---
title: "Introduction to innsight"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{Introduction to innsight}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
size = "huge",
collapse = TRUE,
comment = "#>",
eval = torch::torch_is_installed(),
fig.align = "center",
out.width = "95%"
)
```
```{css, echo = FALSE}
details {
padding: 10px 10px;
}
details > summary {
border: none;
cursor: pointer;
}
details[open] {
border-radius: 10px 10px 10px 10px;
padding: 7px 7px;
border: 3px solid lightgrey;
}
```
```{r, echo = FALSE}
Sys.setenv(LANG = "en_US.UTF-8")
set.seed(1111)
```
<style>
.column-left{
float: left;
width: 70%;
text-align: left;
}
.column-right{
float: right;
width: 30%;
text-align: right;
}
</style>
<style>
img {
border: 0;
}
</style>
In the last decade, it has been demonstrated in an impressive way how
efficiently and successfully neural networks can analyze and understand enormous
amounts of data. They can recognize patterns and associations and transfer this
knowledge to new data points with remarkable accuracy. Moreover, their
flexibility eliminates the feature engineering step that was often necessary
before and allows them to work directly with raw data. Nevertheless, these
associations and internal findings are hidden somewhere in the black box and it
is unclear to the user what the crucial aspects of the prediction are. One way
to open the black box is through so-called **feature attribution** methods.
These are local methods that — based on a single data point (image, tabular
instance,...) — assign a relevance of a previously defined output class or node
to each input variable. In general, only a normal forward pass and a
method-specific backward pass are required, making the implementation much
faster compared to perturbation- or optimization-based methods like LIME or
Occlusion. Figure 1 illustrates the basic approach of the feature attribution
methods.
</br>
```{r pressure, echo=FALSE, fig.cap = "**Figure 1:** Feature attribution methods"}
knitr::include_graphics("images/feature_attribution.png")
```
### Why innsight?
<div class = "column-left">
Of course, we are not the first to provide several feature attribution methods
for neural networks in one package. For example, there are several packages for
Python, such as [**iNNvestigate**](https://github.com/albermax/innvestigate/),
[**captum**](https://captum.ai/) and
[**zennit**](https://github.com/chr5tphr/zennit/). Due to the great and
extremely efficient deep learning libraries Keras/TensorFlow and PyTorch, it is
only reasonable that these are all Python-exclusive. However, in recent years
these libraries have been integrated more and more successfully into the R
programming language. We fill this lack of feature attribution methods for
neural networks in R with our package **innsight**.
</div>
<div class="column-right">
```{r, echo=FALSE, fig.cap = "**Figure 2:** innsight package"}
knitr::include_graphics("images/innsight_torch.png")
```
</div>
In addition to the availability in R, the package is also outstanding for the
following aspects:
**Deep-learning-library-agnostic:** To be as flexible as possible and available
to a range of users, we do not limit ourselves to models from a particular deep
learning library, as is the case with all Python variants. Using the `Converter`,
each passed model (from **keras**, **torch** or **neuralnet**) is first
converted into a list with all relevant information about the model. Then, a
**torch**-model is created from this list, which has the available feature
attribution methods pre-implemented for each layer. If our package does not
support your favorite library, there is also the option to do the converting
step by yourself and pass a list directly.
* **No Python dependency:** In R, there are currently two major deep learning
libraries, namely [**keras**/**tensorflow**](https://tensorflow.rstudio.com/)
and [**torch**](https://torch.mlverse.org/). However, **keras**/**tensorflow**,
accesses the corresponding Python methods via the package **reticulate**. We
use the fast and efficient [**torch** package](https://torch.mlverse.org/) for
all computations, which runs without Python and directly accesses the C++
variant of PyTorch called LibTorch (see Fig. 2).
* **Unified framework:** It does not matter which model and method you choose,
it is always the same three steps that lead to a visual illustration of the
results (see the [next section](#how-to-use) for details):
<div align="center"> model $\xrightarrow{\text{Step 1}}$ `Converter` $\xrightarrow{\text{Step 2}}$ method $\xrightarrow{\text{Step 3}}$ `plot()` or `plot_global()`/`boxplot()` </div></br>
* **Visualization tools:** Our package **innsight** offers several
visualization methods for individual or summarized results regardless of
whether it is tabular, 1D signal, 2D image data or a mix of these.
Additionally, interactive plots can be created based on the **plotly** package.
## How to use
The following is more of a high-level overview that only explains some of the
details of the three steps. In case you are looking for a more detailed overview
of all configuration options, we refer you to the [vignette "In-depth
explanation"](https://bips-hb.github.io/innsight/articles/detailed_overview.html)
(same as `vignette("detailed_overview", package = "innsight")`). The three steps
for explaining individual predictions with the provided methods are unified in
this package and follow a strict scheme. This will hopefully allow any user a
smooth and easy introduction to the possibilities of this package. The steps
are:
```{r, eval = FALSE}
# Step 0: Model creation
model <- ... # this step is left to the user
# Step 1: Convert the model
converter <- convert(model)
converter <- Converter$new(model) # the same but without helper function
# Step 2: Apply selected method to your data
result <- run_method(converter, data)
result <- Method$new(converter, data) # the same but without helper function
# Step 3: Show and plot the results
get_result(result) # get the result as an `array`, `data.frame` or `torch_tensor`
plot(result) # for individual results (local)
plot_global(result) # for summarized results (global)
boxplot(result) # alias for `plot_global` for tabular and signal data
```
### Step 1: Model creation and converting
The **innsight** package aims to be as flexible as possible and independent of
any particular deep learning package in which the passed network was learned
or defined. For this reason, there are several ways in this package to pass
a neural network to the `Converter` object, but the call is always the same:
```{r, eval = FALSE}
# Using the helper function `convert`
converter <- convert(model, ...)
# It simply passes all arguments to the initialization function of
# the corresponding R6 class, i.e., it is equivalent to
converter <- Converter$new(model, ...)
```
Except for a **neuralnet** model, no names of inputs or outputs are stored in
the given model. If no further arguments are set for the `Converter`
instance or `convert()` function, default labels are generated for the input
(e.g. `'X1'`, `'X2'`, ...) and output names (`'Y1'`, `'Y2'`, ... ). In the
converter, however, there is the possibility with the optional arguments
`input_names` and `output_names` to pass the names, which will then be used in
all results and plots created by this object.
#### Usage with torch models
Currently, only models created by
[`torch::nn_sequential`](https://torch.mlverse.org/docs/reference/nn_sequential.html)
are accepted. However, the most popular standard layers and activation functions
are available (see the [detailed
vignette](https://bips-hb.github.io/innsight/articles/detailed_overview.html#package-torch)
for details).
> **`r knitr::asis_output("\U1F4DD")` Note**
> If you want to create an instance of the class `Converter` with a **torch**
model that meets the above conditions, you have to specify the shape of the
inputs with the argument `input_dim` because this information is not stored
in every given **torch** model.
<details>
<summary> **Example** </summary>
```{r}
library(torch)
library(innsight)
torch_manual_seed(123)
# Create model
model <- nn_sequential(
nn_linear(3, 10),
nn_relu(),
nn_linear(10, 2, bias = FALSE),
nn_softmax(2)
)
# Convert the model
conv_dense <- convert(model, input_dim = c(3))
# Convert model with input and output names
conv_dense_with_names <-
convert(model,
input_dim = c(3),
input_names = list(c("Price", "Weight", "Height")),
output_names = list(c("Buy it!", "Don't buy it!"))
)
```
</details>
#### Usage with keras models
Models created by
[`keras_model_sequential`](https://tensorflow.rstudio.com/reference/keras/keras_model_sequential)
or [`keras_model`](https://tensorflow.rstudio.com/reference/keras/keras_model)
with the **keras** package are accepted. Within these functions, the most
popular layers and activation functions are accepted (see the [in-depth
vignette](https://bips-hb.github.io/innsight/articles/detailed_overview.html#package-keras)
for details).
<details>
<summary> **Example** </summary>
```{r, eval = keras::is_keras_available() & torch::torch_is_installed()}
library(keras)
# Create model
model <- keras_model_sequential()
model <- model %>%
layer_conv_2d(4, c(5, 4), input_shape = c(10, 10, 3), activation = "softplus") %>%
layer_max_pooling_2d(c(2, 2), strides = c(1, 1)) %>%
layer_conv_2d(6, c(3, 3), activation = "relu", padding = "same") %>%
layer_max_pooling_2d(c(2, 2)) %>%
layer_conv_2d(4, c(2, 2), strides = c(2, 1), activation = "relu") %>%
layer_flatten() %>%
layer_dense(5, activation = "softmax")
# Convert the model
conv_cnn <- convert(model)
```
</details>
#### Usage with neuralnet models
The usage with nets from the package **neuralnet** is very simple and
straightforward, because the package offers much fewer options than
**torch** or **keras**. The only thing to note is that no custom activation
function can be used. However, the package saves the names of the inputs and
outputs, which can, of course, be overwritten with the arguments
`input_names` and `output_names` when creating the converter object.
<details>
<summary> **Example** </summary>
```{r}
library(neuralnet)
data(iris)
# Create model
model <- neuralnet(Species ~ Petal.Length + Petal.Width, iris,
linear.output = FALSE
)
# Convert model
conv_dense <- convert(model)
```
</details>
#### Usage with a model as a named list
If you have not trained your net with **keras**, **torch** or **neuralnet**,
you can also pass your model as a list, i.e., you write your own wrapper for your
library. But you have to consider a few points, which are explained in detail
in the [in-depth vignette](https://bips-hb.github.io/innsight/articles/detailed_overview.html#model-as-named-list).
<details>
<summary> **Example** </summary>
```{r}
model <- list(
input_dim = 2,
input_names = list(c("X1", "Feat2")),
input_nodes = 1,
output_nodes = 2,
layers = list(
list(
type = "Dense", weight = matrix(rnorm(10), 5, 2), bias = rnorm(5),
activation_name = "relu", input_layers = 0, output_layers = 2
),
list(
type = "Dense", weight = matrix(rnorm(5), 1, 5), bias = rnorm(1),
activation_name = "sigmoid", input_layers = 1, output_layers = -1
)
)
)
converter <- convert(model)
```
</details>
After an instance of the `Converter` class has been created, the base `print()`
method can be used to output the most important components of the object in
summary form:
```{r}
converter
```
### Step 2: Apply selected method
The **innsight** package provides several tools for analyzing black box
neural networks based on dense or convolution layers. For the sake of uniform
usage, all implemented methods inherit from the `InterpretingMethod`
super class (see `?InterpretingMethod` for details) and differ in each case
only by method-specific arguments and settings. Therefore, each method
has the following initialization structure:
```{r, eval = FALSE}
method <- Method$new(converter, data, # required arguments
channels_first = TRUE, # optional settings
output_idx = NULL, # .
ignore_last_act = TRUE, # .
... # other args and method-specific args
)
```
However, you can also use the helper functions (e.g., `run_grad()`,
`run_deeplift()`, etc.) for initializing a new object:
```{r, eval = FALSE}
method <- run_method(converter, data, # required arguments
channels_first = TRUE, # optional settings
output_idx = NULL, # .
ignore_last_act = TRUE, # .
... # other args and method-specific args
)
```
The most important arguments are explained below. For a complete and detailed
explanation, however, we refer to the R documentation (see
`?InterpretingMethod`) or the vignette ["In-depth explanation"](https://bips-hb.github.io/innsight/articles/detailed_overview.html#step-2-apply-selected-method)
(`vignette("detailed_overview", package = "innsight")`).
* `converter`: This is the converter object created in the [first step](#step-1-model-creation-and-converting).
* `data`: The data to which the method is to be applied. These must have the
same format as the input data of the passed model to the converter object.
This means either an `array`, `data.frame`, `torch_tensor` or array-like
format of size $\left(\text{batchsize}, \text{input_dim}\right)$, if e.g.,
the model has only one input layer, or a `list` of the respective input sizes
for each of the input layers.
* `channels_first`: The channel position of the given data (argument `data`).
If `TRUE`, the channel axis is placed at the second position between the
batch size and the remaining input axes, e.g., `c(10,3,32,32)` for a batch
of ten images with three channels and a height and width of 32 pixels.
Otherwise (`FALSE`), the channel axis is at the last position, i.e.,
`c(10,32,32,3)`. If the data has no channel axis, use the default value `TRUE`.
* `output_idx`: These indices specify the output nodes or classes for which
the method is to be applied. If the model has only one output layer, the
values correspond to the indices of the output nodes, e.g., `c(1,3,4)` for the
first, third and fourth output node. If your model has more than one output
layer, you can pass the respective output nodes in a list which is described in
detail in the R documentation (see `?InterpretingMethod`) or in the [in-depth vignette](https://bips-hb.github.io/innsight/articles/detailed_overview.html#argument-output_idx)
* `output_label`: These values specify the output nodes for which the method
is to be applied and can be used as an alternative to the argument `output_idx`.
Only values that were previously passed with the argument `output_names` in
the `converter` can be used.
* `ignore_last_act`: Set this logical value to include the last activation
functions for each output layer, or not (default: `TRUE`)
The package **innsight** now offers the following methods for interpreting your
model. To use them, simply replace the name `"Method"` with one of the method's
names below. There are also method-specific arguments, but these are
explained in detail along with the methods in the R documentation (e.g.,
`?Gradient` or `?LRP`) or in the [in-depth vignette](https://bips-hb.github.io/innsight/articles/detailed_overview.html#methods).
Let $x \in \mathbb{R}^p$ the input instance, $i$ is the feature index of the
input and $c$ the index of the output node or class to be explained:
<ul>
<li> **`Gradient`** : Calculation of the model output *Gradients* with respect
to the model inputs including the attribution method
*Gradient$\times$Input*:
$$
\begin{align}
\text{Gradient}(x)_i^c &= \frac{\partial f(x)_c}{\partial x_i}\\
\text{Gradient$\times$Input}(x)_i^c &= x_i \cdot \text{Gradient}(x)_i^c
\end{align}
$$
<details>
<summary>Examples</summary>
```{r, results='hide', message=FALSE, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Apply method 'Gradient' for the dense network
grad_dense <- Gradient$new(conv_dense, iris[-c(1, 2, 5)])
# You can also use the helper function `run_grad`
grad_dense <- run_grad(conv_dense, iris[-c(1, 2, 5)])
# Apply method 'Gradient x Input' for CNN
x <- torch_randn(c(10, 3, 10, 10))
grad_cnn <- run_grad(conv_cnn, x, times_input = TRUE)
```
</details>
</li>
<li> **`SmoothGrad`** : Calculation of the smoothed model output gradients
([*SmoothGrad*](https://arxiv.org/abs/1706.03825)) with respect to the model
inputs by averaging the gradients over number of inputs with added noise
(including *SmoothGrad$\times$Input*):
$$
\text{SmoothGrad}(x)_i^c = \mathbb{E}_{\varepsilon \sim \mathcal{N}(0, \sigma^2)}\left[\frac{\partial f(x + \varepsilon)_c}{\partial (x + \varepsilon)_i}\right] \approx \frac{1}{n} \sum_{k=1}^n \frac{\partial f(x + \varepsilon_k)_c}{\partial (x + \varepsilon_k)_i}
$$
with $\varepsilon_1, \ldots \varepsilon_n \sim \mathcal{N}(0, \sigma^2)$.
<details>
<summary>Examples</summary>
```{r, results='hide', message=FALSE, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Apply method 'SmoothGrad' for the dense network
smooth_dense <- run_smoothgrad(conv_dense, iris[-c(1, 2, 5)])
# Apply method 'SmoothGrad x Input' for CNN
x <- torch_randn(c(10, 3, 10, 10))
smooth_cnn <- run_smoothgrad(conv_cnn, x, times_input = TRUE)
```
</details>
</li>
<li> **`IntegratedGradient`** : Calculation of the integrated gradients
([Sundararajan et al. (2017)](https://arxiv.org/abs/1703.01365)) with respect
to a reference input $\tilde{x}$:
$$
\text{IntGrad}(x)_i^c = (x - \tilde{x}) \int_{\alpha = 0}^1 \frac{\partial f(\tilde{x} + \alpha (x - \tilde{x}))}{\partial x} d\alpha.
$$
<details>
<summary>Examples</summary>
```{r, results='hide', message=FALSE, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Apply method 'IntegratedGradient' for the dense network
intgrad_dense <- run_intgrad(conv_dense, iris[-c(1, 2, 5)])
# Apply method 'IntegratedGradient' for CNN with the average baseline
x <- torch_randn(c(10, 3, 10, 10))
x_ref <- x$mean(1, keepdim = TRUE)
intgrad_cnn <- run_intgrad(conv_cnn, x, x_ref = x_ref)
```
</details>
</li>
<li> **`ExpectedGradient`** : Calculation of the integrated gradients
([Erion et al., 2021](https://doi.org/10.1038/s42256-021-00343-w)) with respect
to a whole reference dataset $\tilde{X} \sim \tilde{x}$:
$$
\text{ExpGrad}(x)_i^c = \mathbb{E}_{\tilde{x}\sim \tilde{X}, \alpha \sim U(0,1)} \left[(x - \tilde{x}) \times \frac{\partial f(\tilde{x} + \alpha (x - \tilde{x}))}{\partial x} \right]
$$
<details>
<summary>Examples</summary>
```{r, results='hide', message=FALSE, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Apply method 'ExpectedGradient' for the dense network
expgrad_dense <- run_expgrad(conv_dense, iris[-c(1, 2, 5)],
data_ref = iris[-c(1, 2, 5)])
# Apply method 'ExpectedGradient' for CNN
x <- torch_randn(c(10, 3, 10, 10))
data_ref <- torch_randn(c(20, 3, 10, 10))
expgrad_cnn <- run_expgrad(conv_cnn, x, data_ref = data_ref)
```
</details>
</li>
<li> **`LRP`** : Back-propagating the model output to the model input neurons to
obtain relevance scores for the model prediction which is known as
[*Layer-wise Relevance Propagation*](https://doi.org/10.1371/journal.pone.0130140):
$$
f(x)_c \approx \sum_{i=1}^d R_i
$$
with $R_i$ relevance score for input neuron $i$.
<details>
<summary>Examples</summary>
```{r, results='hide', message=FALSE, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Apply method 'LRP' for the dense network
lrp_dense <- run_lrp(conv_dense, iris[-c(1, 2, 5)])
# Apply method 'LRP' for CNN with alpha-beta-rule
x <- torch_randn(c(10, 10, 10, 3))
lrp_cnn <- run_lrp(conv_cnn, x,
rule_name = "alpha_beta", rule_param = 1,
channels_first = FALSE
)
```
</details>
</li>
<li> **`DeepLift`** : Calculation of a decomposition of the model output with
respect to the model inputs and a reference input which is known as
*Deep Learning Important Features*
([*DeepLift*](https://arxiv.org/abs/1704.02685)):
$$
\Delta y_c = f(x)_c - f(x_\text{ref})_c = \sum_{i=1}^d C_i
$$
with $C_i$ contribution score for input neuron $i$ to the
difference-from-reference model output $\Delta y_c$.
<details>
<summary>Examples</summary>
```{r, results='hide', message=FALSE, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Define reference value
x_ref <- array(colMeans(iris[-c(1, 2, 5)]), dim = c(1, 2))
# Apply method 'DeepLift' for the dense network
deeplift_dense <- run_deeplift(conv_dense, iris[-c(1, 2, 5)], x_ref = x_ref)
# Apply method 'DeepLift' for CNN (default is a zero baseline)
x <- torch_randn(c(10, 3, 10, 10))
deeplift_cnn <- run_deeplift(conv_cnn, x)
```
</details>
</li>
<li> **`ConnectionWeights`** : This is a naive and old approach by calculating
the product of all weights from an input to an output neuron and then adding
them up (see [*Connection Weights*](https://doi.org/10.1016/j.ecolmodel.2004.03.013)).
<details>
<summary>Examples</summary>
```{r, results='hide', message=FALSE, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Apply global method 'ConnectionWeights' for a dense network
connectweights_dense <- run_cw(conv_dense)
# Apply local method 'ConnectionWeights' for a CNN
# Note: This variant requires input data
x <- torch_randn(c(10, 3, 10, 10))
connectweights_cnn <- run_cw(conv_cnn, x, times_input = TRUE)
```
</details>
</li>
<li> Additionally, the method **`DeepSHAP`** and the model-agnostic methods
**`LIME`** and **`SHAP`** are implemented (by the functions `run_deepshap()`,
`run_lime()` and `run_shap()`). For details, we refer to our [vignette "In-depth
explanation"](https://bips-hb.github.io/innsight/articles/detailed_overview.html).
</li>
</ul>
> **`r knitr::asis_output("\U1F4DD")` Notes**
>
> * By default, the last activation function is not taken into account
for all data-based methods. Because often, this is a sigmoid/logistic or
softmax function, which has increasingly smaller gradients with a growing
distance from 0, which leads to the so-called
*saturation problem*. But if you still want to consider the last activation
function, use the argument `ignore_last_act = FALSE`.
>
> * For data with channels, it is impossible to determine exactly on which
axis the channels are located. Internally, all data and the converted model are
in the data format *"channels first"*, i.e., directly after the batch dimension
$\left(\text{batchsize}, \text{channels}, \text{input_dim}\right)$. In case you
want to pass data with *"channels last"* (e.g., for MNIST-data
$\left(\text{batchsize}, 28,28,3\right)$),
you have to indicate that with argument `channels_first` in the
applied method.
>
> * It can happen with very large and deep neural networks that the calculation
for all outputs requires the entire memory and takes a very long time.
But often, the results are needed only for certain output nodes. By default,
only the results for the first 10 outputs are calculated, which can be adjusted
individually with the argument `output_idx` by passing the relevant output
indices.
Similar to the instances of the `Converter` class, the default `print()`
function for R6 classes was also overridden for each method object, so that all
important contents of the corresponding method are displayed:
```{r, eval = keras::is_keras_available() & torch::torch_is_installed()}
smooth_cnn
```
### Step 3: Show and plot the results
Once a method object has been created, the results can be returned as an
`array`, `data.frame`, or `torch_tensor`, and can be further processed as
desired. In addition, for each of the three sizes of the inputs (tabular, 1D
signals or 2D images) suitable plot and boxplot functions based on
[**ggplot2**](https://ggplot2.tidyverse.org/) are implemented. Due to the
complexity of higher dimensional inputs, these plots and boxplots can also
be displayed as an interactive [**plotly**](https://plotly.com/r/) plots by using
the argument `as_plotly`.
#### Get results
Each instance of the interpretability methods has the class method
`get_result()`, which is used to return the results. You can choose between
the data formats `array`, `data.frame` or `torch_tensor` by passing the
name as an character for argument `type`. This method is also implemented
as a S3 method. For a deeper view in this method look [this section](https://bips-hb.github.io/innsight/articles/detailed_overview.html#get-results)
in the in-depth vignette.
```{r, eval = FALSE}
# Get the result with the class method
method$get_result(type = "array")
# or use the S3 function
get_result(method, type = "array")
```
**Examples:**
<details>
<summary> `array` (default) </summary>
```{r, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Get result (make sure 'grad_dense' is defined!)
result_array <- grad_dense$get_result()
# or with the S3 method
result_array <- get_result(grad_dense)
# Show the result for data point 1 and 71
result_array[c(1, 71), , ]
```
</details>
<details>
<summary> `data.frame` </summary>
```{r, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Get result as data.frame (make sure 'lrp_cnn' is defined!)
result_data.frame <- lrp_cnn$get_result("data.frame")
# or with the S3 method
result_data.frame <- get_result(lrp_cnn, "data.frame")
# Show the first 5 rows
head(result_data.frame, 5)
```
</details>
<details>
<summary> `torch_tensor` </summary>
```{r, eval = keras::is_keras_available() & torch::torch_is_installed()}
# Get result (make sure 'deeplift_dense' is defined!)
result_torch <- deeplift_dense$get_result("torch_tensor")
# or with the S3 method
result_torch <- get_result(deeplift_dense, "torch_tensor")
# Show for datapoint 1 and 71 the result
result_torch[c(1, 71), , ]
```
</details>
#### Plot results
The package **innsight** also provides methods for visualizing the results.
By default a **ggplot2**-plot is created, but it can also be rendered as an
interactive **plotly** plot with the `as_plotly` argument. You can use the
argument `output_idx` to select the indices of the output nodes
for the plot. In addition, if the results have channels, the
`aggr_channels` argument can be used to determine how the channels are
aggregated. All arguments are explained in detail in the R documentation
(see `?InterpretingMethod`) or [here for `plot()`](https://bips-hb.github.io/innsight/articles/detailed_overview.html#plot-single-results-plot) and
[here for `plot_global()`](https://bips-hb.github.io/innsight/articles/detailed_overview.html#plot-summarized-results-plot_global).
```{r, eval = FALSE}
# Create a plot for single data points
plot(method,
data_idx = 1, # the data point to be plotted
output_idx = NULL, # the indices of the output nodes/classes to be plotted
output_label = NULL, # the class labels to be plotted
aggr_channels = "sum",
as_plotly = FALSE, # create an interactive plot
... # other arguments
)
# Create a plot with summarized results
plot_global(method,
output_idx = NULL, # the indices of the output nodes/classes to be plotted
output_label = NULL, # the class labels to be plotted
ref_data_idx = NULL, # the index of an reference data point to be plotted
aggr_channels = "sum",
as_plotly = FALSE, # create an interactive plot
... # other arguments
)
# Alias for `plot_global` for tabular and signal data
boxplot(...)
```
> **`r knitr::asis_output("\U1F4DD")` Note**
> The argument `output_idx` can be either a vector of indices or a list of
vectors of indices but must be a subset of the indices for which the results were
calculated, i.e., a subset of the argument `output_idx` passed to the
respective method previously. By default (`NULL`), the smallest
index of all computed output nodes and output layers is used.
**Examples:**
<details>
<summary> `plot()` function (**ggplot2**) </summary>
```{r, eval = keras::is_keras_available() & torch::torch_is_installed(), fig.height=6, fig.width=9}
# Plot the result of the first data point (default) for the output classes '1', '2' and '3'
plot(smooth_dense, output_idx = 1:3)
# You can plot several data points at once
plot(smooth_dense, data_idx = c(1, 144), output_idx = 1:3)
# Plot result for the first data point and first and fourth output classes
# and aggregate the channels by taking the Euclidean norm
plot(lrp_cnn, aggr_channels = "norm", output_idx = c(1, 4))
```
</details>
<details>
<summary> `plot()` function (**plotly**) </summary>
```{r, eval = FALSE}
# Create a plotly plot for the first output
plot(lrp_cnn, aggr_channels = "norm", output_idx = c(1), as_plotly = TRUE)
```
```{r, fig.width = 8, fig.height=4, echo = FALSE, message=FALSE, eval=Sys.getenv("RENDER_PLOTLY", unset = 0) == 1}
# You can do the same with the plotly-based plots
p <- plot(lrp_cnn, aggr_channels = "norm", output_idx = c(1), as_plotly = TRUE)
plotly::config(print(p))
```
</details>
<details>
<summary> `plot_global()` function (**ggplot2**) </summary>
```{r, eval = keras::is_keras_available() & torch::torch_is_installed(), fig.height=6, fig.width=9}
# Create boxplot for the first two output classes
plot_global(smooth_dense, output_idx = 1:2)
# Use no preprocess function (default: abs) and plot a reference data point
plot_global(smooth_dense,
output_idx = 1:3, preprocess_FUN = identity,
ref_data_idx = c(55)
)
```
</details>
<details>
<summary> `plot_global()` function (**plotly**) </summary>
```{r, fig.height=6, fig.width=9, eval = FALSE}
# You can do the same with the plotly-based plots
plot_global(smooth_dense,
output_idx = 1:3, preprocess_FUN = identity,
ref_data_idx = c(55), as_plotly = TRUE
)
```
```{r, fig.width = 8, fig.height=4, echo = FALSE, message=FALSE, eval=Sys.getenv("RENDER_PLOTLY", unset = 0) == 1 & torch::torch_is_installed()}
# You can do the same with the plotly-based plots
p <- plot_global(smooth_dense,
output_idx = 1:3, preprocess_FUN = identity,
ref_data_idx = c(55), as_plotly = TRUE
)
plotly::config(print(p))
```
</details>