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