def make_simultaneous_hierarchial_kde_plot(n_mean_plots=100,
infolder='hierarchical-model'):
with pm.Model():
trace = pm.load_trace(os.path.join('uniform-traces', infolder))
psi = trace.get_values('psi')
mu_psi = trace.get_values('mu_psi')
df = pd.DataFrame(
trace.get_values('psi'),
columns=['psi-{}'.format(i) for i in range(psi.shape[1])])
df['mu_psi'] = mu_psi
sigma_psi = pd.Series(trace.get_values('sigma_psi'))
# plotting
f, (ax0, ax1) = plt.subplots(ncols=1,
nrows=2,
figsize=(10, 6),
dpi=200,
sharex=True,
gridspec_kw={'height_ratios': [3, 1]})
plt.sca(ax0)
df.drop('mu_psi', axis=1).plot.kde(ax=plt.gca(),
linestyle='dashed',
legend=None)
plt.sca(ax1)
mean_dist_params = pd.concat((df['mu_psi'], sigma_psi), axis=1)
for _ in trange(n_mean_plots):
mu, sigma = mean_dist_params.sample().values.T
x = np.linspace(0, 50, 500)
y = np.exp(-(x - mu)**2 /
(2 * sigma**2)) / np.sqrt(2 * np.pi * sigma**2)
plt.fill_between(x, np.zeros_like(y), y, alpha=0.008, color='k')
plt.xlim(0, 50)
ax1.set_ylim(0, ax1.get_ylim()[1])
plt.suptitle(r"KDE for individual galaxies' $\psi$ and $\psi_\mu$")
plt.xlabel('Pitch angle (degrees)')
plt.savefig('hierarchial_gal_result.png', bbox_inches='tight')
def load_trace(start, n_weeks):
filename = "../data/mcmc_samples_backup/parameters_covid19_{}".format(start)
model = load_model(start, n_weeks)
with model:
trace = pm.load_trace(filename)
del model
return trace
def estimate_parameters(self, df, lat, lon, map_estimate):
x_fourier = fourier.get_fourier_valid(df, self.modes)
x_fourier_01 = (x_fourier + 1) / 2
x_fourier_01.columns = ["pos" + col for col in x_fourier_01.columns]
dff = pd.concat([df, x_fourier, x_fourier_01], axis=1)
df_subset = dh.get_subset(dff, self.subset, self.seed, self.startdate)
self.model = self.statmodel.setup(df_subset)
outdir_for_cell = dh.make_cell_output_dir(self.output_dir, "traces",
lat, lon, self.variable)
if map_estimate:
try:
with open(outdir_for_cell, 'rb') as handle:
trace = pickle.load(handle)
except Exception as e:
print("Problem with saved trace:", e,
". Redo parameter estimation.")
trace = pm.find_MAP(model=self.model)
if self.save_trace:
with open(outdir_for_cell, 'wb') as handle:
free_params = {
key: value
for key, value in trace.items()
if key.startswith('weights') or key == 'logp'
}
pickle.dump(free_params,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
else:
# FIXME: Rework loading old traces
# print("Search for trace in\n", outdir_for_cell)
# As load_trace does not throw an error when no saved data exists, we here
# test this manually. FIXME: Could be improved, as we check for existence
# of names and number of chains only, but not that the data is not corrupted.
try:
trace = pm.load_trace(outdir_for_cell, model=self.model)
print(trace.varnames)
# for var in self.statmodel.vars_to_estimate:
# if var not in trace.varnames:
# print(var, "is not in trace, rerun sampling.")
# raise IndexError
# if trace.nchains != self.chains:
# raise IndexError("Sample data not completely saved. Rerun.")
print("Successfully loaded sampled data from")
print(outdir_for_cell)
print("Skip this for sampling.")
except Exception as e:
print("Problem with saved trace:", e,
". Redo parameter estimation.")
trace = self.sample()
# print(pm.summary(trace)) # takes too much memory
if self.save_trace:
pm.backends.save_trace(trace,
outdir_for_cell,
overwrite=True)
return trace, dff
def load_trace_window(disease, model_i, start, n_weeks):
filename_params = "../data/mcmc_samples_backup/parameters_{}_{}".format(
disease, start)
model = load_model_window(disease, model_i, start, n_weeks)
with model:
trace = pm.load_trace(filename_params)
del model
return trace
def load_trace_by_i(disease, i):
filename_params = "../data/mcmc_samples_backup/parameters_{}_{}".format(
disease, i)
model = load_model_by_i(disease, i)
with model:
trace = pm.load_trace(filename_params)
del model
return trace
def load_trace(disease, use_age, use_eastwest):
filename_params = "../data/mcmc_samples/parameters_{}_{}_{}".format(disease, use_age, use_eastwest)
model = load_model(disease, use_age, use_eastwest)
with model:
trace = pm.load_trace(filename_params)
del model
return trace
def fig4(name, func, eps):
"""Makes figure 4.
Args:
name (str): Descriptive name of the model. Posterior samples, statistics, and
figures are generated and saved in a subdirectory with this name.
func (:obj:`<class 'function'>): Function for model construction. Should
return a formatted copy of the data.
eps (bool): If True, saves the figures to the manuscript subdirectory in .eps
format.
"""
with pm.Model() as m:
fit_model(name, func)
trace = pm.load_trace(name)
params = sorted(
[p.name for p in m.deterministics if "Lambda" in p.name])
set_fig_defaults()
rcParams["figure.figsize"] = (3, 3 * 2)
fig, axes = plt.subplots(5, 1, constrained_layout=True)
for p, ax in zip(params, axes):
vals, bins, _ = ax.hist(trace[p],
bins=50,
density=True,
histtype="step",
color="lightgray")
ax.set_xlabel(p)
if ax == axes[0]:
ax.set_ylabel("Posterior density")
start, stop = pm.stats.hpd(trace[p])
for n, l, r in zip(vals, bins, bins[1:]):
if l > start:
if r < stop:
ax.fill_between([l, r], 0, [n, n], color="lightgray")
elif l < stop < r:
ax.fill_between([l, stop], 0, [n, n], color="lightgray")
elif l < start < r:
ax.fill_between([start, r], 0, [n, n], color="lightgray")
x = np.linspace(min([bins[0], 0]), max([0, bins[-1]]))
theta = skewnorm.fit(trace[p])
ax.plot(x, skewnorm.pdf(x, *theta), "k", label="Normal approx.")
ax.plot(x, norm.pdf(x), "k--", label="Prior")
ax.plot([0, 0], [skewnorm.pdf(0, *theta), norm.pdf(0)], "ko")
fig.savefig(f"{name}/fig4.png")
if eps is True:
fig.savefig("manuscript/fig4.eps")
def test_save_and_load(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp('data'))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
trace2 = pm.load_trace(directory, model=TestSaveLoad.model())
for var in ('x', 'z'):
assert (self.trace[var] == trace2[var]).all()
def test_save_and_load(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp('data'))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
trace2 = pm.load_trace(directory, model=TestSaveLoad.model())
for var in ('x', 'z'):
assert (self.trace[var] == trace2[var]).all()
def load_final_trace(no_rd=False):
if no_rd:
filename_params = "../data/mcmc_samples_backup/parameters_covid19_final_no_rd"
else:
filename_params = "../data/mcmc_samples_backup/parameters_covid19_final"
model = load_final_model(no_rd=no_rd)
with model:
trace = pm.load_trace(filename_params)
del model
return trace
def load(cls, input_file):
saved_result = pd.read_pickle(input_file)
trace_fname = saved_result.pop('trace', None)
bhsm = cls(saved_result['galaxies'])
if trace_fname is not None:
with bhsm.model:
trace = pm.load_trace(trace_fname)
else:
trace = None
return {**saved_result, 'bhsm': bhsm, 'trace': trace}
def get_trace(path_trace, rhog, cs):
path_results = '../../../../results/'
data = pickle.load(open(path_results + 'data2ch.pickle', 'rb'))
dtslip, tiltsty, tiltstx, tiltsly, tiltslx, gps, stackx, stacky, tstack, xsh, ysh, dsh, xshErr, yshErr, dshErr, strsrc, strsrcErr = data
n = np.arange(1, len(tiltsty) + 1)
tilt_std = 1e-5
dt_std = 3600
gps_std = 1
with pm.Model() as model:
gpsconst = pm.Uniform('gpsconst', lower=-15, upper=15)
A_mod = pm.Uniform('A_mod', lower=0, upper=1e+2)
B_mod = pm.Uniform('B_mod', lower=0, upper=1e+2)
C_mod = pm.Uniform('C_mod', lower=0, upper=1e+2)
D_mod = pm.Uniform('D_mod', lower=0, upper=1e+2)
E_mod = pm.Uniform('E_mod', lower=0, upper=1e+2)
F_mod = pm.Uniform('F_mod', lower=0, upper=1e+2)
Vd_exp = pm.Uniform('Vd_exp', lower=8, upper=11)
Vd_mod = pm.Deterministic('Vd_mod', 10**Vd_exp)
kd_exp = pm.Uniform('kd_exp', lower=7, upper=10)
kd_mod = pm.Deterministic('kd_mod', 10**kd_exp)
R1 = pm.Deterministic('R1', rhog * Vd_mod / (kd_mod * S))
ratio = pm.Uniform('ratio',
lower=30 * 4 * R1 / (1 + R1),
upper=100 * 4 * R1 / (1 + R1))
pspd_mod = pm.Uniform('pspd_mod', lower=1e+5, upper=1e+7)
ptps_mod = pm.Deterministic('ptps_mod', ratio * pspd_mod)
conds_mod = pm.Uniform('conds_mod', lower=1, upper=10)
deltap_mod = pm.Uniform('deltap', lower=1e+5, upper=ptps_mod)
strsrc_mod = pm.Normal('strsrc_Mod', mu=strsrc, sigma=strsrcErr)
Vs_mod = pm.Deterministic('Vs_mod', strsrc_mod / deltap_mod)
#conds_mod = pm.Uniform('conds_mod',lower=1,upper=10)
condd_mod = pm.Uniform('condd_mod', lower=1, upper=30)
dsh_mod = pm.Normal('dsh_mod', mu=dsh, sigma=dshErr)
xsh_mod = pm.Normal('xsh_mod', mu=xsh, sigma=xshErr)
ysh_mod = pm.Normal('ysh_mod', mu=ysh, sigma=yshErr)
coeffx = cs * dsh_mod * (
x - xsh_mod) / (dsh_mod**2 + (x - xsh_mod)**2 +
(y - ysh_mod)**2)**(5. / 2) * Vd_mod
coeffy = cs * dsh_mod * (
y - ysh_mod) / (dsh_mod**2 + (x - xsh_mod)**2 +
(y - ysh_mod)**2)**(5. / 2) * Vd_mod
tau2 = 8 * mu * ld * Vs_mod / (3.14 * condd_mod**4 * kd_mod
) #Model set-up
x_mod = gpsconst + 4 * R1 / rhog * pspd_mod / (1 + R1) * n
pslip_mod = -rhog * x_mod
pstick_mod = pslip_mod + 4 * pspd_mod / (1 + R1)
trace2 = pm.load_trace(path_results + 'trace300000_strsrc_LF')
return trace2
def test_save_and_load(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp("data"))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
trace2 = pm.load_trace(directory, model=TestSaveLoad.model())
for var in ("x", "z"):
assert (self.trace[var] == trace2[var]).all()
assert self.trace.stat_names == trace2.stat_names
for stat in self.trace.stat_names:
assert all(self.trace[stat] == trace2[stat]), (
"Restored value of statistic %s does not match stored value" %
stat)
def test_save_new_model(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp("data"))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
with pm.Model() as model:
w = pm.Normal("w", 0, 1)
new_trace = pm.sample(return_inferencedata=False)
with pytest.raises(OSError):
_ = pm.save_trace(new_trace, directory)
_ = pm.save_trace(new_trace, directory, overwrite=True)
with model:
new_trace_copy = pm.load_trace(directory)
assert (new_trace["w"] == new_trace_copy["w"]).all()
def test_save_new_model(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp('data'))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
with pm.Model() as model:
w = pm.Normal('w', 0, 1)
new_trace = pm.sample()
with pytest.raises(OSError):
_ = pm.save_trace(new_trace, directory)
_ = pm.save_trace(new_trace, directory, overwrite=True)
with model:
new_trace_copy = pm.load_trace(directory)
assert (new_trace['w'] == new_trace_copy['w']).all()
def test_save_new_model(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp('data'))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
with pm.Model() as model:
w = pm.Normal('w', 0, 1)
new_trace = pm.sample()
with pytest.raises(OSError):
_ = pm.save_trace(new_trace, directory)
_ = pm.save_trace(new_trace, directory, overwrite=True)
with model:
new_trace_copy = pm.load_trace(directory)
assert (new_trace['w'] == new_trace_copy['w']).all()
def load_trace(dir_path, bX, by):
with pm.Model() as model: # noqa
length = pm.Gamma("length", alpha=2, beta=1)
eta = pm.HalfCauchy("eta", beta=5)
cov = eta**2 * pm.gp.cov.Matern52(input_dim=1, ls=length)
gp = pm.gp.Latent(cov_func=cov)
f = gp.prior("f", X=bX)
sigma = pm.HalfCauchy("sigma", beta=5)
nu = pm.Gamma("nu", alpha=2, beta=0.1)
y_ = pm.StudentT("y", mu=f, lam=1.0 / sigma, nu=nu,
observed=by) # noqa
trace = pm.load_trace(dir_path)
return model, gp, trace
def table2(name, func, tex):
"""Makes table 2.
Args:
name (str): Descriptive name of the model. Posterior samples, statistics, and
figures are generated and saved in a subdirectory with this name.
func (:obj:`<class 'function'>): Function for model construction. Should
return a formatted copy of the data.
tex (bool): If True, saves the table to the manuscript subdirectory.
"""
with pm.Model() as m:
fit_model(name, func)
trace = pm.load_trace(name)
params = sorted([p.name for p in m.deterministics if "Lambda" in p.name])
df = pm.summary(trace, var_names=params)
table = []
for p, i in zip(params, interps):
theta = skewnorm.fit(trace[p])
p0 = norm.pdf(0)
p1 = skewnorm.pdf(0, *theta)
bf = p0 / p1
a, b, c = df.loc[p, ["mean", "hpd_2.5", "hpd_97.5"]]
dic = {
"Variable": p,
"Posterior mean (95% HPD)": "%s (%s, %s)" % (
latexify(a), latexify(b), latexify(c)),
"During roved-frequency trials ...": i,
"BF": latexify(bf),
"Evidence": interpret(bf),
}
table.append(dic)
# print(p, bf)
df = pd.DataFrame(table)[dic.keys()]
df.to_latex(f"{name}/table2.tex", escape=False, index=False)
if tex is True:
df.to_latex("manuscript/table2.tex", escape=False, index=False)
def test_sample_posterior_predictive(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp("data"))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
rng = np.random.RandomState(10)
with TestSaveLoad.model(rng_seeder=rng):
ppc = pm.sample_posterior_predictive(self.trace)
rng = np.random.RandomState(10)
with TestSaveLoad.model(rng_seeder=rng):
trace2 = pm.load_trace(directory)
ppc2 = pm.sample_posterior_predictive(trace2)
for key, value in ppc.items():
assert (value == ppc2[key]).all()
def test_sample_ppc(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp('data'))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
seed = 10
np.random.seed(seed)
with TestSaveLoad.model():
ppc = pm.sample_ppc(self.trace)
seed = 10
np.random.seed(seed)
with TestSaveLoad.model():
trace2 = pm.load_trace(directory)
ppc2 = pm.sample_ppc(trace2)
for key, value in ppc.items():
assert (value == ppc2[key]).all()
def test_sample_ppc(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp('data'))
save_dir = pm.save_trace(self.trace, directory, overwrite=True)
assert save_dir == directory
seed = 10
np.random.seed(seed)
with TestSaveLoad.model():
ppc = pm.sample_ppc(self.trace)
seed = 10
np.random.seed(seed)
with TestSaveLoad.model():
trace2 = pm.load_trace(directory)
ppc2 = pm.sample_ppc(trace2)
for key, value in ppc.items():
assert (value == ppc2[key]).all()
def sampleandsave(f):
"""Sample from the model in context.
"""
if not exists(f):
# sample and save
trace = pm.sample(8000, tune=2000, chains=1)
pm.save_trace(trace, f)
pm.traceplot(trace, compact=True)
rcParams["font.size"] = 14
plt.savefig(f"{f}/traceplot.png")
ppc = pm.sample_posterior_predictive(trace)["$Y$"]
np.savez_compressed(f"{f}/ppc.npz", ppc)
else:
trace = pm.load_trace(f)
return trace
import corner
import exoplanet as xo
import matplotlib.pyplot as plt
import pymc3 as pm
import os
import src.close.rv.model as m
import src.data as d
from src.constants import *
plotdir = "figures/close/rv/"
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
trace = pm.load_trace(directory="chains/close/rv", model=m.model)
# view summary
df = az.summary(trace, var_names=m.all_vars)
print(df)
# write summary to disk
f = open(f"{plotdir}summary.txt", "w")
df.to_string(f)
f.close()
with az.rc_context(rc={"plot.max_subplots": 60}):
# autocorrelation
az.plot_autocorr(trace, var_names=m.sample_vars)
plt.savefig(f"{plotdir}autocorr.png")
def test_bad_load(self, tmpdir_factory):
directory = str(tmpdir_factory.mktemp("data"))
with pytest.raises(pm.TraceDirectoryError):
pm.load_trace(directory, model=TestSaveLoad.model())
annInput = theano.shared(XsTrain)
annTarget = theano.shared(YsTrain)
errAnnInput = theano.shared(errXsTrain)
errAnnTarget = theano.shared(errYsTrain)
neural_network = construct_nn(annInput, errAnnInput, annTarget, errAnnTarget)
print("Starting the training of the BNN...")
if not os.path.exists(cache_file_bnn):
with neural_network:
#fit model
trace = pm.sample(draws=nsamples,
init='advi+adapt_diag',
n_init=ninit,
tune=ninit // 2,
chains=nchains,
cores=ncores,
nuts_kwargs={'target_accept': 0.90},
discard_tuned_samples=True,
compute_convergence_checks=True,
progressbar=False)
pm.save_trace(trace, directory=cache_file_bnn)
else:
trace = pm.load_trace(cache_file_bnn, model=neural_network)
print("Done...")
phi = 1 * Vs_mod / Vd_mod
#phi = ratio * Vs_mod / kd_mod
stack_mod = A_mod * tt.exp(tstack/tau2*(-T1/2 - phi/2 + tt.sqrt(4*phi + (-T1 + phi - 1)**2)/2 - 1/2)) + B_mod * tt.exp(tstack/tau2*(-T1/2 - phi/2 - tt.sqrt(4*phi + (-T1 + phi - 1)**2)/2 - 1/2)) - E_mod
# stack_mod = A_mod * tt.exp(t*(-condd**4*pi*r/(16*ld*mu) + tt.sqrt((condd**4*pi*r/(8*ld*mu) - condd**4*ks*pi/(8*Vs*ld*mu) - conds**4*ks*pi/(8*Vs*ls*mu))**2 + condd**8*ks*pi**2*r/(16*Vs*ld**2*mu**2))/2 - condd**4*ks*pi/(16*Vs*ld*mu) - conds**4*ks*pi/(16*Vs*ls*mu))) +
# B_mod * tt.exp
#Posterio
tslx_obs = pm.Normal('tslx_obs', mu=tslx_mod, sigma = tilt_std, observed=tiltslx)
tsly_obs = pm.Normal('tsly_obs', mu=tsly_mod, sigma = tilt_std, observed=tiltsly)
tstx_obs = pm.Normal('tstx_obs', mu=tstx_mod, sigma = tilt_std, observed=tiltstx)
tsty_obs = pm.Normal('tsty_obs', mu=tsty_mod, sigma = tilt_std, observed=tiltsty)
x_obs = pm.Normal('x_obs', mu = x_mod, sigma = gps_std, observed=gps)
stack_obs = pm.Normal('stack_obs', mu = stack_mod, sigma = tilt_std*1e+6, observed=stack)
trace2 = pm.load_trace(path_results + 'trace300000_LF')
panda_trace = pm.backends.tracetab.trace_to_dataframe(trace2)
panda_trace['Vs_mod'] = np.log10(panda_trace['Vs_mod'])
panda_trace['kd_mod'] = np.log10(panda_trace['kd_mod'])
panda_trace['Vd_mod'] = np.log10(panda_trace['Vd_mod'])
panda_trace['pspd_mod'] = panda_trace['pspd_mod'] / 1e+6
filename = 'res300000_LF.pickle'
results =pickle.load(open(path_results + filename,'rb'))
R1 = rho * g * results['MAP']['Vs_mod'] /(results['MAP']['kd_mod'] * S)
xMAP = results['MAP']['gpsconst']+ 4 * R1 / (rho * g) * results['MAP']['pspd_mod'] / (1 + R1) * n
pslipMAP = -rho * g * xMAP
pstickMAP = pslipMAP + 4 * results['MAP']['pspd_mod']/ (1 + R1)
T1 = (conds_mod / results['MAP']['condd_mod'] )**4 * ld /ls
phi = 1 * results['MAP']['Vs_mod'] / results['MAP']['Vd_mod']
coeffx = cs * results['MAP']['dsh_mod'] * (x - results['MAP']['xsh_mod']) / (results['MAP']['dsh_mod']**2 + (x - results['MAP']['xsh_mod'])**2 + (y - results['MAP']['ysh_mod'])**2 )**(5./2) * results['MAP']['Vs_mod']
def forecast_main(clusters, cases_df, vel_cases_df, population_df,
cluster_mode, init_cluster_num, max_cluster_num,
initial_date, final_date, final_change_date, num_days_future,
dataset_final_date, run_mode, root_save_path):
rmse_per_cluster_list = []
total_re_per_cluster_list = []
mean_rsquared_per_cluster_list = []
mape_per_cluster_list = []
wape_per_cluster_list = []
mse_per_county_per_cluster_list = []
re_per_county_per_cluster_list = []
rsquared_per_county_per_cluster_list = []
mape_per_county_per_cluster_list = []
wape_per_county_per_cluster_list = []
unclustered_rmse_per_cluster_list = []
unclustered_mse_per_county_per_cluster_list = []
unclustered_total_re_per_cluster_list = []
unclustered_re_per_county_per_cluster_list = []
initial_date = datetime.datetime.strptime(f'{initial_date}/2020',
'%m/%d/%Y')
final_date = datetime.datetime.strptime(f'{final_date}/2020', '%m/%d/%Y')
dataset_final_date = datetime.datetime.strptime(
f'{dataset_final_date}/2020', '%m/%d/%Y')
cluster_colors = {
1: rgb2hex(255, 0, 0),
2: rgb2hex(255, 111, 0),
3: rgb2hex(255, 234, 0),
4: rgb2hex(151, 255, 0),
5: rgb2hex(44, 255, 150),
6: rgb2hex(0, 152, 255),
7: rgb2hex(0, 25, 255)
}
# cluster id 0 was not clustered by Tzu Hsi but I still use it
for cluster_id in range(init_cluster_num, max_cluster_num):
if cluster_mode == 'unclustered':
cluster_id = 'All'
chosen_cluster_series = clusters
else:
chosen_cluster_series = clusters[clusters == cluster_id]
cluster_counties = chosen_cluster_series.index.tolist()
print('-----------------------------')
print('Cluster ID: ', cluster_id)
# ------------- Create save folders --------------
cluster_save_path = root_save_path + f'/cluster_{cluster_id}/'
if os.path.isdir(cluster_save_path) is False:
os.mkdir(cluster_save_path)
cluster_all_save_path = root_save_path + f'/cluster_All/'
# -------------- Data Preprocessing --------------
cluster_cases_df, proc_population_series = preprocess_dataset(
cases_df.copy(), population_df.copy(), cluster_counties)
cluster_vel_cases_df, _ = preprocess_dataset(vel_cases_df.copy(),
population_df.copy(),
cluster_counties)
cluster_cases_df, current_cumulative_cases_df, future_cumulative_cases_df, old_cumulative_infected_cases_series, date_begin_sim, num_days_sim = \
process_date(cluster_cases_df, initial_date, final_date, dataset_final_date, num_days_future)
cluster_vel_cases_df, current_vel_cases_df, future_vel_cases_df, _, _, _ = \
process_date(cluster_vel_cases_df, initial_date, final_date, dataset_final_date, num_days_future)
current_cumulative_cases_series = current_cumulative_cases_df.sum(
axis=1)
current_vel_cases_series = current_vel_cases_df.sum(axis=1)
cluster_total_population = proc_population_series.sum()
future_cumulative_cases = future_cumulative_cases_df.sum(axis=1)[-1]
print('old_cumulative_infected_cases_series:',
old_cumulative_infected_cases_series)
print('Cumulative future cases:', future_cumulative_cases)
print('population:', cluster_total_population)
print('Remaining population:',
cluster_total_population - future_cumulative_cases)
visualize_trend_with_r_not(cluster_id, cluster_vel_cases_df,
cluster_save_path)
# --------------- Get SIR Model -----------------
# convert cumulative infected to daily total infected cases
current_total_cases_series = current_cumulative_cases_series - old_cumulative_infected_cases_series.sum(
)
future_total_cases_df = future_cumulative_cases_df - old_cumulative_infected_cases_series
day_1_cumulative_infected_cases = current_cumulative_cases_series[0]
S_begin_beta = cluster_total_population - day_1_cumulative_infected_cases
I_begin_beta = current_total_cases_series[
0] # day 1 total infected cases
print('day_1_cumulative_infected_cases: ',
day_1_cumulative_infected_cases)
print('S_begin_beta: ', S_begin_beta)
print('I_begin_beta: ', I_begin_beta)
change_points = get_change_points(final_date, final_change_date,
cluster_id)
sir_model = Bayesian_Inference_SEIR.SIR_with_change_points(
S_begin_beta,
I_begin_beta,
current_vel_cases_series.to_numpy(
), # current_total_cases_series.to_numpy(),
change_points_list=change_points,
date_begin_simulation=date_begin_sim,
num_days_sim=num_days_sim,
diff_data_sim=0,
N=cluster_total_population)
# ---------- Estimate Parameters for SIR model ------------
if run_mode == 'train':
Bayesian_Inference_SEIR.run(sir_model,
N_SAMPLES=10000,
cluster_save_path=cluster_save_path)
elif run_mode == 'eval':
trace = pm.load_trace(cluster_save_path + 'sir_model.trace',
model=sir_model)
susceptible_series = proc_population_series - current_cumulative_cases_df.loc[
final_date]
# ---------- Forecast using unclustered data ------------------
t = range(len(future_vel_cases_df.values))
if cluster_mode == 'clustered':
lambda_t, μ = np.load(cluster_all_save_path + 'SIR_params.npy',
allow_pickle=True)
beta, gamma = lambda_t[-1], μ[0]
print('beta, gamma', beta, gamma)
cluster_all_vel_case_forecast = sir_forecast_a_county(
susceptible_series.sum(),
moving_average_from_df(current_vel_cases_df).sum(),
cluster_total_population, t, beta, gamma, '', '')
else:
cluster_all_vel_case_forecast = None
# ----------- Forecast using clustered data ------------------
lambda_t, μ = np.load(cluster_save_path + 'SIR_params.npy',
allow_pickle=True)
beta, gamma = lambda_t[-1], μ[0]
print('beta, gamma', beta, gamma)
cluster_forecast_I0 = np.mean(
trace['new_cases'][:, len(current_vel_cases_series)], axis=0)
cluster_vel_case_forecast = sir_forecast_a_county(
susceptible_series.sum(), cluster_forecast_I0,
cluster_total_population, t, beta, gamma, '', '')
# ----------- Forecast Visualization ---------------
# reorder cluster id to 1,2,3,4,5,6,7 based on severity (rising trend)
if cluster_id == 0:
cluster_id = 7
elif cluster_id == 2:
cluster_id = 1
elif cluster_id == 3:
cluster_id = 3
elif cluster_id == 4:
cluster_id = 2
elif cluster_id == 6:
cluster_id = 5
elif cluster_id == 7:
cluster_id = 6
elif cluster_id == 8:
cluster_id = 4
if cluster_id in cluster_colors.keys():
cluster_color = cluster_colors[cluster_id]
else:
cluster_color = 'black'
plot_cases(cluster_id,
cluster_color,
trace,
current_vel_cases_series,
future_vel_cases_df,
cluster_vel_case_forecast,
cluster_all_vel_case_forecast,
date_begin_sim,
diff_data_sim=0,
num_days_future=num_days_future,
cluster_save_path=cluster_save_path)
# ---------- Evaluation per county -----------
cluster_mse_dict, cluster_re_dict, cluster_rsquared_dict, cluster_mape_dict, cluster_wape_dict = \
eval_per_cluster(susceptible_series, moving_average_from_df(current_vel_cases_df), future_vel_cases_df, proc_population_series, num_days_future,cluster_save_path)
if cluster_mode == 'unclustered':
for cluster_id in range(0, max_cluster_num):
local_mse_list = []
local_re_list = []
chosen_cluster_series = clusters[clusters == cluster_id]
cluster_counties = chosen_cluster_series.index.tolist()
for a_county in cluster_counties:
if a_county in cluster_mse_dict:
local_mse_list.append(cluster_mse_dict[a_county])
if a_county in cluster_re_dict:
local_re_list.append(cluster_re_dict[a_county])
unclustered_rmse_per_cluster_list.append(
math.sqrt(statistics.mean(local_mse_list)))
unclustered_mse_per_county_per_cluster_list.append(
local_mse_list)
unclustered_total_re_per_cluster_list.append(
statistics.mean(local_re_list))
unclustered_re_per_county_per_cluster_list.append(
local_re_list)
elif cluster_mode == 'clustered':
rmse_per_cluster_list.append(
math.sqrt(statistics.mean(cluster_mse_dict.values())))
total_re_per_cluster_list.append(
statistics.mean(cluster_re_dict.values()))
mean_rsquared_per_cluster_list.append(
statistics.mean(cluster_rsquared_dict.values()))
# mape_per_cluster_list.append(statistics.mean(cluster_mape_dict.values()))
wape_per_cluster_list.append(
statistics.mean(cluster_wape_dict.values()))
mse_per_county_per_cluster_list.append(
list(cluster_mse_dict.values()))
re_per_county_per_cluster_list.append(
list(cluster_re_dict.values()))
rsquared_per_county_per_cluster_list.append(
list(cluster_rsquared_dict.values()))
mape_per_county_per_cluster_list.append(
list(cluster_mape_dict.values()))
wape_per_county_per_cluster_list.append(
list(cluster_wape_dict.values()))
if cluster_mode == 'unclustered':
break # only run once for unclustered dataset
return rmse_per_cluster_list, mse_per_county_per_cluster_list, mean_rsquared_per_cluster_list, rsquared_per_county_per_cluster_list, \
mape_per_cluster_list, mape_per_county_per_cluster_list, wape_per_cluster_list, wape_per_county_per_cluster_list, \
total_re_per_cluster_list, re_per_county_per_cluster_list, \
unclustered_rmse_per_cluster_list, unclustered_mse_per_county_per_cluster_list, \
unclustered_total_re_per_cluster_list, unclustered_re_per_county_per_cluster_list
def get_SIR(x, y, y0, country, forecast_len=0, load_post=False):
'''
If 'forecast_len' is nonzero, attempts to load a trace corresponding to the
country of interest from the directory 'traces' and retrieves predicted numbers
of infected and susceptible patients 'forecast_len' days into the future after the
1st case is detected in the country.
'''
# If in 'prediction mode', modify x, y to reflect forecast length
if forecast_len != 0:
ext = np.arange(1, forecast_len + 1).astype(float)
ext += x[-1]
x = np.append(x, ext)
y = np.empty((x.shape[0], y.shape[1]))
# SIR Model
# p[0]: beta, p[1]: lambda
def SIR(y, t, p):
ds = -p[0] * y[0] * y[1] # Susceptible differential
di = p[0] * y[0] * y[1] - p[1] * y[1] # Infected differential
return [ds, di]
# Initialize ODE
sir_ode = DifferentialEquation(func=SIR,
times=x,
n_states=2,
n_theta=2,
t0=0)
load_dir = osp.join('traces', country.lower())
with pm.Model() as model:
sigma = pm.HalfNormal('sigma', 3, shape=2)
# R0 is bounded below by 1 because we see an epidemic has occured
R0 = pm.Normal('R0', 2, 3)
lmbda = pm.Normal('lambda', 0.1, 0.1)
beta = pm.Deterministic('beta', lmbda * R0)
print('Setting up model for ' + country)
sir_curves = sir_ode(y0=y0, theta=[beta, lmbda])
y_obs = pm.Normal('y_obs', mu=sir_curves, sigma=sigma, observed=y)
if forecast_len == 0:
trace = pm.sample(2000,
tune=1000,
cores=2,
chains=2,
progressbar=True)
# Save trace
pm.save_trace(trace, load_dir, overwrite=True)
# Get the posterior
post = pm.sample_posterior_predictive(trace, progressbar=True)
out_post = post
else:
# Load trace
print('Loading trace')
trace = pm.load_trace(load_dir)
print('Computing posterior')
#Get posterior
if not load_post:
post = pm.sample_posterior_predictive(trace[500:],
progressbar=True)
out_post = post
with open(country + '_post.pkl', 'wb') as buff:
pickle.dump({'post': post}, buff)
else:
with open(country + '_post.pkl', 'rb') as buff:
data = pickle.load(buff)
out_post = data['post']
print('Done')
return trace, out_post, x
def main():
# load the data
df = pd.read_csv("../../assets/data/HS.csv", index_col=0)
# define items to keep
item_names = [
"visual",
"cubes",
"paper",
"flags",
"general",
"paragrap",
"sentence",
"wordc",
"wordm",
"addition",
"code",
"counting",
"straight",
"wordr",
"numberr",
"figurer",
"object",
"numberf",
"figurew",
]
# define the factor structure
factors = np.array([
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
])
paths = np.array([
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
])
# iterate over the two schools
for school, sdf in df.groupby("school"):
# define the path to save results
f = f"../data/BSEM examples/{school}"
# select the 19 commonly used variables
items = sdf[item_names]
# for numerical convenience, standardize the data
items = (items - items.mean()) / items.std()
with pm.Model():
# construct the model
bsem(items, factors, paths)
if not exists(f):
# sample and save
trace = pm.sample(chains=2) # 19000, tune=1000,
pm.save_trace(trace, f)
else:
trace = pm.load_trace(f)
pm.traceplot(trace, compact=True)
rcParams["font.size"] = 14
plt.savefig(f"{f}/traceplot.png")
# create a nice summary table
loadings = pd.DataFrame(
trace[r"$\Lambda$"].mean(axis=0).round(3),
index=[v.title() for v in item_names],
columns=["Spatial", "Verbal", "Speed", "Memory", "g"],
)
loadings.to_csv(f"{f}/loadings.csv")
print(tabulate(loadings, tablefmt="pipe", headers="keys"))
#
# # correlations = pd.DataFrame(
# # trace[r"$\Psi$"].mean(axis=0).round(3),
# # index=["Spatial", "Verbal", "Speed", "Memory", "g"],
# # columns=["Spatial", "Verbal", "Speed", "Memory", "g"],
# # )
# # correlations.to_csv(f"{f}/factor_correlations.csv")
#
_paths = pd.DataFrame(
trace[r"$\Gamma$"].mean(axis=0).round(3),
index=["Spatial", "Verbal", "Speed", "Memory", "g"],
columns=["Spatial", "Verbal", "Speed", "Memory", "g"],
)
_paths.to_csv(f"{f}/factor_paths.csv")
print(tabulate(_paths, tablefmt="pipe", headers="keys"))
def show_plot():
plt.close('all')
plt.ioff()
# load in the trace
from glob import glob
import os
global df, trace_o, trace_n, title
df = pd.read_csv('merged_data.csv')
# read in the models
fit_o = models.oocyte_model(df)
fit_n = models.oocyte_model(df)
# read in the traces
trace_o = pm.load_trace('trace_o', fit_o)
trace_n = pm.load_trace('trace_n', fit_n)
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
out = widgets.Output()
#res_df = pd.read_pickle("res.pkl")
def click(b):
global o_obs, o_x, n_obs, n_x, df
mask = df['i_ind'] == choice.value
if (sum(mask) > 0):
title_comp = df.loc[mask, 'ucode'].values[0].split('_')
fig.suptitle("{}".format(title_comp[0]))
ax.set_title("day {}.rep {}, str {}".format(*title_comp[1:]))
Vmax_o = Vmax_o_slide.value
ao = ao_slide.value
to = to_slide.value
Vmin_o = Vmin_o_slide.value
Vmax_n = Vmax_n_slide.value
a0 = a0_slide.value
t0 = t0_slide.value
a1 = a1_slide.value
t1 = t1_slide.value
Vmin_n = Vmin_n_slide.value
#plt.figure(2)
ax.clear()
ax.plot(-o_x, o_obs, 'o', color='red')
ax.plot(-n_x, n_obs, 'o', color='blue')
if (True):
x = np.linspace(-2, 16, num=100)
ax.plot(-x,
bu.rise_only(x, Vmax_o, to, ao, 1.0, Vmin_o),
color='red')
ax.plot(-x,
bu.rise_and_fall(x, Vmax_n, t0, a0, 1.0, t1, a1, 1,
Vmin_n),
color='blue')
ax.plot([-to, -to],
[0, bu.rise_only(to, Vmax_o, to, ao, 1.0, Vmin_o)],
ls='--',
color='red',
lw=1.5)
ax.plot([-t0, -t0], [
0,
bu.rise_and_fall(t0, Vmax_n, t0, a0, 1.0, t1, a1, 1, Vmin_n)
],
ls='--',
color='blue',
lw=1.5)
ax.plot([-t1, -t1], [
0,
bu.rise_and_fall(t1, Vmax_n, t0, a0, 1.0, t1, a1, 1, Vmin_n)
],
ls='--',
color='blue',
lw=1.5)
ax.set_xticks(range(-14, 2, 2))
ax.set_ylim(0, 200)
ax.axhline(Vmax_o, ls='--', color='red')
ax.axvline(0, ls='--', color='grey', lw=1)
[xmin, xmax] = ax.get_xlim()
[ymin, ymax] = ax.get_ylim()
ax.annotate(r'$t_o$', (to + 0.2, ymax - 10))
ax.annotate(r'$Vmax_{o}}$', (xmax - 2, Vmax_o + 10))
with out:
clear_output(wait=True)
display(ax.figure)
#choice=Dropdown(
#
# options='cont_r1 cont_r2 cont_r3 caged_d04_r1 caged_d07_r1'.split(),
# value='cont_r1',
# description='Number:',
# disabled=False,
#)
choice = widgets.IntText(value=0,
min=0,
max=len(df['i_ind'].unique()),
step=1,
description='Test:',
disabled=False,
continuous_update=False,
readout=True,
readout_format='d')
Vmax_o_slide = FloatSlider(description=r'$V$max$_o$',
value=150,
min=0,
max=300,
continuous_update=False)
Vmax_o_slide.observe(click, names='value')
Vmin_o_slide = FloatSlider(description=r'$V$min$_o$',
value=15,
min=0,
max=30,
continuous_update=False)
Vmin_o_slide.observe(click, names='value')
ao_slide = FloatSlider(description=r'$a_o$',
value=0.2,
min=0.,
max=0.75,
continuous_update=False)
ao_slide.observe(click, names='value')
to_slide = FloatSlider(description=r'$t_o$',
value=1,
min=-2,
max=6,
continuous_update=False)
to_slide.observe(click, names='value')
Vmax_n_slide = FloatSlider(description=r'$V$max$_{n}$',
value=150,
min=0,
max=300,
continuous_update=False)
Vmax_n_slide.observe(click, names='value')
Vmin_n_slide = FloatSlider(description=r'$V$min$_n$',
value=15,
min=0,
max=30,
continuous_update=False)
Vmin_n_slide.observe(click, names='value')
a0_slide = FloatSlider(description=r'$a_0$',
value=0.4,
min=0.0,
max=1.5,
continuous_update=False)
a0_slide.observe(click, names='value')
t0_slide = FloatSlider(description=r'$t_0$',
value=0,
min=-4,
max=8,
continuous_update=False)
t0_slide.observe(click, names='value')
a1_slide = FloatSlider(description=r'$a_1$',
value=0.4,
min=0.0,
max=8,
continuous_update=False)
a1_slide.observe(click, names='value')
t1_slide = FloatSlider(description=r'$t_1$',
value=0.5,
min=-2,
max=6,
continuous_update=False)
t1_slide.observe(click, names='value')
def choice_selected(b):
global o_obs, o_x, n_obs, n_x, df, trace_o, trace_n
if (False):
name = choice.value
df = pd.read_csv("results_analyse/{}.csv".format(name))
o_obs = dh.unpack_results(df, 1, 'o', volume=False)
o_x = np.arange(len(o_obs))
n_obs = dh.unpack_results(df, 1, 'n', volume=False)
n_x = np.arange(len(n_obs))
else:
iexp = choice.value
mask = df['i_ind'] == iexp
if (sum(mask) > 0):
o_obs = df.loc[mask, 'Oc_size']
o_x = df.loc[mask, 'pos']
n_obs = df.loc[mask, 'Ns_size']
n_x = o_x
vars_o = 'Vmax_o,t_o,a_o,Vmin_o'.split(',')
vars_n = 'Vmax_n t0 a0 t1 a1 Vmin_n'.split()
theta_o = np.median(bu.pull_post(trace_o, vars_o, iexp),
axis=0)
theta_n = np.median(bu.pull_post(trace_n, vars_n, iexp),
axis=0)
for slide, val in zip(
[Vmax_o_slide, to_slide, ao_slide, Vmin_o_slide], theta_o):
slide.value = val
for slide, val in zip([
Vmax_n_slide, t0_slide, a0_slide, t1_slide, a1_slide,
Vmin_n_slide
], theta_n):
slide.value = val
#rown = "{}_1".format(name)
#Vmax_n_slide.value= res_df.loc[rown,('Vmax_n','m')]
#a0_slide.value= res_df.loc[rown,('a0','m')]
#t0_slide.value= -res_df.loc[rown,('t0','m')]
#a1_slide.value= res_df.loc[rown,('a1','m')]
#t1_slide.value= -res_df.loc[rown,('t1','m')]
#Vmax_o_slide.value= res_df.loc[rown,('Vmax_o','m')]
#ao_slide.value= res_df.loc[rown,('a_o','m')]
#to_slide.value= -res_df.loc[rown,('t_o','m')]
click(None)
#f(Vmax_slide.value, a0_slide.value, t0_slide.value)
return
choice_selected(None)
choice.observe(choice_selected)
#display(VBox([mslide,cslide]))
oocyte_params = widgets.VBox([
Label(value="Oocyte"), Vmax_o_slide, ao_slide, to_slide, Vmin_o_slide
])
nurse_params = widgets.VBox(
[Vmax_n_slide, a0_slide, t0_slide, a1_slide, t1_slide, Vmin_n_slide])
box = widgets.VBox(
[choice, widgets.HBox([oocyte_params, nurse_params]), out])
display(box)
click(None)
with neural_network:
#fit model
trace_hier = pm.sample(draws=nsamples_hier,
init='advi+adapt_diag',
n_init=ninit,
tune=ninit // 2,
chains=nchains_hier,
cores=ncores_hier,
nuts_kwargs={'target_accept': 0.90},
discard_tuned_samples=True,
compute_convergence_checks=True,
progressbar=False)
pm.save_trace(trace_hier, directory=cache_file_hier)
else:
trace_hier = pm.load_trace(cache_file_hier, model=neural_network)
print("Done...")
if not os.path.exists(cache_file_samples):
samples_tmp = defaultdict(list)
samples = {}
for layer_name in layer_names:
for mu, sd in zip(
trace_hier.get_values(layer_name,
burn=nsamples_hier // 2,
combine=True),
trace_hier.get_values(layer_name + '_sd',
burn=nsamples_hier // 2,
from pathlib import Path
import model as m
import twa.data as d
from twa import wide
from twa.constants import *
from twa.plot_utils import plot_cline, plot_nodes
from twa.plot_utils import efficient_autocorr, efficient_trace
plotdir = Path("figures")
diagnostics = True
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
trace = pm.load_trace(directory="chains", model=m.model)
if diagnostics:
ar_data = az.from_pymc3(trace=trace)
# view summary
df = az.summary(ar_data, var_names=m.all_vars)
# write summary to disk
f = open(plotdir / "summary.txt", "w")
df.to_string(f)
f.close()
with az.rc_context(rc={"plot.max_subplots": 80}):
stem = str(plotdir / "autocorr{:}.png")
efficient_autocorr(ar_data, var_names=m.all_vars, figstem=stem)
best_model = pkl.load(f)
for disease in diseases:
use_age = best_model[disease]["use_age"]
use_eastwest = best_model[disease]["use_eastwest"]
prediction_region = "bavaria" if disease == "borreliosis" else "germany"
filename_params = "../data/mcmc_samples/parameters_{}_{}_{}".format(
disease, use_age, use_eastwest)
filename_model = "../data/mcmc_samples/model_{}_{}_{}.pkl".format(
disease, use_age, use_eastwest)
with open(filename_model, "rb") as f:
model = pkl.load(f)
with model:
trace = pm.load_trace(filename_params)
fig = plt.figure(figsize=(12, 14))
grid = GridSpec(1,
2,
top=0.9,
bottom=0.1,
left=0.07,
right=0.97,
hspace=0.25,
wspace=0.15)
W_ia_args = {
"W_ia": {
"color": "C1",
"label": "$W_{IA}$",
