def test_create_db():
# temporary file name
file_ = tempfile.mkstemp(suffix=".db")[1]
# set up history
pyabc.History("sqlite:///" + file_)
# should work just fine though file mostly empty
pyabc.History("sqlite:///" + file_, create=False)
# delete file and check we cannot create a History object
os.remove(file_)
with pytest.raises(ValueError):
pyabc.History("sqlite:///" + file_, create=False)
def setup_module():
"""Set up module. Called before all tests here."""
# create and run some model
observation = {'ss0': p_true['p0'], 'ss1': p_true['p1']}
prior = pyabc.Distribution(
**{
key: pyabc.RV('uniform', limits[key][0], limits[key][1] -
limits[key][0])
for key in p_true.keys()
})
distance = pyabc.PNormDistance(p=2)
n_history = 2
sampler = pyabc.sampler.MulticoreEvalParallelSampler(n_procs=2)
for _ in range(n_history):
abc = pyabc.ABCSMC(model,
prior,
distance,
population_size=100,
sampler=sampler)
abc.new(db_path, observation)
abc.run(minimum_epsilon=.1, max_nr_populations=3)
for j in range(n_history):
history = pyabc.History(db_path)
history.id = j + 1
histories.append(history)
labels.append("Some run " + str(j))
def test_db_import(script_runner):
"""Import an outdated database, assert import raises, and then convert."""
db_file = os.path.join(tempfile.gettempdir(), "pyabc_test_migrate.db")
# this database was created with a previos pyabc version, thus
# import should fail
with pytest.raises(AssertionError):
pyabc.History("sqlite:///" + db_file)
# call the migration script
ret = script_runner.run('abc-migrate', '--src', db_file, '--dst', db_file)
assert ret.success
# now it should work
h = pyabc.History("sqlite:///" + db_file)
h.get_weighted_sum_stats()
# remove file
os.remove(db_file)
prior_distribution = "loguniform"
print(prior_distribution)
para_prior1 = para_prior(lim, prior_distribution, 1)
para_prior2 = para_prior(lim, prior_distribution, 2)
para_prior3 = para_prior(lim, prior_distribution, 3)
para_prior4 = para_prior(lim, prior_distribution, 4)
para_prior5 = para_prior(lim, prior_distribution, 5)
# %% Load database
# change database name
db_path = "sqlite:///db/model5_super.db"
history = pyabc.History(db_path)
print("ID: %d, generations: %d" % (history.id, history.max_t))
# %% Load last population
df, w = history.get_distribution(t=history.max_t - 5)
pca = PCA(n_components=12, svd_solver='full', iterated_power='full')
pca.fit(df)
# plt.plot(pca.explained_variance_ratio_)
# plt.show()
plt.figure(figsize=(11, 5))
def client():
"""A fake server client."""
history = pyabc.History(db_path)
server.app.config["HISTORY"] = history
with server.app.test_client() as client:
yield client
prior_distribution = "loguniform"
print(prior_distribution)
para_prior1 = para_prior(lim, prior_distribution, 1)
para_prior2 = para_prior(lim, prior_distribution, 2)
para_prior3 = para_prior(lim, prior_distribution, 3)
para_prior4 = para_prior(lim, prior_distribution, 4)
para_prior5 = para_prior(lim, prior_distribution, 5)
# %% Load database
# change database name
db_path = "sqlite:///db/model5_24_more.db"
history = pyabc.History(db_path)
print("ID: %d, generations: %d" % (history.id, history.max_t))
# %% Plot
solver = ODESolver()
# change model name
solver.ode_model = solver.ode_model5
result_data(history, solver, nr_population=history.max_t, savefig=True)
# change prior name
result_plot(history, None, para_prior5, history.max_t - 5, savefig=False)
solver = ODESolver()
solver.time_point = solver.time_point_default
obs_data_raw_s = solver.ode_model1(para_true1, flatten=False, add_noise=False)
solver.time_point = solver.time_point_exp
obs_data_raw_s_less = solver.ode_model(para_true1, flatten=False, add_noise=False)
# print("Target data")
# print(obs_data_noisy_s)
# %% Load database
db_path = "sqlite:///db/abcsmc_test.db"
history = pyabc.History(db_path)
print("ID: %d, generations: %d" % (history.id, history.max_t))
# %% Plot
raw_data_path = os.path.abspath(os.curdir) + "/data/rawData.csv"
raw_data = pd.read_csv(raw_data_path).astype("float32")
time_points: object = raw_data.iloc[:, 0].to_numpy()
exp_data = raw_data.iloc[:, 1:].to_numpy()
exp_data = arr2d_to_dict(exp_data)
exp_data_s = raw_data.iloc[:, 1:].to_dict(orient='list')
for k in exp_data_s:
exp_data_s[k] = np.array(exp_data_s[k])
psize = pop_sizes[-1]
eps_list = [8, 4, 2, 1, 0.7, 0.5, 0.33, 0.25]
eps = pyabc.ListEpsilon(eps_list)
iters_LA = 25
iters_ORI = 25
iters_STAT = 3
# In[3]:
histories_LA = []
for i in range(iters_LA):
histories_LA.append(
pyabc.History("sqlite:///" + os.path.join(
basepath, "results/Var" + str(sleepvars[0]) +
"/database", "DYNLA" + str(nodes) + "_" + str(psize) + "_" +
str(i) + ".db")))
histories_ORI = []
for i in range(iters_ORI):
histories_ORI.append(
pyabc.History("sqlite:///" + os.path.join(
basepath, "results/Var" + str(sleepvars[0]) + "/database", "ORI" +
str(nodes) + "_" + str(psize) + "_" + str(i) + ".db")))
histories_STAT = []
for i in range(iters_STAT):
histories_STAT.append(
pyabc.History("sqlite:///" + os.path.join(
basepath, "results/Var" + str(sleepvars[0]) +
"/database", "STATIC" + str(nodes) + "_" + str(psize) + "_" +
import pyabc
import os
import matplotlib.pyplot as plt
from study_abc_noise.model import NonIdAbleModelVars as ModelVars
mv = ModelVars()
db_files = [f for f in os.listdir('.') if os.path.isfile(f) and "db_" in f]
print(f"Using db files {db_files}")
histories = []
labels = []
for db_file in db_files:
id_ = db_file.split('__')[1]
h = pyabc.History("sqlite:///" + db_file)
h.id = 1
histories.append(h)
labels.append(id_)
gt_par = h.get_population(t=-1).get_list()[0].parameter
pyabc.visualization.plot_sample_numbers(histories, labels)
plt.savefig("samples.png")
pyabc.visualization.plot_epsilons(histories, labels, scale="log10")
plt.savefig("epsilons.png")
for h, label in zip(histories, labels):
pyabc.visualization.plot_histogram_matrix(h, bins=100)
plt.savefig("hist_" + label + ".png")
df, w = h.get_distribution()
pyabc.visualization.plot_kde_matrix(df, w,
refval=gt_par) #, limits=mv.limits)
