Metadata-Version: 2.1
Name: actionrules-lukassykora
Version: 1.1.3
Summary: Action rules mining package
Home-page: https://github.com/lukassykora/actionrules
Author: Lukas Sykora
Author-email: lukassykora@seznam.cz
License: UNKNOWN
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.```
        
        ## 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). 
        
        
        ## 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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
