tlc.core.objects.tables.from_table.umap_table#

A Table where a column from the input Table has been dimensionally reduced by the UMAP algorithm.

Module Contents#

Classes#

Class

Description

UMAPTable

A procedural table where a column in the input table column has been has dimensionally reduced by the UMAP algorithm.

Data#

Data

Description

umap

API#

tlc.core.objects.tables.from_table.umap_table.umap = _lazy_import(...)#
class tlc.core.objects.tables.from_table.umap_table.UMAPTable(url: tlc.core.url.Url | None = None, created: str | None = None, description: str | None = None, row_cache_url: tlc.core.url.Url | None = None, row_cache_populated: bool | None = None, input_table_url: tlc.core.url.Url | tlc.core.objects.table.Table | None = None, source_embedding_column: str | None = None, target_embedding_column: str | None = None, retain_source_embedding_column: bool | None = None, standard_scaler_normalize: bool | None = None, n_components: int | None = None, n_neighbors: int | None = None, metric: str | None = None, min_dist: float | None = None, n_jobs: int | None = None, fit_table_url: tlc.core.objects.table.Table | tlc.core.url.Url | None = None, model_url: tlc.core.url.Url | None = None, init_parameters: Any = None, random_state: int | None = None, input_tables: list[tlc.core.url.Url] | None = None)#

Bases: tlc.core.objects.tables.from_table.dimensional_reduction_table._DimensionalReductionTable

A procedural table where a column in the input table column has been has dimensionally reduced by the UMAP algorithm.

Creates a derived table with an (additional) UMAP-ed column based on input column and wanted dimensionality.

Parameters:

  • input_table_url – The input table to apply UMAP to

  • source_embedding_column – The column in the input table to apply UMAP to

  • target_embedding_column – The name of the new column to create in the output table

  • retain_source_embedding_column – Whether to retain the source column in the UMAP table, defaults to False

  • standard_scaler_normalize – Whether to apply the sklearn standard scaler to input before mapping, defaults to False

  • n_components – The dimension of the output embedding

  • n_neighbors – The number of neighbors to use to approximate the manifold structure

  • metric – The metric to use to compute distances in high dimensional space

  • min_dist – The minimum distance between points in the low dimensional embedding

  • n_jobs – The number of threads to use for the reduction. If set to anything other than 1, the random_state parameter of the UMAP algorithm is set to None, which means that the results will not be deterministic.

  • fit_table_url – The table to use for fitting the UMAP transform, if not specified the input table is used

  • model_url – The URL to store/load the UMAP model file. If empty, no model is saved.

  • random_state – The random state to use for the reduction

algorithm_name = UMAP#
property seed: list[int]#