content: from umap import UMAP
  
  from whatlies.transformers._transformer import SklearnTransformer
  
  
  class Umap(SklearnTransformer):
      """
      This transformer transformers all vectors in an [EmbeddingSet][whatlies.embeddingset.EmbeddingSet]
      by means of umap. We're using the implementation in [umap-learn](https://umap-learn.readthedocs.io/en/latest/).
  
      Arguments:
          n_components: the number of compoments to create/add
          kwargs: keyword arguments passed to the UMAP algorithm
  
      Usage:
  
      ```python
      from whatlies.language import SpacyLanguage
      from whatlies.transformers import Umap
  
      words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
               "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
               "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
               "person", "family", "brother", "sister"]
  
      lang = SpacyLanguage("en_core_web_md")
      emb = lang[words]
  
      emb.transform(Umap(3)).plot_interactive_matrix(0, 1, 2)
      ```
      """
  
      def __init__(self, n_components=2, **kwargs):
          super().__init__(
              UMAP, f"umap_{n_components}", n_components=n_components, **kwargs
          )
  