%global _empty_manifest_terminate_build 0 Name: python-TwoSampleHC Version: 0.2.1 Release: 1 Summary: Two-sample Higher Criticism License: MIT License URL: https://github.com/alonkipnis/TwoSampleHC Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4e/51/70605976fb6eb917e428e582849525fb91aefbd642d8d43abc2db127a823/TwoSampleHC-0.2.1.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy Requires: python3-pandas %description # TwoSampleHC -- Higher Criticism Test between Two Frequency Tables This package provides an adaptation of the Donoho-Jin-Tukey Higher-Critisim (HC) test to frequency tables. This adapatation uses a binomial allocation model for the number of occurances of each feature in two samples, each of which is associated with a frequency table. The exact binomial test associated with each feature yields a p-value. The HC statistic combines these P-values to a global test against the null hypothesis that the two tables are two realizations of the same data generating mechanism. This test is particularly useful in identifying non-null effects under weak and sparse alternatives, i.e., when the difference between the tables is due to few features, and the evidence each such feature provide is realtively weak. More details and applications can be found in [1] Alon Kipnis. (2019). Higher Criticism for Discriminating Word Frequency Tables and Testing Authorship. [2] David Donoho and Alon Kipnis. (2020). Two-sample Testing for Large, Sparse High-Dimensional Multinomials under Rare and WeakPerturbations. [3] Alon Kipnis. (2021). Log-Chisquared P-values under Rare and Weak Departures. ## Example: ``` import numpy as np N = 1000 # number of features n = 5 * N #number of samples P = 1 / np.arange(1,N+1) # Zipf base distribution P = P / P.sum() ep = 0.03 #fraction of features to perturb mu = 0.005 #intensity of perturbation TH = np.random.rand(N) < ep Q = P.copy() Q[TH] += mu Q = Q / np.sum(Q) smp_P = np.random.multinomial(n, P) # sample form P smp_Q = np.random.multinomial(n, Q) # sample from Q pv = two_sample_pvals(smp_Q, smp_P) # binomial P-values hc = HC(pv) hv_val, p_th = HC.HCstar(alpha = 0.25) # Small sample Higher Criticism test print("TV distance between P and Q: ", 0.5*np.sum(np.abs(P-Q))) print("Higher-Criticism score for testing P == Q: ", HC) # (HC score rarely goes above 2.5 if P == Q) ``` %package -n python3-TwoSampleHC Summary: Two-sample Higher Criticism Provides: python-TwoSampleHC BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-TwoSampleHC # TwoSampleHC -- Higher Criticism Test between Two Frequency Tables This package provides an adaptation of the Donoho-Jin-Tukey Higher-Critisim (HC) test to frequency tables. This adapatation uses a binomial allocation model for the number of occurances of each feature in two samples, each of which is associated with a frequency table. The exact binomial test associated with each feature yields a p-value. The HC statistic combines these P-values to a global test against the null hypothesis that the two tables are two realizations of the same data generating mechanism. This test is particularly useful in identifying non-null effects under weak and sparse alternatives, i.e., when the difference between the tables is due to few features, and the evidence each such feature provide is realtively weak. More details and applications can be found in [1] Alon Kipnis. (2019). Higher Criticism for Discriminating Word Frequency Tables and Testing Authorship. [2] David Donoho and Alon Kipnis. (2020). Two-sample Testing for Large, Sparse High-Dimensional Multinomials under Rare and WeakPerturbations. [3] Alon Kipnis. (2021). Log-Chisquared P-values under Rare and Weak Departures. ## Example: ``` import numpy as np N = 1000 # number of features n = 5 * N #number of samples P = 1 / np.arange(1,N+1) # Zipf base distribution P = P / P.sum() ep = 0.03 #fraction of features to perturb mu = 0.005 #intensity of perturbation TH = np.random.rand(N) < ep Q = P.copy() Q[TH] += mu Q = Q / np.sum(Q) smp_P = np.random.multinomial(n, P) # sample form P smp_Q = np.random.multinomial(n, Q) # sample from Q pv = two_sample_pvals(smp_Q, smp_P) # binomial P-values hc = HC(pv) hv_val, p_th = HC.HCstar(alpha = 0.25) # Small sample Higher Criticism test print("TV distance between P and Q: ", 0.5*np.sum(np.abs(P-Q))) print("Higher-Criticism score for testing P == Q: ", HC) # (HC score rarely goes above 2.5 if P == Q) ``` %package help Summary: Development documents and examples for TwoSampleHC Provides: python3-TwoSampleHC-doc %description help # TwoSampleHC -- Higher Criticism Test between Two Frequency Tables This package provides an adaptation of the Donoho-Jin-Tukey Higher-Critisim (HC) test to frequency tables. This adapatation uses a binomial allocation model for the number of occurances of each feature in two samples, each of which is associated with a frequency table. The exact binomial test associated with each feature yields a p-value. The HC statistic combines these P-values to a global test against the null hypothesis that the two tables are two realizations of the same data generating mechanism. This test is particularly useful in identifying non-null effects under weak and sparse alternatives, i.e., when the difference between the tables is due to few features, and the evidence each such feature provide is realtively weak. More details and applications can be found in [1] Alon Kipnis. (2019). Higher Criticism for Discriminating Word Frequency Tables and Testing Authorship. [2] David Donoho and Alon Kipnis. (2020). Two-sample Testing for Large, Sparse High-Dimensional Multinomials under Rare and WeakPerturbations. [3] Alon Kipnis. (2021). Log-Chisquared P-values under Rare and Weak Departures. ## Example: ``` import numpy as np N = 1000 # number of features n = 5 * N #number of samples P = 1 / np.arange(1,N+1) # Zipf base distribution P = P / P.sum() ep = 0.03 #fraction of features to perturb mu = 0.005 #intensity of perturbation TH = np.random.rand(N) < ep Q = P.copy() Q[TH] += mu Q = Q / np.sum(Q) smp_P = np.random.multinomial(n, P) # sample form P smp_Q = np.random.multinomial(n, Q) # sample from Q pv = two_sample_pvals(smp_Q, smp_P) # binomial P-values hc = HC(pv) hv_val, p_th = HC.HCstar(alpha = 0.25) # Small sample Higher Criticism test print("TV distance between P and Q: ", 0.5*np.sum(np.abs(P-Q))) print("Higher-Criticism score for testing P == Q: ", HC) # (HC score rarely goes above 2.5 if P == Q) ``` %prep %autosetup -n TwoSampleHC-0.2.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-TwoSampleHC -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.2.1-1 - Package Spec generated