SCALABLE BAYESIAN RULE LISTS

Description

Fit a sbrl model. Learn from the data and create a decision rule list in the format of:

if (condition1) then positive probability = ...

else if (condition2) then positive probability = ...

else if (condition3) ...

...

else (default rule) then positive probability = ...

( See the examples below )

Details

This package contains three functions: sbrl, print.sbrl, show.sbrl, and predict.sbrl

Author(s)

Hongyu Yang, Cynthia Rudin, Margo Seltzer

References

Hongyu Yang, Morris Chen, Cynthia Rudin, Margo Seltzer (2016) Scalable Bayesian Rule Lists. Working paper on arXiv 2016.

Benjamin Letham, Cynthia Rudin, Tyler McCormick and David Madigan (2015) Building Interpretable Classifiers with Rules using Bayesian Analysis. Annals of Applied Statistics, 2015.

See Also

sbrl, print.sbrl, show.sbrl and predict.sbrl

Examples

# Let us use the titactoe dataset
data(tictactoe)
for (name in names(tictactoe)) {tictactoe[name] <- as.factor(tictactoe[,name])}

# Train on two-thirds of the data
b = round(2*nrow(tictactoe)/3, digit=0)
data_train <- tictactoe[1:b, ]
# Test on the remaining one third of the data
data_test <- tictactoe[(b+1):nrow(tictactoe), ]
# data_train, data_test are dataframes with factor columns
# The class column is "label"

# Run the sbrl algorithm on the training set
  sbrl_model <- sbrl(data_train, iters=20000, pos_sign="1",
   neg_sign="0", rule_minlen=1, rule_maxlen=3, 
   minsupport_pos=0.10, minsupport_neg=0.10, 
   lambda=10.0, eta=1.0, nchain=25)
  print(sbrl_model)

# Make predictions on the test set
  yhat <- predict(sbrl_model, data_test)
# yhat will be a list of predicted negative and positive probabilities for the test data. 

#clean up
rm(list = ls())
gc()

GET BINARY MATRIX REPRESENTATION OF THE DATA-FEATURE RELAITONSHIP

Description

Given some features in the form "feature1=x1", "feature2=x2"..., this function will generate a matrix representation of which data are captured by which features.

Usage

get_data_feature_mat(data, featurenames)

Arguments

Value

a binary matrix of size #observations-by-#featurenames

Author(s)

Hongyu Yang, Morris Chen, Cynthia Rudin, Margo Seltzer

Examples

data(tictactoe)
featurenames <- c("c1=b", "c1=o", "c1=x")
get_data_feature_mat(tictactoe, featurenames)
#it will generate a binary matrix representing which observations are captured by which features.

#clean up
rm(list = ls())
gc()

PREDICT THE POSITIVE PROBABILITY FOR THE OBSERVATIONS

Description

Returns a list of probabilities.

Usage

## S3 method for class 'sbrl'
predict(object, tdata, ...)

Arguments

Value

return a list containing 2 lists of probablities for the rule list, corresponding to probability being 0 and 1 for each observation. The two probabilities for each rule add up to 1, P(y=0 | rule r) + p(y=1 | rule r) = 1

Examples

# Let us use the titactoe dataset
data(tictactoe)
for (name in names(tictactoe)) {tictactoe[name] <- as.factor(tictactoe[,name])}

# Train on two-thirds of the data
b = round(2*nrow(tictactoe)/3, digit=0)
data_train <- tictactoe[1:b, ]
# Test on the remaining one third of the data
data_test <- tictactoe[(b+1):nrow(tictactoe), ]
# data_train, data_test are dataframes with factor columns
# The class column is "label"

# Run the sbrl algorithm on the training set
  sbrl_model <- sbrl(data_train, iters=20000, pos_sign="1",
   neg_sign="0", rule_minlen=1, rule_maxlen=3, 
   minsupport_pos=0.10, minsupport_neg=0.10, 
   lambda=10.0, eta=1.0, nchain=25)
  print(sbrl_model)

# Make predictions on the test set
  yhat <- predict(sbrl_model, data_test)
# yhat will be a list of predicted negative and positive probabilities for the test data. 

#clean up
rm(list = ls())
gc()

INTERPRETABLE VERSION OF A SBRL MODEL

Description

This function prints an sbrl object. It is a method for the generic function print of class "sbrl".

Usage

# S3 method for class 'sbrl'
# This complies with the form of the standard generic method print
## S3 method for class 'sbrl'
print(x, useS4=FALSE, ...)
## S3 method for class 'sbrl'
show(x, useS4=FALSE, ...)

Arguments

Details

This function is a method for the generic function print for class "sbrl". It can be invoked by calling print for an object of the appropriate class, or directly by calling print.sbrl regardless of the class of the object.

Value

No return value. This function prints out the logical structure of the sbrl model as a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree.

Examples

# Let us use the titactoe dataset
data(tictactoe)
for (name in names(tictactoe)) {tictactoe[name] <- as.factor(tictactoe[,name])}

# Train on two-thirds of the data
b = round(2*nrow(tictactoe)/3, digit=0)
data_train <- tictactoe[1:b, ]
# Test on the remaining one third of the data
data_test <- tictactoe[(b+1):nrow(tictactoe), ]
# data_train, data_test are dataframes with factor columns
# The class column is "label"

# Run the sbrl algorithm on the training set
  sbrl_model <- sbrl(data_train, iters=20000, pos_sign="1",
   neg_sign="0", rule_minlen=1, rule_maxlen=3, 
   minsupport_pos=0.10, minsupport_neg=0.10, 
   lambda=10.0, eta=1.0, nchain=25)
  print(sbrl_model)

#clean up
rm(list = ls())
gc()

fit the scalable bayesian rule lists model

Description

Fit the scalable bayesian rule lists model with given data and parameters. It generates a model that is a probabilistic classifier that optimizes the posterior of a Bayesian hierarchical model over pre-mined association rules.

Usage

sbrl(tdata, iters=30000, pos_sign="1", 
 neg_sign="0", rule_minlen=1, rule_maxlen=1, 
 minsupport_pos=0.10, minsupport_neg=0.10, 
 lambda=10.0, eta=1.0, alpha=c(1,1), nchain=10)

Arguments

Value

Return a list of :

Author(s)

Hongyu Yang, Morris Chen, Cynthia Rudin, Margo Seltzer

References

Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) Scalable Bayesian Rule Lists. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3921-3930, 2017.

Benjamin Letham, Cynthia Rudin, Tyler McCormick and David Madigan (2015) Building Interpretable Classifiers with Rules using Bayesian Analysis. Annals of Applied Statistics, 2015.

Examples

# Let us use the titactoe dataset
data(tictactoe)
for (name in names(tictactoe)) {tictactoe[name] <- as.factor(tictactoe[,name])}

# Train on two-thirds of the data
b = round(2*nrow(tictactoe)/3, digit=0)
data_train <- tictactoe[1:b, ]
# Test on the remaining one third of the data
data_test <- tictactoe[(b+1):nrow(tictactoe), ]
# data_train, data_test are dataframes with factor columns
# The class column is "label"

# Run the sbrl algorithm on the training set
  sbrl_model <- sbrl(data_train, iters=20000, pos_sign="1",
   neg_sign="0", rule_minlen=1, rule_maxlen=3, 
   minsupport_pos=0.10, minsupport_neg=0.10, 
   lambda=10.0, eta=1.0, nchain=25)
  print(sbrl_model)

# Make predictions on the test set
  yhat <- predict(sbrl_model, data_test)
# yhat will be a list of predicted negative and positive probabilities for the test data. 

#clean up
rm(list = ls())
gc()

SHUFFLED TIC-TAC-TOE-ENDGAME DATASET

Description

This is a shuffled version of the Tic-Tac-Toe Endgame Data Set on UCI Machine Learning Repository.

Usage

data("tictactoe")

Format

A data frame with 958 observations on the following 10 variables.

c1

a factor with levels b, o, x

c2

a factor with levels b, o, x

c3

a factor with levels b, o, x

c4

a factor with levels b, o, x

c5

a factor with levels b, o, x

c6

a factor with levels b, o, x

c7

a factor with levels b, o, x

c8

a factor with levels b, o, x

c9

a factor with levels b, o, x

label

an integer with values 0, 1

Details

This database encodes the complete set of possible board configurations at the end of tic-tac-toe games, where "x" is assumed to have played first. The target concept is "win for x" (i.e., true when "x" has one of 8 possible ways to create a "three-in-a-row").

Source

https://archive.ics.uci.edu/ml/datasets/Tic-Tac-Toe+Endgame

Examples

data(tictactoe)
## maybe str(tictactoe) ; plot(tictactoe) ...

#clean up
rm(list = ls())
gc()