---
title: "Using Self-Organizing Maps with SOMbrero to cluster a numeric dataset"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document:
    theme: journal
    toc: yes
    toc_float:
      collapsed: no
---

<!--
%\VignetteEngine{knitr}
%\VignetteIndexEntry{SOM for numeric data}
%\VignettePackage{SOMbrero}
-->

To limit this documentation size, most figures are not displayed in the version
of the vignette included in the package. To see all figures, you can rerun
the vignettes, changing the following option to `TRUE`:
```{r setup, include=TRUE}
knitr::opts_chunk$set(include = FALSE)
```

Alternatively, this compilation is also available at:
http://sombrero.nathalievialaneix.eu/articles/c-doc-numericSOM.html


## Basic package description

`SOMbrero` implements different variants of the Self-Organizing Map algorithm
(also called Kohonen's algorithm). To run the standard version of the algorithm,
use the function `trainSOM()` on a data frame or matrix with numerical columns.
The standard numeric SOM and its use in `SOMbrero` are illustrated below.

__This documentation only considers the case of numerical data.__

```{r loading, results='hide', echo=FALSE, warning=FALSE, message=FALSE}
library("ggplot2")
library("SOMbrero")
```

### Arguments

The ```trainSOM``` function has several arguments, but only the first one is
required. This argument is ```x.data``` which is the dataset used to train the 
SOM. In this documentation, it is passed to the function as a matrix or a data
frame with numerical variables in columns and observations of these variables in
rows.

The other arguments are the same as the arguments passed to the ```initSOM```
function (they are parameters defining the algorithm, see ```help(initSOM)```
for further details).

### Outputs

The ```trainSOM``` function returns an object of class ```somRes``` (see 
```help(trainSOM)``` for further details on this class).

### Graphics

The following table indicates which graphics are available for a numeric SOM.

| What <br> <br> SOM or SC<br>Type | SOM<br>Energy  <br> <br> <br>| Obs  <br> <br> <br> <br>| Prototypes  <br> <br> <br> <br>| Add  <br> <br> <br> <br>| SuperCluster<br>(no what) <br> <br> <br> | Obs <br> <br> <br> <br> | Prototypes <br> <br> <br> <br> | Add <br> <br> <br> <br> |
|:------------|:--------|:-----|:------------|:-----|:-------------|:-----|:------------|:-----|
| (no type)   | x       |      |             |      |              |      |             |      |
| hitmap      |         | x    |             |      |              | x    |             |      |
| color       |         | x    | x           | x    |              | x    | x           |      |
| lines       |         | x    | x           | x    |              | x    | x           | x    |
| meanline    |         | x    |             | x    |              | x    |             | x    |
| barplot     |         | x    | x           | x    |              | x    | x           | x    |
| pie         |         |      |             | x    |              |      |             | x    |
| boxplot     |         | x    |             | x    |              | x    |             | x    |
| 3d          |         |      | x           |      |              |      |             |      |
| poly.dist   |         |      | x           |      |              |      | x           |      |
| umatrix     |         |      | x           |      |              |      |             |      |
| smooth.dist |         |      | x           |      |              |      |             |      |
| mds         |         |      | x           |      |              |      | x           |      |
| grid.dist   |         |      | x           |      |              |      |             |      |
| words       |         |      |             | x    |              |      |             |      |
| names       |         | x    |             | x    |              |      |             |      |
| grid        |         |      |             |      |              |      | x           |      |
| dendrogram  |         |      |             |      | x            |      |             |      |
| dendro3d    |         |      |             |      | x            |      |             |      |


## First case study: simulated data in $[0,1]^2$

The first case study shows the clustering of points randomly distributed in the
square $[0,1]^2$. The data are generated by:
```{r dataGeneration}
set.seed(4031719)
the.data <- data.frame("x1" = runif(500), "x2" = runif(500))
ggplot(the.data, aes(x = x1, y = x2)) + geom_point() + theme_bw()
```

### Training the SOM

The numeric SOM algorithm is used to cluster the data:
```{r dataTrain}
set.seed(1105)
# run the SOM algorithm with 10 intermediate backups and 2000 iterations
my.som <- trainSOM(x.data=the.data, dimension=c(5,5), nb.save=10, maxit=2000, 
                   scaling="none", radius.type="letremy", topo="square",
                   dist.type = "letremy")
```

The energy evolves as described in the following graphic:
```{r energy, include=TRUE}
plot(my.som, what="energy")
```

### Clustering

The resulting clustering distribution can be visualized by the hitmap:
```{r hitmapObs, fig.height=6, fig.width=6}
plot(my.som, what = "obs", type = "hitmap")
```

The observations are almost uniformly distributed on the map.

The clustering component allows us to plot the initial data according to the 
final clustering.

```{r clusteredData, include=TRUE, cache=TRUE}
# prepare a vector of colors
my.colors <- rainbow(prod(my.som$parameters$the.grid$dim))[my.som$clustering]

# points depicted with the same color are in the same final cluster
plot(my.som$data[,1], my.som$data[,2], col=my.colors, pch=19, xlab="x1", 
     ylab="x2", main="Data according to final clustering")
```

### Clustering interpretation

The values of the prototypes can be represented with the plot function and help
interpret the clusters:
```{r colorProto, fig.width=5, fig.height=2.5, include=TRUE}
par(mfrow=c(1,2))
plot(my.som, what="prototypes", type="color", var=1)
plot(my.som, what="prototypes", type="color", var=2)
```

Here, the interpretation is simple enough: high values of the first variables x1
are located at the top of the map and small values at the bottom of the map. 
Large values of x2 are located at the right hand side of the map, whereas, small
values are located at the left hand side.

We obtain the same results with a similar plot on the observation mean values:
```{r colorObs, fig.width=5, fig.height=2.5}
par(mfrow=c(1,2))
plot(my.som, what="obs", type="color", var=1)
plot(my.som, what="obs", type="color", var=2)
```

The prototypes coordinates are also registered for each intermediate backup so 
they can be displayed on different graphics to see the evolution in the
prototypes organization.
```{r protoEvoluation, fig.width=15, fig.height=6, include=TRUE, echo=FALSE}
# Get the neighbours between prototypes
values <- protoDist(my.som, "neighbors")
tmp <- data.frame("prot1" = rep.int(1:prod(my.som$parameters$the.grid$dim), 
                                    times=sapply(values, length)), 
                  "nei" = as.numeric(as.character(names(unlist(values)))))
tmp <- tmp[tmp[ ,1] < tmp[ ,2], ]

# plot the prototypes
par(mfrow=c(2, 5),mar=c(3,2,2,1))
invisible(sapply(1:my.som$parameters$nb.save, function(ind){
  plot(my.som$backup$prototypes[[ind]][,1], my.som$backup$prototypes[[ind]][,2],
       xlab="", ylab="", main=c("iteration ", my.som$backup$steps[ind]))
  for (i in 1:nrow(tmp)){
    segments(x0=my.som$backup$prototypes[[ind]][tmp[i,1],1], 
             y0=my.som$backup$prototypes[[ind]][tmp[i,1],2],
             x1=my.som$backup$prototypes[[ind]][tmp[i,2],1], 
             y1=my.som$backup$prototypes[[ind]][tmp[i,2],2], 
             col="red", pch=19)
  }
}))
```

At the begining of the algorithm, the prototypes are randomly distributed in
[0,1]^2 and then, they organize as a regular rectangular grid in $[0,1]^2$.

## Second case study: the iris dataset

This second case study is performed on the famous (Fisher's or Anderson's) iris
data set that gives the measurements in centimeters of the variables sepal
length and width and petal length and width, respectively, for 50 flowers from
each of 3 species of iris (setosa, versicolor, and virginica).

### Training the SOM

__NB: In the following analysis, variables are centered and scaled to unit 
variance, which is the default behavior of the algorithm.__

The first four variables of the data set (that are the numeric variables) are 
used to map each flower on the SOM grid.
```{r irisTrain, cache=TRUE, include=TRUE}
set.seed(255)
# run the SOM algorithm with verbose set to TRUE
iris.som <- trainSOM(x.data = iris[,1:4], dimension = c(5,5), verbose = TRUE, 
                     nb.save = 5, topo = "hexagonal")
iris.som
```

As the energy is registered during the intermediate backups, we can have a look
at its evolution.
```{r energyIris, include=TRUE}
plot(iris.som, what="energy")
```

Here the energy does not stabilize as in the case of ```dist.type="letremy"```
because the Gaussian annealing of the neighbourhood is continuous and not 
stepwise.


### Resulting clustering

The clustering component contains the final classification of the dataset. It is 
a vector with length equal to the number of rows of the input dataset.
```{r irisClusters, include=TRUE}
iris.som$clustering
table(iris.som$clustering)
```
which can also be visualized by a hitmap plot:
```{r irisHitmap, include=TRUE}
plot(iris.som, what="obs", type="hitmap")
```

To assess the relevance of each explanatory variable in the definition of the 
clusters, the function ```summary``` includes an ANOVA with the predictor being
the clustering, for each (numeric) input variable.
```{r irisSummary, include=TRUE}
summary(iris.som)
```
Here, all variables have significantly different means among the different
clusters and can thus be considered to be relevant for the clustering
definition.

Another useful function is ```predict.somRes```. This function predicts the 
neuron to which a new observation would be assigned. The first argument must be 
a ```somRes``` object and the second one the new observation. Let us have a try 
on the first observation of the iris data set:
```{r irisPred1, include=TRUE}
# call predict.somRes
predict(iris.som, iris[1,1:4])
# check the result of the final clustering with the SOM algorithm
iris.som$clustering[1]
```

### Clustering interpretation

#### Graphics common to observations and prototypes

Some graphics are shared between observations and prototypes and can be used to
display the prototypes' or the observations' values for the different variables 
in the neurons of the map. For observations, the mean values are sometimes 
displayed instead of the individual values.

```{r irisGraphOP, include=TRUE}
par(mfrow = c(2,2))
plot(iris.som, what = "obs", type = "color", variable = 1)
plot(iris.som, what = "obs", type = "color", variable = 2)
plot(iris.som, what = "obs", type = "color", variable = 3)
plot(iris.som, what = "obs", type = "color", variable = 4)
```

```{r irisGraphOP2}
plot(iris.som, what = "prototypes", type = "lines", show.names = TRUE) + 
  theme(axis.text.x = element_blank())
plot(iris.som, what = "obs", type = "barplot", show.names = TRUE) + 
  theme(axis.text.x = element_blank())
```

Some neurons are empty (no observations affected to them): neurons 3, 7, 8, 9, 
10, 13, 14, 15, 19, 20, 24. They are the illustration of a large difference 
between observations classified in clusters 4-5 with the rest of the 
observations (to some extend, the same can be said about observations in cluster
25).

Clusters 4-5 are characterized by the following facts (visible on `"color"` 
plots): larger values for `Sepal.Width`, smaller values for `Petal.Length` and
`Petal.Width` and average values for `Sepal.Length`. Similar conclusions are 
obtained from the `"lines"` and `"barplot"` plots. Please note that, for these
two last plots, it is advised to display the neuron numbers (option 
`show.names = TRUE`) because the hexagonal display is not properly rendered in 
them.


#### More graphics on observations

Individual information on observations in clustered can be obtained with the
following plots: 

```{r irisObs, warning=FALSE}
plot(iris.som, what = "obs", type = "boxplot", show.names = TRUE)
plot(iris.som, what = "obs", type = "lines", show.names = TRUE)
plot(iris.som, what = "obs", type = "names", show.names = TRUE)
```

They display either the observation distribution within the cluster for all
the variables in the dataset (for `"boxplot"` and `"lines"`) or the names (row
numbers by default) of the observations classified within the cluster.


#### More graphics on prototypes

Some more graphics handling prototypes have been implemented:

* ```"3d"``` provides the same results as `"color"` but on a 3 dimensional plot:
x is the x dimension of the grid, y is the y dimension of the grid and z is the
value of the prototype for the variable ```variable``` (by name or number in the
dataset) of the corresponding neuron. For the hexagonal topology, the plot is
obtained using a linear interpolation on a regular square grid.

```{r irisProto}
par(mfrow=c(2,2))
plot(iris.som, what = "prototypes", type = "3d", variable = 1)
plot(iris.som, what = "prototypes", type = "3d", variable = 2)
plot(iris.som, what = "prototypes", type = "3d", variable = 3)
plot(iris.som, what = "prototypes", type = "3d", variable = 4)
```

Also, some graphics are provided to visualize the distance between prototypes on
the grid:

```{r irisDistProto, warning=FALSE, include=TRUE}
plot(iris.som, what = "prototypes", type = "poly.dist", show.names = FALSE)
```

```{r irisDistProto2, warning=FALSE}
plot(iris.som, what = "prototypes", type = "umatrix")
plot(iris.som, what = "prototypes", type = "smooth.dist")
plot(iris.som, what = "prototypes", type = "mds")
plot(iris.som, what = "prototypes", type = "grid.dist")
```

* ```"poly.dist"``` represents the distances between neighboring prototypes with
polygons plotted for each cell of the grid. The smaller the distance between 
a polygon's vertex and a cell border, the closer the pair of prototypes.
The colors indicates the number of observations in the neuron (white is used 
for empty neurons);

* ```"umatrix"``` fills the neurons of the grid using colors that represent
the average distance between the current prototype and its neighbors;

* ```"smooth.dist"``` plots the mean distance between the current prototype and 
its neighbors with a color gradation;

* ```mds``` plots the number of the neuron on a map according to a Multi
Dimensional Scaling (MDS) projection;

* ```grid.dist``` plots a point for each pair of prototypes, with x coordinates
representing the distance between the prototypes in the input space, and y
coordinates representing the distance between the corresponding neurons on the
grid.

These graphics show that there is a big gap (large distances) between the top 
left corner and the rest of the map and between the top right corner and the 
rest of the map (which is consistent with what was already observed in the 
previous plots). In addition, the bottom right corner of the maps has a few
neurons (21, 22, 23) that are very closed to each others.


#### Graphics showing an additional variable

##### Additional factor

The clustering can be analyzed together with an additional variable (here, the
flower species) using `what = "add"`:
```{r irisAdd1, include=TRUE}
class(iris$Species)
levels(iris$Species)
plot(iris.som, what = "add", type = "pie", variable = iris$Species) +
  scale_fill_brewer(type = "qual") + 
  guides(fill = guide_legend(title = "Species"))
```

This plot shows that the clustering produced by the SOM is indeed relevant
to identify the three different species of iris: they are well separated on the
map and almost all clusters only contain one species of iris. The Setosa species
is the most distinct from the other two, isolated in the top left corner of the
map.


##### Additional numerical vector

The ```"color"``` plot available for ```"add"``` is similar to the ```"obs"``` 
or ```"prototypes"``` cases. Here we choose the first variable of the iris data 
set as an additional variable to illustrate its use. We thus obtain the same 
plot as above (see section __Graphics common to observations and prototypes__).
```{r irisAdd2}
plot(iris.som, what = "add", type = "color", variable = iris$Sepal.Length, 
     show.names = FALSE)
```

##### Additional numerical matrix or data frame

The ```"lines"```, ```"barplot"```, ```"radar"``` and ```"boxplot"``` plots 
available for ```"add"``` are similar to the ```"obs"``` or ```"prototypes"``` 
cases.

```"words"``` is only implemented for an additional variable. In this case, the 
additional variable must be a contingency matrix: the words used on the plot 
are the names of the columns and the presence or lack of the word is expressed 
by respectively 1 or 0. The size of the words on the grid depends on the rate 
of presence in the observations of the current neuron. To illustrate its use,
we define a contingency table `my.cont.mat` that corresponds to the flower
Species:
```{r irisMatCont, echo=FALSE}
my.cont.mat <- matrix(data=c(rep(c(rep(1,50), rep(0,150)), 2), rep(1,50)), 
                      nrow = 150, ncol = 3)
colnames(my.cont.mat) <- levels(iris$Species)
```

```{r irisAdd4}
head(my.cont.mat)
plot(iris.som, what = "add", type = "words", variable = my.cont.mat, 
     show.names = FALSE)
```

##### Additional non-numerical vector

```"names"``` is similar to the ```"names"``` case implemented for ```"obs"```. 
Here we choose to give the argument ```variable``` the row names of the iris 
data set: so we obtain the same plot as above (see 
__More graphics on observations__).
```{r irisAdd5, warning=FALSE}
plot(iris.som, what = "add", type = "names", variable = rownames(iris)) 
```

Similarly, this plot can be used with the variable ```iris$Species```:
```{r irisAdd5bis, warning=FALSE}
plot(iris.som, what = "add", type = "names", variable = iris$Species)
```

which gives exactly the same plot as before for type ```"words"``` with the 
contingency matrix corresponding to the variable ```iris$Species```.


### Analyze the projection quality

```{r irisQuality, include=TRUE}
quality(iris.som)
```

```{r saveQual, echo=FALSE}
qualities <- quality(iris.som)
```

By default, the quality function calculates both quantization and topographic 
errors. It is also possible to specify which one you want using the 
argument ```quality.type```.

The topographic error value varies between 0 (good projection quality) and 1 
(poor projection quality). Here, the topographic quality of the mapping is 
equal to `r round(qualities$topographic, 2)`, which means that around
`r round(qualities$topographic * 100, 1)`% of the observations have a second 
best unit in the neighborhood of the best matching unit.

The quantization error is an unbounded positive number. The closer it is to 0,
the better the projection quality.


### Building super classes from the resulting SOM

In the SOM algorithm, the number of clusters is necessarily close to the number 
of neurons on the grid (not necessarily equal as some neurons may have no 
observations assigned to them). This - quite large - number may not suit the 
original data for a clustering purpose.

A usual way to address clustering with SOM is to perform a hierarchical
clustering on the prototypes. This clustering is directly available in the
package ```SOMbrero``` using the function ```superClass```. To do so, you can
first have a quick overview to decide on the number of super clusters which 
suits your data.
```{r irisSC, include=TRUE}
plot(superClass(iris.som))
```

By default, the function plots both a dendrogram and the evolution of the
percentage of explained variance. Here, 3 super clusters seem to be the best
choice. The output of ```superClass``` is a ```somSC``` class object.
Basic functions have been defined for this class:
```{r irisSC3, include=TRUE}
my.sc <- superClass(iris.som, k = 3)
summary(my.sc)
plot(my.sc, plot.var = FALSE)
```

Like ```plot.somRes```, the function ```plot.somSC``` has an argument ```type```
which offers many different plots and can thus be combined with most of the
graphics produced by ```plot.somSC```:
```{r irisSCplot, fig.width=6, fig.height=4, include=TRUE}
plot(my.sc, type = "grid")
```
```{r irisSCplot3d}
plot(my.sc, type = "dendro3d")
```

Case ```"grid"``` fills the grid with colors according to the super clustering 
(and can provide a legend).
Case ```"dendro3d"``` plots a 3d dendrogram.

A couple of plots from ```plot.somRes``` are also available for the super 
clustering. Some identify the super clusters with colors:
```{r irisSCplot2, fig.width=6, fig.height=5}
plot(my.sc, what = "obs", type = "hitmap", maxsize = 20)
```

```{r irisSCplot2B}
plot(my.sc, what = "prototypes", type = "lines")
plot(my.sc, what = "prototypes", type = "barplot")
```

```{r irisSCplot2C, fig.height=4, fig.width=6}
plot(my.sc, what = "prototypes", type = "mds")
```

And some others identify the super clusters with titles:
```{r irisSCplot3, include=TRUE}
plot(my.sc, what = "prototypes", type = "color", variable = "Sepal.Length")
plot(my.sc, what = "prototypes", type = "poly.dist")
```

It is also possible to consider an additional variable using the 
argument ```what='add'```:
```{r irisSCplot4}
plot(my.sc, what = "add", type = "pie", variable = iris$Species) +
  scale_fill_brewer(type = "qual")
plot(my.sc, what = "add", type = "color", variable = iris$Sepal.Length)
```

Super cluster number 2 is located at the top left hand corner of the map and 
associated with the Setosa species. SC 3 is in the opposite corner and 
associated mainly with Virginica whereas SC 1 is in the diagonal between these
two corners and associated with Versicolor.


### Session information

This vignette has been computed with the following environment:
```{r sessionInfo, include=TRUE}
sessionInfo()
```