inst/doc/variables_importance.R

In ReSurv: Machine Learning Models for Predicting Claim Counts

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ----instancepackage, include=FALSE-------------------------------------------
library(ReSurv)

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  
#  input_data_0 <- data_generator(
#    random_seed = 1,
#    scenario = 0,
#    time_unit = 1 / 360,
#    years = 4,
#    yearly_exposure = 200
#  )
#  
#  individual_data_0 <- IndividualDataPP(
#    data = input_data_0,
#    id = NULL,
#    categorical_features = "claim_type",
#    continuous_features = "AP",
#    accident_period = "AP",
#    calendar_period = "RP",
#    input_time_granularity = "days",
#    output_time_granularity = "quarters",
#    years = 4
#  )
#  

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  # Input data scenario Delta
#  
#  input_data3 <- data_generator(
#    random_seed = 1,
#    scenario = 3,
#    time_unit = 1 / 360,
#    years = 4,
#    yearly_exposure = 200
#  )
#  
#  individual_data_3 <- IndividualDataPP(
#    data = input_data3,
#    id = NULL,
#    categorical_features = "claim_type",
#    continuous_features = "AP",
#    accident_period = "AP",
#    calendar_period = "RP",
#    input_time_granularity = "days",
#    output_time_granularity = "quarters",
#    years = 4
#  )
#  

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  
#  hp_scenario_alpha_xgb <- list(
#    params = list(
#      booster = "gbtree",
#      eta = 0.9887265,
#      subsample = 0.7924135 ,
#      alpha = 10.85342,
#      lambda = 6.213317,
#      min_child_weight = 3.042204,
#      max_depth = 1
#    ),
#    print_every_n = 0,
#    nrounds = 3000,
#    verbose = FALSE,
#    early_stopping_rounds = 500
#  )
#  
#  hp_scenario_alpha_nn <- list(
#    batch_size = as.integer(5000),
#    epochs = as.integer(5500),
#    num_workers = 0,
#    tie = 'Efron',
#    num_layers = 2,
#    num_nodes = 10,
#    optim = "SGD",
#    batch_size = as.integer(5000),
#    lr = 0.3023043,
#    xi = 0.426443,
#    eps = 0,
#    activation = "SELU",
#    early_stopping = TRUE,
#    patience = 350,
#    verbose = FALSE,
#    network_structure = NULL
#  )
#  
#  hp_scenario_delta_xgb <- list(params=list(booster="gbtree",
#                                            eta=0.2717736,
#                                            subsample=0.9043068 ,
#                                            alpha=7.789214,
#                                            lambda=12.09398 ,
#                                            min_child_weight=22.4837 ,
#                                            max_depth = 4),
#                                            print_every_n = 0,
#                                            nrounds=3000,
#                                            verbose= FALSE,
#                                            early_stopping_rounds = 500)
#  
#  hp_scenario_delta_nn <- list(
#    batch_size = as.integer(5000),
#    epochs = as.integer(5500),
#    num_workers = 0,
#    tie = 'Efron',
#    num_layers = 2,
#    num_nodes = 2,
#    optim = "Adam",
#    batch_size = as.integer(5000),
#    lr = 0.3542422,
#    xi = 0.1803953,
#    eps = 0,
#    activation = "LeakyReLU",
#    early_stopping = TRUE,
#    patience = 350,
#    verbose = FALSE,
#    network_structure = NULL
#  )
#  

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  
#  resurv_model_xgb_A <-  ReSurv(individual_data_0,
#                                hazard_model = "XGB",
#                                hparameters = hp_scenario_alpha_xgb)
#  
#  resurv_model_nn_A <-  ReSurv(individual_data_0,
#                               hazard_model = "NN",
#                               hparameters = hp_scenario_alpha_nn)
#  
#  resurv_model_xgb_D <-  ReSurv(individual_data_3,
#                                hazard_model = "XGB",
#                                hparameters = hp_scenario_delta_xgb)
#  
#  resurv_model_nn_D <- ReSurv(individual_data_3,
#                              hazard_model = "NN",
#                              hparameters = hp_scenario_delta_nn)
#  
#  

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  plot(resurv_model_xgb_A)

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  plot(resurv_model_xgb_D)

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  plot(resurv_model_nn_A, nsamples = 10000)

## ----eval=FALSE, include=TRUE-------------------------------------------------
#  plot(resurv_model_nn_D, nsamples=10000)