.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/getting_started/plot_quick_start.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_getting_started_plot_quick_start.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_getting_started_plot_quick_start.py:
.. _example_quick_start:
===========
Quick start
===========
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Machine learning evaluation and diagnostics
===========================================
Evaluate your model using skore's :class:`~skore.CrossValidationReport`:
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.. code-block:: Python
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from skore import CrossValidationReport
X, y = make_classification(n_classes=2, n_samples=100_000, n_informative=4)
clf = LogisticRegression()
cv_report = CrossValidationReport(clf, X, y)
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Display the help tree to see all the insights that are available to you (skore
detected that you are doing binary classification):
.. GENERATED FROM PYTHON SOURCE LINES 31-33
.. code-block:: Python
cv_report.help()
.. rst-class:: sphx-glr-script-out
.. code-block:: none
╭─────────────────── Tools to diagnose estimator LogisticRegression ───────────────────╮
│ CrossValidationReport │
│ ├── .metrics │
│ │ ├── .accuracy(...) (↗︎) - Compute the accuracy score. │
│ │ ├── .brier_score(...) (↘︎) - Compute the Brier score. │
│ │ ├── .log_loss(...) (↘︎) - Compute the log loss. │
│ │ ├── .precision(...) (↗︎) - Compute the precision score. │
│ │ ├── .precision_recall(...) - Plot the precision-recall curve. │
│ │ ├── .recall(...) (↗︎) - Compute the recall score. │
│ │ ├── .roc(...) - Plot the ROC curve. │
│ │ ├── .roc_auc(...) (↗︎) - Compute the ROC AUC score. │
│ │ ├── .custom_metric(...) - Compute a custom metric. │
│ │ └── .report_metrics(...) - Report a set of metrics for our estimator. │
│ ├── .cache_predictions(...) - Cache the predictions for sub-estimators │
│ │ reports. │
│ ├── .clear_cache(...) - Clear the cache. │
│ └── Attributes │
│ ├── .X │
│ ├── .y │
│ ├── .estimator_ │
│ ├── .estimator_name_ │
│ ├── .estimator_reports_ │
│ └── .n_jobs │
│ │
│ │
│ Legend: │
│ (↗︎) higher is better (↘︎) lower is better │
╰──────────────────────────────────────────────────────────────────────────────────────╯
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Display the report metrics that was computed for you:
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.. code-block:: Python
df_cv_report_metrics = cv_report.metrics.report_metrics()
df_cv_report_metrics
.. raw:: html
<div class="output_subarea output_html rendered_html output_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead tr th {
text-align: left;
}
.dataframe thead tr:last-of-type th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th></th>
<th colspan="5" halign="left">LogisticRegression</th>
</tr>
<tr>
<th></th>
<th></th>
<th>Split #0</th>
<th>Split #1</th>
<th>Split #2</th>
<th>Split #3</th>
<th>Split #4</th>
</tr>
<tr>
<th>Metric</th>
<th>Label / Average</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="2" valign="top">Precision</th>
<th>0</th>
<td>0.746546</td>
<td>0.744155</td>
<td>0.740818</td>
<td>0.751271</td>
<td>0.747534</td>
</tr>
<tr>
<th>1</th>
<td>0.719169</td>
<td>0.728469</td>
<td>0.723992</td>
<td>0.724621</td>
<td>0.725310</td>
</tr>
<tr>
<th rowspan="2" valign="top">Recall</th>
<th>0</th>
<td>0.702570</td>
<td>0.719372</td>
<td>0.714000</td>
<td>0.709200</td>
<td>0.712400</td>
</tr>
<tr>
<th>1</th>
<td>0.761524</td>
<td>0.752725</td>
<td>0.750200</td>
<td>0.765200</td>
<td>0.759400</td>
</tr>
<tr>
<th>ROC AUC</th>
<th></th>
<td>0.800497</td>
<td>0.798113</td>
<td>0.798477</td>
<td>0.802839</td>
<td>0.801416</td>
</tr>
<tr>
<th>Brier score</th>
<th></th>
<td>0.181144</td>
<td>0.182536</td>
<td>0.182259</td>
<td>0.180057</td>
<td>0.181266</td>
</tr>
</tbody>
</table>
</div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 41-42
Display the ROC curve that was generated for you:
.. GENERATED FROM PYTHON SOURCE LINES 44-50
.. code-block:: Python
import matplotlib.pyplot as plt
roc_plot = cv_report.metrics.roc()
roc_plot.plot()
plt.tight_layout()
.. image-sg:: /auto_examples/getting_started/images/sphx_glr_plot_quick_start_001.png
:alt: plot quick start
:srcset: /auto_examples/getting_started/images/sphx_glr_plot_quick_start_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 51-53
Skore project: storing some items
=================================
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From your Python code, create and load a skore :class:`~skore.Project`:
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.. code-block:: Python
import skore
my_project = skore.Project("my_project")
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This will create a skore project directory named ``my_project.skore`` in your
current working directory.
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Store some previous results in the skore project for safe-keeping:
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.. code-block:: Python
my_project.put("df_cv_report_metrics", df_cv_report_metrics)
my_project.put("roc_plot", roc_plot)
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Retrieve what was stored:
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.. code-block:: Python
df_get = my_project.get("df_cv_report_metrics")
df_get
.. 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>(LogisticRegression, Split #0)</th>
<th>(LogisticRegression, Split #1)</th>
<th>(LogisticRegression, Split #2)</th>
<th>(LogisticRegression, Split #3)</th>
<th>(LogisticRegression, Split #4)</th>
</tr>
<tr>
<th>Metric</th>
<th>Label / Average</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="2" valign="top">Precision</th>
<th>0</th>
<td>0.746546</td>
<td>0.744155</td>
<td>0.740818</td>
<td>0.751271</td>
<td>0.747534</td>
</tr>
<tr>
<th>1</th>
<td>0.719169</td>
<td>0.728469</td>
<td>0.723992</td>
<td>0.724621</td>
<td>0.725310</td>
</tr>
<tr>
<th rowspan="2" valign="top">Recall</th>
<th>0</th>
<td>0.702570</td>
<td>0.719372</td>
<td>0.714000</td>
<td>0.709200</td>
<td>0.712400</td>
</tr>
<tr>
<th>1</th>
<td>0.761524</td>
<td>0.752725</td>
<td>0.750200</td>
<td>0.765200</td>
<td>0.759400</td>
</tr>
<tr>
<th>ROC AUC</th>
<th></th>
<td>0.800497</td>
<td>0.798113</td>
<td>0.798477</td>
<td>0.802839</td>
<td>0.801416</td>
</tr>
<tr>
<th>Brier score</th>
<th></th>
<td>0.181144</td>
<td>0.182536</td>
<td>0.182259</td>
<td>0.180057</td>
<td>0.181266</td>
</tr>
</tbody>
</table>
</div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 93-96
.. admonition:: What's next?
For a more in-depth guide, see our :ref:`example_skore_getting_started` page!
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 1.685 seconds)
.. _sphx_glr_download_auto_examples_getting_started_plot_quick_start.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_quick_start.ipynb <plot_quick_start.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_quick_start.py <plot_quick_start.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: plot_quick_start.zip <plot_quick_start.zip>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_