action-rules 0.0.5

The package for action rules mining using Action-Apriori (Apriori Modified for Action Rules Mining)..

Action Rules

The package for action rules mining using Action-Apriori (Apriori Modified for Action Rules Mining).

• Documentation: https://lukassykora.github.io/action-rules
• GitHub: https://github.com/lukassykora/action-rules
• PyPI: https://pypi.org/project/action-rules/
• Free software: MIT

Installation

pip install action-rules

Features

Action Rules API

# Import Module
from action_rules import ActionRules
import pandas as pd
# Get Data
transactions = {'Sex': ['M', 'F', 'M', 'M', 'F', 'M', 'F'],
                'Age': ['Y', 'Y', 'O', 'Y', 'Y', 'O', 'Y'],
                'Class': [1, 1, 2, 2, 1, 1, 2],
                'Embarked': ['S', 'C', 'S', 'C', 'S', 'C', 'C'],
                'Survived': [1, 1, 0, 0, 1, 1, 0],
               }
data = pd.DataFrame.from_dict(transactions)
# Initialize ActionRules Miner with Parameters
stable_attributes = ['Age', 'Sex']
flexible_attributes = ['Embarked', 'Class']
target = 'Survived'
min_stable_attributes = 2
min_flexible_attributes = 1 #min 1
min_undesired_support = 1
min_undesired_confidence = 0.5 #min 0.5
min_desired_support = 1
min_desired_confidence = 0.5 #min 0.5
undesired_state = '0'
desired_state = '1'
# Action Rules Mining
action_rules = ActionRules(min_stable_attributes, min_flexible_attributes, min_undesired_support, min_undesired_confidence, min_desired_support,min_desired_confidence, verbose=False)
# Fit
action_rules.fit(
    data,
    stable_attributes,
    flexible_attributes,
    target,
    undesired_state,
    desired_state,
)
# Print rules
for action_rule in action_rules.get_rules().get_ar_notation():
    print(action_rule)
# Print rules (pretty notation)
for action_rule in action_rules.get_rules().get_pretty_ar_notation():
    print(action_rule)
# JSON export
print(action_rules.get_rules().get_export_notation())

Action Rules CLI

action-rules --min_stable_attributes 2 --min_flexible_attributes 1 --min_undesired_support 1 --min_undesired_confidence 0.5 --min_desired_support 1 --min_desired_confidence 0.5 --csv_path 'data.csv' --stable_attributes 'Sex, Age' --flexible_attributes 'Class, Embarked' --target 'Survived' --undesired_state '0' --desired_state '1' --output_json_path 'output.json'

Jupyter Notebook Example

https://lukassykora.github.io/action-rules/notebooks/Example.ipynb

Credits

This package was created with Cookiecutter and the waynerv/cookiecutter-pypackage project template.