actionrules's Introduction

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.

actionrules's People

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actionrules's Issues

ModuleNotFoundError: No module named 'fim'

Hi,

I am currently reproduce experiments of CausalRulesDiscovery, which is implemented based on the actionrules.
However, the error above occurs every time when I execute the files.
Is it possible to substitute another library?
It would be thankful if someone shares idea to resolve this issue.

Kind regards

from where to get this titanic file?

actionRulesDiscovery.read_csv("data/titanic.csv", sep="\t")

from where to get this titanic file?

from here
https://www.kaggle.com/c/titanic/data

will it work in Windows 10?

from algorithmic ( mathematical ) prospective how different this from pycorels and pyARC

from algorithmic ( mathematical ) prospective how different this from

https://github.com/fingoldin/pycorels
and
https://github.com/jirifilip/pyARC
??

Add link to RuleML challenge paper

Problem installing package on linux

pip install https://test.pypi.org/simple/ actionrules-lukassykora

Gives errror
ERROR: Cannot unpack file /tmp/pip-unpack-26bij12f/simple (downloaded from /tmp/pip-req-build-qor_7uw6, content-type: text/html; charset=UTF-8); cannot detect archive format

pip install -i https://test.pypi.org/simple/ actionrules-lukassykora

Gives errror

Collecting actionrules-lukassykora
Using cached https://test-files.pythonhosted.org/packages/4a/63/c1bf588c34102d80b0c3244cc571d6a1636618a2db77cef28cfe9ff6fec3/actionrules_lukassykora-1.0.18-py3-none-any.whl
Collecting typing (from actionrules-lukassykora)
ERROR: Could not find a version that satisfies the requirement typing (from actionrules-lukassykora) (from versions: none)
ERROR: No matching distribution found for typing (from actionrules-lukassykora)

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