.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/tpot/main01.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_tpot_main01.py>`
        to download the full example code

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

.. _sphx_glr__examples_tpot_main01.py:


01. Basic example
=================

Basic example to use tpot.

.. GENERATED FROM PYTHON SOURCE LINES 7-93

.. code-block:: default
   :lineno-start: 8


    # Import
    import numpy as np
    import pandas as pd

    # Specific
    from tpot import TPOTClassifier

    # Import own
    from pySML2.preprocessing.splitters import cvs_hos_split
    from pySML2.preprocessing.splitters import kfolds_split


    # ---------------------------------------------
    # Configuration
    # ---------------------------------------------
    # The input features and label for the algorithm
    features = sorted(['alb', 'alp', 'alt', 'baso', 'bil', 'cl', 'cre', 'crp', 'egfr',
                      'eos', 'k', 'ly',  'mcv', 'mono', 'mpv', 'nrbca', 'plt', 'rbc',
                      'rdw',  'urea', 'wbc'])

    # The labels
    labels = sorted(['micro_confirmed'])

    # The splits
    n_splits = 10

    # Dataset
    # -------
    # Dataset filepath
    filepath = 'data/dataset.csv'

    # ---------------------------------------------
    # Load dataset and format it
    # ---------------------------------------------
    # Read data
    data = pd.read_csv(filepath)
    data.columns = [c.lower() for c in data.columns.values]
    # data = data[features + labels]

    # Missing values
    data['missing'] = data[features].isnull().sum(axis=1)

    # The indexes for complete profiles
    cmp = (data.missing == 0)

    # Split in CVS and HOS
    data['cvs_hos_split'] = cvs_hos_split(data, selected_rows=cmp)

    # ---------------------------------------------
    # Train
    # ---------------------------------------------
    data[(data.missing == 0)].to_csv('outcomes/main01/tpot_data_cvs.csv')
    data[(data.cvs_hos_split == 'hos')].to_csv('outcomes/main01/tpot_data_hos.csv')
    data[(data.cvs_hos_split == 'cvs')].to_csv('outcomes/main01/tpot_data_cvs.csv')
    data[(data.cvs_hos_split == 'hos')].to_csv('outcomes/main01/tpot_data_hos.csv')

    # ---------------------------------------------
    # Train
    # ---------------------------------------------
    # The indexes used for cross validation
    cvs_idxs = (data.cvs_hos_split == 'cvs')
    hos_idxs = (data.cvs_hos_split == 'hos')

    # Create matrices train
    X_train = data[cvs_idxs][features].to_numpy()
    y_train = data[cvs_idxs][labels].to_numpy()

    # Create matrices test
    X_test = data[cvs_idxs][features].to_numpy()
    y_test = data[cvs_idxs][labels].to_numpy()

    # ---------------------------------------------
    # Search
    # ---------------------------------------------
    # Create genetic search
    tpot = TPOTClassifier(generations=5, verbosity=2,
                          scoring='roc_auc', cv=2)

    # Fit
    tpot.fit(X_train, y_train)

    # Score
    score = tpot.score(X_test, y_test)

    # Save
    tpot.export('outcomes/main01/tpot_best_pipeline.py')

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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download__examples_tpot_main01.py:


.. only :: html

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



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

     :download:`Download Python source code: main01.py <main01.py>`



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

     :download:`Download Jupyter notebook: main01.ipynb <main01.ipynb>`


.. only:: html

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

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