-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathREADME.Rmd
94 lines (70 loc) · 3.12 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# brulee <a href="https://brulee.tidymodels.org/"><img src="man/figures/logo.png" align="right" height="139" alt="a dish of creme brulee on a striped background" /></a>
<!-- badges: start -->
[](https://github.com/tidymodels/brulee/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/tidymodels/brulee?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html)
<!-- badges: end -->
The R `brulee` package contains several basic modeling functions that use the `torch` package infrastructure, such as:
* [neural networks](https://brulee.tidymodels.org/reference/brulee_mlp.html)
* [linear regression](https://brulee.tidymodels.org/reference/brulee_linear_reg.html)
* [logistic regression](https://brulee.tidymodels.org/reference/brulee_logistic_reg.html)
* [multinomial regression](https://brulee.tidymodels.org/reference/brulee_multinomial_reg.html)
## Installation
You can install the released version of brulee from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("brulee")
```
And the development version from [GitHub](https://github.com/tidymodels/brulee) with:
``` r
# install.packages("pak")
pak::pak("tidymodels/brulee")
```
## Example
`brulee` has formula, x/y, and recipe user interfaces for each function. For example:
```{r load, include = FALSE}
library(brulee)
library(yardstick)
library(recipes)
```
```{r class-fit-form}
library(brulee)
library(recipes)
library(yardstick)
data(bivariate, package = "modeldata")
set.seed(20)
nn_log_biv <- brulee_mlp(Class ~ log(A) + log(B), data = bivariate_train,
epochs = 150, hidden_units = 3)
# We use the tidymodels semantics to always return a tibble when predicting
predict(nn_log_biv, bivariate_test, type = "prob") %>%
bind_cols(bivariate_test) %>%
roc_auc(Class, .pred_One)
```
A recipe can also be used if the data require some sort of preprocessing (e.g., indicator variables, transformations, or standardization):
```{r class-fit-rec}
library(recipes)
rec <-
recipe(Class ~ ., data = bivariate_train) %>%
step_YeoJohnson(all_numeric_predictors()) %>%
step_normalize(all_numeric_predictors())
set.seed(20)
nn_rec_biv <- brulee_mlp(rec, data = bivariate_train,
epochs = 150, hidden_units = 3)
# A little better
predict(nn_rec_biv, bivariate_test, type = "prob") %>%
bind_cols(bivariate_test) %>%
roc_auc(Class, .pred_One)
```
## Code of Conduct
Please note that the brulee project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.