lime

by thomasp85

thomasp85 /lime

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

420 Stars 97 Forks Last release: 11 months ago (v0.5.1) Other 172 Commits 3 Releases

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lime

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There once was a package called lime,

Whose models were simply sublime,

It gave explanations for their variations,

one observation at a time.

lime-rick by Mara Averick


This is an R port of the Python lime package (https://github.com/marcotcr/lime) developed by the authors of the lime (Local Interpretable Model-agnostic Explanations) approach for black-box model explanations. All credits for the invention of the approach goes to the original developers.

The purpose of

lime
is to explain the predictions of black box classifiers. What this means is that for any given prediction and any given classifier it is able to determine a small set of features in the original data that has driven the outcome of the prediction. To learn more about the methodology of
lime
read the paper and visit the repository of the original implementation.

The

lime
package for R does not aim to be a line-by-line port of its Python counterpart. Instead it takes the ideas laid out in the original code and implements them in an API that is idiomatic to R.

An example

Out of the box

lime
supports a long range of models, e.g. those created with caret, parsnip, and mlr. Support for unsupported models are easy to achieve by adding a
predict_model
and
model_type
method for the given model.

The following shows how a random forest model is trained on the iris data set and how

lime
is then used to explain a set of new observations:
library(caret)
library(lime)

Split up the data set

iris_test # A tibble: 10 x 13 #> model_type case label label_prob model_r2 model_intercept #> #> 1 classific… 1 seto… 1 0.680 0.120 #> 2 classific… 1 seto… 1 0.680 0.120 #> 3 classific… 2 seto… 1 0.675 0.125 #> 4 classific… 2 seto… 1 0.675 0.125 #> 5 classific… 3 seto… 1 0.682 0.122 #> 6 classific… 3 seto… 1 0.682 0.122 #> 7 classific… 4 seto… 1 0.667 0.128 #> 8 classific… 4 seto… 1 0.667 0.128 #> 9 classific… 5 seto… 1 0.678 0.121 #> 10 classific… 5 seto… 1 0.678 0.121 #> # … with 7 more variables: model_prediction , feature , #> # feature_value , feature_weight , feature_desc , #> # data , prediction

And can be visualised directly

plot_features(explanation)

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lime
also supports explaining image and text models. For image explanations the relevant areas in an image can be highlighted:
explanation 

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Here we see that the second most probably class is hardly true, but is due to the model picking up waxy areas of the produce and interpreting them as wax-light surface.

For text the explanation can be shown by highlighting the important words. It even includes a

shiny
application for interactively exploring text models:

interactive text explainer

Installation

lime
is available on CRAN and can be installed using the standard approach:
install.packages('lime')

To get the development version, install from GitHub instead:

# install.packages('devtools')
devtools::install_github('thomasp85/lime')

Code of Conduct

Please note that the ‘lime’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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