Posit AI Weblog: lime v0.4: The Kitten Image Version


I’m completely happy to report a brand new main launch of lime has landed on CRAN. lime is
an R port of the Python library of the identical title by Marco Ribeiro that enables
the consumer to pry open black field machine studying fashions and clarify their
outcomes on a per-observation foundation. It really works by modelling the result of the
black field within the native neighborhood across the commentary to elucidate and utilizing
this native mannequin to elucidate why (not how) the black field did what it did. For
extra details about the speculation of lime I’ll direct you to the article
introducing the methodology.

New options

The meat of this launch facilities round two new options which are considerably
linked: Native help for keras fashions and help for explaining picture fashions.

keras and pictures

J.J. Allaire was sort sufficient to namedrop lime throughout his keynote introduction
of the tensorflow and keras packages and I felt compelled to help them
natively. As keras is by far the preferred solution to interface with tensorflow
it’s first in line for build-in help. The addition of keras implies that
lime now instantly helps fashions from the next packages:

Should you’re engaged on one thing too obscure or leading edge to not be capable to use
these packages it’s nonetheless doable to make your mannequin lime compliant by
offering predict_model() and model_type() strategies for it.

keras fashions are used identical to every other mannequin, by passing it into the lime()
operate together with the coaching knowledge as a way to create an explainer object.
As a result of we’re quickly going to speak about picture fashions, we’ll be utilizing one of many
pre-trained ImageNet fashions that’s accessible from keras itself:

Layer (kind)                              Output Form                         Param #        
input_1 (InputLayer)                      (None, 224, 224, 3)                  0              
block1_conv1 (Conv2D)                     (None, 224, 224, 64)                 1792           
block1_conv2 (Conv2D)                     (None, 224, 224, 64)                 36928          
block1_pool (MaxPooling2D)                (None, 112, 112, 64)                 0              
block2_conv1 (Conv2D)                     (None, 112, 112, 128)                73856          
block2_conv2 (Conv2D)                     (None, 112, 112, 128)                147584         
block2_pool (MaxPooling2D)                (None, 56, 56, 128)                  0              
block3_conv1 (Conv2D)                     (None, 56, 56, 256)                  295168         
block3_conv2 (Conv2D)                     (None, 56, 56, 256)                  590080         
block3_conv3 (Conv2D)                     (None, 56, 56, 256)                  590080         
block3_pool (MaxPooling2D)                (None, 28, 28, 256)                  0              
block4_conv1 (Conv2D)                     (None, 28, 28, 512)                  1180160        
block4_conv2 (Conv2D)                     (None, 28, 28, 512)                  2359808        
block4_conv3 (Conv2D)                     (None, 28, 28, 512)                  2359808        
block4_pool (MaxPooling2D)                (None, 14, 14, 512)                  0              
block5_conv1 (Conv2D)                     (None, 14, 14, 512)                  2359808        
block5_conv2 (Conv2D)                     (None, 14, 14, 512)                  2359808        
block5_conv3 (Conv2D)                     (None, 14, 14, 512)                  2359808        
block5_pool (MaxPooling2D)                (None, 7, 7, 512)                    0              
flatten (Flatten)                         (None, 25088)                        0              
fc1 (Dense)                               (None, 4096)                         102764544      
fc2 (Dense)                               (None, 4096)                         16781312       
predictions (Dense)                       (None, 1000)                         4097000        
Whole params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0

The vgg16 mannequin is a picture classification mannequin that has been construct as a part of
the ImageNet competitors the place the objective is to categorise footage into 1000
classes with the best accuracy. As we are able to see it’s pretty difficult.

With a view to create an explainer we might want to move within the coaching knowledge as
effectively. For picture knowledge the coaching knowledge is admittedly solely used to inform lime that we
are coping with a picture mannequin, so any picture will suffice. The format for the
coaching knowledge is just the trail to the photographs, and since the web runs on
kitten footage we’ll use considered one of these:

img <- image_read('https://www.data-imaginist.com/belongings/pictures/kitten.jpg')
img_path <- file.path(tempdir(), 'kitten.jpg')
image_write(img, img_path)

As with textual content fashions the explainer might want to know methods to put together the enter
knowledge for the mannequin. For keras fashions this implies formatting the picture knowledge as
tensors. Fortunately keras comes with a number of instruments for reshaping picture knowledge:

image_prep <- operate(x) {
  arrays <- lapply(x, operate(path) {
    img <- image_load(path, target_size = c(224,224))
    x <- image_to_array(img)
    x <- array_reshape(x, c(1, dim(x)))
    x <- imagenet_preprocess_input(x)
  do.call(abind::abind, c(arrays, list(alongside = 1)))
explainer <- lime(img_path, mannequin, image_prep)

We now have an explainer mannequin for understanding how the vgg16 neural community
makes its predictions. Earlier than we go alongside, lets see what the mannequin consider our

res <- predict(mannequin, image_prep(img_path))
  class_name class_description      rating
1  n02124075      Egyptian_cat 0.48913878
2  n02123045             tabby 0.15177219
3  n02123159         tiger_cat 0.10270492
4  n02127052              lynx 0.02638111
5  n03793489             mouse 0.00852214

So, it’s fairly positive about the entire cat factor. The explanation we have to use
imagenet_decode_predictions() is that the output of a keras mannequin is all the time
only a anonymous tensor:

[1]    1 1000

We’re used to classifiers realizing the category labels, however this isn’t the case
for keras. Motivated by this, lime now have a solution to outline/overwrite the
class labels of a mannequin, utilizing the as_classifier() operate. Let’s redo our

model_labels <- readRDS(system.file('extdata', 'imagenet_labels.rds', bundle = 'lime'))
explainer <- lime(img_path, as_classifier(mannequin, model_labels), image_prep)

There may be additionally an as_regressor() operate which tells lime, definitely,
that the mannequin is a regression mannequin. Most fashions will be introspected to see
which sort of mannequin they’re, however neural networks doesn’t actually care. lime
guesses the mannequin kind from the activation used within the final layer (linear
activation == regression), but when that heuristic fails then
as_regressor()/as_classifier() can be utilized.

We are actually able to poke into the mannequin and discover out what makes it assume our
picture is of an Egyptian cat. However… first I’ll have to speak about one more
idea: superpixels (I promise I’ll get to the reason half in a bit).

With a view to create significant permutations of our picture (bear in mind, that is the
central thought in lime), we now have to outline how to take action. The permutations wants
to be substantial sufficient to have an effect on the picture, however not a lot that
the mannequin fully fails to recognise the content material in each case – additional,
they need to result in an interpretable outcome. The idea of superpixels lends
itself effectively to those constraints. In brief, a superpixel is a patch of an space
with excessive homogeneity, and superpixel segmentation is a clustering of picture
pixels into various superpixels. By segmenting the picture to elucidate into
superpixels we are able to flip space of contextual similarity on and off through the
permutations and discover out if that space is essential. It’s nonetheless essential to
experiment a bit because the optimum variety of superpixels rely upon the content material of
the picture. Keep in mind, we want them to be massive sufficient to have an effect however not
so massive that the category likelihood turns into successfully binary. lime comes
with a operate to evaluate the superpixel segmentation earlier than starting the
rationalization and it is strongly recommended to play with it a bit — with time you’ll
doubtless get a really feel for the precise values:

# default

# Altering some settings
plot_superpixels(img_path, n_superpixels = 200, weight = 40)

The default is ready to a fairly low variety of superpixels — if the topic of
curiosity is comparatively small it might be obligatory to extend the variety of
superpixels in order that the total topic doesn’t find yourself in a single, or just a few
superpixels. The weight parameter will can help you make the segments extra
compact by weighting spatial distance larger than color distance. For this
instance we’ll persist with the defaults.

Bear in mind that explaining picture
fashions is far heavier than tabular or textual content knowledge. In impact it’ll create 1000
new pictures per rationalization (default permutation dimension for pictures) and run these
by way of the mannequin. As picture classification fashions are sometimes fairly heavy, this
will lead to computation time measured in minutes. The permutation is batched
(default to 10 permutations per batch), so that you shouldn’t be afraid of operating
out of RAM or hard-drive house.

rationalization <- clarify(img_path, explainer, n_labels = 2, n_features = 20)

The output of a picture rationalization is an information body of the identical format as that
from tabular and textual content knowledge. Every characteristic shall be a superpixel and the pixel
vary of the superpixel shall be used as its description. Normally the reason
will solely make sense within the context of the picture itself, so the brand new model of
lime additionally comes with a plot_image_explanation() operate to do exactly that.
Let’s see what our rationalization have to inform us:


We will see that the mannequin, for each the foremost predicted lessons, focuses on the
cat, which is sweet since they’re each completely different cat breeds. The plot operate
bought just a few completely different features that will help you tweak the visible, and it filters low
scoring superpixels away by default. An alternate view that places extra focus
on the related superpixels, however removes the context will be seen through the use of
show = 'block':

plot_image_explanation(rationalization, show = 'block', threshold = 0.01)

Whereas not as frequent with picture explanations additionally it is doable to take a look at the
areas of a picture that contradicts the category:

plot_image_explanation(rationalization, threshold = 0, show_negative = TRUE, fill_alpha = 0.6)

As every rationalization takes longer time to create and must be tweaked on a
per-image foundation, picture explanations usually are not one thing that you simply’ll create in
massive batches as you may do with tabular and textual content knowledge. Nonetheless, just a few
explanations may can help you perceive your mannequin higher and be used for
speaking the workings of your mannequin. Additional, because the time-limiting issue
in picture explanations are the picture classifier and never lime itself, it’s sure
to enhance as picture classifiers turns into extra performant.

Seize again

Other than keras and picture help, a slew of different options and enhancements
have been added. Right here’s a fast overview:

  • All rationalization plots now embody the match of the ridge regression used to make
    the reason. This makes it simple to evaluate how good the assumptions about
    native linearity are saved.
  • When explaining tabular knowledge the default distance measure is now 'gower'
    from the gower bundle. gower makes it doable to measure distances
    between heterogeneous knowledge with out changing all options to numeric and
    experimenting with completely different exponential kernels.
  • When explaining tabular knowledge numerical options will not be sampled from
    a traditional distribution throughout permutations, however from a kernel density outlined
    by the coaching knowledge. This could make sure that the permutations are extra
    consultant of the anticipated enter.

Wrapping up

This launch represents an essential milestone for lime in R. With the
addition of picture explanations the lime bundle is now on par or above its
Python relative, feature-wise. Additional growth will concentrate on enhancing the
efficiency of the mannequin, e.g. by including parallelisation or enhancing the native
mannequin definition, in addition to exploring different rationalization sorts corresponding to

Comfortable Explaining!

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