scipy-yli/tests/test_beta_oddsratio.py

97 lines
3.1 KiB
Python

# scipy-yli: Helpful SciPy utilities and recipes
# Copyright © 2022 Lee Yingtong Li (RunasSudo)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from pytest import approx
import numpy as np
from scipy import stats
import yli
def test_beta_ratio_cdf_vs_empirical():
"""Compare beta_ratio.cdf with empirical distribution"""
# Define the example beta distribution
beta1 = stats.beta(3, 6)
beta2 = stats.beta(12, 7)
dist = yli.beta_oddsratio.from_scipy(beta1, beta2)
# Compute empirical distribution
samples_p1 = beta1.rvs(10_000, random_state=31415)
samples_p2 = beta2.rvs(10_000, random_state=92653)
sample = (samples_p1 / (1 - samples_p1)) / (samples_p2 / (1 - samples_p2))
# Values to check
x = np.linspace(0, 2, 10)
y1 = dist.cdf(x)
y2 = [(sample < xx).sum()/sample.size for xx in x]
# Allow 0.01 tolerance
assert y1 == approx(y2, abs=0.01)
def test_beta_ratio_ppf_vs_empirical():
"""Compare beta_ratio.ppf with empirical distribution"""
# Define the example beta distribution
beta1 = stats.beta(3, 6)
beta2 = stats.beta(12, 7)
dist = yli.beta_oddsratio.from_scipy(beta1, beta2)
# Compute empirical distribution
samples_p1 = beta1.rvs(10_000, random_state=31415)
samples_p2 = beta2.rvs(10_000, random_state=92653)
sample = (samples_p1 / (1 - samples_p1)) / (samples_p2 / (1 - samples_p2))
# Values to check
x = np.linspace(0, 0.9, 10)
y1 = dist.ppf(x)
y2 = np.quantile(sample, x)
# Allow 0.01 tolerance
assert y1 == approx(y2, abs=0.01)
def test_beta_ratio_mean_vs_empirical():
"""Compare beta_ratio.mean (vs _munp) with empirical mean"""
# Define the example beta distribution
beta1 = stats.beta(3, 6)
beta2 = stats.beta(12, 7)
dist = yli.beta_oddsratio.from_scipy(beta1, beta2)
# Compute empirical mean
samples_p1 = beta1.rvs(10_000, random_state=31415)
samples_p2 = beta2.rvs(10_000, random_state=92653)
sample = (samples_p1 / (1 - samples_p1)) / (samples_p2 / (1 - samples_p2))
# Allow 0.01 tolerance
assert dist.mean() == approx(sample.mean(), abs=0.01)
def test_beta_ratio_var_vs_empirical():
"""Compare beta_ratio.var (vs _munp) with empirical variance"""
# Define the example beta distribution
beta1 = stats.beta(3, 6)
beta2 = stats.beta(12, 7)
dist = yli.beta_oddsratio.from_scipy(beta1, beta2)
# Compute empirical mean
samples_p1 = beta1.rvs(10_000, random_state=31415)
samples_p2 = beta2.rvs(10_000, random_state=92653)
sample = (samples_p1 / (1 - samples_p1)) / (samples_p2 / (1 - samples_p2))
# Allow 0.01 tolerance
assert dist.var() == approx(sample.var(), abs=0.01)