From ca3efe6e7927e23e725fdd275d8777aa796a7dfe Mon Sep 17 00:00:00 2001 From: dandls Date: Tue, 15 Oct 2024 08:40:17 +0200 Subject: [PATCH] update vignette html --- vignettes/how-to-add-new-cf-methods.html | 769 ++++++++++++++++++----- vignettes/introduction.html | 329 +++++----- vignettes/other_models.html | 269 ++++---- 3 files changed, 929 insertions(+), 438 deletions(-) diff --git a/vignettes/how-to-add-new-cf-methods.html b/vignettes/how-to-add-new-cf-methods.html index 8541a1f..a81d8e1 100644 --- a/vignettes/how-to-add-new-cf-methods.html +++ b/vignettes/how-to-add-new-cf-methods.html @@ -8,11 +8,10 @@ - -How to extend the package +how-to-add-new-cf-methods + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +

<!DOCTYPE html>

+ + + + + + + +How to extend the package + + - - - - - - - - - - - - -

How to extend the package

- - - -

In the following, we illustrate how further methods can be added to -the counterfactuals package by integrating the featureTweakR -package, which implements Feature Tweaking of Tolomei et al. (2017).

-

(Note that the featureTweakR package has a couple of +

+How to extend the package +

+

+In the following, we illustrate how further methods can be added to the +counterfactuals package by integrating the +featureTweakR +package, which implements Feature Tweaking of Tolomei et al. (2017). +

+

+(Note that the featureTweakR package has a couple of limitations, e.g., that factors in the training data cause problems or that the algorithm is only applicable to randomForests trained on standardized features. Therefore, featureTweakR is not part of the counterfactuals package, but serves as a -suitable example for our purpose here.)

+suitable example for our purpose here.) +

-

Structure of the counterfactuals package

-

Before we dive into the implementation details, we briefly explain -the structure of the counterfactuals package.

+

+Structure of the counterfactuals package +

+

+Before we dive into the implementation details, we briefly explain the +structure of the counterfactuals package. +

-

Class diagram

-

Each counterfactual method is represented by its own R6 +

+Class diagram +

+

+Each counterfactual method is represented by its own R6 class. Depending on whether a counterfactual method supports classification or regression tasks, it inherits from the (abstract) CounterfactualMethodClassif or @@ -363,9 +711,13 @@

Class diagram

Counterfactual methods that support both tasks are split into two separate classes; for instance, as MOC is applicable to classification and regression tasks, we implement two classes: MOCClassif -and MOCRegr.

-

-

Leaf classes (like MOCClassif and MOCRegr) +and MOCRegr. +

+

+ +

+

+Leaf classes (like MOCClassif and MOCRegr) inherit the find_counterfactuals() method from CounterfactualMethodClassif or CounterfactualMethodRegr, respectively. The key advantage @@ -373,47 +725,66 @@

Class diagram

find_counterfactuals() interface to the task at hand: for classification tasks find_counterfactuals() has two arguments desired_class and desired_prob and -for regression tasks it has one argument -desired_outcome.

+for regression tasks it has one argument desired_outcome. +

-

Call graph

-

The find_counterfactuals() method calls a private +

+Call graph +

+

+The find_counterfactuals() method calls a private run() method (1)—implemented by the leaf classes—which performs the search and returns the counterfactuals as a data.table (2). The find_counterfactuals() method then creates a Counterfactuals object, which contains the counterfactuals and provides several methods for their -evaluation and visualization (3).

-

+evaluation and visualization (3). +

+

+ +

-

Integrating a new counterfactuals explanation method

-

To integrate Feature Tweaking, we first need to install +

+Integrating a new counterfactuals explanation method +

+

+To integrate Feature Tweaking, we first need to install featureTweakR and pforeach and load the -required libraries.

-
devtools::install_github("katokohaku/featureTweakR")
+required libraries.
+

+

+
devtools::install_github("katokohaku/featureTweakR")
 # required for FeatureTweakeR
-devtools::install_github("hoxo-m/pforeach")

-
library(counterfactuals)
+devtools::install_github("hoxo-m/pforeach")

+

+

+
library(counterfactuals)
 library(randomForest)
 library(featureTweakR)
-library(R6)

+library(R6)
+
-

Class structure

-

A new leaf class needs at least two methods: -initialize() and run(). The method -print_parameters() is not mandatory, but strongly -recommended as it gives objects of that class an informative -print() output.

-

As elaborated above, the new class inherits from either +

+Class structure +

+

+A new leaf class needs at least two methods: initialize() +and run(). The method print_parameters() is +not mandatory, but strongly recommended as it gives objects of that +class an informative print() output. +

+

+As elaborated above, the new class inherits from either CounterfactualMethodClassif or CounterfactualMethodRegr, depending on which task it supports. Because Feature Tweaking supports classification tasks, the -new FeatureTweakerClassif class inherits from the -former.

-
FeatureTweakerClassif = R6::R6Class("FeatureTweakerClassif", inherit = CounterfactualMethodClassif,
+new FeatureTweakerClassif class inherits from the former.
+

+

+
FeatureTweakerClassif = R6::R6Class("FeatureTweakerClassif", inherit = CounterfactualMethodClassif,
   
   public = list(
     initialize = function() {}
@@ -424,56 +795,100 @@ 
Class structure

     
     print_parameters = function() {}
   )
-)

+)
+
-

Implement the $initialize method

-

The initialize() method must have a predictor argument -that takes an iml::Predictor +

+Implement the \(initialize</code> +method</h3> <p>The <code>initialize()</code> +method must have a predictor argument that takes an <a +href="https://giuseppec.github.io/iml/reference/Predictor.html"><code>iml::Predictor</code></a> object. In addition, it may have further arguments that are specific to -the counterfactual method such as ktree, -epsiron, and resample in this case. For -argument checks, we recommend the checkmate -package. We also fill the print_parameters() method with -the parameters of Feature Tweaking.

-
FeatureTweakerClassif = R6Class("FeatureTweakerClassif", inherit = CounterfactualMethodClassif,
-  
-  public = list(
-     initialize = function(predictor, ktree = NULL, epsiron = 0.1, 
-      resample = FALSE) {
-      # adds predictor to private$predictor field
-      super$initialize(predictor) 
-      private$ktree = ktree
-      private$epsiron = epsiron
-      private$resample = resample
-    }
-  ),
-  
-  private = list(
-    ktree = NULL,
-    epsiron = NULL,
-    resample = NULL,
-    
-    run = function() {},
-    
-    print_parameters = function() {
-      cat(" - epsiron: ", private$epsiron, "\n")
-      cat(" - ktree: ", private$ktree, "\n")
-      cat(" - resample: ", private$resample)
-    }
-  )
-)
-

-
-

Implement the $run method

-

The run() method performs the search for -counterfactuals. Its structure is completely free, which makes it -flexible to add new counterfactual methods to the -counterfactuals package.

-

The workflow of finding counterfactuals with the -featureTweakR package is explained here and -essentially consists of these steps:

-
# Rule extraction
+the counterfactual method such as <code>ktree</code>,
+<code>epsiron</code>, and <code>resample</code>
+in this case. For argument checks, we recommend the <a
+href="https://mllg.github.io/checkmate/"><code>checkmate</code></a>
+package. We also fill the <code>print_parameters()</code>
+method with the parameters of Feature Tweaking.</p> <div
+class="sourceCode" id="cb4"><pre
+class="sourceCode r"><code class="sourceCode
+r"><span id="cb4-1"><a
+href="#cb4-1"
+tabindex="-1"></a>FeatureTweakerClassif <span
+class="ot">=</span> <span
+class="fu">R6Class</span>(<span
+class="st">&quot;FeatureTweakerClassif&quot;</span>,
+<span class="at">inherit =</span>
+CounterfactualMethodClassif,</span> <span
+id="cb4-2"><a href="#cb4-2"
+tabindex="-1"></a> </span> <span
+id="cb4-3"><a href="#cb4-3"
+tabindex="-1"></a> <span
+class="at">public =</span> <span
+class="fu">list</span>(</span> <span
+id="cb4-4"><a href="#cb4-4"
+tabindex="-1"></a> <span
+class="at">initialize =</span> <span
+class="cf">function</span>(predictor, <span
+class="at">ktree =</span> <span
+class="cn">NULL</span>, <span
+class="at">epsiron =</span> <span
+class="fl">0.1</span>, </span> <span
+id="cb4-5"><a href="#cb4-5"
+tabindex="-1"></a> <span
+class="at">resample =</span> <span
+class="cn">FALSE</span>) {</span> <span
+id="cb4-6"><a href="#cb4-6"
+tabindex="-1"></a> <span class="co">#
+adds predictor to private\)predictor field  super\(</span><span
+class="fu">initialize</span>(predictor) </span>
+<span id="cb4-8"><a href="#cb4-8"
+tabindex="-1"></a> private<span
+class="sc">\)ktree =
+ktree 
+private\(</span>epsiron
+<span class="ot">=</span> epsiron</span>
+<span id="cb4-10"><a href="#cb4-10"
+tabindex="-1"></a> private<span
+class="sc">\)resample = resample  }  ),    private = list(  ktree
+= NULL,  epsiron = NULL,  resample = NULL,    run
+= function() {},    print_parameters = function()
+{  cat(" - epsiron: ",
+private\(</span>epsiron, <span
+class="st">&quot;</span><span
+class="sc">\n</span><span
+class="st">&quot;</span>)</span> <span
+id="cb4-23"><a href="#cb4-23"
+tabindex="-1"></a> <span
+class="fu">cat</span>(<span
+class="st">&quot; - ktree: &quot;</span>,
+private<span class="sc">\)ktree, "")
+ cat(" - resample: ",
+private\(</span>resample)</span> <span
+id="cb4-25"><a href="#cb4-25"
+tabindex="-1"></a> }</span> <span
+id="cb4-26"><a href="#cb4-26"
+tabindex="-1"></a> )</span> <span
+id="cb4-27"><a href="#cb4-27"
+tabindex="-1"></a>)</span></code></pre></div>
+</div> <div id="implement-the-run-method"
+class="section level3"> <h3>Implement the
+<code>\)run method
+
+

+The run() method performs the search for counterfactuals.
+Its structure is completely free, which makes it flexible to add new
+counterfactual methods to the counterfactuals package.
+

+

+The workflow of finding counterfactuals with the
+featureTweakR package is explained
+here and
+essentially consists of these steps:
+

+

+
# Rule extraction
 rules = getRules(rf, ktree = 5L)
 # Get e-satisfactory instance 
 es = set.eSatisfactory(rules, epsiron = 0.3)
@@ -481,12 +896,16 @@ 
Implement the $run method

 tweaked = tweak(
   es, rf, x_interest, label.from = ..., label.to = ..., .dopar = FALSE
 )
-tweaked$suggest

-
As long as ktree—the number of trees to parse—is smaller
+tweaked$suggest
+
+

+As long as ktree—the number of trees to parse—is smaller than the total number of trees in the randomForest, the rule extraction is a random process. Hence, these steps can be repeated -resample times to obtain multiple counterfactuals.

-
FeatureTweakerClassif = R6Class("FeatureTweakerClassif", 
+resample times to obtain multiple counterfactuals.
+

+

+
FeatureTweakerClassif = R6Class("FeatureTweakerClassif", 
   inherit = CounterfactualMethodClassif,
   
   public = list(
@@ -528,27 +947,41 @@ 
Implement the $run method

       cat(" - resample: ", private$resample)
     }
   )
-)

+)
+
-

Use Case

-

Now, that we have implemented FeatureTweakerClassif, -let’s look at a short application to the iris dataset.

-

First, we train a randomForest model on the -iris data set and set up the iml::Predictor -object, omitting x_interest from the training data.

-
set.seed(78546)
+

+Use Case
+

+

+Now, that we have implemented FeatureTweakerClassif, let’s
+look at a short application to the iris dataset.
+

+

+First, we train a randomForest model on the
+iris data set and set up the
+iml::Predictor
+object, omitting x_interest from the training data.
+

+

+
set.seed(78546)
 X = subset(iris, select = -Species)[- 130L,]
 y = iris$Species[-130L]
 rf = randomForest(X, y, ntree = 20L)
-predictor = iml::Predictor$new(rf, data = iris[-130L, ], y = "Species", type = "prob")

-
For x_interest, the model predicts a probability of 25%
-for versicolor.

-
x_interest = iris[130L, ]
+predictor = iml::Predictor$new(rf, data = iris[-130L, ], y = "Species", type = "prob")

+

+

+For x_interest, the model predicts a probability of 25% for
+versicolor.
+

+

+
x_interest = iris[130L, ]
 predictor$predict(x_interest)
 #>   setosa versicolor virginica
-#> 1      0        0.3       0.7

+#> 1      0        0.3       0.7
+
#> extracting sampled 10 of 20 trees
 #> Time difference of 0.3100975 secs
 #> set e-satisfactory instance (10 trees)
@@ -558,39 +991,95 @@ 
Use Case

 #> evaluate 0 rules in 7 trees
 #> - evalutate 10 candidate of rules (delta.min=999999999999999967336168804116691273849533185806555472917961779471295845921727862608739868455469056.000)
 #> Time difference of 0.005567312 secs
-

Now, we use Feature Tweaking to address the question: “What would -need to change in x_interest for the model to predict a -probability of at least 60% forversicolor.

-
# Set up FeatureTweakerClassif
+

+Now, we use Feature Tweaking to address the question: “What would need
+to change in x_interest for the model to predict a
+probability of at least 60% forversicolor.
+

+

+
# Set up FeatureTweakerClassif
 ft_classif = FeatureTweakerClassif$new(predictor, ktree = 10L, resample = TRUE)
 
 # Find counterfactuals and create Counterfactuals Object
 cfactuals = ft_classif$find_counterfactuals(
   x_interest = x_interest, desired_class = "versicolor", desired_prob = c(0.6, 1)
-)

-
Just as for the existing methods, the result is a
-Counterfactuals object.

+)
+
+

+Just as for the existing methods, the result is a +Counterfactuals object. +

-

Comments

-

A minor limitation of this basic implementation is that we would not -be able to find counterfactuals for a setting with -max(desired_prob) < 0.5, since -featureTweakR::tweak only searches for instances that would -be predicted as desired_class by majority vote. To enable -this setting, we would need to change some featureTweakR -internal code, but for the sake of clarity we will not do this here.

+

+Comments +

+

+A minor limitation of this basic implementation is that we would not be +able to find counterfactuals for a setting with max(desired_prob) +< 0.5, since featureTweakR::tweak only searches +for instances that would be predicted as desired_class by +majority vote. To enable this setting, we would need to change some +featureTweakR internal code, but for the sake of clarity we +will not do this here. +

-

References

-

Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable +

+References +

+

+Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 465–474. KDD ’17, ACM, New York, NY, USA -(2017). .

+(2017). . +

+
+ + + + + + + + +
+ + + + + diff --git a/vignettes/introduction.html b/vignettes/introduction.html index 9a8fe12..a244448 100644 --- a/vignettes/introduction.html +++ b/vignettes/introduction.html @@ -29,27 +29,27 @@ + code{white-space: pre-wrap;} + span.smallcaps{font-variant: small-caps;} + span.underline{text-decoration: underline;} + div.column{display: inline-block; vertical-align: top; width: 50%;} + div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} + ul.task-list{list-style: none;} + + code{white-space: pre-wrap;} + span.smallcaps{font-variant: small-caps;} + span.underline{text-decoration: underline;} + div.column{display: inline-block; vertical-align: top; width: 50%;} + div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} + ul.task-list{list-style: none;} +