Changes in version 0.4.1 (2024-11-18)                  

New Features

  - Introduce AUTO_VI$save_plot() which is the default method of saving
    a plot by calling save_plot(). This allows user to override the plot
    saving method if needed.
  - Introduce a method AUTO_VI$summary() which allows user to get
    computed statistics provided in AUTO_VI$..str..().
  - Introduce a method AUTO_VI$plot_pair() which allows user to put the
    true residual plot and a null plot side-by-side.
  - Introduce a method AUTO_VI$plot_lineup() which allows user to
    generate a lineup for manual inspection.
  - Introduce AUTO_VI$boot_method() which is the default method of
    generating bootstrapped residuals. This allows user to override the
    bootstrapping scheme if needed.
  - Introduce residual_checker() as a new class constructor of AUTO_VI.
    It has an argument keras_model_name that will be passed to
    get_keras_model().

Changes

  - Integrate the AUTO_VI$select_feature() method into
    AUTO_VI$feature_pca() for clarity. Now the AUTO_VI$feature_pca()
    method has one more parameter pattern for specifying feature name
    pattern.
  - Remove the type parameter and p_value_type parameter from
    AUTO_VI$p_value() and AUTO_VI$check(), respectively, and unify the
    p-value formula. Now the p-value is always calculated as
    mean(c(null_dist, vss) >= vss), where null_dist is a vector of
    visual signal strength for null residual plots, and vss is the
    visual signal strength for the true residual plot.
  - Improve AUTO_VI$feature_pca_plot(). Now the observed point is always
    displayed on top of other groups.
  - AUTO_VI$check() and AUTO_VI$lineup_check() now returns self instead
    of invisible(self) to provide a visible summary of the check result.
  - get_keras_model() now have an option format to specify the format of
    the model to download, including "npz", "SavedModel" and "keras".
    The previous version of autovi downloads the pre-trained model in
    the ".keras", which could cause backward compatibility issue due to
    difference in Python or TensorFlow versions. The "SavedModel" format
    can better handle this aspect but come with a larger file size so it
    may slow down the model loading process. The "npz" format is the
    most recommend one, as it will download a Python script to rebuild
    the model from scratch and load weights from a ".npz" file. This
    overcomes many of the issues mentioned above.

Bug Fix

  - Fix a bug in AUTO_VI$vss() that arguments will be passed incorrectly
    to KERAS_WRAPPER$image_to_array() when a data.frame or a tibble is
    provided by the user to predict visual signal strength.
  - Fix a bug in save_plot() where the path argument was not functioning
    as intended..

                 Changes in version 0.4.0 (2024-06-25)                  

  - First CRAN release