leaspy.algo.personalize.scipy_minimize
======================================

.. py:module:: leaspy.algo.personalize.scipy_minimize

.. autoapi-nested-parse::

   This module defines the `ScipyMinimize` class.

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Classes
-------

.. autoapisummary::

   leaspy.algo.personalize.scipy_minimize.ScipyMinimizeAlgorithm


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

.. py:class:: ScipyMinimizeAlgorithm(settings)

   Bases: :py:obj:`leaspy.algo.personalize.base.PersonalizeAlgorithm`\ [\ :py:obj:`leaspy.models.McmcSaemCompatibleModel`\ , :py:obj:`leaspy.io.outputs.individual_parameters.IndividualParameters`\ ]


   
   Gradient descent based algorithm to compute individual parameters, `i.e.` personalizing a model for a given set of subjects.


   :Parameters:

       **settings** : :class:`.AlgorithmSettings`
           Settings for the algorithm, including the `custom_scipy_minimize_params`
           parameter, which contains keyword arguments passed to
           :func:`scipy.optimize.minimize`.

   :Attributes:

       **scipy_minimize_params** : :obj:`dict`
           Keyword arguments for :func:`scipy.optimize.minimize`, with default values
           depending on the usage of a jacobian (cf.
           `ScipyMinimize.DEFAULT_SCIPY_MINIMIZE_PARAMS_WITH_JACOBIAN` and
           `ScipyMinimize.DEFAULT_SCIPY_MINIMIZE_PARAMS_WITHOUT_JACOBIAN`).
           Customization is possible via the `custom_scipy_minimize_params` in
           :class:`.AlgorithmSettings`.

       **format_convergence_issues** : :obj:`str`
           A format string for displaying convergence issues, which can use the
            following variables:
                - `patient_id`: :obj:`str`
                - `optimization_result_pformat`: :obj:`str`
                - `optimization_result_obj`: dict-like
            The default format is defined in
            `ScipyMinimize.DEFAULT_FORMAT_CONVERGENCE_ISSUES`, but it can be
            customized via the `custom_format_convergence_issues` parameter.

       **logger** : None or callable :obj:`str` -> None
           The function used to display convergence issues returned by :func:`scipy.optimize.minimize`.
           By default, convergence issues are printed only if the BFGS optimization method is not used.
           This can  be customized by setting the `logger` attribute in :class:`.AlgorithmSettings`.













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   .. py:attribute:: name
      :type:  leaspy.algo.base.AlgorithmName


   .. py:attribute:: DEFAULT_SCIPY_MINIMIZE_PARAMS_WITH_JACOBIAN


   .. py:attribute:: DEFAULT_SCIPY_MINIMIZE_PARAMS_WITHOUT_JACOBIAN


   .. py:attribute:: DEFAULT_FORMAT_CONVERGENCE_ISSUES
      :value: Multiline-String

      .. raw:: html

         <details><summary>Show Value</summary>

      .. code-block:: python

         """<!> {patient_id}:
         {optimization_result_pformat}"""

      .. raw:: html

         </details>




   .. py:attribute:: regularity_factor
      :type:  float
      :value: 1.0



   .. py:attribute:: scipy_minimize_params


   .. py:attribute:: format_convergence_issues


   .. py:attribute:: logger


   .. py:method:: obj_no_jac(x, state, scaling)

      
      Objective loss function to minimize in order to get patient's individual parameters.


      :Parameters:

          **x** : numpy.ndarray
              Individual standardized parameters
              At initialization x is full of zeros (mode of priors, scaled by std-dev)

          **state** : :class:`.State`
              The cloned model state that is dedicated to the current individual.
              In particular, individual data variables for the current individual are already loaded into it.

          **scaling** : _AffineScalings1D
              The scaling to be used for individual latent variables.



      :Returns:

          **objective** : :obj:`float`
              Value of the loss function (negative log-likelihood).











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   .. py:method:: obj_with_jac(x, state, scaling)
      :abstractmethod:


      
      Objective loss function to minimize in order to get patient's individual parameters,
      together with its jacobian w.r.t to each of `x` dimension.


      :Parameters:

          **x** : numpy.ndarray
              Individual standardized parameters
              At initialization x is full of zeros (mode of priors, scaled by std-dev)

          **state** : :class:`.State`
              The cloned model state that is dedicated to the current individual.
              In particular, individual data variables for the current individual are already loaded into it.

          **scaling** : _AffineScalings1D
              The scaling to be used for individual latent variables.



      :Returns:

          2-tuple (as expected by :func:`scipy.optimize.minimize` when ``jac=True``)
              * objective : :obj:`float`
              * gradient : array-like[float] with same length as `x` (= all dimensions of individual latent variables, concatenated)











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   .. py:method:: is_jacobian_implemented(model)

      
      Check that the jacobian of model is implemented.
















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