https://github.com/genentech/gamerank
https://github.com/genentech/gamerank
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/genentech/gamerank
- Owner: Genentech
- Created: 2022-02-22T17:40:54.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-09T10:27:20.000Z (over 1 year ago)
- Last Synced: 2024-09-10T11:53:10.624Z (over 1 year ago)
- Language: R
- Size: 19.4 MB
- Stars: 10
- Watchers: 6
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GameRank - An R package for Feature Selection
The GameRank package is an R package that implements functions for
variable and feature selection to create predictive models. This includes a
number of feature selection algorithms, mostly combinatorial
search strategies, which are:
* Forward selection,
* Backward selection,
* Bidirectional search, and
* Plus-L, Minus-R search together with
* Random search, which draws random combinations across the feature space, and
* *GameRank* a novel feature selection algorithm, that employs a
maximum likelihood ranking model to learn the optimal ranking of features.
The following figure illustrates the algorithm:
In addition, it provides methods for feature construction, that is
* functions to screen variable properties,
* evaluate simple variable transformations,
* Box-Cox transformations for regression and binomial models, as well as
* methods for the detection of features with multi-modal distributions.
GameRank allows for direct optimization of measures related to _calibration_ and
_discrimination_ of models. This approach uses a training:validation:test split-set
approach. However, for small sample selection projects, functions for bias-corrections via
cross-validation or bootstrapping are included.
Parts of this code have been used to craft a model for chemo-tolerability
prediction. [https://ascopubs.org/doi/full/10.1200/CCI.21.00121]
The package can be installed directly from GitHub via
```{r}
devtools::install_github("Genentech/GameRank")
```
Here are some few lines of example code to use the package to
**screen features:**
```{r}
library( dplyr )
library( ggplot2 )
vck <- check_variables( toy_data, response_var, list_variables )
vck %>%
ggplot(aes(x=entropy, y=mutual_information) ) +
geom_point()
```
and do a **forward selection:**
```{r}
sel <- forward( toy_data, response_var, list_variables
fn_train_binomial, # Train a logistic regression model
fn_eval_binomial_auroc, # Evaluate the Area under ROC curve
m=4L, # Search for combination of 4 features
splits=2L, # Evaluate on 2 parallel split sets
maximize=TRUE # Maximize the Area under ROC curve
)
sel$variable_selections
```
Have fun!