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.
1 maintainer
Metadata
1 maintainer
Release files for actionrules-lukassykora 1.1.27
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