%global _empty_manifest_terminate_build 0
Name:		python-BOHB-HPO
Version:	0.5.2
Release:	1
Summary:	Bayesian Optimization Hyperband Hyperparameter Optimization
License:	MIT
URL:		https://github.com/goktug97/bohb_hpo
Source0:	https://mirrors.aliyun.com/pypi/web/packages/76/11/1c093976ff45711e0f9060fb6ea0d02629522715f7d17463b397a6ae62c5/BOHB_HPO-0.5.2.tar.gz
BuildArch:	noarch


%description
Implementation for [BOHB](http://proceedings.mlr.press/v80/falkner18a.html)
## Requirements
    - numpy
    - scipy
    - statsmodels
    - dask
    - torch (example)
## Installation
```bash
pip3 install bohb-hpo
```
## Usage
``` Python
from bohb import BOHB
import bohb.configspace as cs
def objective(step, alpha, beta):
    return 1 / (alpha * step + 0.1) + beta
def evaluate(params, n_iterations):
    loss = 0.0
    for i in range(int(n_iterations)):
        loss += objective(**params, step=i)
    return loss/n_iterations
if __name__ == '__main__':
    alpha = cs.CategoricalHyperparameter('alpha', [0.001, 0.01, 0.1])
    beta = cs.CategoricalHyperparameter('beta', [1, 2, 3])
    configspace = cs.ConfigurationSpace([alpha, beta])
    opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1)
    # Parallel
    # opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1, n_proc=4)
    logs = opt.optimize()
```
See [examples](https://github.com/goktug97/bohb-hpo/tree/master/examples)
### Configspace Examples
- Basic
```python
import dehb.configspace as cs
lr = cs.UniformHyperparameter('lr', 1e-4, 1e-1, log=True)
batch_size = cs.CategoricalHyperparameter('batch_size', [8, 16, 32])
configspace = cs.ConfigurationSpace([lr, batch_size], seed=123)
```
- Conditional Parameters
```python
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b = cs.CategoricalHyperparameter('b', ['a', 'b', 'c'], a == 0)
b_default = cs.CategoricalHyperparameter('b', ['d'], ~b.cond)
configspace = cs.ConfigurationSpace([a, b, b_default], seed=123)
```
- Complex Conditional Parameters
```python
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b1 = cs.UniformHyperparameter('b', 0, 0.5, a <= 1)
b2 = cs.UniformHyperparameter('b', 0.5, 1, ~b1.cond)
c1 = cs.CategoricalHyperparameter('c', ['a', 'b', 'c'], b1 < 0.25)
c2 = cs.CategoricalHyperparameter('c', ['c', 'd', 'e'], ~c1.cond)
d1 = cs.UniformHyperparameter('d', 0, 1, (b1 < 0.125) & (c1 == 'b'))
d2 = cs.NormalHyperparameter('d', 0, 0.1, (b1 > 0.125) & (c1 == 'c'))
d3 = cs.IntegerNormalHyperparameter('d', 5, 10, (b2 > 0.750) & (c2 == 'd'))
d4 = cs.UniformHyperparameter('d', 0, 0, ~(d1.cond | d2.cond | d3.cond))
configspace = cs.ConfigurationSpace([a, b1, b2, c1, c2, d1, d2, d3, d4], seed=123)
```
## License
bohb-hpo is licensed under the MIT License.

%package -n python3-BOHB-HPO
Summary:	Bayesian Optimization Hyperband Hyperparameter Optimization
Provides:	python-BOHB-HPO
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-BOHB-HPO
Implementation for [BOHB](http://proceedings.mlr.press/v80/falkner18a.html)
## Requirements
    - numpy
    - scipy
    - statsmodels
    - dask
    - torch (example)
## Installation
```bash
pip3 install bohb-hpo
```
## Usage
``` Python
from bohb import BOHB
import bohb.configspace as cs
def objective(step, alpha, beta):
    return 1 / (alpha * step + 0.1) + beta
def evaluate(params, n_iterations):
    loss = 0.0
    for i in range(int(n_iterations)):
        loss += objective(**params, step=i)
    return loss/n_iterations
if __name__ == '__main__':
    alpha = cs.CategoricalHyperparameter('alpha', [0.001, 0.01, 0.1])
    beta = cs.CategoricalHyperparameter('beta', [1, 2, 3])
    configspace = cs.ConfigurationSpace([alpha, beta])
    opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1)
    # Parallel
    # opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1, n_proc=4)
    logs = opt.optimize()
```
See [examples](https://github.com/goktug97/bohb-hpo/tree/master/examples)
### Configspace Examples
- Basic
```python
import dehb.configspace as cs
lr = cs.UniformHyperparameter('lr', 1e-4, 1e-1, log=True)
batch_size = cs.CategoricalHyperparameter('batch_size', [8, 16, 32])
configspace = cs.ConfigurationSpace([lr, batch_size], seed=123)
```
- Conditional Parameters
```python
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b = cs.CategoricalHyperparameter('b', ['a', 'b', 'c'], a == 0)
b_default = cs.CategoricalHyperparameter('b', ['d'], ~b.cond)
configspace = cs.ConfigurationSpace([a, b, b_default], seed=123)
```
- Complex Conditional Parameters
```python
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b1 = cs.UniformHyperparameter('b', 0, 0.5, a <= 1)
b2 = cs.UniformHyperparameter('b', 0.5, 1, ~b1.cond)
c1 = cs.CategoricalHyperparameter('c', ['a', 'b', 'c'], b1 < 0.25)
c2 = cs.CategoricalHyperparameter('c', ['c', 'd', 'e'], ~c1.cond)
d1 = cs.UniformHyperparameter('d', 0, 1, (b1 < 0.125) & (c1 == 'b'))
d2 = cs.NormalHyperparameter('d', 0, 0.1, (b1 > 0.125) & (c1 == 'c'))
d3 = cs.IntegerNormalHyperparameter('d', 5, 10, (b2 > 0.750) & (c2 == 'd'))
d4 = cs.UniformHyperparameter('d', 0, 0, ~(d1.cond | d2.cond | d3.cond))
configspace = cs.ConfigurationSpace([a, b1, b2, c1, c2, d1, d2, d3, d4], seed=123)
```
## License
bohb-hpo is licensed under the MIT License.

%package help
Summary:	Development documents and examples for BOHB-HPO
Provides:	python3-BOHB-HPO-doc
%description help
Implementation for [BOHB](http://proceedings.mlr.press/v80/falkner18a.html)
## Requirements
    - numpy
    - scipy
    - statsmodels
    - dask
    - torch (example)
## Installation
```bash
pip3 install bohb-hpo
```
## Usage
``` Python
from bohb import BOHB
import bohb.configspace as cs
def objective(step, alpha, beta):
    return 1 / (alpha * step + 0.1) + beta
def evaluate(params, n_iterations):
    loss = 0.0
    for i in range(int(n_iterations)):
        loss += objective(**params, step=i)
    return loss/n_iterations
if __name__ == '__main__':
    alpha = cs.CategoricalHyperparameter('alpha', [0.001, 0.01, 0.1])
    beta = cs.CategoricalHyperparameter('beta', [1, 2, 3])
    configspace = cs.ConfigurationSpace([alpha, beta])
    opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1)
    # Parallel
    # opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1, n_proc=4)
    logs = opt.optimize()
```
See [examples](https://github.com/goktug97/bohb-hpo/tree/master/examples)
### Configspace Examples
- Basic
```python
import dehb.configspace as cs
lr = cs.UniformHyperparameter('lr', 1e-4, 1e-1, log=True)
batch_size = cs.CategoricalHyperparameter('batch_size', [8, 16, 32])
configspace = cs.ConfigurationSpace([lr, batch_size], seed=123)
```
- Conditional Parameters
```python
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b = cs.CategoricalHyperparameter('b', ['a', 'b', 'c'], a == 0)
b_default = cs.CategoricalHyperparameter('b', ['d'], ~b.cond)
configspace = cs.ConfigurationSpace([a, b, b_default], seed=123)
```
- Complex Conditional Parameters
```python
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b1 = cs.UniformHyperparameter('b', 0, 0.5, a <= 1)
b2 = cs.UniformHyperparameter('b', 0.5, 1, ~b1.cond)
c1 = cs.CategoricalHyperparameter('c', ['a', 'b', 'c'], b1 < 0.25)
c2 = cs.CategoricalHyperparameter('c', ['c', 'd', 'e'], ~c1.cond)
d1 = cs.UniformHyperparameter('d', 0, 1, (b1 < 0.125) & (c1 == 'b'))
d2 = cs.NormalHyperparameter('d', 0, 0.1, (b1 > 0.125) & (c1 == 'c'))
d3 = cs.IntegerNormalHyperparameter('d', 5, 10, (b2 > 0.750) & (c2 == 'd'))
d4 = cs.UniformHyperparameter('d', 0, 0, ~(d1.cond | d2.cond | d3.cond))
configspace = cs.ConfigurationSpace([a, b1, b2, c1, c2, d1, d2, d3, d4], seed=123)
```
## License
bohb-hpo is licensed under the MIT License.

%prep
%autosetup -n BOHB_HPO-0.5.2

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

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

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.2-1
- Package Spec generated