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.

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: interactive_text_explanations(explainer) # }