This transformer scales all the vectors in an EmbeddingSet by means of principal component analysis. We're using the implementation found in scikit-learn


Name Type Description Default
n_components the number of compoments to create/add required
**kwargs keyword arguments passed to the PCA from scikit-learn {}


from whatlies.language import SpacyLanguage
from whatlies.transformers import Pca

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(Pca(3)).plot_interactive_matrix(0, 1, 2)