Title:	Leveraging Experiment Lines to Data Analytics
Version:	1.2.727
Description:	The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
License:	MIT + file LICENSE
URL:	https://cefet-rj-dal.github.io/daltoolbox/, https://github.com/cefet-rj-dal/daltoolbox
BugReports:	https://github.com/cefet-rj-dal/daltoolbox/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
RoxygenNote:	7.3.2
Imports:	FNN, caret, class, cluster, dbscan, dplyr, e1071, ggplot2, nnet, randomForest, reshape, tree
NeedsCompilation:	no
Packaged:	2025-06-27 23:40:23 UTC; gpca
Author:	Eduardo Ogasawara [aut, ths, cre], Antonio Castro [aut], Diego Salles [aut], Janio Lima [aut], Lucas Tavares [aut], Diego Carvalho [ctb], Eduardo Bezerra [ctb], Rafaelli Coutinho [ctb], CEFET/RJ [cph]
Maintainer:	Eduardo Ogasawara <eogasawara@ieee.org>
Repository:	CRAN
Date/Publication:	2025-06-28 16:20:01 UTC

Boston Housing Data (Regression)

Description

housing values in suburbs of Boston.

• crim: per capita crime rate by town.

• zn: proportion of residential land zoned for lots over 25,000 sq.ft.

• indus: proportion of non-retail business acres per town

• chas: Charles River dummy variable (= 1 if tract bounds)

• nox: nitric oxides concentration (parts per 10 million)

• rm: average number of rooms per dwelling

• age: proportion of owner-occupied units built prior to 1940

• dis: weighted distances to five Boston employment centres

• rad: index of accessibility to radial highways

• tax: full-value property-tax rate per $10,000

• ptratio: pupil-teacher ratio by town

• black: 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town

• lstat: percentage of lower status of the population

• medv: Median value of owner-occupied homes in $1000's

Usage

data(Boston)

Format

Regression Dataset.

Source

This dataset was obtained from the MASS library.

References

Creator: Harrison, D. and Rubinfeld, D.L. Hedonic prices and the demand for clean air, J. Environ. Economics & Management, vol.5, 81-102, 1978.

Examples

data(Boston)
head(Boston)

Action

Description

Executes the action of model applied in provided data

Usage

action(obj, ...)

Arguments

Value

returns the result of an action of the model applied in provided data

Examples

data(iris)
# an example is minmax normalization
trans <- minmax()
trans <- fit(trans, iris)
tiris <- action(trans, iris)

Action implementation for transform

Description

A default function that defines the action to proxy transform method

Usage

## S3 method for class 'dal_transform'
action(obj, ...)

Arguments

Value

returns a transformed data

Examples

#See ?minmax for an example of transformation

Adjust categorical mapping

Description

Converts a vector into a categorical mapping, where each category is represented by a specific value. By default, the values represent binary categories (true/false)

Usage

adjust_class_label(x, valTrue = 1, valFalse = 0)

Arguments

Value

returns an adjusted categorical mapping

Adjust to data frame

Description

Converts a dataset to a data.frame if it is not already in that format

Usage

adjust_data.frame(data)

Arguments

Value

returns a data.frame

Examples

data(iris)
df <- adjust_data.frame(iris)

Adjust factors

Description

Converts a vector into a factor with specified levels and labels

Usage

adjust_factor(value, ilevels, slevels)

Arguments

Value

returns an adjusted factor

Adjust to matrix

Description

Converts a dataset to a matrix format if it is not already in that format

Usage

adjust_matrix(data)

Arguments

Value

returns an adjusted matrix

Examples

data(iris)
mat <- adjust_matrix(iris)

Autoencoder - Encode

Description

Creates a base class for autoencoder.

Usage

autoenc_base_e(input_size, encoding_size)

Arguments

Value

returns a autoenc_base_e object.

Examples

#See an example of using `autoenc_base_e` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_base_e.md

Autoencoder - Encode-decode

Description

Creates a base class for autoencoder.

Usage

autoenc_base_ed(input_size, encoding_size)

Arguments

Value

returns a autoenc_base_ed object.

Examples

#See an example of using `autoenc_base_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_base_ed.md

Categorical mapping

Description

Categorical mapping provides a way to map the levels of a categorical variable to new values. Each possible value is converted to a binary attribute.

Usage

categ_mapping(attribute)

Arguments

Value

returns a data frame with binary attributes, one for each possible category.

Examples

cm <- categ_mapping("Species")
iris_cm <- transform(cm, iris)

# can be made in a single column
species <- iris[,"Species", drop=FALSE]
iris_cm <- transform(cm, species)

Decision Tree for classification

Description

Creates a classification object that uses the Decision Tree algorithm for classification. It wraps the tree library.

Usage

cla_dtree(attribute, slevels)

Arguments

Value

returns a classification object

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_dtree("Species", slevels)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

K Nearest Neighbor Classification

Description

Classifies using the K-Nearest Neighbor algorithm. It wraps the class library.

Usage

cla_knn(attribute, slevels, k = 1)

Arguments

Value

returns a knn object.

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_knn("Species", slevels, k=3)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

Majority Classification

Description

This function creates a classification object that uses the majority vote strategy to predict the target attribute. Given a target attribute, the function counts the number of occurrences of each value in the dataset and selects the one that appears most often.

Usage

cla_majority(attribute, slevels)

Arguments

Value

returns a classification object.

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_majority("Species", slevels)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

MLP for classification

Description

Creates a classification object that uses the Multi-Layer Perceptron (MLP) method. It wraps the nnet library.

Usage

cla_mlp(attribute, slevels, size = NULL, decay = 0.1, maxit = 1000)

Arguments

Value

returns a classification object

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_mlp("Species", slevels, size=3, decay=0.03)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

Naive Bayes Classifier

Description

Classification using the Naive Bayes algorithm It wraps the e1071 library.

Usage

cla_nb(attribute, slevels)

Arguments

Value

returns a classification object.

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_nb("Species", slevels)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

Random Forest for classification

Description

Creates a classification object that uses the Random Forest method It wraps the randomForest library.

Usage

cla_rf(attribute, slevels, nodesize = 5, ntree = 10, mtry = NULL)

Arguments

Value

returns a classification object

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_rf("Species", slevels, ntree=5)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

SVM for classification

Description

Creates a classification object that uses the Support Vector Machine (SVM) method for classification It wraps the e1071 and svm library.

Usage

cla_svm(attribute, slevels, epsilon = 0.1, cost = 10, kernel = "radial")

Arguments

Value

returns a SVM classification object

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_svm("Species", slevels, epsilon=0.0,cost=20.000)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

Classification Tune

Description

This function performs a grid search or random search over specified hyperparameter values to optimize a base classification model

Usage

cla_tune(base_model, folds = 10, metric = "accuracy")

Arguments

Value

returns a cla_tune object

Examples

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

# hyper parameter setup
tune <- cla_tune(cla_mlp("Species", levels(iris$Species)))
ranges <- list(size=c(3:5), decay=c(0.1))

# hyper parameter optimization
model <- fit(tune, train, ranges)

# testing optimization
test_prediction <- predict(model, test)
test_predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

classification

Description

Ancestor class for classification problems

Usage

classification(attribute, slevels)

Arguments

Value

returns a classification object

Examples

#See ?cla_dtree for a classification example using a decision tree

Clustering Tune

Description

Creates an object for tuning clustering models. This object can be used to fit and optimize clustering algorithms by specifying hyperparameter ranges

Usage

clu_tune(base_model)

Arguments

Value

returns a clu_tune object.

Examples

data(iris)

# fit model
model <- clu_tune(cluster_kmeans(k = 0))
ranges <- list(k = 1:10)
model <- fit(model, iris[,1:4], ranges)
model$k

Cluster

Description

Defines a cluster method

Usage

cluster(obj, ...)

Arguments

Value

clustered data

Examples

#See ?cluster_kmeans for an example of transformation

DBSCAN

Description

Creates a clusterer object that uses the DBSCAN method It wraps the dbscan library.

Usage

cluster_dbscan(minPts = 3, eps = NULL)

Arguments

Value

returns a dbscan object

Examples

# setup clustering
model <- cluster_dbscan(minPts = 3)

#load dataset
data(iris)

# build model
model <- fit(model, iris[,1:4])
clu <- cluster(model, iris[,1:4])
table(clu)

# evaluate model using external metric
eval <- evaluate(model, clu, iris$Species)
eval

k-means

Description

Creates a clusterer object that uses the k-means method It wraps the stats library.

Usage

cluster_kmeans(k = 1)

Arguments

Value

returns a k-means object.

Examples

# setup clustering
model <- cluster_kmeans(k=3)

#load dataset
data(iris)

# build model
model <- fit(model, iris[,1:4])
clu <- cluster(model, iris[,1:4])
table(clu)

# evaluate model using external metric
eval <- evaluate(model, clu, iris$Species)
eval

PAM

Description

Creates a clusterer object that uses the Partition Around Medoids (PAM) method It wraps the cluster library.

Usage

cluster_pam(k = 1)

Arguments

Value

returns PAM object.

Examples

# setup clustering
model <- cluster_pam(k = 3)

#load dataset
data(iris)

# build model
model <- fit(model, iris[,1:4])
clu <- cluster(model, iris[,1:4])
table(clu)

# evaluate model using external metric
eval <- evaluate(model, clu, iris$Species)
eval

Clusterer

Description

Ancestor class for clustering problems

Usage

clusterer()

Value

returns a clusterer object

Examples

#See ?cluster_kmeans for an example of transformation

Class dal_base

Description

The dal_base class is an abstract class for all dal descendants classes. It provides both fit() and action() functions

Usage

dal_base()

Value

returns a dal_base object

Examples

trans <- dal_base()

DAL Learner

Description

A ancestor class for clustering, classification, regression, and time series regression. It also provides the basis for specialized evaluation of learning performance.

An example of a learner is a decision tree (cla_dtree)

Usage

dal_learner()

Value

returns a learner

Examples

#See ?cla_dtree for a classification example using a decision tree

DAL Transform

Description

A transformation method applied to a dataset. If needed, the fit can be called to adjust the transform.

Usage

dal_transform()

Value

returns a dal_transform object.

Examples

#See ?minmax for an example of transformation

DAL Tune

Description

Creates an ancestor class for hyperparameter optimization, allowing the tuning of a base model using cross-validation.

Usage

dal_tune(base_model, folds = 10)

Arguments

Value

returns a dal_tune object

Examples

#See ?cla_tune for classification tuning
#See ?reg_tune for regression tuning
#See ?ts_tune for time series tuning

Data Sample

Description

The data_sample function in R is used to randomly sample data from a given data frame. It can be used to obtain a subset of data for further analysis or modeling.

Two basic specializations of data_sample are sample_random and sample_stratified. They provide random sampling and stratified sampling, respectively.

Data sample provides both training and testing partitioning (train_test) and k-fold partitioning (k_fold) of data.

Usage

data_sample()

Value

returns an object of class data_sample

Examples

#using random sampling
sample <- sample_random()
tt <- train_test(sample, iris)

# distribution of train
table(tt$train$Species)

# preparing dataset into four folds
folds <- k_fold(sample, iris, 4)

# distribution of folds
tbl <- NULL
for (f in folds) {
 tbl <- rbind(tbl, table(f$Species))
}
head(tbl)

PCA

Description

PCA (Principal Component Analysis) is an unsupervised dimensionality reduction technique used in data analysis and machine learning. It transforms a dataset of possibly correlated variables into a new set of uncorrelated variables called principal components.

Usage

dt_pca(attribute = NULL, components = NULL)

Arguments

Value

returns an object of class dt_pca

Examples

mypca <- dt_pca("Species")
# Automatically fitting number of components
mypca <- fit(mypca, iris)
iris.pca <- transform(mypca, iris)
head(iris.pca)
head(mypca$pca.transf)
# Manual establishment of number of components
mypca <- dt_pca("Species", 3)
mypca <- fit(mypca, datasets::iris)
iris.pca <- transform(mypca, iris)
head(iris.pca)
head(mypca$pca.transf)

Evaluate

Description

Evaluate learner performance. The actual evaluate varies according to the type of learner (clustering, classification, regression, time series regression)

Usage

evaluate(obj, ...)

Arguments

Value

returns the evaluation

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_dtree("Species", slevels)
model <- fit(model, iris)
prediction <- predict(model, iris)
predictand <- adjust_class_label(iris[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

Fit

Description

Applies the fit method to a model object to train or configure it using the provided data and optional arguments

Usage

fit(obj, ...)

Arguments

Value

returns a object after fitting

Examples

data(iris)
# an example is minmax normalization
trans <- minmax()
trans <- fit(trans, iris)
tiris <- action(trans, iris)

tune hyperparameters of ml model

Description

Tunes the hyperparameters of a machine learning model for classification

Usage

## S3 method for class 'cla_tune'
fit(obj, data, ranges, ...)

Arguments

Value

a fitted obj

fit dbscan model

Description

Fits a DBSCAN clustering model by setting the eps parameter. If eps is not provided, it is estimated based on the k-nearest neighbor distances. It wraps dbscan library

Usage

## S3 method for class 'cluster_dbscan'
fit(obj, data, ...)

Arguments

Value

returns a fitted obj with the eps parameter set

maximum curvature analysis

Description

Fitting a curvature model in a sequence of observations. It extracts the the maximum curvature computed.

Usage

fit_curvature_max()

Value

returns an object of class fit_curvature_max, which inherits from the fit_curvature and dal_transform classes. The object contains a list with the following elements:

• x: The position in which the maximum curvature is reached.

• y: The value where the the maximum curvature occurs.

• yfit: The value of the maximum curvature.

Examples

x <- seq(from=1,to=10,by=0.5)
dat <- data.frame(x = x, value = -log(x), variable = "log")
myfit <- fit_curvature_max()
res <- transform(myfit, dat$value)
head(res)

minimum curvature analysis

Description

Fitting a curvature model in a sequence of observations. It extracts the the minimum curvature computed.

Usage

fit_curvature_min()

Value

Returns an object of class fit_curvature_max, which inherits from the fit_curvature and dal_transform classes. The object contains a list with the following elements:

• x: The position in which the minimum curvature is reached.

• y: The value where the the minimum curvature occurs.

• yfit: The value of the minimum curvature.

Examples

x <- seq(from=1,to=10,by=0.5)
dat <- data.frame(x = x, value = log(x), variable = "log")
myfit <- fit_curvature_min()
res <- transform(myfit, dat$value)
head(res)

Inverse Transform

Description

Reverses the transformation applied to data.

Usage

inverse_transform(obj, ...)

Arguments

Value

dataset inverse transformed.

Examples

#See ?minmax for an example of transformation

K-fold sampling

Description

k-fold partition of a dataset using a sampling method

Usage

k_fold(obj, data, k)

Arguments

Value

returns a list of k data frames

Examples

#using random sampling
sample <- sample_random()

# preparing dataset into four folds
folds <- k_fold(sample, iris, 4)

# distribution of folds
tbl <- NULL
for (f in folds) {
 tbl <- rbind(tbl, table(f$Species))
}
head(tbl)

Min-max normalization

Description

The minmax performs scales data between [0,1].

minmax = (x-min(x))/(max(x)-min(x))

Usage

minmax()

Value

returns an object of class minmax

Examples

data(iris)
head(iris)

trans <- minmax()
trans <- fit(trans, iris)
tiris <- transform(trans, iris)
head(tiris)

itiris <- inverse_transform(trans, tiris)
head(itiris)

outliers_boxplot

Description

The outliers_boxplot class uses box-plot definition for outliers_boxplot. An outlier is a value that is below than Q_1 - 1.5 \cdot IQR or higher than Q_3 + 1.5 \cdot IQR. The class remove outliers_boxplot for numeric attributes. Users can set alpha to 3 to remove extreme values.

Usage

outliers_boxplot(alpha = 1.5)

Arguments

Value

returns an outlier object

Examples

# code for outlier removal
out_obj <- outliers_boxplot() # class for outlier analysis
out_obj <- fit(out_obj, iris) # computing boundaries
iris.clean <- transform(out_obj, iris) # returning cleaned dataset

#inspection of cleaned dataset
nrow(iris.clean)

idx <- attr(iris.clean, "idx")
table(idx)
iris.outliers_boxplot <- iris[idx,]
iris.outliers_boxplot

outliers_gaussian

Description

The outliers_gaussian class uses box-plot definition for outliers_gaussian. An outlier is a value that is below than \overline{x} - 3 \sigma_x or higher than \overline{x} + 3 \sigma_x. The class remove outliers_gaussian for numeric attributes.

Usage

outliers_gaussian(alpha = 3)

Arguments

Value

returns an outlier object

Examples

# code for outlier removal
out_obj <- outliers_gaussian() # class for outlier analysis
out_obj <- fit(out_obj, iris) # computing boundaries
iris.clean <- transform(out_obj, iris) # returning cleaned dataset

#inspection of cleaned dataset
nrow(iris.clean)

idx <- attr(iris.clean, "idx")
table(idx)
iris.outliers_gaussian <- iris[idx,]
iris.outliers_gaussian

Plot bar graph

Description

this function displays a bar graph from a data frame containing x-axis categories using ggplot2.

Usage

plot_bar(data, label_x = "", label_y = "", colors = NULL, alpha = 1)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

#summarizing iris dataset
data <- iris |> dplyr::group_by(Species) |>
dplyr::summarize(Sepal.Length=mean(Sepal.Length))
head(data)

#ploting data
grf <- plot_bar(data, colors="blue")
plot(grf)

Plot boxplot

Description

this function displays a boxplot graph from a data frame containing x-axis categories and numeric values using ggplot2.

Usage

plot_boxplot(data, label_x = "", label_y = "", colors = NULL, barwidth = 0.25)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

grf <- plot_boxplot(iris, colors="white")
plot(grf)

Boxplot per class

Description

This function generates boxplots grouped by a specified class label from a data frame containing numeric values using ggplot2.

Usage

plot_boxplot_class(
  data,
  class_label,
  label_x = "",
  label_y = "",
  colors = NULL
)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

grf <- plot_boxplot_class(iris |> dplyr::select(Sepal.Width, Species),
class = "Species", colors=c("red", "green", "blue"))
plot(grf)

Plot density

Description

This function generates a density plot from a data frame containing numeric values using ggplot2. If the data frame has multiple columns, densities can be grouped and plotted.

Usage

plot_density(
  data,
  label_x = "",
  label_y = "",
  colors = NULL,
  bin = NULL,
  alpha = 0.25
)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

grf <- plot_density(iris |> dplyr::select(Sepal.Width), colors="blue")
plot(grf)

Plot density per class

Description

This function generates density plots using ggplot2 grouped by a specified class label from a data frame containing numeric values.

Usage

plot_density_class(
  data,
  class_label,
  label_x = "",
  label_y = "",
  colors = NULL,
  bin = NULL,
  alpha = 0.5
)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

grf <- plot_density_class(iris |> dplyr::select(Sepal.Width, Species),
class = "Species", colors=c("red", "green", "blue"))
plot(grf)

Plot grouped bar

Description

This function generates a grouped bar plot from a given data frame using ggplot2.

Usage

plot_groupedbar(data, label_x = "", label_y = "", colors = NULL, alpha = 1)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

#summarizing iris dataset
data <- iris |> dplyr::group_by(Species) |>
dplyr::summarize(Sepal.Length=mean(Sepal.Length), Sepal.Width=mean(Sepal.Width))
head(data)

#ploting data
grf <- plot_groupedbar(data, colors=c("blue", "red"))
plot(grf)

Plot histogram

Description

This function generates a histogram from a specified data frame using ggplot2.

Usage

plot_hist(data, label_x = "", label_y = "", color = "white", alpha = 0.25)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

grf <- plot_hist(iris |> dplyr::select(Sepal.Width), color=c("blue"))
plot(grf)

Plot lollipop

Description

This function creates a lollipop chart using ggplot2.

Usage

plot_lollipop(
  data,
  label_x = "",
  label_y = "",
  colors = NULL,
  color_text = "black",
  size_text = 3,
  size_ball = 8,
  alpha_ball = 0.2,
  min_value = 0,
  max_value_gap = 1
)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

#summarizing iris dataset
data <- iris |> dplyr::group_by(Species) |>
dplyr::summarize(Sepal.Length=mean(Sepal.Length))
head(data)

#ploting data
grf <- plot_lollipop(data, colors="blue", max_value_gap=0.2)
plot(grf)

Plot pie

Description

This function creates a pie chart using ggplot2.

Usage

plot_pieplot(
  data,
  label_x = "",
  label_y = "",
  colors = NULL,
  textcolor = "white",
  bordercolor = "black"
)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

#summarizing iris dataset
data <- iris |> dplyr::group_by(Species) |>
dplyr::summarize(Sepal.Length=mean(Sepal.Length))
head(data)

#ploting data
grf <- plot_pieplot(data, colors=c("red", "green", "blue"))
plot(grf)

Plot points

Description

This function creates a scatter plot using ggplot2.

Usage

plot_points(data, label_x = "", label_y = "", colors = NULL)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

x <- seq(0, 10, 0.25)
data <- data.frame(x, sin=sin(x), cosine=cos(x)+5)
head(data)

grf <- plot_points(data, colors=c("red", "green"))
plot(grf)

Plot radar

Description

This function creates a radar chart using ggplot2.

Usage

plot_radar(data, label_x = "", label_y = "", colors = NULL)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

data <- data.frame(name = "Petal.Length", value = mean(iris$Petal.Length))
data <- rbind(data, data.frame(name = "Petal.Width", value = mean(iris$Petal.Width)))
data <- rbind(data, data.frame(name = "Sepal.Length", value = mean(iris$Sepal.Length)))
data <- rbind(data, data.frame(name = "Sepal.Width", value = mean(iris$Sepal.Width)))

grf <- plot_radar(data, colors="red") + ggplot2::ylim(0, NA)
plot(grf)

Scatter graph

Description

This function creates a scatter plot using ggplot2.

Usage

plot_scatter(data, label_x = "", label_y = "", colors = NULL)

Arguments

Value

return a ggplot2::ggplot graphic

Examples

grf <- plot_scatter(iris |> dplyr::select(x = Sepal.Length,
value = Sepal.Width, variable = Species),
label_x = "Sepal.Length", label_y = "Sepal.Width",
colors=c("red", "green", "blue"))
plot(grf)

Plot series

Description

This function creates a time series plot using ggplot2.

Usage

plot_series(data, label_x = "", label_y = "", colors = NULL)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

x <- seq(0, 10, 0.25)
data <- data.frame(x, sin=sin(x))
head(data)

grf <- plot_series(data, colors=c("red"))
plot(grf)

Plot stacked bar

Description

this function creates a stacked bar chart using ggplot2.

Usage

plot_stackedbar(data, label_x = "", label_y = "", colors = NULL, alpha = 1)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

#summarizing iris dataset
data <- iris |> dplyr::group_by(Species) |>
dplyr::summarize(Sepal.Length=mean(Sepal.Length), Sepal.Width=mean(Sepal.Width))

#plotting data
grf <- plot_stackedbar(data, colors=c("blue", "red"))
plot(grf)

Plot time series chart

Description

This function plots a time series chart with points and a line using ggplot2.

Usage

plot_ts(x = NULL, y, label_x = "", label_y = "", color = "black")

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

x <- seq(0, 10, 0.25)
y <- sin(x)

grf <- plot_ts(x = x, y = y, color=c("red"))
plot(grf)

Plot a time series chart with predictions

Description

This function plots a time series chart with three lines: the original series, the adjusted series, and the predicted series using ggplot2.

Usage

plot_ts_pred(
  x = NULL,
  y,
  yadj,
  ypred = NULL,
  label_x = "",
  label_y = "",
  color = "black",
  color_adjust = "blue",
  color_prediction = "green"
)

Arguments

Value

returns a ggplot2::ggplot graphic

Examples

x <- base::seq(0, 10, 0.25)
yvalues <- sin(x) + rnorm(41,0,0.1)
adjust <- sin(x[1:35])
prediction <- sin(x[36:41])
grf <- plot_ts_pred(y=yvalues, yadj=adjust, ypre=prediction)
plot(grf)

DAL Predict

Description

Ancestor class for regression and classification It provides basis for fit and predict methods. Besides, action method proxies to predict.

An example of learner is a decision tree (cla_dtree)

Usage

predictor()

Value

returns a predictor object

Examples

#See ?cla_dtree for a classification example using a decision tree

Decision Tree for regression

Description

Creates a regression object that uses the Decision Tree method for regression It wraps the tree library.

Usage

reg_dtree(attribute)

Arguments

Value

returns a decision tree regression object

Examples

data(Boston)
model <- reg_dtree("medv")

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

knn regression

Description

Creates a regression object that uses the K-Nearest Neighbors (knn) method for regression

Usage

reg_knn(attribute, k)

Arguments

Value

returns a knn regression object

Examples

data(Boston)
model <- reg_knn("medv", k=3)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

MLP for regression

Description

Creates a regression object that uses the Multi-Layer Perceptron (MLP) method. It wraps the nnet library.

Usage

reg_mlp(attribute, size = NULL, decay = 0.05, maxit = 1000)

Arguments

Value

returns a object of class reg_mlp

Examples

data(Boston)
model <- reg_mlp("medv", size=5, decay=0.54)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

Random Forest for regression

Description

Creates a regression object that uses the Random Forest method. It wraps the randomForest library.

Usage

reg_rf(attribute, nodesize = 1, ntree = 10, mtry = NULL)

Arguments

Value

returns an object of class reg_rfobj

Examples

data(Boston)
model <- reg_rf("medv", ntree=10)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

SVM for regression

Description

Creates a regression object that uses the Support Vector Machine (SVM) method for regression It wraps the e1071 and svm library.

Usage

reg_svm(attribute, epsilon = 0.1, cost = 10, kernel = "radial")

Arguments

Value

returns a SVM regression object

Examples

data(Boston)
model <- reg_svm("medv", epsilon=0.2,cost=40.000)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

Regression Tune

Description

Creates an object for tuning regression models

Usage

reg_tune(base_model, folds = 10)

Arguments

Value

returns a reg_tune object.

Examples

# preparing dataset for random sampling
data(Boston)
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test

# hyper parameter setup
tune <- reg_tune(reg_mlp("medv"))
ranges <- list(size=c(3), decay=c(0.1,0.5))

# hyper parameter optimization
model <- fit(tune, train, ranges)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

Regression

Description

Ancestor class for regression problems. This ancestor class is used to define and manage the target attribute for regression tasks.

Usage

regression(attribute)

Arguments

Value

returns a regression object

Examples

#See ?reg_dtree for a regression example using a decision tree

Sample Random

Description

The sample_random function in R is used to generate a random sample of specified size from a given data set.

Usage

sample_random()

Value

returns an object of class 'sample_random

Examples

#using random sampling
sample <- sample_random()
tt <- train_test(sample, iris)

# distribution of train
table(tt$train$Species)

# preparing dataset into four folds
folds <- k_fold(sample, iris, 4)

# distribution of folds
tbl <- NULL
for (f in folds) {
 tbl <- rbind(tbl, table(f$Species))
}
head(tbl)

Stratified Random Sampling

Description

The sample_stratified function in R is used to generate a stratified random sample from a given dataset. Stratified sampling is a statistical method that is used when the population is divided into non-overlapping subgroups or strata, and a sample is selected from each stratum to represent the entire population. In stratified sampling, the sample is selected in such a way that it is representative of the entire population and the variability within each stratum is minimized.

Usage

sample_stratified(attribute)

Arguments

Value

returns an object of class sample_stratified

Examples

#using stratified sampling
sample <- sample_stratified("Species")
tt <- train_test(sample, iris)

# distribution of train
table(tt$train$Species)

# preparing dataset into four folds
folds <- k_fold(sample, iris, 4)

# distribution of folds
tbl <- NULL
for (f in folds) {
 tbl <- rbind(tbl, table(f$Species))
}
head(tbl)

Selection hyper parameters

Description

Selects the optimal hyperparameters from a dataset resulting from k-fold cross-validation

Usage

select_hyper(obj, hyperparameters)

Arguments

Value

returns the index of selected hyper parameter

selection of hyperparameters

Description

Selects the optimal hyperparameter by maximizing the average classification metric. It wraps dplyr library.

Usage

## S3 method for class 'cla_tune'
select_hyper(obj, hyperparameters)

Arguments

Value

returns a optimized key number of hyperparameters

Assign parameters

Description

set_params function assigns all parameters to the attributes presented in the object.

Usage

set_params(obj, params)

Arguments

Value

returns an object with parameters set

Examples

obj <- set_params(dal_base(), list(x = 0))

Default Assign parameters

Description

Default method for set_params which returns the object unchanged

Usage

## Default S3 method:
set_params(obj, params)

Arguments

Value

returns the object unchanged

Smoothing

Description

Smoothing is a statistical technique used to reduce the noise in a signal or a dataset by removing the high-frequency components. The smoothing level is associated with the number of bins used. There are alternative methods to establish the smoothing: equal interval, equal frequency, and clustering.

Usage

smoothing(n)

Arguments

Value

returns an object of class smoothing

Examples

data(iris)
obj <- smoothing_inter(n = 2)
obj <- fit(obj, iris$Sepal.Length)
sl.bi <- transform(obj, iris$Sepal.Length)
table(sl.bi)
obj$interval

entro <- evaluate(obj, as.factor(names(sl.bi)), iris$Species)
entro$entropy

Smoothing by cluster

Description

Uses clustering method to perform data smoothing. The input vector is divided into clusters using the k-means algorithm. The mean of each cluster is then calculated and used as the smoothed value for all observations within that cluster.

Usage

smoothing_cluster(n)

Arguments

Value

returns an object of class smoothing_cluster

Examples

data(iris)
obj <- smoothing_cluster(n = 2)
obj <- fit(obj, iris$Sepal.Length)
sl.bi <- transform(obj, iris$Sepal.Length)
table(sl.bi)
obj$interval

entro <- evaluate(obj, as.factor(names(sl.bi)), iris$Species)
entro$entropy

Smoothing by Freq

Description

The 'smoothing_freq' function is used to smooth a given time series data by aggregating observations within a fixed frequency.

Usage

smoothing_freq(n)

Arguments

Value

returns an object of class smoothing_freq

Examples

data(iris)
obj <- smoothing_freq(n = 2)
obj <- fit(obj, iris$Sepal.Length)
sl.bi <- transform(obj, iris$Sepal.Length)
table(sl.bi)
obj$interval

entro <- evaluate(obj, as.factor(names(sl.bi)), iris$Species)
entro$entropy

Smoothing by interval

Description

The "smoothing by interval" function is used to apply a smoothing technique to a vector or time series data using a moving window approach.

Usage

smoothing_inter(n)

Arguments

Value

returns an object of class smoothing_inter

Examples

data(iris)
obj <- smoothing_inter(n = 2)
obj <- fit(obj, iris$Sepal.Length)
sl.bi <- transform(obj, iris$Sepal.Length)
table(sl.bi)
obj$interval

entro <- evaluate(obj, as.factor(names(sl.bi)), iris$Species)
entro$entropy

Train-Test Partition

Description

Partitions a dataset into training and test sets using a specified sampling method

Usage

train_test(obj, data, perc = 0.8, ...)

Arguments

Value

returns an list with two elements:

• train: A data frame containing the training set

• test: A data frame containing the test set

Examples

#using random sampling
sample <- sample_random()
tt <- train_test(sample, iris)

# distribution of train
table(tt$train$Species)

k-fold training and test partition object

Description

Splits a dataset into training and test sets based on k-fold cross-validation. The function takes a list of data partitions (folds) and a specified fold index k. It returns the data corresponding to the k-th fold as the test set, and combines all other folds to form the training set.

Usage

train_test_from_folds(folds, k)

Arguments

Value

returns a list with two elements:

• train: A data frame containing the combined data from all folds except the k-th fold, used as the training set.

• test: A data frame corresponding to the k-th fold, used as the test set.

Examples

# Create k-fold partitions of a dataset (e.g., iris)
folds <- k_fold(sample_random(), iris, k = 5)

# Use the first fold as the test set and combine the remaining folds for the training set
train_test_split <- train_test_from_folds(folds, k = 1)

# Display the training set
head(train_test_split$train)

# Display the test set
head(train_test_split$test)

Transform

Description

Defines a transformation method.

Usage

transform(obj, ...)

Arguments

Value

returns a transformed data.

Examples

#See ?minmax for an example of transformation

Z-score normalization

Description

Scale data using z-score normalization.

zscore = (x - mean(x))/sd(x)

Usage

zscore(nmean = 0, nsd = 1)

Arguments

Value

returns the z-score transformation object

Examples

data(iris)
head(iris)

trans <- zscore()
trans <- fit(trans, iris)
tiris <- transform(trans, iris)
head(tiris)

itiris <- inverse_transform(trans, tiris)
head(itiris)