InterpretingMethod: Super class for interpreting methods

In innsight: Get the Insights of Your Neural Network

Super class for interpreting methods

Description

This is a super class for all interpreting methods in the innsight package. Implemented are the following methods:

• Deep Learning Important Features (DeepLift)

• Deep Shapley additive explanations (DeepSHAP)

• Layer-wise Relevance Propagation (LRP)

• Gradient-based methods:

  • Vanilla gradients including Gradient\timesInput (Gradient)

  • Smoothed gradients including SmoothGrad\timesInput (SmoothGrad)

  • Integrated gradients (IntegratedGradient)

  • Expected gradients (ExpectedGradient)

• Connection Weights (global and local) (ConnectionWeights)

• Also some model-agnostic approaches:

  • Local interpretable model-agnostic explanations (LIME)

  • Shapley values (SHAP)

Public fields

data

(list)
The passed data as a list of torch_tensors in the selected data format (field dtype) matching the corresponding shapes of the individual input layers. Besides, the channel axis is moved to the second position after the batch size because internally only the format channels first is used.

converter

(Converter)
An instance of the Converter class that includes the torch-converted model and some other model-specific attributes. See Converter for details.

channels_first

(logical(1))
The channel position of the given data. If TRUE, the channel axis is placed at the second position between the batch size and the rest of the 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). This is especially important for layers like flatten, where the order is crucial and therefore the channels have to be moved from the internal format "channels first" back to the original format before the layer is calculated.

dtype

(character(1))
The data type for the calculations. Either 'float' for torch_float or 'double' for torch_double.

ignore_last_act

(logical(1))
A logical value to include the last activation functions into all the calculations, or not.

result

(list)
The results of the method on the passed data. A unified list structure is used regardless of the complexity of the model: The outer list contains the individual output layers and the inner list the input layers. The results for the respective output and input layer are then stored there as torch tensors in the given data format (field dtype). In addition, the channel axis is moved to its original place and the last axis contains the selected output nodes for the individual output layers (see output_idx).
For example, the structure of the result for two output layers (output node 1 for the first and 2 and 4 for the second) and two input layers with channels_first = FALSE looks like this:

List of 2 # both output layers
  $ :List of 2 # both input layers
    ..$ : torch_tensor [batch_size, dim_in_1, channel_axis, 1]
    ..$ : torch_tensor [batch_size, dim_in_2, channel_axis, 1]
 $ :List of 2 # both input layers
    ..$ : torch_tensor [batch_size, dim_in_1, channel_axis, 2]
    ..$ : torch_tensor [batch_size, dim_in_2, channel_axis, 2]

output_idx

(list)
This list of indices specifies the output nodes to which the method is to be applied. In the order of the output layers, the list contains the respective output nodes indices and unwanted output layers have the entry NULL instead of a vector of indices, e.g., list(NULL, c(1,3)) for the first and third output node in the second output layer.

output_label

(list)
This list of factors specifies the output nodes to which the method is to be applied. In the order of the output layers, the list contains the respective output nodes labels and unwanted output layers have the entry NULL instead of a vector of labels, e.g., list(NULL, c("a", "c")) for the first and third output node in the second output layer.

verbose

(logical(1))
This logical value determines whether a progress bar is displayed for the calculation of the method or not. The default value is the output of the primitive R function interactive().

winner_takes_all

(logical(1))
This logical value is only relevant for models with a MaxPooling layer. Since many zeros are produced during the backward pass due to the selection of the maximum value in the pooling kernel, another variant is implemented, which treats a MaxPooling as an AveragePooling layer in the backward pass to overcome the problem of too many zero relevances. With the default value TRUE, the whole upper-layer relevance is passed to the maximum value in each pooling window. Otherwise, if FALSE, the relevance is distributed equally among all nodes in a pooling window.

preds

(list)
In this field, all calculated predictions are stored as a list of torch_tensors. Each output layer has its own list entry and contains the respective predicted values.

decomp_goal

(list)
In this field, the method-specific decomposition objectives are stored as a list of torch_tensors for each output layer. For example, GradientxInput and LRP attempt to decompose the prediction into feature-wise additive effects. DeepLift and IntegratedGradient decompose the difference between f(x) and f(x'). On the other hand, DeepSHAP and ExpectedGradient aim to decompose f(x) minus the averaged prediction across the reference values.

Methods

Public methods

• InterpretingMethod$new()

• InterpretingMethod$get_result()

• InterpretingMethod$plot()

• InterpretingMethod$plot_global()

• InterpretingMethod$print()

• InterpretingMethod$clone()

---

Method new()

Create a new instance of this super class.

Usage

InterpretingMethod$new(
  converter,
  data,
  channels_first = TRUE,
  output_idx = NULL,
  output_label = NULL,
  ignore_last_act = TRUE,
  winner_takes_all = TRUE,
  verbose = interactive(),
  dtype = "float"
)

Arguments

converter

(Converter)
An instance of the Converter class that includes the torch-converted model and some other model-specific attributes. See Converter for details.

data

(array, data.frame, torch_tensor or list)
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 (batch_size, dim_in), if e.g., the model has only one input layer, or

• a list with the corresponding input data (according to the upper point) for each of the input layers.
  

channels_first

(logical(1))
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 rest of the 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

(integer, list or NULL)
These indices specify the output nodes for which the method is to be applied. In order to allow models with multiple output layers, there are the following possibilities to select the indices of the output nodes in the individual output layers:

• An integer vector of indices: 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 there are multiple output layers, the indices of the output nodes from the first output layer are considered.

• A list of integer vectors of indices: If the method is to be applied to output nodes from different layers, a list can be passed that specifies the desired indices of the output nodes for each output layer. Unwanted output layers have the entry NULL instead of a vector of indices, e.g. list(NULL, c(1,3)) for the first and third output node in the second output layer.

• NULL (default): The method is applied to all output nodes in the first output layer but is limited to the first ten as the calculations become more computationally expensive for more output nodes.
  

output_label

(character, factor, list or NULL)
These values specify the output nodes for which the method is to be applied. Only values that were previously passed with the argument output_names in the converter can be used. In order to allow models with multiple output layers, there are the following possibilities to select the names of the output nodes in the individual output layers:

• A character vector or factor of labels: If the model has only one output layer, the values correspond to the labels of the output nodes named in the passed Converter object, e.g., c("a", "c", "d") for the first, third and fourth output node if the output names are c("a", "b", "c", "d"). If there are multiple output layers, the names of the output nodes from the first output layer are considered.

• A list of charactor/factor vectors of labels: If the method is to be applied to output nodes from different layers, a list can be passed that specifies the desired labels of the output nodes for each output layer. Unwanted output layers have the entry NULL instead of a vector of labels, e.g., list(NULL, c("a", "c")) for the first and third output node in the second output layer.

• NULL (default): The method is applied to all output nodes in the first output layer but is limited to the first ten as the calculations become more computationally expensive for more output nodes.
  

ignore_last_act

(logical(1))
Set this logical value to include the last activation functions for each output layer, or not (default: TRUE). In practice, the last activation (especially for softmax activation) is often omitted.

winner_takes_all

(logical(1))
This logical argument is only relevant for models with a MaxPooling layer. Since many zeros are produced during the backward pass due to the selection of the maximum value in the pooling kernel, another variant is implemented, which treats a MaxPooling as an AveragePooling layer in the backward pass to overcome the problem of too many zero relevances. With the default value TRUE, the whole upper-layer relevance is passed to the maximum value in each pooling window. Otherwise, if FALSE, the relevance is distributed equally among all nodes in a pooling window.

verbose

(logical(1))
This logical argument determines whether a progress bar is displayed for the calculation of the method or not. The default value is the output of the primitive R function interactive().

dtype

(character(1))
The data type for the calculations. Use either 'float' for torch_float or 'double' for torch_double.

---

Method get_result()

This function returns the result of this method for the given data either as an array ('array'), a torch tensor ('torch.tensor', or 'torch_tensor') of size (batch_size, dim_in, dim_out) or as a data.frame ('data.frame'). This method is also implemented as a generic S3 function get_result. For a detailed description, we refer to our in-depth vignette (vignette("detailed_overview", package = "innsight")) or our website.

Usage

InterpretingMethod$get_result(type = "array")

Arguments

type

(character(1))
The data type of the result. Use one of 'array', 'torch.tensor', 'torch_tensor' or 'data.frame' (default: 'array').

Returns

The result of this method for the given data in the chosen type.

---

Method plot()

This method visualizes the result of the selected method and enables a visual in-depth investigation with the help of the S4 classes innsight_ggplot2 and innsight_plotly.
You can use the argument data_idx to select the data points in the given data for the plot. In addition, the individual output nodes for the plot can be selected with the argument output_idx. The different results for the selected data points and outputs are visualized using the ggplot2-based S4 class innsight_ggplot2. You can also use the as_plotly argument to generate an interactive plot with innsight_plotly based on the plot function plotly::plot_ly. For more information and the whole bunch of possibilities, see innsight_ggplot2 and innsight_plotly.

Notes:

1. For the interactive plotly-based plots, the suggested package plotly is required.

2. The ggplot2-based plots for models with multiple input layers are a bit more complex, therefore the suggested packages 'grid', 'gridExtra' and 'gtable' must be installed in your R session.

3. If the global Connection Weights method was applied, the unnecessary argument data_idx will be ignored.

4. The predictions, the sum of relevances, and, if available, the decomposition target are displayed by default in a box within the plot. Currently, these are not generated for plotly plots.

Usage

InterpretingMethod$plot(
  data_idx = 1,
  output_idx = NULL,
  output_label = NULL,
  aggr_channels = "sum",
  as_plotly = FALSE,
  same_scale = FALSE,
  show_preds = TRUE
)

Arguments

data_idx

(integer)
An integer vector containing the numbers of the data points whose result is to be plotted, e.g., c(1,3) for the first and third data point in the given data. Default: 1. This argument will be ignored for the global Connection Weights method.

output_idx

(integer, list or NULL)
The indices of the output nodes for which the results is to be plotted. This can be either a integer vector of indices or a list of integer vectors of indices but must be a subset of the indices for which the results were calculated, i.e., a subset of output_idx from the initialization new() (see argument output_idx in method new() of this R6 class for details). By default (NULL), the smallest index of all calculated output nodes and output layers is used.

output_label

(character, factor, list or NULL)
These values specify the output nodes for which the method is to be applied. Only values that were previously passed with the argument output_names in the converter can be used. In order to allow models with multiple output layers, there are the following possibilities to select the names of the output nodes in the individual output layers:

• A character vector or factor of labels: If the model has only one output layer, the values correspond to the labels of the output nodes named in the passed Converter object, e.g., c("a", "c", "d") for the first, third and fourth output node if the output names are c("a", "b", "c", "d"). If there are multiple output layers, the names of the output nodes from the first output layer are considered.

• A list of charactor/factor vectors of labels: If the method is to be applied to output nodes from different layers, a list can be passed that specifies the desired labels of the output nodes for each output layer. Unwanted output layers have the entry NULL instead of a vector of labels, e.g., list(NULL, c("a", "c")) for the first and third output node in the second output layer.

• NULL (default): The method is applied to all output nodes in the first output layer but is limited to the first ten as the calculations become more computationally expensive for more output nodes.
  

aggr_channels

(character(1) or function)
Pass one of 'norm', 'sum', 'mean' or a custom function to aggregate the channels, e.g., the maximum (base::max) or minimum (base::min) over the channels or only individual channels with function(x) x[1]. By default ('sum'), the sum of all channels is used.
Note: This argument is used only for 2D and 3D input data.

as_plotly

(logical(1))
This logical value (default: FALSE) can be used to create an interactive plot based on the library plotly (see innsight_plotly for details).
Note: Make sure that the suggested package plotly is installed in your R session.

same_scale

(logical)
A logical value that specifies whether the individual plots have the same fill scale across multiple input layers or whether each is scaled individually. This argument is only used if more than one input layer results are plotted.

show_preds

(logical)
This logical value indicates whether the plots display the prediction, the sum of calculated relevances, and, if available, the targeted decomposition value. For example, in the case of GradientxInput, the goal is to obtain a decomposition of the predicted value, while for DeepLift and IntegratedGradient, the goal is the difference between the prediction and the reference value, i.e., f(x) - f(x').

Returns

Returns either an innsight_ggplot2 (as_plotly = FALSE) or an innsight_plotly (as_plotly = TRUE) object with the plotted individual results.

---

Method plot_global()

This method visualizes the results of the selected method summarized as boxplots/median image and enables a visual in-depth investigation of the global behavior with the help of the S4 classes innsight_ggplot2 and innsight_plotly.
You can use the argument output_idx to select the individual output nodes for the plot. For tabular and 1D data, boxplots are created in which a reference value can be selected from the data using the ref_data_idx argument. For images, only the pixel-wise median is visualized due to the complexity. The plot is generated using the ggplot2-based S4 class innsight_ggplot2. You can also use the as_plotly argument to generate an interactive plot with innsight_plotly based on the plot function plotly::plot_ly. For more information and the whole bunch of possibilities, see innsight_ggplot2 and innsight_plotly.

Notes:

1. This method can only be used for the local Connection Weights method, i.e., if times_input is TRUE and data is provided.

2. For the interactive plotly-based plots, the suggested package plotly is required.

3. The ggplot2-based plots for models with multiple input layers are a bit more complex, therefore the suggested packages 'grid', 'gridExtra' and 'gtable' must be installed in your R session.

Usage

InterpretingMethod$plot_global(
  output_idx = NULL,
  output_label = NULL,
  data_idx = "all",
  ref_data_idx = NULL,
  aggr_channels = "sum",
  preprocess_FUN = abs,
  as_plotly = FALSE,
  individual_data_idx = NULL,
  individual_max = 20
)

Arguments

output_idx

(integer, list or NULL)
The indices of the output nodes for which the results is to be plotted. This 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 output_idx from the initialization new() (see argument output_idx in method new() of this R6 class for details). By default (NULL), the smallest index of all calculated output nodes and output layers is used.

output_label

(character, factor, list or NULL)
These values specify the output nodes for which the method is to be applied. Only values that were previously passed with the argument output_names in the converter can be used. In order to allow models with multiple output layers, there are the following possibilities to select the names of the output nodes in the individual output layers:

• A character vector or factor of labels: If the model has only one output layer, the values correspond to the labels of the output nodes named in the passed Converter object, e.g., c("a", "c", "d") for the first, third and fourth output node if the output names are c("a", "b", "c", "d"). If there are multiple output layers, the names of the output nodes from the first output layer are considered.

• A list of charactor/factor vectors of labels: If the method is to be applied to output nodes from different layers, a list can be passed that specifies the desired labels of the output nodes for each output layer. Unwanted output layers have the entry NULL instead of a vector of labels, e.g., list(NULL, c("a", "c")) for the first and third output node in the second output layer.

• NULL (default): The method is applied to all output nodes in the first output layer but is limited to the first ten as the calculations become more computationally expensive for more output nodes.
  

data_idx

(integer)
By default, all available data points are used to calculate the boxplot information. However, this parameter can be used to select a subset of them by passing the indices. For example, with c(1:10, 25, 26) only the first 10 data points and the 25th and 26th are used to calculate the boxplots.

ref_data_idx

(integer(1) or NULL)
This integer number determines the index for the reference data point. In addition to the boxplots, it is displayed in red color and is used to compare an individual result with the summary statistics provided by the boxplot. With the default value (NULL), no individual data point is plotted. This index can be chosen with respect to all available data, even if only a subset is selected with argument data_idx.
Note: Because of the complexity of 2D inputs, this argument is used only for tabular and 1D inputs and disregarded for 2D inputs.

aggr_channels

(character(1) or function)
Pass one of 'norm', 'sum', 'mean' or a custom function to aggregate the channels, e.g., the maximum (base::max) or minimum (base::min) over the channels or only individual channels with function(x) x[1]. By default ('sum'), the sum of all channels is used.
Note: This argument is used only for 2D and 3D input data.

preprocess_FUN

(function)
This function is applied to the method's result before calculating the boxplots or medians. Since positive and negative values often cancel each other out, the absolute value (abs) is used by default. But you can also use the raw results (identity) to see the results' orientation, the squared data (function(x) x^2) to weight the outliers higher or any other function.

as_plotly

(logical(1))
This logical value (default: FALSE) can be used to create an interactive plot based on the library plotly (see innsight_plotly for details).
Note: Make sure that the suggested package plotly is installed in your R session.

individual_data_idx

(integer or NULL)
Only relevant for a plotly plot with tabular or 1D inputs! This integer vector of data indices determines the available data points in a dropdown menu, which are drawn individually analogous to ref_data_idx only for more data points. With the default value NULL, the first individual_max data points are used.
Note: If ref_data_idx is specified, this data point will be added to those from individual_data_idx in the dropdown menu.

individual_max

(integer(1))
Only relevant for a plotly plot with tabular or 1D inputs! This integer determines the maximum number of individual data points in the dropdown menu without counting ref_data_idx. This means that if individual_data_idx has more than individual_max indices, only the first individual_max will be used. A too high number can significantly increase the runtime.

Returns

Returns either an innsight_ggplot2 (as_plotly = FALSE) or an innsight_plotly (as_plotly = TRUE) object with the plotted summarized results.

---

Method print()

Print a summary of the method object. This summary contains the individual fields and in particular the results of the applied method.

Usage

InterpretingMethod$print()

Returns

Returns the method object invisibly via base::invisible.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

InterpretingMethod$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

innsight documentation built on May 29, 2024, 4:36 a.m.