• 1 Classifiers
    • 1.1 Self Learning
    • 1.2 Expectation Maximization
    • 1.3 Least Squares Classifier
    • 1.4 Linear Discriminant Analysis
    • 1.5 Nearest Mean Classifier
    • 1.6 Logistic Regression
  • 2 Measures
  • 3 Artificial datasets
    • 3.1 2ClassGaussian
    • 3.2 2ClassGaussian (alt)
    • 3.3 ABA
    • 3.4 CrescentMoon
    • 3.5 FourClusters
    • 3.6 FourClusters (alt)
    • 3.7 ParallelPlanes
    • 3.8 SlicedCookie
    • 3.9 SlicedCookie (alt)
    • 3.10 TwoCircles
    • 3.11 Spirals
  • 4 Real datasets
    • 4.1 Iris
    • 4.2 Spambase

Semi-Supervised Learning

RSSL provides implementations of several semi-supervised learning methods. The code can be found on Github. It is described in the following paper: arXiv. More R libraries are used in this notebook for visualization.

library(RSSL)
library(ggplot2)
library(grid)
library(gridExtra)
library(plot3D)
set.seed(1)

1 Classifiers

There are many classifiers available in the package. The main types are defined below: Least Squares Classifier, Linear Discriminant Analysis, Nearest Mean Classifier and Logistic Regression. They are used together with Self Learning and Expectation Maximization. classifiers is a list of two classifiers, with one supervised classifier and one self learning classifier. Each classifier is a function that accepts 4 arguments: a numeric design matrix of the labeled objects, a factor of labels, a numeric design matrix of unlabeled objects and a factor of labels for the unlabeled objects.

Classifiers available in RSSL

1.1 Self Learning

Self Learning

1.2 Expectation Maximization

Expectation Maximization

1.3 Least Squares Classifier

Least Squares Classifier

classifiers_LS <- list(
    "LS" = function(X, y, X_u, y_u) {
        LeastSquaresClassifier(X, y, lambda = 0)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LeastSquaresClassifier)
    },
    "EM" = function(X, y, X_u, y_u) {
        EMLeastSquaresClassifier(X, y, X_u)
    }
)

1.4 Linear Discriminant Analysis

Linear Discriminant Analysis

classifiers_LD <- list(
    "LD" = function(X, y, X_u, y_u) {
        LinearDiscriminantClassifier(X, y)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LinearDiscriminantClassifier)
    },
    "EM" = function(X, y, X_u, y_u) {
        EMLinearDiscriminantClassifier(X, y, X_u)
    }
)

1.5 Nearest Mean Classifier

Nearest Mean Classifier

classifiers_NM <- list(
    "NM" = function(X, y, X_u, y_u) {
        NearestMeanClassifier(X, y)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, NearestMeanClassifier)
    },
    "EM" = function(X, y, X_u, y_u) {
        EMNearestMeanClassifier(X, y, X_u)
    }
)

1.6 Logistic Regression

Logistic Regression

classifiers_LR <- list(
    "LR" = function(X, y, X_u, y_u) {
        LogisticRegression(X, y)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LogisticRegression)
    }
)

2 Measures

There are five performance measures available: accuracy, error, test loss, labeled loss and train loss. measures is a list of performance measures that we want to select. Our aim is to improve the accuracy on the test set (or reduce the error). We can look at the losses of each split to know how the model is learning. The time is also an important measure when working with big datasets.

Measures

measures <- list(
    "Accuracy" = measure_accuracy,
    "Error" = measure_error,
    "Loss Test" = measure_losstest,
    "Loss Labeled" = measure_losslab,
    "Loss Train" = measure_losstrain
)

3 Artificial datasets

RSSL offers many artificial datasets. In the next sections I will generate and visualize them.

Simulated Datasets. Each can be generated using a function of the form generateDataset, where Dataset should be replaced by the name of the dataset. (alt) indicates non-default parameters were used when calling the function.

3.1 2ClassGaussian

data <- generate2ClassGaussian(2000, d = 2, var = 0.6, expected = TRUE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

LearningCurveSSL evaluates semi-supervised classifiers for different amounts of unlabeled training examples or different fractions of unlabeled vs. labeled examples. This function allows for two different types of learning curves to be generated.

If type="unlabeled", the number of labeled objects remains fixed at the value of n_l, where sizes controls the number of unlabeled objects. n_test controls the number of objects used for the test set, while all remaining objects are used if with_replacement=FALSE in which case objects are drawn without replacement from the input dataset. We make sure each class is represented by at least n_min labeled objects of each class. For n_l, additional options include: “enough” which takes the max of the number of features and 20, max(ncol(X)+5,20), “d” which takes the number of features or “2d” which takes 2 times the number of features.

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "unlabeled", n_l = "enough", repeats = 3
)

plot(lc)

If type="fraction" the total number of objects remains fixed, while the fraction of labeled objects is changed. frac sets the fractions of labeled objects that should be considered, while test_fraction determines the fraction of the total number of objects left out to serve as the test set.

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LD, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_NM, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LR, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.2 2ClassGaussian (alt)

data <- generate2ClassGaussian(2000, d = 2, var = 0.6, expected = FALSE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.3 ABA

data <- generateABA(2000, d = 2, var = 0.6)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.4 CrescentMoon

data <- generateCrescentMoon(150, 2, 1)
plot(data$X1, data$X2, col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 2:3]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.5 FourClusters

data <- generateFourClusters(1000, distance = 6, expected = TRUE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.6 FourClusters (alt)

data <- generateFourClusters(1000, distance = 6, expected = FALSE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.7 ParallelPlanes

classifiers_LS_SL <- list(
    "LS" = function(X, y, X_u, y_u) {
        LeastSquaresClassifier(X, y, lambda = 0)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LeastSquaresClassifier)
    }
)
data <- generateParallelPlanes(100, 3)
plot(data[, 1], data[, 2], col = data$Class)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS_SL, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.8 SlicedCookie

data <- generateSlicedCookie(1000, expected = TRUE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.9 SlicedCookie (alt)

data <- generateSlicedCookie(1000, expected = FALSE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.10 TwoCircles

data <- generateTwoCircles(n = 100, noise_var = 0.2)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

3.11 Spirals

data <- generateSpirals(100, sigma = 0.1)
plot3D::scatter3D(data$x, data$y, data$z, col = data$Class)
Warning in `[<-.factor`(`*tmp*`, is.na(Col), value = "white") :
  invalid factor level, NA generated

lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)

4 Real datasets

4.1 Iris

Iris

data("iris")
iris[sample(nrow(iris), 10), ]
ABCDEFGHIJ0123456789
 
 
Sepal.Length
<dbl>
Sepal.Width
<dbl>
Petal.Length
<dbl>
Petal.Width
<dbl>
Species
<fctr>
484.63.21.40.2setosa
1005.72.84.11.3versicolor
455.13.81.90.4setosa
1336.42.85.62.2virginica
825.52.43.71.0versicolor
215.43.41.70.2setosa
995.12.53.01.1versicolor
15.13.51.40.2setosa
915.52.64.41.2versicolor
605.22.73.91.4versicolor
1-10 of 10 rows
pairs(iris[1:4], main = "Iris Scatter Plots", pch = 21, bg = c("red", "green3", "blue")[unclass(iris$Species)])

BpSl <- ggplot(iris, aes(Species, Sepal.Length, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Sepal Length (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

BpSw <- ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Sepal Width (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

BpPl <- ggplot(iris, aes(Species, Petal.Length, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Petal Length (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

BpPw <- ggplot(iris, aes(Species, Petal.Width, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Petal Width (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

# Plot all visualizations
grid.arrange(BpSl + ggtitle(""),
    BpSw + ggtitle(""),
    BpPl + ggtitle(""),
    BpPw + ggtitle(""),
    nrow = 2,
    top = textGrob("Iris Box Plots",
        gp = gpar(fontsize = 15)
    )
)

classifiers_LS_SL <- list(
    "LS" = function(X, y, X_u, y_u) {
        LeastSquaresClassifier(X, y, lambda = 0)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LeastSquaresClassifier)
    }
)
lc_iris <- LearningCurveSSL(as.matrix(iris[1:4]), iris$Species,
    classifiers = classifiers_LS_SL, measures = measures,
    type = "fraction", fracs = seq(0.1, 0.8, 0.1),
    test_fraction = 0.5, repeats = 3
)

plot(lc_iris)

4.2 Spambase

Spam email Non spam email

spambase <- read.csv("../data/spambase.csv", header = TRUE, sep = ",")
spambase$class <- as.factor(spambase$class)
spambase[sample(nrow(spambase), 10), ]
ABCDEFGHIJ0123456789
 
 
word_freq_make
<dbl>
word_freq_address
<dbl>
word_freq_all
<dbl>
word_freq_3d
<dbl>
word_freq_our
<dbl>
43450.000.600.0000.60
41160.000.000.0000.00
44870.000.620.6200.00
44470.000.000.6800.68
5210.000.000.0000.00
11410.180.000.5401.09
5510.000.000.4000.40
45050.000.000.6400.00
22520.090.000.1900.09
5900.000.000.0000.00
1-10 of 10 rows | 1-6 of 58 columns
lc_spambase <- LearningCurveSSL(as.matrix(spambase[1:57]), spambase$class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", fracs = seq(0.1, 0.8, 0.1),
    test_fraction = 0.5, repeats = 3
)

plot(lc_spambase)

---
title: 'RSSL: Semi-Supervised Learning in R'
output:
  html_notebook: 
    toc: yes
    toc_float: yes
    number_sections: yes
---

![Semi-Supervised Learning](../images/semi_supervised_learning.png)

RSSL provides implementations of several semi-supervised learning methods. The code can be found on [Github](https://github.com/jkrijthe/RSSL). It is described in the following paper: [arXiv](https://arxiv.org/pdf/1612.07993.pdf). More R libraries are used in this notebook for visualization.

```{r}
library(RSSL)
library(ggplot2)
library(grid)
library(gridExtra)
library(plot3D)
set.seed(1)
```

# Classifiers

There are many classifiers available in the package. The main types are defined below: Least Squares Classifier, Linear Discriminant Analysis, Nearest Mean Classifier and Logistic Regression. They are used together with Self Learning and Expectation Maximization. `classifiers` is a list of two classifiers, with one supervised classifier and one self learning classifier. Each
classifier is a function that accepts 4 arguments: a
numeric design matrix of the labeled objects, a factor of labels,
a numeric design matrix of unlabeled objects and a factor of
labels for the unlabeled objects.

![Classifiers available in RSSL](../images/classifiers.png)

## Self Learning

![Self Learning](../images/SL.png)

## Expectation Maximization

![Expectation Maximization](../images/EM.png)

## Least Squares Classifier

![Least Squares Classifier](../images/LS.png)

```{r}
classifiers_LS <- list(
    "LS" = function(X, y, X_u, y_u) {
        LeastSquaresClassifier(X, y, lambda = 0)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LeastSquaresClassifier)
    },
    "EM" = function(X, y, X_u, y_u) {
        EMLeastSquaresClassifier(X, y, X_u)
    }
)
```

## Linear Discriminant Analysis

![Linear Discriminant Analysis](../images/LD.jpg)

```{r}
classifiers_LD <- list(
    "LD" = function(X, y, X_u, y_u) {
        LinearDiscriminantClassifier(X, y)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LinearDiscriminantClassifier)
    },
    "EM" = function(X, y, X_u, y_u) {
        EMLinearDiscriminantClassifier(X, y, X_u)
    }
)
```


## Nearest Mean Classifier

![Nearest Mean Classifier](../images/NM.png)

```{r}
classifiers_NM <- list(
    "NM" = function(X, y, X_u, y_u) {
        NearestMeanClassifier(X, y)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, NearestMeanClassifier)
    },
    "EM" = function(X, y, X_u, y_u) {
        EMNearestMeanClassifier(X, y, X_u)
    }
)
```

## Logistic Regression

![Logistic Regression](../images/LR.jpg)

```{r}
classifiers_LR <- list(
    "LR" = function(X, y, X_u, y_u) {
        LogisticRegression(X, y)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LogisticRegression)
    }
)
```

# Measures

There are five performance measures available: accuracy, error, test loss, labeled loss and train loss. `measures` is a list of performance measures that we want to select. Our aim is to improve the accuracy on the test set (or reduce the error). We can look at the losses of each split to know how the model is learning. The time is also an important measure when working with big datasets.

![Measures](../images/measures.png)

```{r}
measures <- list(
    "Accuracy" = measure_accuracy,
    "Error" = measure_error,
    "Loss Test" = measure_losstest,
    "Loss Labeled" = measure_losslab,
    "Loss Train" = measure_losstrain
)
```

# Artificial datasets

RSSL offers many artificial datasets. In the next sections I will generate and visualize them.

![Simulated Datasets. Each can be generated using a function of the form
generateDataset, where Dataset should be replaced by the name of the dataset. (alt) indicates
non-default parameters were used when calling the function.](../images/artificial_datasets.png)

## 2ClassGaussian

```{r}
data <- generate2ClassGaussian(2000, d = 2, var = 0.6, expected = TRUE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

`LearningCurveSSL` evaluates semi-supervised classifiers for different amounts of
unlabeled training examples or different fractions of unlabeled
vs. labeled examples. This function allows for two different types of learning curves to
be generated. 

If `type="unlabeled"`, the number of labeled objects
remains fixed at the value of `n_l`, where `sizes` controls the
number of unlabeled objects. `n_test` controls the number of
objects used for the test set, while all remaining objects are
used if `with_replacement=FALSE` in which case objects are drawn
without replacement from the input dataset. We make sure each
class is represented by at least `n_min` labeled objects of each
class. For `n_l`, additional options include: "enough" which takes
the max of the number of features and 20, max(ncol(X)+5,20), "d"
which takes the number of features or "2d" which takes 2 times the
number of features.

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "unlabeled", n_l = "enough", repeats = 3
)

plot(lc)
```

If `type="fraction"` the total number of objects remains fixed,
while the fraction of labeled objects is changed. `frac` sets the
fractions of labeled objects that should be considered, while
`test_fraction` determines the fraction of the total number of
objects left out to serve as the test set.

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LD, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_NM, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LR, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## 2ClassGaussian (alt)

```{r}
data <- generate2ClassGaussian(2000, d = 2, var = 0.6, expected = FALSE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```


## ABA

```{r}
data <- generateABA(2000, d = 2, var = 0.6)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## CrescentMoon

```{r}
data <- generateCrescentMoon(150, 2, 1)
plot(data$X1, data$X2, col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 2:3]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## FourClusters

```{r}
data <- generateFourClusters(1000, distance = 6, expected = TRUE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## FourClusters (alt)

```{r}
data <- generateFourClusters(1000, distance = 6, expected = FALSE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## ParallelPlanes

```{r}
classifiers_LS_SL <- list(
    "LS" = function(X, y, X_u, y_u) {
        LeastSquaresClassifier(X, y, lambda = 0)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LeastSquaresClassifier)
    }
)
```

```{r}
data <- generateParallelPlanes(100, 3)
plot(data[, 1], data[, 2], col = data$Class)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS_SL, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## SlicedCookie

```{r}
data <- generateSlicedCookie(1000, expected = TRUE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## SlicedCookie (alt)

```{r}
data <- generateSlicedCookie(1000, expected = FALSE)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## TwoCircles

```{r}
data <- generateTwoCircles(n = 100, noise_var = 0.2)
plot(data[, 1], data[, 2], col = data$Class, asp = 1)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

## Spirals

```{r}
data <- generateSpirals(100, sigma = 0.1)
plot3D::scatter3D(data$x, data$y, data$z, col = data$Class)
```

```{r}
lc <- LearningCurveSSL(as.matrix(data[, 1:2]),
    data$Class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", test_fraction = 0.5, repeats = 3
)

plot(lc)
```

# Real datasets

## Iris

![Iris](../images/iris.png)

```{r}
data("iris")
iris[sample(nrow(iris), 10), ]
```

```{r}
pairs(iris[1:4], main = "Iris Scatter Plots", pch = 21, bg = c("red", "green3", "blue")[unclass(iris$Species)])
```

```{r}
BpSl <- ggplot(iris, aes(Species, Sepal.Length, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Sepal Length (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

BpSw <- ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Sepal Width (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

BpPl <- ggplot(iris, aes(Species, Petal.Length, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Petal Length (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

BpPw <- ggplot(iris, aes(Species, Petal.Width, fill = Species)) +
    geom_boxplot() +
    scale_y_continuous("Petal Width (cm)", breaks = seq(0, 30, by = .5)) +
    theme(legend.position = "none")

# Plot all visualizations
grid.arrange(BpSl + ggtitle(""),
    BpSw + ggtitle(""),
    BpPl + ggtitle(""),
    BpPw + ggtitle(""),
    nrow = 2,
    top = textGrob("Iris Box Plots",
        gp = gpar(fontsize = 15)
    )
)
```

```{r}
classifiers_LS_SL <- list(
    "LS" = function(X, y, X_u, y_u) {
        LeastSquaresClassifier(X, y, lambda = 0)
    },
    "SL" = function(X, y, X_u, y_u) {
        SelfLearning(X, y, X_u, LeastSquaresClassifier)
    }
)
```

```{r}
lc_iris <- LearningCurveSSL(as.matrix(iris[1:4]), iris$Species,
    classifiers = classifiers_LS_SL, measures = measures,
    type = "fraction", fracs = seq(0.1, 0.8, 0.1),
    test_fraction = 0.5, repeats = 3
)

plot(lc_iris)
```

## Spambase

![Spam email](../images/spam.png)
![Non spam email](../images/nonspam.png)

```{r}
spambase <- read.csv("../data/spambase.csv", header = TRUE, sep = ",")
spambase$class <- as.factor(spambase$class)
spambase[sample(nrow(spambase), 10), ]
```

```{r}
lc_spambase <- LearningCurveSSL(as.matrix(spambase[1:57]), spambase$class,
    classifiers = classifiers_LS, measures = measures,
    type = "fraction", fracs = seq(0.1, 0.8, 0.1),
    test_fraction = 0.5, repeats = 3
)

plot(lc_spambase)
```
