Package 'UAHDataScienceSC'

Title: Learn Supervised Classification Methods Through Examples and Code
Description: Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.
Authors: Víctor Amador Padilla [aut], Juan Jose Cuadrado Gallego [ctb] , Andriy Protsak Protsak [aut, cre], Universidad de Alcala [cph]
Maintainer: Andriy Protsak Protsak <[email protected]>
License: MIT + file LICENSE
Version: 1.0.0
Built: 2025-02-17 13:32:01 UTC
Source: CRAN

Help Index

Activation Function

Description

Upon a received input, calculates the output based on the selected activation function

Usage

act_method(method, x)

Arguments

method

Activation function to be used. It must be one of "step", "sine", "tangent", "linear", "relu", "gelu" or "swish".
x

Input value to be used in the activation function.

Details

Formulae used:

step

f(x)={0if x<threshold1if x≥thresholdf(x) = \begin{cases} 0 & \text{if } x < \text{threshold} \\ 1 & \text{if } x \geq \text{threshold} \end{cases}

sine

f(x)=sinh⁡(x)f(x) = \sinh(x)

tangent

f(x)=tanh⁡(x)f(x) = \tanh(x)

linear

xx

relu

f(x)={xif x>00if x≤0f(x) = \begin{cases} x & \text{if } x > 0 \\ 0 & \text{if } x \leq 0 \end{cases}

gelu

f(x)=12⋅x⋅(1+tanh⁡(2π⋅(x+0.044715⋅x3)))f(x) = \frac{1}{2} \cdot x \cdot \left(1 + \tanh\left(\sqrt{\frac{2}{\pi}} \cdot (x + 0.044715 \cdot x^3)\right)\right)

swish

f(x)=x1+e−xf(x) = \frac{x}{1 + e^{-x}}

Value

List with the weights of the inputs.

Author(s)

Víctor Amador Padilla, [email protected]

Examples

# example code
act_method("step", 0.3)
act_method("gelu", 0.7)

Test Database 5

Description

Test Database 5

Usage

db_flowers

Format

## 'db_flowers' A data frame representing features of flowers. It has 4 independent variables (first 4 columns) and one independent variable (last column).

Test Database 2

Description

Test Database 2

Usage

db_per_and

Format

## 'db_per_and' A data frame with 3 independent variables (first 3 columns) and one independent variable (last column). It represents a 3 input "AND" logic gate.

Test Database 3

Description

Test Database 3

Usage

db_per_or

Format

## 'db_per_or' A data frame with 3 independent variables (first 3 columns) and one independent variable (last column). It represents a 3 input "OR" logic gate.

Test Database 4

Description

Test Database 4

Usage

db_per_xor

Format

## 'db_per_xor' A data frame with 3 independent variables (first 3 columns) and one independent variable (last column). It represents a 3 input "XOR" logic gate.

Test Database 8

Description

Test Database 8

Usage

db_tree_struct

Format

## 'db_tree_struct' Decision tree structure. output of the decision_tree() function "decision_tree(db2, "VehicleType", 4, "gini")"

Test Database 1

Description

Test Database 1

Usage

db1rl

Format

## 'db1rl' A data frame with 4 independent variables (first 4 columns, representing different line types). The last column is the independent variable.

Test Database 6

Description

Test Database 6

Usage

db2

Format

## 'db2' A data frame with 3 independent variables (first 3 columns) and one independent variable (last column). It has information about vehicles.

Test Database 7

Description

Test Database 7

Usage

db3

Format

## 'db3' A data frame with 3 independent variables (first 3 columns) and one independent variable (last column). It has information about vehicles. Similar to db2 but a little bit more complex.

Decision Tree

Description

This function creates a decision tree based of an example dataset, calculating the best classifier possible in each step. Only creates perfect divisions, this means, if the rule doesn't create a classified group, it is not considered. It is specifically designed for categorical values. Continues values are not recommended as they will be treated as categorical ones.

Usage

decision_tree(
  data,
  classy,
  m,
  method = "entropy",
  learn = FALSE,
  waiting = TRUE
)

Arguments

data

A data frame with already classified observations. Each column represents a parameter of the value. Each row is a different observation. The column names in the parameter "data" must not contain the sequence of characters " or ". As this is supposed to be a binary decision rules generator and not a binary decision tree generator, no tree structures are used, except for the information gain formulas.
classy

Name of the column we want the data to be classified by. the set of rules obtained will be calculated according to this.
m

Maximum numbers of child nodes each node can have.
method

The definition of Gain. It must be one of "Entropy", "Gini"or "Error".
learn

Boolean value. If it is set to "TRUE" multiple clarifications and explanations are printed along the code
waiting

If TRUE while learn = TRUE. The code will stop in each "block" of code and wait for the user to press "enter" to continue.

Details

If data is not perfectly classifiable, the code will not finish.

Available information gain methods are:

Entropy

The formula to calculate the entropy works as follows:pi=−∑fipi⋅log⁡2pip_{i} = -\sum{f_{i} p_{i} \cdot \log2 p_{i}}

Gini

The formula to calculate gini works as follows:pi=1−∑fipi2p_{i} = 1 -\sum{f_{i} p_{i}^{2}}

Error

The formula to calculate error works as follows:pi=1−max⁡(fipi)p_{i} = 1 -\max{(f_{i} p_{i}})

Once the impurity is calculated, the information gain is calculated as follows:

IG=Ifather−∑count(sonvalues)count(fathervalues)⋅IsonIG = I_{father} - \sum{\frac{count(sonvalues)}{count(fathervalues)} \cdot I_{son}}

Value

Structure of the tree. List with a list per tree level. Each of these contains a list per level node, each of these contains a list with the node's filtered data, the node's id, the father's node id, the height that node is at, the variable it filters by, the value that variable is filtered by and the information gain of the division

Author(s)

Víctor Amador Padilla, [email protected]

Examples

# example code
decision_tree(db3, "VehicleType", 5, "entropy", learn = TRUE, waiting = FALSE)
decision_tree(db2, "VehicleType", 4, "gini")

K-Nearest Neighbors

Description

This function applies knn algorithm to classify data.

Usage

knn(
  data,
  ClassLabel,
  p1,
  d_method = "euclidean",
  k,
  p = 3,
  learn = FALSE,
  waiting = TRUE
)

Arguments

data

Data frame with already classified observations. Each column represents a parameter of the values. The last column contains the output, this means, the expected output when the other column values are inputs. Each row is a different observation.
ClassLabel

String containing the name of the column of the classes we want to classify
p1

Vector containing the parameters of the new value that we want to classify.
d_method

String with the name of the distance method that will be used. It must be one of "Euclidean", "Manhattan", "Cosine", "Chebyshev", "Minkowski", "Canberra", "Octile", "Hamming", "Binary"or "Jaccard". Where both "Hamming" and "Binary" use the same method, as it is known by both names.
k

Number of closest values that will be considered in order to classify the new value ("p1").
p

Exponent used in the Minkowski distance. 3 by default, otherwise if specified.
learn

Boolean value. If it is set to "TRUE" multiple clarifications and explanations are printed along the code
waiting

If TRUE while learn = TRUE. The code will stop in each "block" of code and wait for the user to press "enter" to continue.

Value

Value of the new classified example.

Author(s)

Víctor Amador Padilla, [email protected]

Examples

# example code
knn(db_flowers,"ClassLabel", c(4.7, 1.2, 5.3, 2.1), "chebyshev", 4)
knn(db_flowers,"ClassLabel", c(4.7, 1.5, 5.3, 2.1), "chebyshev", 5)
knn(db_flowers,"ClassLabel", c(6.7, 1.5, 5.3, 2.1), "Euclidean", 2, learn = TRUE, waiting = FALSE)
knn(db_per_or,"y", c(1,1,1), "Hamming", 3, learn = TRUE, waiting = FALSE)

Multivariate Linear Regression

Description

Calculates and plots the linear regression of a given set of values. Being all of them independent values but one, which is the dependent value. It provides information about the process and intermediate values used to calculate the line equation.

Usage

multivariate_linear_regression(data, learn = FALSE, waiting = TRUE)

Arguments

data

x*y data frame with already classified observations. Each column represents a parameter of the values (independent variable). The last column represents the classification value (dependent variable). Each row is a different observation.
learn

Boolean value. If it is set to "TRUE" multiple clarifications and explanations are printed along the code
waiting

If TRUE while learn = TRUE. The code will stop in each "block" of code and wait for the user to press "enter" to continue.

Value

List containing a list for each independent variable, each one contains, the variable name, the intercept and the slope.

Author(s)

Víctor Amador Padilla, [email protected]

Examples

# example code
multivariate_linear_regression(db1rl)

Perceptron

Description

Binary classification algorithm that learns to separate two classes of data points by finding an optimal decision boundary (hyper plane) in the feature space.

Usage

perceptron(
  training_data,
  to_clasify,
  activation_method,
  max_iter,
  learning_rate,
  learn = FALSE,
  waiting = TRUE
)

Arguments

training_data

Data frame with already classified observations. Each column represents a parameter of the values. The last column contains the output, this means, the expected output when the other column values are inputs. Each row is a different observation. It works as training data.
to_clasify

Vector containing the parameters of the new value that we want to classify.
activation_method

Activation function to be used. It must be one of "step", "sine", "tangent", "linear", "relu", "gelu" or "swish".
max_iter

Maximum epoch during the training phase.
learning_rate

Value at which the perceptron will learn from previous epochs mistakes.
learn

Boolean value. If it is set to "TRUE" multiple clarifications and explanations are printed along the code
waiting

If TRUE while learn = TRUE. The code will stop in each "block" of code and wait for the user to press "enter" to continue.

Details

Functioning:

Step 1

Generate a random weight for each independent variable.

Step 2

Check if the weights classify correctly. If they do, go to step 4

Step 3

Adjust weights based on the error between the expected output and the real output. If max_iter is reached go to step 4. If not, go to step 2.

Step 4

Return the weights and use them to classify the new value

Value

List with the weights of the inputs.

Author(s)

Víctor Amador Padilla, [email protected]

Examples

# example code
perceptron(db_per_or, c(1, 1, 1), "gelu", 1000, 0.1)
perceptron(db_per_and, c(0,0,1), "swish", 1000, 0.1, TRUE, FALSE)

Multivariate Polynomial Regression

Description

Calculates and plots the polynomial regression of a given set of values. Being all of them independent values but one, which is the dependent value. It provides (if asked) information about the process and intermediate values used to calculate the line equation. The approximation depends entirely in the degree of the equations.

Usage

polynomial_regression(data, degree, learn = FALSE, waiting = TRUE)

Arguments

data

x*y data frame with already classified observations. Each column represents a parameter of the values (independent variable). The last column represents the classification value (dependent variable). Each row is a different observation.
degree

Degree of the equations approximation.
learn

Boolean value. If it is set to "TRUE" multiple clarifications and explanations are printed along the code
waiting

If TRUE while learn = TRUE. The code will stop in each "block" of code and wait for the user to press "enter" to continue.

Value

List containing a list for each independent variable, each one contains the equation coefficients.

Author(s)

Víctor Amador Padilla, [email protected]

Examples

# example code
polynomial_regression(db1rl,4, TRUE, FALSE)
polynomial_regression(db1rl,6)

Print Tree Structure

Description

This function prints the structure of a tree, generated by the decision_tree function.

Usage

## S3 method for class 'tree_struct'
print(x, ...)

Arguments

x

The tree structure.
...

Extra useless parameters.

Details

It must receive a tree_struct data type.

Value

nothing.

Author(s)

Víctor Amador Padilla, [email protected]

Examples

# example code
print(db_tree_struct)