.. 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>cce3ae2e27e68a514cac89b53cde05b4</th>
          <td>lr-regularization-1.0e-07</td>
          <td>2026-04-02T09:45:08.711362+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>0.116555</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>8c86ee4ef8ff78a5ca37b10cb262058f</th>
          <td>lr-regularization-2.9e-07</td>
          <td>2026-04-02T09:45:08.938459+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>0.109650</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>65613126249b44b8175d8ae325bd65e1</th>
          <td>lr-regularization-8.6e-07</td>
          <td>2026-04-02T09:45:09.149259+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>0.098044</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>2ddb65892fe191190cdc90328e315aa6</th>
          <td>lr-regularization-2.5e-06</td>
          <td>2026-04-02T09:45:09.297476+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>0.079087</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>ad19396cf8328436c243fb2a67218c51</th>
          <td>lr-regularization-7.4e-06</td>
          <td>2026-04-02T09:45:09.426943+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>0.060447</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>1d2b0654d2a90ccf76ea1028f0e74873</th>
          <td>lr-regularization-2.2e-05</td>
          <td>2026-04-02T09:45:09.555109+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>0.060979</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>e8dd75340ac6aeb8eb14c77fab2643fa</th>
          <td>lr-regularization-6.3e-05</td>
          <td>2026-04-02T09:45:09.680315+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>0.058468</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>17eb85b7e258d235663c295104e1d666</th>
          <td>lr-regularization-1.8e-04</td>
          <td>2026-04-02T09:45:09.841745+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>0.087663</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>859e3d1c0c3dfbff4bffa061950e8ba4</th>
          <td>lr-regularization-5.4e-04</td>
          <td>2026-04-02T09:45:09.968284+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>0.058607</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>da896014956de20c0390456429a31043</th>
          <td>lr-regularization-1.6e-03</td>
          <td>2026-04-02T09:45:10.094302+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>0.059772</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>b39f3fe57cfb68eead3a8324b63a6603</th>
          <td>lr-regularization-4.6e-03</td>
          <td>2026-04-02T09:45:10.220633+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>0.058976</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>28b9ed500dc596ee52d60be59727d91c</th>
          <td>lr-regularization-1.4e-02</td>
          <td>2026-04-02T09:45:10.347112+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>0.058664</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>5e6e445b2dc06d315aa34ea7f134df02</th>
          <td>lr-regularization-4.0e-02</td>
          <td>2026-04-02T09:45:10.488758+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>0.073961</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>0a7f6d731bff33ec7f9afe73952c3385</th>
          <td>lr-regularization-1.2e-01</td>
          <td>2026-04-02T09:45:10.614921+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>0.060026</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>6d0d2e106414a5d4f3e2e0fa67c0375c</th>
          <td>lr-regularization-3.4e-01</td>
          <td>2026-04-02T09:45:10.756787+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>0.061970</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>4691e621be223bfa183d0cc6354b7330</th>
          <td>lr-regularization-1.0e+00</td>
          <td>2026-04-02T09:45:10.968552+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>0.112566</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>3aa766701a6290d7b9024eccca2ad751</th>
          <td>lr-regularization-2.9e+00</td>
          <td>2026-04-02T09:45:11.103412+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>0.058800</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>7a6061054b4f7e2d88ecd45b1b5f5f7a</th>
          <td>lr-regularization-8.6e+00</td>
          <td>2026-04-02T09:45:11.242869+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>0.061946</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>dc7c4fea5042890f3212c1421c6b92a2</th>
          <td>lr-regularization-2.5e+01</td>
          <td>2026-04-02T09:45:11.474981+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>0.113050</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>d5014c9b5099ad62dee8175eb645fcda</th>
          <td>lr-regularization-7.4e+01</td>
          <td>2026-04-02T09:45:11.694812+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>0.113761</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>c316ff197d97c41f7d5097c97fc7d661</th>
          <td>lr-regularization-2.2e+02</td>
          <td>2026-04-02T09:45:11.938019+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>0.121294</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>de7dcf8e1e2dd20c1160b29c14770220</th>
          <td>lr-regularization-6.3e+02</td>
          <td>2026-04-02T09:45:12.182035+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>0.122729</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>ff0ff147c5f4840caf38a98784180128</th>
          <td>lr-regularization-1.8e+03</td>
          <td>2026-04-02T09:45:12.420282+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.284873</td>
          <td>0.990473</td>
          <td>0.117328</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>c5758aba3db34c94d6718d3fd016572c</th>
          <td>lr-regularization-5.4e+03</td>
          <td>2026-04-02T09:45:12.566418+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.471375</td>
          <td>0.989838</td>
          <td>0.065183</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>62cc5e2027cd5a454265097732cc4580</th>
          <td>lr-regularization-1.6e+04</td>
          <td>2026-04-02T09:45:12.705363+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.545335</td>
          <td>0.991108</td>
          <td>0.072387</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>74a1970587c236336167541bf05110b8</th>
          <td>lr-regularization-4.6e+04</td>
          <td>2026-04-02T09:45:12.845980+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>0.934770</td>
          <td>0.983963</td>
          <td>0.074678</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>b86a94323efcaa0ce3d87ab21b2814d3</th>
          <td>lr-regularization-1.4e+05</td>
          <td>2026-04-02T09:45:12.988707+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.246488</td>
          <td>0.985551</td>
          <td>0.076210</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>2c46f17e7ef508634d408676e0d1c929</th>
          <td>lr-regularization-4.0e+05</td>
          <td>2026-04-02T09:45:13.132949+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.336316</td>
          <td>0.985551</td>
          <td>0.076458</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>cc70e300d3b7a9562cb38c061b0f89a3</th>
          <td>lr-regularization-1.2e+06</td>
          <td>2026-04-02T09:45:13.271350+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.670465</td>
          <td>0.975865</td>
          <td>0.072319</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>38c0fb37e971a9c852ec4015dad1dbff</th>
          <td>lr-regularization-3.4e+06</td>
          <td>2026-04-02T09:45:13.412681+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.605194</td>
          <td>0.975865</td>
          <td>0.074787</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>7310fec94e5fc975ab3b0c8f473b79df</th>
          <td>lr-regularization-1.0e+07</td>
          <td>2026-04-02T09:45:13.551545+00:00</td>
          <td>LogisticRegression</td>
          <td>binary-classification</td>
          <td>estimator</td>
          <td>a36fcd73fd752ec31662e617f0f6e127</td>
          <td>None</td>
          <td>1.875108</td>
          <td>0.975548</td>
          <td>0.072363</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, 'cce3ae2e27e68a514cac89b53cde05b4') to (30, '7310fec94e5fc975ab3b0c8f473b79df')
    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>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>0.060026</td>
          <td>0.061970</td>
          <td>0.112566</td>
          <td>0.058800</td>
        </tr>
        <tr>
          <th>Predict time (s)</th>
          <td>0.035935</td>
          <td>0.034916</td>
          <td>0.062869</td>
          <td>0.035129</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 5.912 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>`_