.. 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>319600054297863044292889970164137448300</th>
          <td>lr-regularization-1.0e-07</td>
          <td>2026-04-20T17:52:26.655009+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.682846</td>
          <td>0.984122</td>
          <td>0.099246</td>
          <td>0.057546</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>1</th>
          <th>133777232867550309543251718287987382666</th>
          <td>lr-regularization-2.9e-07</td>
          <td>2026-04-20T17:52:26.969326+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.682710</td>
          <td>0.984122</td>
          <td>0.093573</td>
          <td>0.056238</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>2</th>
          <th>239967432476237296588070632678057615642</th>
          <td>lr-regularization-8.6e-07</td>
          <td>2026-04-20T17:52:27.284817+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.682313</td>
          <td>0.984122</td>
          <td>0.092248</td>
          <td>0.056558</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>3</th>
          <th>336450492414157125226136412595429195995</th>
          <td>lr-regularization-2.5e-06</td>
          <td>2026-04-20T17:52:27.561180+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.681153</td>
          <td>0.984122</td>
          <td>0.091662</td>
          <td>0.057417</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>4</th>
          <th>326233199582433253816322387949474905112</th>
          <td>lr-regularization-7.4e-06</td>
          <td>2026-04-20T17:52:27.888749+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.677790</td>
          <td>0.984122</td>
          <td>0.100166</td>
          <td>0.060627</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>5</th>
          <th>42509192854748068163401802872263961737</th>
          <td>lr-regularization-2.2e-05</td>
          <td>2026-04-20T17:52:28.212103+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.668210</td>
          <td>0.984122</td>
          <td>0.093845</td>
          <td>0.058580</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>6</th>
          <th>265003138106978757403547898607147676060</th>
          <td>lr-regularization-6.3e-05</td>
          <td>2026-04-20T17:52:28.534110+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.642328</td>
          <td>0.985075</td>
          <td>0.092312</td>
          <td>0.057040</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>7</th>
          <th>260531295652550002599510715870368447238</th>
          <td>lr-regularization-1.8e-04</td>
          <td>2026-04-20T17:52:28.849819+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.581579</td>
          <td>0.985392</td>
          <td>0.093260</td>
          <td>0.057658</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>8</th>
          <th>28586379743841536348353532421836850869</th>
          <td>lr-regularization-5.4e-04</td>
          <td>2026-04-20T17:52:29.199737+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.476411</td>
          <td>0.985710</td>
          <td>0.109397</td>
          <td>0.069763</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>9</th>
          <th>131167287054830192511133112243932267338</th>
          <td>lr-regularization-1.6e-03</td>
          <td>2026-04-20T17:52:29.555287+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.355309</td>
          <td>0.987615</td>
          <td>0.088961</td>
          <td>0.069654</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>10</th>
          <th>308126891139553944768317731282096072120</th>
          <td>lr-regularization-4.6e-03</td>
          <td>2026-04-20T17:52:29.900827+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.252513</td>
          <td>0.990156</td>
          <td>0.111219</td>
          <td>0.068957</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>11</th>
          <th>279841620087507710598663049879387116984</th>
          <td>lr-regularization-1.4e-02</td>
          <td>2026-04-20T17:52:30.265086+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.178052</td>
          <td>0.992379</td>
          <td>0.105894</td>
          <td>0.069630</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>12</th>
          <th>303556171485881400658178284044789202666</th>
          <td>lr-regularization-4.0e-02</td>
          <td>2026-04-20T17:52:30.657004+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.128754</td>
          <td>0.995872</td>
          <td>0.131898</td>
          <td>0.052352</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>13</th>
          <th>135990661535727214012674790622028980054</th>
          <td>lr-regularization-1.2e-01</td>
          <td>2026-04-20T17:52:30.957141+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.098897</td>
          <td>0.996189</td>
          <td>0.063245</td>
          <td>0.068229</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>14</th>
          <th>121263790949956478747844362450892180457</th>
          <td>lr-regularization-3.4e-01</td>
          <td>2026-04-20T17:52:31.368102+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.083941</td>
          <td>0.995872</td>
          <td>0.099731</td>
          <td>0.063710</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>15</th>
          <th>140925836346298906342559439993018073165</th>
          <td>lr-regularization-1.0e+00</td>
          <td>2026-04-20T17:52:31.714664+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.080457</td>
          <td>0.995554</td>
          <td>0.101894</td>
          <td>0.066344</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>16</th>
          <th>32564207787422562201767276785550655976</th>
          <td>lr-regularization-2.9e+00</td>
          <td>2026-04-20T17:52:32.136321+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.089466</td>
          <td>0.994601</td>
          <td>0.093928</td>
          <td>0.043496</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>17</th>
          <th>209842753366844399909375563662864096650</th>
          <td>lr-regularization-8.6e+00</td>
          <td>2026-04-20T17:52:32.342166+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.104241</td>
          <td>0.993649</td>
          <td>0.057646</td>
          <td>0.062464</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>18</th>
          <th>308220362541405739951712962468733593787</th>
          <td>lr-regularization-2.5e+01</td>
          <td>2026-04-20T17:52:32.692314+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.121671</td>
          <td>0.992379</td>
          <td>0.108864</td>
          <td>0.060596</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>19</th>
          <th>128368093804667374367373605304158866373</th>
          <td>lr-regularization-7.4e+01</td>
          <td>2026-04-20T17:52:33.031396+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.142282</td>
          <td>0.991426</td>
          <td>0.096144</td>
          <td>0.069804</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>20</th>
          <th>40121472873410394581867485699536654387</th>
          <td>lr-regularization-2.2e+02</td>
          <td>2026-04-20T17:52:33.415477+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.162450</td>
          <td>0.991426</td>
          <td>0.115205</td>
          <td>0.070214</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>21</th>
          <th>93151925567776315627936559444622672498</th>
          <td>lr-regularization-6.3e+02</td>
          <td>2026-04-20T17:52:33.799109+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.204586</td>
          <td>0.991108</td>
          <td>0.118077</td>
          <td>0.072886</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>22</th>
          <th>331234454705907022678060942328598120906</th>
          <td>lr-regularization-1.8e+03</td>
          <td>2026-04-20T17:52:34.193172+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.284873</td>
          <td>0.990473</td>
          <td>0.123701</td>
          <td>0.062961</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>23</th>
          <th>35634477182579873469410034264653080021</th>
          <td>lr-regularization-5.4e+03</td>
          <td>2026-04-20T17:52:34.569009+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.471375</td>
          <td>0.989838</td>
          <td>0.124675</td>
          <td>0.068780</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>24</th>
          <th>31235435463640014562497384215926340693</th>
          <td>lr-regularization-1.6e+04</td>
          <td>2026-04-20T17:52:34.966175+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.545335</td>
          <td>0.991108</td>
          <td>0.137036</td>
          <td>0.068199</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>25</th>
          <th>17961718234908200650545727235354059010</th>
          <td>lr-regularization-4.6e+04</td>
          <td>2026-04-20T17:52:35.365284+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.934770</td>
          <td>0.983963</td>
          <td>0.138423</td>
          <td>0.069656</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>26</th>
          <th>218764210745362156583888086669867226547</th>
          <td>lr-regularization-1.4e+05</td>
          <td>2026-04-20T17:52:35.799254+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.246488</td>
          <td>0.985551</td>
          <td>0.172180</td>
          <td>0.069340</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>27</th>
          <th>240846162326983660378265340807226494271</th>
          <td>lr-regularization-4.0e+05</td>
          <td>2026-04-20T17:52:36.194319+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.336316</td>
          <td>0.985551</td>
          <td>0.136745</td>
          <td>0.073131</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>28</th>
          <th>189726406070659159571586070476222409921</th>
          <td>lr-regularization-1.2e+06</td>
          <td>2026-04-20T17:52:36.595981+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.670465</td>
          <td>0.975865</td>
          <td>0.140221</td>
          <td>0.121888</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>29</th>
          <th>286683080110144804324803819587089008944</th>
          <td>lr-regularization-3.4e+06</td>
          <td>2026-04-20T17:52:37.042814+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.605194</td>
          <td>0.975865</td>
          <td>0.127628</td>
          <td>0.065117</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>30</th>
          <th>185543021202053829588081373607202680230</th>
          <td>lr-regularization-1.0e+07</td>
          <td>2026-04-20T17:52:37.424612+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.875108</td>
          <td>0.975548</td>
          <td>0.128567</td>
          <td>0.066552</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, '319600054297863044292889970164137448300') to (30, '185543021202053829588081373607202680230')
    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       31 non-null     float64 
     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(4), object(11)
    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>Log loss</th>
          <td>0.098897</td>
          <td>0.083941</td>
          <td>0.080457</td>
          <td>0.089466</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>0.063245</td>
          <td>0.099731</td>
          <td>0.101894</td>
          <td>0.093928</td>
        </tr>
        <tr>
          <th>Predict time (s)</th>
          <td>0.068895</td>
          <td>0.067794</td>
          <td>0.062137</td>
          <td>0.054768</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)





.. 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 12.662 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>`_