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

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

.. _sphx_glr_auto_examples_technical_details_plot_skore_local_project.py:


.. _example_skore_local_project:

===================
Local skore Project
===================

This example shows how to use :class:`~skore.Project` in **local** mode: store
reports on your machine and inspect them. A key point is that
:meth:`~skore.Project.summarize` returns a :class:`~skore.project._summary.Summary`,
which is a :class:`pandas.DataFrame`. In Jupyter you get an interactive widget, but
you can always inspect and filter the summary as a DataFrame if you prefer.

.. GENERATED FROM PYTHON SOURCE LINES 16-22

Create a local project and store reports
=========================================

We use a temporary directory as the workspace so the example is self-contained.
In practice you can omit ``workspace`` to use the default (e.g. a ``skore/``
directory in your user cache).

.. GENERATED FROM PYTHON SOURCE LINES 22-31

.. code-block:: Python

    from pathlib import Path
    from tempfile import TemporaryDirectory

    from skore import Project

    tmp_dir = TemporaryDirectory()
    tmp_path = Path(tmp_dir.name)
    project = Project("example-project", workspace=tmp_path)








.. GENERATED FROM PYTHON SOURCE LINES 32-39

.. code-block:: Python

    from sklearn.datasets import load_breast_cancer
    from sklearn.linear_model import LogisticRegression
    from skrub import tabular_pipeline

    X, y = load_breast_cancer(return_X_y=True, as_frame=True)
    estimator = tabular_pipeline(LogisticRegression(max_iter=1_000))








.. GENERATED FROM PYTHON SOURCE LINES 40-54

.. code-block:: Python

    import numpy as np
    from sklearn.base import clone
    from skore import evaluate

    for regularization in np.logspace(-7, 7, 31):
        report = evaluate(
            clone(estimator).set_params(logisticregression__C=regularization),
            X,
            y,
            splitter=0.2,
            pos_label=1,
        )
        project.put(f"lr-regularization-{regularization:.1e}", report)








.. GENERATED FROM PYTHON SOURCE LINES 55-61

Summarize: you get a DataFrame
==============================

:meth:`~skore.Project.summarize` returns a :class:`~skore.project._summary.Summary`,
which subclasses :class:`pandas.DataFrame`. In a Jupyter environment it renders
an interactive parallel-coordinates widget by default.

.. GENERATED FROM PYTHON SOURCE LINES 61-63

.. code-block:: Python

    summary = project.summarize()








.. GENERATED FROM PYTHON SOURCE LINES 64-66

To see the normal DataFrame table instead of the widget (e.g. in scripts or
when you prefer the table), wrap the summary in :class:`pandas.DataFrame`:

.. GENERATED FROM PYTHON SOURCE LINES 66-71

.. code-block:: Python

    import pandas as pd

    pandas_summary = pd.DataFrame(summary)
    pandas_summary






.. 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></th>
          <th>key</th>
          <th>date</th>
          <th>learner</th>
          <th>ml_task</th>
          <th>report_type</th>
          <th>dataset</th>
          <th>rmse</th>
          <th>log_loss</th>
          <th>roc_auc</th>
          <th>fit_time</th>
          <th>predict_time</th>
          <th>rmse_mean</th>
          <th>log_loss_mean</th>
          <th>roc_auc_mean</th>
          <th>fit_time_mean</th>
          <th>predict_time_mean</th>
        </tr>
        <tr>
          <th></th>
          <th>id</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <th>82f01bdfce9cdd853c862abba834c33f</th>
          <td>lr-regularization-1.0e-07</td>
          <td>2026-03-27T13:06:51.134009+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.682846</td>
          <td>0.984122</td>
          <td>0.135748</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>1</th>
          <th>e33eb3207cac10b32041d281ce14a94a</th>
          <td>lr-regularization-2.9e-07</td>
          <td>2026-03-27T13:06:51.412339+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.682710</td>
          <td>0.984122</td>
          <td>0.133380</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>2</th>
          <th>b5733fdc8270138fb13e8dc9cdff36ed</th>
          <td>lr-regularization-8.6e-07</td>
          <td>2026-03-27T13:06:51.659784+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.682313</td>
          <td>0.984122</td>
          <td>0.109869</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>3</th>
          <th>95c77b566cbccb8e314cdaf66201ca4a</th>
          <td>lr-regularization-2.5e-06</td>
          <td>2026-03-27T13:06:51.914574+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.681153</td>
          <td>0.984122</td>
          <td>0.126737</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>4</th>
          <th>0db14e4bbe06a5987998dd1be9f17a4f</th>
          <td>lr-regularization-7.4e-06</td>
          <td>2026-03-27T13:06:52.169691+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.677790</td>
          <td>0.984122</td>
          <td>0.124913</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>5</th>
          <th>8a7e72257c6aa07dc07956aeb741d7e5</th>
          <td>lr-regularization-2.2e-05</td>
          <td>2026-03-27T13:06:52.311711+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.668210</td>
          <td>0.984122</td>
          <td>0.065759</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>6</th>
          <th>d1bde468c8902a124aacb847e7c67f45</th>
          <td>lr-regularization-6.3e-05</td>
          <td>2026-03-27T13:06:52.454790+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.642328</td>
          <td>0.985075</td>
          <td>0.064632</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>7</th>
          <th>97790c77be5deec39d7807df747d2e3c</th>
          <td>lr-regularization-1.8e-04</td>
          <td>2026-03-27T13:06:52.597939+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.581579</td>
          <td>0.985392</td>
          <td>0.066439</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>8</th>
          <th>0ae095221b46c4ae7887b0e14363e7db</th>
          <td>lr-regularization-5.4e-04</td>
          <td>2026-03-27T13:06:52.743813+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.476411</td>
          <td>0.985710</td>
          <td>0.068008</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>9</th>
          <th>8d5a6b6285b6efc64461726d689c784d</th>
          <td>lr-regularization-1.6e-03</td>
          <td>2026-03-27T13:06:52.988513+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.355309</td>
          <td>0.987615</td>
          <td>0.117831</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>10</th>
          <th>ed5beb595855d9f955d6638241cbb021</th>
          <td>lr-regularization-4.6e-03</td>
          <td>2026-03-27T13:06:53.244467+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.252513</td>
          <td>0.990156</td>
          <td>0.124471</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>11</th>
          <th>6730776adb2457ccfe344329f69cb8d1</th>
          <td>lr-regularization-1.4e-02</td>
          <td>2026-03-27T13:06:53.512177+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.178052</td>
          <td>0.992379</td>
          <td>0.127996</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>12</th>
          <th>8c263d5b5286d970bc461a2fcac97705</th>
          <td>lr-regularization-4.0e-02</td>
          <td>2026-03-27T13:06:53.710221+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.128754</td>
          <td>0.995872</td>
          <td>0.069371</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>13</th>
          <th>4b0033ad94e6eb31867a93e29deb8b3f</th>
          <td>lr-regularization-1.2e-01</td>
          <td>2026-03-27T13:06:53.951030+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.098897</td>
          <td>0.996189</td>
          <td>0.120337</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>14</th>
          <th>14eed12ea458f86074c549f4049ff0dd</th>
          <td>lr-regularization-3.4e-01</td>
          <td>2026-03-27T13:06:54.181244+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.083941</td>
          <td>0.995872</td>
          <td>0.107787</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>15</th>
          <th>7dc451f75559509dd8d0f5fd28ec44f9</th>
          <td>lr-regularization-1.0e+00</td>
          <td>2026-03-27T13:06:54.398317+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.080457</td>
          <td>0.995554</td>
          <td>0.104451</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>16</th>
          <th>0be0241e877f49981567f879d8c3c028</th>
          <td>lr-regularization-2.9e+00</td>
          <td>2026-03-27T13:06:54.659038+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.089466</td>
          <td>0.994601</td>
          <td>0.123010</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>17</th>
          <th>35086d42ac2337ab23b9516411524a21</th>
          <td>lr-regularization-8.6e+00</td>
          <td>2026-03-27T13:06:54.924768+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.104241</td>
          <td>0.993649</td>
          <td>0.134518</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>18</th>
          <th>34bf50b369ddb17f0a0a7c36ce1f3113</th>
          <td>lr-regularization-2.5e+01</td>
          <td>2026-03-27T13:06:55.201254+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.121671</td>
          <td>0.992379</td>
          <td>0.133600</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>19</th>
          <th>97e9c70dd0b66056d8023f6d15326805</th>
          <td>lr-regularization-7.4e+01</td>
          <td>2026-03-27T13:06:55.477897+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.142282</td>
          <td>0.991426</td>
          <td>0.130623</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>20</th>
          <th>c8d2bd255e12dd5424ce7ad8345c7780</th>
          <td>lr-regularization-2.2e+02</td>
          <td>2026-03-27T13:06:55.763546+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.162450</td>
          <td>0.991426</td>
          <td>0.144351</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>21</th>
          <th>68fdda1bf33f5c8e4e6b6357d78f7dfc</th>
          <td>lr-regularization-6.3e+02</td>
          <td>2026-03-27T13:06:56.044868+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.204586</td>
          <td>0.991108</td>
          <td>0.139917</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>22</th>
          <th>e40d6892b033bcb716f57458dbceac8f</th>
          <td>lr-regularization-1.8e+03</td>
          <td>2026-03-27T13:06:56.303821+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.291953</td>
          <td>0.990473</td>
          <td>0.130204</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>23</th>
          <th>4bf896d370cd074bcd8555b48ac49aee</th>
          <td>lr-regularization-5.4e+03</td>
          <td>2026-03-27T13:06:56.454334+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.471382</td>
          <td>0.989838</td>
          <td>0.074480</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>24</th>
          <th>c8decbec09eac575487f87087cf96b0a</th>
          <td>lr-regularization-1.6e+04</td>
          <td>2026-03-27T13:06:56.616195+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.796526</td>
          <td>0.983169</td>
          <td>0.085537</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>25</th>
          <th>3c54d53897a22b0444bdabc22dd85268</th>
          <td>lr-regularization-4.6e+04</td>
          <td>2026-03-27T13:06:56.781637+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.133167</td>
          <td>0.984757</td>
          <td>0.090190</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>26</th>
          <th>6e7d7650fcecf949dd60df4f894a8a5a</th>
          <td>lr-regularization-1.4e+05</td>
          <td>2026-03-27T13:06:56.944262+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.229179</td>
          <td>0.985551</td>
          <td>0.087516</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>27</th>
          <th>1deb098c94f48756de8ea2cbad3399ce</th>
          <td>lr-regularization-4.0e+05</td>
          <td>2026-03-27T13:06:57.107860+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.276041</td>
          <td>0.985869</td>
          <td>0.085979</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>28</th>
          <th>c8d89a1921e299c7d220ab23f155637b</th>
          <td>lr-regularization-1.2e+06</td>
          <td>2026-03-27T13:06:57.271814+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.447319</td>
          <td>0.975865</td>
          <td>0.086354</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>29</th>
          <th>4443a11e69e532a9e24b116d6431e76f</th>
          <td>lr-regularization-3.4e+06</td>
          <td>2026-03-27T13:06:57.432890+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.448698</td>
          <td>0.976500</td>
          <td>0.083347</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>30</th>
          <th>dc795b85826c054835edf0069bd6afba</th>
          <td>lr-regularization-1.0e+07</td>
          <td>2026-03-27T13:06:57.593093+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.897034</td>
          <td>0.976183</td>
          <td>0.083862</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 72-74

Basically, our summary contains metadata related to various information that we need
to quickly help filtering the reports.

.. GENERATED FROM PYTHON SOURCE LINES 74-76

.. code-block:: Python

    summary.info()





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

 .. code-block:: none

    <class 'skore._project._summary.Summary'>
    MultiIndex: 31 entries, (0, '82f01bdfce9cdd853c862abba834c33f') to (30, 'dc795b85826c054835edf0069bd6afba')
    Data columns (total 16 columns):
     #   Column             Non-Null Count  Dtype   
    ---  ------             --------------  -----   
     0   key                31 non-null     object  
     1   date               31 non-null     object  
     2   learner            31 non-null     category
     3   ml_task            31 non-null     object  
     4   report_type        31 non-null     object  
     5   dataset            31 non-null     object  
     6   rmse               0 non-null      object  
     7   log_loss           31 non-null     float64 
     8   roc_auc            31 non-null     float64 
     9   fit_time           31 non-null     float64 
     10  predict_time       0 non-null      object  
     11  rmse_mean          0 non-null      object  
     12  log_loss_mean      0 non-null      object  
     13  roc_auc_mean       0 non-null      object  
     14  fit_time_mean      0 non-null      object  
     15  predict_time_mean  0 non-null      object  
    dtypes: category(1), float64(3), object(12)
    memory usage: 5.3+ KB




.. GENERATED FROM PYTHON SOURCE LINES 77-79

Filter reports by metric (e.g. keep only those above a given accuracy) and
work with the result as a table.

.. GENERATED FROM PYTHON SOURCE LINES 79-81

.. code-block:: Python

    summary.query("log_loss < 0.1")["key"].tolist()





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

 .. code-block:: none


    ['lr-regularization-1.2e-01', 'lr-regularization-3.4e-01', 'lr-regularization-1.0e+00', 'lr-regularization-2.9e+00']



.. GENERATED FROM PYTHON SOURCE LINES 82-84

Use :meth:`~skore.project._summary.Summary.reports` to load the corresponding
reports from the project (optionally after filtering the summary).

.. GENERATED FROM PYTHON SOURCE LINES 84-87

.. code-block:: Python

    reports = summary.query("log_loss < 0.1").reports(return_as="comparison")
    len(reports.reports_)





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

 .. code-block:: none


    4



.. GENERATED FROM PYTHON SOURCE LINES 88-90

Since we got a :class:`~skore.ComparisonReport`, we can use the metrics accessor
to summarize the metrics across the reports.

.. GENERATED FROM PYTHON SOURCE LINES 90-92

.. code-block:: Python

    reports.metrics.summarize().frame()






.. 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>Estimator</th>
          <th>LogisticRegression_1</th>
          <th>LogisticRegression_2</th>
          <th>LogisticRegression_3</th>
          <th>LogisticRegression_4</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Accuracy</th>
          <td>0.947368</td>
          <td>0.964912</td>
          <td>0.964912</td>
          <td>0.964912</td>
        </tr>
        <tr>
          <th>Precision</th>
          <td>0.942029</td>
          <td>0.970149</td>
          <td>0.970149</td>
          <td>0.970149</td>
        </tr>
        <tr>
          <th>Recall</th>
          <td>0.970149</td>
          <td>0.970149</td>
          <td>0.970149</td>
          <td>0.970149</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <td>0.996189</td>
          <td>0.995872</td>
          <td>0.995554</td>
          <td>0.994601</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <td>0.027157</td>
          <td>0.024990</td>
          <td>0.025149</td>
          <td>0.026218</td>
        </tr>
        <tr>
          <th>Fit time (s)</th>
          <td>0.120337</td>
          <td>0.107787</td>
          <td>0.104451</td>
          <td>0.123010</td>
        </tr>
        <tr>
          <th>Predict time (s)</th>
          <td>0.040571</td>
          <td>0.040740</td>
          <td>0.043242</td>
          <td>0.056472</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 93-95

.. code-block:: Python

    reports.metrics.roc().plot(subplot_by=None)




.. image-sg:: /auto_examples/technical_details/images/sphx_glr_plot_skore_local_project_001.png
   :alt: ROC Curve Positive label: 1 Data source: Test set
   :srcset: /auto_examples/technical_details/images/sphx_glr_plot_skore_local_project_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none



    <Figure size 600x825 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 96-98

.. code-block:: Python

    project.delete("example-project", workspace=tmp_path)
    tmp_dir.cleanup()








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

   **Total running time of the script:** (0 minutes 7.800 seconds)


.. _sphx_glr_download_auto_examples_technical_details_plot_skore_local_project.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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