.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_03_joint.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_03_joint.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_03_joint.py:


Joint Model
=====================================================
This notebook contains the code for a simple implementation of the Leaspy Joint model on synthetic data.

.. GENERATED FROM PYTHON SOURCE LINES 8-9

The following imports are required libraries for numerical computation and data manipulation.

.. GENERATED FROM PYTHON SOURCE LINES 9-23

.. code-block:: Python

    import os

    import pandas as pd

    import leaspy
    from leaspy.io.data import Data

    leaspy_root = os.path.dirname(leaspy.__file__)

    data_path = os.path.join(leaspy_root, "datasets/data/simulated_data_for_joint.csv")

    df = pd.read_csv(data_path, dtype={"ID": str}, sep=";")
    print(df.head())





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        ID    TIME  EVENT_TIME  EVENT_BOOL       Y0    Y1   Y2   Y3
    0  116  78.461        85.5           1  0.44444  0.04  0.0  0.0
    1  116  78.936        85.5           1  0.60000  0.00  0.0  0.2
    2  116  79.482        85.5           1  0.39267  0.04  0.0  0.2
    3  116  79.939        85.5           1  0.58511  0.00  0.0  0.0
    4  116  80.491        85.5           1  0.57044  0.00  0.0  0.0




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To use the Joint Model in Leaspy, your dataset must include the following columns:

1. **ID** : Patient identifier
2. **TIME** : Time of measurement
3. **EVENT_TIME** : Time of the event
4. **EVENT_BOOL** : Event indicator:
   - `1` if the event occurred
   - `0` if censored
   - `2` if a competing event occurred

For one patient, the event time and event bool are the same for each row.

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We load the Joint Model from the `leaspy.models` and transform the dataset in a leaspy-compatible form with the built-in functions.

.. GENERATED FROM PYTHON SOURCE LINES 39-44

.. code-block:: Python

    from leaspy.models import JointModel

    data = Data.from_dataframe(df, "joint")
    model = JointModel(name="test_model", nb_events=1)








.. GENERATED FROM PYTHON SOURCE LINES 45-52

The parameter `nb_events` should match the number of distinct event types
present in the `EVENT_BOOL` column:

- If `EVENT_BOOL` contains values {0, 1}, then `nb_events=1`.
- If it contains values {0, 1, 2}, then `nb_events=2`.

Once the model is initialized, we can fit it to the data.

.. GENERATED FROM PYTHON SOURCE LINES 52-56

.. code-block:: Python


    model.fit(data, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/leaspy/checkouts/476/src/leaspy/models/time_reparametrized.py:288: UserWarning: You did not provide `source_dimension` hyperparameter for multivariate model, setting it to ⌊√dimension⌋ = 2.
      warnings.warn(
     ==> Setting seed to 1312

    Fit with `AlgorithmName.FIT_MCMC_SAEM` took: 1s




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The Joint Model includes specific parameters such as `log_rho_mean` and `zeta_mean`.

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.. code-block:: Python

    print(model.parameters)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    {'betas_mean': tensor([[-0.0163, -0.0908],
            [-0.0079, -0.0469],
            [-0.1058, -0.0088]]), 'log_g_mean': tensor([0.1157, 2.8874, 2.5624, 1.3001]), 'log_rho_mean': tensor([1.8040]), 'log_v0_mean': tensor([-3.0789, -3.8272, -3.8023, -2.7624]), 'n_log_nu_mean': tensor([-1.9862]), 'noise_std': tensor(0.0947, dtype=torch.float64), 'tau_mean': tensor([78.4523], dtype=torch.float64), 'tau_std': tensor([5.7890], dtype=torch.float64), 'xi_std': tensor([0.4379], dtype=torch.float64), 'zeta_mean': tensor([[0.0421],
            [0.0660]])}





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.377 seconds)


.. _sphx_glr_download_auto_examples_plot_03_joint.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_03_joint.ipynb <plot_03_joint.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_03_joint.py <plot_03_joint.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_03_joint.zip <plot_03_joint.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_