original_docstring: 
          Projects this embedding space into a lower dimensionality. The method
          parameter can be a callable, which will define a dimensionality
          reduction technique that takes as input a numpy array and a list of IDs,
          as well as any keyword arguments given to the params argument of this
          method, and returns a dimension-reduced matrix. If no metric is provided
          in the keyword params, the default metric of this Embedding is used.
          
          Returns: A new Embedding object with the Field.POSITION value set to the
              result of the projection.
          
docstring: Projects this embedding space into a lower dimensionality. The method
  parameter can be a callable, which will define a dimensionality
  reduction technique that takes as input a numpy array and a list of IDs,
  as well as any keyword arguments given to the params argument of this
  method, and returns a dimension-reduced matrix. If no metric is provided
  in the keyword params, the default metric of this Embedding is used.
  
  A new Embedding object with the Field.POSITION value set to the
  result of the projection.
docstring_tokens: Projects this embedding space into a lower dimensionality . The method parameter can be a callable which will define a dimensionality reduction technique that takes as input a numpy array and a list of IDs as well as any keyword arguments given to the params argument of this method and returns a dimension - reduced matrix . If no metric is provided in the keyword params the default metric of this Embedding is used . A new Embedding object with the Field . POSITION value set to the result of the projection .
code: def project(self, method=ProjectionTechnique.UMAP, **params):
          """
          Projects this embedding space into a lower dimensionality. The method
          parameter can be a callable, which will define a dimensionality
          reduction technique that takes as input a numpy array and a list of IDs,
          as well as any keyword arguments given to the params argument of this
          method, and returns a dimension-reduced matrix. If no metric is provided
          in the keyword params, the default metric of this Embedding is used.
          
          Returns: A new Embedding object with the Field.POSITION value set to the
              result of the projection.
          """
          hi_d = self.field(Field.POSITION)
          params = params or {}
          if method != ProjectionTechnique.PCA:
              params["metric"] = params.get("metric", self.metric)
          
          if method == ProjectionTechnique.UMAP:
              import umap
              lo_d = umap.UMAP(**params).fit_transform(hi_d)
          elif method == ProjectionTechnique.TSNE:
              lo_d = TSNE(**params).fit_transform(hi_d)
          elif method == ProjectionTechnique.PCA:
              lo_d = PCA(**params).fit_transform(hi_d)
          elif callable(method):
              lo_d = method(hi_d, self.ids, **params)
          else:
              raise ValueError("Unrecognized projection technique '{}'".format(method))
          
          return self.copy_with_fields({Field.POSITION: lo_d}, clear_neighbors=True)
code_tokens: def project ( self , method = ProjectionTechnique . UMAP , ** params ) : hi_d = self . field ( Field . POSITION ) params = params or { } if method != ProjectionTechnique . PCA : params [ "metric" ] = params . get ( "metric" , self . metric ) if method == ProjectionTechnique . UMAP : import umap lo_d = umap . UMAP ( ** params ) . fit_transform ( hi_d ) elif method == ProjectionTechnique . TSNE : lo_d = TSNE ( ** params ) . fit_transform ( hi_d ) elif method == ProjectionTechnique . PCA : lo_d = PCA ( ** params ) . fit_transform ( hi_d ) elif callable ( method ) : lo_d = method ( hi_d , self . ids , ** params ) else : raise ValueError ( "Unrecognized projection technique '{}'" . format ( method ) ) return self . copy_with_fields ( { Field . POSITION : lo_d } , clear_neighbors = True )
short_docstring: Projects this embedding space into a lower dimensionality.
short_docstring_tokens: Projects this embedding space into a lower dimensionality .
comment: """
          Projects this embedding space into a lower dimensionality. The method
          parameter can be a callable, which will define a dimensionality
          reduction technique that takes as input a numpy array and a list of IDs,
          as well as any keyword arguments given to the params argument of this
          method, and returns a dimension-reduced matrix. If no metric is provided
          in the keyword params, the default metric of this Embedding is used.
          
          Returns: A new Embedding object with the Field.POSITION value set to the
              result of the projection.
          """