Bayesian Rule Set Mining

Find the rule set from the data The input data should follow the following format: X has to be a pandas DataFrame all the column names can not contain '_' or '<' and the column names can not be pure numbers The categorical data should be represented in string (For example, gender needs to be 'male'/'female', or '0'/'1' to represent male and female respectively.) The parser will only recognize this format of data. So transform the data set first before using the functions. y hass to be a numpy.ndarray

reference: Wang, Tong, et al. "Bayesian rule sets for interpretable classification." Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016.

The program is very picky on the input data format X needs to be a pandas DataFrame, y needs to be a nd.array

Parameters

max_rules : int, default 5000

Maximum number of rules when generating rules

max_iter : int, default 200

Maximun number of iteratations to find the rule set

chians : int, default 1

Number of chains that run in parallel

support : int, default 5

The support is the percentile threshold for the itemset to be selected.

maxlen : int, default 3

The maximum number of items in a rule

#note need to replace all the alpha_1 to alpha+ alpha_1 : float, default 100 alpha+

beta_1 : float, default 1

beta+

alpha_2 : float, default 100

alpha-

beta_2 : float, default 1

beta-

alpha_l : float array, shape (maxlen+1,)

default all elements to be 1

beta_l : float array, shape (maxlen+1,)

default corresponding patternSpace

level : int, default 4

Number of intervals to deal with numerical continous features

neg : boolean, default True

Negate the features

add_rules : list, default empty

User defined rules to add it needs user to add numerical version of the rules

criteria : str, default 'precision'

When there are rules more than max_rules, the criteria used to filter rules

greedy_initilization : boolean, default False

Wether start the rule set using a greedy initilization (according to accuracy)

greedy_threshold : float, default 0.05

Threshold for the greedy algorithm to find the starting rule set

propose_threshold : float, default 0.1

Threshold for a proposal to be accepted

method : str, default 'fpgrowth'

The method used to generate rules. Can be 'fpgrowth' or 'forest' Notice that if there are potentially many rules then fpgrowth is not a good method as it will have memory issue (because the rule screening is after rule generations).

Sample usage:

import ruleset as rs import pandas as pd import numpy as np from sklearn.model_selection import train_test_split

df = pd.read_csv('data/adult.dat', header=None, sep=',', names=['age', 'workclass', 'fnlwgt', 'education', 'educationnum', 'matritalstatus', 'occupation', 'relationship', 'race', 'sex', 'capitalgain', 'capitalloss', 'hoursperweek', 'nativecountary', 'income']) y = (df['income'] == '>50K').as_matrix() df.drop('income', axis=1, inplace=True)

X_train, X_test, y_train, y_test = train_test_split(

df, y, test_size=0.3)

model = rs.BayesianRuleSet(method='forest') model.fit(X_train, y_train) yhat = model.predict(X_test) TP, FP, TN, FN = rs.get_confusion(yhat, y_test)