def test_resume(db_path, gt_model):
def model(parameter):
return {"data": parameter["mean"] + sp.randn()}
prior = Distribution(mean=RV("uniform", 0, 5))
def distance(x, y):
x_data = x["data"]
y_data = y["data"]
return abs(x_data - y_data)
abc = ABCSMC(model, prior, distance, population_size=10)
history = abc.new(db_path, {"data": 2.5}, gt_model=gt_model)
run_id = history.id
print("Run ID:", run_id)
hist_new = abc.run(minimum_epsilon=0, max_nr_populations=1)
assert hist_new.n_populations == 1
abc_continued = ABCSMC(model, prior, distance)
run_id_continued = abc_continued.load(db_path, run_id)
print("Run ID continued:", run_id_continued)
hist_contd = abc_continued.run(minimum_epsilon=0, max_nr_populations=1)
assert hist_contd.n_populations == 2
assert hist_new.n_populations == 2
# 'delay': DiscreteJumpTransition(domain=delay_domain),
# 'n01': DiscreteJumpTransition(domain=n01_domain),
# 'n02': DiscreteJumpTransition(domain=n02_domain),
'k': DiscreteJumpTransition(domain=k_domain, p_stay=.8),
('n01', 'n02', 'log_p', 'p_inf'): GridSearchCV()
})
id = 'n=3e5_2'
db = "sqlite:///" + os.path.join(os.getcwd(), id + ".db")
abc = ABCSMC(
model,
prior,
distance,
transitions=transition,
population_size=200,
)
# abc.load(db, int(np.load('run_id_'+id+'.npy')))#, {'cases': data})
abc.load(db, 1)
# history = abc.new(db, {'cases': data})
np.save('run_id_' + id + '.npy', abc.history.id)
abc.run(max_nr_populations=3)
history = abc.history
print(history.max_t)
df, w = history.get_distribution()
# kde = GridSearchCV().fit(df, w)
# print(kde.rvs()['log_p'])
# df['p'] = 10 ** (-df['log_p'])
plot_kde_matrix(df, w)
plt.show()
'k':
DiscreteJumpTransition(domain=k_domain, p_stay=0.7),
('n01', 'n02', 'log_p', 'p_inf'):
MultivariateNormalTransition(scaling=0.8)
})
db = "sqlite:///" + os.path.join(os.getcwd(), "n=3e5_new.db")
with Pool(processes=5) as pool:
abc = ABCSMC(
model,
prior,
distance,
transitions=transition,
sampler=MappingSampler(pool.map), # SingleCoreSampler(),
# population_size=AdaptivePopulationSize(100, max_population_size=500),
# population_size=25
)
# abc.load(db, np.load('run_id.npy'))
abc.load(db, 10)
# history = abc.new(db, {'cases': data})
abc.run(max_nr_populations=10)
pool.close()
pool.join()
history = abc.history
np.save('run_id_n=3e5_new.npy', history.id)
# print(history.max_t)
df, w = history.get_distribution()
plot_kde_matrix(df, w)
plt.show()
kmin, kmax = 1, 11
k_domain = np.arange(kmin, kmax + 1)
# print(len(data_ext[delaymax:tmax+delaymax]), tmax)
prior = Distribution(k=RV('randint', kmin, kmax),
log_p=RV('uniform', 0, 7),
p_inf=RV('uniform', 0.01, 0.03))
model = MyStochasticProcess(n, t1, data1)
transition = AggregatedTransition(
mapping={
'k': DiscreteJumpTransition(domain=k_domain),
('log_p', 'p_inf'): GridSearchCV()
})
id_old = 'n=3e5_new'
db_old = "sqlite:///" + os.path.join(os.getcwd(), id_old + ".db")
abc_old = ABCSMC(model, prior, distance)
abc_old.load(db_old, int(np.load('run_id_' + id_old + '.npy')))
model = ContinuedSpread(n, t1, t2, data2, abc_old.history)
id = 'n=3e5_late'
db = 'sqlite:///' + os.path.join(os.getcwd(), id + '.db')
abc = ABCSMC(model,
prior,
distance,
transitions=transition,
population_size=200)
# abc.load(db, int(np.load('run_id_'+id+'.npy')))
# print(int(np.load('run_id_'+id+'.npy')))
# abc.load(db, 3)
history = abc.new(db, {'cases': data2, 'firstcases': data1})
# history = abc.history
np.save('run_id_' + id + '.npy', history.id)
data = np.load('cases_de_feb26_mar16.npy') #* n / 1e6
# print(data[0])
# assert data < 3
tmax = len(data)
prior = Distribution(n01=RV('uniform', 0, 3 * round(data[0])),
n02=RV('uniform', 0, 10 * round(data[0])),
k=RV('randint', k_domain.min(), k_domain.max()+1),
log_p=RV('uniform', 0, 6),
p_inf=RV('uniform', 0.01, 0.07))
model = MyStochasticProcess(n, tmax, data)
transition = AggregatedTransition(mapping={
# 'n01': DiscreteJumpTransition(domain=np.arange(int(data.max()))),
# 'n02': DiscreteJumpTransition(domain=np.arange(10 * int(data.max()))),
'k': DiscreteJumpTransition(domain=k_domain, p_stay=0.7),
('n01', 'n02', 'log_p', 'p_inf'): GridSearchCV()
})
db = "sqlite:///" + os.path.join(os.getcwd(), "early_growth.db")
abc = ABCSMC(model, prior, distance, transitions=transition)
abc.load(db, int(np.load('run_id_early_growth.npy')), {'cases': data})
# history = abc.new(db, {'cases': data})
abc.run(max_nr_populations=1)
history = abc.history
np.save('run_id_early_growth.npy', history.id)
# print(history.max_t)
df, w = history.get_distribution()
plot_kde_matrix(df, w)
plt.show()
