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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_plot_pipeline.py:


=====================
Building Pipelines
=====================

HiClass can be adopted in scikit-learn pipelines, and fully supports sparse matrices as input.
This example desmonstrates the use of both of these features.

.. GENERATED FROM PYTHON SOURCE LINES 10-46




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

 Out:

 .. code-block:: none

    [['Credit reporting' 'Reports']
     ['Loan' 'Student loan']]






|

.. code-block:: default

    from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import Pipeline

    from hiclass import LocalClassifierPerParentNode

    # Define data
    X_train = [
        "Struggling to repay loan",
        "Unable to get annual report",
    ]
    X_test = [
        "Unable to get annual report",
        "Struggling to repay loan",
    ]
    Y_train = [["Loan", "Student loan"], ["Credit reporting", "Reports"]]

    # We will use logistic regression classifiers for every parent node
    lr = LogisticRegression()

    # Let's build a pipeline using CountVectorizer and TfidfTransformer
    # to extract features as sparse matrices
    pipeline = Pipeline(
        [
            ("count", CountVectorizer()),
            ("tfidf", TfidfTransformer()),
            ("lcppn", LocalClassifierPerParentNode(local_classifier=lr)),
        ]
    )

    # Now, let's train a local classifier per parent node
    pipeline.fit(X_train, Y_train)

    # Finally, let's predict using the pipeline
    predictions = pipeline.predict(X_test)
    print(predictions)


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

   **Total running time of the script:** ( 0 minutes  0.022 seconds)


.. _sphx_glr_download_auto_examples_plot_pipeline.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: plot_pipeline.py <plot_pipeline.py>`



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

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


.. only:: html

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

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