innsight_sugar {innsight}

Syntactic sugar for object construction

Description

Since all methods and the preceding conversion step in the innsight package were implemented using R6 classes and these always require a call to classname$new() for initialization, the following functions are defined to shorten the construction of the corresponding R6 objects:

• convert() for Converter

• run_grad() for Gradient

• run_smoothgrad() for SmoothGrad

• run_intgrad() for IntegratedGradient

• run_expgrad() for ExpectedGradient

• run_lrp() for LRP

• run_deeplift() for DeepLift

• run_deepshap for DeepSHAP

• run_cw for ConnectionWeights

• run_lime for LIME

• run_shap for SHAP

Usage

# Create a new `Converter` object of the given `model`
convert(model, ...)

# Apply the `Gradient` method to the passed `data` to be explained
run_grad(converter, data, ...)

# Apply the `SmoothGrad` method to the passed `data` to be explained
run_smoothgrad(converter, data, ...)

# Apply the `IntegratedGradient` method to the passed `data` to be explained
run_intgrad(converter, data, ...)

# Apply the `ExpectedGradient` method to the passed `data` to be explained
run_expgrad(converter, data, ...)

# Apply the `LRP` method to the passed `data` to be explained
run_lrp(converter, data, ...)

# Apply the `DeepLift` method to the passed `data` to be explained
run_deeplift(converter, data, ...)

# Apply the `DeepSHAP` method to the passed `data` to be explained
run_deepshap(converter, data, ...)

# Apply the `ConnectionWeights` method (argument `data` is not always required)
run_cw(converter, ...)

# Apply the `LIME` method to explain `data` by using the dataset `data_ref`
run_lime(model, data, data_ref, ...)

# Apply the `SHAP` method to explain `data` by using the dataset `data_ref`
run_shap(model, data, data_ref, ...)

Arguments

model

(nn_sequential, keras_model, neuralnet or list)
A trained neural network for classification or regression tasks to be interpreted. Only models from the following types or packages are allowed: nn_sequential, keras_model, keras_model_sequential, neuralnet or a named list (see details).
Note: For the model-agnostic methods, an arbitrary fitted model for a classification or regression task can be passed. A Converter object can also be passed. In order for the package to know how to make predictions with the given model, a prediction function must also be passed with the argument pred_fun. However, for models created by nn_sequential, keras_model, neuralnet or Converter, these have already been pre-implemented and do not need to be specified.

...

Other arguments passed to the individual constructor functions of the methods R6 classes.

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.

Note: For the model-agnostic methods, only models with a single input and output layer is allowed!

data_ref

(array, data.frame or torch_tensor)
The dataset to which the method is to be applied. These must have the same format as the input data of the passed model and has to be either matrix, an array, a data.frame or a torch_tensor.
Note: For the model-agnostic methods, only models with a single input and output layer is allowed!

Value

R6::R6Class object of the respective type.