Assess neural estimator using trained estimator

A wrapper around NeuralEstimators::assess() that automatically unpacks the trained estimator and ABI input from a trained estimator object created by abi_train.

Usage

abi_assess(
  trained_estimator,
  estimator_name = NULL,
  use_gpu = TRUE,
  verbose = TRUE
)

Arguments

trained_estimator

A trained estimator object returned by abi_train. Must be of class eam_abi_trained_estimator and contain trained_estimator and abi_input elements.

estimator_name

Character string; optional name for the estimator (default: NULL).

use_gpu

Logical; whether to use GPU for assessment (default: TRUE).

verbose

Logical; whether to print progress information (default: TRUE).

Value

A list with class eam_abi_assess containing:

estimates

Data frame with columns: m, k, j, estimator, parameter, estimate, truth

runtimes

Data frame with runtime information

Details

This function extracts the trained estimator and ABI input from the trained estimator object, then extracts test parameters and summary statistics from the ABI input, along with parameter names (theta), and passes them to NeuralEstimators::assess(). The test set (theta_test and Z_test) is used for assessment.

The returned object has class eam_abi_assess, which enables the use of S3 methods like plot_cv_recovery for visualization.

Note

This function initializes the global Julia environment on first call.

Examples

if (FALSE) { # \dontrun{
# Train an estimator first
trained_estimator <- abi_train(
  estimator = estimator,
  abi_input = abi_input,
  epochs = 100
)

# Assess the trained estimator
assessment <- abi_assess(
  trained_estimator = trained_estimator,
  estimator_name = "MyEstimator",
  use_gpu = TRUE,
  verbose = TRUE
)

# View the assessment results
str(assessment)

# Plot parameter recovery
plot_cv_recovery(assessment)
} # }