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Improving tests on sampleable distributions (more robust; using seed); adding test for LogCustomKernel

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This commit is contained in:
Nicolas Mounet 2021-06-02 12:45:49 +02:00
parent 61b06fc1f0
commit 387fda67d8
1 changed files with 42 additions and 15 deletions
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57
cara/tests/test_sampleable_distribution.py
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@ -3,8 +3,8 @@ import numpy.testing as npt
import pytest
from cara.monte_carlo import sampleable
# TODO: seed deterministically the random number generators, here (to
# avoid random issues with tests)
# TODO: seed better the random number generators
np.random.seed(2000)
@pytest.mark.parametrize(
"mean, std",[
@ -17,12 +17,14 @@ def test_normal(mean, std):
sample_size = 2000000
samples = sampleable.Normal(mean, std).generate_samples(sample_size)
histogram, bins = np.histogram(samples,bins=100, density=True)
x = (bins[1:]+bins[:-1])/2
exact_dist = 1/(np.sqrt(2*np.pi)*std) * np.exp(-((x-mean)/std)**2/2)
selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
(bins[1:]+bins[:-1])/2,histogram) if b>=0.25 and b<=1.75])
exact_dist = 1/(np.sqrt(2*np.pi)*std) * np.exp(
-((np.array(selected_bins)-mean)/std)**2/2)
assert len(samples) == sample_size
npt.assert_allclose([samples.mean(), samples.std()], [mean, std], atol=mean/100)
npt.assert_allclose(histogram, exact_dist, atol=exact_dist.mean()/20)
npt.assert_allclose([samples.mean(), samples.std()], [mean, std], rtol=0.01)
npt.assert_allclose(selected_histogram, exact_dist, rtol=0.02)
@pytest.mark.parametrize(
@ -37,17 +39,19 @@ def test_lognormal(mean_gaussian, std_gaussian):
samples = sampleable.LogNormal(mean_gaussian, std_gaussian
).generate_samples(sample_size)
histogram, bins = np.histogram(samples,bins=50, density=True)
x = (bins[1:]+bins[:-1])/2
exact_dist = ( 1/(x*np.sqrt(2*np.pi)*std_gaussian) *
np.exp(-((np.log(x)-mean_gaussian)/std_gaussian)**2/2) )
selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
(bins[1:]+bins[:-1])/2,histogram) if b>=0.4 and b<=0.6])
selected_bins = np.array(selected_bins)
exact_dist = ( 1/(selected_bins*np.sqrt(2*np.pi)*std_gaussian) *
np.exp(-((np.log(selected_bins)-mean_gaussian)/std_gaussian)**2/2) )
exact_mean = np.exp(mean_gaussian + std_gaussian**2/2)
exact_std = np.sqrt( (np.exp(std_gaussian**2)-1) *
np.exp(2*mean_gaussian + std_gaussian**2) )
assert len(samples) == sample_size
npt.assert_allclose([samples.mean(), samples.std()],
[exact_mean, exact_std], atol=exact_mean/100)
npt.assert_allclose(histogram, exact_dist, atol=exact_dist.mean()/20)
[exact_mean, exact_std], rtol=0.01)
npt.assert_allclose(selected_histogram, exact_dist, rtol=0.02)
@pytest.mark.parametrize(
@ -71,13 +75,36 @@ def test_custom(use_kernel):
samples = sampleable.CustomKernel(variable, frequencies,
kernel_bandwidth=0.1
).generate_samples(sample_size)
tolerance = max_function/10
else:
samples = sampleable.Custom((0, 10), function, max_function
).generate_samples(sample_size)
tolerance = max_function/50
histogram, bins = np.histogram(samples, bins=100, density=True)
correct_dist = function((bins[1:]+bins[:-1])/2)
selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
(bins[1:]+bins[:-1])/2,histogram) if b>=1 and b<=9])
correct_dist = function(np.array(selected_bins))
assert len(samples) == sample_size
npt.assert_allclose(histogram, correct_dist, atol=tolerance)
npt.assert_allclose(selected_histogram, correct_dist, rtol=0.05)
def test_logcustomkernel():
# test that the sample has approximately the right distribution
# function, for the LogCustomKernel.
# the distribution function is an inverted parabola vs. the log of
# the variable (normalized)
norm = 500/3.
function = lambda x: (-(5 - x)**2 + 25)/norm
sample_size = 2000000
log_variable = np.linspace(0.1,9.9,100)
frequencies = function(log_variable)
samples = sampleable.LogCustomKernel(log_variable, frequencies,
kernel_bandwidth=0.1
).generate_samples(sample_size)
histogram, bins = np.histogram(np.log10(samples), bins=100, density=True)
selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
(bins[1:]+bins[:-1])/2,histogram) if b>=1 and b<=9])
correct_dist = function(np.array(selected_bins))
assert len(samples) == sample_size
npt.assert_allclose(selected_histogram, correct_dist, rtol=0.05)