问题描述
限时送ChatGPT账号..我阅读了 tidymodels 的介绍，但我对配方包中的 prep()、bake() 和 juice() 对数据的作用感到困惑.各自做什么?老实说，我发现为函数命名这样的名称令人困惑，从烹饪类比中，每个函数的名称会更直观吗?
I read the introduction to tidymodels and I am confused about what prep(), bake() and juice() from the recipes package do to the data. What each does? I honestly found confusing to have such names for functions, what would be a more intuitive name for each one out of the culinary analogy?
推荐答案
让我们来看看每个函数的作用.首先，让我们通过几个步骤定义一个配方.请注意，进入配方的数据是训练数据.
Let's walk through what each of these functions does. First, let's define a recipe with a couple of steps. Notice that the data going into the recipe is the training data.
library(recipes)
cars_train <- mtcars[1:20,]
cars_test <- mtcars[21:32,]
cars_rec <- recipe(mpg ~ ., data = cars_train) %>%
step_log(disp) %>%
step_center(all_predictors())
cars_rec
#> Data Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 10
#>
#> Operations:
#>
#> Log transformation on disp
#> Centering for all_predictors
预处理配方 cars_rec 已定义，但尚未估计任何值.例如，没有为 disp 取对数，也没有为预测变量计算平均值，以便它们可以居中.
The preprocessing recipe cars_rec has been defined but no values have been estimated. For example, the log has not been taken for disp, and the mean has not been calculated for predictors so that they can be centered.
prep() 函数获取定义的对象并计算所有内容，以便执行预处理步骤.例如，在此示例中计算了每个预测变量的平均值，因此可以将预测变量居中.这是通过训练数据完成的.
The prep() function takes that defined object and computes everything so that the preprocessing steps can be executed. For example, the mean of each predictor is calculated in this example so the predictors can be centered. This is done with the training data.
cars_prep <- prep(cars_rec)
cars_prep
#> Data Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 10
#>
#> Training data contained 20 data points and no missing data.
#>
#> Operations:
#>
#> Log transformation on disp [trained]
#> Centering for cyl, disp, hp, drat, wt, qsec, vs, am, ... [trained]
请注意，之前未准备的配方仅表示 Centering for all_predictors，因为尚未对其进行评估.现在已经对其进行了评估，我们知道哪些列是预测变量以及它们的均值是什么.
Notice that before, with the unprepped recipe, it just said Centering for all_predictors because it had not been evaluated yet. Now it has been evaluated and we know which columns are predictors and what their means are.
bake() 和 juice() 函数都返回数据，而不是预处理配方对象.bake() 函数采用一个准备好的配方(一个已经根据训练数据估计了所有数量的配方)并将其应用于 new_data.new_data 可能又是训练数据...
The bake() and juice() functions both return data, not a preprocessing recipe object. The bake() function takes a prepped recipe (one that has had all quantities estimated from training data) and applies it to new_data. That new_data could be the training data again...
bake(cars_prep, new_data = cars_train)
#> # A tibble: 20 x 11
#> cyl disp hp drat wt qsec vs am gear carb mpg
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -0.2 -0.222 -26.2 0.355 -0.778 -1.98 -0.5 0.7 0.5 1.30 21
#> 2 -0.2 -0.222 -26.2 0.355 -0.523 -1.42 -0.5 0.7 0.5 1.30 21
#> 3 -2.2 -0.615 -43.2 0.305 -1.08 0.169 0.5 0.7 0.5 -1.7 22.8
#> 4 -0.2 0.256 -26.2 -0.465 -0.183 0.999 0.5 -0.3 -0.5 -1.7 21.4
#> 5 1.8 0.589 38.8 -0.395 0.0415 -1.42 -0.5 -0.3 -0.5 -0.7 18.7
#> 6 -0.2 0.119 -31.2 -0.785 0.0615 1.78 0.5 -0.3 -0.5 -1.7 18.1
#> 7 1.8 0.589 109. -0.335 0.172 -2.60 -0.5 -0.3 -0.5 1.30 14.3
#> 8 -2.2 -0.309 -74.2 0.145 -0.208 1.56 0.5 -0.3 0.5 -0.7 24.4
#> 9 -2.2 -0.350 -41.2 0.375 -0.248 4.46 0.5 -0.3 0.5 -0.7 22.8
#> 10 -0.2 -0.176 -13.2 0.375 0.0415 -0.141 0.5 -0.3 0.5 1.30 19.2
#> 11 -0.2 -0.176 -13.2 0.375 0.0415 0.459 0.5 -0.3 0.5 1.30 17.8
#> 12 1.8 0.323 43.8 -0.475 0.672 -1.04 -0.5 -0.3 -0.5 0.300 16.4
#> 13 1.8 0.323 43.8 -0.475 0.332 -0.841 -0.5 -0.3 -0.5 0.300 17.3
#> 14 1.8 0.323 43.8 -0.475 0.382 -0.441 -0.5 -0.3 -0.5 0.300 15.2
#> 15 1.8 0.860 68.8 -0.615 1.85 -0.461 -0.5 -0.3 -0.5 1.30 10.4
#> 16 1.8 0.834 78.8 -0.545 2.03 -0.621 -0.5 -0.3 -0.5 1.30 10.4
#> 17 1.8 0.790 93.8 -0.315 1.95 -1.02 -0.5 -0.3 -0.5 1.30 14.7
#> 18 -2.2 -0.932 -70.2 0.535 -1.20 1.03 0.5 0.7 0.5 -1.7 32.4
#> 19 -2.2 -0.970 -84.2 1.38 -1.78 0.079 0.5 0.7 0.5 -0.7 30.4
#> 20 -2.2 -1.03 -71.2 0.675 -1.56 1.46 0.5 0.7 0.5 -1.7 33.9
也可以是测试数据.在这种情况下，来自训练数据的列均值应用于测试数据，因为这就是建模工作流中 IRL 发生的情况.否则就是数据泄露.
Or it could be the testing data. In this case, the column means from the training data are applied to the testing data, because that is what happens IRL in a modeling workflow. To do otherwise is data leakage.
bake(cars_prep, new_data = cars_test)
#> # A tibble: 12 x 11
#> cyl disp hp drat wt qsec vs am gear carb mpg
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -2.2 -0.509 -39.2 0.155 -0.933 1.57 0.5 -0.3 -0.5 -1.7 21.5
#> 2 1.8 0.465 13.8 -0.785 0.122 -1.57 -0.5 -0.3 -0.5 -0.7 15.5
#> 3 1.8 0.420 13.8 -0.395 0.0366 -1.14 -0.5 -0.3 -0.5 -0.7 15.2
#> 4 1.8 0.561 109. 0.185 0.442 -3.03 -0.5 -0.3 -0.5 1.30 13.3
#> 5 1.8 0.694 38.8 -0.465 0.447 -1.39 -0.5 -0.3 -0.5 -0.7 19.2
#> 6 -2.2 -0.928 -70.2 0.535 -1.46 0.459 0.5 0.7 0.5 -1.7 27.3
#> 7 -2.2 -0.507 -45.2 0.885 -1.26 -1.74 -0.5 0.7 1.5 -0.7 26
#> 8 -2.2 -0.742 -23.2 0.225 -1.89 -1.54 0.5 0.7 1.5 -0.7 30.4
#> 9 1.8 0.564 128. 0.675 -0.228 -3.94 -0.5 0.7 1.5 1.30 15.8
#> 10 -0.2 -0.320 38.8 0.075 -0.628 -2.94 -0.5 0.7 1.5 3.3 19.7
#> 11 1.8 0.410 199. -0.00500 0.172 -3.84 -0.5 0.7 1.5 5.3 15
#> 12 -2.2 -0.501 -27.2 0.565 -0.618 0.159 0.5 0.7 0.5 -0.7 21.4
juice() 函数是一个不错的小捷径.由于准备好的配方是根据训练数据估计的，因此您只能从中处理训练数据.想象自己挤压准备好的配方以获取训练数据，您用来估计预处理参数开始.
The juice() function is a nice little shortcut. Because the prepped recipe was estimated from the training data, you can process the training data only from it. Picture yourself squeezing the prepped recipe to get the training data back out that you used to estimate the preprocessing parameters with to start with.
juice(cars_prep)
#> # A tibble: 20 x 11
#> cyl disp hp drat wt qsec vs am gear carb mpg
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -0.2 -0.222 -26.2 0.355 -0.778 -1.98 -0.5 0.7 0.5 1.30 21
#> 2 -0.2 -0.222 -26.2 0.355 -0.523 -1.42 -0.5 0.7 0.5 1.30 21
#> 3 -2.2 -0.615 -43.2 0.305 -1.08 0.169 0.5 0.7 0.5 -1.7 22.8
#> 4 -0.2 0.256 -26.2 -0.465 -0.183 0.999 0.5 -0.3 -0.5 -1.7 21.4
#> 5 1.8 0.589 38.8 -0.395 0.0415 -1.42 -0.5 -0.3 -0.5 -0.7 18.7
#> 6 -0.2 0.119 -31.2 -0.785 0.0615 1.78 0.5 -0.3 -0.5 -1.7 18.1
#> 7 1.8 0.589 109. -0.335 0.172 -2.60 -0.5 -0.3 -0.5 1.30 14.3
#> 8 -2.2 -0.309 -74.2 0.145 -0.208 1.56 0.5 -0.3 0.5 -0.7 24.4
#> 9 -2.2 -0.350 -41.2 0.375 -0.248 4.46 0.5 -0.3 0.5 -0.7 22.8
#> 10 -0.2 -0.176 -13.2 0.375 0.0415 -0.141 0.5 -0.3 0.5 1.30 19.2
#> 11 -0.2 -0.176 -13.2 0.375 0.0415 0.459 0.5 -0.3 0.5 1.30 17.8
#> 12 1.8 0.323 43.8 -0.475 0.672 -1.04 -0.5 -0.3 -0.5 0.300 16.4
#> 13 1.8 0.323 43.8 -0.475 0.332 -0.841 -0.5 -0.3 -0.5 0.300 17.3
#> 14 1.8 0.323 43.8 -0.475 0.382 -0.441 -0.5 -0.3 -0.5 0.300 15.2
#> 15 1.8 0.860 68.8 -0.615 1.85 -0.461 -0.5 -0.3 -0.5 1.30 10.4
#> 16 1.8 0.834 78.8 -0.545 2.03 -0.621 -0.5 -0.3 -0.5 1.30 10.4
#> 17 1.8 0.790 93.8 -0.315 1.95 -1.02 -0.5 -0.3 -0.5 1.30 14.7
#> 18 -2.2 -0.932 -70.2 0.535 -1.20 1.03 0.5 0.7 0.5 -1.7 32.4
#> 19 -2.2 -0.970 -84.2 1.38 -1.78 0.079 0.5 0.7 0.5 -0.7 30.4
#> 20 -2.2 -1.03 -71.2 0.675 -1.56 1.46 0.5 0.7 0.5 -1.7 33.9
由 reprex 包 (v0.3.0) 于 2020 年 6 月 4 日创建
Created on 2020-06-04 by the reprex package (v0.3.0)
它与bake(cars_prep, new_data = cars_train) 相同，只是一个快捷方式.
It is the same as bake(cars_prep, new_data = cars_train) and is just a shortcut.
这篇关于R包“食谱"中的prep/bake/juice有什么区别?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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