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Draw a summary Plot for principal component analysis conducted on extracted features

Source: R/zzz_auto_visual_inference.R
AUTO_VI-cash-feature_pca_plot.Rd

This function draws a summary Plot for principal component analysis conducted on extracted features

Usage

AUTO_VI$feature_pca_plot(
  feature_pca = self$feature_pca(),
  x = PC1,
  y = PC2,
  col_by_set = TRUE)

Arguments

feature_pca

Dataframe. A data frame containing the rotated features.

x

Symbol. The x variable. See also ggplot2::tidyeval.

y

Symbol. The y variable. See also ggplot2::tidyeval.

col_by_set

Boolena. Whether to color points by sets (observed, null, and boot).

Value

A ggplot.

Details

By default, it will visualize PC2 vs PC1. User can choose to visualize other principal components.

Examples

keras_model <- try(get_keras_model("vss_phn_32"))
if (!inherits(keras_model, "try-error")) {
  myvi <- auto_vi(lm(dist ~ speed, data = cars), keras_model)

  myvi$lineup_check(extract_feature_from_layer = "global_max_pooling2d")
  myvi$feature_pca_plot()
}
#> ✔ Generate null data.
#> ✔ Generate null plots.
#> ✔ Compute auxilary inputs.
#> ✔ Predict visual signal strength for 19 images.
#> ✔ Predict visual signal strength for 1 image.