explain: Explain the output of machine learning models with more...

In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values

Explain the output of machine learning models with more accurately estimated Shapley values

Description

Computes dependence-aware Shapley values for observations in x_explain from the specified model by using the method specified in approach to estimate the conditional expectation.

Usage

explain(
  model,
  x_explain,
  x_train,
  approach,
  prediction_zero,
  n_combinations = NULL,
  group = NULL,
  n_samples = 1000,
  n_batches = NULL,
  seed = 1,
  keep_samp_for_vS = FALSE,
  predict_model = NULL,
  get_model_specs = NULL,
  MSEv_uniform_comb_weights = TRUE,
  timing = TRUE,
  verbose = 0,
  ...
)

Arguments

model	The model whose predictions we want to explain. Run get_supported_models() for a table of which models explain supports natively. Unsupported models can still be explained by passing predict_model and (optionally) get_model_specs, see details for more information.
x_explain	A matrix or data.frame/data.table. Contains the the features, whose predictions ought to be explained.
x_train	Matrix or data.frame/data.table. Contains the data used to estimate the (conditional) distributions for the features needed to properly estimate the conditional expectations in the Shapley formula.
approach	Character vector of length 1 or one less than the number of features. All elements should, either be "gaussian", "copula", "empirical", "ctree", "vaeac", "categorical", "timeseries", or "independence". See details for more information.
prediction_zero	Numeric. The prediction value for unseen data, i.e. an estimate of the expected prediction without conditioning on any features. Typically we set this value equal to the mean of the response variable in our training data, but other choices such as the mean of the predictions in the training data are also reasonable.
n_combinations	Integer. If group = NULL, n_combinations represents the number of unique feature combinations to sample. If group != NULL, n_combinations represents the number of unique group combinations to sample. If n_combinations = NULL, the exact method is used and all combinations are considered. The maximum number of combinations equals 2^m, where m is the number of features.
group	List. If NULL regular feature wise Shapley values are computed. If provided, group wise Shapley values are computed. group then has length equal to the number of groups. The list element contains character vectors with the features included in each of the different groups.
n_samples	Positive integer. Indicating the maximum number of samples to use in the Monte Carlo integration for every conditional expectation. See also details.
n_batches	Positive integer (or NULL). Specifies how many batches the total number of feature combinations should be split into when calculating the contribution function for each test observation. The default value is NULL which uses a reasonable trade-off between RAM allocation and computation speed, which depends on approach and n_combinations. For models with many features, increasing the number of batches reduces the RAM allocation significantly. This typically comes with a small increase in computation time.
seed	Positive integer. Specifies the seed before any randomness based code is being run. If NULL the seed will be inherited from the calling environment.
keep_samp_for_vS	Logical. Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in internal$output)
predict_model	Function. The prediction function used when model is not natively supported. (Run get_supported_models() for a list of natively supported models.) The function must have two arguments, model and newdata which specify, respectively, the model and a data.frame/data.table to compute predictions for. The function must give the prediction as a numeric vector. NULL (the default) uses functions specified internally. Can also be used to override the default function for natively supported model classes.
get_model_specs	Function. An optional function for checking model/data consistency when model is not natively supported. (Run get_supported_models() for a list of natively supported models.) The function takes model as argument and provides a list with 3 elements: labels Character vector with the names of each feature. classes Character vector with the classes of each features. factor_levels Character vector with the levels for any categorical features. If NULL (the default) internal functions are used for natively supported model classes, and the checking is disabled for unsupported model classes. Can also be used to override the default function for natively supported model classes.
MSEv_uniform_comb_weights	Logical. If TRUE (default), then the function weights the combinations uniformly when computing the MSEv criterion. If FALSE, then the function use the Shapley kernel weights to weight the combinations when computing the MSEv criterion. Note that the Shapley kernel weights are replaced by the sampling frequency when not all combinations are considered.
timing	Logical. Whether the timing of the different parts of the explain() should saved in the model object.
verbose	An integer specifying the level of verbosity. If 0, shapr will stay silent. If 1, it will print information about performance. If 2, some additional information will be printed out. Use 0 (default) for no verbosity, 1 for low verbose, and 2 for high verbose. TODO: Make this clearer when we end up fixing this and if they should force a progressr bar.
...	Arguments passed on to setup_approach.empirical, setup_approach.independence, setup_approach.gaussian, setup_approach.copula, setup_approach.ctree, setup_approach.vaeac, setup_approach.categorical, setup_approach.timeseries empirical.type Character. (default = "fixed_sigma") Should be equal to either "independence","fixed_sigma", "AICc_each_k" "AICc_full". TODO: Describe better what the methods do here. empirical.eta Numeric. (default = 0.95) Needs to be ⁠0 < eta <= 1⁠. Represents the minimum proportion of the total empirical weight that data samples should use. If e.g. eta = .8 we will choose the K samples with the largest weight so that the sum of the weights accounts for 80\ eta is the \eta parameter in equation (15) of Aas et al (2021). empirical.fixed_sigma Positive numeric scalar. (default = 0.1) Represents the kernel bandwidth in the distance computation used when conditioning on all different combinations. Only used when empirical.type = "fixed_sigma" empirical.n_samples_aicc Positive integer. (default = 1000) Number of samples to consider in AICc optimization. Only used for empirical.type is either "AICc_each_k" or "AICc_full". empirical.eval_max_aicc Positive integer. (default = 20) Maximum number of iterations when optimizing the AICc. Only used for empirical.type is either "AICc_each_k" or "AICc_full". empirical.start_aicc Numeric. (default = 0.1) Start value of the sigma parameter when optimizing the AICc. Only used for empirical.type is either "AICc_each_k" or "AICc_full". empirical.cov_mat Numeric matrix. (Optional, default = NULL) Containing the covariance matrix of the data generating distribution used to define the Mahalanobis distance. NULL means it is estimated from x_train. internal Not used. gaussian.mu Numeric vector. (Optional) Containing the mean of the data generating distribution. NULL means it is estimated from the x_train. gaussian.cov_mat Numeric matrix. (Optional) Containing the covariance matrix of the data generating distribution. NULL means it is estimated from the x_train. ctree.mincriterion Numeric scalar or vector. (default = 0.95) Either a scalar or vector of length equal to the number of features in the model. Value is equal to 1 - \alpha where \alpha is the nominal level of the conditional independence tests. If it is a vector, this indicates which value to use when conditioning on various numbers of features. ctree.minsplit Numeric scalar. (default = 20) Determines minimum value that the sum of the left and right daughter nodes required for a split. ctree.minbucket Numeric scalar. (default = 7) Determines the minimum sum of weights in a terminal node required for a split ctree.sample Boolean. (default = TRUE) If TRUE, then the method always samples n_samples observations from the leaf nodes (with replacement). If FALSE and the number of observations in the leaf node is less than n_samples, the method will take all observations in the leaf. If FALSE and the number of observations in the leaf node is more than n_samples, the method will sample n_samples observations (with replacement). This means that there will always be sampling in the leaf unless sample = FALSE AND the number of obs in the node is less than n_samples. vaeac.depth Positive integer (default is 3). The number of hidden layers in the neural networks of the masked encoder, full encoder, and decoder. vaeac.width Positive integer (default is 32). The number of neurons in each hidden layer in the neural networks of the masked encoder, full encoder, and decoder. vaeac.latent_dim Positive integer (default is 8). The number of dimensions in the latent space. vaeac.lr Positive numeric (default is 0.001). The learning rate used in the torch::optim_adam() optimizer. vaeac.activation_function An torch::nn_module() representing an activation function such as, e.g., torch::nn_relu() (default), torch::nn_leaky_relu(), torch::nn_selu(), or torch::nn_sigmoid(). vaeac.n_vaeacs_initialize Positive integer (default is 4). The number of different vaeac models to initiate in the start. Pick the best performing one after vaeac.extra_parameters$epochs_initiation_phase epochs (default is 2) and continue training that one. vaeac.epochs Positive integer (default is 100). The number of epochs to train the final vaeac model. This includes vaeac.extra_parameters$epochs_initiation_phase, where the default is 2. vaeac.extra_parameters Named list with extra parameters to the vaeac approach. See vaeac_get_extra_para_default() for description of possible additional parameters and their default values. categorical.joint_prob_dt Data.table. (Optional) Containing the joint probability distribution for each combination of feature values. NULL means it is estimated from the x_train and x_explain. categorical.epsilon Numeric value. (Optional) If joint_probability_dt is not supplied, probabilities/frequencies are estimated using x_train. If certain observations occur in x_train and NOT in x_explain, then epsilon is used as the proportion of times that these observations occurs in the training data. In theory, this proportion should be zero, but this causes an error later in the Shapley computation. timeseries.fixed_sigma_vec Numeric. (Default = 2) Represents the kernel bandwidth in the distance computation. TODO: What length should it have? 1? timeseries.bounds Numeric vector of length two. (Default = c(NULL, NULL)) If one or both of these bounds are not NULL, we restrict the sampled time series to be between these bounds. This is useful if the underlying time series are scaled between 0 and 1, for example.

Details

The most important thing to notice is that shapr has implemented six different approaches for estimating the conditional distributions of the data, namely "empirical", "gaussian", "copula", "ctree", "vaeac", "categorical", "timeseries", and "independence". In addition, the user also has the option of combining the different approaches. E.g., if you're in a situation where you have trained a model that consists of 10 features, and you'd like to use the "gaussian" approach when you condition on a single feature, the "empirical" approach if you condition on 2-5 features, and "copula" version if you condition on more than 5 features this can be done by simply passing approach = c("gaussian", rep("empirical", 4), rep("copula", 4)). If "approach[i]" = "gaussian" means that you'd like to use the "gaussian" approach when conditioning on i features. Conditioning on all features needs no approach as that is given by the complete prediction itself, and should thus not be part of the vector.

For approach="ctree", n_samples corresponds to the number of samples from the leaf node (see an exception related to the sample argument). For approach="empirical", n_samples is the K parameter in equations (14-15) of Aas et al. (2021), i.e. the maximum number of observations (with largest weights) that is used, see also the empirical.eta argument.

Value

Object of class c("shapr", "list"). Contains the following items:

shapley_values

data.table with the estimated Shapley values

internal

List with the different parameters, data and functions used internally

pred_explain

Numeric vector with the predictions for the explained observations

MSEv

List with the values of the MSEv evaluation criterion for the approach.

shapley_values is a data.table where the number of rows equals the number of observations you'd like to explain, and the number of columns equals m +1, where m equals the total number of features in your model.

If shapley_values[i, j + 1] > 0 it indicates that the j-th feature increased the prediction for the i-th observation. Likewise, if shapley_values[i, j + 1] < 0 it indicates that the j-th feature decreased the prediction for the i-th observation. The magnitude of the value is also important to notice. E.g. if shapley_values[i, k + 1] and shapley_values[i, j + 1] are greater than 0, where j != k, and shapley_values[i, k + 1] > shapley_values[i, j + 1] this indicates that feature j and k both increased the value of the prediction, but that the effect of the k-th feature was larger than the j-th feature.

The first column in dt, called none, is the prediction value not assigned to any of the features (\phi₀). It's equal for all observations and set by the user through the argument prediction_zero. The difference between the prediction and none is distributed among the other features. In theory this value should be the expected prediction without conditioning on any features. Typically we set this value equal to the mean of the response variable in our training data, but other choices such as the mean of the predictions in the training data are also reasonable.

Author(s)

Martin Jullum

References

Aas, K., Jullum, M., & L<U+00F8>land, A. (2021). Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502.

Examples

# Load example data
data("airquality")
airquality <- airquality[complete.cases(airquality), ]
x_var <- c("Solar.R", "Wind", "Temp", "Month")
y_var <- "Ozone"

# Split data into test- and training data
data_train <- head(airquality, -3)
data_explain <- tail(airquality, 3)

x_train <- data_train[, x_var]
x_explain <- data_explain[, x_var]

# Fit a linear model
lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var, collapse = " + ")))
model <- lm(lm_formula, data = data_train)

# Explain predictions
p <- mean(data_train[, y_var])

# Empirical approach
explain1 <- explain(
  model = model,
  x_explain = x_explain,
  x_train = x_train,
  approach = "empirical",
  prediction_zero = p,
  n_samples = 1e2
)

# Gaussian approach
explain2 <- explain(
  model = model,
  x_explain = x_explain,
  x_train = x_train,
  approach = "gaussian",
  prediction_zero = p,
  n_samples = 1e2
)

# Gaussian copula approach
explain3 <- explain(
  model = model,
  x_explain = x_explain,
  x_train = x_train,
  approach = "copula",
  prediction_zero = p,
  n_samples = 1e2
)

# ctree approach
explain4 <- explain(
  model = model,
  x_explain = x_explain,
  x_train = x_train,
  approach = "ctree",
  prediction_zero = p,
  n_samples = 1e2
)

# Combined approach
approach <- c("gaussian", "gaussian", "empirical")
explain5 <- explain(
  model = model,
  x_explain = x_explain,
  x_train = x_train,
  approach = approach,
  prediction_zero = p,
  n_samples = 1e2
)

# Print the Shapley values
print(explain1$shapley_values)

# Plot the results
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot(explain1)
  plot(explain1, plot_type = "waterfall")
}

# Group-wise explanations
group_list <- list(A = c("Temp", "Month"), B = c("Wind", "Solar.R"))

explain_groups <- explain(
  model = model,
  x_explain = x_explain,
  x_train = x_train,
  group = group_list,
  approach = "empirical",
  prediction_zero = p,
  n_samples = 1e2
)
print(explain_groups$shapley_values)

NorskRegnesentral/shapr documentation built on April 14, 2024, 9:16 a.m.