def test_function(self):
fun = pnl.NormalDist(mean=5.0).function
spec = SampleSpec(function=fun)
sample_iterator = SampleIterator(specification=spec)
expected = [
5.978737984105739, 7.240893199201458, 6.867557990149967,
4.022722120123589, 5.950088417525589
]
for i in range(5):
assert np.allclose(next(sample_iterator), expected[i])
def test_int_num(self):
spec = SampleSpec(num=6, start=0, stop=10)
sample_iterator = SampleIterator(specification=spec)
expected = [0, 2, 4, 6, 8, 10]
for i in range(6):
assert np.allclose(next(sample_iterator), expected[i])
assert next(sample_iterator, None) is None
sample_iterator.reset()
for i in range(6):
assert np.allclose(next(sample_iterator), expected[i])
assert next(sample_iterator, None) is None
def test_float_step(self):
# Need to decide whether stop should be exclusive
spec = SampleSpec(step=2.79, start=0.65, stop=10.25)
sample_iterator = SampleIterator(specification=spec)
expected = [0.65, 3.44, 6.23, 9.02]
for i in range(4):
assert np.allclose(next(sample_iterator), expected[i])
assert next(sample_iterator, None) is None
sample_iterator.reset()
for i in range(4):
assert np.allclose(next(sample_iterator), expected[i])
assert next(sample_iterator, None) is None
def test_moving_average():
# Set an arbitrary seed and a global random state to keep the randomly generated quantities the same between runs
seed = 20170530 # this will be separately given to ELFI
np.random.seed(seed)
# true parameters
t1_true = 0.6
t2_true = 0.2
# Define a function that simulates a 2nd order moving average, assuming mean zero:
# y_t = w_t + t1*w_t-1 + t2*w_t-2
# where t1 and t2 are real and w_k is i.i.d sequence white noise with N(0,1)
def MA2(input=[0],
t1=0.5,
t2=0.5,
n_obs=100,
batch_size=1,
random_state=None):
# FIXME: Convert arguments to scalar if they are not. Why is this nescessary?
# PsyNeuLink, when creating a user defined function, seems to expect the function
# to support inputs of type np.ndarray even when they are only allowed to be
# scalars.
n_obs = n_obs[0] if (type(n_obs) is np.ndarray) else n_obs
batch_size = batch_size[0] if (
type(batch_size) is np.ndarray) else batch_size
# Make inputs 2d arrays for numpy broadcasting with w
t1 = np.asanyarray(t1).reshape((-1, 1))
t2 = np.asanyarray(t2).reshape((-1, 1))
random_state = random_state or np.random
w = random_state.randn(int(batch_size),
int(n_obs) + 2) # i.i.d. sequence ~ N(0,1)
x = w[:, 2:] + t1 * w[:, 1:-1] + t2 * w[:, :-2]
return x
# Lets make some observed data. This will be the data we try to fit parameters for.
y_obs = MA2(t1_true, t2_true)
# Make a processing mechanism out of our simulator.
ma_mech = ProcessingMechanism(function=MA2,
size=1,
name='Moving Average (2nd Order)')
# Now lets add it to a composition
comp = Composition(name="Moving_Average")
comp.add_node(ma_mech)
# Now lets setup some control signals for the parameters we want to
# infer. This is where we would like to specify priors.
signalSearchRange = SampleSpec(start=0.1, stop=2.0, step=0.2)
t1_control_signal = ControlSignal(projections=[('t1', ma_mech)],
allocation_samples=signalSearchRange,
modulation=OVERRIDE)
t2_control_signal = ControlSignal(projections=[('t2', ma_mech)],
allocation_samples=signalSearchRange,
modulation=OVERRIDE)
# A function to calculate the auto-covariance with specific lag for a
# time series. We will use this function to compute the summary statistics
# for generated and observed data so that we can compute a metric between the
# two. In PsyNeuLink terms, this will be part of an ObjectiveMechanism.
# A function to calculate the auto-covariance with specific lag for a
# time series. We will use this function to compute the summary statistics
# for generated and observed data so that we can compute a metric between the
# two. In PsyNeuLink terms, this will be part of an ObjectiveMechanism.
def autocov(agent_rep, x=None, lag=1):
if x is None:
return np.asarray(0.0)
C = np.mean(x[:, lag:] * x[:, :-lag], axis=1)
return C
# # Lets make one function that computes all the summary stats in one go because PsyNeuLink
# # objective mechanism expect a single function.
# def objective_function(x):
# return np.concatenate((autocov(x), autocov(x, lag=2)))
#
# # Objective Mechanism and its function currently need to be specified in the script.
# # (In future versions, this will be set up automatically)
# objective_mech = ObjectiveMechanism(function=objective_function,
# monitor=[ma_mech])
# Setup the controller with the ParamEstimationFunction
comp.add_controller(controller=OptimizationControlMechanism(
agent_rep=comp,
function=ParamEstimationFunction(
priors={
't1': (scipy.stats.uniform, 0, 2),
't2': (scipy.stats.uniform, 0, 2)
},
observed=y_obs,
summary=[(autocov, 1), (autocov, 2)],
discrepancy='euclidean',
n_samples=3,
quantile=0.01, # Set very small now cause things are slow.
seed=seed),
objective_mechanism=False,
control_signals=[t1_control_signal, t2_control_signal]))
comp.disable_all_history()
# Lets setup some input to the mechanism, not that it uses it for anything.
stim_list_dict = {ma_mech: [0]}
# # FIXME: Show graph fails when the controller doesn't have an objective mechanism.
# comp.show_graph(show_controller=True,
# show_projection_labels=True,
# show_node_structure=True,
# show_cim=True,
# show_dimensions=True)
comp.run(inputs=stim_list_dict)
# FIXME: The final test should be to check if the true parameters set above are
# recovered approximately. Not sure how to get all the samples out from
# above yet though so just pass for now.
assert True
import numpy as np
import psyneulink.core.llvm as pnlvm
import psyneulink.core.components.functions.function as Function
import psyneulink.core.components.functions.objectivefunctions as Functions
import psyneulink.core.components.functions.optimizationfunctions as OPTFunctions
import psyneulink.core.globals.keywords as kw
from psyneulink.core.globals.sampleiterator import SampleIterator, SampleSpec
import pytest
SIZE = 5
# Some metrics (CROSS_ENTROPY) don't like 0s
test_var = np.random.rand(SIZE) + Function.EPSILON
EPS = float(Function.EPSILON)
search_space = [
SampleIterator([EPS, 1.0] if i %
2 == 0 else SampleSpec(start=EPS, stop=1.0, num=2))
for i in range(SIZE)
]
results = {
Functions.Stability: {
kw.ENERGY: {
True: {
OPTFunctions.MINIMIZE: {
'FIRST': ((1.0, 1.0, 1.0, 1.0, 1.0), -0.4, [], []),
'RANDOM': ((1.0, 1.0, 1.0, 1.0, 1.0), -0.4, [], []),
},
OPTFunctions.MAXIMIZE: {
'FIRST': ((EPS, EPS, EPS, EPS, EPS),
-1.9721522630525296e-32, [], []),
'RANDOM': ((1.0, EPS, EPS, EPS, EPS),
-1.9721522630525296e-32, [], []),
def test_neither_num_nor_step(self):
with pytest.raises(SampleIteratorError) as error_text:
SampleSpec(start=0, stop=10)
assert "Must specify one of 'step', 'num' or 'function'" in str(
error_text.value)