tidyrules

Description

tidyrules package provides a framework to work with decision rules. Rules can be extracted from supported models using tidy, augmented using validation data by augment, manipulated using standard dataframe operations, (modified) rulelists can be used to predict on unseen (test) data. Utilities include: Create a rulelist manually (as_rulelist), Export a rulelist to SQL (to_sql_case) and so on. The package offers two classes; rulelist and ruleset based on dataframe.

Author(s)

Maintainer: Srikanth Komala Sheshachala sri.teach@gmail.com

Authors:

• Amith Kumar Ullur Raghavendra amith54@gmail.com

See Also

rulelist, tidy, augment, predict

Add backquotes

Description

(vectorized) Add backquotes when a string has a space in it

Usage

addBackquotes(string)

Arguments

Value

character vector

Examples

tidyrules:::addBackquotes(c("ab", "a b"))

as_rulelist generic from tidyrules package

Description

as_rulelist generic

Usage

as_rulelist(x, ...)

Arguments

Value

A rulelist

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

as_rulelist method for a data.frame

Description

Convert a set of rules in a dataframe to a rulelist

Usage

## S3 method for class 'data.frame'
as_rulelist(x, keys = NULL, model_type = NULL, estimation_type, ...)

Arguments

Details

Input dataframe should contain these columns: rule_nbr, LHS, RHS. Providing other inputs helps augment better.

Value

rulelist object

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Examples

rules_df = tidytable::tidytable(rule_nbr = 1:2,
                                LHS      = c("var_1 > 50", "var_2 < 30"),
                                RHS      = c(2, 1)
                                )
as_rulelist(rules_df, estimation_type = "regression")

Get a ruleset from a rulelist

Description

Returns a ruleset object

Usage

as_ruleset(rulelist)

Arguments

Value

A ruleset

See Also

rulelist

Examples

model_class_party = partykit::ctree(species ~ .,
                                    data = palmerpenguins::penguins
                                    )
as_ruleset(tidy(model_class_party))

augment is re-export of generics::augment from tidyrules package

Description

See augment.rulelist

Usage

augment(x, ...)

Arguments

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Augment a rulelist

Description

augment outputs a rulelist with an additional column named augmented_stats based on summary statistics calculated using attribute validation_data.

Usage

## S3 method for class 'rulelist'
augment(x, ...)

Arguments

Details

The dataframe-column augmented_stats will have these columns corresponding to the estimation_type:

• For regression: support, IQR, RMSE

• For classification: support, confidence, lift

along with custom aggregations.

Value

A rulelist with a new dataframe-column named augmented_stats.

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Examples

# Examples for augment ------------------------------------------------------
library("magrittr")

# C5 ----
att = modeldata::attrition
set.seed(100)
train_index = sample(c(TRUE, FALSE), nrow(att), replace = TRUE)

model_c5 = C50::C5.0(Attrition ~., data = att[train_index, ], rules = TRUE)
tidy_c5  =
  model_c5 %>%
  tidy() %>%
  set_validation_data(att[!train_index, ], "Attrition")

tidy_c5

augment(tidy_c5) %>%
  tidytable::unnest(augmented_stats, names_sep = "__") %>%
  tidytable::glimpse()

# augment with custom aggregator
augment(tidy_c5,output_counts = list(table(Attrition))) %>%
  tidytable::unnest(augmented_stats, names_sep = "__") %>%
  tidytable::glimpse()

# rpart ----
set.seed(100)
train_index = sample(c(TRUE, FALSE), nrow(iris), replace = TRUE)

model_class_rpart = rpart::rpart(Species ~ ., data = iris[train_index, ])
tidy_class_rpart  = tidy(model_class_rpart) %>%
  set_validation_data(iris[!train_index, ], "Species")
tidy_class_rpart

model_regr_rpart = rpart::rpart(Sepal.Length ~ ., data = iris[train_index, ])
tidy_regr_rpart  = tidy(model_regr_rpart) %>%
  set_validation_data(iris[!train_index, ], "Sepal.Length")
tidy_regr_rpart

# augment (classification case)
augment(tidy_class_rpart) %>%
  tidytable::unnest(augmented_stats, names_sep = "__") %>%
  tidytable::glimpse()

# augment (regression case)
augment(tidy_regr_rpart) %>%
  tidytable::unnest(augmented_stats, names_sep = "__") %>%
  tidytable::glimpse()

# party ----
pen = palmerpenguins::penguins %>%
  tidytable::drop_na(bill_length_mm)
set.seed(100)
train_index = sample(c(TRUE, FALSE), nrow(pen), replace = TRUE)

model_class_party = partykit::ctree(species ~ ., data = pen[train_index, ])
tidy_class_party  = tidy(model_class_party) %>%
  set_validation_data(pen[!train_index, ], "species")
tidy_class_party

model_regr_party =
  partykit::ctree(bill_length_mm ~ ., data = pen[train_index, ])
tidy_regr_party  = tidy(model_regr_party) %>%
  set_validation_data(pen[!train_index, ], "bill_length_mm")
tidy_regr_party

# augment (classification case)
augment(tidy_class_party) %>%
  tidytable::unnest(augmented_stats, names_sep = "__") %>%
  tidytable::glimpse()

# augment (regression case)
augment(tidy_regr_party) %>%
  tidytable::unnest(augmented_stats, names_sep = "__") %>%
  tidytable::glimpse()

# cubist ----
att         = modeldata::attrition
set.seed(100)
train_index = sample(c(TRUE, FALSE), nrow(att), replace = TRUE)
cols_att    = setdiff(colnames(att), c("MonthlyIncome", "Attrition"))

model_cubist = Cubist::cubist(x = att[train_index, cols_att],
                              y = att[train_index, "MonthlyIncome"]
                              )

tidy_cubist = tidy(model_cubist) %>%
  set_validation_data(att[!train_index, ], "MonthlyIncome")
tidy_cubist

augment(tidy_cubist) %>%
  tidytable::unnest(augmented_stats, names_sep = "__") %>%
  tidytable::glimpse()

as the name says

Description

as the name says not to be exported

Usage

augment_class_keys(x, new_data, y_name, weight, ...)

as the name says

Description

as the name says not to be exported

Usage

augment_class_no_keys(x, new_data, y_name, weight, ...)

as the name says

Description

as the name says not to be exported

Usage

augment_regr_keys(x, new_data, y_name, weight, ...)

as the name says

Description

as the name says not to be exported

Usage

augment_regr_no_keys(x, new_data, y_name, weight, ...)

calculate is re-export of generics::calculate from tidyrules package

Description

See calculate.rulelist

Usage

calculate(x, ...)

Arguments

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

calculate metrics for a rulelist

Description

Computes some metrics (based on estimation_type) in cumulative window function style over the rulelist (in the same order) ignoring the keys.

Usage

## S3 method for class 'rulelist'
calculate(x, metrics_to_exclude = NULL, ...)

Arguments

Details

Default Metrics

These metrics are calculated by default:

• cumulative_coverage: For nth rule in the rulelist, number of distinct row_nbrs (of new_data) covered by nth and all preceding rules (in order). In weighted case, we sum the weights corresponding to the distinct row_nbrs.

• cumulative_overlap: Up til nth rule in the rulelist, number of distinct row_nbrs (of new_data) already covered by some preceding rule (in order). In weighted case, we sum the weights corresponding to the distinct row_nbrs.

For classification:

• cumulative_accuracy: For nth rule in the rulelist, fraction of row_nbrs such that RHS matches the y_name column (of new_data) by nth and all preceding rules (in order). In weighted case, weighted accuracy is computed.

For regression:

• cumulative_RMSE: For nth rule in the rulelist, weighted RMSE of all predictions (RHS) predicted by nth rule and all preceding rules.

Custom metrics

Custom metrics to be computed should be passed a named list of function(s) in .... The custom metric function should take these arguments in same order: rulelist, new_data, y_name, weight. The custom metric function should return a numeric vector of same length as the number of rows of rulelist.

Value

A dataframe of metrics with a rule_nbr column.

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Examples

library("magrittr")
model_c5  = C50::C5.0(Attrition ~., data = modeldata::attrition, rules = TRUE)
tidy_c5   = tidy(model_c5) %>%
            set_validation_data(modeldata::attrition, "Attrition") %>%
            set_keys(NULL)

# calculate default metrics (classification)
calculate(tidy_c5)

model_rpart = rpart::rpart(MonthlyIncome ~., data = modeldata::attrition)
tidy_rpart  =
  tidy(model_rpart) %>%
  set_validation_data(modeldata::attrition, "MonthlyIncome") %>%
  set_keys(NULL)

# calculate default metrics (regression)
calculate(tidy_rpart)

# calculate default metrics with a custom metric
#' custom function to get cumulative MAE
library("tidytable")
get_cumulative_MAE = function(rulelist, new_data, y_name, weight){

  priority_df =
    rulelist %>%
    select(rule_nbr) %>%
    mutate(priority = 1:nrow(rulelist)) %>%
    select(rule_nbr, priority)

  pred_df =
    predict(rulelist, new_data) %>%
    left_join(priority_df, by = "rule_nbr") %>%
    mutate(weight = local(weight)) %>%
    select(rule_nbr, row_nbr, weight, priority)

  new_data2 =
    new_data %>%
    mutate(row_nbr = 1:n()) %>%
    select(all_of(c("row_nbr", y_name)))

  rmse_till_rule = function(rn){

    if (is.character(rulelist$RHS)) {
      inter_df =
        pred_df %>%
        tidytable::filter(priority <= rn) %>%
        left_join(mutate(new_data, row_nbr = 1:n()), by = "row_nbr") %>%
        left_join(select(rulelist, rule_nbr, RHS), by = "rule_nbr") %>%
        nest(.by = c("RHS", "rule_nbr", "row_nbr", "priority", "weight")) %>%
        mutate(RHS = purrr::map2_dbl(RHS,
                                     data,
                                     ~ eval(parse(text = .x), envir = .y)
                                     )
               ) %>%
        unnest(data)
    } else {

      inter_df =
        pred_df %>%
        tidytable::filter(priority <= rn) %>%
        left_join(new_data2, by = "row_nbr") %>%
        left_join(select(rulelist, rule_nbr, RHS), by = "rule_nbr")
    }

    inter_df %>%
      summarise(rmse = MetricsWeighted::mae(RHS,
                                             .data[[y_name]],
                                             weight,
                                             na.rm = TRUE
                                             )
                ) %>%
      `[[`("rmse")
  }

  res = purrr::map_dbl(1:nrow(rulelist), rmse_till_rule)
  return(res)
}

calculate(tidy_rpart,
          metrics_to_exclude = NULL,
          list("cumulative_mae" = get_cumulative_MAE)
          )

Convert a R parsable rule to python/sql parsable rule

Description

Convert a R parsable rule to python/sql parsable rule

Usage

convert_rule_flavor(rule, flavor)

Arguments

Value

(chr vector) of rules

See Also

rulelist, tidy, augment, predict, to_sql_case

Other Auxiliary Rulelist Utility: to_sql_case()

Plot method for prune_rulelist class

Description

Plot method for prune_rulelist class

Usage

## S3 method for class 'prune_rulelist'
plot(x, ...)

Arguments

Value

ggplot2 object (invisibly)

Plot method for rulelist

Description

Plots a heatmap with rule_nbr's on x-side and clusters of row_nbr's on y-side of a binary matrix with 1 if a rule is applicable for a row.

Usage

## S3 method for class 'rulelist'
plot(x, thres_cluster_rows = 1000, dist_metric = "jaccard", ...)

Arguments

Details

Number of clusters is set to min(number of unique rows in the row_nbr X rule_nbr matrix and thres_cluster_rows)

Examples

library("magrittr")
att = modeldata::attrition
tidy_c5 =
  C50::C5.0(Attrition ~., data = att, rules = TRUE) %>%
  tidy() %>%
  set_validation_data(att, "Attrition") %>%
  set_keys(NULL)

plot(tidy_c5)

Position of space outside single quotes

Description

(vectorised) Detect the position of space in a string not within a pair of single quotes

Usage

positionSpaceOutsideSinglequotes(string)

Arguments

Value

A integer vector of positions

Examples

tidyrules:::positionSpaceOutsideSinglequotes(c("hello", "hel' 'o "))

predict method for a rulelist

Description

Predicts rule_nbr applicable (as per the order in rulelist) for a row_nbr (per key) in new_data

Usage

## S3 method for class 'rulelist'
predict(object, new_data, multiple = FALSE, ...)

Arguments

Details

If a row_nbr is covered more than one rule_nbr per 'keys', then rule_nbr appearing earlier (as in row order of the rulelist) takes precedence.

Output Format

• When multiple is FALSE(default), output is a dataframe with three or more columns: row_number (int), columns corresponding to 'keys', rule_nbr (int).

• When multiple is TRUE, output is a dataframe with three or more columns: row_number (int), columns corresponding to 'keys', rule_nbr (list column of integers).

• If a row number and 'keys' combination is not covered by any rule, then rule_nbr column has missing value.

Value

A dataframe. See Details.

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Examples

model_c5 = C50::C5.0(species ~.,
                     data = palmerpenguins::penguins,
                     trials = 5,
                     rules = TRUE
                     )
tidy_c5 = tidy(model_c5)
tidy_c5

output_1 = predict(tidy_c5, palmerpenguins::penguins)
output_1 # different rules per 'keys' (`trial_nbr` here)

output_2 = predict(tidy_c5, palmerpenguins::penguins, multiple = TRUE)
output_2 # `rule_nbr` is a list-column of integer vectors

predict method for a ruleset

Description

Predicts multiple rule_nbr(s) applicable for a row_nbr (per key) in new_data

Usage

## S3 method for class 'ruleset'
predict(object, new_data, ...)

Arguments

Value

A dataframe with three or more columns: row_number (int), columns corresponding to 'keys', rule_nbr (list column of integers). If a row number and 'keys' combination is not covered by any rule, then rule_nbr column has missing value.

See Also

predict.rulelist

Examples

model_c5 = C50::C5.0(species ~.,
                     data = palmerpenguins::penguins,
                     trials = 5,
                     rules = TRUE
                     )
tidy_c5_ruleset = as_ruleset(tidy(model_c5))
tidy_c5_ruleset

predict(tidy_c5_ruleset, palmerpenguins::penguins)

as the name says

Description

as the name says

Usage

predict_all_nokeys_rulelist(rulelist, new_data)

Arguments

Value

dataframe

with or without keys

Description

uses predict_all_nokeys_rulelist

Usage

predict_all_rulelist(rulelist, new_data)

Arguments

Value

dataframe

as the name says

Description

as the name says

Usage

predict_nokeys_rulelist(rulelist, new_data)

Arguments

Value

dataframe

with or without keys

Description

uses predict_nokeys_rulelist

Usage

predict_rulelist(rulelist, new_data)

Arguments

Value

dataframe

Print method for prune_rulelist class

Description

Print method for prune_rulelist class

Usage

## S3 method for class 'prune_rulelist'
print(x, ...)

Arguments

Print method for rulelist class

Description

Prints rulelist attributes and first few rows.

Usage

## S3 method for class 'rulelist'
print(x, banner = TRUE, ...)

Arguments

Value

input rulelist (invisibly)

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Print method for ruleset class

Description

Prints the ruleset object

Usage

## S3 method for class 'ruleset'
print(x, banner = TRUE, ...)

Arguments

Value

(invisibly) Returns the ruleset object

See Also

print.rulelist

Examples

model_class_party = partykit::ctree(species ~ .,
                                    data = palmerpenguins::penguins
                                    )
as_ruleset(tidy(model_class_party))

prune is re-export of generics::prune from tidyrules package

Description

See prune.rulelist

Usage

prune(tree, ...)

Arguments

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

prune rules of a rulelist

Description

Prune the rulelist by suggesting to keep first 'k' rules based on metrics computed by calculate

Usage

## S3 method for class 'rulelist'
prune(
  tree,
  metrics_to_exclude = NULL,
  stop_expr_string = "relative__cumulative_coverage >= 0.9",
  min_n_rules = 1,
  ...
)

Arguments

Details

1. Metrics are computed using calculate. 2. Relative metrics (prepended by 'relative__') are calculated by dividing each metric by its max value. 3. The first rule in rulelist order which meets the 'stop_expr_string' criteria is stored (say 'pos'). Print method suggests to keep rules until pos.

Value

Object of class 'prune_ruleslist' with these components: 1. pruned: ruleset keeping only first 'pos' rows. 2. n_pruned_rules: pos. If stop criteria is never met, then pos = nrow(ruleset) 3. n_total_rules: nrow(ruleset), 4. metrics_df: Dataframe with metrics and relative metrics 5. stop_expr_string

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Examples

library("magrittr")
model_c5  = C50::C5.0(Attrition ~., data = modeldata::attrition, rules = TRUE)
tidy_c5   = tidy(model_c5) %>%
            set_validation_data(modeldata::attrition, "Attrition") %>%
            set_keys(NULL)

#' prune with defaults
prune_obj = prune(tidy_c5)
#' note that all other metrics are visible in the print output
prune_obj
plot(prune_obj)
prune_obj$pruned

#' prune with a different stop_expr_string threshold
prune_obj = prune(tidy_c5,
                  stop_expr_string = "relative__cumulative_coverage >= 0.2"
                  )
prune_obj #' as expected, has smaller then 10 rules as compared to default args
plot(prune_obj)
prune_obj$pruned

#' prune with a different stop_expr_string metric
st = "relative__cumulative_overlap <= 0.7 & relative__cumulative_overlap > 0"
prune_obj = prune(tidy_c5, stop_expr_string = st)
prune_obj #' as expected, has smaller then 10 rules as compared to default args
plot(prune_obj)
prune_obj$pruned

Remove empty lines

Description

Remove empty strings from a character vector

Usage

removeEmptyLines(strings)

Arguments

Value

A character vector

Examples

tidyrules:::removeEmptyLines(c("abc", "", "d"))

reorder generic

Description

reorder generic for rulelist

Usage

reorder(x, ...)

Arguments

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Reorder the rules/rows of a rulelist

Description

Implements a greedy strategy to add one rule at a time which maximizes/minimizes a metric.

Usage

## S3 method for class 'rulelist'
reorder(x, metric = "cumulative_coverage", minimize = FALSE, init = NULL, ...)

Arguments

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Examples

library("magrittr")
att = modeldata::attrition
tidy_c5 =
  C50::C5.0(Attrition ~., data = att, rules = TRUE) %>%
  tidy() %>%
  set_validation_data(att, "Attrition") %>%
  set_keys(NULL) %>%
  head(5)

# with defaults
reorder(tidy_c5)

# use 'cumulative_overlap' to break ties (if any)
reorder(tidy_c5, metric = c("cumulative_coverage", "cumulative_overlap"))

# reorder after 2 rules
reorder(tidy_c5, init = 2)

Rulelist

Description

Structure

A rulelist is ordered list of rules stored as a dataframe. Each row, specifies a rule (LHS), expected outcome (RHS) and some other details.

It has these mandatory columns:

• rule_nbr: (integer vector) Rule number

• LHS: (character vector) A rule is a string that can be parsed using base::parse()

• RHS: (character vector or a literal)

Example

| rule_nbr|LHS                                                                  |RHS       | support| confidence|     lift|
|--------:|:--------------------------------------------------------------------|:---------|-------:|----------:|--------:|
|        1|( island %in% c('Biscoe') ) & ( flipper_length_mm > 203 )            |Gentoo    |     122|  1.0000000| 2.774193|
|        2|( island %in% c('Biscoe') ) & ( flipper_length_mm <= 203 )           |Adelie    |      46|  0.9565217| 2.164760|
|        3|( island %in% c('Dream', 'Torgersen') ) & ( bill_length_mm > 44.1 )  |Chinstrap |      65|  0.9538462| 4.825339|
|        4|( island %in% c('Dream', 'Torgersen') ) & ( bill_length_mm <= 44.1 ) |Adelie    |     111|  0.9459459| 2.140825|

Create a rulelist

A rulelist can be created using tidy() on some supported model fits (run: utils::methods(tidy)). It can also be created manually from a existing dataframe using as_rulelist.

Keys and attributes

Columns identified as 'keys' along with rule_nbr form a unique combination – a group of rules. For example, rule-based C5 model with multiple trials creates rules per each trial_nbr. predict method understands 'keys', thereby provides/predicts a rule number (for each row in new data / test data) within the same trial_nbr.

A rulelist has these mandatory attributes:

• estimation_type: One among regression, classification

  A rulelist has these optional attributes:

• keys: (character vector)Names of the column that forms a key.

• model_type: (string) Name of the model

Set Validation data

This helps a few methods like augment, calculate, prune, reorder require few additional attributes which can be set using set_validation_data.

Methods for rulelist

1. Predict: Given a dataframe (possibly without a dependent variable column aka 'test data'), predicts the first rule (as ordered in the rulelist) per 'keys' that is applicable for each row. When multiple = TRUE, returns all rules applicable for a row (per key).

2. Augment: Outputs summary statistics per rule over validation data and returns a rulelist with a new dataframe-column.

3. Calculate: Computes metrics for a rulelist in a cumulative manner such as cumulative_coverage, cumulative_overlap, cumulative_accuracy.

4. Prune: Suggests pruning a rulelist such that some expectation are met (based on metrics). Example: cumulative_coverage of 80% can be met with a first few rules.

5. Reorder: Reorders a rulelist in order to maximize a metric.

Manipulating a rulelist

Rulelists are essentially dataframes. Hence, any dataframe operations which preferably preserve attributes will output a rulelist. as_rulelist and as.data.frame will help in moving back and forth between rulelist and dataframe worlds.

Utilities for a rulelist

1. as_rulelist: Create a rulelist from a dataframe with some mandatory columns.

2. set_keys: Set or Unset 'keys' of a rulelist.

3. to_sql_case: Outputs a SQL case statement for a rulelist.

4. convert_rule_flavor: Converts R-parsable rule strings to python/SQL parsable rule strings.

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Ruleset

Description

ruleset class is a piggyback class that inherits rulelist class for convenience of print and predict methods.

Set keys for a rulelist

Description

'keys' are a set of column(s) which identify a group of rules in a rulelist. Methods like predict, augment produce output per key combination.

Usage

set_keys(x, keys, reset = FALSE)

Arguments

Details

A new rulelist is returned with attr keys is modified. The input rulelist object is unaltered.

Value

A rulelist object

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Other Core Rulelist Utility: set_validation_data()

Examples

model_c5 = C50::C5.0(Attrition ~., data = modeldata::attrition, rules = TRUE)
tidy_c5 = tidy(model_c5)
tidy_c5 # keys are: "trial_nbr"

tidy_c5[["rule_nbr"]] = 1:nrow(tidy_c5)
new_tidy_c5 = set_keys(tidy_c5, NULL) # remove all keys
new_tidy_c5

new_2_tidy_c5 = set_keys(new_tidy_c5, "trial_nbr") # set "trial_nbr" as key
new_2_tidy_c5

# Note that `tidy_c5` and `new_tidy_c5` are not altered.
tidy_c5
new_tidy_c5

Add validation_data to a rulelist

Description

Returns a rulelist with three new attributes set: validation_data, y_name and weight. Methods such as augment, calculate, prune, reorder require this to be set.

Usage

set_validation_data(x, validation_data, y_name, weight = 1)

Arguments

Value

A rulelist with some extra attributes set.

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Other Core Rulelist Utility: set_keys()

Examples

att = modeldata::attrition
set.seed(100)
index = sample(c(TRUE, FALSE), nrow(att), replace = TRUE)
model_c5 = C50::C5.0(Attrition ~., data = att[index, ], rules = TRUE)

tidy_c5 = tidy(model_c5)
tidy_c5

tidy_c5_2 = set_validation_data(tidy_c5,
                                validation_data = att[!index, ],
                                y_name = "Attrition",
                                weight = 1 # default
                                )
tidy_c5_2
tidy_c5 # not altered

Vectorized semantic equivalent of 'head' for a string

Description

Picks the substring starting from the first character

Usage

strHead(string, n)

Arguments

Details

'n' can be in the interval [-len + 1, len] (both ends inclusive)

Value

A string

Examples

tidyrules:::strHead(c("string", "string2"), 2)
tidyrules:::strHead(c("string", "string2"), -1)

Sequential string replace

Description

Sequential string replace via reduce

Usage

strReplaceReduce(string, pattern, replacement)

Arguments

Value

character vector

Examples

tidyrules:::strReplaceReduce("abcd", c("ab", "dc"), c("cd", "ab"))

String split a string

Description

and return a character vector (not a list)

Usage

strSplitSingle(string, pattern)

Arguments

Value

A character vector

Examples

tidyrules:::strSplitSingle("abc,d", ",")

Vectorized semantic equivalent of tail for a string

Description

Picks the substring starting from the first character

Usage

strTail(string, n)

Arguments

Details

'n' can be in the interval [-len + 1, len] (both ends inclusive)

Value

A string

Examples

tidyrules:::strTail(c("string", "string2"), 2)
tidyrules:::strTail(c("string", "string2"), -1)

tidy is re-export of generics::tidy from tidyrules package

Description

tidy applied on a supported model fit creates a rulelist. See Also section links to documentation of specific methods.

Usage

tidy(x, ...)

Arguments

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Other Core Tidy Utility: tidy.C5.0(), tidy.cubist(), tidy.rpart()

Get the rulelist from a C5 model

Description

Each row corresponds to a rule per trial_nbr

Usage

## S3 method for class 'C5.0'
tidy(x, ...)

Arguments

Details

• The output columns are: rule_nbr, trial_nbr, LHS, RHS, support, confidence, lift.

• Rules per trial_nbr are sorted in this order: desc(confidence), desc(lift), desc(support).

Optional named arguments:

• laplace (flag, default: TRUE) is supported. This computes confidence with laplace correction as documented under 'Rulesets' here: C5 doc.

Value

A rulelist object

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Other Core Tidy Utility: tidy(), tidy.cubist(), tidy.rpart()

Examples

model_c5 = C50::C5.0(Attrition ~., data = modeldata::attrition, rules = TRUE)
tidy(model_c5)

Get the rulelist from a party model

Description

Each row corresponds to a rule

Usage

## S3 method for class 'constparty'
tidy(x, ...)

Arguments

Details

These types of party models are supported: regression (y is numeric), classification (y is factor)

For party classification model:

• Output columns are: rule_nbr, LHS, RHS, support, confidence, lift, terminal_node_id.

• Rules are sorted in this order: desc(confidence), desc(lift), desc(support).

For party regression model:

• Output columns are: rule_nbr, LHS, RHS, support, IQR, RMSE, terminal_node_id.

• Rules are sorted in this order: RMSE, desc(support).

Value

A rulelist object

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Examples

pen = palmerpenguins::penguins
model_class_party = partykit::ctree(species ~ ., data = pen)
tidy(model_class_party)
model_regr_party = partykit::ctree(bill_length_mm ~ ., data = pen)
tidy(model_regr_party)

Get the rulelist from a cubist model

Description

Each row corresponds to a rule per committee

Usage

## S3 method for class 'cubist'
tidy(x, ...)

Arguments

Details

• The output columns are: rule_nbr, committee, LHS, RHS, support, mean, min, max, error.

• Rules are sorted in this order per committee: error, desc(support)

Value

A rulelist object

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Other Core Tidy Utility: tidy(), tidy.C5.0(), tidy.rpart()

Examples

att = modeldata::attrition
cols_att    = setdiff(colnames(att), c("MonthlyIncome", "Attrition"))
model_cubist = Cubist::cubist(x = att[, cols_att],
                              y = att[["MonthlyIncome"]]
                              )
tidy(model_cubist)

Get the rulelist from a rpart model

Description

Each row corresponds to a rule

Usage

## S3 method for class 'rpart'
tidy(x, ...)

Arguments

Details

For rpart rules, one should build the model without ordered factor variable. We recommend you to convert ordered factor to factor or integer class.

For rpart::rpart classification model:

• Output columns are: rule_nbr, LHS, RHS, support, confidence, lift.

• The rules are sorted in this order: desc(confidence), desc(lift), desc(support).

For rpart::rpart regression(anova) model:

• Output columns are: rule_nbr, LHS, RHS, support.

• The rules are sorted in this order: desc(support).

Value

A rulelist object

See Also

rulelist, tidy, augment, predict, calculate, prune, reorder

Other Core Tidy Utility: tidy(), tidy.C5.0(), tidy.cubist()

Examples

model_class_rpart = rpart::rpart(Species ~ ., data = iris)
tidy(model_class_rpart)

model_regr_rpart = rpart::rpart(Sepal.Length ~ ., data = iris)
tidy(model_regr_rpart)

Extract SQL case statement from a rulelist

Description

Extract SQL case statement from a rulelist

Usage

to_sql_case(rulelist, rhs_column_name = "RHS", output_colname = "output")

Arguments

Details

As a side-effect, the SQL statement is cat to stdout. The output contains newline character.

Value

(string invisibly) SQL case statement

See Also

rulelist, tidy, augment, predict, convert_rule_flavor

Other Auxiliary Rulelist Utility: convert_rule_flavor()

Examples

model_c5 = C50::C5.0(Attrition ~., data = modeldata::attrition, rules = TRUE)
tidy(model_c5)
to_sql_case(tidy(model_c5))

Get variable specification for a Cubist/C5 object

Description

Obtain variable names, type (numeric, ordered, factor) and levels as a tidytable

Usage

varSpec(object)

Arguments

Value

A tidytable with three columns: variable(character), type(character) and levels(a list-column). For numeric variables, levels are set to NA.

Examples

## Not run: 
data("attrition", package = "modeldata")
cols_att = setdiff(colnames(attrition), c("MonthlyIncome", "Attrition"))

cb_att = Cubist::cubist(x = attrition[, cols_att],
                        y = attrition[["MonthlyIncome"]]
                        )
varSpec(cb_att)

## End(Not run)