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unit test for sim_cat #209

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Dec 12, 2022
Merged

unit test for sim_cat #209

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8188247
unit test for sim_cat
khawajasim Nov 17, 2022
21ddaad
testing_likelihood
khawajasim Nov 17, 2022
eb19a44
name correction
khawajasim Nov 17, 2022
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minor corrections
khawajasim Nov 17, 2022
6b12645
correct indent
khawajasim Nov 17, 2022
2dd39f4
fixing imports
khawajasim Nov 17, 2022
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khawajasim Nov 17, 2022
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2 changes: 1 addition & 1 deletion csep/core/binomial_evaluations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,7 @@ def _binary_likelihood_test(forecast_data, observed_data, num_simulations=1000,
# data structures to store results
sim_fore = numpy.zeros(sampling_weights.shape)
simulated_ll = []
n_active_cells = len(np.unique(np.nonzero(observed_data.ravel())))
n_active_cells = len(numpy.unique(numpy.nonzero(observed_data.ravel())))
n_fore = numpy.sum(forecast_data)
expected_forecast_count = int(n_active_cells)

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42 changes: 35 additions & 7 deletions tests/test_evaluations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,8 @@
import numpy
import unittest

from csep.core.poisson_evaluations import _simulate_catalog, _poisson_likelihood_test
from csep.core.binomial_evaluations import binary_joint_log_likelihood_ndarray

import csep.core.poisson_evaluations as poisson
import csep.core.binomial_evaluations as binary

def get_datadir():
root_dir = os.path.dirname(os.path.abspath(__file__))
Expand Down Expand Up @@ -48,21 +47,21 @@ def test_simulate_catalog(self):

# this is taken from the test likelihood function
sim_fore = numpy.empty(sampling_weights.shape)
sim_fore = _simulate_catalog(num_events, sampling_weights, sim_fore,
sim_fore = poisson._simulate_catalog(num_events, sampling_weights, sim_fore,
random_numbers=self.random_matrix)

# final statement
numpy.testing.assert_allclose(expected_catalog, sim_fore)

# test again to ensure that fill works properply
sim_fore = _simulate_catalog(num_events, sampling_weights, sim_fore,
sim_fore = poisson._simulate_catalog(num_events, sampling_weights, sim_fore,
random_numbers=self.random_matrix)

# final statement
numpy.testing.assert_allclose(expected_catalog, sim_fore)

def test_likelihood(self):
qs, obs_ll, simulated_ll = _poisson_likelihood_test(self.forecast_data, self.observed_data, num_simulations=1,
qs, obs_ll, simulated_ll = poisson._poisson_likelihood_test(self.forecast_data, self.observed_data, num_simulations=1,
random_numbers=self.random_matrix, use_observed_counts=True)

# very basic result to pass "laugh" test
Expand All @@ -78,13 +77,42 @@ def test_likelihood(self):
class TestBinomialLikelihood(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.seed = 0
numpy.random.seed(self.seed)
self.forecast_data = numpy.array([[0.1, 0.3, 0.4], [0.2, 0.1, 0.1]])
self.observed_data = numpy.array([[0, 1, 2], [1, 1, 0]])
self.random_matrix = numpy.random.rand(1, 9)

def test_joint_likelihood_calculation(self):
bill = binary_joint_log_likelihood_ndarray(self.forecast_data, self.observed_data)
bill = binary.binary_joint_log_likelihood_ndarray(self.forecast_data, self.observed_data)
numpy.testing.assert_allclose(bill, -6.7197988064)

def test_simulate_active_cells(self):
#With fixed seed we get the same random numbers if we get all the number at once or one by one.
#Making sure random number generated by seed 0 match.
expected_random_numbers = numpy.array([[0.5488135, 0.71518937, 0.60276338, 0.54488318, 0.4236548, 0.64589411,
0.4375872112626925, 0.8917730007820798, 0.9636627605010293]])

numpy.testing.assert_allclose(expected_random_numbers, self.random_matrix)

#We can expect the following catalog, if we get the above random numbers.
#We get 4 active cells after 9th random sample.
expected_catalog = [0, 0, 1, 1, 1, 1]

sampling_weights = numpy.cumsum(self.forecast_data.ravel()) / numpy.sum(self.forecast_data)
sim_fore = numpy.zeros(sampling_weights.shape)
obs_active_cells = len(numpy.unique(numpy.nonzero(self.observed_data.ravel())))
#resetting seed again to 0, to make sure _simulate_catalog uses this.
seed = 0
numpy.random.seed(seed)
sim_fore = binary._simulate_catalog(obs_active_cells, sampling_weights, sim_fore)
numpy.testing.assert_allclose(expected_catalog, sim_fore)

def test_binomial_likelihood(self):
qs, bill, simulated_ll = binary._binary_likelihood_test(self.forecast_data,self.observed_data, num_simulations=1,seed=0, verbose=True)
numpy.testing.assert_allclose(bill, -6.7197988064)
numpy.testing.assert_allclose(qs, 1)
numpy.testing.assert_allclose(simulated_ll[0], -7.921741654647629)


if __name__ == '__main__':
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