model_export.Rmd

---
title: "model_export"
output: github_document
---

First attach the `randomForest` package and load the model and test data.

```{r}
suppressPackageStartupMessages(library("randomForest"))

lst <- readRDS("thRS500.RDS")
varslist <- lst$varslist
fmodel <- lst$fmodel
buzztest <- lst$buzztest
rm(list = "lst")
```

Now covert the model to `SQL`.

```{r}
sql <- tidypredict::tidypredict_sql(
  fmodel,
  dbplyr::simulate_mssql())

length(sql)

substr(sql[[1]], 1, 500)

substr(sql[[2]], 1, 200)
```

```{r}
# From: https://shiring.github.io/machine_learning/2017/03/16/rf_plot_ggraph

library(dplyr)
library(ggraph)
library(igraph)

tree_func <- function(final_model, 
                      tree_num) {
  
  # get tree by index
  tree <- randomForest::getTree(final_model, 
                                k = tree_num, 
                                labelVar = TRUE) %>%
    tibble::rownames_to_column() %>%
    # make leaf split points to NA, so the 0s won't get plotted
    mutate(`split point` = ifelse(is.na(prediction), `split point`, NA))
  
  # prepare data frame for graph
  graph_frame <- data.frame(from = rep(tree$rowname, 2),
                            to = c(tree$`left daughter`, tree$`right daughter`))
  
  # convert to graph and delete the last node that we don't want to plot
  graph <- graph_from_data_frame(graph_frame) %>%
    delete_vertices("0")
  
  # set node labels
  V(graph)$node_label <- gsub("_", " ", as.character(tree$`split var`))
  V(graph)$leaf_label <- as.character(tree$prediction)
  V(graph)$split <- as.character(round(tree$`split point`, digits = 2))
  
  # plot
  plot <- ggraph(graph, 'dendrogram') + 
    theme_bw() +
    geom_edge_link() +
    geom_node_point() +
    geom_node_text(aes(label = node_label), na.rm = TRUE, repel = TRUE) +
    geom_node_label(aes(label = split), vjust = 2.5, na.rm = TRUE, fill = "white") +
    geom_node_label(aes(label = leaf_label, fill = leaf_label), na.rm = TRUE, 
					repel = TRUE, colour = "white", fontface = "bold", show.legend = FALSE) +
    theme(panel.grid.minor = element_blank(),
          panel.grid.major = element_blank(),
          panel.background = element_blank(),
          plot.background = element_rect(fill = "white"),
          panel.border = element_blank(),
          axis.line = element_blank(),
          axis.text.x = element_blank(),
          axis.text.y = element_blank(),
          axis.ticks = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y = element_blank(),
          plot.title = element_text(size = 18))
  
  print(plot)
}
```

```{r}
tree_func(fmodel, 1)
```