leaspy.io.data.individual_data
==============================

.. py:module:: leaspy.io.data.individual_data


Classes
-------

.. autoapisummary::

   leaspy.io.data.individual_data.IndividualData


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

.. py:class:: IndividualData(idx)

   
   Container for an individual's data


   :Parameters:

       **idx** : IDType
           Unique ID

   :Attributes:

       **idx** : :class:`~leaspy.utils.typing.IDType`
           Unique ID

       **timepoints** : :obj:`np.ndarray` [:obj:`float`]
           Timepoints associated with the observations 1D array

       **observations** : :obj:`np.ndarray` [:obj:`float`]
           Observed data points,
           Shape is ``(n_timepoints, n_features)``

       **cofactors** : :obj:`dict` [:class:`~leaspy.utils.typing.FeatureType`, :class:`~leaspy.utils.typing.Any`]
           Cofactors in the form {cofactor_name: cofactor_value}

       **event_time** : :obj:`float`
           Time of an event, if the event is censored, the time correspond to the last patient observation

       **event_bool** : :obj:`bool`
           Boolean to indicate if an event is censored or not: 1 observed, 0 censored













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   .. py:attribute:: idx
      :type:  leaspy.utils.typing.IDType


   .. py:attribute:: timepoints
      :type:  numpy.ndarray
      :value: None



   .. py:attribute:: observations
      :type:  numpy.ndarray
      :value: None



   .. py:attribute:: event_time
      :type:  Optional[numpy.ndarray]
      :value: None



   .. py:attribute:: event_bool
      :type:  Optional[numpy.ndarray]
      :value: None



   .. py:attribute:: cofactors
      :type:  dict[leaspy.utils.typing.FeatureType, Any]


   .. py:attribute:: covariates
      :type:  Optional[numpy.ndarray]
      :value: None



   .. py:method:: add_observations(timepoints, observations)

      
      Include new observations and associated timepoints


      :Parameters:

          **timepoints** : :obj:`array-like` [:obj:`float`]
              Timepoints associated with the observations to include, 1D array

          **observations** : :obj:`array-like` [:obj:`float`]
              Observations to include, 2D array







      :Raises:

          :exc:`.LeaspyDataInputError`
              ..







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   .. py:method:: add_event(event_time, event_bool)

      
      Include event time and associated censoring bool


      :Parameters:

          **event_time** : :obj:`float`
              Time of the event

          **event_bool** : :obj:`float`
              0 if censored (not observed) and 1 if observed














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   .. py:method:: add_covariates(covariates)

      
      Include covariates


      :Parameters:

          **covariates** : :obj:`array-like` [:obj:`float`]
              Covariates to include, 2D array














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   .. py:method:: add_cofactors(cofactors)

      
      Include new cofactors


      :Parameters:

          **cofactors** : :obj:`dict` [:class:`~leaspy.utils.typing.FeatureType`, :class:`~leaspy.utils.typing.Any`]
              Cofactors to include, in the form `{name: value}`







      :Raises:

          :exc:`.LeaspyDataInputError`
              ..

          :exc:`.LeaspyTypeError`
              ..







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   .. py:method:: to_frame(headers, event_time_name, event_bool_name, covariate_names)

      
      Convert the individual data to a pandas DataFrame


      :Parameters:

          **headers** : :obj:`list` [:obj:`str`]
              List of feature names for the observations

          **event_time_name** : :obj:`str`
              Name of the column for the event time

          **event_bool_name** : :obj:`str`
              Name of the column for the event boolean (0 or 1)

          **covariate_names** : :obj:`list` [:obj:`str`]
              List of covariate names



      :Returns:

          :obj:`pd.DataFrame`
              DataFrame containing the individual's data with the following columns:
                  * ID: Unique identifier for the individual
                  * TIME: Timepoints associated with the observations
                  * Observations: Observed data points for each feature
                  * Event Time: Time of the event (if any)
                  * Event Boolean: Boolean indicating if the event was observed (1) or censored (0)
                  * Covariates: Values of the covariates for the individual











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