collapse_sims(mort_draws)
unlogit_est = gpr.collapse_sims(gpr.inv_logit(mort_draws))
'''
Fit model with best parameters
'''
# fit model, get predictions
if (len(data_year) == 0): # no data model
[M, C] = gpr.gpmodel_nodata(pyear=prior_year,
pmort=prior_mort,
scale=best_scale,
predictionyears=predictionyears,
sim=1000,
amp2x=best_amp2x,
mse=mse)
else: # data model
[M, C] = gpr.gpmodel(ihme_loc_id, region_name, data_year, data_mort,
data_var, data_category, prior_year, prior_mort, mse,
best_scale, best_amp2x, predictionyears)
## find mean and standard error, drawing from M and C
draws = 1000
mort_draws = np.zeros((draws, len(predictionyears)))
gpr_seeds = [x + 123456 for x in range(1, 1001)]
for draw in range(draws):
np.random.seed(gpr_seeds[draw])
mort_draws[draw, :] = Realization(M, C)(predictionyears)
# collapse across draws
# note: space transformations need to be performed at the draw level
logit_est = gpr.collapse_sims(mort_draws)
unlogit_est = gpr.collapse_sims(gpr.inv_logit(mort_draws))
if (len(test_mort) > 0):
all_err = [] # set up holder for holdout results
all_est = [] # set up holder for predictions
for amp2x in amp2x_list: # loop through amp
for scale in scale_list:
print('amp2x %f and scale %f' % (amp2x, scale))
## fit model
if (len(train_year) == 0): # no data model
[M,C] = gpr.gpmodel_all(prior_mort, prior_year, scale, mse*amp2x)
d = gpr.gpmodel_all_pred(M, C, 100, predictionyears)
else: # data model
[gpmort, allobs, allyear, allvar, bias_vr, bias_sibs] = gpr.gpmodel(prior_mort, prior_year, train_mort, train_year, train_stderr, train_category, scale, mse*amp2x, cc)
d = gpr.gpmodel_pred(gpmort, allyear, allvar, allobs, bias_vr, bias_sibs, 500, 300, 2, predictionyears)
log_est = gpr.results(d)
if (transform == 'log10'):
unlog_est = gpr.results(10**d) # log base 10 space
#print('1')
elif (transform == 'ln'):
unlog_est = gpr.results(math.e**d) # natural log space
#print('2')
elif (transform == 'logit'):
unlog_est = gpr.results((math.e**d)/(1+(math.e**d))) # logit space
#print('3')
elif (transform == 'logit10'):
unlog_est = gpr.results((10**d)/(1+(10**d))) # logit10 space
#print('4')
# best_amp2 = 2
'''
Test all parameter combinations
'''
# fit model, get predictions
if (len(data_year) == 0): # no data model
[M, C] = gpr.gpmodel_all(prior_mort, prior_year, best_scale,
best_amp2)
d = gpr.gpmodel_all_pred(M, C, dr, predictionyears)
else: # data model
if (dr == int(10)):
[gpmort, allobs, allyear, allvar, bias_vr,
bias_sibs] = gpr.gpmodel(prior_mort, prior_year, data_mort,
data_year, data_stderr,
data_category, best_scale, best_amp2,
cc)
d = gpr.gpmodel_pred(gpmort, allyear, allvar, allobs, bias_vr,
bias_sibs, 5000, 3000, 2, predictionyears)
else:
[gpmort, allobs, allyear, allvar, bias_vr,
bias_sibs] = gpr.gpmodel(prior_mort, prior_year, data_mort,
data_year, data_stderr,
data_category, best_scale, best_amp2,
cc)
d = gpr.gpmodel_pred(gpmort, allyear, allvar, allobs, bias_vr,
bias_sibs, 5000, 3000, 2, predictionyears)
if (transform == 'log10'):
unlog_est = gpr.results(10**d) # log base 10 space
elif (transform == 'ln'):
for amp2x in amp2x_list: # loop through amp
for scale in scale_list:
print('amp2x %f and scale %f' % (amp2x, scale))
## fit model
if (len(train_year) == 0): # no data model
[M, C] = gpr.gpmodel_nodata(pyear=prior_year,
pmort=prior_mort,
scale=scale,
predictionyears=predictionyears,
sim=100,
amp2x=amp2x,
mse=mse)
else: # data model
[M, C] = gpr.gpmodel(cc, rr, train_year, train_mort, train_var,
train_category, prior_year, prior_mort,
mse, scale, amp2x, predictionyears)
## find mean and standard error, drawing from M and C
draws = 1000
#not setting seed here because the holdouts are random anyway
mort_draws = np.zeros((draws, len(predictionyears)))
for draw in range(draws):
mort_draws[draw, :] = Realization(M, C)(predictionyears)
# collapse across draws
# note: space transformations need to be performed at the draw level
logit_est = gpr.collapse_sims(mort_draws)
unlogit_est = gpr.collapse_sims(gpr.inv_logit(mort_draws))
## save the predictions