.. 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.EstimatorReport`:

.. GENERATED FROM PYTHON SOURCE LINES 16-29

.. code-block:: Python

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

    from skore import EstimatorReport

    X, y = make_classification(n_classes=2, n_samples=20_000, n_informative=4)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    log_report = EstimatorReport(
        LogisticRegression(), X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test
    )








.. GENERATED FROM PYTHON SOURCE LINES 30-32

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 34-36

.. code-block:: Python

    log_report.help()





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

 .. code-block:: none

    ╭─────────────────── Tools to diagnose estimator LogisticRegression ───────────────────╮
    │ EstimatorReport                                                                      │
    │ ├── .metrics                                                                         │
    │ │   ├── .accuracy(...)         (↗︎)     - Compute the accuracy score.                 │
    │ │   ├── .brier_score(...)      (↘︎)     - Compute the Brier score.                    │
    │ │   ├── .confusion_matrix(...)         - Plot the confusion matrix.                  │
    │ │   ├── .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.  │
    │ ├── .feature_importance                                                              │
    │ │   ├── .coefficients(...)             - Retrieve the coefficients of a linear       │
    │ │   │   model, including the intercept.                                              │
    │ │   └── .permutation(...)              - Report the permutation feature importance.  │
    │ ├── .cache_predictions(...)            - Cache estimator's predictions.              │
    │ ├── .clear_cache(...)                  - Clear the cache.                            │
    │ ├── .get_predictions(...)              - Get estimator's predictions.                │
    │ └── Attributes                                                                       │
    │     ├── .X_test                        - Testing data                                │
    │     ├── .X_train                       - Training data                               │
    │     ├── .y_test                        - Testing target                              │
    │     ├── .y_train                       - Training target                             │
    │     ├── .estimator_                    - The cloned or copied estimator              │
    │     ├── .estimator_name_               - The name of the estimator                   │
    │     ├── .fit_time_                     - The time taken to fit the estimator, in     │
    │     │   seconds                                                                      │
    │     └── .ml_task                       - No description available                    │
    │                                                                                      │
    │                                                                                      │
    │ Legend:                                                                              │
    │ (↗︎) higher is better (↘︎) lower is better                                             │
    ╰──────────────────────────────────────────────────────────────────────────────────────╯




.. GENERATED FROM PYTHON SOURCE LINES 37-38

Display the report metrics that was computed for you:

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: Python

    df_report_metrics = log_report.metrics.report_metrics(pos_label=1)
    df_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 th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>LogisticRegression</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Precision</th>
          <td>0.815981</td>
        </tr>
        <tr>
          <th>Recall</th>
          <td>0.805899</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <td>0.895035</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <td>0.131878</td>
        </tr>
        <tr>
          <th>Fit time (s)</th>
          <td>0.010845</td>
        </tr>
        <tr>
          <th>Predict time (s)</th>
          <td>0.000488</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 44-45

Display the ROC curve that was generated for you:

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: Python

    roc_plot = log_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 51-53

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

.. GENERATED FROM PYTHON SOURCE LINES 55-56

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

.. GENERATED FROM PYTHON SOURCE LINES 58-62

.. code-block:: Python

    import skore

    my_project = skore.Project("my_project")








.. GENERATED FROM PYTHON SOURCE LINES 70-72

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

.. GENERATED FROM PYTHON SOURCE LINES 74-75

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

.. GENERATED FROM PYTHON SOURCE LINES 77-79

.. code-block:: Python

    my_project.put("log_report", log_report)








.. GENERATED FROM PYTHON SOURCE LINES 80-81

Let us store a second model:

.. GENERATED FROM PYTHON SOURCE LINES 83-94

.. code-block:: Python

    from sklearn.ensemble import RandomForestClassifier

    rf_report = EstimatorReport(
        RandomForestClassifier(),
        X_train=X_train,
        X_test=X_test,
        y_train=y_train,
        y_test=y_test,
    )
    my_project.put("rf_report", rf_report)








.. GENERATED FROM PYTHON SOURCE LINES 95-96

Now, let us retrieve the data that we previously stored:

.. GENERATED FROM PYTHON SOURCE LINES 98-101

.. code-block:: Python

    metadata = my_project.reports.metadata()
    print(type(metadata))





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

 .. code-block:: none

    <class 'skore.project.metadata.Metadata'>




.. GENERATED FROM PYTHON SOURCE LINES 102-103

We can perform some queries on our stored data:

.. GENERATED FROM PYTHON SOURCE LINES 105-110

.. code-block:: Python

    from pprint import pprint

    report_get = metadata.query("ml_task.str.contains('classification')").reports()
    pprint(report_get)





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

 .. code-block:: none

    [EstimatorReport(estimator=LogisticRegression(), ...),
     EstimatorReport(estimator=RandomForestClassifier(), ...)]




.. GENERATED FROM PYTHON SOURCE LINES 111-112

For example, we can retrieve the report metrics from the first estimator report:

.. GENERATED FROM PYTHON SOURCE LINES 114-118

.. code-block:: Python



    report_get[0].metrics.report_metrics(pos_label=1)






.. 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</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Precision</th>
          <td>0.815981</td>
        </tr>
        <tr>
          <th>Recall</th>
          <td>0.805899</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <td>0.895035</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <td>0.131878</td>
        </tr>
        <tr>
          <th>Fit time (s)</th>
          <td>0.010845</td>
        </tr>
        <tr>
          <th>Predict time (s)</th>
          <td>0.000363</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 122-129

.. note::
  If rendered in a Jupyter notebook, ``metadata`` would render an interactive
  parallel coordinate plot to search for your preferred model based on some metrics.
  Here is a screenshot:

  .. image:: /_static/images/screenshot_quick_start.png
      :alt: Screenshot of the widget in a Jupyter notebook

.. GENERATED FROM PYTHON SOURCE LINES 131-134

.. 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 7.555 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>`_