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Action Rules

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 It is the best explanation of all possibilities.
Telco A brief demonstration.
Ras Based on the example in (Ras, Zbigniew W and Wyrzykowska, ARAS: Action rules discovery based on agglomerative strategy, 2007).
Attrition 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

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

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.

KIZI/actionrules

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