library(largeVis)
library(ggplot2)
library(dplyr)

#setwd("D:/heal/documents/trunk/Publications/2018/GPTP/data");
setwd("C:/reps/HEAL/Publications-2018-GPTP/data");
sentenceFileName <- "evaluations_allSentences_2018-04-13_16-40_TreeSize-7_1d.csv.gz";

# read from CSV and store as R binary (must be done once to produce the .rds file)
#evalData <- read.csv(sentenceFileName,header = TRUE, sep = ";", dec=",");
#saveRDS(evalData, "evaluations_allSentences_2018-04-13_16-40_TreeSize-7_1d.csv.rds");

# read from R binary (faster)
evalData <- readRDS("evaluations_allSentences_2018-04-13_16-40_TreeSize-7_1d.csv.rds");

max(evalData$R2.keijzer4);
max(evalData$R2.keijzer9);
max(evalData$R2.pagie);
max(evalData$R2.nguyen5);
max(evalData$R2.nguyen6);
max(evalData$R2.nguyen7);

outputs <- evalData[,10:109];

#check zero mean, unit variance
#mean(t(outputs[2,]))
#sd(t(outputs[2,]))

# check
# plot(t(outputs[4,]))

#apprNN <- randomProjectionTreeSearch(t(outputs), K=100, n_trees=50, distance_method="Euclidean", verbose=TRUE)
# check ANN
#cluster_1 <- tidyr::gather(dplyr::tbl_df(t(outputs)[,apprNN[,5]]), "rowNum", "value");
#xs <- rep(seq(1:100),100)
#ggplot(cluster_1, aes(x=xs, y=value, c=rowNum)) + geom_line();

#edgeMatrix <- buildEdgeMatrix(t(outputs),apprNN, verbose=TRUE);
#clusters <- hdbscan(edgeMatrix, apprNN, minPts = 10, K = 5, verbose=TRUE);

# check cluster
#cluster_1 <- tidyr::gather(dplyr::tbl_df(t(outputs)[,!is.na(clusters$clusters) & clusters$clusters==3]), "rowNum", "value");
#xs <- rep(seq(1:100),nrow(cluster_1)/100) # reps must be the number of functions in the cluster
#ggplot(cluster_1, aes(x=xs, y=value, c=rowNum)) + geom_line();


lv <- largeVis(t(outputs), dim=2, distance_method="Cosine",
               perplexity=100, K = 100, n_trees = 150, threads=4, 
               save_neighbors = TRUE, save_edges = TRUE, verbose=TRUE) ;
clusters <- hdbscan(lv, verbose=TRUE, threads=4, minPts = 10, K = 20);


# calculate quality distribution for each cluster
qualities <- evalData$R2.keijzer4;
clusterQualities <- data.frame(Qualities = qualities, Clusters = clusters$clusters, x=t(lv$coords)[,1], y=t(lv$coords)[,2] );

clusterQualityAvg <- clusterQualities %>% group_by(Clusters) %>% summarize(AvgQuality = mean(Qualities)) ;
clusterQualityStdDev <- clusterQualities %>% group_by(Clusters) %>% summarize(StdDevQuality = sd(Qualities));
clusterQualityCount <- clusterQualities %>% group_by(Clusters) %>% summarize(Count = n());
clusterXCenter <- clusterQualities %>% group_by(Clusters) %>% summarize(meanX = mean(x));
clusterYCenter <- clusterQualities %>% group_by(Clusters) %>% summarize(meanY = mean(y));
clusterStats <- clusterQualityAvg %>% full_join(clusterQualityStdDev, by="Clusters") %>% full_join(clusterQualityCount, by="Clusters") %>% full_join(clusterXCenter, by ="Clusters") %>% full_join(clusterYCenter, by="Clusters");
clusterStats <- dplyr::arrange(clusterStats, desc(AvgQuality));
clusterStats$Rank <- seq(1:nrow(clusterStats));
ggplot(clusterStats, aes(x = Rank, y=AvgQuality)) + geom_point();

write.csv2(clusters$clusters, "cluster_assignment_new.csv", sep = " ", dec = ".");

#check clusters
for(i in seq(1:nrow(clusterStats))) {
  clusterNumber <- clusterStats$Clusters[i] # number of cluster with smallest quality (error!)
  cluster_i <- tidyr::gather(dplyr::tbl_df(t(outputs)[,!is.na(clusters$clusters) & clusters$clusters==clusterNumber]), "rowNum", "value");
  xs <- rep(seq(1:100),nrow(cluster_i)/100) # reps must be the number of functions in the cluster
  ggplot(cluster_i, aes(x=xs, y=value, c=rowNum)) +
    theme_void() +
    geom_line(alpha=0.1);
  
  ggsave(paste(as.character(i), as.character(round(clusterStats$meanX[i], 3)), as.character(round(clusterStats$meanY[i], 3)), ".png"));
}

funs_in_cluster <- t(outputs)[,!is.na(clusters$clusters) & clusters$clusters==1748]
cor(method="pearson", target_keijzer4, t(outputs[20281, ]))
plot(funs_in_cluster[,4], target_keijzer4)

xi <- seq(0,9.99,0.1);
#  x³  * exp(-x) * cos(x) * sin(x) * (sin(x)² * cos(x) - 1)
target_keijzer4 <- xi^3 * exp(-xi) * cos(xi) * sin(xi) * (sin(xi)*sin(xi) * cos(xi) - 1);
plot(xi, target_keijzer4);

m <- data.frame(x=t(lv$coords)[,1], y=t(lv$coords)[,2], c=clusters$clusters, q=qualities, outputs)
m_sub <- m[m$q<1.0,];

# plot mapped points (clusters)
ggplot(data=m, aes(x=x, y=y)) +
  geom_point(aes(color=c))  +
  theme(legend.position = "none")
#  scale_color_gradient(low = "red",high = "black") 
;
ggsave("phenotypic_clusters.png")


# plot mapped points (qualities)
ggplot(data=m, aes(x=x, y=y)) +
  geom_point(aes(color=q))  +
  scale_color_gradientn(colors=heat.colors(30)) 
;


#write.csv2(m, "mapping_evaluations_allSentences_2018-04-13_16-40_TreeSize-7_1d.csv");
m <- read.csv2("mapping_evaluations_allSentences_2018-04-13_16-40_TreeSize-7_1d.csv");

cluster_n <- dplyr::filter(m, c==9);
cluster_evals <- data.frame(x=seq(1,100,1), t(cluster_n[,5:104]))
evals_cluster_n <- tidyr::gather(cluster_evals,"f", "fx", 2:ncol(cluster_evals))

p <- ggplot(evals_cluster_n, aes(x=x, y=fx,color=f)) + geom_line();
p