| Title: | Bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition |
|---|---|
| Description: | The bsnsing package provides functions for training a decision tree classifier, making predictions and generating latex code for plotting. It solves the two-class and multi-class classification problems under the supervised learning paradigm. While building a decision tree, bsnsing uses a Boolean rule involving multiple variables to split a node. Each split rule is identified by solving an optimization problem. Use the bsnsing function to build a tree, the predict function to make predictions and the show function to plot the tree. The paper is at <arXiv:2205.15263>. Source code and more data sets are at <https://github.com/profyliu/bsnsing>. |
| Authors: | Yanchao Liu |
| Maintainer: | Yanchao Liu <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.1 |
| Built: | 2025-02-15 05:40:37 UTC |
| Source: | https://github.com/profyliu/bsnsing |
The bsnsing package provides functions for building a decision tree classifier and making predictions. It solves a mixed-integer programming (MIP) model to maximize the Gini reduction at each node split, and each node split rule can utilize multiple input variables. Benchmarking experiments on 75 open data sets suggest that bsnsing trees are the most capable of discriminating new cases compared to trees trained by other decision tree codes including the rpart, C50, party and tree packages in R. Compared to other optimal decision tree packages, including DL8.5, OSDT, GOSDT and indirectly more, bsnsing stands out in its training speed, ease of use and broader applicability without losing in prediction accuracy. For more information, please check out the paper https://arxiv.org/abs/2205.15263, to be published in INFORMS Journal on Computing.
The default method for solving the MIP model is the implicit enumeration (ENUM) algorithm, while other solvers including GUROBI, CPLEX and lpSolve can be used (via specifying the opt.solver option in the bsnsing function). However, the users are strongly suggested to compile the bslearn.c file, make it into a shared library (e.g., .dylib, .so or .dll binary file) and paste the binary file in the work directory. In this way, the bsnsing will leverage the compiled code (instead of the R code) for the ENUM algorithm, which runs much (~40x) faster. All benchmarking experiments were run using the compiled ENUM algorithm. The C source file and the MAKE file can be found at https://github.com/profyliu/bsnsing. Pre-compiled binary files for different target platforms are also provided there for the convenience of the users (just download the .dylib, .so or the .dll file, depending on the operating system, and put it in the work directory). Future updates of this package will internalize the compilation step, but for now only the R implementation of the ENUM algorithm is included in the package source, so serious users please take the extra step outlined above.
Several data frames (i.e., auto, iris, GlaucomaMVF and BreastCancer) used in the example code are included in this package. More two-class and multi-class classification data sets can be found at https://github.com/profyliu/bsnsing.
The learn (train) functions include bsnsing, bsnsing.formula and bsnsing.default.
The predict functions include: predict.bsnsing and predict.mbsnsing.
A bsnsing object (tree) can be plotted into a PDF file, or in the form of latex code, by the function show.bsnsing. The ROC curve can be plotted using the function ROC_func.
Here is a list of internal functions of the package that are open for end users.
summary.bsnsing
summary.mbsnsing
binarize,
binarize.numeric,
binarize.factor,
binarize.y,
bslearn,
bscontrol
Yanchao Liu
A test data set.
autoauto
A data frame with 392 rows and 8 variables
http://github.com/profyliu/bsnsing
Create a set of variables (columns) with binary values for each column in the input data. For a variable with values of 0 and 1, the column is retained and no new column is created. For a numeric variable, the function binarize.numeric is called. For a factor column, the function binarize.factor is called.
binarize( x, y, target = stop("'target' (0 or 1) must be provided"), control = bscontrol() )binarize( x, y, target = stop("'target' (0 or 1) must be provided"), control = bscontrol() )
x |
a data frame or matrix to be binarized. |
y |
a vector with two unique values (0 and 1). It is the response variable that guides the optimal discretization of variables in |
target |
the level of y (0 or 1) which indicates the boolean rule target |
control |
a list or a |
a data frame containing binary variables, or a character string describing the rule that perfectly split the target.
## Not run: # Load and prepare data x <- auto[, c('mpg', 'cylinders', 'displacement')] x$cylinders <- as.factor(x$cylinders) y <- ifelse(auto$origin == 'USA', 1L, 0L) # binarize x by y = 1 bx1 <- binarize(x, y, target = 1) head(bx1) # binarize x by y = 0 bx0 <- binarize(x, y, target = 0) head(bx0) # when selecting only one column from a data frame, use drop = FALSE to maintain binarize(auto[,'mpg', drop = FALSE], y, target = 1) ## End(Not run)## Not run: # Load and prepare data x <- auto[, c('mpg', 'cylinders', 'displacement')] x$cylinders <- as.factor(x$cylinders) y <- ifelse(auto$origin == 'USA', 1L, 0L) # binarize x by y = 1 bx1 <- binarize(x, y, target = 1) head(bx1) # binarize x by y = 0 bx0 <- binarize(x, y, target = 0) head(bx0) # when selecting only one column from a data frame, use drop = FALSE to maintain binarize(auto[,'mpg', drop = FALSE], y, target = 1) ## End(Not run)
Create binary dummy variables based on a factor variable. This function is used internally by binarize.
binarize.factor(x, name, y, segments = 10, bin.size = 5)binarize.factor(x, name, y, segments = 10, bin.size = 5)
x |
a numeric vector. |
name |
a character string, the variable name of |
y |
a numeric or integer vector of the same length as |
segments |
a positive integer indicating the maximum number of levels allowed in the factor variable. |
bin.size |
a positive integer. It is the minimum number of observations required to fall into each bin. |
a data frame with binary (0 and 1) entries. The column names are indicative of the conditions used to form the corresponding columns.
Discretize a continuous variable x by splitting its range at a sequence of cutpoints. The cutpoints are determined so as to effectively split the binary target y. This function is used internally by binarize.
binarize.numeric( x, name, y, target = stop("Must provide a target, 0 or 1"), segments = 10, bin.size = 5, node.size = 10 )binarize.numeric( x, name, y, target = stop("Must provide a target, 0 or 1"), segments = 10, bin.size = 5, node.size = 10 )
x |
a numeric vector. |
name |
a character string, the variable name of |
y |
a numeric or integer vector of the same length as |
target |
a scalar, valued 0 or 1, indicating the target level of |
segments |
a positive integer, any value below 3 is set to 3. It is the maximum number of segments the range of |
bin.size |
a positive integer. It is the minimum number of observations required to fall into each bin. |
node.size |
a positive integer. If either child node is smaller than the node.size, do not return the perfect rule. |
a data frame with binary (0 and 1) entries, or a character string describing the rule that perfectly splits y. If a data frame is returned, the column names are indicative of the conditions used to form the corresponding columns.
Recode a Variable with Two Unique Values into an 0/1 Vector
binarize.y(y, verbose = F)binarize.y(y, verbose = F)
y |
a vector, must contain two unique values. |
verbose |
a logical value, TRUE or FALSE, indicating whehter details are to be printed on the screen. |
a list with three elements: y, a vector of the same length as y, whose entries are coded to 0 and 1, coding.scheme, a character string describing the map from the original coding to 0/1 coding, and ycode, a character vector containing the original level names of y.
y <- factor(c('good', 'bad', 'good', 'good', 'bad')) (yb <- binarize.y(y)) y <- c(TRUE, FALSE, FALSE, FALSE, TRUE) (yb <- binarize.y(y)) y <- c(1, 2, 2, 1, 2) (yb <- binarize.y(y))y <- factor(c('good', 'bad', 'good', 'good', 'bad')) (yb <- binarize.y(y)) y <- c(TRUE, FALSE, FALSE, FALSE, TRUE) (yb <- binarize.y(y)) y <- c(1, 2, 2, 1, 2) (yb <- binarize.y(y))
A test data set.
BreastCancerBreastCancer
A data frame with 699 rows and 10 variables.
http://github.com/profyliu/bsnsing
bsnsing FitDefine Parameters for the bsnsing Fit
bscontrol( bin.size = 5, nseg.numeric = 20, nseg.factor = 20, num2factor = 10, node.size = 0, stop.prob = 0.9999, opt.solver = c("enum_c", "enum", "greedy", "hybrid", "gurobi", "lpSolve", "cplex"), solver.timelimit = 180, max.rules = 2, opt.model = c("gini", "error"), greedy.level = 0.9, import.external = T, suppress.internal = F, no.same.gender.children = F, n0n1.cap = 40000, verbose = F )bscontrol( bin.size = 5, nseg.numeric = 20, nseg.factor = 20, num2factor = 10, node.size = 0, stop.prob = 0.9999, opt.solver = c("enum_c", "enum", "greedy", "hybrid", "gurobi", "lpSolve", "cplex"), solver.timelimit = 180, max.rules = 2, opt.model = c("gini", "error"), greedy.level = 0.9, import.external = T, suppress.internal = F, no.same.gender.children = F, n0n1.cap = 40000, verbose = F )
bin.size |
the minimum number of observations required in a binarization bucket. |
nseg.numeric |
the maximum number of segments the range of a numeric variable is divided into for each inequality direction. |
nseg.factor |
the maximum number of unique levels allowed in a factor variable. |
num2factor |
an equality binarization rule will be created for each unique value of a numeric variable (in addition to the inequality binarization attempt), if the number of unique values of the numeric variable is less than |
node.size |
if the number of training cases falling into a tree node is fewer than |
stop.prob |
if the proportion of the majority class in a tree node is greater than |
opt.solver |
a character string in the set 'enum', 'enum_c', 'gurobi', 'cplex', 'lpSolve', 'greedy' indicating the optimization solver to be used in the program. The choice of 'cplex' requires the package |
solver.timelimit |
the solver time limit in seconds. Currently only applicable to 'gurobi', 'enum' and 'enum_c' solvers. |
max.rules |
the maximum number of features allowed to enter an OR-clause split rule. A small max.rules reduces the search space and regulates model complexity. Default is 3. |
opt.model |
a character string in the set 'gini','error' indicating the optimization model to solve in the program. The default is 'gini'. The choice of 'error' is faster because the optimization model is smaller. The default is 'gini'. |
greedy.level |
a proportion value between 0 and 1, applicable only when opt.solver is 'greedy'. In the greedy forward selection process of split rules, a candidate rule is added to the OR-clause only if the split performance (gini reduction or accuracy) after the addition multiplied by greedy.level would still be greater than the split performance before the addition. A higher value of greedy.level tend to more aggressively produce multi-variable splits. |
import.external |
logical value indicating whether or not to try importing candidate split rules from other decision tree packages. Default is True. |
suppress.internal |
logical value indicating whether or not to suppress the feature binarization process that creates the pool of binary features. If it is set to True, then only the features imported from external methods (if import.external is True) will be used in the optimal rule selection model. Default is False. |
no.same.gender.children |
logical value indicating whether or not to suppress splits that would result in both children having the same majority class. Default is False. |
n0n1.cap |
a positive integer. It is applicable only when the opt.solver is 'hybrid' and the opt.model is 'gini'. When the bslearn function is called, if the product of the number of negative cases (n0) and the number of positive cases (n1) is greater than this number, 'enum' solver will be used; otherwise, gurobi solver will be used. |
verbose |
a logical value (TRUE or FALSE) indicating whether the solution details are to be printed on the screen. |
An object of class bscontrol.
bscontrol() # display the default parameters bsc <- bscontrol(stop.prob = 0.8, nseg.numeric = 10, verbose = TRUE) bscbscontrol() # display the default parameters bsc <- bscontrol(stop.prob = 0.8, nseg.numeric = 10, verbose = TRUE) bsc
The function solves a mixed integer program (MIP) to either maximize the Gini reduction (opt.model = 'gini') or the number of misclassifications (opt.model = 'error'). The optimal rule serves as the split condition in the classification tree built by bsnsing.
bslearn(bx, y, control = bscontrol())bslearn(bx, y, control = bscontrol())
bx |
a data frame with binary (0 and 1) entries. |
y |
an integer vector with binary entries. |
control |
an object of class |
a list containing the splitting solution.
## Not run: x <- auto[, c('mpg', 'cylinders', 'displacement')] y <- ifelse(auto$origin == 'USA', 1L, 0L) # binarize x by y = 1 bx <- binarize(x, y, target = 1) # learn the optimal Boolean rule bssol <- bslearn(bx, y, bscontrol(opt.solver = 'enum')) cat(paste("Optimal rule:" , bssol$rules, "\n")) ## End(Not run)## Not run: x <- auto[, c('mpg', 'cylinders', 'displacement')] y <- ifelse(auto$origin == 'USA', 1L, 0L) # binarize x by y = 1 bx <- binarize(x, y, target = 1) # learn the optimal Boolean rule bssol <- bslearn(bx, y, bscontrol(opt.solver = 'enum')) cat(paste("Optimal rule:" , bssol$rules, "\n")) ## End(Not run)
Depending on the arguments provided, either bsnsing.default or bsnsing.formula will be called.
bsnsing(x, ...)bsnsing(x, ...)
x |
a data frame or a |
... |
arguments passed on to |
an object of class bsnsing for a two-class problem or an object of class mbsnsing for a multi-class problem.
## Not run: # Use the formula format bs <- bsnsing(Class~., data = BreastCancer) summary(bs) # For multi-class classification bs <- bsnsing(Species ~ ., data = iris) summary(bs) summary(bs[[1]]) # display the tree for the first class summary(bs[[2]]) # display the tree for the second class summary(bs[[3]]) # display the tree for the third class predict(bs, type = 'class') # the fitted class membership predict(bs, type = 'prob') # the fitted probabilities # Use the (x, y) format, y must have two levels y <- ifelse(iris$Species == 'setosa', 1L, 0L) x <- iris[, c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width')] bs <- bsnsing(x, y, verbose = TRUE) summary(bs) ## End(Not run)## Not run: # Use the formula format bs <- bsnsing(Class~., data = BreastCancer) summary(bs) # For multi-class classification bs <- bsnsing(Species ~ ., data = iris) summary(bs) summary(bs[[1]]) # display the tree for the first class summary(bs[[2]]) # display the tree for the second class summary(bs[[3]]) # display the tree for the third class predict(bs, type = 'class') # the fitted class membership predict(bs, type = 'prob') # the fitted probabilities # Use the (x, y) format, y must have two levels y <- ifelse(iris$Species == 'setosa', 1L, 0L) x <- iris[, c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width')] bs <- bsnsing(x, y, verbose = TRUE) summary(bs) ## End(Not run)
This is the default method for bsnsing and handles binary classification only. bsnsing.formula, which calls bsnsing.default as the basic tree builder, can handle multiclass classification problems. Missing values in numeric variables are imputed as the median of the non-missing ones, and missing values in factor variables are treated as a separate level named 'NA'.
## Default S3 method: bsnsing(x, y, controls = bscontrol(), ...)## Default S3 method: bsnsing(x, y, controls = bscontrol(), ...)
x |
a data frame containing independent variables. Columns can be of numeric, integer, factor and logical types. The column names must be proper identifiers (e.g., must start with a letter, cannot contain special characters and spaces, etc.). |
y |
a vector of the response variable. The response variable can be of an integer, numeric, logical or factor type, but must have only two unique values. Typical coding of a binary response variable is 0 (for negative case) and 1 (for positive cases). |
controls |
an object of class |
... |
further argument to be passed to bsnsing.default. |
an object of class bsnsing.
## Not run: y <- ifelse(iris$Species == 'setosa', 1L, 0L) x <- iris[, c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width')] bs <- bsnsing(x, y, verbose = TRUE) summary(bs) ## End(Not run)## Not run: y <- ifelse(iris$Species == 'setosa', 1L, 0L) x <- iris[, c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width')] bs <- bsnsing(x, y, verbose = TRUE) summary(bs) ## End(Not run)
The program builds a binary classification tree for each unique value in the response variable. Each tree classifies a target value against all the other values (internally coded as 'all.other') in the response variable. If the response variable is a numeric type, the number of unique values must not exceed 30. There is no programmatic restriction on the number of unique levels for a factor response.
## S3 method for class 'formula' bsnsing(formula, data, subset, na.action = stats::na.pass, ...)## S3 method for class 'formula' bsnsing(formula, data, subset, na.action = stats::na.pass, ...)
formula |
an object of class " |
data |
an optional data frame, list or environment (or object coercible by |
subset |
an optional vector specifying a subset (in terms of index numbers, not actual data) of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data contain NAs. If |
... |
additional arguments to be passed to the low level fitting functions, e.g., elements in the |
an object of bsnsing for a two-class problem or an object of mbsnsing for a multi-class problem.
# Multi-class classification ## Not run: bs <- bsnsing(Species ~ ., data = iris) summary(bs) summary(bs[[1]]) # display the tree for the first class summary(bs[[2]]) # display the tree for the second class summary(bs[[3]]) # display the tree for the third class # Two-class classification bs <- bsnsing(origin ~ mpg + displacement + horsepower + weight, data = auto, verbose = TRUE) summary(bs) ## End(Not run)# Multi-class classification ## Not run: bs <- bsnsing(Species ~ ., data = iris) summary(bs) summary(bs[[1]]) # display the tree for the first class summary(bs[[2]]) # display the tree for the second class summary(bs[[3]]) # display the tree for the third class # Two-class classification bs <- bsnsing(origin ~ mpg + displacement + horsepower + weight, data = auto, verbose = TRUE) summary(bs) ## End(Not run)
This function is for internal use, to determine the file extension of the bslearn shared library. .dylib for oxs, .so for linux and .dll for windows.
get_os()get_os()
a character string indicating the OS type
A test dataset
GlaucomaMVFGlaucomaMVF
A data frame with 170 rows and 67 variables:
http://github.com/profyliu/bsnsing
A test data set.
irisiris
A data frame with 150 rows and 5 variables.
http://github.com/profyliu/bsnsing
A class that contains multi-class classification model built by bsnsing. Can be used in summary and predict functions.
If the file argument is supplied, this function will invoke the external programs latex, dvips and ps2pdf. If these programs are not available, only the latex code will be generated. If the file argument is left empty, the latex code will be written to the console screen. The latex code utilizes the following packages: pstricks, pst-node, pst-tree.
## S3 method for class 'bsnsing' plot( object, file = "", class_labels = c(), class_colors = c("red", "green"), rule_font = c("footnotesize", "scriptsize", "tiny", "normalsize", "small"), rule_color = "blue", footnote = F, landscape = F, papersize = c("a0paper", "a1paper", "a2paper", "a3paper", "a4paper", "a5paper", "a6paper", "b0paper", "b1paper", "b2paper", "b3paper", "b4paper", "b5paper", "b6paper", "c0paper", "c1paper", "c2paper", "c3paper", "c4paper", "c5paper", "c6paper", "b0j", "b1j", "b2j", "b3j", "b4j", "b5j", "b6j", "ansiapaper", "ansibpaper", "ansicpaper", "ansidpaper", "ansiepaper", "letterpaper", "executivepaper", "legalpaper"), verbose = F, ... )## S3 method for class 'bsnsing' plot( object, file = "", class_labels = c(), class_colors = c("red", "green"), rule_font = c("footnotesize", "scriptsize", "tiny", "normalsize", "small"), rule_color = "blue", footnote = F, landscape = F, papersize = c("a0paper", "a1paper", "a2paper", "a3paper", "a4paper", "a5paper", "a6paper", "b0paper", "b1paper", "b2paper", "b3paper", "b4paper", "b5paper", "b6paper", "c0paper", "c1paper", "c2paper", "c3paper", "c4paper", "c5paper", "c6paper", "b0j", "b1j", "b2j", "b3j", "b4j", "b5j", "b6j", "ansiapaper", "ansibpaper", "ansicpaper", "ansidpaper", "ansiepaper", "letterpaper", "executivepaper", "legalpaper"), verbose = F, ... )
object |
an object of class |
file |
a writable connection or a character string naming the file to write to. If not supplied, the output will be written to the console. |
class_labels |
a character vector of two elements for leaf node label (for 0 and 1). If empty, the labels will be read from the bsnsing object. |
class_colors |
a character vector of two elements for leaf node color (for 0 and 1) |
rule_font |
a string specifying the font size of the split rule at each non-leaf node |
rule_color |
a string specifying the color of the split rule and node, e.g., blue, gray, black, etc. For a list of all usable colors, see https://en.wikibooks.org/wiki/LaTeX/Colors |
footnote |
a boolean value indicating whether to add annotation text to the PDF page. The default is False. |
landscape |
a boolean value indicating if the PDF page should be in landscape layout. The default is False. |
papersize |
a string specifying the PDF paper size. The default is 'a0paper'. |
NA
# Suppose bs is a bsnsing object ## Not run: plot(bs) plot(bs, file = "/path/to/destination/filename.tex") ## End(Not run)# Suppose bs is a bsnsing object ## Not run: plot(bs) plot(bs, file = "/path/to/destination/filename.tex") ## End(Not run)
Generate latex code for plotting the bsnsing tree
## S3 method for class 'mbsnsing' plot(object)## S3 method for class 'mbsnsing' plot(object)
object |
an object of class |
bsnsing ModelImplements the generic predict function to make predictions on new data using a trained bsnsing model.
## S3 method for class 'bsnsing' predict(object, newdata = NULL, type = c("prob", "class"), ...)## S3 method for class 'bsnsing' predict(object, newdata = NULL, type = c("prob", "class"), ...)
object |
a |
newdata |
a optional data frame in which to look for variables for prediction. If omitted, the fitted class or probability will be returned. |
type |
a character string indicating the type of prediction. 'prob' predicts the probability of being a positive case (i.e., y = 1), and 'class' predicts the class membership. |
... |
further arguments to predict.bsnsing. |
a vector containing the predicted values.
## Not run: # Load data n <- nrow(GlaucomaMVF) train_index = sample(1:n, round(0.5*n)) test_index = setdiff(1:n, train_index) # Fit a model using training set bs <- bsnsing(Class ~ ., data = GlaucomaMVF, subset = train_index) # Make predictions on the test set pred <- predict(bs, GlaucomaMVF[test_index, ], type = 'class') # Display the confusion matrix table(pred, actual = GlaucomaMVF[test_index, 'Class']) ## End(Not run)## Not run: # Load data n <- nrow(GlaucomaMVF) train_index = sample(1:n, round(0.5*n)) test_index = setdiff(1:n, train_index) # Fit a model using training set bs <- bsnsing(Class ~ ., data = GlaucomaMVF, subset = train_index) # Make predictions on the test set pred <- predict(bs, GlaucomaMVF[test_index, ], type = 'class') # Display the confusion matrix table(pred, actual = GlaucomaMVF[test_index, 'Class']) ## End(Not run)
bsnsing ModelMake Predictions with a bsnsing Model
## S3 method for class 'mbsnsing' predict(object, newdata = NULL, type = c("prob", "class"), ...)## S3 method for class 'mbsnsing' predict(object, newdata = NULL, type = c("prob", "class"), ...)
object |
an object of class |
newdata |
an optional data frame in which to look for variables for prediction. If omitted, the fitted class or probability will be returned. |
type |
a character string indicating the type of prediction. 'prob' predicts the probability of being in each class, and 'class' predicts the class membership. |
... |
further arguments to predict.mbsnsing. |
a data frame containing the predicted values.
## Not run: n <- nrow(iris) train_index <- sample(1:n, round(0.5*n)) test_index <- setdiff(1:n, train_index) # Fit a model on the training set bs <- bsnsing(Species ~ ., data = iris, subset = train_index) # Make predictions on the test set pred <- predict(bs, iris[test_index, ], type = 'class') # Display the confusion matrix table(pred, actual = iris[test_index, 'Species']) # Predict the probabilities predprob <- predict(bs, iris[test_index, ], type = 'prob') head(predprob) ## End(Not run)## Not run: n <- nrow(iris) train_index <- sample(1:n, round(0.5*n)) test_index <- setdiff(1:n, train_index) # Fit a model on the training set bs <- bsnsing(Species ~ ., data = iris, subset = train_index) # Make predictions on the test set pred <- predict(bs, iris[test_index, ], type = 'class') # Display the confusion matrix table(pred, actual = iris[test_index, 'Species']) # Predict the probabilities predprob <- predict(bs, iris[test_index, ], type = 'prob') head(predprob) ## End(Not run)
bscontrol
Print the Object of Class bscontrol
## S3 method for class 'bscontrol' print(x = bscontrol(), ...)## S3 method for class 'bscontrol' print(x = bscontrol(), ...)
x |
an object of class |
... |
further arguments to the print function. |
bsnsing
Print the Object of Class bsnsing
## S3 method for class 'bsnsing' print(x, print.call = T, ...)## S3 method for class 'bsnsing' print(x, print.call = T, ...)
x |
an object of class |
print.call |
print out the function called, default TRUE |
... |
further arguments |
mbsnsing
Print the Object of Class mbsnsing
## S3 method for class 'mbsnsing' print(x, ...)## S3 method for class 'mbsnsing' print(x, ...)
x |
an object of class |
... |
further arguments. |
bsnsing ModelPrint the Summary of bsnsing Model
## S3 method for class 'summary.bsnsing' print(x, print.call = T, ...)## S3 method for class 'summary.bsnsing' print(x, print.call = T, ...)
x |
an object of class |
print.call |
a logical value, print out the function called if TRUE. |
... |
further arguments. |
mbsnsing model fitsPrint the summary of mbsnsing model fits
## S3 method for class 'summary.mbsnsing' print(x, ...)## S3 method for class 'summary.mbsnsing' print(x, ...)
x |
an object of class |
... |
further arguments. |
bscontrol
Print the Object of Class bscontrol
prt.bscontrol(control = bscontrol())prt.bscontrol(control = bscontrol())
control |
an object of class |
bsnsing
Print the Object of Class bsnsing
prt.bsnsing(object, print.call = T, ...)prt.bsnsing(object, print.call = T, ...)
object |
an object of class |
print.call |
print out the function called, default TRUE |
... |
further arguments |
mbsnsing
Print the Object of Class mbsnsing
prt.mbsnsing(object, ...)prt.mbsnsing(object, ...)
object |
an object of class |
... |
further arguments. |
bsnsing ModelPrint the Summary of bsnsing Model
prt.summary.bsnsing(object, print.call = T, ...)prt.summary.bsnsing(object, print.call = T, ...)
object |
an object of class |
print.call |
a logical value, print out the function called if TRUE. |
... |
further arguments. |
mbsnsing model fitsPrint the summary of mbsnsing model fits
prt.summary.mbsnsing(object, ...)prt.summary.mbsnsing(object, ...)
object |
an object of class |
... |
further arguments. |
This is a general utility function, not part of the bsnsing functionality.
ROC_func( df, label_colnum, score_colnum, pos.label = "1", plot.ROC = F, add_on = F, color = "black", lty = 1 )ROC_func( df, label_colnum, score_colnum, pos.label = "1", plot.ROC = F, add_on = F, color = "black", lty = 1 )
df |
a data frame which must contain at least these two columns: the prediction scores (numeric values, not necessarily be between 0 and 1) and the true class labels. |
label_colnum |
the column index of the scores column in df |
score_colnum |
the column index of the true class labels column in df |
pos.label |
a character string matching the positive class label used in the class labels column |
plot.ROC |
a logical value indicating whether the ROC curve should be plotted |
add_on |
a logical value indicating whether the ROC curve should be added to an existing plot |
color |
a character string specifying the color of the ROC curve in the plot |
lty |
line type used in the plot (1 solid, 2 dashed, etc.) |
the area under the curve (AUC) value
## Not run: n <- nrow(BreastCancer) trainset <- sample(1:n, 0.7*n) # randomly sample 70\ testset <- setdiff(1:n, trainset) # the remaining is for testing # Build a tree to predict Class, using all default options bs <- bsnsing(Class~., data = BreastCancer[trainset,]) summary(bs) # display the tree structure pred <- predict(bs, BreastCancer[testset,], type='class') actual <- BreastCancer[testset, 'Class'] table(pred, actual) # display the confusion matrix # Plot the ROC curve and display the AUC ROC_func(data.frame(predict(bs, BreastCancer[testset,]), BreastCancer[testset,'Class']), 2, 1, pos.label = 'malignant', plot.ROC=TRUE) # Plot the tree to PDF and generate the .tex file plot(bs, file='../bsnsing_test/fig/BreastCancer.pdf') ## End(Not run)## Not run: n <- nrow(BreastCancer) trainset <- sample(1:n, 0.7*n) # randomly sample 70\ testset <- setdiff(1:n, trainset) # the remaining is for testing # Build a tree to predict Class, using all default options bs <- bsnsing(Class~., data = BreastCancer[trainset,]) summary(bs) # display the tree structure pred <- predict(bs, BreastCancer[testset,], type='class') actual <- BreastCancer[testset, 'Class'] table(pred, actual) # display the confusion matrix # Plot the ROC curve and display the AUC ROC_func(data.frame(predict(bs, BreastCancer[testset,]), BreastCancer[testset,'Class']), 2, 1, pos.label = 'malignant', plot.ROC=TRUE) # Plot the tree to PDF and generate the .tex file plot(bs, file='../bsnsing_test/fig/BreastCancer.pdf') ## End(Not run)
If the file argument is supplied, this function will invoke the external programs latex, dvips and ps2pdf. If these programs are not available, only the latex code will be generated. If the file argument is left empty, the latex code will be written to the console screen. The latex code utilizes the following packages: pstricks, pst-node, pst-tree.
show.bsnsing( object, file = "", class_labels = c(), class_colors = c("red", "green"), rule_font = c("footnotesize", "scriptsize", "tiny", "normalsize", "small"), rule_color = "blue", footnote = F, landscape = F, papersize = c("a0paper", "a1paper", "a2paper", "a3paper", "a4paper", "a5paper", "a6paper", "b0paper", "b1paper", "b2paper", "b3paper", "b4paper", "b5paper", "b6paper", "c0paper", "c1paper", "c2paper", "c3paper", "c4paper", "c5paper", "c6paper", "b0j", "b1j", "b2j", "b3j", "b4j", "b5j", "b6j", "ansiapaper", "ansibpaper", "ansicpaper", "ansidpaper", "ansiepaper", "letterpaper", "executivepaper", "legalpaper"), verbose = F, ... )show.bsnsing( object, file = "", class_labels = c(), class_colors = c("red", "green"), rule_font = c("footnotesize", "scriptsize", "tiny", "normalsize", "small"), rule_color = "blue", footnote = F, landscape = F, papersize = c("a0paper", "a1paper", "a2paper", "a3paper", "a4paper", "a5paper", "a6paper", "b0paper", "b1paper", "b2paper", "b3paper", "b4paper", "b5paper", "b6paper", "c0paper", "c1paper", "c2paper", "c3paper", "c4paper", "c5paper", "c6paper", "b0j", "b1j", "b2j", "b3j", "b4j", "b5j", "b6j", "ansiapaper", "ansibpaper", "ansicpaper", "ansidpaper", "ansiepaper", "letterpaper", "executivepaper", "legalpaper"), verbose = F, ... )
object |
an object of class |
file |
a writable connection or a character string naming the file to write to. If not supplied, the output will be written to the console. |
class_labels |
a character vector of two elements for leaf node label (for 0 and 1). If empty, the labels will be read from the bsnsing object. |
class_colors |
a character vector of two elements for leaf node color (for 0 and 1) |
rule_font |
a string specifying the font size of the split rule at each non-leaf node |
rule_color |
a string specifying the color of the split rule and node, e.g., blue, gray, black, etc. For a list of all usable colors, see https://en.wikibooks.org/wiki/LaTeX/Colors |
footnote |
a boolean value indicating whether to add annotation text to the PDF page. The default is False. |
landscape |
a boolean value indicating if the PDF page should be in landscape layout. The default is False. |
papersize |
a string specifying the PDF paper size. The default is 'a0paper'. |
verbose |
a logical value, default is FALSE. |
... |
further parameters to the plot function. |
NA
# Suppose bs is a bsnsing object ## Not run: plot(bs) plot(bs, file = "/path/to/destination/filename.tex") ## End(Not run)# Suppose bs is a bsnsing object ## Not run: plot(bs) plot(bs, file = "/path/to/destination/filename.tex") ## End(Not run)
Generate latex code for plotting the bsnsing tree
show.mbsnsing(object, ...)show.mbsnsing(object, ...)
object |
an object of class |
... |
further arguments to the plot function. |
Summarize the bsnsing Model Fits
## S3 method for class 'bsnsing' summary(object = stop("no 'object' arg"), ...)## S3 method for class 'bsnsing' summary(object = stop("no 'object' arg"), ...)
object |
an object of class |
... |
further arguments. |
a list of detailed information in the given object.
Summarize mbsnsing Model Fits
## S3 method for class 'mbsnsing' summary(object = stop("no 'object' arg"), ...)## S3 method for class 'mbsnsing' summary(object = stop("no 'object' arg"), ...)
object |
an object of class |
... |
further arguments. |
a list of detailed information in the given object.