Based on the example code on https://www.tidymodels.org/learn/models/parsnip-nnet/
library(tidymodels)
#> ─ Attaching packages ──────────────────────────────────────────── tidymodels 0.1.0 ─
#> ✓ broom 0.5.6 ✓ recipes 0.1.12
#> ✓ dials 0.0.6 ✓ rsample 0.0.6
#> ✓ dplyr 0.8.5 ✓ tibble 3.0.1
#> ✓ ggplot2 3.3.0.9000 ✓ tune 0.1.0
#> ✓ infer 0.5.1 ✓ workflows 0.1.1
#> ✓ parsnip 0.1.0 ✓ yardstick 0.0.6
#> ✓ purrr 0.3.4
#> ─ Conflicts ────────────────────────────────────────────── tidymodels_conflicts() ─
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x ggplot2::margin() masks dials::margin()
#> x recipes::step() masks stats::step()
data(bivariate)
biv_rec <-
recipe(Class ~ ., data = bivariate_train) %>%
step_BoxCox(all_predictors())%>%
step_normalize(all_predictors()) %>%
prep(training = bivariate_train, retain = TRUE)
val_normalized <- bake(biv_rec, new_data = bivariate_val, all_predictors())
set.seed(57974)
create_xgb_dmatrix <- function(d) {
o <- parsnip:::convert_form_to_xy_fit(Class ~ ., d, composition = "matrix")
xgboost::xgb.DMatrix(data = o$x, label = as.integer(o$y == "One"), missing = NA)
}
train_normalized <- bake(biv_rec, new_data = bivariate_train)
test_normalized <- bake(biv_rec, new_data = bivariate_test)
watchlist <- list(
train = create_xgb_dmatrix(train_normalized),
test = create_xgb_dmatrix(test_normalized)
)
fit <-
boost_tree(
tree_depth = 7,
trees = 10000,
learn_rate = 0.03
) %>%
set_mode("classification") %>%
set_engine("xgboost",
watchlist = watchlist,
early_stopping_rounds = 200,
verbose = 2
) %>%
fit(Class ~ ., data = juice(biv_rec))
#> [1] train-error:0.805748 test-error:0.763380
#> Multiple eval metrics are present. Will use test_error for early stopping.
#> Will train until test_error hasn't improved in 200 rounds.
#>
#> [2] train-error:0.805748 test-error:0.763380
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#> [305] train-error:0.911794 test-error:0.747887
#> [306] train-error:0.911794 test-error:0.747887
#> [307] train-error:0.911794 test-error:0.747887
#> [308] train-error:0.911794 test-error:0.747887
#> [309] train-error:0.911794 test-error:0.747887
#> [310] train-error:0.911794 test-error:0.747887
#> [311] train-error:0.911794 test-error:0.747887
#> [312] train-error:0.911794 test-error:0.746479
#> [313] train-error:0.911794 test-error:0.746479
#> [314] train-error:0.911794 test-error:0.746479
#> [315] train-error:0.911794 test-error:0.746479
#> [316] train-error:0.911794 test-error:0.746479
#> [317] train-error:0.911794 test-error:0.746479
#> [318] train-error:0.911794 test-error:0.745070
#> [319] train-error:0.911794 test-error:0.745070
#> [320] train-error:0.911794 test-error:0.745070
#> [321] train-error:0.911794 test-error:0.746479
#> [322] train-error:0.911794 test-error:0.746479
#> [323] train-error:0.911794 test-error:0.746479
#> [324] train-error:0.911794 test-error:0.746479
#> [325] train-error:0.911794 test-error:0.745070
#> [326] train-error:0.912785 test-error:0.746479
#> [327] train-error:0.912785 test-error:0.745070
#> [328] train-error:0.912785 test-error:0.746479
#> [329] train-error:0.913776 test-error:0.745070
#> [330] train-error:0.913776 test-error:0.746479
#> [331] train-error:0.913776 test-error:0.746479
#> [332] train-error:0.913776 test-error:0.746479
#> [333] train-error:0.913776 test-error:0.746479
#> [334] train-error:0.913776 test-error:0.746479
#> [335] train-error:0.913776 test-error:0.746479
#> [336] train-error:0.913776 test-error:0.746479
#> [337] train-error:0.913776 test-error:0.746479
#> [338] train-error:0.913776 test-error:0.746479
#> [339] train-error:0.914767 test-error:0.746479
#> [340] train-error:0.914767 test-error:0.746479
#> [341] train-error:0.914767 test-error:0.746479
#> [342] train-error:0.916749 test-error:0.746479
#> [343] train-error:0.916749 test-error:0.746479
#> [344] train-error:0.916749 test-error:0.746479
#> [345] train-error:0.915758 test-error:0.746479
#> [346] train-error:0.915758 test-error:0.746479
#> [347] train-error:0.915758 test-error:0.746479
#> [348] train-error:0.915758 test-error:0.746479
#> [349] train-error:0.915758 test-error:0.746479
#> [350] train-error:0.915758 test-error:0.747887
#> [351] train-error:0.915758 test-error:0.747887
#> [352] train-error:0.916749 test-error:0.746479
#> [353] train-error:0.916749 test-error:0.747887
#> [354] train-error:0.916749 test-error:0.747887
#> [355] train-error:0.917740 test-error:0.747887
#> [356] train-error:0.918731 test-error:0.749296
#> [357] train-error:0.918731 test-error:0.749296
#> [358] train-error:0.918731 test-error:0.749296
#> [359] train-error:0.918731 test-error:0.747887
#> [360] train-error:0.918731 test-error:0.747887
#> [361] train-error:0.918731 test-error:0.747887
#> [362] train-error:0.918731 test-error:0.747887
#> [363] train-error:0.917740 test-error:0.749296
#> [364] train-error:0.917740 test-error:0.750704
#> [365] train-error:0.917740 test-error:0.750704
#> [366] train-error:0.917740 test-error:0.750704
#> [367] train-error:0.917740 test-error:0.750704
#> [368] train-error:0.917740 test-error:0.752113
#> [369] train-error:0.917740 test-error:0.752113
#> [370] train-error:0.917740 test-error:0.752113
#> [371] train-error:0.917740 test-error:0.752113
#> [372] train-error:0.918731 test-error:0.750704
#> [373] train-error:0.918731 test-error:0.750704
#> [374] train-error:0.918731 test-error:0.752113
#> [375] train-error:0.918731 test-error:0.752113
#> [376] train-error:0.918731 test-error:0.752113
#> [377] train-error:0.918731 test-error:0.749296
#> [378] train-error:0.918731 test-error:0.749296
#> [379] train-error:0.920714 test-error:0.749296
#> [380] train-error:0.920714 test-error:0.749296
#> [381] train-error:0.920714 test-error:0.749296
#> [382] train-error:0.920714 test-error:0.749296
#> [383] train-error:0.920714 test-error:0.749296
#> [384] train-error:0.920714 test-error:0.749296
#> [385] train-error:0.920714 test-error:0.749296
#> [386] train-error:0.920714 test-error:0.749296
#> [387] train-error:0.920714 test-error:0.749296
#> [388] train-error:0.920714 test-error:0.749296
#> [389] train-error:0.920714 test-error:0.749296
#> [390] train-error:0.920714 test-error:0.749296
#> [391] train-error:0.920714 test-error:0.749296
#> [392] train-error:0.920714 test-error:0.749296
#> [393] train-error:0.920714 test-error:0.749296
#> [394] train-error:0.920714 test-error:0.749296
#> [395] train-error:0.920714 test-error:0.749296
#> [396] train-error:0.920714 test-error:0.749296
#> [397] train-error:0.920714 test-error:0.747887
#> [398] train-error:0.920714 test-error:0.747887
#> [399] train-error:0.921705 test-error:0.747887
#> [400] train-error:0.922696 test-error:0.747887
#> [401] train-error:0.922696 test-error:0.749296
#> [402] train-error:0.922696 test-error:0.749296
#> [403] train-error:0.922696 test-error:0.749296
#> [404] train-error:0.922696 test-error:0.749296
#> [405] train-error:0.922696 test-error:0.749296
#> [406] train-error:0.922696 test-error:0.749296
#> [407] train-error:0.922696 test-error:0.749296
#> [408] train-error:0.922696 test-error:0.749296
#> [409] train-error:0.922696 test-error:0.749296
#> [410] train-error:0.922696 test-error:0.749296
#> [411] train-error:0.922696 test-error:0.749296
#> [412] train-error:0.922696 test-error:0.749296
#> [413] train-error:0.922696 test-error:0.749296
#> [414] train-error:0.922696 test-error:0.749296
#> [415] train-error:0.922696 test-error:0.749296
#> [416] train-error:0.922696 test-error:0.749296
#> Stopping. Best iteration:
#> [216] train-error:0.895937 test-error:0.745070
val_results <-
bivariate_val %>%
bind_cols(
predict(fit, new_data = val_normalized),
predict(fit, new_data = val_normalized, type = "prob")
)
val_results %>% roc_auc(truth = Class, .pred_One)
#> # A tibble: 1 x 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc binary 0.788
val_results %>% accuracy(truth = Class, .pred_class)
#> # A tibble: 1 x 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 accuracy binary 0.713Created on 2020-05-06 by the reprex package (v0.3.0)
Hi @yutannihilation, I believe you are passing in the wrong data into
fit(Class ~ ., data = juice(biv_rec)) it should be the train set only.