R API for Infogram

[exalate-issue-sync]

{noformat}ig <- h2o.infogram(x, y, training_frame,
protected_columns = NULL,
algorithm = c("gbm", "automl", ...),
algorithm_params = NULL,
thresholds = c(0.1, 0.1), #x, y thresholds
top_n_features = 50,
...){noformat}

Note: You can pass plot arguments to the {{...}} (aes, etc.).

Function returns a {{H2OInfogram}} object, which contains several slots for data, as well as the plots (ggplot object in R and a matplotlib object in Python). This is very similar to the H2O Explain module and the {{H2OExplanation}} class.

The {{ig}} object contains the following slots (note possibly change {{_columns}} to {{_features}}, though {{top_n_features}} is used to denote an integer in H2O Explain):

• {{admissible_columns}}: list of column names which are in the top-right of the Infogram.
• {{protected_columns}}: this might be useful to store just for reference.
• {{top_n_columns}}: let's record which columns were found as "top N"
• {{plots}}: ggplot/matplot lib object
• {{admissible_score}} (or better name): An H2OFrame to store the (x,y) index values on the infogram plot, containing four columns: feature name, and then the values for the (x,y) plot (two additional columns), and boolean "admissible":
  ** x: {{relevance_index}} (aka total information)
  ** y: {{core_index}} (aka conditional/net information) or {{safety_index}}

Add a plot methods such that to plot the Infogram, you do: {{plot(ig)}} in R.

[exalate-issue-sync]

Erin LeDell commented: Since the {{h2o.admissibleML()}} function will need to have {{infogram_algorithm}} and {{infogram_algorithm_params}} (to differentiate from the main model training algorithm ({{automl_params}}, etc)), it might make sense to be overly verbose here and change the shorter, nicer {{algorithm}} and {{algorithm_params}} to the more lengthy above versions. The longer version is what’s currently implemented: [https://github.com//pull/5572/files|https://github.com//pull/5572/files|smart-link]

[exalate-issue-sync]

Erin LeDell commented: Is {{sensitive_columns}} a better name than {{protected_columns}}?

[exalate-issue-sync]

Wendy commented: Erin: I prefer the threshold parameter to be broken into two parts, one for cmi, one for relevance so that people don’t have to remember the order of which one is for which.

[exalate-issue-sync]

Erin LeDell commented: Note to self: update the algorithm description in admissible.R to:

{noformat}#' The infogram is an information-theoretic graphical tool which allows the user to quickly spot the "core" decision-making variables
#' that are driving the response, while minimizing redundancy, for supervised learning problems. The user can can also define protected
#' features to set the admissibility criterion. All other features will be checked to make sure that their pathway to the response
#' is not through a protected feature. A measure of how safe each feature is is provided on the y-axis of the infogram, while the relevance
#' to the response (how much the variable drives the response) is plotted on the x-axis of the infogram. {noformat}

[h2o-ops]

JIRA Issue Details

Jira Issue: PUBDEV-8222
Assignee: Erin LeDell
Reporter: Erin LeDell
State: Resolved
Fix Version: 3.36.0.1
Attachments: N/A
Development PRs: Available

[h2o-ops]

Linked PRs from JIRA

#5933