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

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

.. _sphx_glr_auto_examples_getting_started_plot_skore_getting_started.py:


.. _example_skore_getting_started:

======================
Skore: getting started
======================

.. GENERATED FROM PYTHON SOURCE LINES 10-26

This getting started guide illustrates how to use skore and why:

#.    Get assistance when developing your ML/DS projects to avoid common pitfalls
      and follow recommended practices.

      *   :class:`skore.EstimatorReport`: get an insightful report on your estimator

      *   :class:`skore.CrossValidationReport`: get an insightful report on your
          cross-validation results

      *   :class:`skore.ComparisonReport`: benchmark your skore estimator reports

      *   :func:`skore.train_test_split`: get diagnostics when splitting your data

#.    Track your ML/DS results using skore's :class:`~skore.Project`
      (for storage).

.. GENERATED FROM PYTHON SOURCE LINES 28-34

Machine learning evaluation and diagnostics
===========================================

Skore implements new tools or wraps some key scikit-learn class / functions to
automatically provide insights and diagnostics when using them, as a way to
facilitate good practices and avoid common pitfalls.

.. GENERATED FROM PYTHON SOURCE LINES 36-44

Model evaluation with skore
^^^^^^^^^^^^^^^^^^^^^^^^^^^

In order to assist its users when programming, skore has implemented a
:class:`skore.EstimatorReport` class.

Let us load some synthetic data and get the estimator report for a
:class:`~sklearn.linear_model.LogisticRegression`:

.. GENERATED FROM PYTHON SOURCE LINES 46-61

.. code-block:: Python

    from sklearn.datasets import make_classification
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split

    from skore import EstimatorReport

    X, y = make_classification(n_classes=2, n_samples=100_000, n_informative=4)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    log_reg = LogisticRegression(random_state=0)

    log_reg_report = EstimatorReport(
        log_reg, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test
    )








.. GENERATED FROM PYTHON SOURCE LINES 62-64

Now, we can display the help tree to see all the insights that are available to us
(skore detected that we are doing binary classification):

.. GENERATED FROM PYTHON SOURCE LINES 66-68

.. code-block:: Python

    log_reg_report.help()





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

 .. code-block:: none

    ╭────────────────── Tools to diagnose estimator LogisticRegression ───────────────────╮
    │ EstimatorReport                                                                     │
    │ ├── .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 estimator's predictions.             │
    │ ├── .clear_cache(...)                  - Clear the cache.                           │
    │ └── Attributes                                                                      │
    │     ├── .X_test                                                                     │
    │     ├── .X_train                                                                    │
    │     ├── .y_test                                                                     │
    │     ├── .y_train                                                                    │
    │     ├── .estimator_                                                                 │
    │     └── .estimator_name_                                                            │
    │                                                                                     │
    │                                                                                     │
    │ Legend:                                                                             │
    │ (↗︎) higher is better (↘︎) lower is better                                            │
    ╰─────────────────────────────────────────────────────────────────────────────────────╯




.. GENERATED FROM PYTHON SOURCE LINES 69-70

We can get the report metrics that was computed for us:

.. GENERATED FROM PYTHON SOURCE LINES 72-75

.. code-block:: Python

    df_log_reg_report_metrics = log_reg_report.metrics.report_metrics()
    df_log_reg_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 th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>LogisticRegression</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th>Label / Average</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="2" valign="top">Precision</th>
          <th>0</th>
          <td>0.875310</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.871969</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">Recall</th>
          <th>0</th>
          <td>0.872449</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.874839</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <th></th>
          <td>0.944256</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <th></th>
          <td>0.092121</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 76-77

We can also plot the ROC curve that was generated for us:

.. GENERATED FROM PYTHON SOURCE LINES 79-85

.. code-block:: Python

    import matplotlib.pyplot as plt

    roc_plot = log_reg_report.metrics.roc()
    roc_plot.plot()
    plt.tight_layout()




.. image-sg:: /auto_examples/getting_started/images/sphx_glr_plot_skore_getting_started_001.png
   :alt: plot skore getting started
   :srcset: /auto_examples/getting_started/images/sphx_glr_plot_skore_getting_started_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 86-90

.. seealso::

  For more information about the motivation and usage of
  :class:`skore.EstimatorReport`, see :ref:`example_estimator_report`.

.. GENERATED FROM PYTHON SOURCE LINES 93-98

Cross-validation with skore
^^^^^^^^^^^^^^^^^^^^^^^^^^^

skore has also (re-)implemented a :class:`skore.CrossValidationReport` class that
contains several :class:`skore.EstimatorReport` for each fold.

.. GENERATED FROM PYTHON SOURCE LINES 100-104

.. code-block:: Python

    from skore import CrossValidationReport

    cv_report = CrossValidationReport(log_reg, X, y, cv_splitter=5)








.. GENERATED FROM PYTHON SOURCE LINES 105-106

We display the cross-validation report helper:

.. GENERATED FROM PYTHON SOURCE LINES 108-110

.. 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                                             │
    ╰──────────────────────────────────────────────────────────────────────────────────────╯




.. GENERATED FROM PYTHON SOURCE LINES 111-112

We display the metrics for each fold:

.. GENERATED FROM PYTHON SOURCE LINES 114-117

.. 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>0.872391</td>
          <td>0.875463</td>
          <td>0.874875</td>
          <td>0.877140</td>
          <td>0.875163</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.876635</td>
          <td>0.873640</td>
          <td>0.872531</td>
          <td>0.867285</td>
          <td>0.870365</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">Recall</th>
          <th>0</th>
          <td>0.877449</td>
          <td>0.873451</td>
          <td>0.872251</td>
          <td>0.865654</td>
          <td>0.869652</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.871549</td>
          <td>0.875650</td>
          <td>0.875150</td>
          <td>0.878651</td>
          <td>0.875850</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <th></th>
          <td>0.946881</td>
          <td>0.945183</td>
          <td>0.944682</td>
          <td>0.945068</td>
          <td>0.945238</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <th></th>
          <td>0.090180</td>
          <td>0.091689</td>
          <td>0.092021</td>
          <td>0.092021</td>
          <td>0.091441</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 118-119

We display the ROC curves for each fold:

.. GENERATED FROM PYTHON SOURCE LINES 121-125

.. code-block:: Python

    roc_plot_cv = cv_report.metrics.roc()
    roc_plot_cv.plot()
    plt.tight_layout()




.. image-sg:: /auto_examples/getting_started/images/sphx_glr_plot_skore_getting_started_002.png
   :alt: plot skore getting started
   :srcset: /auto_examples/getting_started/images/sphx_glr_plot_skore_getting_started_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 126-128

We can retrieve the estimator report of a specific fold to investigate further,
for example the first fold:

.. GENERATED FROM PYTHON SOURCE LINES 130-134

.. code-block:: Python

    log_reg_report_fold = cv_report.estimator_reports_[0]
    df_log_reg_report_fold_metrics = log_reg_report_fold.metrics.report_metrics()
    df_log_reg_report_fold_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 th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>LogisticRegression</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th>Label / Average</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="2" valign="top">Precision</th>
          <th>0</th>
          <td>0.872391</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.876635</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">Recall</th>
          <th>0</th>
          <td>0.877449</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.871549</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <th></th>
          <td>0.946881</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <th></th>
          <td>0.090180</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 135-139

.. seealso::

  For more information about the motivation and usage of
  :class:`skore.CrossValidationReport`, see :ref:`example_use_case_employee_salaries`.

.. GENERATED FROM PYTHON SOURCE LINES 141-149

Comparing estimators reports
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

:class:`skore.ComparisonReport` enables users to compare several estimator reports
(corresponding to several estimators) on a same test set, as in a benchmark of
estimators.

Apart from the previous ``log_reg_report``, let use define another estimator report:

.. GENERATED FROM PYTHON SOURCE LINES 151-158

.. code-block:: Python

    from sklearn.ensemble import RandomForestClassifier

    rf = RandomForestClassifier(max_depth=2, random_state=0)
    rf_report = EstimatorReport(
        rf, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test
    )








.. GENERATED FROM PYTHON SOURCE LINES 159-161

Now, let us compare these two estimator reports, that were applied to the exact
same test set:

.. GENERATED FROM PYTHON SOURCE LINES 163-167

.. code-block:: Python

    from skore import ComparisonReport

    comparison_report = ComparisonReport(reports=[log_reg_report, rf_report])








.. GENERATED FROM PYTHON SOURCE LINES 168-170

As for the :class:`~skore.EstimatorReport` and the
:class:`~skore.CrossValidationReport`, we have a helper:

.. GENERATED FROM PYTHON SOURCE LINES 172-174

.. code-block:: Python

    comparison_report.help()





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

 .. code-block:: none

    ╭──────────────────────────── Tools to compare estimators ─────────────────────────────╮
    │ ComparisonReport                                                                     │
    │ ├── .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 the estimators. │
    │ ├── .cache_predictions(...)            - Cache the predictions for sub-estimators    │
    │ │   reports.                                                                         │
    │ ├── .clear_cache(...)                  - Clear the cache.                            │
    │ └── Attributes                                                                       │
    │     ├── .estimator_reports_                                                          │
    │     ├── .n_jobs                                                                      │
    │     └── .report_names_                                                               │
    │                                                                                      │
    │                                                                                      │
    │ Legend:                                                                              │
    │ (↗︎) higher is better (↘︎) lower is better                                             │
    ╰──────────────────────────────────────────────────────────────────────────────────────╯




.. GENERATED FROM PYTHON SOURCE LINES 175-176

Let us display the result of our benchmark:

.. GENERATED FROM PYTHON SOURCE LINES 178-181

.. code-block:: Python

    benchmark_metrics = comparison_report.metrics.report_metrics()
    benchmark_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 th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Estimator</th>
          <th>LogisticRegression</th>
          <th>RandomForestClassifier</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th>Label / Average</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="2" valign="top">Precision</th>
          <th>0</th>
          <td>0.875310</td>
          <td>0.830577</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.871969</td>
          <td>0.861436</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">Recall</th>
          <th>0</th>
          <td>0.872449</td>
          <td>0.868782</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.874839</td>
          <td>0.821532</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <th></th>
          <td>0.944256</td>
          <td>0.919813</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <th></th>
          <td>0.092121</td>
          <td>0.149600</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 182-183

We have the result of our benchmark.

.. GENERATED FROM PYTHON SOURCE LINES 185-187

We display the ROC curve for the two estimator reports we want to compare, by
superimposing them on the same figure:

.. GENERATED FROM PYTHON SOURCE LINES 189-193

.. code-block:: Python

    comparison_report.metrics.roc().plot()
    plt.tight_layout()





.. image-sg:: /auto_examples/getting_started/images/sphx_glr_plot_skore_getting_started_003.png
   :alt: plot skore getting started
   :srcset: /auto_examples/getting_started/images/sphx_glr_plot_skore_getting_started_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 194-201

Train-test split with skore
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Skore has implemented a :func:`skore.train_test_split` function that wraps
scikit-learn's :func:`sklearn.model_selection.train_test_split`.

Let us load a dataset containing some time series data:

.. GENERATED FROM PYTHON SOURCE LINES 203-211

.. code-block:: Python

    import pandas as pd
    from skrub.datasets import fetch_employee_salaries

    dataset = fetch_employee_salaries()
    X, y = dataset.X, dataset.y
    X["date_first_hired"] = pd.to_datetime(X["date_first_hired"])
    X.head(2)





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

 .. code-block:: none

    Downloading 'employee_salaries' from https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/employee_salaries.zip (attempt 1/3)


.. 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>gender</th>
          <th>department</th>
          <th>department_name</th>
          <th>division</th>
          <th>assignment_category</th>
          <th>employee_position_title</th>
          <th>date_first_hired</th>
          <th>year_first_hired</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>F</td>
          <td>POL</td>
          <td>Department of Police</td>
          <td>MSB Information Mgmt and Tech Division Records...</td>
          <td>Fulltime-Regular</td>
          <td>Office Services Coordinator</td>
          <td>1986-09-22</td>
          <td>1986</td>
        </tr>
        <tr>
          <th>1</th>
          <td>M</td>
          <td>POL</td>
          <td>Department of Police</td>
          <td>ISB Major Crimes Division Fugitive Section</td>
          <td>Fulltime-Regular</td>
          <td>Master Police Officer</td>
          <td>1988-09-12</td>
          <td>1988</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 212-214

We can observe that there is a ``date_first_hired`` which is time-based.
Now, let us apply :func:`skore.train_test_split` on this data:

.. GENERATED FROM PYTHON SOURCE LINES 216-222

.. code-block:: Python

    import skore

    X_train, X_test, y_train, y_test = skore.train_test_split(
        X, y, random_state=0, shuffle=False
    )





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

 .. code-block:: none

    ╭─────────────────────────────── TimeBasedColumnWarning ───────────────────────────────╮
    │ We detected some time-based columns (column "date_first_hired") in your data. We     │
    │ recommend using scikit-learn's TimeSeriesSplit instead of train_test_split.          │
    │ Otherwise you might train on future data to predict the past, or get inflated model  │
    │ performance evaluation because natural drift will not be taken into account.         │
    ╰──────────────────────────────────────────────────────────────────────────────────────╯




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We get a ``TimeBasedColumnWarning`` advising us to use
:class:`sklearn.model_selection.TimeSeriesSplit` instead!
Indeed, we should not shuffle time-ordered data!

.. GENERATED FROM PYTHON SOURCE LINES 228-233

.. seealso::

  More methodological advice is available.
  For more information about the motivation and usage of
  :func:`skore.train_test_split`, see :ref:`example_train_test_split`.

.. GENERATED FROM PYTHON SOURCE LINES 235-240

Tracking: skore project
=======================

A key feature of skore is its :class:`~skore.Project` that allows to store
items of many types.

.. GENERATED FROM PYTHON SOURCE LINES 242-244

Setup: creating and loading a skore project
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 246-248

Let's start by creating a skore project directory named ``my_project.skore`` in our
current directory:

.. GENERATED FROM PYTHON SOURCE LINES 250-254

.. code-block:: Python


    my_project = skore.Project("my_project")









.. GENERATED FROM PYTHON SOURCE LINES 264-269

Skore project: storing and retrieving some items
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Now that the project exists, we can store some useful items in it (in the same
directory) using :func:`~skore.Project.put`), with a "universal" key-value convention:

.. GENERATED FROM PYTHON SOURCE LINES 271-275

.. code-block:: Python

    my_project.put("my_int", 3)
    my_project.put("df_cv_report_metrics", df_cv_report_metrics)
    my_project.put("roc_plot", roc_plot)








.. GENERATED FROM PYTHON SOURCE LINES 276-280

.. note ::
  With the skore :func:`~skore.Project.put`, there is no need to remember the API for
  each type of object: ``df.to_csv(...)``, ``plt.savefig(...)``, ``np.save(...)``,
  etc.

.. GENERATED FROM PYTHON SOURCE LINES 282-283

We can retrieve the value of an item:

.. GENERATED FROM PYTHON SOURCE LINES 285-287

.. code-block:: Python

    my_project.get("my_int")





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

 .. code-block:: none


    3



.. GENERATED FROM PYTHON SOURCE LINES 288-290

.. code-block:: Python

    my_project.get("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 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>0.872391</td>
          <td>0.875463</td>
          <td>0.874875</td>
          <td>0.877140</td>
          <td>0.875163</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.876635</td>
          <td>0.873640</td>
          <td>0.872531</td>
          <td>0.867285</td>
          <td>0.870365</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">Recall</th>
          <th>0</th>
          <td>0.877449</td>
          <td>0.873451</td>
          <td>0.872251</td>
          <td>0.865654</td>
          <td>0.869652</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.871549</td>
          <td>0.875650</td>
          <td>0.875150</td>
          <td>0.878651</td>
          <td>0.875850</td>
        </tr>
        <tr>
          <th>ROC AUC</th>
          <th></th>
          <td>0.946881</td>
          <td>0.945183</td>
          <td>0.944682</td>
          <td>0.945068</td>
          <td>0.945238</td>
        </tr>
        <tr>
          <th>Brier score</th>
          <th></th>
          <td>0.090180</td>
          <td>0.091689</td>
          <td>0.092021</td>
          <td>0.092021</td>
          <td>0.091441</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 291-296

.. seealso::

  For more information about the functionalities and the different types
  of items that we can store in a skore :class:`~skore.Project`,
  see :ref:`example_working_with_projects`.

.. GENERATED FROM PYTHON SOURCE LINES 298-300

Tracking the history of items
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 302-303

Suppose we store several values for a same item called ``my_key_metric``:

.. GENERATED FROM PYTHON SOURCE LINES 305-311

.. code-block:: Python

    my_project.put("my_key_metric", 4)

    my_project.put("my_key_metric", 9)

    my_project.put("my_key_metric", 16)








.. GENERATED FROM PYTHON SOURCE LINES 312-314

Skore does not overwrite items with the same name (key): instead, it stores
their history so that nothing is lost:

.. GENERATED FROM PYTHON SOURCE LINES 316-319

.. code-block:: Python

    history = my_project.get("my_key_metric", version="all")
    history





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

 .. code-block:: none


    [4, 9, 16]



.. GENERATED FROM PYTHON SOURCE LINES 323-327

These tracking functionalities are very useful to:

* never lose some key machine learning metrics,
* and observe the evolution over time / runs.

.. GENERATED FROM PYTHON SOURCE LINES 329-333

.. seealso::

  For more functionalities about the tracking of items using their history,
  see :ref:`example_tracking_items`.

.. GENERATED FROM PYTHON SOURCE LINES 335-343

.. admonition:: Stay tuned!

  These are only the initial features: skore is a work in progress and aims to be
  an end-to-end library for data scientists.

  Feedbacks are welcome: please feel free to join our
  `Discord <http://discord.probabl.ai>`_ or
  `create an issue <https://github.com/probabl-ai/skore/issues>`_.


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

   **Total running time of the script:** (0 minutes 9.853 seconds)


.. _sphx_glr_download_auto_examples_getting_started_plot_skore_getting_started.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_getting_started.ipynb <plot_skore_getting_started.ipynb>`

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

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

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

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


.. only:: html

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

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