我试图用Tidymodels预测R的房地产价格。我遵循这个教程。一切都很顺利，直到我试图对我的测试数据进行预测。
请参阅下面的代码示例和最后的错误。
我看了两个类似的问题(这里和这里)，但似乎我已经定义了可变角色，并为我的工作流程提供了一个未准备好的配方。
# libraries ---------------------------------------------------------------
library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 0.1.2 ──
#> ✓ broom 0.7.3 ✓ recipes 0.1.15
#> ✓ dials 0.0.9 ✓ rsample 0.0.8
#> ✓ dplyr 1.0.3 ✓ tibble 3.0.5
#> ✓ ggplot2 3.3.3 ✓ tidyr 1.1.2
#> ✓ infer 0.5.4 ✓ tune 0.1.2
#> ✓ modeldata 0.1.0 ✓ workflows 0.2.1
#> ✓ parsnip 0.1.5 ✓ yardstick 0.0.7
#> ✓ 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 recipes::step() masks stats::step()
library(data.table)
library(purrr)
# data --------------------------------------------------------------------
# 're' means real estate
# I'm using data.table in general. Using tribble below for cleaner data definition.
real_estate_data <- tibble::tribble(
~re_id, ~price_per_sqm_huf_mil, ~district, ~num_room,
"30876343", 0.534722222222222, 1, 3,
"31914489", 0.476119402985075, 1, 1,
"30972289", 0.507352941176471, 1, 2,
"31739730", 0.472972972972973, 1, 3,
"31783137", 0.49875, 2, 3,
"31809435", 0.439705882352941, 2, 2,
"31943408", 0.469117647058824, 2, 3,
"31944348", 0.56231884057971, 2, 1,
"31961146", 0.472972972972973, 3, 3,
"24314388", 0.649550561797753, 3, 2,
"29840270", 0.719178082191781, 3, 3,
"29840429", 0.719178082191781, 3, 3,
"30873484", 0.822857142857143, 4, 3,
"30969673", 0.533802816901408, 4, 3,
"31333120", 0.741511627906977, 4, 3,
"31788730", 0.527142857142857, 4, 2,
"31948441", 0.734848484848485, 5, 2,
"31962350", 0.8, 5, 3,
"31962779", 0.670454545454545, 5, 3,
"31979128", 0.689054054054054, 5, 1
)
real_estate_data <- as.data.table(real_estate_data) %>% .[, district := factor(district)]
# train/test split --------------------------------------------------------
set.seed(123)
re_split <- initial_split(real_estate_data)
re_train <- training(re_split)
re_test <- testing(re_split)
# workflow (w/ recipe) ----------------------------------------------------
re_rec <- recipe(re_train,
formula = price_per_sqm_huf_mil ~ .) %>%
update_role(re_id, new_role = "ID") %>%
step_center(all_numeric(), - district) %>%
step_scale(all_predictors(), all_numeric(), - district) %>%
step_dummy(district) %>%
step_zv(all_predictors())
summary(re_rec)
#> # A tibble: 4 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 re_id nominal ID original
#> 2 district nominal predictor original
#> 3 num_room numeric predictor original
#> 4 price_per_sqm_huf_mil numeric outcome original
lr_model <-
linear_reg() %>%
set_engine("lm")
re_wflow <-
workflow() %>%
add_model(lr_model) %>%
add_recipe(re_rec)
# model training and prediction -------------------------------------------
re_fit <-
re_wflow %>%
fit(data = re_train)
re_pred <- predict(re_fit, re_test)
#> Error: Can't subset columns that don't exist.
#> x Column `price_per_sqm_huf_mil` doesn't exist.
由reprex包(v0.3.0)在2021-01-25创建
多谢!
这里的问题是您使用step_center()来转换结果(price_per_sqm_huf_mil)，预测时无结果。您可以指定您想要居中all_predictors() & all_numeric()，像这样:
library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 0.1.2 ──
#> ✓ broom 0.7.3 ✓ recipes 0.1.15
#> ✓ dials 0.0.9 ✓ rsample 0.0.8
#> ✓ dplyr 1.0.3 ✓ tibble 3.0.5
#> ✓ ggplot2 3.3.3 ✓ tidyr 1.1.2
#> ✓ infer 0.5.4 ✓ tune 0.1.2
#> ✓ modeldata 0.1.0 ✓ workflows 0.2.1
#> ✓ parsnip 0.1.5 ✓ yardstick 0.0.7
#> ✓ 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 recipes::step() masks stats::step()
library(dplyr)
real_estate_data <- tibble::tribble(
~re_id, ~price_per_sqm_huf_mil, ~district, ~num_room,
"30876343", 0.534722222222222, 1, 3,
"31914489", 0.476119402985075, 1, 1,
"30972289", 0.507352941176471, 1, 2,
"31739730", 0.472972972972973, 1, 3,
"31783137", 0.49875, 2, 3,
"31809435", 0.439705882352941, 2, 2,
"31943408", 0.469117647058824, 2, 3,
"31944348", 0.56231884057971, 2, 1,
"31961146", 0.472972972972973, 3, 3,
"24314388", 0.649550561797753, 3, 2,
"29840270", 0.719178082191781, 3, 3,
"29840429", 0.719178082191781, 3, 3,
"30873484", 0.822857142857143, 4, 3,
"30969673", 0.533802816901408, 4, 3,
"31333120", 0.741511627906977, 4, 3,
"31788730", 0.527142857142857, 4, 2,
"31948441", 0.734848484848485, 5, 2,
"31962350", 0.8, 5, 3,
"31962779", 0.670454545454545, 5, 3,
"31979128", 0.689054054054054, 5, 1
) %>%
mutate(district = factor(district))
set.seed(123)
re_split <- initial_split(real_estate_data)
re_train <- training(re_split)
re_test <- testing(re_split)
re_rec <- recipe(re_train,
formula = price_per_sqm_huf_mil ~ .) %>%
update_role(re_id, new_role = "ID") %>%
step_center(all_predictors() & all_numeric()) %>%
step_scale(all_predictors() & all_numeric()) %>%
step_dummy(district) %>%
step_zv(all_predictors())
summary(re_rec)
#> # A tibble: 4 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 re_id nominal ID original
#> 2 district nominal predictor original
#> 3 num_room numeric predictor original
#> 4 price_per_sqm_huf_mil numeric outcome original
lr_model <-
linear_reg() %>%
set_engine("lm")
re_wflow <-
workflow() %>%
add_model(lr_model) %>%
add_recipe(re_rec)
re_fit <-
re_wflow %>%
fit(data = re_train)
predict(re_fit, new_data = re_test)
#> # A tibble: 5 x 1
#> .pred
#> <dbl>
#> 1 0.486
#> 2 0.611
#> 3 0.688
#> 4 0.688
#> 5 0.768
由reprex包(v0.3.0)在2021-01-25创建
这已经绊倒了比你更多的人，所以我们正在努力添加一组新的选择器，将很快合并。如果您确实想要尝试转换结果，可以考虑的另一种选择是使用skip = TRUE。