here to show you how to use xgboost (extreme gradient boosting) models in pipelearner.

Why a post on xgboost and pipelearner?

xgboost is one of the most powerful machine-learning libraries, so there’s a good reason to use it. pipelearner helps to create machine-learning pipelines that make it easy to do cross-fold validation, hyperparameter grid searching, and more. So bringing them together will make for an awesome combination!

The only problem - out of the box, xgboost doesn’t play nice with pipelearner. Let’s work out how to deal with this.

Setup

To follow this post you’ll need the following packages:

# Install (if necessary)install.packages(c("xgboost", "tidyverse", "devtools"))devtools::install_github("drsimonj/pipelearner") # Attach library(tidyverse) library(xgboost) library(pipelearner) library(lazyeval)

Our example will be to try and predict whether tumours are cancerous or not using the . Set up as follows:

data_url <- 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'd <- read_csv(  data_url,  col_names = c('id', 'thinkness', 'size_uniformity',                'shape_uniformity', 'adhesion', 'epith_size', 'nuclei', 'chromatin', 'nucleoli', 'mitoses', 'cancer')) %>% select(-id) %>% # Remove id; not useful here filter(nuclei != '?') %>% # Remove records with missing data mutate(cancer = cancer == 4) %>% # one-hot encode 'cancer' as 1=malignant;0=benign mutate_all(as.numeric) # All to numeric; needed for XGBoost d #> # A tibble: 683 × 10 #> thinkness size_uniformity shape_uniformity adhesion epith_size nuclei #> 
     
      
       
        
         
         
           #> 1 5 1 1 1 2 1 #> 2 5 4 4 5 7 10 #> 3 3 1 1 1 2 2 #> 4 6 8 8 1 3 4 #> 5 4 1 1 3 2 1 #> 6 8 10 10 8 7 10 #> 7 1 1 1 1 2 10 #> 8 2 1 2 1 2 1 #> 9 2 1 1 1 2 1 #> 10 4 2 1 1 2 1 #> # ... with 673 more rows, and 4 more variables: chromatin 
          
           , #> # nucleoli 
           
            , mitoses 
            
             , cancer 
             
            
           
          
         
        
       
      
     
    

pipelearner

pipelearner makes it easy to do lots of routine machine learning tasks, many of which you can check out . For this example, we’ll use pipelearner to perform a grid search of some xgboost hyperparameters.

Grid searching is easy with pipelearner. For detailed instructions, check out my previous post: . As a quick reminder, we declare a data frame, machine learning function, formula, and hyperparameters as vectors. Here’s an example that would grid search multiple values of minsplit and maxdepth for an rpart decision tree:

pipelearner(d, rpart::rpart, cancer ~ .,            minsplit = c(2, 4, 6, 8, 10), maxdepth = c(2, 3, 4, 5))

The challenge for xgboost:

pipelearner expects a model function that has two arguments: data andformula

xgboost

Here’s an xgboost model:

# Prep data (X) and labels (y)X <- select(d, -cancer) %>% as.matrix()y <- d$cancer# Fit the modelfit <- xgboost(X, y, nrounds = 5, objective = "reg:logistic")#> [1] train-rmse:0.372184 #> [2] train-rmse:0.288560 #> [3] train-rmse:0.230171 #> [4] train-rmse:0.188965 #> [5] train-rmse:0.158858 # Examine accuracy predicted <- as.numeric(predict(fit, X) >= .5) mean(predicted == y) #> [1] 0.9838946

Look like we have a model with 98.39% accuracy on the training data!

Regardless, notice that first two arguments to xgboost() are a numeric data matrix and a numeric label vector. This is not what pipelearner wants!

Wrapper function to parse data and formula

To make xgboost compatible with pipelearner we need to write a wrapper function that accepts data and formula, and uses these to pass a feature matrix and label vector to xgboost:

pl_xgboost <- function(data, formula, ...) {  data <- as.data.frame(data)  X_names <- as.character(f_rhs(formula))  y_name  <- as.character(f_lhs(formula))  if (X_names == '.') {    X_names <- names(data)[names(data) != y_name]  }  X <- data.matrix(data[, X_names])  y <- data[[y_name]]  xgboost(data = X, label = y, ...) }

Let’s try it out:

pl_fit <- pl_xgboost(d, cancer ~ ., nrounds = 5, objective = "reg:logistic")#> [1]  train-rmse:0.372184 #> [2]  train-rmse:0.288560 #> [3] train-rmse:0.230171 #> [4] train-rmse:0.188965 #> [5] train-rmse:0.158858 # Examine accuracy pl_predicted <- as.numeric(predict(pl_fit, as.matrix(select(d, -cancer))) >= .5) mean(pl_predicted == y) #> [1] 0.9838946

Perfect!

Bringing it all together

We can now use pipelearner and pl_xgboost() for easy grid searching:

pl <- pipelearner(d, pl_xgboost, cancer ~ .,                  nrounds = c(5, 10, 25),                  eta = c(.1, .3), max_depth = c(4, 6)) fits <- pl %>% learn() #> [1] train-rmse:0.453832 #> [2] train-rmse:0.412548 #> ... fits #> # A tibble: 12 × 9 #> models.id cv_pairs.id train_p fit target model #> 
     
      
       
        
         
         
           #> 1 1 1 1 
          
            cancer pl_xgboost #> 2 10 1 1 
           
             cancer pl_xgboost #> 3 11 1 1 
            
              cancer pl_xgboost #> 4 12 1 1 
             
               cancer pl_xgboost #> 5 2 1 1 
              
                cancer pl_xgboost #> 6 3 1 1 
               
                 cancer pl_xgboost #> 7 4 1 1 
                
                  cancer pl_xgboost #> 8 5 1 1 
                 
                   cancer pl_xgboost #> 9 6 1 1 
                  
                    cancer pl_xgboost #> 10 7 1 1 
                   
                     cancer pl_xgboost #> 11 8 1 1 
                    
                      cancer pl_xgboost #> 12 9 1 1 
                     
                       cancer pl_xgboost #> # ... with 3 more variables: params 
                      
                       , train 
                       
                        , test 
                        
                       
                      
                     
                    
                   
                  
                 
                
               
              
             
            
           
          
         
        
       
      
     
    

Looks like all the models learned OK. Let’s write a custom function to extract model accuracy and examine the results:

accuracy <- function(fit, data, target_var) {  # Convert resample object to data frame  data <- as.data.frame(data)  # Get feature matrix and labels  X <- data %>%    select(-matches(target_var)) %>%     as.matrix()  y <- data[[target_var]]  # Obtain predicted class  y_hat <- as.numeric(predict(fit, X) > .5) # Return accuracy mean(y_hat == y) } results <- fits %>% mutate( # hyperparameters nrounds = map_dbl(params, "nrounds"), eta = map_dbl(params, "eta"), max_depth = map_dbl(params, "max_depth"), # Accuracy accuracy_train = pmap_dbl(list(fit, train, target), accuracy), accuracy_test = pmap_dbl(list(fit, test, target), accuracy) ) %>% # Select columns and order rows select(nrounds, eta, max_depth, contains("accuracy")) %>% arrange(desc(accuracy_test), desc(accuracy_train)) results #> # A tibble: 12 × 5 #> nrounds eta max_depth accuracy_train accuracy_test #> 
     
      
       
        
        
          #> 1 25 0.3 6 1.0000000 0.9489051 #> 2 25 0.3 4 1.0000000 0.9489051 #> 3 10 0.3 6 0.9981685 0.9489051 #> 4 5 0.3 6 0.9945055 0.9489051 #> 5 10 0.1 6 0.9945055 0.9489051 #> 6 25 0.1 6 0.9945055 0.9489051 #> 7 5 0.1 6 0.9926740 0.9489051 #> 8 25 0.1 4 0.9890110 0.9489051 #> 9 10 0.3 4 0.9871795 0.9489051 #> 10 5 0.3 4 0.9853480 0.9489051 #> 11 10 0.1 4 0.9853480 0.9416058 #> 12 5 0.1 4 0.9835165 0.9416058
        
       
      
     
    

Our top model, which got 94.89% on a test set, had nrounds = 25, eta = 0.3, and max_depth = 6.

Either way, the trick was the wrapper function pl_xgboost() that let us bridge xgboost and pipelearner. Note that this same principle can be used for any other machine learning functions that don’t play nice with pipelearner.

Bonus: bootstrapped cross validation

For those of you who are comfortable, below is a bonus example of using 100 boostrapped cross validation samples to examine consistency in the accuracy. It doesn’t get much easier than using pipelearner!

results <- pipelearner(d, pl_xgboost, cancer ~ ., nrounds = 25) %>%   learn_cvpairs(n = 100) %>%   learn() %>%   mutate(    test_accuracy  = pmap_dbl(list(fit, test,  target), accuracy)  )#> [1]  train-rmse:0.357471 #> [2]  train-rmse:0.256735 #> ... results %>% ggplot(aes(test_accuracy)) + geom_histogram(bins = 30) + scale_x_continuous(labels = scales::percent) + theme_minimal() + labs(x = "Accuracy", y = "Number of samples", title = "Test accuracy distribution for\n100 bootstrapped samples")

Sign off

Thanks for reading and I hope this was useful for you.

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If you’d like the code that produced this blog, check out the .

 

转自：https://drsimonj.svbtle.com/with-our-powers-combined-xgboost-and-pipelearner