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diff --git a/python-actionrules-lukassykora.spec b/python-actionrules-lukassykora.spec new file mode 100644 index 0000000..f2b895f --- /dev/null +++ b/python-actionrules-lukassykora.spec @@ -0,0 +1,363 @@ +%global _empty_manifest_terminate_build 0 +Name: python-actionrules-lukassykora +Version: 1.1.26 +Release: 1 +Summary: Action rules mining package +License: MIT License +URL: https://github.com/lukassykora/actionrules +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f4/93/b1690f7d8083c26ee27c8d0454c763191a77a9f547b2b22ff1a329cec32b/actionrules-lukassykora-1.1.26.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-pyfim + +%description +# Action Rules + [](https://opensource.org/licenses/MIT) + +Action Rules (actionrules) is an implementation of Action Rules from Classification Rules algorithm described in + +```Dardzinska, A. (2013). Action rules mining. Berlin: Springer.``` + +If you use this package, please cite: + +```Sýkora, Lukáš, and Tomáš Kliegr. "Action Rules: Counterfactual Explanations in Python." RuleML Challenge 2020. CEUR-WS. ``` http://ceur-ws.org/Vol-2644/paper36.pdf + + +## GIT repository + +https://github.com/lukassykora/actionrules + +## Installation + +pip install actionrules-lukassykora + +## Jupyter Notebooks + +- [Titanic](https://github.com/lukassykora/actionrules/blob/master/notebooks/Titanic%20-%20Action%20Rules.ipynb) It is the best explanation of all possibilities. +- [Telco](https://github.com/lukassykora/actionrules/blob/master/notebooks/Telco%20-%20Action%20Rules.ipynb) A brief demonstration. +- [Ras](https://github.com/lukassykora/actionrules/blob/master/notebooks/Ras%20-%20Acton%20Rules.ipynb) Based on the example in (Ras, Zbigniew W and Wyrzykowska, ARAS: Action rules discovery based on agglomerative strategy, 2007). +- [Attrition](https://github.com/lukassykora/actionrules/blob/master/notebooks/Employee%20Attrition%20-%20High%20Utility%20Action%20Rules.ipynb) High-Utility Action Rules Mining example. + +## Example 1 +Get data from csv. +Get action rules from classification rules. Classification rules have confidence 55% and support 3%. +Stable part of action rule is "Age". +Flexible attributes are "Embarked", "Fare", "Pclass". +Target is a Survived value 1.0. +No nan values. +Use reduction tables for speeding up. +Minimal 1 stable antecedent +Minimal 1 flexible antecedent + + +```python +from actionrules.actionRulesDiscovery import ActionRulesDiscovery + +actionRulesDiscovery = ActionRulesDiscovery() +actionRulesDiscovery.read_csv("data/titanic.csv", sep="\t") +actionRulesDiscovery.fit(stable_attributes = ["Age"], + flexible_attributes = ["Embarked", "Fare", "Pclass"], + consequent = "Survived", + conf=55, + supp=3, + desired_classes = ["1.0"], + is_nan=False, + is_reduction=True, + min_stable_attributes=1, + min_flexible_attributes=1, + max_stable_attributes=5, + max_flexible_attributes=5) +actionRulesDiscovery.get_action_rules() +``` + +The output is a list where the first part is an action rule and the second part is a tuple of (support before, support after, action rule support) and (confidence before, confidence after, action rule confidence). + +## Example 2 +Get data from pandas dataframe. +Get action rules from classification rules. Classification rules have confidence 50% and support 3%. +Stable attributes are "Age" and "Sex". +Flexible attributes are "Embarked", "Fare", "Pclass". +Target is a Survived that changes from 0.0 to 1.0. +No nan values. +Use reduction tables for speeding up. +Minimal 1 stable antecedent +Minimal 1 flexible antecedent + + +```python +from actionrules.actionRulesDiscovery import ActionRulesDiscovery +import pandas as pd + +dataFrame = pd.read_csv("data/titanic.csv", sep="\t") +actionRulesDiscovery = ActionRulesDiscovery() +actionRulesDiscovery.load_pandas(dataFrame) +actionRulesDiscovery.fit(stable_attributes = ["Age", "Sex"], + flexible_attributes = ["Embarked", "Fare", "Pclass"], + consequent = "Survived", + conf=50, + supp=3, + desired_changes = [["0.0", "1.0"]], + is_nan=False, + is_reduction=True, + min_stable_attributes=1, + min_flexible_attributes=1, + max_stable_attributes=5, + max_flexible_attributes=5) +actionRulesDiscovery.get_pretty_action_rules() +``` + +The output is a list of action rules in pretty text form. + + +%package -n python3-actionrules-lukassykora +Summary: Action rules mining package +Provides: python-actionrules-lukassykora +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-actionrules-lukassykora +# Action Rules + [](https://opensource.org/licenses/MIT) + +Action Rules (actionrules) is an implementation of Action Rules from Classification Rules algorithm described in + +```Dardzinska, A. (2013). Action rules mining. Berlin: Springer.``` + +If you use this package, please cite: + +```Sýkora, Lukáš, and Tomáš Kliegr. "Action Rules: Counterfactual Explanations in Python." RuleML Challenge 2020. CEUR-WS. ``` http://ceur-ws.org/Vol-2644/paper36.pdf + + +## GIT repository + +https://github.com/lukassykora/actionrules + +## Installation + +pip install actionrules-lukassykora + +## Jupyter Notebooks + +- [Titanic](https://github.com/lukassykora/actionrules/blob/master/notebooks/Titanic%20-%20Action%20Rules.ipynb) It is the best explanation of all possibilities. +- [Telco](https://github.com/lukassykora/actionrules/blob/master/notebooks/Telco%20-%20Action%20Rules.ipynb) A brief demonstration. +- [Ras](https://github.com/lukassykora/actionrules/blob/master/notebooks/Ras%20-%20Acton%20Rules.ipynb) Based on the example in (Ras, Zbigniew W and Wyrzykowska, ARAS: Action rules discovery based on agglomerative strategy, 2007). +- [Attrition](https://github.com/lukassykora/actionrules/blob/master/notebooks/Employee%20Attrition%20-%20High%20Utility%20Action%20Rules.ipynb) High-Utility Action Rules Mining example. + +## Example 1 +Get data from csv. +Get action rules from classification rules. Classification rules have confidence 55% and support 3%. +Stable part of action rule is "Age". +Flexible attributes are "Embarked", "Fare", "Pclass". +Target is a Survived value 1.0. +No nan values. +Use reduction tables for speeding up. +Minimal 1 stable antecedent +Minimal 1 flexible antecedent + + +```python +from actionrules.actionRulesDiscovery import ActionRulesDiscovery + +actionRulesDiscovery = ActionRulesDiscovery() +actionRulesDiscovery.read_csv("data/titanic.csv", sep="\t") +actionRulesDiscovery.fit(stable_attributes = ["Age"], + flexible_attributes = ["Embarked", "Fare", "Pclass"], + consequent = "Survived", + conf=55, + supp=3, + desired_classes = ["1.0"], + is_nan=False, + is_reduction=True, + min_stable_attributes=1, + min_flexible_attributes=1, + max_stable_attributes=5, + max_flexible_attributes=5) +actionRulesDiscovery.get_action_rules() +``` + +The output is a list where the first part is an action rule and the second part is a tuple of (support before, support after, action rule support) and (confidence before, confidence after, action rule confidence). + +## Example 2 +Get data from pandas dataframe. +Get action rules from classification rules. Classification rules have confidence 50% and support 3%. +Stable attributes are "Age" and "Sex". +Flexible attributes are "Embarked", "Fare", "Pclass". +Target is a Survived that changes from 0.0 to 1.0. +No nan values. +Use reduction tables for speeding up. +Minimal 1 stable antecedent +Minimal 1 flexible antecedent + + +```python +from actionrules.actionRulesDiscovery import ActionRulesDiscovery +import pandas as pd + +dataFrame = pd.read_csv("data/titanic.csv", sep="\t") +actionRulesDiscovery = ActionRulesDiscovery() +actionRulesDiscovery.load_pandas(dataFrame) +actionRulesDiscovery.fit(stable_attributes = ["Age", "Sex"], + flexible_attributes = ["Embarked", "Fare", "Pclass"], + consequent = "Survived", + conf=50, + supp=3, + desired_changes = [["0.0", "1.0"]], + is_nan=False, + is_reduction=True, + min_stable_attributes=1, + min_flexible_attributes=1, + max_stable_attributes=5, + max_flexible_attributes=5) +actionRulesDiscovery.get_pretty_action_rules() +``` + +The output is a list of action rules in pretty text form. + + +%package help +Summary: Development documents and examples for actionrules-lukassykora +Provides: python3-actionrules-lukassykora-doc +%description help +# Action Rules + [](https://opensource.org/licenses/MIT) + +Action Rules (actionrules) is an implementation of Action Rules from Classification Rules algorithm described in + +```Dardzinska, A. (2013). Action rules mining. Berlin: Springer.``` + +If you use this package, please cite: + +```Sýkora, Lukáš, and Tomáš Kliegr. "Action Rules: Counterfactual Explanations in Python." RuleML Challenge 2020. CEUR-WS. ``` http://ceur-ws.org/Vol-2644/paper36.pdf + + +## GIT repository + +https://github.com/lukassykora/actionrules + +## Installation + +pip install actionrules-lukassykora + +## Jupyter Notebooks + +- [Titanic](https://github.com/lukassykora/actionrules/blob/master/notebooks/Titanic%20-%20Action%20Rules.ipynb) It is the best explanation of all possibilities. +- [Telco](https://github.com/lukassykora/actionrules/blob/master/notebooks/Telco%20-%20Action%20Rules.ipynb) A brief demonstration. +- [Ras](https://github.com/lukassykora/actionrules/blob/master/notebooks/Ras%20-%20Acton%20Rules.ipynb) Based on the example in (Ras, Zbigniew W and Wyrzykowska, ARAS: Action rules discovery based on agglomerative strategy, 2007). +- [Attrition](https://github.com/lukassykora/actionrules/blob/master/notebooks/Employee%20Attrition%20-%20High%20Utility%20Action%20Rules.ipynb) High-Utility Action Rules Mining example. + +## Example 1 +Get data from csv. +Get action rules from classification rules. Classification rules have confidence 55% and support 3%. +Stable part of action rule is "Age". +Flexible attributes are "Embarked", "Fare", "Pclass". +Target is a Survived value 1.0. +No nan values. +Use reduction tables for speeding up. +Minimal 1 stable antecedent +Minimal 1 flexible antecedent + + +```python +from actionrules.actionRulesDiscovery import ActionRulesDiscovery + +actionRulesDiscovery = ActionRulesDiscovery() +actionRulesDiscovery.read_csv("data/titanic.csv", sep="\t") +actionRulesDiscovery.fit(stable_attributes = ["Age"], + flexible_attributes = ["Embarked", "Fare", "Pclass"], + consequent = "Survived", + conf=55, + supp=3, + desired_classes = ["1.0"], + is_nan=False, + is_reduction=True, + min_stable_attributes=1, + min_flexible_attributes=1, + max_stable_attributes=5, + max_flexible_attributes=5) +actionRulesDiscovery.get_action_rules() +``` + +The output is a list where the first part is an action rule and the second part is a tuple of (support before, support after, action rule support) and (confidence before, confidence after, action rule confidence). + +## Example 2 +Get data from pandas dataframe. +Get action rules from classification rules. Classification rules have confidence 50% and support 3%. +Stable attributes are "Age" and "Sex". +Flexible attributes are "Embarked", "Fare", "Pclass". +Target is a Survived that changes from 0.0 to 1.0. +No nan values. +Use reduction tables for speeding up. +Minimal 1 stable antecedent +Minimal 1 flexible antecedent + + +```python +from actionrules.actionRulesDiscovery import ActionRulesDiscovery +import pandas as pd + +dataFrame = pd.read_csv("data/titanic.csv", sep="\t") +actionRulesDiscovery = ActionRulesDiscovery() +actionRulesDiscovery.load_pandas(dataFrame) +actionRulesDiscovery.fit(stable_attributes = ["Age", "Sex"], + flexible_attributes = ["Embarked", "Fare", "Pclass"], + consequent = "Survived", + conf=50, + supp=3, + desired_changes = [["0.0", "1.0"]], + is_nan=False, + is_reduction=True, + min_stable_attributes=1, + min_flexible_attributes=1, + max_stable_attributes=5, + max_flexible_attributes=5) +actionRulesDiscovery.get_pretty_action_rules() +``` + +The output is a list of action rules in pretty text form. + + +%prep +%autosetup -n actionrules-lukassykora-1.1.26 + +%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-actionrules-lukassykora -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.26-1 +- Package Spec generated |