smoother.models.reduction._sppca
================================

.. py:module:: smoother.models.reduction._sppca


Classes
-------

.. autoapisummary::

   smoother.models.reduction._sppca.SpatialPCA


Module Contents
---------------

.. py:class:: SpatialPCA(adata: anndata.AnnData, layer: Optional[str] = None, n_latent: int = 10, spatial_loss: Optional[smoother.SpatialLoss] = None, lambda_spatial_loss=0.1)

   Bases: :py:obj:`torch.nn.Module`


   Solving PCA using stochastic gradient descent.


   .. py:attribute:: n_latent
      :value: 10



   .. py:attribute:: n_feature


   .. py:attribute:: _data
      :value: None



   .. py:attribute:: spatial_loss
      :value: None



   .. py:attribute:: l_sp_loss
      :value: 0.1



   .. py:attribute:: U


   .. py:attribute:: dr_configs


   .. py:attribute:: dr_logs


   .. py:method:: load_adata(adata: anndata.AnnData, layer: Optional[str] = None)

      Load data to project.

      :param adata: data to project.
      :type adata: AnnData
      :param layer: layer to project. If None, use adata.X.
      :type layer: str



   .. py:method:: _gram_schmidt(U)

      Project the PC basis matrix to the feasible space.

      :param U: PC basis matrix, num_feature x num_pc.
      :type U: 2D tensor



   .. py:method:: forward(x)

      Project x to the lower PC space.

      :param x: data to project, num_feature x num_sample.
      :type x: 2D tensor



   .. py:method:: _init_with_svd(x)

      Initialize model with svd solution.

      :param x: data to project, num_feature x num_sample.
      :type x: 2D tensor



   .. py:method:: get_latent_representation()

      Project loaded data to the lower PC space and return the latent representation.




   .. py:method:: reduce(lr=1.0, max_epochs=1000, patience=10, tol=1e-05, init_with_svd=False, verbose=True, quite=False, clear_logs=True)

      Reduce the dimension of the expression matrix.

      :param lr: The learning rate.
      :type lr: float
      :param max_epochs: The maximum number of epochs.
      :type max_epochs: int
      :param patience: The patience for early stopping.
      :type patience: int
      :param tol: The tolerated convergence error.
      :type tol: float
      :param init_with_svd: Whether to initialize with analytical solution calculated
                            using `torch.svd_lowrank()`.
      :type init_with_svd: bool
      :param verbose: If True, print out loss while training.
      :type verbose: bool
      :param quite: If True, no output printed.
      :type quite: bool
      :param clear_logs: If True, clear logs before training.
      :type clear_logs: bool



   .. py:method:: from_rna_model(rna_model, st_adata: anndata.AnnData, layer: Optional[str] = None, spatial_loss: Optional[smoother.SpatialLoss] = None, lambda_spatial_loss=0.1)
      :classmethod:


      Initialize a spatial model from a pre-trained RNA model.



   .. py:method:: from_scanpy(adata: anndata.AnnData, layer: Optional[str] = None, spatial_loss: Optional[smoother.SpatialLoss] = None, lambda_spatial_loss=0.1)
      :classmethod:


      Initialize a spatial model from a pre-trained RNA model.