def validate_once(true_cf=[pl.ones(3) / 3.0,
pl.ones(3) / 3.0],
true_std=0.01 * pl.ones(3),
std_bias=[1., 1., 1.],
save=False,
dir='',
i=0):
"""
Generate a set of simulated estimates for the provided true cause fractions; Fit the bad model and
the latent simplex model to this simulated data and calculate quality metrics.
"""
# generate simulation data
X = data.sim_data_for_validation(1000, true_cf, true_std, std_bias)
# fit bad model, calculate fit metrics
bad_model = models.bad_model(X)
bad_model_metrics = calc_quality_metrics(true_cf, true_std, std_bias,
bad_model)
retrieve_estimates(bad_model, True, 'bad_model', dir, i)
# fit latent simplex model, calculate fit metrics
m, latent_simplex = models.fit_latent_simplex(X)
latent_simplex_metrics = calc_quality_metrics(true_cf, true_std, std_bias,
latent_simplex)
retrieve_estimates(latent_simplex, True, 'latent_simplex', dir, i)
# either write results to disk or return them
if save:
pl.rec2csv(bad_model_metrics, '%s/metrics_bad_model_%i.csv' % (dir, i))
pl.rec2csv(latent_simplex_metrics,
'%s/metrics_latent_simplex_%i.csv' % (dir, i))
else:
return bad_model_metrics, latent_simplex_metrics
def validate_once(true_cf = [pl.ones(3)/3.0, pl.ones(3)/3.0], true_std = 0.01*pl.ones(3), std_bias = [1., 1., 1.], save=False, dir='', i=0):
"""
Generate a set of simulated estimates for the provided true cause fractions; Fit the bad model and
the latent simplex model to this simulated data and calculate quality metrics.
"""
# generate simulation data
X = data.sim_data_for_validation(1000, true_cf, true_std, std_bias)
# fit bad model, calculate fit metrics
bad_model = models.bad_model(X)
bad_model_metrics = calc_quality_metrics(true_cf, true_std, std_bias, bad_model)
retrieve_estimates(bad_model, True, 'bad_model', dir, i)
# fit latent simplex model, calculate fit metrics
m, latent_simplex = models.fit_latent_simplex(X)
latent_simplex_metrics = calc_quality_metrics(true_cf, true_std, std_bias, latent_simplex)
retrieve_estimates(latent_simplex, True, 'latent_simplex', dir, i)
# either write results to disk or return them
if save:
pl.rec2csv(bad_model_metrics, '%s/metrics_bad_model_%i.csv' % (dir, i))
pl.rec2csv(latent_simplex_metrics, '%s/metrics_latent_simplex_%i.csv' % (dir, i))
else:
return bad_model_metrics, latent_simplex_metrics
def combine_output(J, T, model, dir, reps, save=False):
"""
Combine output on absolute error, relative error, csmf_accuracy, and coverage from from
multiple runs of validate_once. Either saves the output to the disk, or returns arays
for each.
"""
cause = pl.zeros(J*T, dtype='f').view(pl.recarray)
time = pl.zeros(J*T, dtype='f').view(pl.recarray)
abs_err = pl.zeros(J*T, dtype='f').view(pl.recarray)
rel_err = pl.zeros(J*T, dtype='f').view(pl.recarray)
coverage = pl.zeros(J*T, dtype='f').view(pl.recarray)
csmf_accuracy = pl.zeros(J*T, dtype='f').view(pl.recarray)
for i in range(reps):
metrics = pl.csv2rec('%s/metrics_%s_%i.csv' % (dir, model, i))
cause = pl.vstack((cause, metrics.cause))
time = pl.vstack((time, metrics.time))
abs_err = pl.vstack((abs_err, metrics.abs_err))
rel_err = pl.vstack((rel_err, metrics.rel_err))
coverage = pl.vstack((coverage, metrics.coverage))
csmf_accuracy = pl.vstack((csmf_accuracy, metrics.csmf_accuracy))
cause = cause[1:,]
time = time[1:,]
abs_err = abs_err[1:,]
rel_err = rel_err[1:,]
coverage = coverage[1:,]
csmf_accuracy = csmf_accuracy[1:,]
mean_abs_err = abs_err.mean(0)
median_abs_err = pl.median(abs_err, 0)
mean_rel_err = rel_err.mean(0)
median_rel_err = pl.median(rel_err, 0)
mean_csmf_accuracy = csmf_accuracy.mean(0)
median_csmf_accuracy = pl.median(csmf_accuracy, 0)
mean_coverage_bycause = coverage.mean(0)
mean_coverage = coverage.reshape(reps, T, J).mean(0).mean(1)
percent_total_coverage = (coverage.reshape(reps, T, J).sum(2)==3).mean(0)
mean_coverage = pl.array([[i for j in range(J)] for i in mean_coverage]).ravel()
percent_total_coverage = pl.array([[i for j in range(J)] for i in percent_total_coverage]).ravel()
models = pl.array([[model for j in range(J)] for i in range(T)]).ravel()
true_cf = metrics.true_cf
true_std = metrics.true_std
std_bias = metrics.std_bias
all = pl.np.core.records.fromarrays([models, cause[0], time[0], true_cf, true_std, std_bias, mean_abs_err, median_abs_err, mean_rel_err, median_rel_err,
mean_csmf_accuracy, median_csmf_accuracy, mean_coverage_bycause, mean_coverage, percent_total_coverage],
names=['model', 'cause', 'time', 'true_cf', 'true_std', 'std_bias', 'mean_abs_err', 'median_abs_err',
'mean_rel_err', 'median_rel_err', 'mean_csmf_accuracy', 'median_csmf_accuracy',
'mean_covearge_bycause', 'mean_coverage', 'percent_total_coverage'])
if save:
pl.rec2csv(all, '%s/%s_summary.csv' % (dir, model))
else:
return all
def sites_and_env(session, species, layer_names, glob_name, glob_channels, buffer_width, n_pseudoabsences, dblock=None, simdata=False):
"""
Queries the DB to get a list of locations. Writes it out along with matching
extractions of the requested layers to a temporary csv file, which serves the
dual purpose of caching the extraction and making it easier to get data into
the BRT package.
"""
breaks, x, found, zero, others_found, multipoints, eo = sites_as_ndarray(session, species)
if simdata:
print 'Process %i simulating presences for species %s.'%(multiprocessing.current_process().ident,species[1])
x = get_pseudoabsences(eo, -1, n_pseudoabsences, layer_names, glob_name)
found = np.ones(n_pseudoabsences)
fname = hashlib.sha1(str(x)+found.tostring()+\
glob_name+'channel'.join([str(i) for i in glob_channels])+\
'layer'.join(layer_names)).hexdigest()+'.csv'
pseudoabsences = get_pseudoabsences(eo, buffer_width, n_pseudoabsences, layer_names, glob_name)
x_found = x[np.where(found)]
x = np.vstack((x_found, pseudoabsences))
found = np.concatenate((np.ones(len(x_found)), np.zeros(n_pseudoabsences)))
if fname in os.listdir('anopheles-caches'):
pass
else:
# Makes list of (key, value) tuples
env_layers = map(lambda ln: extract_environment(ln, x, lock=dblock), layer_names)\
+ map(lambda ch: (os.path.basename(glob_name)+'_'+str(ch), extract_environment(glob_name,x,\
postproc=lambda d: d==ch, id_=ch, lock=dblock)[1]), glob_channels)
arrays = [(found>0).astype('int')] + [l[1] for l in env_layers]
names = ['found'] + [l[0] for l in env_layers]
data = np.rec.fromarrays(arrays, names=','.join(names))
nancheck = np.array([np.any(np.isnan(row.tolist())) for row in data])
if np.any(nancheck):
print 'There were some NaNs in the data, probably points in the sea'
singletons = 0
for e in env_layers:
if len(set(e[1][np.where(True-np.isnan(e[1]))]))==1:
singletons += 1
if singletons == len(env_layers):
raise ValueError, 'All environmental layer evaluations contained only single values.'
data = data[np.where(True-nancheck)]
rec2csv(data, os.path.join('anopheles-caches',fname))
return fname, pseudoabsences, x
def knockout_uniformly_at_random(in_fname='noisy_data.csv', out_fname='missing_noisy_data.csv', pct=20.):
""" replace data.csv y column with uniformly random missing entries
Parameters
----------
pct : float, percent to knockout
"""
data = pl.csv2rec(in_fname)
for i, row in enumerate(data):
if pl.rand() < pct/100.:
data[i].y = pl.nan
pl.rec2csv(data, out_fname)
def knockout_uniformly_at_random(in_fname='noisy_data.csv', out_fname='missing_noisy_data.csv', pct=20.):
""" replace data.csv y column with uniformly random missing entries
Parameters
----------
pct : float, percent to knockout
"""
data = pl.csv2rec(in_fname)
for i, row in enumerate(data):
if pl.rand() < pct/100.:
data[i].y = pl.nan
pl.rec2csv(data, out_fname)
def compile_all_results (scenarios, dir='../data'):
"""
Compiles the results across multiple scenarios produced by running run_on_cluster on each
one into a single sv file. The specified directory must be where where the results of
running run_on_cluster for each scenario are stored (each is a sub-directory named v0, v1, etc.)
and is also where the output from this function will be saved.
"""
models = []
causes = []
time = []
true_cf = []
true_std = []
std_bias = []
mean_abs_err = []
median_abs_err = []
mean_rel_err = []
median_rel_err = []
mean_csmf_accuracy = []
median_csmf_accuracy = []
mean_coverage_bycause = []
mean_coverage = []
percent_total_coverage = []
scenario = []
for i in range(scenarios):
for j in ['bad_model', 'latent_simplex']:
read = csv.reader(open('%s/v%s/%s_summary.csv' % (dir, i, j)))
read.next()
for row in read:
models.append(row[0])
causes.append(row[1])
time.append(row[2])
true_cf.append(row[3])
true_std.append(row[4])
std_bias.append(row[5])
mean_abs_err.append(row[6])
median_abs_err.append(row[7])
mean_rel_err.append(row[8])
median_rel_err.append(row[9])
mean_csmf_accuracy.append(row[10])
median_csmf_accuracy.append(row[11])
mean_coverage_bycause.append(row[12])
mean_coverage.append(row[13])
percent_total_coverage.append(row[14])
scenario.append(i)
all = pl.np.core.records.fromarrays([scenario, models, time, true_cf, true_std, causes, mean_abs_err, median_abs_err, mean_rel_err, median_rel_err,
mean_csmf_accuracy, median_csmf_accuracy, mean_coverage_bycause, mean_coverage, percent_total_coverage],
names=['scenario', 'model', 'time', 'true_cf', 'true_std', 'cause', 'mean_abs_err', 'median_abs_err',
'mean_rel_err', 'median_rel_err', 'mean_csmf_accuracy', 'median_csmf_accuracy',
'mean_covearge_bycause', 'mean_coverage', 'percent_total_coverage'])
pl.rec2csv(all, fname='%s/all_summary_metrics.csv' % (dir))
def add_sampling_error(in_fname='data.csv', out_fname='noisy_data.csv', std=1.):
""" add normally distributed noise to data.csv y column
Parameters
----------
std : float, or array of floats
standard deviation of noise
"""
data = pl.csv2rec(in_fname)
if type(std) == float:
std = std * pl.ones(len(data))
for i, row in enumerate(data):
data[i].y += std[i] * pl.randn(1)
data[i].se += std[i]
pl.rec2csv(data, out_fname)
def add_sampling_error(in_fname='data.csv', out_fname='noisy_data.csv', std=1.):
""" add normally distributed noise to data.csv y column
Parameters
----------
std : float, or array of floats
standard deviation of noise
"""
data = pl.csv2rec(in_fname)
if type(std) == float:
std = std * pl.ones(len(data))
for i, row in enumerate(data):
data[i].y += std[i] * pl.randn(1)
data[i].se += std[i]
pl.rec2csv(data, out_fname)
def retrieve_estimates(preds, save=False, model='', dir='', i=0):
"""
calculates the posterior mean for pi as well as the 95% hpd region and
optionally saves this output
"""
T, J = preds.shape[1:]
mean = preds.mean(0).ravel()
hpd = mc.utils.hpd(preds, 0.05)
lower = hpd[:,:,0].ravel()
upper = hpd[:,:,1].ravel()
time = pl.array([[t for j in range(J)] for t in range(T)]).ravel()
cause = pl.array([[j for j in range(J)] for t in range(T)]).ravel()
results = pl.np.core.records.fromarrays([time, cause, mean, lower, upper],
names=['time', 'cause', 'med', 'lower', 'upper'])
if (save):
pl.rec2csv(results, '%s/%s_estimates%i.csv' % (dir, model, i))
else:
return(results)
def retrieve_estimates(preds, save=False, model='', dir='', i=0):
"""
calculates the posterior mean for pi as well as the 95% hpd region and
optionally saves this output
"""
T, J = preds.shape[1:]
mean = preds.mean(0).ravel()
hpd = mc.utils.hpd(preds, 0.05)
lower = hpd[:, :, 0].ravel()
upper = hpd[:, :, 1].ravel()
time = pl.array([[t for j in range(J)] for t in range(T)]).ravel()
cause = pl.array([[j for j in range(J)] for t in range(T)]).ravel()
results = pl.np.core.records.fromarrays(
[time, cause, mean, lower, upper],
names=['time', 'cause', 'med', 'lower', 'upper'])
if (save):
pl.rec2csv(results, '%s/%s_estimates%i.csv' % (dir, model, i))
else:
return (results)
def trees_to_diagnostics(brt_evaluator, fname, species_name, n_pseudopresences, n_pseudoabsences, config_filename):
"""
Takes the BRT evaluator and sees how well it does at predicting the training dataset.
"""
from diagnostics import simple_assessments, roc, plot_roc_
din = csv2rec(os.path.join('anopheles-caches',fname))
found = din.found
din = dict([(k,din[k]) for k in brt_evaluator.nice_tree_dict.iterkeys()])
probs = pm.flib.invlogit(brt_evaluator(din))
print 'Species %s: fraction %f correctly classified.'%(species_name, ((probs>.5)*found+(probs<.5)*(True-found)).sum()/float(len(probs)))
result_dirname = get_result_dir(config_filename)
resdict = {}
for f in simple_assessments:
resdict[f.__name__] = f(probs>.5, found)
pstack = np.array([pm.rbernoulli(probs) for i in xrange(10000)])
fp, tp, AUC = roc(pstack, found)
resdict['AUC'] = AUC
fout=file(os.path.join(result_dirname,'simple-diagnostics.txt'),'w')
fout.write('presences: %i\n'%(found.sum()-n_pseudopresences))
fout.write('pseudopresences: %i\n'%n_pseudopresences)
fout.write('pseudoabsences: %i\n'%n_pseudoabsences)
for k in resdict.iteritems():
fout.write('%s: %s\n'%k)
import pylab as pl
pl.clf()
plot_roc_(fp,tp,AUC)
pl.savefig(os.path.join(result_dirname,'roc.pdf'))
r = np.rec.fromarrays([fp,tp],names='false,true')
rec2csv(r,os.path.join(result_dirname,'roc.csv'))
def measure_fit(self):
''' Provide metrics of fit to determine how well the model performed '''
# TODO: code up RMSE for non-holdout predictions
if self.training_type == 'make predictions':
print 'RMSE for non-holdout data not yet implemented'
# calculate age-adjusted rates on the test data
else:
predicted = self.predictions[['country','year','age','pop','actual_deaths', 'mean_deaths', 'upper_deaths', 'lower_deaths']].view(np.recarray)
predicted = recfunctions.append_fields(predicted, 'mean_rate', predicted.mean_deaths / predicted.pop * 100000.).view(np.recarray)
predicted = recfunctions.append_fields(predicted, 'actual_rate', predicted.actual_deaths / predicted.pop * 100000.).view(np.recarray)
predicted = recfunctions.append_fields(predicted, 'weight', np.ones(predicted.shape[0])).view(np.recarray)
for a in self.age_list:
predicted.weight[np.where(predicted.age==a)[0]] = self.age_weights.weight[np.where(self.age_weights.age==a)[0]]
predicted.mean_rate = predicted.mean_rate * predicted.weight
predicted.actual_rate = predicted.actual_rate * predicted.weight
from matplotlib import mlab
adj_rates = mlab.rec_groupby(predicted, ('country','year'), (('mean_rate', np.sum, 'adj_mean_rate'),('actual_rate', np.sum, 'adj_actual_rate')))
# calculate RMSE/RMdSE
err = adj_rates.adj_mean_rate - adj_rates.adj_actual_rate
sq_err = err ** 2.
mse = np.mean(sq_err)
mdse = np.median(sq_err)
rmse = np.sqrt(mse)
rmdse = np.sqrt(mdse)
# calculate AARE/MdARE
abs_rel_err = np.abs(err / adj_rates.adj_actual_rate)
aare = np.mean(abs_rel_err)
mdare = np.median(abs_rel_err)
# calculate coverage (age-specific, not age-adjusted)
coverage = np.array((predicted.upper_deaths >= predicted.actual_deaths) & (predicted.lower_deaths <= predicted.actual_deaths)).astype(np.int).mean()
# output fit metrics
print 'Root Mean Square Error: ' + str(rmse), '\nRoot Median Square Error: ' + str(rmdse), '\nAverage Absolute Relative Error: ' + str(aare), '\nMedian Absolute Relative Error: ' + str(mdare), '\nCoverage: ' + str(coverage)
pl.rec2csv(np.core.records.fromarrays([np.array(('rmse','rmdse','aare','mdare','coverage')),np.array((rmse,rmdse,aare,mdare,coverage))], names=['metric','value']), '/home/j/Project/Causes of Death/CoDMod/tmp/' + self.name + '_fits_' + self.cause + '_' + self.sex + '.csv')
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
# save basic estimates
model_estimates = model.trace('estimate')[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields( rec = data,
names = ['mean', 'lower', 'upper'],
arrs = [mean_estimate, lower_estimate, upper_estimate])
pl.rec2csv(output, proj_dir + 'outputs/model results/spatial smoothing/SWRI_with_spatial.csv')
'''
### plot diagnostics
# setup plotting
#import matplotlib.pyplot as pp
#pp.switch_backend('acc')
plot_me = [mu_si, mu_ss, mu_ci, mu_cs, sigma_si, sigma_ss, sigma_ci, sigma_cs, state_intercepts, state_slopes, cause_intercepts, cause_slopes]
# plot traces
os.chdir(proj_dir + '/outputs/model results/simple random effects by state/mcmc plots/traces/')
for p in plot_me:
mc.Matplot.plot(p, suffix='_trace')
if len(p.shape) == 0:
plt.close()
weights = np.array(1)
sumval = 1.0
else:
indexer[axis] = slice(i, i+2)
j = i + 1
weights = np.array([(j - index), (index - i)],float)
wshape = [1]*sorted.ndim
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
# save basic estimates
model_estimates = model.trace('estimate')[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields( rec = data,
names = ['mean', 'lower', 'upper'],
arrs = [mean_estimate, lower_estimate, upper_estimate])
pl.rec2csv(output, proj_dir + 'outputs/model results/spatial smoothing/' + mod_name + '_' + str(sex) + '_' + age + '.csv')
# save draws
draws = pl.rec_append_fields(
rec = data,
names = ['draw_' + str(i+1) for i in range(100)],
arrs = [model.trace('estimate')[i] for i in range(100)])
pl.rec2csv(draws, proj_dir + 'outputs/model results/spatial smoothing/' + mod_name + '_draws_' + str(sex) + '_' + age + '.csv')
tfr_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(tfr_draws)
unlogit_est = gpr.collapse_sims(np.exp(tfr_draws)*tfr_bound/(1+np.exp(tfr_draws))) # get the inverse logit
os.chdir('FILEPATH')
all_est = []
for i in range(len(predictionyears)):
all_est.append((cc, predictionyears[i], unlogit_est['med'][i], unlogit_est['lower'][i], unlogit_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('year', '<f8'), ('med', '<f8'), ('lower', '<f8'), ('upper', '<f8')])
pl.rec2csv(all_est, 'gpr_%s.txt' %(cc+'_'+ str(best_amp2x) + '_' + str(best_scale)))
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(draws):
all_sim.append((cc, predictionyears[i], s, np.exp(tfr_draws[s][i])*tfr_bound/ (1+np.exp(tfr_draws[s][i])) ))
all_sim = pl.array(all_sim, [('ihme_loc', '|S32'), ('year', '<f8'), ('sim', '<f8'), ('fert', '<f8')])
pl.rec2csv(all_sim, 'gpr_%s_sim.txt' %(cc+ '_' + str(best_amp2x) + '_' + str(best_scale)))
def fit_GPR(infile, outfile, dv_list, scale, number_submodels, test,
spacetime_iters, top_submodel):
# load in the data
all_data = csv2rec(infile, use_mrecords=False)
for m in range(number_submodels):
if all_data['spacetime_' + str(m + 1)].dtype == 'float64':
all_data = np.delete(
all_data,
np.where(np.isnan(all_data['spacetime_' + str(m + 1)]))[0],
axis=0)
# find the list of years for which we need to predict
year_list = np.unique(all_data.year)
# find the list of country/age groups
country_age = np.array([
str(all_data.iso3[i]) + '_' + str(all_data.age_group[i])
for i in range(len(all_data))
])
country_age_list = np.repeat(np.unique(country_age), len(year_list))
# make empty arrays in which to store the results
total_iters = np.sum(spacetime_iters)
draws = [
np.empty(len(country_age_list), 'float') for i in range(total_iters)
]
if (top_submodel > 0):
top_submodel_draws = [
np.empty(len(country_age_list), 'float') for i in range(100)
]
iso3 = np.empty(len(country_age_list), '|S3')
age_group = np.empty(len(country_age_list), 'int')
year = np.empty(len(country_age_list), 'int')
# loop through country/age groups
for ca in np.unique(country_age_list):
print('GPRing ' + ca)
# subset the data for this particular country/age
ca_data = all_data[country_age == ca]
# subset just the observed data
if ca_data['lt_cf'].dtype != '|O8':
ca_observed = ca_data[(np.isnan(ca_data['lt_cf']) == 0)
& (ca_data['test_' + test] == 0)]
if len(ca_observed) > 1:
has_data = True
else:
has_data = False
else:
has_data = False
# keep track of how many iterations have been added for this model
iter_counter = 0
# loop through each submodel
for m in range(number_submodels):
# identify the dependent variable for this model
dv = dv_list[m]
# continue making predictions if we actually need draws for this model
if (spacetime_iters[m] > 0) or (m + 1 == top_submodel):
# skip models with no spacetime results
if all_data['spacetime_' + str(m + 1)].dtype != 'float64':
for i in range(spacetime_iters[m]):
draws[iter_counter][country_age_list == ca] = np.NaN
iter_counter += 1
if (m + 1 == top_submodel):
for i in range(100):
top_submodel_draws[i][country_age_list ==
ca] = np.NaN
continue
# make a list of the spacetime predictions
ca_prior = np.array([
np.mean(ca_data['spacetime_' +
str(m + 1)][ca_data.year == y])
for y in year_list
])
# find the amplitude for this country/age
amplitude = np.mean(ca_data['spacetime_amplitude_' +
str(m + 1)])
# make a linear interpolation of the spatio-temporal predictions to use as the mean function for GPR
def mean_function(x):
return np.interp(x, year_list, ca_prior)
# setup the covariance function
M = gp.Mean(mean_function)
C = gp.Covariance(eval_fun=gp.matern.euclidean,
diff_degree=2,
amp=amplitude,
scale=scale)
# observe the data if there is any
if has_data:
gp.observe(M=M,
C=C,
obs_mesh=ca_observed.year,
obs_V=ca_observed['spacetime_data_variance_' +
str(m + 1)],
obs_vals=ca_observed[dv])
# draw realizations from the data
realizations = [
gp.Realization(M, C) for i in range(spacetime_iters[m])
]
# save the data for this country/age into the results array
iso3[country_age_list == ca] = ca[0:3]
age_group[country_age_list == ca] = ca[4:]
year[country_age_list == ca] = year_list.T
for i in range(spacetime_iters[m]):
try:
draws[iter_counter][country_age_list ==
ca] = realizations[i](year_list)
except:
print('Failure in ' + ca)
iter_counter += 1
# if it's the top submodel, do 100 additional draws
if (m + 1 == top_submodel):
realizations = [gp.Realization(M, C) for i in range(100)]
for i in range(100):
try:
top_submodel_draws[i][country_age_list ==
ca] = realizations[i](
year_list)
except:
print('Failure in ' + ca)
# save the results
print('Saving GPR results')
names = ['iso3', 'age_group', 'year']
results = np.core.records.fromarrays([iso3, age_group, year], names=names)
for i in range(total_iters):
results = recfunctions.append_fields(results,
'ensemble_d' + str(i + 1),
draws[i])
if (top_submodel > 0):
for i in range(100):
results = recfunctions.append_fields(results,
'top_submodel_d' + str(i + 1),
top_submodel_draws[i])
rec2csv(results, outfile)
def fit_GPR(infile, outfile, dv_list, scale, number_submodels, test, spacetime_iters, top_submodel):
# load in the data
all_data = csv2rec(infile, use_mrecords=False)
for m in range(number_submodels):
if all_data['spacetime_' + str(m+1)].dtype == 'float64':
all_data = np.delete(all_data, np.where(np.isnan(all_data['spacetime_' + str(m+1)]))[0], axis=0)
# find the list of years for which we need to predict
year_list = np.unique(all_data.year)
# find the list of country/age groups
country_age = np.array([str(all_data.iso3[i]) + '_' + str(all_data.age_group[i]) for i in range(len(all_data))])
country_age_list = np.repeat(np.unique(country_age), len(year_list))
# make empty arrays in which to store the results
total_iters = np.sum(spacetime_iters)
draws = [np.empty(len(country_age_list), 'float') for i in range(total_iters)]
if (top_submodel > 0):
top_submodel_draws = [np.empty(len(country_age_list), 'float') for i in range(100)]
iso3 = np.empty(len(country_age_list), '|S3')
age_group = np.empty(len(country_age_list), 'int')
year = np.empty(len(country_age_list), 'int')
# loop through country/age groups
for ca in np.unique(country_age_list):
print('GPRing ' + ca)
# subset the data for this particular country/age
ca_data = all_data[country_age==ca]
# subset just the observed data
if ca_data['lt_cf'].dtype != '|O8':
ca_observed = ca_data[(np.isnan(ca_data['lt_cf'])==0) & (ca_data['test_' + test]==0)]
if len(ca_observed) > 1:
has_data = True
else:
has_data = False
else:
has_data = False
# keep track of how many iterations have been added for this model
iter_counter = 0
# loop through each submodel
for m in range(number_submodels):
# identify the dependent variable for this model
dv = dv_list[m]
# continue making predictions if we actually need draws for this model
if (spacetime_iters[m] > 0) or (m+1 == top_submodel):
# skip models with no spacetime results
if all_data['spacetime_' + str(m+1)].dtype != 'float64':
for i in range(spacetime_iters[m]):
draws[iter_counter][country_age_list==ca] = np.NaN
iter_counter += 1
if (m+1 == top_submodel):
for i in range(100):
top_submodel_draws[i][country_age_list==ca] = np.NaN
continue
# make a list of the spacetime predictions
ca_prior = np.array([np.mean(ca_data['spacetime_' + str(m+1)][ca_data.year==y]) for y in year_list])
# find the amplitude for this country/age
amplitude = np.mean(ca_data['spacetime_amplitude_' + str(m+1)])
# make a linear interpolation of the spatio-temporal predictions to use as the mean function for GPR
def mean_function(x) :
return np.interp(x, year_list, ca_prior)
# setup the covariance function
M = gp.Mean(mean_function)
C = gp.Covariance(eval_fun=gp.matern.euclidean, diff_degree=2, amp=amplitude, scale=scale)
# observe the data if there is any
if has_data:
gp.observe(M=M, C=C, obs_mesh=ca_observed.year, obs_V=ca_observed['spacetime_data_variance_' + str(m+1)], obs_vals=ca_observed[dv])
# draw realizations from the data
realizations = [gp.Realization(M, C) for i in range(spacetime_iters[m])]
# save the data for this country/age into the results array
iso3[country_age_list==ca] = ca[0:3]
age_group[country_age_list==ca] = ca[4:]
year[country_age_list==ca] = year_list.T
for i in range(spacetime_iters[m]):
try:
draws[iter_counter][country_age_list==ca] = realizations[i](year_list)
except:
print('Failure in ' + ca)
iter_counter += 1
# if it's the top submodel, do 100 additional draws
if (m+1 == top_submodel):
realizations = [gp.Realization(M, C) for i in range(100)]
for i in range(100):
try:
top_submodel_draws[i][country_age_list==ca] = realizations[i](year_list)
except:
print('Failure in ' + ca)
# save the results
print('Saving GPR results')
names = ['iso3','age_group','year']
results = np.core.records.fromarrays([iso3,age_group,year], names=names)
for i in range(total_iters):
results = recfunctions.append_fields(results, 'ensemble_d' + str(i+1), draws[i])
if (top_submodel > 0):
for i in range(100):
results = recfunctions.append_fields(results, 'top_submodel_d' + str(i+1), top_submodel_draws[i])
rec2csv(results, outfile)
def rec2csv_2d(Y, fname):
"""
write a 2-dimensional recarray to a csv file
"""
pl.rec2csv(pl.np.core.records.fromarrays(Y.T), fname)
for i in range(len(predictionyears)):
all_est.append((cc, ss, predictionyears[i], unlog_est['med'][i],
unlog_est['lower'][i], unlog_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('sex', '|S32'),
('year', '<f8'), ('mort_med', '<f8'),
('mort_lower', '<f8'),
('mort_upper', '<f8')])
## no need to save the summary if we're doing the HIV draws version
if (huncert == int(1)):
os.chdir('strPath')
else:
os.chdir('%s/strPath' %
('/home/j' if os.name == 'posix' else 'J:'))
pl.rec2csv(all_est, 'gpr_%s_%s_not_scaled.txt' % (cc, ss))
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(dr):
if (transform == 'log10'):
all_sim.append((cc, ss, predictionyears[i], s,
10**d[s][i])) # log base 10 space
elif (transform == 'ln'):
all_sim.append((cc, ss, predictionyears[i], s,
math.e**d[s][i])) # natural log space
elif (transform == 'logit'):
all_sim.append((cc, ss, predictionyears[i], s,
((math.e**d[s][i]) /
weights = np.array(1)
sumval = 1.0
else:
indexer[axis] = slice(i, i+2)
j = i + 1
weights = np.array([(j - index), (index - i)],float)
wshape = [1]*sorted.ndim
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
# save basic estimates
model_estimates = model.trace('estimate')[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields( rec = data,
names = ['mean', 'lower', 'upper'],
arrs = [mean_estimate, lower_estimate, upper_estimate])
pl.rec2csv(output, proj_dir + 'outputs/model results/random effects plus flex time/' + mod_name + '_' + str(sex) + '_' + age + '.csv')
# save draws
draws = pl.rec_append_fields(
rec = data,
names = ['draw_' + str(i+1) for i in range(100)],
arrs = [model.trace('estimate')[i] for i in range(100)])
pl.rec2csv(draws, proj_dir + 'outputs/model results/random effects plus flex time/' + mod_name + '_draws_' + str(sex) + '_' + age + '.csv')
for i in range(len(predictionyears)):
all_est.append((cc, ss, predictionyears[i], unlog_est['med'][i],
unlog_est['lower'][i], unlog_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('sex', '|S32'),
('year', '<f8'), ('mort_med', '<f8'),
('mort_lower', '<f8'),
('mort_upper', '<f8')])
## no need to save the summary if we're doing the HIV draws version
if (huncert == int(1)):
os.chdir('filepath')
else:
os.chdir('filepath')
pl.rec2csv(all_est, 'filepath' % (cc, ss))
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(dr):
if (transform == 'log10'):
all_sim.append((cc, ss, predictionyears[i], s,
10**d[s][i])) # log base 10 space
elif (transform == 'ln'):
all_sim.append((cc, ss, predictionyears[i], s,
math.e**d[s][i])) # natural log space
elif (transform == 'logit'):
all_sim.append((cc, ss, predictionyears[i], s,
((math.e**d[s][i]) /
def predict_test(self, save_csv=False):
''' Use the MCMC traces to predict the test data '''
# setup constants
num_test_rows = self.test_data.shape[0]
num_iters = self.mod_mc.beta.trace().shape[0]
# indices
t_index = dict([(t, i) for i, t in enumerate(self.year_list)])
a_index = dict([(a, i) for i, a in enumerate(self.age_list)])
# fixed effects
X = np.array([self.test_data['x%d'%i] for i in range(self.mod_mc.beta.value.shape[0])])
BX = np.dot(self.mod_mc.beta.trace(), X)
# exposure
'''
if self.training_type == 'make predictions':
E = np.ones((num_iters, num_test_rows))*self.test_data.envelope
else:
E = np.random.binomial(np.round(self.test_data.envelope).astype('int'), (self.test_data.sample_size/self.test_data.envelope), (num_iters, num_test_rows))
'''
E = np.ones((num_iters, num_test_rows))*self.test_data.envelope
# pi_s
s_index = [np.where(self.test_data.super_region==s) for s in self.super_region_list]
t_by_s = [[t_index[self.test_data.year[j]] for j in s_index[s][0]] for s in range(len(self.super_region_list))]
a_by_s = [[a_index[self.test_data.age[j]] for j in s_index[s][0]] for s in range(len(self.super_region_list))]
pi_s = np.zeros((num_iters, num_test_rows))
for s in range(len(self.super_region_list)):
pi_s[:,s_index[s][0]] = self.mod_mc.pi_s_list.trace()[:,s][:,a_by_s[s],t_by_s[s]]
self.test_s_index = s_index
# pi_r
r_index = [np.where(self.test_data.region==r) for r in self.region_list]
t_by_r = [[t_index[self.test_data.year[j]] for j in r_index[r][0]] for r in range(len(self.region_list))]
a_by_r = [[a_index[self.test_data.age[j]] for j in r_index[r][0]] for r in range(len(self.region_list))]
pi_r = np.zeros((num_iters, num_test_rows))
for r in range(len(self.region_list)):
pi_r[:,r_index[r][0]] = self.mod_mc.pi_r_list.trace()[:,r][:,a_by_r[r],t_by_r[r]]
self.test_r_index = r_index
# pi_c
c_index = [np.where(self.test_data.country==c) for c in self.country_list]
t_by_c = [[t_index[self.test_data.year[j]] for j in c_index[c][0]] for c in range(len(self.country_list))]
a_by_c = [[a_index[self.test_data.age[j]] for j in c_index[c][0]] for c in range(len(self.country_list))]
pi_c = np.zeros((num_iters, num_test_rows))
for c in range(len(self.country_list)):
pi_c[:,c_index[c][0]] = self.mod_mc.pi_c_list.trace()[:,c][:,a_by_c[c],t_by_c[c]]
self.test_c_index = c_index
# make predictions
import os
os.chdir('/home/j/Project/Causes of Death/CoDMod/codmod2/')
import percentile
predictions = np.exp(BX + np.log(E) + pi_s + pi_r + pi_c)
mean = predictions.mean(axis=0)
lower = percentile.percentile(predictions, 2.5, axis=0)
upper = percentile.percentile(predictions, 97.5, axis=0)
self.predictions = self.test_data[['country','region','super_region','year','age','pop']]
self.predictions = recfunctions.append_fields(self.predictions, 'mean_deaths', mean)
self.predictions = recfunctions.append_fields(self.predictions, 'lower_deaths', lower)
self.predictions = recfunctions.append_fields(self.predictions, 'upper_deaths', upper)
if self.training_type != 'make predictions':
self.predictions = recfunctions.append_fields(self.predictions, 'actual_deaths', self.test_data.cf*self.test_data.envelope)
self.predictions = self.predictions.view(np.recarray)
# save the predictions
if save_csv == True:
pl.rec2csv(self.predictions, '/home/j/Project/Causes of Death/CoDMod/tmp/' + self.name + '_predictions_' + self.cause + '_' + self.sex + '.csv')
def load(self, save_cache=False, use_cache=False, dir='/home/j/Project/Causes of Death/CoDMod/tmp/'):
'''
If use_cache=True, loads data from a previous call to the MySQL server.
Otherwise, loads codmod data from the MySQL server.
The resulting query will get all the data for a specified cause and sex, plus any covariates specified.
If save_cache is True, then the results from this will be saved as csvs.
'''
# use cached data if specified
if use_cache == True:
self.use_cache(dir)
# otherwise, load in the data from MySQL
else:
# make the sql covariate query
covs = ''
for i in list(set(self.covariates_untransformed)):
if i != 'year':
covs = covs + i + ', '
covs = covs[0:-2]
# load observed deaths plus covariates
obs_sql = 'SELECT iso3 as country, a.region, a.super_region, age, year, sex, cf, sample_size, a.envelope, a.pop, ' + covs + ' FROM full_cod_database AS a LEFT JOIN all_covariates USING (iso3,year,sex,age) WHERE a.cod_id="' + self.cause + '";'
obs = mysql_to_recarray(self.cursor, obs_sql)
obs = obs[np.where((obs.year >= self.year_range[0]) & (obs.year <= self.year_range[1]) & (obs.age >= self.age_range[0]) & (obs.age <= self.age_range[1]) & (obs.sex == self.sex_num))[0]]
# load in just covariates (for making predictions)
all_sql = 'SELECT iso3 as country, region, super_region, age, year, sex, envelope, pop, ' + covs + ' FROM all_covariates;'
all = mysql_to_recarray(self.cursor, all_sql)
all = all[np.where((all.year >= self.year_range[0]) & (all.year <= self.year_range[1]) & (all.age >= self.age_range[0]) & (all.age <= self.age_range[1]) & (all.sex == self.sex_num))[0]]
# get rid of rows for which covariates are unavailable
for i in list(set(self.covariates_untransformed)):
all = np.delete(all, np.where(np.isnan(all[i]))[0], axis=0)
obs = np.delete(obs, np.where(np.isnan(obs[i]))[0], axis=0)
# remove observations in which the CF is missing or outside of (0,1), or where sample size/envelope is missing
obs = np.delete(obs, np.where((np.isnan(obs.cf)) | (obs.cf > 1.) | (obs.cf < 0.) | (np.isnan(obs.sample_size)) | (obs.sample_size < 1.) | np.isnan(obs.envelope))[0], axis=0)
# make lists of all the countries/regions/ages/years to predict for
self.country_list = np.unique(all.country)
self.region_list = np.unique(all.region)
self.super_region_list = np.unique(all.super_region)
self.age_list = np.unique(all.age)
self.year_list = np.unique(all.year)
# apply a moving average (5 year window) on cause fractions of 0 or 1, or where sample size is less than 100
age_lookups = {}
for a in self.age_list:
age_lookups[a] = np.where(obs.age == a)[0]
country_lookups = {}
country_age_lookups = {}
for c in self.country_list:
country_lookups[c] = np.where(obs.country == c)[0]
for a in self.age_list:
country_age_lookups[c+'_'+str(a)] = np.intersect1d(country_lookups[c], age_lookups[a])
year_window_lookups = {}
for y in range(self.year_range[0],self.year_range[1]+1):
year_window_lookups[y] = np.where((obs.year >= y-2.) & (obs.year <= y+2.))[0]
smooth_me = np.where((obs.cf==0.) | (obs.cf==1.) | (obs.sample_size<100.))[0]
for i in smooth_me:
obs.cf[i] = obs.cf[np.intersect1d(country_age_lookups[obs.country[i]+'_'+str(obs.age[i])],year_window_lookups[obs.year[i]])].mean()
# for cases in which the CF is still 0 or 1 after the moving average, use the smallest/largest non-0/1 CF observed in that region-age
region_age_lookups = {}
region_lookups = {}
for r in self.region_list:
region_lookups[r] = np.where(obs.region == r)[0]
for a in self.age_list:
region_age_lookups[str(r)+'_'+str(a)] = np.intersect1d(region_lookups[r], age_lookups[a])
validcfs = np.where((obs.cf>0.) & (obs.cf<1.))[0]
for i in np.where(obs.cf==0.)[0]:
candidates = np.intersect1d(region_age_lookups[str(obs.region[i])+'_'+str(obs.age[i])], validcfs)
if candidates.shape[0] == 0:
obs.cf[i] = 0.
else:
obs.cf[i] = obs.cf[candidates].min()
for i in np.where(obs.cf==1.)[0]:
candidates = np.intersect1d(region_age_lookups[str(obs.region[i])+'_'+str(obs.age[i])], validcfs)
if candidates.shape[0] == 0:
obs.cf[i] = 1.
else:
obs.cf[i] = obs.cf[candidates].max()
# finally, any CF that is still 0 or 1 after the above corrections should simply be dropped
obs = np.delete(obs, np.where((obs.cf == 0.) | (obs.cf == 1.))[0], axis=0)
# we treat our envelope as truth, so never allow sample size to exceed it
shrink_me = np.where(obs.sample_size > obs.envelope*.999)[0]
obs.sample_size[shrink_me] = obs.envelope[shrink_me]*.999
# make covariate matrices (including transformations and normalization)
obs_vectors = [obs.country, obs.region, obs.super_region, obs.year, obs.age, obs.cf, obs.sample_size, obs.envelope, obs.pop, np.ones(obs.shape[0])]
obs_names = ['country', 'region', 'super_region', 'year', 'age', 'cf', 'sample_size', 'envelope', 'pop', 'x0']
all_vectors = [all.country, all.region, all.super_region, all.year, all.age, all.envelope, all.pop, np.ones(all.shape[0])]
all_names = ['country', 'region', 'super_region', 'year', 'age', 'envelope', 'pop', 'x0']
self.covariate_dict = {'x0': 'constant'}
for i in range(len(self.covariate_list)):
a = all[self.covariates_untransformed[i]]
o = obs[self.covariates_untransformed[i]]
if self.covariate_transformations[i] == 'ln':
a = np.log(a)
o = np.log(o)
elif self.covariate_transformations[i] == 'ln+sq':
a = (np.log(a))**2
o = (np.log(o))**2
elif self.covariate_transformations[i] == 'sq':
a = a**2
o = o**2
if self.normalize == True:
cov_mean = np.mean(a)
cov_sd = np.std(a)
a = ((a-cov_mean)/cov_sd)
o = ((o-cov_mean)/cov_sd)
all_vectors.append(a)
all_names.append('x' + str(i+1))
obs_vectors.append(o)
obs_names.append('x' + str(i+1))
self.covariate_dict['x' + str(i+1)] = self.covariate_list[i]
# create age dummies if specified
if self.age_dummies == True:
pre_ref = 1
for i,j in enumerate(self.age_list):
if j == self.age_ref:
pre_ref = 0
elif pre_ref == 1:
all_vectors.append(np.array(all.age==j).astype(np.float))
all_names.append('x' + str(len(self.covariate_list)+i+1))
obs_vectors.append(np.array(obs.age==j).astype(np.float))
obs_names.append('x' + str(len(self.covariate_list)+i+1))
self.covariate_dict['x' + str(len(self.covariate_list)+i+1)] = 'Age ' + str(j)
else:
all_vectors.append(np.array(all.age==j).astype(np.float))
all_names.append('x' + str(len(self.covariate_list)+i))
obs_vectors.append(np.array(obs.age==j).astype(np.float))
obs_names.append('x' + str(len(self.covariate_list)+i))
self.covariate_dict['x' + str(len(self.covariate_list)+i)] = 'Age ' + str(j)
# return the prediction and observation matrices
self.prediction_matrix = np.core.records.fromarrays(all_vectors, names=all_names)
self.observation_matrix = np.core.records.fromarrays(obs_vectors, names=obs_names)
# prep all the in-sample data
self.data_rows = self.observation_matrix.shape[0]
print 'Data Rows:', self.data_rows
self.training_split()
# cache the data if requested
if save_cache == True:
pl.rec2csv(self.prediction_matrix, '/home/j/Project/Causes of Death/CoDMod/tmp/prediction_matrix_' + self.cause + '_' + self.sex + '.csv')
pl.rec2csv(self.observation_matrix, '/home/j/Project/Causes of Death/CoDMod/tmp/observation_matrix_' + self.cause + '_' + self.sex + '.csv')
# load in age weights for creating age adjusted rates later
age_weights = mysql_to_recarray(self.cursor, 'SELECT age,weight FROM age_weights;')
age_weights = recfunctions.append_fields(age_weights, 'keep', np.zeros(age_weights.shape[0])).view(np.recarray)
for a in self.age_list:
age_weights.keep[np.where(age_weights.age==a)[0]] = 1
age_weights = np.delete(age_weights, np.where(age_weights.keep==0)[0], axis=0)
age_weights.weight = age_weights.weight/age_weights.weight.sum()
self.age_weights = age_weights
def predict_test(self, save_csv=False):
''' Use the MCMC traces to predict the test data '''
# setup constants
num_test_rows = self.test_data.shape[0]
num_iters = self.approxs['beta'].shape[0]
# indices
t_index = dict([(t, i) for i, t in enumerate(self.year_list)])
a_index = dict([(a, i) for i, a in enumerate(self.age_list)])
# fixed effects
X = np.array([self.test_data['x%d'%i] for i in range(self.mod_mc.beta.value.shape[0])])
BX = np.dot(self.approxs['beta'], X)
# exposure
'''
if self.training_type == 'make predictions':
E = np.ones((num_iters, num_test_rows))*self.test_data.envelope
else:
E = np.random.binomial(np.round(self.test_data.envelope).astype('int'), (self.test_data.sample_size/self.test_data.envelope), (num_iters, num_test_rows))
'''
E = np.ones((num_iters, num_test_rows))*self.test_data.envelope
# interpolation parameters
x_samples = self.sample_points[:,0]
y_samples = self.sample_points[:,1]
xb = self.age_list[0]
xe = self.age_list[-1]
yb = self.year_list[0]
ye = self.year_list[-1]
kx = 3 if len(self.age_samples) > 3 else len(self.age_samples)-1
ky = 3 if len(self.year_samples) > 3 else len(self.year_samples)-1
# pi_s
s_index = [np.where(self.test_data.super_region==s) for s in self.super_region_list]
t_by_s = [[t_index[self.test_data.year[j]] for j in s_index[s][0]] for s in range(len(self.super_region_list))]
a_by_s = [[a_index[self.test_data.age[j]] for j in s_index[s][0]] for s in range(len(self.super_region_list))]
pi_s = np.zeros((num_iters, num_test_rows))
for s in range(len(self.super_region_list)):
for i in range(num_iters):
interpolator = interpolate.bisplrep(x=x_samples, y=y_samples, z=self.approxs['pi_s_'+str(s)][i], xb=xb, xe=xe, yb=yb, ye=ye, kx=kx, ky=ky)
pi_s[i,s_index[s][0]] = interpolate.bisplev(x=self.age_list, y=self.year_list, tck=interpolator)[a_by_s[s],t_by_s[s]]
mean_pi_s = pi_s[:,s_index[s][0]].mean(axis=1)
pi_s[:,s_index[s][0]] = pi_s[:,s_index[s][0]][np.argsort(mean_pi_s)]
# pi_r
r_index = [np.where(self.test_data.region==r) for r in self.region_list]
t_by_r = [[t_index[self.test_data.year[j]] for j in r_index[r][0]] for r in range(len(self.region_list))]
a_by_r = [[a_index[self.test_data.age[j]] for j in r_index[r][0]] for r in range(len(self.region_list))]
pi_r = np.zeros((num_iters, num_test_rows))
for r in range(len(self.region_list)):
for i in range(num_iters):
interpolator = interpolate.bisplrep(x=x_samples, y=y_samples, z=self.approxs['pi_r_'+str(r)][i], xb=xb, xe=xe, yb=yb, ye=ye, kx=kx, ky=ky)
pi_r[i,r_index[r][0]] = interpolate.bisplev(x=self.age_list, y=self.year_list, tck=interpolator)[a_by_r[r],t_by_r[r]]
mean_pi_r = pi_r[:,r_index[r][0]].mean(axis=1)
pi_r[:,r_index[r][0]] = pi_r[:,r_index[r][0]][np.argsort(mean_pi_r)]
# pi_c
c_index = [np.where(self.test_data.country==c) for c in self.country_list]
t_by_c = [[t_index[self.test_data.year[j]] for j in c_index[c][0]] for c in range(len(self.country_list))]
a_by_c = [[a_index[self.test_data.age[j]] for j in c_index[c][0]] for c in range(len(self.country_list))]
pi_c = np.zeros((num_iters, num_test_rows))
for c in range(len(self.country_list)):
for i in range(num_iters):
interpolator = interpolate.bisplrep(x=x_samples, y=y_samples, z=self.approxs['pi_c_'+str(c)][i], xb=xb, xe=xe, yb=yb, ye=ye, kx=kx, ky=ky)
pi_c[i,c_index[c][0]] = interpolate.bisplev(x=self.age_list, y=self.year_list, tck=interpolator)[a_by_c[c],t_by_c[c]]
mean_pi_c = pi_c[:,c_index[c][0]].mean(axis=1)
pi_s[:,c_index[c][0]] = pi_c[:,c_index[c][0]][np.argsort(mean_pi_c)]
# make predictions
import os
os.chdir('/home/j/Project/Causes of Death/CoDMod/codmod2/')
import percentile
predictions = np.exp(BX + np.log(E) + pi_s + pi_r + pi_c)
mean = predictions.mean(axis=0)
lower = percentile.percentile(predictions, 2.5, axis=0)
upper = percentile.percentile(predictions, 97.5, axis=0)
self.predictions = self.test_data[['country','region','super_region','year','age','pop']]
self.predictions = recfunctions.append_fields(self.predictions, 'mean_deaths', mean)
self.predictions = recfunctions.append_fields(self.predictions, 'lower_deaths', lower)
self.predictions = recfunctions.append_fields(self.predictions, 'upper_deaths', upper)
if self.training_type != 'make predictions':
self.predictions = recfunctions.append_fields(self.predictions, 'actual_deaths', self.test_data.cf*self.test_data.envelope)
self.predictions = self.predictions.view(np.recarray)
# save the predictions
if save_csv == True:
pl.rec2csv(self.predictions, '/home/j/Project/Causes of Death/CoDMod/tmp/' + self.name + '_predictions_' + self.cause + '_' + self.sex + '.csv')
## 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)
logit_est = gpr.collapse_sims(mort_draws)
unlogit_est = gpr.collapse_sims(mort_draws)
os.chdir('FILEPATH' + sex + '_' + age + '/')
all_est = []
for i in range(len(predictionyears)):
all_est.append((cc, predictionyears[i], unlogit_est['med'][i],
unlogit_est['lower'][i], unlogit_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('year', '<f8'),
('med', '<f8'), ('lower', '<f8'),
('upper', '<f8')])
pl.rec2csv(all_est, 'gpr_' + cc + '_' + sex + '_' + age + '.txt')
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(draws):
all_sim.append((cc, predictionyears[i], s, mort_draws[s][i]))
all_sim = pl.array(all_sim, [('ihme_loc_id', '|S32'), ('year', '<f8'),
('sim', '<f8'), ('mort', '<f8')])
pl.rec2csv(all_sim, 'gpr_' + cc + '_' + sex + '_' + age + '_sim.txt')
total_var = var + logit_est['std'][pred_index]**2
coverage = int(
(logit_est['med'][pred_index] -
1.96 * pl.sqrt(total_var)) < mort <
(logit_est['med'][pred_index] + 1.96 * pl.sqrt(total_var)))
all_err.append((rr, cc, ho, scale, amp2x, mse * amp2x, year,
mort, re, coverage))
## write files
if os.name == 'posix':
os.chdir('FILEPATH')
else:
os.chdir('FILEPATH')
all_est = pl.array(all_est, [('gbd_region', '|S64'), ('iso3', '|S32'),
('ho', '<f8'), ('scale', '<f8'), ('amp2x', '<f8'),
('amp2', '<f8'), ('year', '<f8'), ('mort', '<f8'),
('std', '<f8')])
pl.rec2csv(all_est, 'gpr_%s_%i.txt' % (cc, ho))
if os.name == 'posix':
os.chdir('FILEPATH')
else:
os.chdir('FILEPATH')
all_err = pl.array(all_err, [('gbd_region', '|S64'), ('iso3', '|S32'),
('ho', '<f8'), ('scale', '<f8'), ('amp2x', '<f8'),
('amp2', '<f8'), ('year', '<f8'), ('mort', '<f8'),
('re', '<f8'), ('coverage', '<f8')])
pl.rec2csv(all_err, 'loss_%s_%i.txt' % (cc, ho))
coldict['lat'] = np.concatenate((duffy_data.lat, vivax_data.lat))
coldict['n'] = np.concatenate((duffy_data.n, vivax_data.pos + vivax_data.neg))
coldict['vivax_pos'] = np.concatenate((duffy_nan, vivax_data.pos))
coldict['vivax_neg'] = np.concatenate((duffy_nan, vivax_data.neg))
coldict['datatype'] = np.concatenate(
(duffy_data.datatype, np.repeat('vivax', n_vivax)))
for colname in vivaxcols:
coldict[colname] = np.concatenate((duffy_nan, vivax_data[colname]))
for colname in duffycols:
coldict[colname] = np.concatenate((duffy_data[colname], vivax_nan))
allcols = coldict.keys()
combined_data = np.rec.fromarrays([coldict[col] for col in allcols],
names=allcols)
# FIXME: Do the Sahel instead.
def box_data(data, llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat):
indicator = (data.lon > llcrnrlon) * (data.lon < urcrnrlon) * (
data.lat > llcrnrlat) * (data.lat < urcrnrlat)
return data[np.where(indicator)]
# Write out
# warnings.warn('Boxing')
# combined_data = combined_data[np.where((combined_data.lon>-19)*(combined_data.lon<54)*(combined_data.lat>0))]
# combined_data = box_data(combined_data, 31.5, 11.5, 64, 32)
rec2csv(combined_data, combined_datafile)
for i in range(len(predictionyears)):
all_est.append((cc, ss, predictionyears[i], unlog_est['med'][i],
unlog_est['lower'][i], unlog_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('sex', '|S32'),
('year', '<f8'), ('mort_med', '<f8'),
('mort_lower', '<f8'),
('mort_upper', '<f8')])
## no need to save the summary if we're doing the HIV draws version
if (hiv_uncert == int(1)):
pass
else:
est_file = "FILEPATH"
pl.rec2csv(all_est, est_file)
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(dr):
if (transform == 'log10'):
all_sim.append((cc, ss, predictionyears[i], s,
10**d[s][i])) # log base 10 space
elif (transform == 'ln'):
all_sim.append((cc, ss, predictionyears[i], s,
math.e**d[s][i])) # natural log space
elif (transform == 'logit'):
all_sim.append((cc, ss, predictionyears[i], s,
((math.e**d[s][i]) /
# collapse across draws
# note: space transformations need to be performed at the draw level
# not actually doing any transformations here, we'll do after
logit_est = gpr.collapse_sims(mort_draws)
unlogit_est = gpr.collapse_sims(mort_draws)
# save the predictions
all_est = []
for i in range(len(predictionyears)):
all_est.append((cc, predictionyears[i], unlogit_est['med'][i],
unlogit_est['lower'][i], unlogit_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('year', '<f8'),
('med', '<f8'), ('lower', '<f8'),
('upper', '<f8')])
output_file = "FILEPATH"
pl.rec2csv(all_est, output_file)
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(draws):
all_sim.append((cc, predictionyears[i], s, mort_draws[s][i]))
all_sim = pl.array(all_sim, [('ihme_loc_id', '|S32'), ('year', '<f8'),
('sim', '<f8'), ('mort', '<f8')])
output_file = "FILEPATH"
pl.rec2csv(all_sim, output_file)
def combine_output(J, T, model, dir, reps, save=False):
"""
Combine output on absolute error, relative error, csmf_accuracy, and coverage from from
multiple runs of validate_once. Either saves the output to the disk, or returns arays
for each.
"""
cause = pl.zeros(J * T, dtype='f').view(pl.recarray)
time = pl.zeros(J * T, dtype='f').view(pl.recarray)
abs_err = pl.zeros(J * T, dtype='f').view(pl.recarray)
rel_err = pl.zeros(J * T, dtype='f').view(pl.recarray)
coverage = pl.zeros(J * T, dtype='f').view(pl.recarray)
csmf_accuracy = pl.zeros(J * T, dtype='f').view(pl.recarray)
for i in range(reps):
metrics = pl.csv2rec('%s/metrics_%s_%i.csv' % (dir, model, i))
cause = pl.vstack((cause, metrics.cause))
time = pl.vstack((time, metrics.time))
abs_err = pl.vstack((abs_err, metrics.abs_err))
rel_err = pl.vstack((rel_err, metrics.rel_err))
coverage = pl.vstack((coverage, metrics.coverage))
csmf_accuracy = pl.vstack((csmf_accuracy, metrics.csmf_accuracy))
cause = cause[1:, ]
time = time[1:, ]
abs_err = abs_err[1:, ]
rel_err = rel_err[1:, ]
coverage = coverage[1:, ]
csmf_accuracy = csmf_accuracy[1:, ]
mean_abs_err = abs_err.mean(0)
median_abs_err = pl.median(abs_err, 0)
mean_rel_err = rel_err.mean(0)
median_rel_err = pl.median(rel_err, 0)
mean_csmf_accuracy = csmf_accuracy.mean(0)
median_csmf_accuracy = pl.median(csmf_accuracy, 0)
mean_coverage_bycause = coverage.mean(0)
mean_coverage = coverage.reshape(reps, T, J).mean(0).mean(1)
percent_total_coverage = (coverage.reshape(reps, T, J).sum(2) == 3).mean(0)
mean_coverage = pl.array([[i for j in range(J)]
for i in mean_coverage]).ravel()
percent_total_coverage = pl.array([[i for j in range(J)]
for i in percent_total_coverage
]).ravel()
models = pl.array([[model for j in range(J)] for i in range(T)]).ravel()
true_cf = metrics.true_cf
true_std = metrics.true_std
std_bias = metrics.std_bias
all = pl.np.core.records.fromarrays(
[
models, cause[0], time[0], true_cf, true_std, std_bias,
mean_abs_err, median_abs_err, mean_rel_err, median_rel_err,
mean_csmf_accuracy, median_csmf_accuracy, mean_coverage_bycause,
mean_coverage, percent_total_coverage
],
names=[
'model', 'cause', 'time', 'true_cf', 'true_std', 'std_bias',
'mean_abs_err', 'median_abs_err', 'mean_rel_err', 'median_rel_err',
'mean_csmf_accuracy', 'median_csmf_accuracy',
'mean_covearge_bycause', 'mean_coverage', 'percent_total_coverage'
])
if save:
pl.rec2csv(all, '%s/%s_summary.csv' % (dir, model))
else:
return all
else:
indexer[axis] = slice(i, i + 2)
j = i + 1
weights = np.array([(j - index), (index - i)], float)
wshape = [1] * sorted.ndim
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer] * weights, axis=axis, out=out) / sumval
# save basic estimates
model_estimates = model.trace("estimate")[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields(
rec=data, names=["mean", "lower", "upper"], arrs=[mean_estimate, lower_estimate, upper_estimate]
)
pl.rec2csv(
output, proj_dir + "outputs/model results/spatial smoothing/" + mod_name + "_" + str(sex) + "_" + age + ".csv"
)
# save draws
draws = pl.rec_append_fields(
rec=data, names=["draw_" + str(i + 1) for i in range(500)], arrs=[model.trace("estimate")[i] for i in range(500)]
)
pl.rec2csv(
draws, proj_dir + "outputs/model results/spatial smoothing/" + mod_name + "_draws_" + str(sex) + "_" + age + ".csv"
)
# 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 hivsims == 0:
os.chdir('FILEPATH')
all_est = []
for i in range(len(predictionyears)):
all_est.append((cc, predictionyears[i], unlogit_est['med'][i],
unlogit_est['lower'][i], unlogit_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('year', '<f8'),
('med', '<f8'), ('lower', '<f8'),
('upper', '<f8')])
pl.rec2csv(all_est, 'gpr_%s.txt' % cc)
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(draws):
all_sim.append(
(cc, predictionyears[i], s, gpr.inv_logit(mort_draws[s][i])))
all_sim = pl.array(all_sim, [('ihme_loc_id', '|S32'), ('year', '<f8'),
('sim', '<f8'), ('mort', '<f8')])
if hivsims == 1:
os.chdir('FILEPATH')
pl.rec2csv(all_sim, 'gpr_%s_%s_sim.txt' % (cc, rnum))
else:
full_dir = '%s/v02_prep_%s' % (indir, iso3)
# get cause list
causes = list(set([file.split('+')[1] for file in os.listdir(full_dir) if re.search(age, file)]))
causes.remove('HIV') # temporary until Miriam fixes the HIV files
# gather data and fit model
cf = data.get_cod_data(full_dir, causes, age, iso3, sex)
m, pi = models.fit_latent_simplex(cf)
# calculate summary measures
N, T, J = pi.shape
mean = pi.mean(0)
lower = pl.array([[st.mquantiles(pi[:,t,j], 0.025)[0] for j in range(J)] for t in range(T)])
upper = pl.array([[st.mquantiles(pi[:,t,j], 0.975)[0] for j in range(J)] for t in range(T)])
# format summary and save
output = pl.np.core.records.fromarrays(mean.T, names=['%s_mean' % c for c in causes])
output = pl.rec_append_fields(output, ['%s_lower' % c for c in causes], lower.T)
output = pl.rec_append_fields(output, ['%s_upper' % c for c in causes], upper.T)
pl.rec2csv(output, '%s/%s+%s+%s+summary.csv' % (outdir, iso3, age, sex))
# format all sims and save
pi.shape = (N*T, J)
years = pl.array([t for s in range(N) for t in range(1980, 2012)])
sim = pl.array([s for s in range(N) for t in range(1980, 2012)])
output = pl.np.core.records.fromarrays(pi.T, names=causes)
output = pl.rec_append_fields(output, 'year', years)
output = pl.rec_append_fields(output, 'sim', sim)
pl.rec2csv(output, '%s/%s+%s+%s.csv' % (outdir, iso3, age, sex))
def fit_GPR(infile, outfile, dv_list, scale, number_submodels, iters):
# load in the data
all_data = csv2rec(infile, use_mrecords=False)
for m in range(number_submodels):
all_data = np.delete(
all_data,
np.where(np.isnan(all_data['spacetime_' + str(m + 1)]))[0],
axis=0)
# Investigate error thrown for HKG, MAC, and SGP... they don't have data, but don't know why this is breaking line 62
all_data = all_data[all_data['iso3'] != "HKG"]
all_data = all_data[all_data['iso3'] != "MAC"]
all_data = all_data[all_data['iso3'] != "SGP"]
# find the list of years for which we need to predict
year_list = np.unique(all_data.year)
# find the list of country/age groups
country_age = np.array([all_data.iso3[i] for i in range(len(all_data))])
country_age_list = np.repeat(np.unique(country_age), len(year_list))
# make empty arrays in which to store the results
draws = [
np.empty(len(country_age_list), 'float')
for i in range(iters * number_submodels * 2)
]
iso3 = np.empty(len(country_age_list), '|S3')
# age_group = np.empty(len(country_age_list), 'int')
year = np.empty(len(country_age_list), 'int')
# loop through country/age groups
for ca in np.unique(country_age_list):
print('GPRing ' + ca)
# subset the data for this particular country/age
ca_data = all_data[country_age == ca]
# subset just the observed data
if ca_data['lt_prev'].dtype != '|O8':
ca_observed = ca_data[(np.isnan(ca_data['lt_prev']) == 0)]
if len(ca_observed) > 1:
has_data = True
else:
has_data = False
else:
has_data = False
# loop through each submodel
for m in range(number_submodels):
# identify the dependent variable for this model
dv = dv_list[m]
# loop through spacetime/linear
for x, t in enumerate(['spacetime']):
# make a list of the spacetime predictions
ca_prior = np.array([
np.mean(ca_data[t + '_' + str(m + 1)][ca_data.year == y])
for y in year_list
])
# find the amplitude for this country/age
amplitude = np.mean(ca_data[t + '_amplitude_' + str(m + 1)])
# make a linear interpolation of the spatio-temporal predictions to use as the mean function for GPR
def mean_function(x):
return np.interp(x, year_list, ca_prior)
# setup the covariance function
M = gp.Mean(mean_function)
C = gp.Covariance(eval_fun=gp.matern.euclidean,
diff_degree=2,
amp=amplitude,
scale=scale)
# observe the data if there is any
if has_data:
gp.observe(M=M,
C=C,
obs_mesh=ca_observed.year,
obs_V=ca_observed[t + '_data_variance_' +
str(m + 1)],
obs_vals=ca_observed['lt_prev'])
# draw realizations from the data
realizations = [gp.Realization(M, C) for i in range(iters)]
# save the data for this country/age into the results array
iso3[country_age_list == ca] = ca[0:3]
# age_group[country_age_list==ca] = ca[4:]
year[country_age_list == ca] = year_list.T
for i in range(iters):
draws[((2 * m + x) * iters) + i][
country_age_list == ca] = realizations[i](year_list)
# save the results
print('Saving GPR results')
names = ['iso3', 'age_group', 'year']
results = np.core.records.fromarrays([iso3, year], names=names)
for m in range(number_submodels):
for x, t in enumerate(['spacetime']):
for i in range(iters):
results = recfunctions.append_fields(
results, 'gpr_' + str(m + 1) + '_' + t + '_d' + str(i + 1),
draws[((2 * m + x) * iters) + i])
results = recfunctions.append_fields(
results, 'gpr_' + str(m + 1) + '_' + t + '_mean',
np.mean(draws[((2 * m + x) * iters):((2 * m + x + 1) * iters)],
axis=0))
rec2csv(results, outfile)
lon_ind = np.argmin(np.abs(np.subtract.outer(lon_old, lon_new)), axis=0)
lat_ind = np.argmin(np.abs(np.subtract.outer(lat_old, lat_new)), axis=0)
out = lat_new*0
for i in xrange(len(lon_new)):
lai, loi = lat_ind[i], lon_ind[i]
if data.mask[lai, loi]:
for d in xrange(10):
if True-np.all(data.mask[lai-d:lai+d,loi-d:loi+d]):
out[i] = mode(data.data[lai-d:lai+d,loi-d:loi+d][np.where(True-data.mask[lai-d:lai+d,loi-d:loi+d])])
break
else:
out[i] = data[lai,loi]
if np.any(np.isnan(out)):
raise ValueError
return out
for fname in map(lambda n: n+'.hdf5', covariate_names):
print 'Evaluating %s'%fname
colname = os.path.splitext(fname)[0]
hf = tb.openFile(os.path.join(covariate_path,fname))
cols[colname] = map_utils.interp_geodata(hf.root.lon[:],hf.root.lat[:],hf.root.data[:],cols['lon'],cols['lat'],hf.root.mask[:],order=0,nan_handler=nan_callback)
if np.any(np.isnan(cols[colname])):
raise ValueError
hf.close()
keys = cols.keys()
data_out = np.rec.fromarrays([cols[k] for k in keys], names=keys)
rec2csv(data_out, os.path.splitext(os.path.basename(sys.argv[1]))[0]+'_with_covariates.csv')
weights = np.array(1)
sumval = 1.0
else:
indexer[axis] = slice(i, i+2)
j = i + 1
weights = np.array([(j - index), (index - i)],float)
wshape = [1]*sorted.ndim
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
# save basic estimates
model_estimates = model.trace('estimate')[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields( rec = data,
names = ['mean', 'lower', 'upper'],
arrs = [mean_estimate, lower_estimate, upper_estimate])
pl.rec2csv(output, proj_dir + 'outputs/model results/spatial smoothing/spatial_intercept_' + str(sex) + '_' + age + '.csv')
# save draws
draws = pl.rec_append_fields(
rec = data,
names = ['draw_' + str(i+1) for i in range(100)],
arrs = [model.trace('estimate')[i] for i in range(100)])
pl.rec2csv(draws, proj_dir + 'outputs/model results/spatial smoothing/spatial_intercept_draws_' + str(sex) + '_' + age + '.csv')
## 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)
logit_est = gpr.collapse_sims(mort_draws)
unlogit_est = gpr.collapse_sims(mort_draws)
os.chdir('FILEPATH')
all_est = []
for i in range(len(predictionyears)):
all_est.append((cc, predictionyears[i], unlogit_est['med'][i], unlogit_est['lower'][i], unlogit_est['upper'][i]))
all_est = pl.array(all_est, [('ihme_loc_id', '|S32'), ('year', '<f8'), ('med', '<f8'), ('lower', '<f8'), ('upper', '<f8')])
pl.rec2csv(all_est, 'gpr_%s.txt' %cc)
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(draws):
all_sim.append((cc, predictionyears[i], s, mort_draws[s][i]))
all_sim = pl.array(all_sim, [('ihme_loc_id', '|S32'), ('year', '<f8'), ('sim', '<f8'), ('mort', '<f8')])
pl.rec2csv(all_sim, 'gpr_%s_sim.txt' %cc)
C = pm.gp.FullRankCovariance(my_st, amp=1, scale=1, inc=np.pi/4, ecc=.3,st=.1, sd=.5, tlc=.2, sf = .1)
dm = np.vstack((lon,lat,t)).T
C_eval = C(dm,dm)
f = pm.rmv_normal_cov(np.sum([cv[name]*vals[name] for name in names],axis=0), C_eval) + np.random.normal(size=n_data+n_pred)*np.sqrt(V)
p = pm.flib.invlogit(f)
ns = 100
pos = pm.rbinomial(ns, p)
neg = ns - pos
print p
ra_data = np.rec.fromarrays((pos[:n_data], neg[:n_data], lon[:n_data], lat[:n_data]) + tuple([cv[name][:n_data] for name in names]), names=['pos','neg','lon','lat']+names)
pl.rec2csv(ra_data,'test_data.csv')
ra_pred = np.rec.fromarrays((pos[n_data:], neg[n_data:], lon[n_data:], lat[n_data:]) + tuple([cv[name][n_data:] for name in names]), names=['pos','neg','lon','lat']+names)
pl.rec2csv(ra_pred,'test_pred.csv')
os.system('infer cov_test test_db test_data.csv -t 10 -n 8 -i 100000')
# os.system('cov-test-predict test test_pred.csv 1000 100')
#
# # ra_data = pl.csv2rec('test_data.csv')
# # ra_pred = pl.csv2rec('test_pred.csv')
# samps = np.fromfile('test_samps.csv',sep=',').reshape((n_pred,-1))
#
# pos_pred = pos[n_data:]
# neg_pred = neg[n_data:]
# p_pred = (pos_pred+1.)/(pos_pred+neg_pred+2.)
#
coldict = {}
coldict["t"] = np.concatenate((duffy_nan, (tstart + tend) / 2.0))
coldict["lon"] = np.concatenate((duffy_data.lon, vivax_data.lon))
coldict["lat"] = np.concatenate((duffy_data.lat, vivax_data.lat))
coldict["n"] = np.concatenate((duffy_data.n, vivax_data.pos + vivax_data.neg))
coldict["vivax_pos"] = np.concatenate((duffy_nan, vivax_data.pos))
coldict["vivax_neg"] = np.concatenate((duffy_nan, vivax_data.neg))
coldict["datatype"] = np.concatenate((duffy_data.datatype, np.repeat("vivax", n_vivax)))
for colname in vivaxcols:
coldict[colname] = np.concatenate((duffy_nan, vivax_data[colname]))
for colname in duffycols:
coldict[colname] = np.concatenate((duffy_data[colname], vivax_nan))
allcols = coldict.keys()
combined_data = np.rec.fromarrays([coldict[col] for col in allcols], names=allcols)
# FIXME: Do the Sahel instead.
def box_data(data, llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat):
indicator = (data.lon > llcrnrlon) * (data.lon < urcrnrlon) * (data.lat > llcrnrlat) * (data.lat < urcrnrlat)
return data[np.where(indicator)]
# Write out
# warnings.warn('Boxing')
# combined_data = combined_data[np.where((combined_data.lon>-19)*(combined_data.lon<54)*(combined_data.lat>0))]
# combined_data = box_data(combined_data, 31.5, 11.5, 64, 32)
rec2csv(combined_data, combined_datafile)
def compile_all_results(scenarios, dir='../data'):
"""
Compiles the results across multiple scenarios produced by running run_on_cluster on each
one into a single sv file. The specified directory must be where where the results of
running run_on_cluster for each scenario are stored (each is a sub-directory named v0, v1, etc.)
and is also where the output from this function will be saved.
"""
models = []
causes = []
time = []
true_cf = []
true_std = []
std_bias = []
mean_abs_err = []
median_abs_err = []
mean_rel_err = []
median_rel_err = []
mean_csmf_accuracy = []
median_csmf_accuracy = []
mean_coverage_bycause = []
mean_coverage = []
percent_total_coverage = []
scenario = []
for i in range(scenarios):
for j in ['bad_model', 'latent_simplex']:
read = csv.reader(open('%s/v%s/%s_summary.csv' % (dir, i, j)))
read.next()
for row in read:
models.append(row[0])
causes.append(row[1])
time.append(row[2])
true_cf.append(row[3])
true_std.append(row[4])
std_bias.append(row[5])
mean_abs_err.append(row[6])
median_abs_err.append(row[7])
mean_rel_err.append(row[8])
median_rel_err.append(row[9])
mean_csmf_accuracy.append(row[10])
median_csmf_accuracy.append(row[11])
mean_coverage_bycause.append(row[12])
mean_coverage.append(row[13])
percent_total_coverage.append(row[14])
scenario.append(i)
all = pl.np.core.records.fromarrays(
[
scenario, models, time, true_cf, true_std, causes, mean_abs_err,
median_abs_err, mean_rel_err, median_rel_err, mean_csmf_accuracy,
median_csmf_accuracy, mean_coverage_bycause, mean_coverage,
percent_total_coverage
],
names=[
'scenario', 'model', 'time', 'true_cf', 'true_std', 'cause',
'mean_abs_err', 'median_abs_err', 'mean_rel_err', 'median_rel_err',
'mean_csmf_accuracy', 'median_csmf_accuracy',
'mean_covearge_bycause', 'mean_coverage', 'percent_total_coverage'
])
pl.rec2csv(all, fname='%s/all_summary_metrics.csv' % (dir))
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))
# save the sims
all_sim = []
for i in range(len(predictionyears)):
for s in range(draws):
all_sim.append((ihme_loc_id, predictionyears[i], s,
gpr.inv_logit(mort_draws[s][i])))
all_sim = pl.array(all_sim, [('ihme_loc_id', '|S32'), ('year', '<f8'),
('sim', '<f8'), ('mort', '<f8')])
pl.rec2csv(all_sim, "FILEPATH")
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
import time
print 'Finished at %s' % time.ctime()
# save basic predictions
predictions = model.trace('predicted')[:]
mean_prediction = predictions.mean(axis=0)
lower_prediction = percentile(predictions, 2.5, axis=0)
upper_prediction = percentile(predictions, 97.5, axis=0)
output = pl.rec_append_fields( rec = data,
names = ['mean', 'lower', 'upper'],
arrs = [mean_prediction, lower_prediction, upper_prediction])
pl.rec2csv(output, proj_dir + 'outputs/model results/epi transition by state/all_cause_males.csv')
# plot surfaces
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages(proj_dir + 'outputs/model results/epi transition by state/surfaces.pdf')
fig = plt.figure()
ax = fig.gca(projection='3d')
X,Y = np.meshgrid(years, ages)
Z = model.trace('alpha_surf')[:].mean(axis=0)
ax.plot_wireframe(X, Y, Z, color='#315B7E')
ax.set_title('National')
pp.savefig()
for g in g_list:
fig = plt.figure()
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
# save basic estimates
model_estimates = model.trace('estimate')[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields( rec = data,
names = ['mean', 'lower', 'upper'],
arrs = [mean_estimate, lower_estimate, upper_estimate])
pl.rec2csv(output, proj_dir + 'outputs/model results/random effects plus flex time/srw_plus_interaction_results.csv')
'''
### plot diagnostics
# setup plotting
#import matplotlib.pyplot as pp
#pp.switch_backend('acc')
plot_me = [mu_si, mu_ss, mu_ci, mu_cs, sigma_si, sigma_ss, sigma_ci, sigma_cs, state_intercepts, state_slopes, cause_intercepts, cause_slopes]
# plot traces
os.chdir(proj_dir + '/outputs/model results/simple random effects by state/mcmc plots/traces/')
for p in plot_me:
mc.Matplot.plot(p, suffix='_trace')
if len(p.shape) == 0:
plt.close()
wshape = [1] * sorted.ndim
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer] * weights, axis=axis, out=out) / sumval
# save basic estimates
model_estimates = model.trace("estimate")[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields(
rec=data, names=["mean", "lower", "upper"], arrs=[mean_estimate, lower_estimate, upper_estimate]
)
pl.rec2csv(output, proj_dir + "outputs/model results/simple random effects by state/pymc_results.csv")
### plot diagnostics
# setup plotting
# import matplotlib.pyplot as pp
# pp.switch_backend('acc')
plot_me = [
mu_si,
mu_ss,
mu_ci,
mu_cs,
sigma_si,
sigma_ss,
sigma_ci,
sigma_cs,
def fit_GPR(infile, outfile, dv_list, scale, number_submodels, test):
# load in the data
all_data = csv2rec(infile, use_mrecords=False)
for m in range(number_submodels):
if all_data['spacetime_' + str(m+1)].dtype == 'float64':
all_data = np.delete(all_data, np.where(np.isnan(all_data['spacetime_' + str(m+1)]))[0], axis=0)
# find the list of years for which we need to predict
year_list = np.unique(all_data.year)
# find the list of country/age groups
country_age = np.array([str(all_data.iso3[i]) + '_' + str(all_data.age_group[i]) for i in range(len(all_data))])
country_age_list = np.repeat(np.unique(country_age), len(year_list))
# make empty arrays in which to store the results
draws = [np.empty(len(country_age_list), 'float') for i in range(number_submodels)]
iso3 = np.empty(len(country_age_list), '|S3')
age_group = np.empty(len(country_age_list), 'int')
year = np.empty(len(country_age_list), 'int')
# loop through country/age groups
for ca in np.unique(country_age_list):
print('GPRing ' + ca)
# subset the data for this particular country/age
ca_data = all_data[country_age==ca]
# subset just the observed data
if ca_data['lt_cf'].dtype != '|O8':
ca_observed = ca_data[(np.isnan(ca_data['lt_cf'])==0) & (ca_data['test_' + test]==0)]
if len(ca_observed) > 1:
has_data = True
else:
has_data = False
else:
has_data = False
# loop through each submodel
for m in range(number_submodels):
# skip models with no spacetime results
if all_data['spacetime_' + str(m+1)].dtype != 'float64':
draws[m][country_age_list==ca] = np.NaN
continue
# identify the dependent variable for this model
dv = dv_list[m]
# make a list of the spacetime predictions
ca_prior = np.array([np.mean(ca_data['spacetime_' + str(m+1)][ca_data.year==y]) for y in year_list])
# find the amplitude for this country/age
amplitude = np.mean(ca_data['spacetime_amplitude_' + str(m+1)])
# make a linear interpolation of the spatio-temporal predictions to use as the mean function for GPR
def mean_function(x) :
return np.interp(x, year_list, ca_prior)
# setup the covariance function
M = gp.Mean(mean_function)
C = gp.Covariance(eval_fun=gp.matern.euclidean, diff_degree=2, amp=amplitude, scale=scale)
# observe the data if there is any
if has_data:
gp.observe(M=M, C=C, obs_mesh=ca_observed.year, obs_V=ca_observed['spacetime_data_variance_' + str(m+1)], obs_vals=ca_observed[dv])
# save the data for this country/age into the results array
iso3[country_age_list==ca] = ca[0:3]
age_group[country_age_list==ca] = ca[4:]
year[country_age_list==ca] = year_list.T
draws[m][country_age_list==ca] = M(year_list)
# save the results
print('Saving GPR results')
names = ['iso3','age_group','year']
results = np.core.records.fromarrays([iso3,age_group,year], names=names)
for m in range(number_submodels):
results = recfunctions.append_fields(results, 'gpr_' + str(m+1) + '_spacetime_mean', draws[m])
rec2csv(results, outfile)
def rec2csv_2d(Y, fname):
"""
write a 2-dimensional recarray to a csv file
"""
pl.rec2csv(pl.np.core.records.fromarrays(Y.T), fname)
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
return np.add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
# save basic estimates
model_estimates = model.trace('estimate')[:]
mean_estimate = model_estimates.mean(axis=0)
lower_estimate = percentile(model_estimates, 2.5, axis=0)
upper_estimate = percentile(model_estimates, 97.5, axis=0)
output = pl.rec_append_fields( rec = data,
names = ['mean', 'lower', 'upper'],
arrs = [mean_estimate, lower_estimate, upper_estimate])
pl.rec2csv(output, proj_dir + 'outputs/model results/random effects plus flex time/pymc_results.csv')
'''
### plot diagnostics
# setup plotting
#import matplotlib.pyplot as pp
#pp.switch_backend('acc')
plot_me = [mu_si, mu_ss, mu_ci, mu_cs, sigma_si, sigma_ss, sigma_ci, sigma_cs, state_intercepts, state_slopes, cause_intercepts, cause_slopes]
# plot traces
os.chdir(proj_dir + '/outputs/model results/simple random effects by state/mcmc plots/traces/')
for p in plot_me:
mc.Matplot.plot(p, suffix='_trace')
if len(p.shape) == 0:
plt.close()
if (transform == 'log10'):
re = (10**mort - unlog_est['med'][pred_index])/(10**mort) # log base 10
#print('1')
elif (transform == 'ln'):
re = (math.e**mort - unlog_est['med'][pred_index])/(math.e**mort) # natural log
#print('2')
elif (transform == 'logit'):
re = (((math.e**mort)/(1+(math.e**mort))) - unlog_est['med'][pred_index])/((math.e**mort)/(1+(math.e**mort))) # logit
#print('3')
elif (transform == 'logit10'):
re = (((10**mort)/(1+(10**mort))) - unlog_est['med'][pred_index])/((10**mort)/(1+(10**mort))) # logit
#print('4')
total_var = stderr**2 + log_est['std'][pred_index]**2 # This evaluates coverage as if any part of uncertainty of data and estimates overlap
# total_var = log_est['std'][pred_index]**2 # This calculates coverage based only on the uncertainty of the estimate
coverage = int((log_est['med'][pred_index] - 1.96*pl.sqrt(total_var)) < mort < (log_est['med'][pred_index] + 1.96*pl.sqrt(total_var)))
all_err.append((rr, cc, ss, ho, scale, amp2x, lam, zeta, mse*amp2x, year, mort, re, coverage))
## write files
os.chdir('/strPath')
all_est = pl.array(all_est, [('region_name', '|S64'), ('ihme_loc_id', '|S32'), ('sex', '|S32'), ('ho', '<f8'),
('scale', '<f8'), ('amp2x', '<f8'), ('lambda', '<f8'), ('zeta', '<f8'), ('amp2', '<f8'), ('year', '<f8'),
('mort', '<f8'), ('std', '<f8')])
pl.rec2csv(all_est, 'gpr_%s_%s_%i_%s_%s.txt' %(cc, ss, ho, lam, zeta))
os.chdir('strPath')
all_err = pl.array(all_err, [('region_name', '|S64'), ('ihme_loc_id', '|S32'), ('sex', '|S32'), ('ho', '<f8'),
('scale', '<f8'), ('amp2x', '<f8'), ('lambda', '<f8'), ('zeta', '<f8'),('amp2', '<f8'), ('year', '<f8'),
('mort', '<f8'), ('re', '<f8'), ('coverage', '<f8')])
pl.rec2csv(all_err, 'loss_%s_%s_%i_%s_%s.txt' %(cc, ss, ho, lam, zeta))