data_describe.dimensionality_reduction.dimensionality_reduction
======================================================================

.. py:module:: data_describe.dimensionality_reduction.dimensionality_reduction





.. autoapisummary::

   data_describe.dimensionality_reduction.dimensionality_reduction.dim_reduc
   data_describe.dimensionality_reduction.dimensionality_reduction.run_pca
   data_describe.dimensionality_reduction.dimensionality_reduction.run_ipca
   data_describe.dimensionality_reduction.dimensionality_reduction.run_tsne
   data_describe.dimensionality_reduction.dimensionality_reduction.run_tsvd


.. function:: dim_reduc(data, n_components: int, dim_method: str, apply_tsvd: bool = True, compute_backend=None)

   Reduces the number of dimensions of the input data.

   :param data: The dataframe
   :param n_components: Desired dimensionality for the data set prior to modeling
   :param dim_method: {'pca', 'ipca', 'tsne', 'tsvd'}
   :param - pca: Principal Component Analysis
   :param - ipca: Incremental Principal Component Analysis. Highly suggested for very large datasets
   :param - tsne: T-distributed Stochastic Neighbor Embedding
   :param - tsvd: Truncated Singular Value Decomposition
   :param apply_tsvd: If True, TSVD will be run before t-SNE. This is highly recommended when running t-SNE

   :returns: The dimensionally-reduced dataframe and reduction object


.. function:: run_pca(data, n_components, compute_backend=None)

   Reduces the number of dimensions of the input data using PCA.

   :param data: The dataframe
   :param n_components: Desired dimensionality for the data set prior to modeling

   :returns: The dimensionally-reduced dataframe
             pca: The applied PCA object
   :rtype: reduc_df


.. function:: run_ipca(data, n_components, compute_backend=None)

   Reduces the number of dimensions of the input data using Incremental PCA.

   :param data: The dataframe
   :param n_components: Desired dimensionality for the data set prior to modeling

   :returns: The dimensionally-reduced dataframe
             ipca: The applied IncrementalPCA object
   :rtype: reduc_df


.. function:: run_tsne(data, n_components, apply_tsvd=True, compute_backend=None)

   Reduces the number of dimensions of the input data using t-SNE.

   :param data: The dataframe
   :param n_components: Desired dimensionality for the output dataset
   :param apply_tsvd: If True, TSVD will be run before t-SNE. This is highly recommended when running t-SNE

   :returns: The dimensionally-reduced dataframe
             tsne: The applied t-SNE object
   :rtype: reduc_df


.. function:: run_tsvd(data, n_components, compute_backend=None)

   Reduces the number of dimensions of the input data using TSVD.

   :param data: The dataframe
   :param n_components: Desired dimensionality for the output dataset

   :returns: The dimensionally-reduced dataframe
             tsne: The applied TSVD object
   :rtype: reduc_df