.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_sklearn_randomforest.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_sklearn_randomforest.py:


Getting started with Lab and scikit-learn
=========================================

This example illustrates how Lab can be used to create and run a simple
classifier on the iris dataset.

Begin by creating a new Lab Project:

    >>> echo "scikit-learn" > requirements.txt
    >>> lab init --name simple-iris


.. code-block:: default


    import argparse
    from sklearn import datasets
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score, precision_score

    from lab.experiment import Experiment

    parser = argparse.ArgumentParser('Test arguments')

    parser.add_argument('--n_estimators', type=int, dest='n_estimators')
    args = parser.parse_args()

    n_estimators=args.n_estimators

    if n_estimators is None:
        n_estimators=100
        max_depth=2

    if __name__ == "__main__":
        e = Experiment(dataset='iris_75')

        @e.start_run
        def train():
            iris = datasets.load_iris()
            X = iris.data
            y = iris.target

            X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                                test_size=0.25,
                                                                random_state=42)

            e.log_features(['Sepal Length', 'Sepal Width', 'Petal Length',
                            'Petal Width'])
            clf = RandomForestClassifier(n_estimators=n_estimators)

            clf.fit(X_train, y_train)

            y_pred = clf.predict(X_test)
            accuracy = accuracy_score(y_test, y_pred)
            precision = precision_score(y_test, y_pred, average = 'macro')

            e.log_metric('accuracy_score', accuracy)
            e.log_metric('precision_score', precision)

            e.log_parameter('n_estimators', n_estimators)
            e.log_parameter('max_depth', max_depth)

            e.log_model('randomforest', clf)


After execute training script through the `lab run` command.

>>> lab run train.py
>>> lab ls


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

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


.. _sphx_glr_download_auto_examples_sklearn_randomforest.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: sklearn_randomforest.py <sklearn_randomforest.py>`



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

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


.. only:: html

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

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