Function reference • autovi

Pre-trained computer vision models

Pre-trained computer vision models for visual signal strength prediction

list_keras_model(): List all available pre-trained computer vision models

get_keras_model(): Download and load the keras model

Keras model wrapper class

Keras model wrapper class

keras_wrapper(): KERAS_WRAPPER class environment

KERAS_WRAPPER$..init..: Initialization method

KERAS_WRAPPER$..str..: String representation of the object

KERAS_WRAPPER$get_input_height: Get keras model input image height

KERAS_WRAPPER$get_input_width: Get keras model input image width

KERAS_WRAPPER$image_to_array: Load an image as numpy array

KERAS_WRAPPER$list_layer_name: List all layer names

KERAS_WRAPPER$predict: Predict visual signal strength

Auto visual inference class

Auto visual inference class

auto_vi() residual_checker(): AUTO_VI class environment

AUTO_VI$..init..: Initialization method

AUTO_VI$..str..: String representation of the object

AUTO_VI$get_fitted_and_resid: Get fitted values and residuals out of a model object

AUTO_VI$get_data: Get data out of a model object

AUTO_VI$plot_resid: Draw a standard residual plot

AUTO_VI$plot_pair: Draw a pair of standard residual plots

AUTO_VI$plot_lineup: Draw a lineup of standard residual plots

AUTO_VI$save_plot: Save plot(s)

AUTO_VI$vss: Predict the visual signal strength

AUTO_VI$rotate_resid: Get rotated residuals from a fitted linear model

AUTO_VI$null_method: Get null residuals from a fitted model

AUTO_VI$boot_method: Get bootstrapped residuals from a fitted model

AUTO_VI$null_vss: Simulate null plots and predict the visual signal strength

AUTO_VI$boot_vss: Predict visual signal strength for bootstrapped residual plots

AUTO_VI$check: Conduct a auto visual inference check with a computer vision model

AUTO_VI$lineup_check: Conduct a auto visual inference lineup check with a computer vision model

AUTO_VI$check_result: List of diagnostic results

AUTO_VI$likelihood_ratio: Compute the likelihood ratio using the simulated result

AUTO_VI$auxiliary: Compute auxiliary variables for the keras model

AUTO_VI$p_value: Compute the p-value based on the check result

AUTO_VI$summary: Summary of the object

AUTO_VI$summary_plot: Draw a summary plot for the result

AUTO_VI$summary_density_plot: Draw a summary density plot for the result

AUTO_VI$summary_rank_plot: Draw a summary rank plot for the result

AUTO_VI$feature_pca: Conduct principal component analysis for features extracted from keras model

AUTO_VI$feature_pca_plot: Draw a summary Plot for principal component analysis conducted on extracted features

Utilties

Utilties

check_python_library_available(): Check python library availability

save_plot(): Save plot(s)

remove_plot(): Remove a plot