def test_mean(scale: Scale):
true_mean = scale.low + scale.width / 2
rv = norm(loc=true_mean, scale=scale.width / 10)
xs = constants.target_xs
pairs = [{
"x": x,
"density": rv.pdf(x)
} for x in scale.denormalize_points(xs)]
pd_norm = PointDensity.from_pairs(pairs, scale)
calculated_mean = float(pd_norm.mean())
assert true_mean == pytest.approx(calculated_mean, rel=1e-3, abs=1e-3)
def test_density_norm_denorm_roundtrip(scale: Scale):
rv = scipy.stats.logistic(loc=0.5, scale=0.15)
normed_xs = np.linspace(0.01, 1, 201)
normed_densities_truth_set = rv.pdf(normed_xs)
xs = scale.denormalize_points(normed_xs)
denormed_densities = scale.denormalize_densities(
xs, normed_densities_truth_set)
normed_densities = scale.normalize_densities(normed_xs, denormed_densities)
assert np.allclose(normed_densities_truth_set,
normed_densities) # type: ignore
def point_density_from_scale(scale: Scale):
scale_mid = scale.low + scale.width / 2
rv = logistic(loc=scale_mid, scale=scale.width / 30)
xs = scale.denormalize_points(constants.target_xs)
densities = rv.pdf(xs)
pairs = [{
"x": x,
"density": density
} for (x, density) in zip(xs, densities)]
return PointDensity.from_pairs(pairs, scale)
def test_pdf(xscale: Scale):
normed_test_loc = 0.5
normed_test_s = 0.1
test_loc = xscale.denormalize_point(normed_test_loc)
test_s = normed_test_s * xscale.width
ergoLogisticMixture = LogisticMixture(
components=[
Logistic(
loc=xscale.denormalize_point(0.2),
s=0.5 * xscale.width,
scale=xscale,
),
Logistic(loc=test_loc, s=test_s, scale=xscale),
],
probs=[1.8629593e-29, 1.0],
)
ergoLogistic = Logistic(loc=test_loc, s=test_s, scale=xscale)
## Make sure it integrates to 1
_xs = xscale.denormalize_points(np.linspace(0, 1, 100))
densities_logistic = np.array([float(ergoLogistic.pdf(x)) for x in _xs])
densities_mixture = np.array(
[float(ergoLogisticMixture.pdf(x)) for x in _xs])
auc_logistic = float(trapz(densities_logistic, x=_xs))
auc_mixture = float(trapz(densities_mixture, x=_xs))
assert (1 == pytest.approx(auc_logistic, abs=0.03) == pytest.approx(
auc_mixture, abs=0.03))
if not isinstance(xscale, LogScale):
scipydist = scipy.stats.logistic(test_loc, test_s)
for x in np.linspace(xscale.denormalize_point(0),
xscale.denormalize_point(1), 10):
assert (scipydist.pdf(x) == pytest.approx(
float(ergoLogistic.pdf(x)), rel=1e-3) == pytest.approx(
float(ergoLogisticMixture.pdf(x)), rel=1e-3))
def denormalize(self, scale: Scale):
denormed_xs = scale.denormalize_points(self.xs)
denormed_densities = scale.denormalize_densities(denormed_xs, self.densities)
return self.__class__(denormed_xs, denormed_densities, self.weight)
