Package 'bcn' reference manual

Title:	Boosted Configuration Networks
Description:	Boosted Configuration (neural) Networks for supervised learning.
Authors:	T. Moudiki
Maintainer:	T. Moudiki <[email protected]>
License:	BSD_3_clause Clear + file LICENSE
Version:	0.7.0
Built:	2024-11-21 03:35:01 UTC
Source:	https://github.com/Techtonique/bcn

Boosted Configuration Networks (BCN)

Description

Boosted Configuration Networks (BCN)

Usage

bcn(
  x,
  y,
  B = 10,
  nu = 0.1,
  col_sample = 1,
  lam = 0.1,
  r = 0.3,
  tol = 0,
  n_clusters = NULL,
  type_optim = c("nlminb", "nmkb", "hjkb", "randomsearch", "adam", "sgd"),
  activation = c("sigmoid", "tanh"),
  hidden_layer_bias = TRUE,
  verbose = 0,
  show_progress = TRUE,
  seed = 123,
  ...
)
bcn(
  x,
  y,
  B = 10,
  nu = 0.1,
  col_sample = 1,
  lam = 0.1,
  r = 0.3,
  tol = 0,
  n_clusters = NULL,
  type_optim = c("nlminb", "nmkb", "hjkb", "randomsearch", "adam", "sgd"),
  activation = c("sigmoid", "tanh"),
  hidden_layer_bias = TRUE,
  verbose = 0,
  show_progress = TRUE,
  seed = 123,
  ...
)

Arguments

`x`	a matrix, containing the explanatory variables
`y`	a factor, containing the variable to be explained
`B`	a numeric, the number of iterations of the algorithm
`nu`	a numeric, the learning rate of the algorithm
`col_sample`	a numeric in [0, 1], the percentage of columns adjusted at each iteration
`lam`	a numeric, defining lower and upper bounds for neural network's weights
`r`	a numeric, with 0 < r < 1. Controls the convergence rate of residuals.
`tol`	a numeric, convergence tolerance for an early stopping
`n_clusters`	a numeric, the number of clusters to be used in the algorithm (for now, kmeans)
`type_optim`	a string, the type of optimization procedure used for finding neural network's weights at each iteration ("nlminb", "nmkb", "hjkb", "adam", "sgd", "randomsearch")
`activation`	a string, the activation function (must be bounded). Currently: "sigmoid", "tanh".
`hidden_layer_bias`	a boolean, saying if there is a bias parameter in neural network's weights
`verbose`	an integer (0, 1, 2, 3). Controls verbosity (for checks). The higher, the more verbosity.
`show_progress`	a boolean, if TRUE, a progress bar is displayed
`seed`	an integer, for reproducibility of results
`...`	additional parameters to be passed to the optimizer (especially, to the `control` parameter)

Value

a list, an object of class 'bcn'

Examples


# iris dataset
set.seed(1234)
train_idx <- sample(nrow(iris), 0.8 * nrow(iris))
X_train <- as.matrix(iris[train_idx, -ncol(iris)])
X_test <- as.matrix(iris[-train_idx, -ncol(iris)])
y_train <- iris$Species[train_idx]
y_test <- iris$Species[-train_idx]

fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 10, nu = 0.335855,
lam = 10**0.7837525, r = 1 - 10**(-5.470031), tol = 10**-7,
activation = "tanh", type_optim = "nlminb")

print(predict(fit_obj, newx = X_test) == y_test)
print(mean(predict(fit_obj, newx = X_test) == y_test))


# Boston dataset (dataset has an ethical problem)
library(MASS)
data("Boston")

set.seed(1234)
train_idx <- sample(nrow(Boston), 0.8 * nrow(Boston))
X_train <- as.matrix(Boston[train_idx, -ncol(Boston)])
X_test <- as.matrix(Boston[-train_idx, -ncol(Boston)])
y_train <- Boston$medv[train_idx]
y_test <- Boston$medv[-train_idx]

fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 500, nu = 0.5646811,
lam = 10**0.5106108, r = 1 - 10**(-7), tol = 10**-7,
col_sample = 0.5, activation = "tanh", type_optim = "nlminb")
print(sqrt(mean((predict(fit_obj, newx = X_test) - y_test)**2)))


# iris dataset
set.seed(1234)
train_idx <- sample(nrow(iris), 0.8 * nrow(iris))
X_train <- as.matrix(iris[train_idx, -ncol(iris)])
X_test <- as.matrix(iris[-train_idx, -ncol(iris)])
y_train <- iris$Species[train_idx]
y_test <- iris$Species[-train_idx]

fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 10, nu = 0.335855,
lam = 10**0.7837525, r = 1 - 10**(-5.470031), tol = 10**-7,
activation = "tanh", type_optim = "nlminb")

print(predict(fit_obj, newx = X_test) == y_test)
print(mean(predict(fit_obj, newx = X_test) == y_test))


# Boston dataset (dataset has an ethical problem)
library(MASS)
data("Boston")

set.seed(1234)
train_idx <- sample(nrow(Boston), 0.8 * nrow(Boston))
X_train <- as.matrix(Boston[train_idx, -ncol(Boston)])
X_test <- as.matrix(Boston[-train_idx, -ncol(Boston)])
y_train <- Boston$medv[train_idx]
y_test <- Boston$medv[-train_idx]

fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 500, nu = 0.5646811,
lam = 10**0.5106108, r = 1 - 10**(-7), tol = 10**-7,
col_sample = 0.5, activation = "tanh", type_optim = "nlminb")
print(sqrt(mean((predict(fit_obj, newx = X_test) - y_test)**2)))

The breast cancer wisconsin dataset.

Description

The breast cancer wisconsin dataset for binary classification (benign or malignant)

Usage

breast_cancer
breast_cancer

Format

A data frame with 569 rows and 31 variables (30 covariates):

Source

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

The digits dataset.

Description

The digits dataset for multi-class classification (handwritten digits recognition).

Usage

digits
digits

Format

A data frame with 1797 rows and 65 variables (64 covariates):

Source

https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

Do K-means clustering

Description

Do K-means clustering

Usage

get_clusters(x, centers = 2L, seed = 123L, clustering_obj = NULL)
get_clusters(x, centers = 2L, seed = 123L, clustering_obj = NULL)

Arguments

`x`	a numeric matrix(like object) of predictors
`centers`	number of clusters
`seed`	random seed for reproducibility
`clustering_obj`	a list of kmeans results. Default is NULL, at training time. Must be provided at prediction time.

Value

a list of kmeans results, with additional attributes: xm, xsd, encoded_x

Examples


n <- 7 ; p <- 3

X <- matrix(rnorm(n * p), n, p) # no intercept!

print(get_clusters(X))


n <- 7 ; p <- 3

X <- matrix(rnorm(n * p), n, p) # no intercept!

print(get_clusters(X))

Size measurements for adult foraging penguins near Palmer Station, Antarctica

Description

Includes measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex. This is a subset of penguins_raw.

Usage

penguins
penguins

Format

A data frame with 344 rows and 8 variables:

species: a factor denoting penguin species (Adelie, Chinstrap and Gentoo)
island: a factor denoting island in Palmer Archipelago, Antarctica (Biscoe, Dream or Torgersen)
bill_length_mm: a number denoting bill length (millimeters)
bill_depth_mm: a number denoting bill depth (millimeters)
flipper_length_mm: an integer denoting flipper length (millimeters)
body_mass_g: an integer denoting body mass (grams)
sex: a factor denoting penguin sex (female, male)
year: an integer denoting the study year (2007, 2008, or 2009)

Source

Adelie penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Adelie penguins (Pygoscelis adeliae) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative https://doi.org/10.6073/pasta/98b16d7d563f265cb52372c8ca99e60f

Gentoo penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Gentoo penguin (Pygoscelis papua) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative https://doi.org/10.6073/pasta/7fca67fb28d56ee2ffa3d9370ebda689

Chinstrap penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Chinstrap penguin (Pygoscelis antarcticus) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 6. Environmental Data Initiative https://doi.org/10.6073/pasta/c14dfcfada8ea13a17536e73eb6fbe9e

Originally published in: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. doi:10.1371/journal.pone.0090081

Predict method for Boosted Configuration Networks (BCN)

Description

Predict method for Boosted Configuration Networks (BCN)

Usage

## S3 method for class 'bcn'
predict(object, newx, type = c("response", "probs"))
## S3 method for class 'bcn'
predict(object, newx, type = c("response", "probs"))

Arguments

`object`	a object of class 'bcn'
`newx`	new data, with no intersection with training data
`type`	a string, "response" is the class, "probs" are the classifier's probabilities

Examples


set.seed(1234)
train_idx <- sample(nrow(iris), 0.8 * nrow(iris))
X_train <- as.matrix(iris[train_idx, -ncol(iris)])
X_test <- as.matrix(iris[-train_idx, -ncol(iris)])
y_train <- iris$Species[train_idx]
y_test <- iris$Species[-train_idx]

fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 10, nu = 0.335855,
lam = 10**0.7837525, r = 1 - 10**(-5.470031), tol = 10**-7,
activation = "tanh", type_optim = "nlminb")

print(predict(fit_obj, newx = X_test) == y_test)
print(mean(predict(fit_obj, newx = X_test) == y_test))

print(predict(fit_obj, newx = X_test, type="probs"))

set.seed(1234)
train_idx <- sample(nrow(iris), 0.8 * nrow(iris))
X_train <- as.matrix(iris[train_idx, -ncol(iris)])
X_test <- as.matrix(iris[-train_idx, -ncol(iris)])
y_train <- iris$Species[train_idx]
y_test <- iris$Species[-train_idx]

fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 10, nu = 0.335855,
lam = 10**0.7837525, r = 1 - 10**(-5.470031), tol = 10**-7,
activation = "tanh", type_optim = "nlminb")

print(predict(fit_obj, newx = X_test) == y_test)
print(mean(predict(fit_obj, newx = X_test) == y_test))

print(predict(fit_obj, newx = X_test, type="probs"))

Random Search

Description

Random Search derivative-free optimization

Usage

random_search(
  objective,
  lower,
  upper,
  seed = 123,
  control = list(iter.max = 100)
)
random_search(
  objective,
  lower,
  upper,
  seed = 123,
  control = list(iter.max = 100)
)

Arguments

`objective`	objective function to be minimized
`lower`	lower bound for search
`upper`	upper bound for search
`seed`	an integer, for reproducing the result
`control`	a list of control parameters. For now `control = list(iter.max=100)`, where `iter.max` is the maximum number of iterations allowed

Value

A list with components

par the best set of parameters found

objective the value of objective corresponding to par

iterations number of iterations performed

Examples


fr <- function(x) {   ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}

random_search(fr, lower = c(-2, -2), upper = c(2, 2), control = list(iter.max=1000))

fr <- function(x) {   ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}

random_search(fr, lower = c(-2, -2), upper = c(2, 2), control = list(iter.max=1000))

sgd optimizer

Description

sgd optimizer

Usage

sgd(start, objective, n_iter = 100, alpha = 0.1, mass = 0.9)
sgd(start, objective, n_iter = 100, alpha = 0.1, mass = 0.9)

Arguments

mass

The wine dataset.

Description

The wine dataset for multi-class classification.

Usage

wine
wine

Format

A data frame with 178 rows and 14 variables (13 covariates):

Source

https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

Package 'bcn'

Help Index

adam optimizer

Description

Usage

Arguments

Boosted Configuration Networks (BCN)

Description

Usage

Arguments

Value

Examples

The breast cancer wisconsin dataset.

Description

Usage

Format

Source

The digits dataset.

Description

Usage

Format

Source

Do K-means clustering

Description

Usage

Arguments

Value

Examples

Size measurements for adult foraging penguins near Palmer Station, Antarctica

Description

Usage

Format

Source

Predict method for Boosted Configuration Networks (BCN)

Description

Usage

Arguments

Examples

Random Search

Description

Usage

Arguments

Value

Examples

sgd optimizer

Description

Usage

Arguments

The wine dataset.

Description

Usage

Format

Source