| Type: | Package |
| Title: | Customer Intelligence Tool for Rapid Understandable Segmentation |
| Version: | 1.0.2 |
| Maintainer: | Dom Clarke <dom.clarke@peak.ai> |
| Copyright: | See the file COPYRIGHTS |
| Description: | A tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models. |
| License: | MIT + file LICENSE |
| Suggests: | testthat |
| Depends: | R (≥ 3.5.0) |
| Imports: | ggplot2 (≥ 3.3.0), GGally (≥ 2.0.0), clustMixType (≥ 0.1-16), treeClust (≥ 1.1-7), rpart (≥ 4.1-15), tibble (≥ 3.0.0), rpart.plot (≥ 3.0.7), stringr (≥ 1.3.0), dplyr (≥ 1.0.6), RColorBrewer (≥ 1.1-2), rlang (≥ 0.4.9), methods |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.0 |
| NeedsCompilation: | no |
| Packaged: | 2022-06-17 13:30:02 UTC; oskarnummedal |
| Author: | Dom Clarke [aut, cre], Cinzia Braglia [aut], Oskar Nummedal [aut], Leo McCarthy [aut], Rebekah Yates [aut], Stuart Davie [aut], Joash Alonso [aut], PEAK AI LIMITED [cph] |
| Repository: | CRAN |
| Date/Publication: | 2022-06-17 15:50:02 UTC |
Creates pair plot from data table
Description
Creates pair plot from data table
Usage
citrus_pair_plot(model, vars = NULL)
Arguments
model |
list, a citrus segmentation model |
vars |
data.frame, the data to segment |
Value
GGally object displaying the segment feature pair plots.
k-clusters model
Description
k-clusters method for segmentation. It can handle segmentation for both numerical data types only, by using k-means algorithm, and mixed data types (numerical and categorical) by using k-prototypes algorithm
Usage
k_clusters(data, hyperparameters, verbose = TRUE)
Arguments
data |
data.frame, the data to segment |
hyperparameters |
list of hyperparameters to pass. They include
centers: number of clusters or a set of initial (distinct) cluster centers, or 'auto'. When 'auto' is chosen, the number of clusters is optimised; |
verbose |
logical whether information about the clustering procedure should be given. |
Value
A class called "k-clusters" containing a list of the model definition, the hyper-parameters, a table of outliers, the elbow plot (ggplot object) used to determine the optimal no. of clusters, and a lookup table containing segment predictions for customers.
Model management function
Description
Saves the model and its settings so that it can be recreated
Usage
model_management(model, hyperparameters)
Arguments
model |
data.frame, the model to save |
hyperparameters |
list, list of hyperparameters of the model |
Value
No return value. Called to save model and settings locally.
Output Table
Description
Generates the output table for model and data
Usage
output_table(data, model)
Arguments
data |
A dataframe generated from the pre-processing step |
model |
A model object used to classify ids with, generated from the model selection layer |
Value
A tibble providing high-level segment attributes such as mean and max (numeric) or mode (categorical) for the segmentation features used.
Preprocess Function
Description
Transforms a transactional table into an id aggregated table with custom options for aggregation methods for numeric and categorical columns.
Usage
preprocess(
df,
samplesize = NA,
numeric_operation_list = c("mean"),
categories = NULL,
target = NA,
target_agg = "mean",
verbose = TRUE
)
Arguments
df |
data.frame, the data to preprocess |
samplesize |
numeric, the fraction of ids used to create a sub-sample of the input df |
numeric_operation_list |
list, a list of the aggregation functions to apply to numeric columns |
categories |
list, a list of the categorical columns to aggregate |
target |
character, the column to use as a response variable for supervised learning |
target_agg |
character, the aggregation function to use to aggregate the target column |
verbose |
logical whether information about the preprocessing should be given |
Value
An id attributes data frame, e.g. customer attributes if the id represents customer IDs. A single row per unique id.
Segmentation preprocessed data
Description
A sample customer dataset for the purpose of demonstrating the segmentation algorithm.
Usage
data(preprocessed_data)
Format
Data frame on a customer level. Contains 402 rows and 8 columns.
Examples
data(preprocessed_data)
rpart.lists function
Description
THIS HAS BEEN COPIED FROM THE ARCHIVED rpart.utils PACKAGE AND THIS CODE WAS WRITTEN BY THE AUTHORS OF THAT PACKAGE Creates lists of variable values (factor levels) associated with each rule in an rpart object.
Usage
rpart.lists(object)
Arguments
object |
an rpart object |
Value
a list of lists
Examples
library(rpart)
fit<-rpart(Reliability~.,data=car.test.frame)
rpart.lists(fit)
Plot a prettified rpart model
Description
Plot an rpart model and prettifies it. Wrap around the rpart.plot::prp function
Usage
rpart.plot_pretty(
model,
main = "",
sub,
caption,
palettes,
type = 2,
fontfamily = "sans",
...
)
Arguments
model |
an rpart model object |
main |
main title |
sub |
fixing captions in line |
caption |
character, caption to use in the plot |
palettes |
list, list of colours to use in the plot |
type |
type of plot. Default is 2. Possible values are: 0 Default. Draw a split label at each split and a node label at each leaf. 1 Label all nodes, not just leaves. 2 Like 1 but draw the split labels below the node labels. 3 Draw separate split labels for the left and right directions. 4 Like 3 but label all nodes, not just leaves. 5 Show the split variable name in the interior nodes. |
fontfamily |
Names of the font family to use for the text in the plots. |
... |
Additional arguments. |
Value
An rpart.plot object. This plot object can be plotted using the rpart::prp function.
rpart.rules function
Description
THIS HAS BEEN COPIED FROM THE ARCHIVED rpart.utils PACKAGE AND THIS CODE WAS WRITTEN BY THE AUTHORS OF THAT PACKAGE Returns a list of strings summarizing the branch path to each node.
Usage
rpart.rules(object)
Arguments
object |
an rpart object |
Examples
library(rpart)
fit<-rpart(Reliability~.,data=car.test.frame)
rpart.rules(fit)
rpart.rules.table function
Description
THIS HAS BEEN COPIED FROM THE ARCHIVED rpart.utils PACKAGE AND THIS CODE WAS WRITTEN BY THE AUTHORS OF THAT PACKAGE Returns an unpivoted table of branch paths (subrules) associated with each node.
Usage
rpart.rules.table(object)
Arguments
object |
an rpart object |
Examples
library(rpart)
fit<-rpart(Reliability~.,data=car.test.frame)
rpart.rules.table(fit)
rpart.subrules.table function
Description
THIS HAS BEEN COPIED FROM THE ARCHIVED rpart.utils PACKAGE AND THIS CODE WAS WRITTEN BY THE AUTHORS OF THAT PACKAGE Returns an unpivoted table of variable values (factor levels) associated with each branch.
Usage
rpart.subrules.table(object)
Arguments
object |
an rpart object |
Examples
library(rpart)
fit<-rpart(Reliability~.,data=car.test.frame)
rpart.subrules.table(fit)
Segment Function
Description
Segments the data by running all steps in the segmentation pipeline, including output table
Usage
segment(
data,
modeltype = c("tree", "k-clusters"),
FUN = NULL,
FUN_preprocess = NULL,
steps = c("preprocess", "model"),
prettify = FALSE,
print_plot = FALSE,
hyperparameters = NULL,
force = FALSE,
verbose = FALSE
)
Arguments
data |
data.frame, the data to segment |
modeltype |
character, the type of model to use to segment choices are: 'tree', 'k-clusters' |
FUN |
function, A user specified function to segment, if the standard methods are not wanting to be used |
FUN_preprocess |
function, A user specified function to preprocess, if the standard methods are not wanting to be used |
steps |
list, names of the steps the user want to run the data on. Options are 'preprocess' and 'model' |
prettify |
logical, TRUE if want cleaned up outputs, FALSE for raw output |
print_plot |
logical, TRUE if want to print the plot |
hyperparameters |
list of hyperparameters to use in the model. |
force |
logical, TRUE to ignore errors in validation step and force model execution. |
verbose |
logical whether information about the segmentation pipeline should be given. |
Value
A list of three objects. A tibble providing high-level segment attributes, a lookup table (data frame) with the id and predicted segment number, and an rpart object defining the model.
Segmentation transactional data
Description
A sample customer dataset for the purpose of demonstrating the segmentation algorithm.
Usage
data(transactional_data)
Format
Data frame on a transactional level. Contains 10,000 rows and 6 columns.
Examples
data(transactional_data)
Abstraction layer function
Description
Organises the model outputs, predictions and settings in a general structure
Usage
tree_abstract(model)
Arguments
model |
The model to organise |
Value
A structure with the class name "tree_model" which contains a list of all the relevant model data, including the rpart model object, hyper-parameters, segment table and the labelled customer lookup table.
Tree Segment Function
Description
Runs decision tree optimisation on the data to segment ids.
Usage
tree_segment(data, hyperparameters, verbose = TRUE)
Arguments
data |
data.frame, the data to segment |
hyperparameters |
list, list of hyperparameters to pass. They include segmentation_variables: a vector or list with variable names that will be used as segmentation variables; dependent_variable: a string with the name of the dependent variable that is used in the clustering; min_segmentation_fraction: integer, the minimum segment size as a proportion of the total data set; number_of_segments: integer, number of leaves you want the decision tree to have. |
verbose |
logical whether information about the segmentation procedure should be given. |
Value
List of 4 objects. The rpart object defining the model, a data frame providing high-level segment attributes, a lookup table (data frame) with the id and predicted segment number, and a list of the hyperparameters used.
Author(s)
Stuart Davie, stuart.davie@peak.ai
Tree Segment Prettify Function
Description
Returns a prettier version of the decision tree.
Usage
tree_segment_prettify(tree, char_length = 20, print_plot = FALSE)
Arguments
tree |
The decision tree model to prettify |
char_length |
integer, the character limit before truncating categories and putting them into an "other" group |
print_plot |
logical, indicates whether to print the generated plot or not |
Value
A formatted and "prettified" rpart.plot object. This plot object can be plotted using the rpart::prp function.
Validation function
Description
Validates that the input data adheres to the expected format for modelling.
Usage
validate(df, supervised = TRUE, force, hyperparameters)
Arguments
df |
data.frame, the data to validate |
supervised |
logical, TRUE for supervised learning, FALSE for k-clusters |
force |
logical, TRUE to ignore error on categorical columns |
hyperparameters |
list of hyperparameters used in the model |
Value
'TRUE' if all checks are passed. Otherwise an error is raised.