| Title: | Model Wrappers for Rule-Based Models |
| Version: | 1.0.2 |
| Description: | Bindings for additional models for use with the 'parsnip' package. Models include prediction rule ensembles (Friedman and Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992 ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013) <doi:10.1007/978-1-4614-6849-3>. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/tidymodels/rules, https://rules.tidymodels.org/ |
| BugReports: | https://github.com/tidymodels/rules/issues |
| Depends: | parsnip (≥ 0.2.1.9004), R (≥ 3.4) |
| Imports: | dials (≥ 0.1.1.9001), dplyr, generics (≥ 0.1.0), purrr, rlang, stats, stringr, tidyr, utils |
| Suggests: | C50, covr, Cubist, knitr, modeldata, recipes, rmarkdown, spelling, testthat (≥ 3.0.0), tibble, xrf (≥ 0.2.0) |
| Config/Needs/website: | tidyr, tidyverse/tidytemplate, recipes, xrf |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-03-08 23:14:01 UTC; emilhvitfeldt |
| Author: | Emil Hvitfeldt 
[aut, cre],
Max Kuhn [aut],
Posit Software, PBC [cph, fnd] |
| Maintainer: | Emil Hvitfeldt <emil.hvitfeldt@posit.co> |
| Repository: | CRAN |
| Date/Publication: | 2023-03-08 23:40:02 UTC |
rules: Model Wrappers for Rule-Based Models
Description
Bindings for additional models for use with the 'parsnip' package. Models include prediction rule ensembles (Friedman and Popescu, 2008) doi:10.1214/07-AOAS148, C5.0 rules (Quinlan, 1992 ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013) doi:10.1007/978-1-4614-6849-3.
Author(s)
Maintainer: Emil Hvitfeldt emil.hvitfeldt@posit.co (ORCID)
Authors:
Max Kuhn max@posit.co (ORCID)
Other contributors:
Posit Software, PBC [copyright holder, funder]
See Also
Useful links:
Report bugs at https://github.com/tidymodels/rules/issues
Internal function wrappers
Description
These are not supported when called by the user.
Usage
c5_fit(x, y, trials = 1, minCases = 2, cost = NULL, ...)
c5_pred(object, new_data, trials = object$fit$trials["Actual"], ...)
## S3 method for class 'C5_rules'
tunable(x, ...)
cubist_fit(x, y, committees = 1, neighbors = 0, max_rules = NA, ...)
get_neighbors(x)
## S3 method for class 'cubist_rules'
tunable(x, ...)
xrf_fit(
formula,
data,
max_depth = 6,
nrounds = 15,
eta = 0.3,
colsample_bynode = NULL,
colsample_bytree = NULL,
min_child_weight = 1,
gamma = 0,
subsample = 1,
validation = 0,
early_stop = NULL,
counts = TRUE,
event_level = c("first", "second"),
lambda = 0.1,
...
)
xrf_pred(object, new_data, lambda = object$fit$lambda, type, ...)
## S3 method for class 'rule_fit'
tunable(x, ...)
Parameter functions for Cubist models
Description
Committee-based models enact a boosting-like procedure to produce ensembles.
committees parameter is for the number of models in the ensembles while
max_rules can be used to limit the number of possible rules.
Usage
committees(range = c(1L, 100L), trans = NULL)
max_rules(range = c(1L, 500L), trans = NULL)
Arguments
range |
A two-element vector holding the defaults for the smallest and largest possible values, respectively. |
trans |
A |
Value
A function with classes "quant_param" and "param"
Examples
committees()
committees(4:5)
max_rules()
multi_predict() methods for rule-based models
Description
multi_predict() methods for rule-based models
Usage
## S3 method for class ''_cubist''
multi_predict(object, new_data, type = NULL, neighbors = NULL, ...)
## S3 method for class ''_xrf''
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)
Arguments
object |
A |
new_data |
A rectangular data object, such as a data frame. |
type |
A single character value or |
neighbors |
A numeric vector of neighbors values between zero and nine. |
... |
Not currently used. |
penalty |
Non-negative penalty values. |
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- dials
- generics
- parsnip
Turn C5.0 and rule-based models into tidy tibbles
Description
Turn C5.0 and rule-based models into tidy tibbles
Usage
## S3 method for class 'C5.0'
tidy(x, trees = x$trials["Actual"], ...)
## S3 method for class 'cubist'
tidy(x, committees = x$committee, ...)
## S3 method for class 'xrf'
tidy(x, penalty = NULL, unit = c("rules", "columns"), ...)
Arguments
x |
A |
trees |
The number of boosting iterations to tidy (defaults to the entire ensemble). |
... |
Not currently used. |
committees |
The number of committees to tidy (defaults to the entire ensemble). |
penalty |
A single numeric value for the |
unit |
What data should be returned? For |
Details
The outputs for these tidy functions are different since the model structures are different.
Let’s look at Cubist and RuleFit first, using the Ames data, then C5.0 with a different data set.
An example using the Ames data
First we will fit a Cubist model and tidy it:
library(tidymodels)
library(rules)
library(rlang)
data(ames, package = "modeldata")
ames <- ames %>%
mutate(Sale_Price = log10(Sale_Price)) %>%
select(Sale_Price, Longitude, Latitude, Central_Air)
cb_fit <-
cubist_rules(committees = 10) %>%
set_engine("Cubist") %>%
fit(Sale_Price ~ ., data = ames)
cb_res <- tidy(cb_fit)
cb_res
## # A tibble: 223 x 5 ## committee rule_num rule estimate statistic ## <int> <int> <chr> <list> <list> ## 1 1 1 ( Central_Air == 'N' ) & ( Latitude <=~ <tibble> <tibble> ## 2 1 2 ( Latitude <= 41.992611 ) & ( Latitude~ <tibble> <tibble> ## 3 1 3 ( Central_Air == 'N' ) & ( Latitude > ~ <tibble> <tibble> ## 4 1 4 ( Latitude <= 42.026997 ) & ( Longitud~ <tibble> <tibble> ## 5 1 5 ( Longitude > -93.63002 ) & ( Latitude~ <tibble> <tibble> ## 6 1 6 ( Latitude <= 42.035858 ) & ( Longitud~ <tibble> <tibble> ## 7 1 7 ( Latitude <= 42.024029 ) & ( Latitude~ <tibble> <tibble> ## 8 1 8 ( Longitude > -93.602348 ) & ( Latitud~ <tibble> <tibble> ## 9 1 9 ( Latitude <= 41.991756 ) & ( Longitud~ <tibble> <tibble> ## 10 1 10 ( Latitude > 42.041813 ) & ( Longitude~ <tibble> <tibble> ## # ... with 213 more rows
Since Cubist fits linear regressions within the data from each rule, the
coefficients are in the estimate column and other information are in
statistic:
cb_res$estimate[[1]]
## # A tibble: 3 x 2 ## term estimate ## <chr> <dbl> ## 1 (Intercept) -509. ## 2 Longitude -5.05 ## 3 Latitude 0.99
cb_res$statistic[[1]]
## # A tibble: 1 x 6 ## num_conditions coverage mean min max error ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 3 38 4.87 4.12 5.22 0.149
Note that we can get the data for this rule by using
rlang::parse_expr() with it:
rule_1_expr <- parse_expr(cb_res$rule[1]) rule_1_expr
## (Central_Air == "N") & (Latitude <= 42.026997) & (Longitude > ## -93.639572)
then use it to get the data back:
filter(ames, !!rule_1_expr)
## # A tibble: 38 x 4 ## Sale_Price Longitude Latitude Central_Air ## <dbl> <dbl> <dbl> <fct> ## 1 5.04 -93.6 42.0 N ## 2 4.74 -93.6 42.0 N ## 3 4.75 -93.6 42.0 N ## 4 4.54 -93.6 42.0 N ## 5 4.64 -93.6 42.0 N ## 6 5.22 -93.6 42.0 N ## 7 4.80 -93.6 42.0 N ## 8 4.99 -93.6 42.0 N ## 9 5.09 -93.6 42.0 N ## 10 4.89 -93.6 42.0 N ## # ... with 28 more rows
Now let’s fit a RuleFit model. First, we’ll use a recipe to convert the Central Air predictor to an indicator:
xrf_reg_mod <-
rule_fit(trees = 3, penalty = .001) %>%
set_engine("xrf") %>%
set_mode("regression")
# Make dummy variables since xgboost will not
ames_rec <-
recipe(Sale_Price ~ ., data = ames) %>%
step_dummy(Central_Air) %>%
step_zv(all_predictors())
ames_processed <- prep(ames_rec) %>% bake(new_data = NULL)
xrf_reg_fit <-
xrf_reg_mod %>%
fit(Sale_Price ~ ., data = ames_processed)
xrf_rule_res <- tidy(xrf_reg_fit, penalty = .001)
xrf_rule_res
## # A tibble: 8 x 3 ## rule_id rule estimate ## <chr> <chr> <dbl> ## 1 (Intercept) ( TRUE ) 16.4 ## 2 Central_Air_Y ( Central_Air_Y ) 0.0567 ## 3 Latitude ( Latitude ) -0.424 ## 4 Longitude ( Longitude ) -0.0694 ## 5 r1_1 ( Longitude < -93.6299744 ) 0.102 ## 6 r2_3 ( Central_Air_Y < 0.5 ) & ( Latitude < 42.0460129 ) -0.136 ## 7 r2_5 ( Latitude >= 42.0460129 ) & ( Longitude < -93.650901~ 0.302 ## 8 r2_6 ( Latitude >= 42.0460129 ) & ( Longitude >= -93.650901~ 0.0853
Here, the focus is on the model coefficients produced by glmnet. We
can also break down the results and sort them by the original predictor
columns:
tidy(xrf_reg_fit, penalty = .001, unit = "columns")
## # A tibble: 11 x 3 ## rule_id term estimate ## <chr> <chr> <dbl> ## 1 r1_1 Longitude 0.102 ## 2 r2_3 Latitude -0.136 ## 3 r2_5 Latitude 0.302 ## 4 r2_6 Latitude 0.0853 ## 5 r2_3 Central_Air_Y -0.136 ## 6 r2_5 Longitude 0.302 ## 7 r2_6 Longitude 0.0853 ## 8 (Intercept) (Intercept) 16.4 ## 9 Longitude Longitude -0.0694 ## 10 Latitude Latitude -0.424 ## 11 Central_Air_Y Central_Air_Y 0.0567
C5.0 classification models
Here, we’ll use the Palmer penguin data:
data(penguins, package = "modeldata") penguins <- drop_na(penguins)
First, let’s fit a boosted rule-based model and tidy:
rule_model <- C5_rules(trees = 3) %>% fit(island ~ ., data = penguins) rule_info <- tidy(rule_model) rule_info
## # A tibble: 25 x 4 ## trial rule_num rule statistic ## <int> <int> <chr> <list> ## 1 1 1 ( bill_length_mm > 37.5 ) <tibble> ## 2 1 2 ( species == 'Chinstrap' ) <tibble> ## 3 1 3 ( body_mass_g > 3200 ) & ( body_mass_g < 3700 ) & (~ <tibble> ## 4 1 4 ( flipper_length_mm < 193 ) <tibble> ## 5 1 5 ( species == 'Adelie' ) & ( bill_length_mm > 38.299~ <tibble> ## 6 1 6 ( bill_length_mm < 40.799999 ) & ( bill_depth_mm > ~ <tibble> ## 7 1 7 ( species == 'Adelie' ) & ( bill_length_mm > 41.599~ <tibble> ## 8 1 8 ( species == 'Adelie' ) & ( bill_depth_mm > 18.9 ) ~ <tibble> ## 9 2 1 ( species == 'Gentoo' ) <tibble> ## 10 2 2 ( body_mass_g > 3700 ) & ( sex == 'female' ) <tibble> ## # ... with 15 more rows
# The statistic column has the pre-computed data about the # data covered by the rule: rule_info$statistic[[1]]
## # A tibble: 1 x 4 ## num_conditions coverage lift class ## <dbl> <dbl> <dbl> <chr> ## 1 1 286 1.10 Biscoe
Tree-based models can also be tidied. Rather than saving the results in a recursive tree structure, we can show the paths to each of the terminal nodes (which is just a rule).
Let’s fit a model and tidy:
tree_model <-
boost_tree(trees = 3) %>%
set_engine("C5.0") %>%
set_mode("classification") %>%
fit(island ~ ., data = penguins)
tree_info <- tidy(tree_model)
tree_info
## # A tibble: 34 x 4 ## trial node rule statistic ## <int> <int> <chr> <list> ## 1 1 1 "( species %in% c(\"Adelie\") ) & ( sex == \"female\" ~ <tibble> ## 2 1 2 "( species %in% c(\"Adelie\") ) & ( sex == \"female\" ~ <tibble> ## 3 1 3 "( species %in% c(\"Adelie\") ) & ( sex == \"female\" ~ <tibble> ## 4 1 4 "( species %in% c(\"Adelie\") ) & ( sex == \"female\" ~ <tibble> ## 5 1 5 "( species %in% c(\"Adelie\") ) & ( sex == \"female\" ~ <tibble> ## 6 1 6 "( species %in% c(\"Adelie\") ) & ( sex == \"female\" ~ <tibble> ## 7 1 7 "( species %in% c(\"Adelie\") ) & ( sex == \"female\" ~ <tibble> ## 8 1 8 "( species %in% c(\"Adelie\") ) & ( sex == \"male\" ) ~ <tibble> ## 9 1 9 "( species %in% c(\"Adelie\") ) & ( sex == \"male\" ) ~ <tibble> ## 10 1 10 "( species %in% c(\"Adelie\") ) & ( sex == \"male\" ) ~ <tibble> ## # ... with 24 more rows
# The statistic column has the class breakdown: tree_info$statistic[[1]]
## # A tibble: 3 x 2 ## value count ## <chr> <dbl> ## 1 Biscoe 3 ## 2 Dream 1 ## 3 Torgersen 0
Note that C5.0 models can have fractional estimates of counts in the terminal nodes.