.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/getting_started/plot_quick_start.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_getting_started_plot_quick_start.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_getting_started_plot_quick_start.py:


.. _example_quick_start:

===========
Quick start
===========

.. GENERATED FROM PYTHON SOURCE LINES 10-11

From your Python code, create and load a skore :class:`~skore.Project`:

.. GENERATED FROM PYTHON SOURCE LINES 13-17

.. code-block:: Python

    import skore

    my_project = skore.open("my_project", create=True)








.. GENERATED FROM PYTHON SOURCE LINES 18-22

This will create a skore project directory named ``quick_start.skore`` in your
current working directory. Note that `overwrite=True` will overwrite any pre-existing
project with the same path (which you might not want to do that depending on your use
case).

.. GENERATED FROM PYTHON SOURCE LINES 24-25

Evaluate your model using skore's :class:`~skore.CrossValidationReport`:

.. GENERATED FROM PYTHON SOURCE LINES 27-37

.. code-block:: Python

    from sklearn.datasets import make_classification
    from sklearn.linear_model import LogisticRegression

    from skore import CrossValidationReport

    X, y = make_classification(n_classes=2, n_samples=100_000, n_informative=4)
    clf = LogisticRegression()

    cv_report = CrossValidationReport(clf, X, y)





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

 .. code-block:: none

      Processing cross-validation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%
      for LogisticRegression                                                   




.. GENERATED FROM PYTHON SOURCE LINES 38-40

Display the help tree to see all the insights that are available to you (given that
you are doing binary classification):

.. GENERATED FROM PYTHON SOURCE LINES 42-44

.. code-block:: Python

    cv_report.help()





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

 .. code-block:: none

    ╭─────────────────── Tools to diagnose estimator LogisticRegression ───────────────────╮
    │ report                                                                               │
    │ ├── .metrics                                                                         │
    │ │   ├── .accuracy(...)         (↗︎)     - Compute the accuracy score.                 │
    │ │   ├── .brier_score(...)      (↘︎)     - Compute the Brier score.                    │
    │ │   ├── .log_loss(...)         (↘︎)     - Compute the log loss.                       │
    │ │   ├── .precision(...)        (↗︎)     - Compute the precision score.                │
    │ │   ├── .recall(...)           (↗︎)     - Compute the recall score.                   │
    │ │   ├── .roc_auc(...)          (↗︎)     - Compute the ROC AUC score.                  │
    │ │   ├── .custom_metric(...)            - Compute a custom metric.                    │
    │ │   ├── .report_metrics(...)           - Report a set of metrics for our estimator.  │
    │ │   └── .plot                                                                        │
    │ │       ├── .precision_recall(...)     - Plot the precision-recall curve.            │
    │ │       └── .roc(...)                  - Plot the ROC curve.                         │
    │ ├── .cache_predictions(...)            - Cache the predictions for sub-estimators    │
    │ │   reports.                                                                         │
    │ ├── .clear_cache(...)                  - Clear the cache.                            │
    │ └── Attributes                                                                       │
    │     ├── .X                                                                           │
    │     ├── .y                                                                           │
    │     ├── .estimator_                                                                  │
    │     ├── .estimator_name_                                                             │
    │     ├── .estimator_reports_                                                          │
    │     └── .n_jobs                                                                      │
    │                                                                                      │
    │                                                                                      │
    │ Legend:                                                                              │
    │ (↗︎) higher is better (↘︎) lower is better                                             │
    ╰──────────────────────────────────────────────────────────────────────────────────────╯




.. GENERATED FROM PYTHON SOURCE LINES 45-46

Display the report metrics that was computed for you:

.. GENERATED FROM PYTHON SOURCE LINES 48-51

.. code-block:: Python

    df_cv_report_metrics = cv_report.metrics.report_metrics()
    df_cv_report_metrics





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

 .. code-block:: none

      Compute metric for each split ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th></th>
          <th>Metric</th>
          <th colspan="2" halign="left">Precision (↗︎)</th>
          <th colspan="2" halign="left">Recall (↗︎)</th>
          <th>ROC AUC (↗︎)</th>
          <th>Brier score (↘︎)</th>
        </tr>
        <tr>
          <th></th>
          <th>Class label</th>
          <th>0</th>
          <th>1</th>
          <th>0</th>
          <th>1</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">LogisticRegression</th>
          <th>Split #0</th>
          <td>0.711855</td>
          <td>0.679716</td>
          <td>0.652961</td>
          <td>0.735906</td>
          <td>0.742988</td>
          <td>0.203268</td>
        </tr>
        <tr>
          <th>Split #1</th>
          <td>0.708920</td>
          <td>0.676875</td>
          <td>0.649560</td>
          <td>0.733507</td>
          <td>0.739072</td>
          <td>0.204214</td>
        </tr>
        <tr>
          <th>Split #2</th>
          <td>0.716189</td>
          <td>0.677460</td>
          <td>0.645258</td>
          <td>0.744502</td>
          <td>0.733268</td>
          <td>0.206385</td>
        </tr>
        <tr>
          <th>Split #3</th>
          <td>0.705876</td>
          <td>0.675994</td>
          <td>0.650160</td>
          <td>0.729308</td>
          <td>0.736770</td>
          <td>0.205158</td>
        </tr>
        <tr>
          <th>Split #4</th>
          <td>0.713305</td>
          <td>0.681327</td>
          <td>0.654927</td>
          <td>0.737031</td>
          <td>0.739471</td>
          <td>0.204202</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 52-53

Display the ROC curve that was generated for you:

.. GENERATED FROM PYTHON SOURCE LINES 55-61

.. code-block:: Python

    import matplotlib.pyplot as plt

    roc_plot = cv_report.metrics.plot.roc()
    roc_plot
    plt.tight_layout()




.. image-sg:: /auto_examples/getting_started/images/sphx_glr_plot_quick_start_001.png
   :alt: plot quick start
   :srcset: /auto_examples/getting_started/images/sphx_glr_plot_quick_start_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

      Computing predictions for display ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%




.. GENERATED FROM PYTHON SOURCE LINES 62-63

Store the results in the skore project for safe-keeping:

.. GENERATED FROM PYTHON SOURCE LINES 65-68

.. code-block:: Python

    my_project.put("df_cv_report_metrics", df_cv_report_metrics)
    my_project.put("roc_plot", roc_plot)








.. GENERATED FROM PYTHON SOURCE LINES 69-72

.. admonition:: What's next?

   For a more in-depth guide, see our :ref:`example_skore_getting_started` page!

.. GENERATED FROM PYTHON SOURCE LINES 74-79

Cleanup the project
-------------------

Let's clear the skore project (to avoid any conflict with other documentation
examples).

.. GENERATED FROM PYTHON SOURCE LINES 81-82

.. code-block:: Python

    my_project.clear()








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

   **Total running time of the script:** (0 minutes 1.961 seconds)


.. _sphx_glr_download_auto_examples_getting_started_plot_quick_start.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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