def test_stochmet_with_prediction():
uni_prior = uniform_prior.UniformPrior(dmin, true_params * 0.6)
met = stochmet.StochMET(sim=simulator2,
sampler=uni_prior,
summarystats=summaries)
met.compute(n_points=50, chunk_size=2)
x_0 = met.data.s.reshape((50, 12))
y_0 = np.zeros(50)
uni_prior = uniform_prior.UniformPrior(true_params * 1.5, dmax)
met = stochmet.StochMET(sim=simulator2,
sampler=uni_prior,
summarystats=summaries)
met.compute(n_points=50, chunk_size=2)
x_1 = met.data.s.reshape((50, 12))
y_1 = np.ones(50)
X = np.vstack((x_0, x_1))
y = np.hstack((y_0, y_1))
clf = SVC()
clf.fit(X, y)
def predictor(x):
return clf.predict(x)
#multi-processing mode
uni_prior = uniform_prior.UniformPrior(dmin, dmax)
met = stochmet.StochMET(sim=simulator2,
sampler=uni_prior,
summarystats=summaries)
met.compute(n_points=10, chunk_size=2, predictor=predictor)
np.testing.assert_equal(met.data.s.shape, (10, 1, 12))
np.testing.assert_equal(met.data.ts.shape, (10, 1, 2, 101))
np.testing.assert_equal(met.data.x.shape, (10, 5))
np.testing.assert_equal(met.data.user_labels.shape, (10, ))
np.testing.assert_equal(met.data.y.shape, (10, 1))
#cluster-mode
c = Client()
met.compute(n_points=10, chunk_size=2, predictor=predictor)
np.testing.assert_equal(met.data.s.shape, (20, 1, 12))
np.testing.assert_equal(met.data.ts.shape, (20, 1, 2, 101))
np.testing.assert_equal(met.data.x.shape, (20, 5))
np.testing.assert_equal(met.data.user_labels.shape, (20, ))
np.testing.assert_equal(met.data.y.shape, (20, 1))
c.close()
def abc_test_run():
"""
Perform a test abc run
:return: ABC mean absolute error
"""
dmin, dmax = get_bounds()
uni_prior = uniform_prior.UniformPrior(dmin, dmax)
fixed_data = get_fixed_data()
summ_func = auto_tsfresh.SummariesTSFRESH()
ns = naive_squared.NaiveSquaredDistance()
abc = ABC(fixed_data,
sim=simulate,
prior_function=uni_prior,
summaries_function=summ_func.compute,
distance_function=ns,
use_logger=True)
abc.compute_fixed_mean(chunk_size=2)
res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)
true_params = get_true_param()
mae_inference = mean_absolute_error(true_params,
abc.results['inferred_parameters'])
return mae_inference
def simulator2(x):
return simulator(x, model=toggle_model)
# Set up the prior
default_param = np.array(list(toggle_model.listOfParameters.items()))[:, 1]
bound = []
for exp in default_param:
bound.append(float(exp.expression))
true_params = np.array(bound)
dmin = true_params * 0.5
dmax = true_params * 2.0
uni_prior = uniform_prior.UniformPrior(dmin, dmax)
default_fc_params = {
'mean':
None,
'variance':
None,
'skewness':
None,
'agg_autocorrelation': [{
'f_agg': 'mean',
'maxlag': 5
}, {
'f_agg': 'median',
'maxlag': 5
}, {
def test_uniform_prior():
lb = np.asarray([1, 1])
ub = np.asarray([5, 5])
num_samples = 5
prior_func = uniform_prior.UniformPrior(lb, ub)
# multiprocessing mode
samples = prior_func.draw(num_samples, chunk_size=1)
assert len(
samples
) == 5, "UniformPrior functional test error, expected chunk count mismatch"
samples, = dask.compute(samples)
samples = np.asarray(samples)
assert samples.shape[
0] == num_samples, "UniformPrior functional test error, expected sample count mismatch"
assert samples.shape[
1] == 1, "UniformPrior functional test error, expected chunk size mismatch"
assert samples.shape[2] == len(
lb), "UniformPrior functional test error, dimension mismatch"
samples = samples.reshape(-1, len(lb))
axis_mins = np.min(samples, 0)
axis_maxs = np.max(samples, 0)
assert axis_mins[0] > lb[0] and axis_maxs[0] < ub[0] and axis_mins[1] > lb[1] and axis_maxs[1] < ub[1], \
"UniformPrior functional test error, drawn samples out of bounds"
# Cluster mode
c = Client()
samples = prior_func.draw(num_samples, chunk_size=1)
assert len(
samples
) == 5, "UniformPrior functional test error, expected chunk count mismatch"
samples, = dask.compute(samples)
samples = np.asarray(samples)
assert samples.shape[
0] == num_samples, "UniformPrior functional test error, expected sample count mismatch"
assert samples.shape[
1] == 1, "UniformPrior functional test error, expected chunk size mismatch"
assert samples.shape[2] == len(
lb), "UniformPrior functional test error, dimension mismatch"
samples = samples.reshape(-1, len(lb))
axis_mins = np.min(samples, 0)
axis_maxs = np.max(samples, 0)
assert axis_mins[0] > lb[0] and axis_maxs[0] < ub[0] and axis_mins[1] > lb[1] and axis_maxs[1] < ub[1], \
"UniformPrior functional test error, drawn samples out of bounds"
# chunk_size = 2
samples = prior_func.draw(num_samples, chunk_size=2)
assert len(
samples
) == 3, "UniformPrior functional test error, expected chunk count mismatch"
samples, = dask.compute(samples)
samples = np.asarray(samples)
assert samples.shape[
0] == 3, "UniformPrior functional test error, expected sample count mismatch"
assert samples[-1].shape[
0] == 2, "UniformPrior functional test error, expected chunk size mismatch"
assert samples[-1].shape[1] == len(
lb), "UniformPrior functional test error, dimension mismatch"
samples = core._reshape_chunks(samples)
axis_mins = np.min(samples, 0)
axis_maxs = np.max(samples, 0)
assert axis_mins[0] > lb[0] and axis_maxs[0] < ub[0] and axis_mins[1] > lb[1] and axis_maxs[1] < ub[1], \
"UniformPrior functional test error, drawn samples out of bounds"
c.close()
# Imports
from sciope.utilities.priors import uniform_prior
from sciope.inference import abc_inference
from sciope.utilities.summarystats import burstiness as bs
import numpy as np
import vilar
from sklearn.metrics import mean_absolute_error
# Load data
data = np.loadtxt("datasets/vilar_dataset_specieA_100trajs_150time.dat",
delimiter=",")
# Set up the prior
dmin = [30, 200, 0, 30, 30, 1, 1, 0, 0, 0, 0.5, 0.5, 1, 30, 80]
dmax = [70, 600, 1, 70, 70, 10, 12, 1, 2, 0.5, 1.5, 1.5, 3, 70, 120]
mm_prior = uniform_prior.UniformPrior(np.asarray(dmin), np.asarray(dmax))
bs_stat = bs.Burstiness(mean_trajectories=False)
# Set up ABC
abc_instance = abc_inference.ABC(data,
vilar.simulate,
epsilon=0.1,
prior_function=mm_prior,
summaries_function=bs_stat)
# Perform ABC; require 30 samples
abc_instance.infer(30)
# Results
true_params = [[
50.0, 100.0, 50.0, 500.0, 0.01, 50.0, 50.0, 5.0, 1.0, 10.0, 0.5, 0.2, 1.0,