These functions extract various elements from a tidyflow object. If they do
not exist yet, an error is thrown. Most of these steps can only be executed
after the tidyflow has been fitted.
pull_tflow_rawdata() returns the complete raw/untrained data.
pull_tflow_split() returns the split object from the function specified
in plug_split.
pull_tflow_training() turns the training data from the split. Only
works when a split has been specified with plug_split.
If prep = TRUE, the preprocessing (either recipe or formula)
is applied to the data.
pull_tflow_testing() returns the testing data from the split. Only
works when a split has been specified with plug_split
If prep = TRUE, the preprocessing (either recipe or formula)
is applied to the data.
pull_tflow_preprocessor() returns either the formula or recipe used
for preprocessing. Note that if the recipe has a tune() argument,
it won't be finalized.
pull_tflow_resample() returns the resample object from the function
specified in plug_resample. The resample object does not
have the preprocessor applied (either formula or recipe).
pull_tflow_grid() returns the grid data frame from which the tuning
parameter was made.
pull_tflow_prepped_recipe() returns the prepped recipe.
pull_tflow_spec() returns the parsnip model specification.
pull_tflow_fit() returns the parsnip model fit.
pull_tflow_fit_tuning() returns the resample result from model tuning.
pull_tflow_rawdata(x)
pull_tflow_split(x)
pull_tflow_training(x, prep = FALSE)
pull_tflow_testing(x, prep = FALSE)
pull_tflow_resample(x)
pull_tflow_grid(x)
pull_tflow_preprocessor(x)
pull_tflow_prepped_recipe(x)
pull_tflow_spec(x)
pull_tflow_fit(x)
pull_tflow_fit_tuning(x)A tidyflow
A logical stating whether the training/testing data should be
returned with the preprocessing step applied (either the formula or the recipe
preprocessing). By default it is set to FALSE.
The extracted value from the tidyflow, x, as described in the description
section.
library(parsnip)
library(recipes)
library(rsample)
model <- set_engine(linear_reg(), "lm")
recipe <- ~ recipe(.x, mpg ~ cyl + disp) %>% step_log(disp)
tflow <-
mtcars %>%
tidyflow() %>%
plug_split(initial_split) %>%
plug_model(model)
recipe_tflow <- plug_recipe(tflow, recipe)
formula_tflow <- plug_formula(tflow, mpg ~ cyl + log(disp))
fit_recipe_tflow <- fit(recipe_tflow)
fit_formula_tflow <- fit(formula_tflow)
pull_tflow_rawdata(fit_recipe_tflow)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
pull_tflow_rawdata(fit_formula_tflow)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# The preprocessor is either the recipe function or a formula
pull_tflow_preprocessor(fit(recipe_tflow))
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 2
#>
#> Operations:
#>
#> Log transformation on disp
pull_tflow_preprocessor(fit(formula_tflow))
#> mpg ~ cyl + log(disp)
#> <environment: 0x7fbde5031e78>
# The `spec` is the parsnip spec before it has been fit.
# The `fit` is the fit parsnip model.
pull_tflow_spec(fit_formula_tflow)
#> Linear Regression Model Specification (regression)
#>
#> Computational engine: lm
#>
pull_tflow_fit(fit_formula_tflow)
#> parsnip model object
#>
#>
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#>
#> Coefficients:
#> (Intercept) cyl `log(disp)`
#> 72.82463 0.06753 -10.06176
#>
# The raw training and testing
pull_tflow_training(fit_recipe_tflow)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
pull_tflow_testing(fit_recipe_tflow)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# Or with the preprocessor (recipe/formula) applied
pull_tflow_training(fit_recipe_tflow, prep = TRUE)
#> mpg cyl disp
#> 1 14.3 8 5.886104
#> 2 21.5 4 4.788325
#> 3 15.5 8 5.762051
#> 4 18.1 6 5.416100
#> 5 22.8 4 4.947340
#> 6 24.4 4 4.988390
#> 7 33.9 4 4.264087
#> 8 32.4 4 4.365643
#> 9 21.4 6 5.552960
#> 10 19.2 6 5.121580
#> 11 16.4 8 5.619676
#> 12 17.8 6 5.121580
#> 13 15.2 8 5.717028
#> 14 30.4 4 4.554929
#> 15 17.3 8 5.619676
#> 16 22.8 4 4.682131
#> 17 21.4 4 4.795791
#> 18 19.7 6 4.976734
#> 19 26.0 4 4.789989
#> 20 19.2 8 5.991465
#> 21 15.8 8 5.860786
#> 22 18.7 8 5.886104
#> 23 21.0 6 5.075174
#> 24 27.3 4 4.369448
pull_tflow_testing(fit_recipe_tflow, prep = TRUE)
#> mpg cyl disp
#> 1 21.0 6 5.075174
#> 2 15.2 8 5.619676
#> 3 10.4 8 6.156979
#> 4 10.4 8 6.131226
#> 5 14.7 8 6.086775
#> 6 30.4 4 4.326778
#> 7 13.3 8 5.857933
#> 8 15.0 8 5.707110
# A useful shortcut is to extract the prepped recipe from the tidyflow
pull_tflow_prepped_recipe(fit_recipe_tflow)
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 2
#>
#> Training data contained 24 data points and no missing data.
#>
#> Operations:
#>
#> Log transformation on disp [trained]