---
title: "Toy example interpreting the co-ranking Matrix"
author: "Guido Kraemer"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup}
library(knitr)
library(scatterplot3d)
library(Rtsne)
library(coRanking)
npoints <- 1000

theta <- runif(npoints, 0, 2 *pi)
u <- runif(npoints, -1, 0.8)

data <- list()
data$x <- sqrt(1-u^2)*cos(theta)
data$y <- sqrt(1-u^2)*sin(theta)
data$z <- u
data$col <- rgb(colorRamp(colors = c('red','yellow','green'))((data$z+1)/2),maxColorValue = 255)
data <- as.data.frame(data, stringsAsFactors = F)
```

# Introduction
 
The co-ranking matrix is a tool to assess the quality of a dimensionality reduciton.

The fishbowl data set consists of a sphere with a hole on top:
```{r, fig.width = 10, fig.height = 10, out.width = "95%"}
scatterplot3d(data$x, data$y, data$z,
              xlab = "x", ylab = "y", zlab = "z",
              color = data$col)
 ```
 
Dimensionality reductions:
```{r, fig.show='hold', fig.width = 7, fig.height = 7, out.width = '45%'}
dim.red <- list()
## dim.red$isomap <- isomap(dist(data[c('x','y','z')]), k = 20)
## dim.red$kpca <- kpca(~x+y+z, data)
dim.red$tsne <- Rtsne(data[c('x','y','z')])
dim.red$pca <- princomp(data[c('x','y','z')])
##plot(dim.red$isomap$points, col = data$col)
## plot(rotated(dim.red$kpca), col = data$col)
plot(dim.red$tsne$Y, col = data$col,
     xlab = 'tsne I', ylab = 'tsne II',
     main = 't-SNE')
plot(dim.red$pca$scores, col = data$col,
     xlab = 'PCA I', ylab = 'PCA II',
     main = 'PCA')
```

the corresponding co-ranking matrices are:

```{r, fig.show='hold', fig.height = 7, fig.width = 7, out.width = "45%"}
Q.tsne <- coranking(data[c('x','y','z')],dim.red$tsne$Y)
Q.pca <- coranking(data[c('x','y','z')],dim.red$pca$scores[,1:2])
imageplot(Q.tsne, main = 't-SNE')
imageplot(Q.pca, main = 'PCA')
```	

As one can observe, the t-SNE *tears* the data, points that were
close are far apart, e.g. the upper border of the fish bowl. This is
reflected in the co-ranking matrix in higher values in the upper right
part.
The PCA *crushes* the points, the data set gets flattened and points
that were far away are very close now, which manifests in more values
in the lower left part of the co-ranking matrix.

t-SNE also tends to keep the smaller distances between points,
i.e. values close to the diagonal are higher in the upper left part, while
PCA keeps the larger distances, i.e. the values close to the diagonal
on the lower right are higher.
	
# A parameter free quality measure for dimensionality reduction
Defined by Chen and Buja (2006). A general quality measure depending on $K$
could be defined as:

$$\text{LCMC}(K) = \frac {1}{KN} \sum_{i,j\in {1, \ldots, K}} q_{i,j} - \frac{K}{N-1}$$

```{r, fig.show='hold', fig.width = 7, fig.height = 7, out.width = "45%"}
lcmc.tsne <- numeric(nrow(Q.tsne))
lcmc.pca <- numeric(nrow(Q.tsne))
lcmc.tsne <- LCMC(Q.tsne)
lcmc.pca <- LCMC(Q.pca)
Kmax.tsne <- which.max(lcmc.tsne)
Kmax.pca <- which.max(lcmc.pca)
plot(lcmc.tsne, main = "t-SNE", xlab = "K", ylab = "LCMC", type = "l")
plot(lcmc.pca, main = "PCA", xlab = "K", ylab = "LCMC", type = "l")
```

$Q_\text{local}$ is the mean over the values left of the maximum,
$Q_\text{global}$ is the mean over the values right of the maximum.

* *t-SNE*: 
	+ $K_\text{max}    = `r Kmax.tsne`$
	+ $Q_\text{local}  = `r mean(lcmc.tsne[1:Kmax.tsne])`$
	+ $Q_\text{global} = `r mean(lcmc.tsne[(Kmax.tsne+1):nrow(Q.tsne)])`$
* *PCA*: 
	+ $K_\text{max}    = `r Kmax.pca`$
	+ $Q_\text{local}  = `r mean(lcmc.pca[1:Kmax.pca])`$
	+ $Q_\text{global} = `r mean(lcmc.pca[(Kmax.pca+1):nrow(Q.pca)])`$
	
$Q_\text{local}$ is considered to be more important than $Q_\text{global}$.
