Action rules mining package
Project description
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for actionrules-lukassykora-1.1.27.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | be16bef1c69191ccc9a7840ae9decafd09f73cfb1a3dda81c3618abfce99bf3e |
|
MD5 | 43bba13faffc93257544e6fb0ef6967f |
|
BLAKE2b-256 | 5806eee417a3786be86efc5119bf3b79d5f7608073d77e53366b20cf8297c01e |
Hashes for actionrules_lukassykora-1.1.27-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c413e9f1a86d2db44b39ec6b0ad7400ad92f99770f7c942e94185c501e165bd9 |
|
MD5 | 0848d0c8d5f1190708326d8d037245b5 |
|
BLAKE2b-256 | 8df2f2c2fe1e5a6d561123351a7753801eb02e9468a2bc02e92327ab9c0584c6 |