def new_gpr_draws(df2, response, amplitude, prior, scale, has_data, draws):
'''
(data frame, str, float, data frame, int, int) -> array
Using the input parameters given above runs an instance of gaussian process
smoothing in order to account for years where we do not have data. The data
frame (df2) is specific to a location-age.
'''
all_indices = df2.index
data_indices = df2[(df2.ix[:, -4])].index
years = df2.loc[all_indices]["year"].values
data = pd.DataFrame({"year": years, "prior": prior})
data.sort_values("year", inplace=True)
data.drop_duplicates(inplace=True)
def mean_function(x):
return np.interp(x, data.year.values, data.prior.values)
M = gp.Mean(mean_function)
C = gp.Covariance(eval_fun=gp.matern.euclidean, diff_degree=2,
amp=amplitude, scale=scale)
df4 = df2.loc[data_indices]
if has_data:
gp.observe(M=M, C=C, obs_mesh=df4.year.values,
obs_vals=df4[response].values,
obs_V=(df4[response + "_sd"].values)**2 +
df4[response + "_nsv"].values)
ca_draws = np.array([gp.Realization(M, C)(years) for d in range(draws)]).T
return ca_draws
def ca_realizations(gpr_dict, years, draws):
'''
(dict, array, int) -> matrix
Make draws for a specific country age group.
'''
realizations = np.array([gp.Realization(gpr_dict["M"], gpr_dict["C"])(years)
for d in range(draws)]).T
return realizations
delta_true = float(options.delta)
sigma_true = float(options.sigma) * pl.ones(5)
replicate = int(options.replicate)
print 'Running random effects validation for:'
print 'N', N
print 'delta_true', delta_true
print 'sigma_true', sigma_true
print 'replicate', replicate
mc.np.random.seed(1234567 + replicate)
M = gp.Mean(validate_consistent_re_model.quadratic)
C = gp.Covariance(gp.matern.euclidean, amp=1., diff_degree=2, scale=50)
gp.observe(M, C, [0, 25, 100], [-5, -3, -5])
true = {}
li = gp.Realization(M, C)
true['i'] = lambda x: pl.exp(li(x))
lr = gp.Realization(M, C)
true['r'] = lambda x: pl.exp(lr(x))
lf = gp.Realization(M, C)
true['f'] = lambda x: pl.exp(lf(x))
model = validate_consistent_re_model.validate_consistent_re(
N, delta_true, sigma_true, true)
model.results.to_csv(
'%s/%s/%s-%s-%s-%s.csv' %
(output_dir, validation_name, options.numberofrows, options.delta,
options.sigma, options.replicate))
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)
def draw(self):
'''Generate a random realization of the spline, based on the data.'''
if not self.setup:
self._setup()
self.realization = GP.Realization(self.M, self.C)
def fit_gpr(df,
amp,
obs_variable='observed_data',
obs_var_variable='obs_data_variance',
mean_variable='st_prediction',
year_variable='year',
scale=40,
diff_degree=2,
draws=0):
initial_columns = list(df.columns)
data = df.ix[(pd.notnull(df[obs_variable]))
& (pd.notnull(df[obs_var_variable]))]
mean_prior = df[[year_variable, mean_variable]].drop_duplicates()
def mean_function(x):
return np.interp(x, mean_prior[year_variable],
mean_prior[mean_variable])
M = gp.Mean(mean_function)
C = gp.Covariance(eval_fun=gp.matern.euclidean,
diff_degree=diff_degree,
amp=amp,
scale=scale)
if len(data) > 0:
gp.observe(M=M,
C=C,
obs_mesh=data[year_variable],
obs_V=data[obs_var_variable],
obs_vals=data[obs_variable])
model_mean = M(mean_prior[year_variable]).T
#model_variance = np.diagonal(C(p_years,p_years)).T
model_variance = C(mean_prior[year_variable])
model_lower = model_mean - np.sqrt(model_variance) * 1.96
model_upper = model_mean + np.sqrt(model_variance) * 1.96
if draws > 0:
realizations = [
gp.Realization(M, C)(range(min(mean_prior['year']),
max(mean_prior['year']) + 1))
for i in range(draws)
]
real_draws = pd.DataFrame({
year_variable: mean_prior[year_variable],
'gpr_mean': model_mean,
'gpr_var': model_variance,
'gpr_lower': model_lower,
'gpr_upper': model_upper
})
for i, r in enumerate(realizations):
real_draws["draw" + str(i)] = r
real_draws = pd.merge(df, real_draws, on=year_variable, how='left')
gpr_columns = list(set(real_draws.columns) - set(initial_columns))
initial_columns.extend(gpr_columns)
return real_draws[initial_columns]
else:
results = pd.DataFrame({
year_variable: mean_prior[year_variable],
'gpr_mean': model_mean,
'gpr_var': model_variance,
'gpr_lower': model_lower,
'gpr_upper': model_upper
})
gpr_columns = list(set(results.columns) - set(initial_columns))
initial_columns.extend(gpr_columns)
results = pd.merge(df, results, on=year_variable, how='left')
return results
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)
print "\ninspect with:\nresults.unstack()['mare', '50%'].unstack() # for example"
print "or: results.unstack()['mare', '50%'].unstack(2).reindex(columns='Very Moderately Slightly'.split())"
else:
N = int(options.numberofrows)
delta_true = float(options.delta)
sigma_true = float(options.sigma) * pl.ones(5)
replicate = int(options.replicate)
smoothness = options.smoothing
print 'Running random effects validation for:'
print 'N', N
print 'delta_true', delta_true
print 'sigma_true', sigma_true
print 'replicate', replicate
print 'smoothness', smoothness
M = gp.Mean(validate_age_integrating_re.quadratic)
C = gp.Covariance(gp.matern.euclidean, amp=1., diff_degree=2, scale=50)
gp.observe(M, C, [0, 25, 100], [-5, -3, -5])
log_p = gp.Realization(M, C)
true_p = lambda x: pl.exp(log_p(x))
model = validate_age_integrating_re.validate_ai_re(
N, delta_true, sigma_true, true_p, smoothness)
model.results.to_csv(
'%s/%s/%s-%s-%s-%s.csv' %
(output_dir, validation_name, options.numberofrows, options.delta,
options.sigma, options.replicate))
delta_true = float(options.delta)
replicate = int(options.replicate)
bias = float(options.bias)
sigma_prior = float(options.sigma)
print 'Running random effects validation for:'
print 'N', N
print 'delta_true', delta_true
print 'bias', bias
print 'sigma_prior', sigma_prior
print 'replicate', replicate
M = gp.Mean(validate_similarity.quadratic)
C = gp.Covariance(gp.matern.euclidean, amp=1., diff_degree=2, scale=50)
gp.observe(M, C, [0, 30, 100], [-5, -3, -5])
true = {}
lp = gp.Realization(M, C)
true_p = lambda x: pl.exp(lp(x))
model = validate_similarity.generate_data(N, delta_true, true_p,
'Unusable', bias,
sigma_prior)
for het in 'Very Moderately Slightly'.split():
model.parameters['p']['heterogeneity'] = het
validate_similarity.fit(model)
model.results.to_csv(
'%s/%s/%s-%s-%s-%s-%s-%s.csv' %
(output_dir, validation_name, options.numberofrows,
options.delta, het, bias, sigma_prior, options.replicate))
def draw(self):
'''Generate a random realization of the spline, based on the data.'''
self.realization = GP.Realization(self.M, self.C)
