:py:mod:`deeprvat.deeprvat.models`
==================================

.. py:module:: deeprvat.deeprvat.models

.. autodoc2-docstring:: deeprvat.deeprvat.models
   :allowtitles:

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

Classes
~~~~~~~

.. list-table::
   :class: autosummary longtable
   :align: left

   * - :py:obj:`BaseModel <deeprvat.deeprvat.models.BaseModel>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel
          :summary:
   * - :py:obj:`DeepSetAgg <deeprvat.deeprvat.models.DeepSetAgg>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSetAgg
          :summary:
   * - :py:obj:`DeepSet <deeprvat.deeprvat.models.DeepSet>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSet
          :summary:
   * - :py:obj:`LinearAgg <deeprvat.deeprvat.models.LinearAgg>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.LinearAgg
          :summary:
   * - :py:obj:`TwoLayer <deeprvat.deeprvat.models.TwoLayer>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.TwoLayer
          :summary:

Functions
~~~~~~~~~

.. list-table::
   :class: autosummary longtable
   :align: left

   * - :py:obj:`get_hparam <deeprvat.deeprvat.models.get_hparam>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.get_hparam
          :summary:

Data
~~~~

.. list-table::
   :class: autosummary longtable
   :align: left

   * - :py:obj:`logger <deeprvat.deeprvat.models.logger>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.logger
          :summary:
   * - :py:obj:`METRICS <deeprvat.deeprvat.models.METRICS>`
     - .. autodoc2-docstring:: deeprvat.deeprvat.models.METRICS
          :summary:

API
~~~

.. py:data:: logger
   :canonical: deeprvat.deeprvat.models.logger
   :value: 'getLogger(...)'

   .. autodoc2-docstring:: deeprvat.deeprvat.models.logger

.. py:data:: METRICS
   :canonical: deeprvat.deeprvat.models.METRICS
   :value: None

   .. autodoc2-docstring:: deeprvat.deeprvat.models.METRICS

.. py:function:: get_hparam(module: pytorch_lightning.LightningModule, param: str, default: typing.Any)
   :canonical: deeprvat.deeprvat.models.get_hparam

   .. autodoc2-docstring:: deeprvat.deeprvat.models.get_hparam

.. py:class:: BaseModel(config: dict, n_annotations: typing.Dict[str, int], n_covariates: typing.Dict[str, int], n_genes: typing.Dict[str, int], phenotypes: typing.List[str], stage: str = 'train', **kwargs)
   :canonical: deeprvat.deeprvat.models.BaseModel

   Bases: :py:obj:`pytorch_lightning.LightningModule`

   .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel

   .. rubric:: Initialization

   .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.__init__

   .. py:method:: configure_optimizers() -> torch.optim.Optimizer
      :canonical: deeprvat.deeprvat.models.BaseModel.configure_optimizers

      .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.configure_optimizers

   .. py:method:: training_step(batch: dict, batch_idx: int) -> torch.Tensor
      :canonical: deeprvat.deeprvat.models.BaseModel.training_step

      .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.training_step

   .. py:method:: validation_step(batch: dict, batch_idx: int)
      :canonical: deeprvat.deeprvat.models.BaseModel.validation_step

      .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.validation_step

   .. py:method:: validation_epoch_end(prediction_y: typing.List[typing.Dict[str, typing.Dict[str, torch.Tensor]]])
      :canonical: deeprvat.deeprvat.models.BaseModel.validation_epoch_end

      .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.validation_epoch_end

   .. py:method:: test_step(batch: dict, batch_idx: int)
      :canonical: deeprvat.deeprvat.models.BaseModel.test_step

      .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.test_step

   .. py:method:: test_epoch_end(prediction_y: typing.List[typing.Dict[str, torch.Tensor]])
      :canonical: deeprvat.deeprvat.models.BaseModel.test_epoch_end

      .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.test_epoch_end

   .. py:method:: configure_callbacks()
      :canonical: deeprvat.deeprvat.models.BaseModel.configure_callbacks

      .. autodoc2-docstring:: deeprvat.deeprvat.models.BaseModel.configure_callbacks

.. py:class:: DeepSetAgg(n_annotations: int, phi_layers: int, phi_hidden_dim: int, rho_layers: int, rho_hidden_dim: int, activation: str, pool: str, output_dim: int = 1, dropout: typing.Optional[float] = None, use_sigmoid: bool = False, reverse: bool = False)
   :canonical: deeprvat.deeprvat.models.DeepSetAgg

   Bases: :py:obj:`pytorch_lightning.LightningModule`

   .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSetAgg

   .. rubric:: Initialization

   .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSetAgg.__init__

   .. py:method:: set_reverse(reverse: bool = True)
      :canonical: deeprvat.deeprvat.models.DeepSetAgg.set_reverse

      .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSetAgg.set_reverse

   .. py:method:: forward(x)
      :canonical: deeprvat.deeprvat.models.DeepSetAgg.forward

      .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSetAgg.forward

.. py:class:: DeepSet(config: dict, n_annotations: typing.Dict[str, int], n_covariates: typing.Dict[str, int], n_genes: typing.Dict[str, int], phenotypes: typing.List[str], agg_model: typing.Optional[torch.nn.Module] = None, use_sigmoid: bool = False, reverse: bool = False, **kwargs)
   :canonical: deeprvat.deeprvat.models.DeepSet

   Bases: :py:obj:`deeprvat.deeprvat.models.BaseModel`

   .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSet

   .. rubric:: Initialization

   .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSet.__init__

   .. py:method:: forward(batch)
      :canonical: deeprvat.deeprvat.models.DeepSet.forward

      .. autodoc2-docstring:: deeprvat.deeprvat.models.DeepSet.forward

.. py:class:: LinearAgg(n_annotations: int, pool: str, output_dim: int = 1, reverse: bool = False)
   :canonical: deeprvat.deeprvat.models.LinearAgg

   Bases: :py:obj:`pytorch_lightning.LightningModule`

   .. autodoc2-docstring:: deeprvat.deeprvat.models.LinearAgg

   .. rubric:: Initialization

   .. autodoc2-docstring:: deeprvat.deeprvat.models.LinearAgg.__init__

   .. py:method:: set_reverse(reverse: bool = True)
      :canonical: deeprvat.deeprvat.models.LinearAgg.set_reverse

      .. autodoc2-docstring:: deeprvat.deeprvat.models.LinearAgg.set_reverse

   .. py:method:: forward(x)
      :canonical: deeprvat.deeprvat.models.LinearAgg.forward

      .. autodoc2-docstring:: deeprvat.deeprvat.models.LinearAgg.forward

.. py:class:: TwoLayer(config: dict, n_annotations: int, n_covariates: int, n_genes: int, agg_model: typing.Optional[torch.nn.Module] = None, **kwargs)
   :canonical: deeprvat.deeprvat.models.TwoLayer

   Bases: :py:obj:`deeprvat.deeprvat.models.BaseModel`

   .. autodoc2-docstring:: deeprvat.deeprvat.models.TwoLayer

   .. rubric:: Initialization

   .. autodoc2-docstring:: deeprvat.deeprvat.models.TwoLayer.__init__

   .. py:method:: forward(batch)
      :canonical: deeprvat.deeprvat.models.TwoLayer.forward

      .. autodoc2-docstring:: deeprvat.deeprvat.models.TwoLayer.forward