README.md

In statist-bhfz/stackeR: Easy model stacking

stackeR

stackeR package implements classical model stacking algorithm called SuperLearner. It works around resampleR and grideR and has as little dependecies as possible.

Installation

You can install dev version of stackeR from GitHub GitHub with:

# install.packages("devtools")
devtools::install_github("statist-bhfz/stackeR")

Example

We will use BostonHousing data to predict median value of owner-occupied homes in USD 1000’s (medv):

library(grideR)
#> Loading required package: data.table
#> Warning: replacing previous import 'lightgbm::getinfo' by
#> 'xgboost::getinfo' when loading 'grideR'
#> Warning: replacing previous import 'lightgbm::slice' by 'xgboost::slice'
#> when loading 'grideR'
#> Warning: replacing previous import 'lightgbm::setinfo' by
#> 'xgboost::setinfo' when loading 'grideR'
library(stackeR)
library(mlbench)
library(ggplot2)

# Input data
data(BostonHousing)
dt <- as.data.table(BostonHousing)
dt[, chas := as.numeric(chas) - 1]
set.seed(42)
dt <- dt[sample(1:.N, .N), ] # random shuffle
# Train/test split
train_test_split <- c(rep(0, 400), rep(1, 106)) # ~80%/20%
dt_train <- dt[train_test_split == 0, ]
dt_test <- dt[train_test_split == 1, ]

Lets fit metamodel (simple linear model) with two base models - xgboost and catboost:

# data.table with resamples
splits <- resampleR::cv_base(dt_train, "medv")

# List of models
models <- list("xgboost" = xgb_fit, "catboost" = catboost_fit)

# Model parameters
xgb_params <- data.table(
    max_depth = 6,
    eta = 0.025,
    colsample_bytree = 0.9,
    subsample = 0.8,
    gamma = 0,
    min_child_weight = 5,
    alpha = 0,
    lambda = 1
)
xgb_args <- list(
    nrounds = 500,
    early_stopping_rounds = 10,
    booster = "gbtree",
    eval_metric = "rmse",
    objective = "reg:linear",
    verbose = 0
)

catboost_params <- data.table(
    iterations = 1000,
    learning_rate = 0.05,
    depth = 8,
    loss_function = "RMSE",
    eval_metric = "RMSE",
    random_seed = 42,
    od_type = 'Iter',
    od_wait = 10,
    use_best_model = TRUE,
    logging_level = "Silent"
)
catboost_args <- NULL

model_params <- list(xgb_params, catboost_params)
model_args <- list(xgb_args, catboost_args)

# Dumb preprocessing function
# Real function will contain imputation, feature engineering etc.
# with all statistics computed on train folds and applied to validation fold
preproc_fun_example <- function(data) return(data[])
# List of preprocessing fuctions for each model
preproc_funs <- list(preproc_fun_example, preproc_fun_example)

metamodel_obj <- metamodel_fit(data = dt_train,
                               target = "medv",
                               split = splits,
                               models = models,
                               model_params = model_params,
                               model_args = model_args,
                               preproc_funs = preproc_funs,
                               metamodel = lm,
                               metamodel_params = list(),
                               metamodel_interface = "formula"
                               )
metamodel_obj$call$formula
#> ground_truth ~ . - split
#> <environment: 0x557c13856770>
metamodel_obj$coefficients
#> (Intercept)     xgboost    catboost 
#>  -0.9140859   0.9510828   0.1034517

The final step is to fit base models in the entire train dataset, get base models predictions on test set and get metamodel predictions using first level predictions as input:

first_level_preds <- across_models(data = dt,
                                   target = "medv",
                                   split = train_test_split,
                                   models = models,
                                   model_params = model_params,
                                   model_args = model_args,
                                   preproc_funs = preproc_funs)
first_level_preds[, split := "split_1"] # to match lm formula

# Quick check to ensure that correlation between base model predictions isn't so strong
ggplot(first_level_preds) + 
    geom_point(aes(ground_truth, xgboost), color = "red") +
    geom_point(aes(ground_truth, catboost), color = "green")

test_preds <- data.table(
    ground_truth = dt_test[, medv],
    preds = predict.lm(metamodel_obj, newdata = first_level_preds)
)

ggplot(test_preds) + 
    geom_point(aes(ground_truth, preds))

ggplot(test_preds) + 
    geom_point(aes(ground_truth, preds), size = 3) +
    geom_point(aes(ground_truth, xgboost), 
               first_level_preds, 
               color = "red", 
               alpha = 0.7) +
    geom_point(aes(ground_truth, catboost), 
               first_level_preds, 
               color = "green", 
               alpha = 0.7) +
    geom_abline(intercept = 0, slope = 1)

rmse <- function(ground_truth, prediction) {
    sqrt(mean((ground_truth - prediction)^2))
}

rmse(first_level_preds$ground_truth, first_level_preds$xgboost)
#> [1] 3.065806
rmse(first_level_preds$ground_truth, first_level_preds$catboost)
#> [1] 3.951509
rmse(test_preds$ground_truth, test_preds$preds) # smallest rmse
#> [1] 2.933041

statist-bhfz/stackeR documentation built on Aug. 7, 2019, 4:57 a.m.