%global _empty_manifest_terminate_build 0
Name:		python-abito
Version:	0.1.3
Release:	1
Summary:	Package for hypothesis testing in A/B-experiments
License:	MIT
URL:		https://github.com/avito-tech/abito
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/7c/ad/8a631821b1f0d93d62f87ce0013d866ced16c3371b6be3df42c685256bc0/abito-0.1.3.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-scipy

%description
# abito
[![Build Status](https://travis-ci.com/avito-tech/abito.svg?branch=master)](https://travis-ci.com/avito-tech/abito)
[![Coverage Status](https://coveralls.io/repos/github/avito-tech/abito/badge.svg?branch=master)](https://coveralls.io/github/avito-tech/abito?branch=master)

Python package for hypothesis testing. Suitable for using in A/B-testing software.
Tested for Python >= 3.5. Based on numpy and scipy.

##### Features
1. Convenient interface to run significance tests.
2. Support of ratio-samples. Linearization included (delta-method).
3. Bootstrapping: can measure significance of any statistic, even quantiles. Multiprocessing is supported.
4. Ntile-bucketing: compress samples to get better performance.
5. Trim: get rid of heavy tails.


## Installation
```
pip install abito
```

## Usage

The most powerful tool in this package is the Sample:
```python
import abito as ab
```

Let's draw some observations from Poisson distribution and initiate Sample instance from them.
```python
import numpy as np

observations = np.random.poisson(1, size=10**6)
sample = ab.sample(observations)
```

Now we can calculate any statistic in numpy-way.
```python
print(sample.mean())
print(sample.std())
print(sample.quantile(q=[0.05, 0.95]))
```

To compare with other sample we can use t_test or mann_whitney_u_test:
```python
observations_control = np.random.poisson(1.005, size=10**6)
sample_control = Sample(observations_control)

print(sample.t_test(sample_control))
print(sample.mann_whitney_u_test(sample_control))
```

### Bootstrap
Or we can use bootstrap to compare any statistic:
```python
sample.bootstrap_test(sample_control, stat='mean', n_iters=100)
```

To improve performance, it's better to provide observations in weighted form: unique values + counts. Or, we can compress samples, using built-in method:
```python
sample.reweigh(inplace=True)
sample_control.reweigh(inplace=True)
sample.bootstrap_test(sample_control, stat='mean', n_iters=10000)
```
Now bootstrap is working lightning-fast. To improve performance further you can set parameter n_threads > 1 to run bootstrapping using multiprocessing.

### Compress
```python
observations = np.random.normal(100, size=10**8)
sample = ab.sample(observations)

compressed = sample.compress(n_buckets=100, stat='mean')

%timeit sample.std()
%timeit compressed.std()
```



%package -n python3-abito
Summary:	Package for hypothesis testing in A/B-experiments
Provides:	python-abito
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-abito
# abito
[![Build Status](https://travis-ci.com/avito-tech/abito.svg?branch=master)](https://travis-ci.com/avito-tech/abito)
[![Coverage Status](https://coveralls.io/repos/github/avito-tech/abito/badge.svg?branch=master)](https://coveralls.io/github/avito-tech/abito?branch=master)

Python package for hypothesis testing. Suitable for using in A/B-testing software.
Tested for Python >= 3.5. Based on numpy and scipy.

##### Features
1. Convenient interface to run significance tests.
2. Support of ratio-samples. Linearization included (delta-method).
3. Bootstrapping: can measure significance of any statistic, even quantiles. Multiprocessing is supported.
4. Ntile-bucketing: compress samples to get better performance.
5. Trim: get rid of heavy tails.


## Installation
```
pip install abito
```

## Usage

The most powerful tool in this package is the Sample:
```python
import abito as ab
```

Let's draw some observations from Poisson distribution and initiate Sample instance from them.
```python
import numpy as np

observations = np.random.poisson(1, size=10**6)
sample = ab.sample(observations)
```

Now we can calculate any statistic in numpy-way.
```python
print(sample.mean())
print(sample.std())
print(sample.quantile(q=[0.05, 0.95]))
```

To compare with other sample we can use t_test or mann_whitney_u_test:
```python
observations_control = np.random.poisson(1.005, size=10**6)
sample_control = Sample(observations_control)

print(sample.t_test(sample_control))
print(sample.mann_whitney_u_test(sample_control))
```

### Bootstrap
Or we can use bootstrap to compare any statistic:
```python
sample.bootstrap_test(sample_control, stat='mean', n_iters=100)
```

To improve performance, it's better to provide observations in weighted form: unique values + counts. Or, we can compress samples, using built-in method:
```python
sample.reweigh(inplace=True)
sample_control.reweigh(inplace=True)
sample.bootstrap_test(sample_control, stat='mean', n_iters=10000)
```
Now bootstrap is working lightning-fast. To improve performance further you can set parameter n_threads > 1 to run bootstrapping using multiprocessing.

### Compress
```python
observations = np.random.normal(100, size=10**8)
sample = ab.sample(observations)

compressed = sample.compress(n_buckets=100, stat='mean')

%timeit sample.std()
%timeit compressed.std()
```



%package help
Summary:	Development documents and examples for abito
Provides:	python3-abito-doc
%description help
# abito
[![Build Status](https://travis-ci.com/avito-tech/abito.svg?branch=master)](https://travis-ci.com/avito-tech/abito)
[![Coverage Status](https://coveralls.io/repos/github/avito-tech/abito/badge.svg?branch=master)](https://coveralls.io/github/avito-tech/abito?branch=master)

Python package for hypothesis testing. Suitable for using in A/B-testing software.
Tested for Python >= 3.5. Based on numpy and scipy.

##### Features
1. Convenient interface to run significance tests.
2. Support of ratio-samples. Linearization included (delta-method).
3. Bootstrapping: can measure significance of any statistic, even quantiles. Multiprocessing is supported.
4. Ntile-bucketing: compress samples to get better performance.
5. Trim: get rid of heavy tails.


## Installation
```
pip install abito
```

## Usage

The most powerful tool in this package is the Sample:
```python
import abito as ab
```

Let's draw some observations from Poisson distribution and initiate Sample instance from them.
```python
import numpy as np

observations = np.random.poisson(1, size=10**6)
sample = ab.sample(observations)
```

Now we can calculate any statistic in numpy-way.
```python
print(sample.mean())
print(sample.std())
print(sample.quantile(q=[0.05, 0.95]))
```

To compare with other sample we can use t_test or mann_whitney_u_test:
```python
observations_control = np.random.poisson(1.005, size=10**6)
sample_control = Sample(observations_control)

print(sample.t_test(sample_control))
print(sample.mann_whitney_u_test(sample_control))
```

### Bootstrap
Or we can use bootstrap to compare any statistic:
```python
sample.bootstrap_test(sample_control, stat='mean', n_iters=100)
```

To improve performance, it's better to provide observations in weighted form: unique values + counts. Or, we can compress samples, using built-in method:
```python
sample.reweigh(inplace=True)
sample_control.reweigh(inplace=True)
sample.bootstrap_test(sample_control, stat='mean', n_iters=10000)
```
Now bootstrap is working lightning-fast. To improve performance further you can set parameter n_threads > 1 to run bootstrapping using multiprocessing.

### Compress
```python
observations = np.random.normal(100, size=10**8)
sample = ab.sample(observations)

compressed = sample.compress(n_buckets=100, stat='mean')

%timeit sample.std()
%timeit compressed.std()
```



%prep
%autosetup -n abito-0.1.3

%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-abito -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.3-1
- Package Spec generated