autovi 0.4.1
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..
autovi 0.4.0