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AUTO_VI$save_plot() which is the default
method of saving a plot by calling save_plot(). This allows
user to override the plot saving method if needed.AUTO_VI$summary() which allows user
to get computed statistics provided in
AUTO_VI$..str..().AUTO_VI$plot_pair() which allows
user to put the true residual plot and a null plot side-by-side.AUTO_VI$plot_lineup() which allows
user to generate a lineup for manual inspection.AUTO_VI$boot_method() which is the default
method of generating bootstrapped residuals. This allows user to
override the bootstrapping scheme if needed.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().AUTO_VI$select_feature() method into
AUTO_VI$feature_pca() for clarity. Now the
AUTO_VI$feature_pca() method has one more parameter
pattern for specifying feature name pattern.type parameter and p_value_type
parameter from AUTO_VI$p_value() and
AUTO_VI$check(), respectively, and unify the p-value
formula. Now the p-value is always calculated as
mean(c(null_dist, vss) >= vss), where
null_dist is a vector of visual signal strength for null
residual plots, and vss is the visual signal strength for
the true residual plot.AUTO_VI$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.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.save_plot() where the path
argument was not functioning as intended..These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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