automatic import of python-actionrules-lukassykora

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+/actionrules-lukassykora-1.1.26.tar.gz
diff --git a/python-actionrules-lukassykora.spec b/python-actionrules-lukassykora.spec
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+%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
+ [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](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
+ [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](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
+ [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](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
diff --git a/sources b/sources
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--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d6e6987efd682dde3d347edd88559b5d  actionrules-lukassykora-1.1.26.tar.gz