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Plot embeddings via PCA, colored by arbitrary labels

Source: R/plot_embeddings.R
plot_embeddings_pca.Rd

Reads an embeddings Parquet dataset (produced by embed_corpus()) with columns id and V1..Vd, computes a PCA on the embedding matrix, and returns a scatter plot of the first two principal components. Points are colored by labels provided via labels. Rows not found in labels are shown as "other".

Usage

plot_embeddings_pca(
  embeddings,
  labels,
  center = TRUE,
  scale. = FALSE,
  point_size = 2,
  alpha = 0.5
)

Arguments

embeddings

Path to a Parquet file or dataset directory containing columns id and V1..Vd.

labels

Label mapping for ids. Supported formats:

  1. data frame with columns id and label,

  2. path to CSV with columns id and label,

  3. named character vector where names are ids and values are labels,

  4. named list where each element is an id vector for that label.

center, scale.

Passed to stats::prcomp() for PCA. Defaults center = TRUE, scale. = FALSE.

point_size, alpha

Point size and transparency for points in the plot. Defaults point_size = 2, alpha = 0.5.

Value

A ggplot object with points mapped to PC1 vs PC2 and colored by group.

Examples

if (FALSE) { # \dontrun{
p <- plot_embeddings_pca(
  embeddings = "inst/examples/embedings/",
  labels = data.frame(
    id = c("W1", "W2", "W10"),
    label = c("reference", "reference", "corpus")
  )
)
print(p)
} # }