.. 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>284093228217490968778865723131983556241</th>
          <td>lr-regularization-1.0e-07</td>
          <td>2026-05-27T07:59:17.122115+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>0.129016</td>
          <td>0.087332</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>1</th>
          <th>187124531107489554712585294680137485213</th>
          <td>lr-regularization-2.9e-07</td>
          <td>2026-05-27T07:59:17.578104+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>0.137199</td>
          <td>0.077172</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>2</th>
          <th>255572861750338550086951979958917350907</th>
          <td>lr-regularization-8.6e-07</td>
          <td>2026-05-27T07:59:17.998726+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>0.113589</td>
          <td>0.064240</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>3</th>
          <th>118137799761384911433456339563806335964</th>
          <td>lr-regularization-2.5e-06</td>
          <td>2026-05-27T07:59:18.397637+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>0.126430</td>
          <td>0.073544</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>4</th>
          <th>269873250232530704378680498425703122099</th>
          <td>lr-regularization-7.4e-06</td>
          <td>2026-05-27T07:59:18.774880+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>0.095717</td>
          <td>0.057962</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>5</th>
          <th>108456935244834542735306799514453316528</th>
          <td>lr-regularization-2.2e-05</td>
          <td>2026-05-27T07:59:19.125381+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>0.107499</td>
          <td>0.065768</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>6</th>
          <th>23516806759272617501250838066184165185</th>
          <td>lr-regularization-6.3e-05</td>
          <td>2026-05-27T07:59:19.490057+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>0.109727</td>
          <td>0.073067</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>7</th>
          <th>226416952655437036927258701929029645846</th>
          <td>lr-regularization-1.8e-04</td>
          <td>2026-05-27T07:59:19.883623+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>0.117158</td>
          <td>0.073161</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>8</th>
          <th>101205377183891174150497585505854257324</th>
          <td>lr-regularization-5.4e-04</td>
          <td>2026-05-27T07:59:20.280599+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>0.120897</td>
          <td>0.074223</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>9</th>
          <th>20756074311773863042752298315277459239</th>
          <td>lr-regularization-1.6e-03</td>
          <td>2026-05-27T07:59:20.687135+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>0.120795</td>
          <td>0.062921</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>10</th>
          <th>76103546144602923813113170563604190915</th>
          <td>lr-regularization-4.6e-03</td>
          <td>2026-05-27T07:59:21.060111+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>0.127296</td>
          <td>0.077641</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>11</th>
          <th>329856455932364756479133574012727079906</th>
          <td>lr-regularization-1.4e-02</td>
          <td>2026-05-27T07:59:21.474669+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>0.120312</td>
          <td>0.073737</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>12</th>
          <th>12790112837799415491224872989446528369</th>
          <td>lr-regularization-4.0e-02</td>
          <td>2026-05-27T07:59:21.874487+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>0.120977</td>
          <td>0.066205</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>13</th>
          <th>256457119736808341817496702573720782744</th>
          <td>lr-regularization-1.2e-01</td>
          <td>2026-05-27T07:59:22.280348+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>0.136284</td>
          <td>0.074866</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>14</th>
          <th>10600963640072191759637782152006052600</th>
          <td>lr-regularization-3.4e-01</td>
          <td>2026-05-27T07:59:22.691288+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>0.124531</td>
          <td>0.069589</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>15</th>
          <th>188512052453481463715206434928113039338</th>
          <td>lr-regularization-1.0e+00</td>
          <td>2026-05-27T07:59:23.097988+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>0.126883</td>
          <td>0.071788</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>16</th>
          <th>313280988523725479988958801166690741906</th>
          <td>lr-regularization-2.9e+00</td>
          <td>2026-05-27T07:59:23.483237+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>0.116288</td>
          <td>0.071801</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>17</th>
          <th>139797029602366530085144210029418639913</th>
          <td>lr-regularization-8.6e+00</td>
          <td>2026-05-27T07:59:23.871149+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>0.119199</td>
          <td>0.070794</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>18</th>
          <th>324806226343292816414284855810191245469</th>
          <td>lr-regularization-2.5e+01</td>
          <td>2026-05-27T07:59:24.257902+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>0.119365</td>
          <td>0.071102</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>19</th>
          <th>306456581851611774278301759695704667115</th>
          <td>lr-regularization-7.4e+01</td>
          <td>2026-05-27T07:59:24.646868+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>0.119881</td>
          <td>0.070795</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>20</th>
          <th>245146307623671139999261302766137455442</th>
          <td>lr-regularization-2.2e+02</td>
          <td>2026-05-27T07:59:24.917254+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>0.077111</td>
          <td>0.042141</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>21</th>
          <th>282027601573083288322832643196977963132</th>
          <td>lr-regularization-6.3e+02</td>
          <td>2026-05-27T07:59:25.159756+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>0.078119</td>
          <td>0.041098</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>22</th>
          <th>138037448102193612206679638083219062629</th>
          <td>lr-regularization-1.8e+03</td>
          <td>2026-05-27T07:59:25.400235+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.291953</td>
          <td>0.990473</td>
          <td>0.079603</td>
          <td>0.042939</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>23</th>
          <th>121677214771911742771517337895787610609</th>
          <td>lr-regularization-5.4e+03</td>
          <td>2026-05-27T07:59:25.652044+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.471382</td>
          <td>0.989838</td>
          <td>0.084534</td>
          <td>0.043277</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>24</th>
          <th>154457705516942024331015364969730581866</th>
          <td>lr-regularization-1.6e+04</td>
          <td>2026-05-27T07:59:25.920765+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>0.796526</td>
          <td>0.983169</td>
          <td>0.094053</td>
          <td>0.042422</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>25</th>
          <th>144546556981004345707470729603841290914</th>
          <td>lr-regularization-4.6e+04</td>
          <td>2026-05-27T07:59:26.184830+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.133167</td>
          <td>0.984757</td>
          <td>0.096843</td>
          <td>0.043191</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>26</th>
          <th>196497950024616241038880694831102142712</th>
          <td>lr-regularization-1.4e+05</td>
          <td>2026-05-27T07:59:26.450619+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.229179</td>
          <td>0.985551</td>
          <td>0.096811</td>
          <td>0.041906</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>27</th>
          <th>109328361929574213298851959484156363177</th>
          <td>lr-regularization-4.0e+05</td>
          <td>2026-05-27T07:59:26.714024+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.276041</td>
          <td>0.985869</td>
          <td>0.095933</td>
          <td>0.042468</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>28</th>
          <th>330108745408851579067801943053500206367</th>
          <td>lr-regularization-1.2e+06</td>
          <td>2026-05-27T07:59:26.973371+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.447319</td>
          <td>0.975865</td>
          <td>0.093343</td>
          <td>0.042523</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>29</th>
          <th>205153643696139539283671099743102182057</th>
          <td>lr-regularization-3.4e+06</td>
          <td>2026-05-27T07:59:27.230801+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.448698</td>
          <td>0.976500</td>
          <td>0.090737</td>
          <td>0.042536</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
        </tr>
        <tr>
          <th>30</th>
          <th>264755494987473349840243633281080082399</th>
          <td>lr-regularization-1.0e+07</td>
          <td>2026-05-27T07:59:27.488791+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>9f4622fc73c9ccd9bc3725923827edeb</td>
          <td>None</td>
          <td>1.897034</td>
          <td>0.976183</td>
          <td>0.090789</td>
          <td>0.043725</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, '284093228217490968778865723131983556241') to (30, '264755494987473349840243633281080082399')
    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>0.136284</td>
          <td>0.124531</td>
          <td>0.126883</td>
          <td>0.116288</td>
        </tr>
        <tr>
          <th>Predict time (s)</th>
          <td>0.041241</td>
          <td>0.042053</td>
          <td>0.043687</td>
          <td>0.042517</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





.. 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 11.991 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>`_