入門機械学習読書会9章 MDS

130824_ML_9

set.seed(851982) 
ex.matrix <- matrix(sample(c(-1, 0, 1), 24, replace = TRUE),
                    nrow = 4,
                    ncol = 6)
row.names(ex.matrix) <- c('A', 'B', 'C', 'D')
colnames(ex.matrix) <- c('P1', 'P2', 'P3', 'P4', 'P5', 'P6')

ex.matrix
ex.mult <- ex.matrix %*% t(ex.matrix)
ex.mult

ex.dist <- dist(ex.mult)
ex.dist

ex.mds <- cmdscale(ex.dist)
plot(ex.mds, type = 'n')
text(ex.mds, c('A', 'B', 'C', 'D'))

library('foreign')
library('ggplot2')
library(gridExtra)

setwd("/Users/kzfm/lang/rcode/ML_for_Hackers/09-MDS/")
data.dir <- file.path("data", "roll_call")
data.files <- list.files(data.dir)

data.files

rollcall.data <- lapply(data.files,
                        function(f)
                        {
                          read.dta(file.path(data.dir, f), convert.factors = FALSE)
                        })

dim(rollcall.data[[1]])

rollcall.simplified <- function(df)
{
  no.pres <- subset(df, state < 99)
  
  for(i in 10:ncol(no.pres))
  {
    no.pres[,i] <- ifelse(no.pres[,i] > 6, 0, no.pres[,i])
    no.pres[,i] <- ifelse(no.pres[,i] > 0 & no.pres[,i] < 4, 1, no.pres[,i])
    no.pres[,i] <- ifelse(no.pres[,i] > 1, -1, no.pres[,i])
  }
  
  return(as.matrix(no.pres[,10:ncol(no.pres)]))
}

rollcall.simple <- lapply(rollcall.data, rollcall.simplified)

rollcall.dist <- lapply(rollcall.simple, function(m) dist(m %*% t(m)))

rollcall.mds <- lapply(rollcall.dist,
                       function(d) as.data.frame((cmdscale(d, k = 2)) * -1))

congresses <- 101:111

for(i in 1:length(rollcall.mds))
{
  names(rollcall.mds[[i]]) <- c("x", "y")
  congress <- subset(rollcall.data[[i]], state < 99)
  congress.names <- sapply(as.character(congress$name),
                           function(n) strsplit(n, "[, ]")[[1]][1])
  rollcall.mds[[i]] <- transform(rollcall.mds[[i]],
                                 name = congress.names,
                                 party = as.factor(congress$party),
                                 congress = congresses[i])
}

cong.110 <- rollcall.mds[[9]]

base.110 <- ggplot(cong.110, aes(x = x, y = y)) +
  scale_size(range = c(2,2), guide = "none") +
  scale_alpha(guide = "none") + theme_bw() +
  labs(title = "Roll Call Vote MDS Clustering for 110th U.S. Senate") +
  theme(axis.ticks = element_blank(),
       axis.text.x = element_blank(),
       axis.text.y = element_blank(),
       panel.grid.major = element_blank()) +
  xlab("") +
  ylab("") +
  scale_shape(name = "Party", breaks = c("100", "200", "328"),
              labels = c("Dem.", "Rep.", "Ind."), solid = FALSE) +
  scale_color_manual(name = "Party", values = c("100" = "black",
                                                "200" = "dimgray",
                                                "328"="grey"),
                     breaks = c("100", "200", "328"),
                     labels = c("Dem.", "Rep.", "Ind."))

g1 <- base.110 + geom_point(aes(shape = party, alpha = 0.75, size = 2))
g2 <- base.110 + geom_text(aes(shape = party, alpha = 0.75, size = 2, label = cong.110$name))

grid.arrange(g1, g2, ncol=2, nrow=1)

all.mds <- do.call(rbind, rollcall.mds)
all.plot <- ggplot(all.mds, aes(x = x, y = y)) +
  geom_point(aes(shape = party, alpha = 0.75, size = 2)) +
  scale_size(range = c(2, 2), guide = "none") +
  scale_alpha(guide = "none") +
  theme_bw() +
  labs(title="Roll Call Vote MDS Clustering for U.S. Senate (101st - 111th Congress)") +
  theme(axis.ticks = element_blank(),
       axis.text.x = element_blank(),
       axis.text.y = element_blank(),
       panel.grid.major = element_blank()) +
  xlab("") +
  ylab("") +
  scale_shape(name = "Party",
              breaks = c("100", "200", "328"),
              labels = c("Dem.", "Rep.", "Ind."),
              solid = FALSE) +
  facet_wrap(~ congress)

print(all.plot)