.. 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 10-14

Machine learning evaluation and diagnostics
===========================================

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

.. GENERATED FROM PYTHON SOURCE LINES 16-26

.. 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)








.. GENERATED FROM PYTHON SOURCE LINES 27-29

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

.. GENERATED FROM PYTHON SOURCE LINES 31-33

.. code-block:: Python

    cv_report.help()





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

 .. code-block:: none

    ╭─────────────────── Tools to diagnose estimator LogisticRegression ───────────────────╮
    │ CrossValidationReport                                                                │
    │ ├── .metrics                                                                         │
    │ │   ├── .accuracy(...)         (↗︎)     - Compute the accuracy score.                 │
    │ │   ├── .brier_score(...)      (↘︎)     - Compute the Brier score.                    │
    │ │   ├── .log_loss(...)         (↘︎)     - Compute the log loss.                       │
    │ │   ├── .precision(...)        (↗︎)     - Compute the precision score.                │
    │ │   ├── .precision_recall(...)         - Plot the precision-recall curve.            │
    │ │   ├── .recall(...)           (↗︎)     - Compute the recall score.                   │
    │ │   ├── .roc(...)                      - Plot the ROC curve.                         │
    │ │   ├── .roc_auc(...)          (↗︎)     - Compute the ROC AUC score.                  │
    │ │   ├── .timings(...)                  - Get all measured processing times related   │
    │ │   │   to the estimator.                                                            │
    │ │   ├── .custom_metric(...)            - Compute a custom metric.                    │
    │ │   └── .report_metrics(...)           - Report a set of metrics for our estimator.  │
    │ ├── .cache_predictions(...)            - Cache the predictions for sub-estimators    │
    │ │   reports.                                                                         │
    │ ├── .clear_cache(...)                  - Clear the cache.                            │
    │ ├── .get_predictions(...)              - Get estimator's predictions.                │
    │ └── Attributes                                                                       │
    │     ├── .X                             - The data to fit                             │
    │     ├── .y                             - The target variable to try to predict in    │
    │     │   the case of supervised learning                                              │
    │     ├── .estimator_                    - The cloned or copied estimator              │
    │     ├── .estimator_name_               - The name of the estimator                   │
    │     ├── .estimator_reports_            - The estimator reports for each split        │
    │     └── .n_jobs                        - Number of jobs to run in parallel           │
    │                                                                                      │
    │                                                                                      │
    │ Legend:                                                                              │
    │ (↗︎) higher is better (↘︎) lower is better                                             │
    ╰──────────────────────────────────────────────────────────────────────────────────────╯




.. GENERATED FROM PYTHON SOURCE LINES 34-35

Display the report metrics that was computed for you:

.. GENERATED FROM PYTHON SOURCE LINES 37-40

.. code-block:: Python

    df_cv_report_metrics = cv_report.metrics.report_metrics(pos_label=1)
    df_cv_report_metrics






.. 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;
        }

        .dataframe thead tr:last-of-type th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th></th>
          <th colspan="2" halign="left">LogisticRegression</th>
        </tr>
        <tr>
          <th></th>
          <th>mean</th>
          <th>std</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Precision</th>
          <td>0.866950</td>
          <td>0.001279</td>
        </tr>
        <tr>
          <th>Recall</th>
          <td>0.893577</td>
          <td>0.003474</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <td>0.942389</td>
          <td>0.001236</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <td>0.090748</td>
          <td>0.000894</td>
        </tr>
        <tr>
          <th>Fit time</th>
          <td>0.181586</td>
          <td>0.028864</td>
        </tr>
        <tr>
          <th>Predict time</th>
          <td>0.001423</td>
          <td>0.001392</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 41-42

Display the ROC curve that was generated for you:

.. GENERATED FROM PYTHON SOURCE LINES 44-47

.. code-block:: Python

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




.. 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





.. GENERATED FROM PYTHON SOURCE LINES 48-50

Skore project: storing some items
=================================

.. GENERATED FROM PYTHON SOURCE LINES 52-53

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

.. GENERATED FROM PYTHON SOURCE LINES 55-59

.. code-block:: Python

    import skore

    my_project = skore.Project("my_project")








.. GENERATED FROM PYTHON SOURCE LINES 69-71

This will create a skore project directory named ``my_project.skore`` in your
current working directory.

.. GENERATED FROM PYTHON SOURCE LINES 73-74

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

.. GENERATED FROM PYTHON SOURCE LINES 76-79

.. 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 80-81

Retrieve what was stored:

.. GENERATED FROM PYTHON SOURCE LINES 83-86

.. code-block:: Python

    df_get = my_project.get("df_cv_report_metrics")
    df_get






.. 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 th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>(LogisticRegression, mean)</th>
          <th>(LogisticRegression, std)</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Precision</th>
          <td>0.866950</td>
          <td>0.001279</td>
        </tr>
        <tr>
          <th>Recall</th>
          <td>0.893577</td>
          <td>0.003474</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <td>0.942389</td>
          <td>0.001236</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <td>0.090748</td>
          <td>0.000894</td>
        </tr>
        <tr>
          <th>Fit time</th>
          <td>0.181586</td>
          <td>0.028864</td>
        </tr>
        <tr>
          <th>Predict time</th>
          <td>0.001423</td>
          <td>0.001392</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 90-93

.. seealso::

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


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

   **Total running time of the script:** (0 minutes 1.866 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>`_