https://github.com/dataxujing/gcforest_r
:art: :art: R package for gcForest [ https://CRAN.R-project.org/package=gcForest ][ https://github.com/cran/gcForest ]
https://github.com/dataxujing/gcforest_r
algorithm r
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:art: :art: R package for gcForest [ https://CRAN.R-project.org/package=gcForest ][ https://github.com/cran/gcForest ]
- Host: GitHub
- URL: https://github.com/dataxujing/gcforest_r
- Owner: DataXujing
- Created: 2018-09-25T07:25:23.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-10-07T02:53:33.000Z (about 7 years ago)
- Last Synced: 2025-08-16T15:41:14.136Z (2 months ago)
- Topics: algorithm, r
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 6
- Watchers: 2
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# gcForest 
Xu Jing
## 1.Introduction
The gcForest algorithm was suggested in Zhou and Feng 2017 ( , refer for this paper for technical details) and pylablanche() who provide a Python3.X implementation of this algorithm on github (). We provide a R package called gcForest which is the R interface of the pylablanche's gcForest module (Python3.X). And if you want to known more about gcForest, please read the source paper (Deep Forest).
## 2.Prerequisites
As mentioned in the previous paragraph, we provide a R interface of the Python3.X module gcForest(by pylablanche), If you want to use this R packages like the R's tensorflow and keras you should hava a Python3.X environment first, and then, you will need to have the following installed on your computer to make it work:
+ Python 3.X
+ Numpy >= 1.12.0
+ Scikit-learn >= 0.18.1
after these, you can install this R package like:
+ `install.packages('gcForest')`
+ `devtools::install_github('DataXujing/gcForest_r')`
run `library(gcForest)` in your R Console no mistakes.
## 3.Using gcForest
Supported APIs:
+ `fit(X,y)` Training the gcForest on input data X and associated target y;
+ `predict(X)` Predict the class of unknown samples X;
+ `predict_proba(X)` Predict the class probabilities of unknown samples X;
+ `mg_scanning(X, y=None)` Performs a Multi Grain Scanning on input data;
+ `window_slicing_pred_prob(X, window, shape_1X, y=None)` Performs a window slicing of the input data and send them through Random Forests. If target values 'y' are provided sliced data are then used to train the Random Forests;
+ `cascade_forest(X, y=None)` Perform (or train if 'y' is not None) a cascade forest estimator;
+ `gcdata(x)` Tansform R data structure to Python data structure;
+ `model_save(model,path)` To save trained model in disk;
+ `model_load(path)` To load trained model from disk to R environment;
Example1: iris data set
```r
library(gcForest)
sk <- reticulate::import('sklearn')
train_test_split <- sk$model_selection$train_test_split
data <- sk$datasets$load_iris
iris <- data()
X = iris$data
y = iris$target
data_split = train_test_split(X, y, test_size=0.33)
X_tr <- data_split[[1]]
X_te <- data_split[[2]]
y_tr <- data_split[[3]]
y_te <- data_split[[4]]
gcforest_m <- gcforest(shape_1X=4L, window=2L, tolerance=0.0)
gcforest_m$fit(X_tr,y_tr)
gcf_model <- model_save(gcforest_m,'../gcforest_model.model')
gcf <- model_load('../gcforest_model.model')
gcf$fit(X_tr, y_tr)
```
Example2: Digits data set
```r
library(gcForest)
sk <- sk <- reticulate::import('sklearn')
train_test_split <- sk$model_selection$train_test_split
data <- sk$datasets$load_digits
digits <- data()
X = digits$data
y = digits$target
data_split = train_test_split(X, y, test_size=0.4)
gcforest_m <- gcforest(shape_1X=c(8L,8L), window=c(4L,6L), tolerance=0.0, min_samples_mgs=10L, min_samples_cascade=7L)
gcforest_m$fit(X_tr,y_tr)
gcf_model <- model_save(gcforest_m,'../gcforest_model.model')
gcf <- model_load('../gcforest_model.model')
gcf$fit(X_tr, y_tr)
```
Example3: Using mg-scanning and cascade_forest Sperately
```r
# mg-scanning
gcforest_m <- gcForest(shape_1X=c(8L,8L), window=5L, min_samples_mgs=10L, min_samples_cascade=7L)
X_tr_mgs <- gcforest_m$mg_scanning(X_tr, y_tr)
X_te_mgs <- gcforest_m$mg_scanning(X_te)
# cascade_forest
gcforest_m <- gcForest(tolerance=0.0, min_samples_mgs=10L, min_samples_cascade=7L)
cf <- gcforest_m$cascade_forest(X_tr_mgs, y_tr)
pred_proba <- gcforest_m$cascade_forest(X_te_mgs)
pred_proba <- reticulate::py_to_r(pred_proba)
# then do mean and max
```
Example4: Skipping mg_scanning
```r
gcforest_m <- gcForest(tolerance=0.0, min_samples_cascade=20L)
cf <- gcforest_m$cascade_forest(X_tr, y_tr)
pred_proba <- gcforest_m$cascade_forest(X_te)
pred_proba <- reticulate::py_to_r(pred_proba)
# then do mean and max
```
## 4.Notes
Thanks for the paper of Deep Forest( Zhou and Feng 2017 ()) and the author of the [gcForest Python3.X moulde](https://github.com/pylablanche/gcForest) (pylablanche ). And We constantly improve gcForest R package, and even consider putting official modules which provide by [LAMDA(Learning And Mining from DatA)](http://lamda.nju.edu.cn/code_gcForest.ashx?AspxAutoDetectCookieSupport=1)in gcForest R package.