问题描述
我正在使用R编程语言.我在此处合并了自己的代码以及冗长的教程: https://michael.hahsler.net/SMU/EMIS7332/R/viz_classifier.html .最后，我制作了一个可视化的图".(请参见此代码的末尾"final_plot")
I am using the R programming language. I incorporated my own code along with a lengthy tutorial over here : https://michael.hahsler.net/SMU/EMIS7332/R/viz_classifier.html . In the end, I produced a visual "plot" (see the end of this code, "final_plot")
library(cluster)
library(Rtsne)
library(dplyr)
library(randomForest)
library(caret)
library(ggplot2)
library(plotly)
#PART 1 : Create Data
#generate 4 random variables : response_variable ~ var_1 , var_2, var_3
var_1 <- rnorm(10000,1,4)
var_2<-rnorm(10000,10,5)
var_3 <- sample( LETTERS[1:4], 10000, replace=TRUE, prob=c(0.1, 0.2, 0.65, 0.05) )
response_variable <- sample( LETTERS[1:2], 10000, replace=TRUE, prob=c(0.4, 0.6) )
#put them into a data frame called "f"
f <- data.frame(var_1, var_2, var_3, response_variable)
#declare var_3 and response_variable as factors
f$response_variable = as.factor(f$response_variable)
f$var_3 = as.factor(f$var_3)
#create id
f$ID <- seq_along(f[,1])
#PART 2: random forest
#split data into train set and test set
index = createDataPartition(f$response_variable, p=0.7, list = FALSE)
train = f[index,]
test = f[-index,]
#create random forest statistical model
rf = randomForest(response_variable ~ var_1 + var_2 + var_3, data=train, ntree=20, mtry=2)
#have the model predict the test set
pred = predict(rf, test, type = "prob")
labels = as.factor(ifelse(pred[,2]>0.5, "A", "B"))
confusionMatrix(labels, test$response_variable)
#PART 3: Visualize in 2D (source: https://dpmartin42.github.io/posts/r/cluster-mixed-types)
gower_dist <- daisy(test[, -c(4,5)],
metric = "gower")
gower_mat <- as.matrix(gower_dist)
labels = data.frame(labels)
labels$ID = test$ID
tsne_obj <- Rtsne(gower_dist, is_distance = TRUE)
tsne_data <- tsne_obj$Y %>%
data.frame() %>%
setNames(c("X", "Y")) %>%
mutate(cluster = factor(labels$labels),
name = labels$ID)
plot = ggplot(aes(x = X, y = Y), data = tsne_data) +
geom_point(aes(color = labels$labels))
plotly_plot = ggplotly(plot)
a = tsne_obj$Y
a = data.frame(a)
data = a
data$class = labels$labels
decisionplot <- function(model, data, class = NULL, predict_type = "class",
resolution = 100, showgrid = TRUE, ...) {
if(!is.null(class)) cl <- data[,class] else cl <- 1
data <- data[,1:2]
k <- length(unique(cl))
plot(data, col = as.integer(cl)+1L, pch = as.integer(cl)+1L, ...)
# make grid
r <- sapply(data, range, na.rm = TRUE)
xs <- seq(r[1,1], r[2,1], length.out = resolution)
ys <- seq(r[1,2], r[2,2], length.out = resolution)
g <- cbind(rep(xs, each=resolution), rep(ys, time = resolution))
colnames(g) <- colnames(r)
g <- as.data.frame(g)
### guess how to get class labels from predict
### (unfortunately not very consistent between models)
p <- predict(model, g, type = predict_type)
if(is.list(p)) p <- p$class
p <- as.factor(p)
if(showgrid) points(g, col = as.integer(p)+1L, pch = ".")
z <- matrix(as.integer(p), nrow = resolution, byrow = TRUE)
contour(xs, ys, z, add = TRUE, drawlabels = FALSE,
lwd = 2, levels = (1:(k-1))+.5)
invisible(z)
}
model <- randomForest(class ~ ., data=data, mtry=2, ntrees=500)
final_plot = decisionplot(model, data, class = "class", main = "rf (1)")
现在，我想将其转变为交互式"广告.使用R中的plotly库进行绘图:
Now, I would like to turn this into an "interactive" plot using the plotly library in R:
plotly_plot = ggplotly(final_plot)
但是我遇到了以下错误:
But I got the following error:
Error in UseMethod("ggplotly", p) :
no applicable method for 'ggplotly' applied to an object of class "c('matrix', 'array', 'integer', 'numeric')"
有没有一种方法可以将"Regular"转换为绘制为"ggplot"在R中?我的"final_plot"可以吗?通过密谋"对象?
Is there a way to convert "Regular" plots to "ggplot" in R? Can my "final_plot" be passed through a "plotly" object?
推荐答案
正如@ mischva11所评论的那样，我认为从头开始创建ggplot比较容易.您的函数实际上是返回一个矩阵，而不是一种绘图对象. plot 和 countour 函数直接在活动图形窗口中绘制绘图.我不确定是否有办法将这些基本图转换为ggplot(也许有).
As @mischva11 commented, I think it is easier to create the ggplot from scratch. Your function is actually returning a matrix and not a kind of plot object. the plot and countour functions draw the plots directly in the active graphic window. I am not sure if there is a way to convert these base plots to ggplot (maybe there is).
这是一种创建与ggplot中相似的图并将其转换为plotly的方法.
Here is a way to create a similar plot as you have in ggplot and then convert it to plotly.
decisionplot <- function(model, data, class = NULL, predict_type = "class", resolution = 100, showgrid = TRUE) {
# create ggplot with minimal theme and no grid lines
g <- ggplot() + theme_minimal() + theme(panel.grid = element_blank())
# make grid values for contour and grid points
r <- sapply(data[ ,1:2], range, na.rm = TRUE)
xs <- seq(r[1,1], r[2,1], length.out = resolution)
ys <- seq(r[1,2], r[2,2], length.out = resolution)
g1 <- cbind(rep(xs, each=resolution), rep(ys, time = resolution))
colnames(g1) <- colnames(r)
g1 <- as.data.frame(g1)
### guess how to get class labels from predict
### (unfortunately not very consistent between models)
p <- predict(model, g1, type = predict_type)
if(is.list(p)) p <- p$class
g1$class <- as.factor(p)
if(showgrid) {
# add labeled grid points to ggplot
g <- g + geom_point(data=g1, aes(x=X1, y=X2, col = class), shape = ".")
}
# add points to plot
g <- g + geom_point(data=data, aes(x=X1, y=X2, col = class, shape = class))
# add contour curves
g <- g + geom_contour(data=g1, aes(x=X1, y=X2, z=as.integer(class)), colour='black', linetype=1, size=rel(0.2), bins=length(unique(g1$class)))
# return ggplot object
return(g)
}
# get ggplot object
final_plot <- decisionplot(model, data, class = "class")
# convert to plotly
ggplotly(final_plot)
这有效.最终的图看起来不太好，但是您可以使用参数.
This works. The final plot does not look that good, but you can play around with the parameters.
我认为可以使最终绘图更好的一件事是使用 geom_raster 绘制具有不同标签预测的区域(而不是绘制小点).但是，当我这样做时，转换为 plotly 花费了很多时间(我实际上放弃了).我认为，当您为 geom_raster 使用离散标签时，转换为plotly是有问题的，因为当我将离散标签转换为数值时，它转换为plotly的速度非常快.
One thing that in my opinion could make the final plot better is to use geom_raster to plot the regions with different label predictions (instead of plotting the small points). However, when I did this the conversion to plotly took forever (I actually gave up). I think there is an issue in the conversion to plotly when you use discrete labels for geom_raster, because when i converted the discrete labels to numeric values, it converted to plotly very fast.
另一种选择是直接在plot_ly中工作，但是我对此没有太多经验.
Another option is to work directly in plot_ly, but I don't have much experience on this.
希望这行得通.
这篇关于转换“常规"绘制到ggplot对象(然后绘制)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！