pca_vector_dim_reduction

The job performs Principal Component Analysis (PCA) on your data to reduce the dimensionality of the vectors. This reduction facilitates visualization by simplifying the data, making it easier to interpret and analyze. It also improves computational efficiency and enhances the performance of your models by focusing on the most significant features.

You can apply pca both to indexed vectors or to vectors uploaded with the job.

Required Account Privileges: "read"

Request JSON ["inputs"]:

   "archive":
      string (3 <= len <= 30) unique in account 
      null NOT allowed
      A unique string identifier for the archive within your account.

   "archive_content_ids_subset": 
      list of ints
      null allowed
      Optional. A list of integers representing the IDs of the specific contents to consider for filtering. If not provided, all contents in the archive will be considered.

   "nr_of_dimensions":
      int (>=1)
      null NOT allowed
      An integer specifying the number of dimensions to reduce the vectors to.

   "file_urls":
      list of strings
      null allowed
      An optional list of strings containing the URLs of files to be downloaded.
      
   "download_from_batch_cloud_folder":
      bool
      null NOT allowed
      A boolean indicating whether to download files from the batch cloud folder.

Response JSON ["results"]

   "identifier_to_pca_vector":
      dict
      {"identifier"(string or int): list of floats}