lime (version 0.4.0)

interactive_text_explanations: Interactive explanations

Description

Display text explanation in an interactive way. You can :

Create an output to insert text explanation plot in Shiny application.

Render the text explanations in Shiny application.

Usage

interactive_text_explanations(explainer,
  window_title = "Text model explainer",
  title = "Local Interpretable Model-agnostic Explanations",
  place_holder = "Put here the text to explain", minimum_lentgh = 3,
  minimum_lentgh_error = "Text provided is too short to be explained (>= 3).",
  max_feature_to_select = 20)
text_explanations_output(outputId, width = "100%", height = "400px")
render_text_explanations(expr, env = parent.frame(), quoted = FALSE)

Arguments

explainer

parameters

window_title, title, place_holder, minimum_lentgh_error

text to be displayed on the page

minimum_lentgh

don't update display if text is shorter than this parameter

max_feature_to_select

up limit to the number of words that can be selected

outputId

output variable to read from

width, height

Must be a valid CSS unit or a number, which will be coerced to a string and have "px" appended.

expr

An expression that generates an HTML widget

env

The environment in which to evaluate expr.

quoted

Is expr a quoted expression (with quote())? This is useful if you want to save an expression in a variable.

Value

An output function that enables the use of the widget within Shiny applications.

A render function that enables the use of the widget within Shiny applications.

Details

• send a new sentence

• update the parameters of the explainer

Examples

# NOT RUN {
library(text2vec)
library(xgboost)

data(train_sentences)
data(test_sentences)

get_matrix <- function(text) {
  it <- itoken(text, progressbar = FALSE)
  create_dtm(it, vectorizer = hash_vectorizer())
}

dtm_train = get_matrix(train_sentences$text)

xgb_model <- xgb.train(list(max_depth = 7, eta = 0.1, objective = "binary:logistic",
                 eval_metric = "error", nthread = 1),
                 xgb.DMatrix(dtm_train, label = train_sentences$class.text == "OWNX"),
                 nrounds = 50)

sentences <- head(test_sentences[test_sentences$class.text == "OWNX", "text"], 1)
explainer <- lime(train_sentences$text, xgb_model, get_matrix)

# The explainer can now be queried interactively:
# }
# NOT RUN {
interactive_text_explanations(explainer)
# }