optimal.Rmd

```{r setup, include=FALSE} 
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```

```{r}
library(readr)
library(dplyr)
library(ggplot2)
library(rjson)
library(gridExtra)

library(reticulate)
```


```{r}
history <- read_csv("model/optimal/history.csv")
```

```{r}
np <- import("numpy")

confusion_matrix <- np$load("model/optimal/confusion_matrix.npy")

confusion_df <- data.frame(Predicted=c("True", "False", "True", "False"), Actual=c("True", "True", "False", "False"), Value=c(confusion_matrix[1], confusion_matrix[2], confusion_matrix[3], confusion_matrix[4]))

confusion_plot <- ggplot(data=confusion_df, aes(x=Actual, y=Predicted, fill=Value)) +
        geom_tile() +
        geom_text(aes(label=Value), color="white", size=10) +
        scale_x_discrete(limits = rev)
confusion_plot
```

```{r}

loss <- ggplot(data=history, aes(x=as.numeric(row.names(history)), y=loss)) +
            geom_point() +
            geom_line() +
            ggtitle("Accuracy vs. Epoch") +
            xlab("Epoch") +
            ylab("Accuracy")
loss
```

```{r}
accuracy <- ggplot(data=history, aes(x=as.numeric(row.names(history)), y=accuracy)) +
            geom_point() +
            geom_line() +
            ggtitle("Accuracy vs. Epoch") +
            xlab("Epoch") +
            ylab("Accuracy")
accuracy
```

```{r}
grid.arrange(loss, accuracy, confusion_plot, nrow=2, ncol=2)
```

```{r}

results <- fromJSON(file="model/optimal/results.json")
print(paste("Uncleaned Model Accuracy:", results["accuracy"]))
print(paste("Uncleaned Model Loss:", results["loss"]))
```


```{r}
ggsave(filename="model/optimal/plots/loss.png", plot=loss)
ggsave(filename="model/optimal/plots/accuracy.png", plot=accuracy)
```