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README.Rmd
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---
output: github_document
---
<head>
<link rel="stylesheet" type="text/css" href="https://d335w9rbwpvuxm.cloudfront.net/semantic.min.css"/>'
</head>
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
warning = F, error = F, message = F,
collapse = T,
comment = "#>",
fig.path = "man/figures/README-",
fig.pos = "center"
#out.width = "100%"
)
```
# mlgraph <img src="data/mlgraph.png" width="160px" align="right" />
```{r, echo = FALSE, results='asis', eval = T, eval = T}
library(badger)
git_repo <- "systats/mlgraph"
cat(
#badge_travis(git_repo),
#"[](https://codecov.io/gh/favstats/peRspective?branch=master)",
badge_code_size(git_repo),
badge_last_commit(git_repo),
badge_lifecycle("experimental", "blue")
)
```
`mlgraph` provides performance visualizations for standardized ml models with linear, binary or multi tasks. It is implemented as an extension of [deeplyr](). At the moment several graphics packages are implemented including
* `gg_` ggplot2
* `hc_` highcharter
* `ax_` apexcharter
Computations are based on
* [Metrics](https://github.com/mfrasco/Metrics)
* [yardstick](https://github.com/tidymodels/yardstick)
Several task specific plots are available:
* Linear
+ ...
* Binary
+ Confusion Matrix
+ ROC
+ Density
* Multi
+ Confusion Matrix
+ ROC
+ Density
# Installation
Get the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("systats/mlgraph")
```
```{r, echo = F}
pacman::p_load(tidyverse, mlgraph)
ggplot2::theme_set(ggplot2::theme_bw())
```
```{r}
preds <- readRDS("data/preds.rds") %>%
glimpse
```
```{r}
df <- mlgraph::eval_classifier(preds, target, pred, prob1) %>% glimpse
```
### ggplot2
```{r, eval = F}
gg_plot2 <- gridExtra::grid.arrange(
gg_plot_confusion(df$confusion),
gg_plot_roc(df$roc),
gg_plot_density(df$dens), nrow = 1
)
ggsave(gg_plot2, filename = "man/figures/gg_plot2.png", width = 18, height = 6)
```
### highcharter
```{r, eval = F}
pacman::p_load(shiny, shiny.semantic)
shiny.semantic::semanticPage(
div(class= "ui three column grid",
div(class = "column",
hc_plot_confusion(df$confusion)
),
div(class = "column",
hc_plot_roc(df$roc)
),
div(class = "column",
hc_plot_density(df$dens)
)
)
)
```
### apexcharter
```{r, eval = F}
pacman::p_load(shiny, shiny.semantic)
shiny.semantic::semanticPage(
div(class= "ui three column grid",
div(class = "column",
ax_plot_confusion(df$confusion)
),
div(class = "column",
ax_plot_roc(df$roc)
),
div(class = "column",
ax_plot_density(df$dens)
)
)
)
#htmltools::html_print(out)
```
```{r}
sessionInfo()
```