Merge pull request #13 from bips-hb/v_0_3_0

Version 0.3.0

.github/workflows/R-CMD-check.yaml

@@ -16,7 +16,7 @@ jobs:
   R-CMD-check:
     runs-on: ${{ matrix.config.os }}
 
-    name: ${{ matrix.config.os }} (tf-${{ matrix.config.tf }}, R-${{ matrix.config.r }})
+    name: ${{ matrix.config.os }} (R-${{ matrix.config.r }})
 
     strategy:
       fail-fast: false
@@ -30,15 +30,15 @@ jobs:
           - {os: 'ubuntu-latest', r: 'release'}
           - {os: 'ubuntu-latest', r: 'oldrel-1'}
           - {os: 'ubuntu-latest', r: 'oldrel-2'}
-          - {os: 'ubuntu-latest', r: 'oldrel-3'}
-          - {os: 'ubuntu-latest', r: 'oldrel-4'}
 
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       TORCH_TEST: 1
       TORCH_INSTALL: 1
       TORCH_COMMIT_SHA: "none"
+      PYTORCH_ENABLE_MPS_FALLBACK: 1
+      PYTORCH_MPS_HIGH_WATERMARK_RATIO: 0.0
 
     steps:
       - uses: actions/checkout@v3
@@ -62,7 +62,7 @@ jobs:
         shell: Rscript {0}
 
       - name: Install Tensorflow + Keras deps
-        run: keras::install_keras(tensorflow = '${{ matrix.config.tf }}-cpu')
+        run: keras::install_keras(tensorflow = 'default-cpu')
         shell: Rscript {0}
 
       - name: Check if torch is installed
@@ -77,4 +77,6 @@ jobs:
 
       - uses: r-lib/actions/check-r-package@v2
         with:
+          error-on: '"error"'
+          args: 'c("--no-multiarch", "--no-manual", "--as-cran")'
           upload-snapshots: true

DESCRIPTION

@@ -1,15 +1,16 @@
 Type: Package
 Package: innsight
 Title: Get the Insights of Your Neural Network
-Version: 0.2.1
+Version: 0.3.0
 Authors@R: c(
     person("Niklas", "Koenen", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-4623-8271")),
     person("Raphael", "Baudeu", , "[email protected]", role = "ctb")
   )
-Description: Interpretability methods to analyze the behavior and
-    individual predictions of modern neural networks. Implemented methods
-    are: 'Connection Weights' described by Olden et al. (2004)
+Description: Interpretation methods for analyzing the behavior and individual
+    predictions of modern neural networks in a three-step procedure: Converting 
+    the model, running the interpretation method, and visualizing the results. 
+    Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004)
     <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance
     propagation ('LRP') described by Bach et al. (2015)
     <doi:10.1371/journal.pone.0130140>, deep learning important features
@@ -32,17 +33,20 @@ Imports:
     torch
 Suggests:
     covr,
+    fastshap,
     GGally,
     grid,
     gridExtra,
     gtable,
     keras,
     knitr,
+    lime,
     luz,
     neuralnet,
     palmerpenguins,
     plotly,
     rmarkdown,
+    ranger,
     spelling,
     tensorflow,
     testthat (>= 3.0.0)
@@ -54,6 +58,8 @@ Language: en-US
 Roxygen: list(markdown = TRUE, r6 = TRUE)
 RoxygenNote: 7.2.3
 Collate: 
+    'AgnosticMethods.R'
+    'AgnosticWrapper.R'
     'ConnectionWeights.R'
     'Convert_keras.R'
     'Convert_neuralnet.R'
@@ -75,5 +81,6 @@ Collate:
     'utils.R'
     'utils_ggplot.R'
     'utils_plotly.R'
+    'innsight_sugar.R'
     'innsight_ggplot2.R'
     'innsight_plotly.R'

NAMESPACE

@@ -1,17 +1,35 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(boxplot,ConnectionWeights)
-S3method(boxplot,DeepLift)
-S3method(boxplot,GradientBased)
-S3method(boxplot,LRP)
+S3method(boxplot,InterpretingMethod)
 S3method(get_result,InterpretingMethod)
+S3method(lime::model_type,innsight_agnostic_wrapper)
+S3method(lime::predict_model,innsight_agnostic_wrapper)
+S3method(plot_global,InterpretingMethod)
+export(AgnosticWrapper)
 export(ConnectionWeights)
 export(Converter)
 export(DeepLift)
+export(DeepSHAP)
+export(ExpectedGradient)
 export(Gradient)
+export(IntegratedGradient)
+export(LIME)
 export(LRP)
+export(SHAP)
 export(SmoothGrad)
+export(convert)
 export(get_result)
+export(plot_global)
+export(run_cw)
+export(run_deeplift)
+export(run_deepshap)
+export(run_expgrad)
+export(run_grad)
+export(run_intgrad)
+export(run_lime)
+export(run_lrp)
+export(run_shap)
+export(run_smoothgrad)
 exportMethods("+")
 exportMethods("[")
 exportMethods("[<-")
@@ -51,5 +69,6 @@ importFrom(methods,show)
 importFrom(stats,aggregate)
 importFrom(stats,ave)
 importFrom(stats,median)
+importFrom(stats,predict)
 importFrom(stats,quantile)
 importFrom(utils,packageVersion)

NEWS.md

@@ -1,3 +1,60 @@
+# innsight 0.3.0
+
+This is a minor release but does contain a range of substantial new features 
+as well as visual changes, along with some bug fixes. For users, however, 
+nothing changes that is not set by default as in the previous version or made 
+aware by warnings. An exception to this are the graphics that are created 
+using `plot()`. These now contain a small box with information about the 
+prediction, the sum of the relevances and the goal of the method.
+
+### Breaking changes
+
+* By default, the `plot()` method now creates a small box within the plot 
+with the prediction for the instance and the corresponding class. This info 
+box also contains the sum of the relevances and, if available, the 
+decomposition target of the method. Displaying the box can be toggled with 
+the `show_preds` argument.
+
+* The `boxplot()` function for the interpretation methods has been 
+renamed `plot_global()` due to the inappropriate name (especially for images). 
+The old method `boxplot()` can still be used, but throws a warning for image 
+data and executes the method `plot_global()` internally.
+
+### New features
+
+* We have now implemented the following other feature attribution methods:
+    * Integrated Gradients (`IntegratedGradient`)
+    * Expected Gradients (`ExpectedGradient`)
+    * DeepSHAP (`DeepSHAP`)
+    * and the model-agnostic approaches LIME (`LIME`) and Shapley values 
+    (`SHAP`). Both can be applied to arbitrary models (by providing the 
+    prediction function `pref_fun`) and wrap the suggested packages `lime` and
+    `fastshap`. However, they can only be applied to models with a single input
+    and output layer.
+
+* We have added functions for the initialization of R6 classes. In this way,
+we don't require prior knowledge of R6 syntax for our package. We implemented the
+following methods:
+    * `convert(...)` for `Converter$new(...)`
+    * `run_grad(...)` for `Gradient$new(...)`
+    * `run_smoothgrad(...)` for `SmoothGrad$new(...)`
+    * `run_intgrad(...)` for `IntegratedGradient$new(...)`
+    * `run_expgrad(...)` for `ExpectedGradient$new(...)`
+    * `run_deeplift(...)` for `DeepLift$new(...)`
+    * `run_deepshap(...)` for `DeepSHAP$new(...)`
+    * `run_lrp(...)` for `LRP$new(...)`
+    * `run_cw(...)` for `ConnectionWeights$new(...)`
+    * `run_lime(...)` for `LIME$new(...)`
+    * `run_shap(...)` for `SHAP$new(...)`
+
+* In addition to the output index with `output_idx`, the new argument 
+`output_label` for the  output label can now also be specified in order to 
+calculate or visualize only certain output nodes. 
+
+### Documentation and vignettes
+
+* Update documentation and vignettes according to the new features and changes
+
 # innsight 0.2.1
 
 * Add `if(require("pkgname"))` for suggested packages in examples