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@@ -0,0 +1 @@ +/stochatreat-0.0.14.tar.gz diff --git a/python-stochatreat.spec b/python-stochatreat.spec new file mode 100644 index 0000000..b5843ba --- /dev/null +++ b/python-stochatreat.spec @@ -0,0 +1,379 @@ +%global _empty_manifest_terminate_build 0 +Name: python-stochatreat +Version: 0.0.14 +Release: 1 +Summary: Stratified random assignment using pandas +License: MIT License +URL: https://github.com/manmartgarc/stochatreat +Source0: https://mirrors.aliyun.com/pypi/web/packages/b8/fb/7c5557fbe821815cd5f9364fb20ed144448f59a87aac91d78baa171b9519/stochatreat-0.0.14.tar.gz +BuildArch: noarch + +Requires: python3-pandas + +%description +# Stochatreat + + + +## Introduction + +This is a Python tool to employ stratified randomization or sampling with uneven numbers in some strata using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within *blocks* or *strata*. The tool also supports having multiple treatments with different probability of assignment within each block or stratum. + +## Installation + +```bash +pip install stochatreat +``` + +## Usage + +Single cluster: + +```python +from stochatreat import stochatreat +import numpy as np +import pandas as pd + +# make 1000 households in 5 different neighborhoods. +np.random.seed(42) +df = pd.DataFrame( + data={'id': list(range(1000)), + 'nhood': np.random.randint(1, 6, size=1000)}) + +# randomly assign treatments by neighborhoods. +treats = stochatreat( + data=df, # your dataframe + stratum_cols='nhood', # the blocking variable + treats=2, # including control + idx_col='id', # the unique id column + random_state=42, # random seed + misfit_strategy='stratum') # the misfit strategy to use +# merge back with original data +df = df.merge(treats, how='left', on='id') + +# check for allocations +df.groupby('nhood')['treat'].value_counts().unstack() + +# previous code should return this +treat 0 1 +nhood +1 105 105 +2 95 95 +3 95 95 +4 103 103 +5 102 102 +``` + +Multiple clusters and treatment probabilities: + +```python +from stochatreat import stochatreat +import numpy as np +import pandas as pd + +# make 1000 households in 5 different neighborhoods, with a dummy indicator +np.random.seed(42) +df = pd.DataFrame(data={'id': list(range(1000)), + 'nhood': np.random.randint(1, 6, size=1000), + 'dummy': np.random.randint(0, 2, size=1000)}) + +# randomly assign treatments by neighborhoods and dummy status. +treats = stochatreat(data=df, + stratum_cols=['nhood', 'dummy'], + treats=2, + probs=[1/3, 2/3], + idx_col='id', + random_state=42, + misfit_strategy='global') +# merge back with original data +df = df.merge(treats, how='left', on='id') + +# check for allocations +df.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack() + +# previous code should return this +treat 0 1 +nhood dummy +1 0 37 75 + 1 33 65 +2 0 35 69 + 1 29 57 +3 0 30 58 + 1 34 68 +4 0 36 72 + 1 32 66 +5 0 33 68 + 1 35 68 +``` + +## Acknowledgments + +- `stochatreat` is totally inspired by [Alvaro Carril's](https://acarril.github.io/) fantastic Stata package: [`randtreat`](https://acarril.github.io/posts/randtreat), which was published in [The Stata Journal](https://www.stata-journal.com/article.html?article=st0490). +- [David McKenzie's](http://blogs.worldbank.org/impactevaluations/tools-of-the-trade-doing-stratified-randomization-with-uneven-numbers-in-some-strata) fantastic post (and blog) about running RCTs for the World Bank. +- [*In Pursuit of Balance: Randomization in Practice in Development Field Experiments.* Bruhn, McKenzie, 2009](https://www.aeaweb.org/articles?id=10.1257/app.1.4.200) + + + + +%package -n python3-stochatreat +Summary: Stratified random assignment using pandas +Provides: python-stochatreat +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-stochatreat +# Stochatreat + + + +## Introduction + +This is a Python tool to employ stratified randomization or sampling with uneven numbers in some strata using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within *blocks* or *strata*. The tool also supports having multiple treatments with different probability of assignment within each block or stratum. + +## Installation + +```bash +pip install stochatreat +``` + +## Usage + +Single cluster: + +```python +from stochatreat import stochatreat +import numpy as np +import pandas as pd + +# make 1000 households in 5 different neighborhoods. +np.random.seed(42) +df = pd.DataFrame( + data={'id': list(range(1000)), + 'nhood': np.random.randint(1, 6, size=1000)}) + +# randomly assign treatments by neighborhoods. +treats = stochatreat( + data=df, # your dataframe + stratum_cols='nhood', # the blocking variable + treats=2, # including control + idx_col='id', # the unique id column + random_state=42, # random seed + misfit_strategy='stratum') # the misfit strategy to use +# merge back with original data +df = df.merge(treats, how='left', on='id') + +# check for allocations +df.groupby('nhood')['treat'].value_counts().unstack() + +# previous code should return this +treat 0 1 +nhood +1 105 105 +2 95 95 +3 95 95 +4 103 103 +5 102 102 +``` + +Multiple clusters and treatment probabilities: + +```python +from stochatreat import stochatreat +import numpy as np +import pandas as pd + +# make 1000 households in 5 different neighborhoods, with a dummy indicator +np.random.seed(42) +df = pd.DataFrame(data={'id': list(range(1000)), + 'nhood': np.random.randint(1, 6, size=1000), + 'dummy': np.random.randint(0, 2, size=1000)}) + +# randomly assign treatments by neighborhoods and dummy status. +treats = stochatreat(data=df, + stratum_cols=['nhood', 'dummy'], + treats=2, + probs=[1/3, 2/3], + idx_col='id', + random_state=42, + misfit_strategy='global') +# merge back with original data +df = df.merge(treats, how='left', on='id') + +# check for allocations +df.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack() + +# previous code should return this +treat 0 1 +nhood dummy +1 0 37 75 + 1 33 65 +2 0 35 69 + 1 29 57 +3 0 30 58 + 1 34 68 +4 0 36 72 + 1 32 66 +5 0 33 68 + 1 35 68 +``` + +## Acknowledgments + +- `stochatreat` is totally inspired by [Alvaro Carril's](https://acarril.github.io/) fantastic Stata package: [`randtreat`](https://acarril.github.io/posts/randtreat), which was published in [The Stata Journal](https://www.stata-journal.com/article.html?article=st0490). +- [David McKenzie's](http://blogs.worldbank.org/impactevaluations/tools-of-the-trade-doing-stratified-randomization-with-uneven-numbers-in-some-strata) fantastic post (and blog) about running RCTs for the World Bank. +- [*In Pursuit of Balance: Randomization in Practice in Development Field Experiments.* Bruhn, McKenzie, 2009](https://www.aeaweb.org/articles?id=10.1257/app.1.4.200) + + + + +%package help +Summary: Development documents and examples for stochatreat +Provides: python3-stochatreat-doc +%description help +# Stochatreat + + + +## Introduction + +This is a Python tool to employ stratified randomization or sampling with uneven numbers in some strata using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within *blocks* or *strata*. The tool also supports having multiple treatments with different probability of assignment within each block or stratum. + +## Installation + +```bash +pip install stochatreat +``` + +## Usage + +Single cluster: + +```python +from stochatreat import stochatreat +import numpy as np +import pandas as pd + +# make 1000 households in 5 different neighborhoods. +np.random.seed(42) +df = pd.DataFrame( + data={'id': list(range(1000)), + 'nhood': np.random.randint(1, 6, size=1000)}) + +# randomly assign treatments by neighborhoods. +treats = stochatreat( + data=df, # your dataframe + stratum_cols='nhood', # the blocking variable + treats=2, # including control + idx_col='id', # the unique id column + random_state=42, # random seed + misfit_strategy='stratum') # the misfit strategy to use +# merge back with original data +df = df.merge(treats, how='left', on='id') + +# check for allocations +df.groupby('nhood')['treat'].value_counts().unstack() + +# previous code should return this +treat 0 1 +nhood +1 105 105 +2 95 95 +3 95 95 +4 103 103 +5 102 102 +``` + +Multiple clusters and treatment probabilities: + +```python +from stochatreat import stochatreat +import numpy as np +import pandas as pd + +# make 1000 households in 5 different neighborhoods, with a dummy indicator +np.random.seed(42) +df = pd.DataFrame(data={'id': list(range(1000)), + 'nhood': np.random.randint(1, 6, size=1000), + 'dummy': np.random.randint(0, 2, size=1000)}) + +# randomly assign treatments by neighborhoods and dummy status. +treats = stochatreat(data=df, + stratum_cols=['nhood', 'dummy'], + treats=2, + probs=[1/3, 2/3], + idx_col='id', + random_state=42, + misfit_strategy='global') +# merge back with original data +df = df.merge(treats, how='left', on='id') + +# check for allocations +df.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack() + +# previous code should return this +treat 0 1 +nhood dummy +1 0 37 75 + 1 33 65 +2 0 35 69 + 1 29 57 +3 0 30 58 + 1 34 68 +4 0 36 72 + 1 32 66 +5 0 33 68 + 1 35 68 +``` + +## Acknowledgments + +- `stochatreat` is totally inspired by [Alvaro Carril's](https://acarril.github.io/) fantastic Stata package: [`randtreat`](https://acarril.github.io/posts/randtreat), which was published in [The Stata Journal](https://www.stata-journal.com/article.html?article=st0490). +- [David McKenzie's](http://blogs.worldbank.org/impactevaluations/tools-of-the-trade-doing-stratified-randomization-with-uneven-numbers-in-some-strata) fantastic post (and blog) about running RCTs for the World Bank. +- [*In Pursuit of Balance: Randomization in Practice in Development Field Experiments.* Bruhn, McKenzie, 2009](https://www.aeaweb.org/articles?id=10.1257/app.1.4.200) + + + + +%prep +%autosetup -n stochatreat-0.0.14 + +%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-stochatreat -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.14-1 +- Package Spec generated @@ -0,0 +1 @@ +fd3d3ad619c202078db46d8d7b66ebf6 stochatreat-0.0.14.tar.gz |