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|
%global _empty_manifest_terminate_build 0
Name: python-sklearn-nature-inspired-algorithms
Version: 0.11.0
Release: 1
Summary: Search using nature inspired algorithms over specified parameter values for an sklearn estimator.
License: MIT
URL: https://github.com/timzatko/Sklearn-Nature-Inspired-Algorithms
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b8/a5/2f6b9c53291274b4247226728d230b482db2e31caee0ed201d32d7e9c20f/sklearn_nature_inspired_algorithms-0.11.0.tar.gz
BuildArch: noarch
Requires: python3-toml
Requires: python3-scikit-learn
Requires: python3-numpy
Requires: python3-matplotlib
Requires: python3-seaborn
Requires: python3-pandas
Requires: python3-niapy
Requires: python3-scipy
%description
# Nature-Inspired Algorithms for scikit-learn
[](https://github.com/timzatko/Sklearn-Nature-Inspired-Algorithms/actions?query=workflow:CI+branch:master)
[](https://codeclimate.com/github/timzatko/Sklearn-Nature-Inspired-Algorithms/maintainability)
[](https://pypi.org/project/sklearn-nature-inspired-algorithms/)
[](https://pypi.org/project/sklearn-nature-inspired-algorithms/)
[](https://src.fedoraproject.org/rpms/python-sklearn-nature-inspired-algorithms)
Nature-inspired algorithms for hyper-parameter tuning of [scikit-learn](https://github.com/scikit-learn/scikit-learn) models. This package uses algorithms implementation from [NiaPy](https://github.com/NiaOrg/NiaPy).
## Installation
```shell script
$ pip install sklearn-nature-inspired-algorithms
```
To install this package on Fedora, run:
```sh
$ dnf install python3-sklearn-nature-inspired-algorithms
```
## Usage
The usage is similar to using sklearn's `GridSearchCV`. Refer to the [documentation](https://sklearn-nature-inspired-algorithms.readthedocs.io/en/stable/) for more detailed guides and more examples.
```python
from sklearn_nature_inspired_algorithms.model_selection import NatureInspiredSearchCV
from sklearn.ensemble import RandomForestClassifier
param_grid = {
'n_estimators': range(20, 100, 20),
'max_depth': range(2, 40, 2),
'min_samples_split': range(2, 20, 2),
'max_features': ["auto", "sqrt", "log2"],
}
clf = RandomForestClassifier(random_state=42)
nia_search = NatureInspiredSearchCV(
clf,
param_grid,
algorithm='hba', # hybrid bat algorithm
population_size=50,
max_n_gen=100,
max_stagnating_gen=10,
runs=3,
random_state=None, # or any number if you want same results on each run
)
nia_search.fit(X_train, y_train)
# the best params are stored in nia_search.best_params_
# finally you can train your model with best params from nia search
new_clf = RandomForestClassifier(**nia_search.best_params_, random_state=42)
```
Also you plot the search process with _line plot_ or _violin plot_.
```python
from sklearn_nature_inspired_algorithms.helpers import score_by_generation_lineplot, score_by_generation_violinplot
# line plot will plot all of the runs, you can specify the metric to be plotted ('min', 'max', 'median', 'mean')
score_by_generation_lineplot(nia_search, metric='max')
# in violin plot you need to specify the run to be plotted
score_by_generation_violinplot(nia_search, run=0)
```
Jupyter notebooks with full examples are available in [here](examples/notebooks).
### Using a Custom Nature-Inspired Algorithm
If you do not want to use any of the pre-defined algorithm configurations, you can use any algorithm from the [NiaPy](https://github.com/NiaOrg/NiaPy) collection.
This will allow you to have more control of the algorithm behavior.
Refer to their [documentation](https://niapy.readthedocs.io/en/latest/) and [examples](https://github.com/NiaOrg/NiaPy/tree/master/examples) for the usage.
__Note:__ Use version >2.x.x of NiaPy package
```python
from niapy.algorithms.basic import GeneticAlgorithm
algorithm = GeneticAlgorithm() # when custom algorithm is provided random_state is ignored
algorithm.set_parameters(NP=50, Ts=5, Mr=0.25)
nia_search = NatureInspiredSearchCV(
clf,
param_grid,
algorithm=algorithm,
population_size=50,
max_n_gen=100,
max_stagnating_gen=20,
runs=3,
)
nia_search.fit(X_train, y_train)
```
## Contributing
Detailed information on the contribution guidelines are in the [CONTRIBUTING.md](./CONTRIBUTING.md).
%package -n python3-sklearn-nature-inspired-algorithms
Summary: Search using nature inspired algorithms over specified parameter values for an sklearn estimator.
Provides: python-sklearn-nature-inspired-algorithms
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-sklearn-nature-inspired-algorithms
# Nature-Inspired Algorithms for scikit-learn
[](https://github.com/timzatko/Sklearn-Nature-Inspired-Algorithms/actions?query=workflow:CI+branch:master)
[](https://codeclimate.com/github/timzatko/Sklearn-Nature-Inspired-Algorithms/maintainability)
[](https://pypi.org/project/sklearn-nature-inspired-algorithms/)
[](https://pypi.org/project/sklearn-nature-inspired-algorithms/)
[](https://src.fedoraproject.org/rpms/python-sklearn-nature-inspired-algorithms)
Nature-inspired algorithms for hyper-parameter tuning of [scikit-learn](https://github.com/scikit-learn/scikit-learn) models. This package uses algorithms implementation from [NiaPy](https://github.com/NiaOrg/NiaPy).
## Installation
```shell script
$ pip install sklearn-nature-inspired-algorithms
```
To install this package on Fedora, run:
```sh
$ dnf install python3-sklearn-nature-inspired-algorithms
```
## Usage
The usage is similar to using sklearn's `GridSearchCV`. Refer to the [documentation](https://sklearn-nature-inspired-algorithms.readthedocs.io/en/stable/) for more detailed guides and more examples.
```python
from sklearn_nature_inspired_algorithms.model_selection import NatureInspiredSearchCV
from sklearn.ensemble import RandomForestClassifier
param_grid = {
'n_estimators': range(20, 100, 20),
'max_depth': range(2, 40, 2),
'min_samples_split': range(2, 20, 2),
'max_features': ["auto", "sqrt", "log2"],
}
clf = RandomForestClassifier(random_state=42)
nia_search = NatureInspiredSearchCV(
clf,
param_grid,
algorithm='hba', # hybrid bat algorithm
population_size=50,
max_n_gen=100,
max_stagnating_gen=10,
runs=3,
random_state=None, # or any number if you want same results on each run
)
nia_search.fit(X_train, y_train)
# the best params are stored in nia_search.best_params_
# finally you can train your model with best params from nia search
new_clf = RandomForestClassifier(**nia_search.best_params_, random_state=42)
```
Also you plot the search process with _line plot_ or _violin plot_.
```python
from sklearn_nature_inspired_algorithms.helpers import score_by_generation_lineplot, score_by_generation_violinplot
# line plot will plot all of the runs, you can specify the metric to be plotted ('min', 'max', 'median', 'mean')
score_by_generation_lineplot(nia_search, metric='max')
# in violin plot you need to specify the run to be plotted
score_by_generation_violinplot(nia_search, run=0)
```
Jupyter notebooks with full examples are available in [here](examples/notebooks).
### Using a Custom Nature-Inspired Algorithm
If you do not want to use any of the pre-defined algorithm configurations, you can use any algorithm from the [NiaPy](https://github.com/NiaOrg/NiaPy) collection.
This will allow you to have more control of the algorithm behavior.
Refer to their [documentation](https://niapy.readthedocs.io/en/latest/) and [examples](https://github.com/NiaOrg/NiaPy/tree/master/examples) for the usage.
__Note:__ Use version >2.x.x of NiaPy package
```python
from niapy.algorithms.basic import GeneticAlgorithm
algorithm = GeneticAlgorithm() # when custom algorithm is provided random_state is ignored
algorithm.set_parameters(NP=50, Ts=5, Mr=0.25)
nia_search = NatureInspiredSearchCV(
clf,
param_grid,
algorithm=algorithm,
population_size=50,
max_n_gen=100,
max_stagnating_gen=20,
runs=3,
)
nia_search.fit(X_train, y_train)
```
## Contributing
Detailed information on the contribution guidelines are in the [CONTRIBUTING.md](./CONTRIBUTING.md).
%package help
Summary: Development documents and examples for sklearn-nature-inspired-algorithms
Provides: python3-sklearn-nature-inspired-algorithms-doc
%description help
# Nature-Inspired Algorithms for scikit-learn
[](https://github.com/timzatko/Sklearn-Nature-Inspired-Algorithms/actions?query=workflow:CI+branch:master)
[](https://codeclimate.com/github/timzatko/Sklearn-Nature-Inspired-Algorithms/maintainability)
[](https://pypi.org/project/sklearn-nature-inspired-algorithms/)
[](https://pypi.org/project/sklearn-nature-inspired-algorithms/)
[](https://src.fedoraproject.org/rpms/python-sklearn-nature-inspired-algorithms)
Nature-inspired algorithms for hyper-parameter tuning of [scikit-learn](https://github.com/scikit-learn/scikit-learn) models. This package uses algorithms implementation from [NiaPy](https://github.com/NiaOrg/NiaPy).
## Installation
```shell script
$ pip install sklearn-nature-inspired-algorithms
```
To install this package on Fedora, run:
```sh
$ dnf install python3-sklearn-nature-inspired-algorithms
```
## Usage
The usage is similar to using sklearn's `GridSearchCV`. Refer to the [documentation](https://sklearn-nature-inspired-algorithms.readthedocs.io/en/stable/) for more detailed guides and more examples.
```python
from sklearn_nature_inspired_algorithms.model_selection import NatureInspiredSearchCV
from sklearn.ensemble import RandomForestClassifier
param_grid = {
'n_estimators': range(20, 100, 20),
'max_depth': range(2, 40, 2),
'min_samples_split': range(2, 20, 2),
'max_features': ["auto", "sqrt", "log2"],
}
clf = RandomForestClassifier(random_state=42)
nia_search = NatureInspiredSearchCV(
clf,
param_grid,
algorithm='hba', # hybrid bat algorithm
population_size=50,
max_n_gen=100,
max_stagnating_gen=10,
runs=3,
random_state=None, # or any number if you want same results on each run
)
nia_search.fit(X_train, y_train)
# the best params are stored in nia_search.best_params_
# finally you can train your model with best params from nia search
new_clf = RandomForestClassifier(**nia_search.best_params_, random_state=42)
```
Also you plot the search process with _line plot_ or _violin plot_.
```python
from sklearn_nature_inspired_algorithms.helpers import score_by_generation_lineplot, score_by_generation_violinplot
# line plot will plot all of the runs, you can specify the metric to be plotted ('min', 'max', 'median', 'mean')
score_by_generation_lineplot(nia_search, metric='max')
# in violin plot you need to specify the run to be plotted
score_by_generation_violinplot(nia_search, run=0)
```
Jupyter notebooks with full examples are available in [here](examples/notebooks).
### Using a Custom Nature-Inspired Algorithm
If you do not want to use any of the pre-defined algorithm configurations, you can use any algorithm from the [NiaPy](https://github.com/NiaOrg/NiaPy) collection.
This will allow you to have more control of the algorithm behavior.
Refer to their [documentation](https://niapy.readthedocs.io/en/latest/) and [examples](https://github.com/NiaOrg/NiaPy/tree/master/examples) for the usage.
__Note:__ Use version >2.x.x of NiaPy package
```python
from niapy.algorithms.basic import GeneticAlgorithm
algorithm = GeneticAlgorithm() # when custom algorithm is provided random_state is ignored
algorithm.set_parameters(NP=50, Ts=5, Mr=0.25)
nia_search = NatureInspiredSearchCV(
clf,
param_grid,
algorithm=algorithm,
population_size=50,
max_n_gen=100,
max_stagnating_gen=20,
runs=3,
)
nia_search.fit(X_train, y_train)
```
## Contributing
Detailed information on the contribution guidelines are in the [CONTRIBUTING.md](./CONTRIBUTING.md).
%prep
%autosetup -n sklearn-nature-inspired-algorithms-0.11.0
%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-sklearn-nature-inspired-algorithms -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.11.0-1
- Package Spec generated
|
