.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/08_join_aggregation.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_08_join_aggregation.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_08_join_aggregation.py:


Self-aggregation on MovieLens
=============================

MovieLens is a famous movie dataset used for both explicit
and implicit recommender systems. It provides a main table,
"ratings", that can be viewed as logs or transactions, comprised
of only 4 columns: ``userId``, ``movieId``, ``rating`` and ``timestamp``.
MovieLens also gives a contextual table "movies", including
``movieId``, ``title`` and ``types``, to enable content-based feature extraction.

From the perspective of machine-learning pipelines, one challenge is to
transform the transaction log into features that can be fed to supervised learning.

In this notebook, we only deal with the main table "ratings".
Our objective is **not to achieve state-of-the-art performance** on
the explicit regression task, but rather to illustrate how to perform
feature engineering in a simple way using |AggJoiner| and |AggTarget|.
Note that our performance is higher than the baseline of using the mean
rating per movies.

The benefit of using  |AggJoiner| and |AggTarget| is that they readily
provide a full pipeline, from the original tables to the prediction, that can
be cross-validated or applied to new data to serve prediction. At the end of
this example, we showcase hyper-parameter optimization on the whole pipeline.


.. |AggJoiner| replace::
     :class:`~skrub.AggJoiner`

.. |AggTarget| replace::
     :class:`~skrub.AggTarget`

.. |TableVectorizer| replace::
     :class:`~skrub.TableVectorizer`

.. |DatetimeEncoder| replace::
     :class:`~skrub.DatetimeEncoder`

.. |TargetEncoder| replace::
     :class:`~sklearn.preprocessing.TargetEncoder`

.. |make_pipeline| replace::
     :class:`~sklearn.pipeline.make_pipeline`

.. |Pipeline| replace::
     :class:`~sklearn.pipeline.Pipeline`

.. |GridSearchCV| replace::
     :class:`~sklearn.model_selection.GridSearchCV`

.. |TimeSeriesSplit| replace::
     :class:`~sklearn.model_selection.TimeSeriesSplit`

.. |HGBR| replace::
     :class:`~sklearn.ensemble.HistGradientBoostingRegressor`

.. GENERATED FROM PYTHON SOURCE LINES 60-65

The data
--------

We begin with loading the ratings table from MovieLens.
Note that we use the light version (100k rows).

.. GENERATED FROM PYTHON SOURCE LINES 65-76

.. code-block:: Python

    import pandas as pd

    from skrub.datasets import fetch_movielens

    ratings = fetch_movielens(dataset_id="ratings")
    ratings = ratings.X.sort_values("timestamp").reset_index(drop=True)
    ratings["timestamp"] = pd.to_datetime(ratings["timestamp"], unit="s")

    X = ratings[["userId", "movieId", "timestamp"]]
    y = ratings["rating"]
    X.shape, y.shape




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

 .. code-block:: none


    ((100836, 3), (100836,))



.. GENERATED FROM PYTHON SOURCE LINES 77-79

.. code-block:: Python

    X.head()






.. 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>userId</th>
          <th>movieId</th>
          <th>timestamp</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>429</td>
          <td>165</td>
          <td>1996-03-29 18:36:55</td>
        </tr>
        <tr>
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          <td>429</td>
          <td>161</td>
          <td>1996-03-29 18:36:55</td>
        </tr>
        <tr>
          <th>2</th>
          <td>429</td>
          <td>150</td>
          <td>1996-03-29 18:36:55</td>
        </tr>
        <tr>
          <th>3</th>
          <td>429</td>
          <td>22</td>
          <td>1996-03-29 18:36:55</td>
        </tr>
        <tr>
          <th>4</th>
          <td>429</td>
          <td>432</td>
          <td>1996-03-29 18:36:55</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 80-86

Encoding the timestamp with a TableVectorizer
---------------------------------------------

Our first step is to extract features from the timestamp, using the
|TableVectorizer|. Natively, it uses the |DatetimeEncoder| on datetime
columns, and doesn't interact with numerical columns.

.. GENERATED FROM PYTHON SOURCE LINES 86-92

.. code-block:: Python

    from skrub import DatetimeEncoder, TableVectorizer

    table_vectorizer = TableVectorizer(datetime=DatetimeEncoder(add_weekday=True))
    X_date_encoded = table_vectorizer.fit_transform(X)
    X_date_encoded.head()






.. 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>userId</th>
          <th>movieId</th>
          <th>timestamp_year</th>
          <th>timestamp_month</th>
          <th>timestamp_day</th>
          <th>timestamp_hour</th>
          <th>timestamp_total_seconds</th>
          <th>timestamp_weekday</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>429.0</td>
          <td>165.0</td>
          <td>1996.0</td>
          <td>3.0</td>
          <td>29.0</td>
          <td>18.0</td>
          <td>828124608.0</td>
          <td>5.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>429.0</td>
          <td>161.0</td>
          <td>1996.0</td>
          <td>3.0</td>
          <td>29.0</td>
          <td>18.0</td>
          <td>828124608.0</td>
          <td>5.0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>429.0</td>
          <td>150.0</td>
          <td>1996.0</td>
          <td>3.0</td>
          <td>29.0</td>
          <td>18.0</td>
          <td>828124608.0</td>
          <td>5.0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>429.0</td>
          <td>22.0</td>
          <td>1996.0</td>
          <td>3.0</td>
          <td>29.0</td>
          <td>18.0</td>
          <td>828124608.0</td>
          <td>5.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>429.0</td>
          <td>432.0</td>
          <td>1996.0</td>
          <td>3.0</td>
          <td>29.0</td>
          <td>18.0</td>
          <td>828124608.0</td>
          <td>5.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 93-94

We can now make a couple of plots and gain some insight on our dataset.

.. GENERATED FROM PYTHON SOURCE LINES 94-121

.. code-block:: Python

    import seaborn as sns
    from matplotlib import pyplot as plt

    sns.set_style("darkgrid")


    def make_barplot(x, y, title):
        fig, ax = plt.subplots(layout="constrained")
        norm = plt.Normalize(y.min(), y.max())
        cmap = plt.get_cmap("magma")

        sns.barplot(x=x, y=y, palette=cmap(norm(y)), ax=ax)
        ax.set_title(title)
        ax.set_xticks(ax.get_xticks(), labels=ax.get_xticklabels(), rotation=30)
        ax.set_ylabel(None)


    # O is Monday, 6 is Sunday

    daily_volume = X_date_encoded["timestamp_weekday"].value_counts().sort_index()

    make_barplot(
        x=daily_volume.index,
        y=daily_volume.values,
        title="Daily volume of ratings",
    )




.. image-sg:: /auto_examples/images/sphx_glr_08_join_aggregation_001.png
   :alt: Daily volume of ratings
   :srcset: /auto_examples/images/sphx_glr_08_join_aggregation_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/circleci/project/examples/08_join_aggregation.py:105: FutureWarning: 

    Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

      sns.barplot(x=x, y=y, palette=cmap(norm(y)), ax=ax)
    /home/circleci/project/examples/08_join_aggregation.py:105: UserWarning: Numpy array is not a supported type for `palette`. Please convert your palette to a list. This will become an error in v0.14
      sns.barplot(x=x, y=y, palette=cmap(norm(y)), ax=ax)




.. GENERATED FROM PYTHON SOURCE LINES 122-123

We also display the distribution of our target ``y``.

.. GENERATED FROM PYTHON SOURCE LINES 123-132

.. code-block:: Python

    rating_count = y.value_counts().sort_index()

    make_barplot(
        x=rating_count.index,
        y=rating_count.values,
        title="Distribution of ratings given to movies",
    )





.. image-sg:: /auto_examples/images/sphx_glr_08_join_aggregation_002.png
   :alt: Distribution of ratings given to movies
   :srcset: /auto_examples/images/sphx_glr_08_join_aggregation_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/circleci/project/examples/08_join_aggregation.py:105: FutureWarning: 

    Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

      sns.barplot(x=x, y=y, palette=cmap(norm(y)), ax=ax)
    /home/circleci/project/examples/08_join_aggregation.py:105: UserWarning: Numpy array is not a supported type for `palette`. Please convert your palette to a list. This will become an error in v0.14
      sns.barplot(x=x, y=y, palette=cmap(norm(y)), ax=ax)




.. GENERATED FROM PYTHON SOURCE LINES 133-156

AggTarget: aggregate y, then join
---------------------------------

We have just extracted datetime features from timestamps.

Let's now perform an expansion for the target ``y``, by aggregating it before
joining it back on the main table. The biggest risk of doing target expansion
with multiple dataframe operations yourself is to end up leaking the target.

To solve this, the |AggTarget| transformer allows you to
aggregate the target ``y`` before joining it on the main table, without
risk of leaking. Note that to perform aggregation then joining on the features
``X``, you need to use |AggJoiner| instead.

You can also think of it as a generalization of the |TargetEncoder|, which
encodes categorical features based on the target.

We only focus on aggregating the target by **users**, but later we will
also consider aggregating by **movies**. Here, we compute the histogram of the
target with 3 bins, before joining it back on the initial table.

This feature answer questions like
*"How many times has this user given a bad, medium or good rate to movies?"*.

.. GENERATED FROM PYTHON SOURCE LINES 156-166

.. code-block:: Python

    from skrub import AggTarget

    agg_target_user = AggTarget(
        main_key="userId",
        suffix="_user",
        operation="hist(3)",
    )
    X_transformed = agg_target_user.fit_transform(X, y)

    X_transformed.shape




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

 .. code-block:: none


    (100836, 7)



.. GENERATED FROM PYTHON SOURCE LINES 167-169

.. code-block:: Python

    X_transformed.head()






.. 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>userId</th>
          <th>movieId</th>
          <th>timestamp</th>
          <th>index</th>
          <th>rating_(0.499, 2.0]_user</th>
          <th>rating_(2.0, 3.5]_user</th>
          <th>rating_(3.5, 5.0]_user</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>429</td>
          <td>165</td>
          <td>1996-03-29 18:36:55</td>
          <td>428</td>
          <td>2</td>
          <td>14</td>
          <td>42</td>
        </tr>
        <tr>
          <th>1</th>
          <td>429</td>
          <td>161</td>
          <td>1996-03-29 18:36:55</td>
          <td>428</td>
          <td>2</td>
          <td>14</td>
          <td>42</td>
        </tr>
        <tr>
          <th>2</th>
          <td>429</td>
          <td>150</td>
          <td>1996-03-29 18:36:55</td>
          <td>428</td>
          <td>2</td>
          <td>14</td>
          <td>42</td>
        </tr>
        <tr>
          <th>3</th>
          <td>429</td>
          <td>22</td>
          <td>1996-03-29 18:36:55</td>
          <td>428</td>
          <td>2</td>
          <td>14</td>
          <td>42</td>
        </tr>
        <tr>
          <th>4</th>
          <td>429</td>
          <td>432</td>
          <td>1996-03-29 18:36:55</td>
          <td>428</td>
          <td>2</td>
          <td>14</td>
          <td>42</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 170-174

Similarly, we join on ``movieId`` instead of ``userId``.

This feature answer questions like
*"How many times has this movie received a bad, medium or good rate from users?"*.

.. GENERATED FROM PYTHON SOURCE LINES 174-181

.. code-block:: Python

    agg_target_movie = AggTarget(
        main_key="movieId",
        suffix="_movie",
        operation="hist(3)",
    )
    X_transformed = agg_target_movie.fit_transform(X, y)
    X_transformed.shape




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

 .. code-block:: none


    (100836, 7)



.. GENERATED FROM PYTHON SOURCE LINES 182-184

.. code-block:: Python

    X_transformed.head()






.. 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>userId</th>
          <th>movieId</th>
          <th>timestamp</th>
          <th>index</th>
          <th>rating_(0.499, 2.0]_movie</th>
          <th>rating_(2.0, 3.5]_movie</th>
          <th>rating_(3.5, 5.0]_movie</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>429</td>
          <td>165</td>
          <td>1996-03-29 18:36:55</td>
          <td>138</td>
          <td>14</td>
          <td>64</td>
          <td>66</td>
        </tr>
        <tr>
          <th>1</th>
          <td>429</td>
          <td>161</td>
          <td>1996-03-29 18:36:55</td>
          <td>134</td>
          <td>8</td>
          <td>38</td>
          <td>57</td>
        </tr>
        <tr>
          <th>2</th>
          <td>429</td>
          <td>150</td>
          <td>1996-03-29 18:36:55</td>
          <td>123</td>
          <td>13</td>
          <td>61</td>
          <td>127</td>
        </tr>
        <tr>
          <th>3</th>
          <td>429</td>
          <td>22</td>
          <td>1996-03-29 18:36:55</td>
          <td>21</td>
          <td>4</td>
          <td>24</td>
          <td>8</td>
        </tr>
        <tr>
          <th>4</th>
          <td>429</td>
          <td>432</td>
          <td>1996-03-29 18:36:55</td>
          <td>376</td>
          <td>18</td>
          <td>29</td>
          <td>8</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 185-191

Chaining everything together in a pipeline
------------------------------------------

To perform cross-validation and enable hyper-parameter tuning, we gather
all elements into a scikit-learn |Pipeline| by using |make_pipeline|,
and define a scikit-learn |HGBR|.

.. GENERATED FROM PYTHON SOURCE LINES 191-203

.. code-block:: Python

    from sklearn.ensemble import HistGradientBoostingRegressor
    from sklearn.pipeline import make_pipeline

    pipeline = make_pipeline(
        table_vectorizer,
        agg_target_user,
        agg_target_movie,
        HistGradientBoostingRegressor(learning_rate=0.1, max_depth=4, max_iter=40),
    )

    pipeline






.. raw:: html

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      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
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        /* Redefinition of color scheme for dark theme */
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         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    #sk-container-id-6 div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
      background-position: center center;
    }

    /* Parallel-specific style estimator block */

    #sk-container-id-6 div.sk-parallel-item::after {
      content: "";
      width: 100%;
      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
      flex-grow: 1;
    }

    #sk-container-id-6 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-6 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-6 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-6 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-6 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-6 div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
      padding-left: 1em;
    }


    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
    clickable and can be expanded/collapsed.
    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-6 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

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    #sk-container-id-6 label.sk-toggleable__label {
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      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
    }

    #sk-container-id-6 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    #sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-6 div.sk-toggleable__content {
      max-height: 0;
      max-width: 0;
      overflow: hidden;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-6 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-6 div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-6 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      max-height: 200px;
      max-width: 100%;
      overflow: auto;
    }

    #sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-6 div.sk-label label.sk-toggleable__label,
    #sk-container-id-6 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-6 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      display: inline-block;
      line-height: 1.2em;
    }

    #sk-container-id-6 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-6 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-6 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-6 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-6 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 1ex;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
      color: var(--sklearn-color-unfitted-level-1);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-6 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-6 a.estimator_doc_link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-6 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-6 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;tablevectorizer&#x27;,
                     TableVectorizer(datetime=DatetimeEncoder(add_weekday=True))),
                    (&#x27;aggtarget-1&#x27;,
                     AggTarget(main_key=&#x27;userId&#x27;, operation=&#x27;hist(3)&#x27;,
                               suffix=&#x27;_user&#x27;)),
                    (&#x27;aggtarget-2&#x27;,
                     AggTarget(main_key=&#x27;movieId&#x27;, operation=&#x27;hist(3)&#x27;,
                               suffix=&#x27;_movie&#x27;)),
                    (&#x27;histgradientboostingregressor&#x27;,
                     HistGradientBoostingRegressor(max_depth=4, max_iter=40))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label  sk-toggleable__label-arrow ">&nbsp;&nbsp;Pipeline<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;tablevectorizer&#x27;,
                     TableVectorizer(datetime=DatetimeEncoder(add_weekday=True))),
                    (&#x27;aggtarget-1&#x27;,
                     AggTarget(main_key=&#x27;userId&#x27;, operation=&#x27;hist(3)&#x27;,
                               suffix=&#x27;_user&#x27;)),
                    (&#x27;aggtarget-2&#x27;,
                     AggTarget(main_key=&#x27;movieId&#x27;, operation=&#x27;hist(3)&#x27;,
                               suffix=&#x27;_movie&#x27;)),
                    (&#x27;histgradientboostingregressor&#x27;,
                     HistGradientBoostingRegressor(max_depth=4, max_iter=40))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label  sk-toggleable__label-arrow ">tablevectorizer: TableVectorizer</label><div class="sk-toggleable__content "><pre>TableVectorizer(datetime=DatetimeEncoder(add_weekday=True))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label  sk-toggleable__label-arrow ">datetime: DatetimeEncoder</label><div class="sk-toggleable__content "><pre>DatetimeEncoder(add_weekday=True)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label  sk-toggleable__label-arrow ">DatetimeEncoder</label><div class="sk-toggleable__content "><pre>DatetimeEncoder(add_weekday=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-38" type="checkbox" ><label for="sk-estimator-id-38" class="sk-toggleable__label  sk-toggleable__label-arrow ">high_cardinality: GapEncoder</label><div class="sk-toggleable__content "><pre>GapEncoder(n_components=30)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-39" type="checkbox" ><label for="sk-estimator-id-39" class="sk-toggleable__label  sk-toggleable__label-arrow ">GapEncoder</label><div class="sk-toggleable__content "><pre>GapEncoder(n_components=30)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-40" type="checkbox" ><label for="sk-estimator-id-40" class="sk-toggleable__label  sk-toggleable__label-arrow ">low_cardinality: OneHotEncoder</label><div class="sk-toggleable__content "><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;, dtype=&#x27;float32&#x27;, handle_unknown=&#x27;ignore&#x27;,
                  sparse_output=False)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label  sk-toggleable__label-arrow ">&nbsp;OneHotEncoder<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content "><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;, dtype=&#x27;float32&#x27;, handle_unknown=&#x27;ignore&#x27;,
                  sparse_output=False)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label  sk-toggleable__label-arrow ">numeric: PassThrough</label><div class="sk-toggleable__content "><pre>PassThrough()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label  sk-toggleable__label-arrow ">PassThrough</label><div class="sk-toggleable__content "><pre>PassThrough()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label  sk-toggleable__label-arrow ">AggTarget</label><div class="sk-toggleable__content "><pre>AggTarget(main_key=&#x27;userId&#x27;, operation=&#x27;hist(3)&#x27;, suffix=&#x27;_user&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-45" type="checkbox" ><label for="sk-estimator-id-45" class="sk-toggleable__label  sk-toggleable__label-arrow ">AggTarget</label><div class="sk-toggleable__content "><pre>AggTarget(main_key=&#x27;movieId&#x27;, operation=&#x27;hist(3)&#x27;, suffix=&#x27;_movie&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-46" type="checkbox" ><label for="sk-estimator-id-46" class="sk-toggleable__label  sk-toggleable__label-arrow ">&nbsp;HistGradientBoostingRegressor<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(max_depth=4, max_iter=40)</pre></div> </div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 204-216

Hyper-parameters tuning and cross validation
--------------------------------------------

We can finally create our hyper-parameter search space, and use a
|GridSearchCV|. We select the cross validation splitter to be
the |TimeSeriesSplit| to prevent leakage, since our data are timestamped
logs.

Note that you need the name of the pipeline elements to assign them
hyper-parameters search.

You can lookup the name of the pipeline elements by doing:

.. GENERATED FROM PYTHON SOURCE LINES 216-218

.. code-block:: Python

    list(pipeline.named_steps)





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

 .. code-block:: none


    ['tablevectorizer', 'aggtarget-1', 'aggtarget-2', 'histgradientboostingregressor']



.. GENERATED FROM PYTHON SOURCE LINES 219-224

Alternatively, you can use scikit-learn |Pipeline| to name your transformers:
``Pipeline([("agg_target_user", agg_target_user), ...])``

We now perform the grid search over the ``AggTarget`` transformers to find the
operation maximizing our validation score.

.. GENERATED FROM PYTHON SOURCE LINES 224-243

.. code-block:: Python

    from sklearn.model_selection import GridSearchCV, TimeSeriesSplit

    operations = ["mean", "hist(3)", "hist(5)", "hist(7)", "value_counts"]
    param_grid = [
        {
            "aggtarget-2__operation": [op],
        }
        for op in operations
    ]

    cv = GridSearchCV(pipeline, param_grid, cv=TimeSeriesSplit(n_splits=10))
    cv.fit(X, y)

    results = pd.DataFrame(cv.cv_results_)

    cols = [f"split{idx}_test_score" for idx in range(10)]
    results = results.set_index("param_aggtarget-2__operation")[cols].T
    results





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

 .. code-block:: none

    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_810cdf0b__
    Feature names seen at fit time, yet now missing:
    - index__skrub_e167678c__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_9f9696d7__
    Feature names seen at fit time, yet now missing:
    - index__skrub_4407c69f__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_a2515f49__
    Feature names seen at fit time, yet now missing:
    - index__skrub_59337b8a__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_ef51ebab__
    Feature names seen at fit time, yet now missing:
    - index__skrub_f0df999b__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_b10f184b__
    Feature names seen at fit time, yet now missing:
    - index__skrub_6d0ed8a6__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_604fdf98__
    Feature names seen at fit time, yet now missing:
    - index__skrub_111f8501__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_41ebad1d__
    Feature names seen at fit time, yet now missing:
    - index__skrub_64a1f497__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_5cc4f8c1__
    Feature names seen at fit time, yet now missing:
    - index__skrub_e8900710__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_865a1293__
    Feature names seen at fit time, yet now missing:
    - index__skrub_76873db3__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_7366f221__
    Feature names seen at fit time, yet now missing:
    - index__skrub_f59e789e__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_425bbf46__
    Feature names seen at fit time, yet now missing:
    - index__skrub_398f8277__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_8b780d1d__
    Feature names seen at fit time, yet now missing:
    - index__skrub_4e7b27bd__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_564a8f68__
    Feature names seen at fit time, yet now missing:
    - index__skrub_94146b12__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_9755619b__
    Feature names seen at fit time, yet now missing:
    - index__skrub_fa1258d7__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_a597b339__
    Feature names seen at fit time, yet now missing:
    - index__skrub_7dac1e94__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_9bcae509__
    Feature names seen at fit time, yet now missing:
    - index__skrub_0a697bad__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_776a5c59__
    Feature names seen at fit time, yet now missing:
    - index__skrub_5b9b5817__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_afc3f99b__
    Feature names seen at fit time, yet now missing:
    - index__skrub_828e5984__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_dda274dc__
    Feature names seen at fit time, yet now missing:
    - index__skrub_f9f9d4ef__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_c96a5c95__
    Feature names seen at fit time, yet now missing:
    - index__skrub_37a737c9__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_90761ca3__
    Feature names seen at fit time, yet now missing:
    - index__skrub_6bbeba03__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_ec150c55__
    Feature names seen at fit time, yet now missing:
    - index__skrub_2cade33d__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_2371788b__
    Feature names seen at fit time, yet now missing:
    - index__skrub_89e505b8__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_3efa16b7__
    Feature names seen at fit time, yet now missing:
    - index__skrub_140cf913__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_a4060719__
    Feature names seen at fit time, yet now missing:
    - index__skrub_ffb426a2__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_d93252f7__
    Feature names seen at fit time, yet now missing:
    - index__skrub_6fbfbbe1__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_eea4f81a__
    Feature names seen at fit time, yet now missing:
    - index__skrub_75682b04__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_8a9592f8__
    Feature names seen at fit time, yet now missing:
    - index__skrub_3d7e30c9__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_51a5b045__
    Feature names seen at fit time, yet now missing:
    - index__skrub_c973ccbd__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_06420b70__
    Feature names seen at fit time, yet now missing:
    - index__skrub_8de721b8__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_c76abfb1__
    Feature names seen at fit time, yet now missing:
    - index__skrub_0cf67b9e__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_64dddc45__
    Feature names seen at fit time, yet now missing:
    - index__skrub_946f4521__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_55ea7f79__
    Feature names seen at fit time, yet now missing:
    - index__skrub_7a894d21__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_14c5f4ec__
    Feature names seen at fit time, yet now missing:
    - index__skrub_f4bb9574__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_a809766f__
    Feature names seen at fit time, yet now missing:
    - index__skrub_16efe28a__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_aba9d923__
    Feature names seen at fit time, yet now missing:
    - index__skrub_c48929de__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_ee951e56__
    Feature names seen at fit time, yet now missing:
    - index__skrub_6aebee84__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_e4594399__
    Feature names seen at fit time, yet now missing:
    - index__skrub_f75016dc__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_6d430c5b__
    Feature names seen at fit time, yet now missing:
    - index__skrub_c8369667__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:982: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
    Traceback (most recent call last):
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py", line 971, in _score
        scores = scorer(estimator, X_test, y_test, **score_params)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/metrics/_scorer.py", line 455, in __call__
        return estimator.score(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/pipeline.py", line 1007, in score
        return self.steps[-1][1].score(Xt, y, **score_params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 848, in score
        y_pred = self.predict(X)
                 ^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1769, in predict
        return self._loss.link.inverse(self._raw_predict(X).ravel())
                                       ^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 1278, in _raw_predict
        X = self._preprocess_X(X, reset=False)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py", line 268, in _preprocess_X
        return self._validate_data(X, reset=False, **check_X_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 608, in _validate_data
        self._check_feature_names(X, reset=reset)
      File "/home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/base.py", line 535, in _check_feature_names
        raise ValueError(message)
    ValueError: The feature names should match those that were passed during fit.
    Feature names unseen at fit time:
    - index__skrub_a00b5363__
    Feature names seen at fit time, yet now missing:
    - index__skrub_6ecda470__


      warnings.warn(
    /home/circleci/project/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1052: UserWarning: One or more of the test scores are non-finite: [0.08466469        nan        nan        nan        nan]
      warnings.warn(


.. 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>param_aggtarget-2__operation</th>
          <th>mean</th>
          <th>hist(3)</th>
          <th>hist(5)</th>
          <th>hist(7)</th>
          <th>value_counts</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>split0_test_score</th>
          <td>0.034264</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split1_test_score</th>
          <td>0.051295</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split2_test_score</th>
          <td>0.088247</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split3_test_score</th>
          <td>0.046735</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split4_test_score</th>
          <td>0.143554</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split5_test_score</th>
          <td>0.105031</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split6_test_score</th>
          <td>0.079159</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split7_test_score</th>
          <td>0.065022</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split8_test_score</th>
          <td>0.106097</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>split9_test_score</th>
          <td>0.127243</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 244-253

The score used in this regression task is the R2. Remember that the R2
evaluates the relative performance compared to the naive baseline consisting
in always predicting the mean value of ``y_test``.
Therefore, the R2 is 0 when ``y_pred = y_true.mean()`` and is upper bounded
to 1 when ``y_pred = y_true``.

To get a better sense of the learning performances of our simple pipeline,
we also compute the average rating of each movie in the training set,
and uses this average to predict the ratings in the test set.

.. GENERATED FROM PYTHON SOURCE LINES 253-290

.. code-block:: Python

    from sklearn.metrics import r2_score


    def baseline_r2(X, y, train_idx, test_idx):
        """Compute the average rating for all movies in the train set,
        and map these averages to the test set as a prediction.

        If a movie in the test set is not present in the training set,
        we simply predict the global average rating of the training set.
        """
        X_train, y_train = X.iloc[train_idx].copy(), y.iloc[train_idx]
        X_test, y_test = X.iloc[test_idx], y.iloc[test_idx]

        X_train["y"] = y_train

        movie_avg_rating = X_train.groupby("movieId")["y"].mean().to_frame().reset_index()

        y_pred = X_test.merge(movie_avg_rating, on="movieId", how="left")["y"]
        y_pred = y_pred.fillna(y_pred.mean())

        return r2_score(y_true=y_test, y_pred=y_pred)


    all_baseline_r2 = []
    for train_idx, test_idx in TimeSeriesSplit(n_splits=10).split(X, y):
        all_baseline_r2.append(baseline_r2(X, y, train_idx, test_idx))

    results.insert(0, "naive mean estimator", all_baseline_r2)

    # we only keep the 5 out of 10 last results
    # because the initial size of the train set is rather small
    fig, ax = plt.subplots(layout="constrained")
    sns.boxplot(results.tail(5), palette="magma", ax=ax)
    ax.set_ylabel("R2 score")
    ax.set_title("Hyper parameters grid-search results")
    plt.tight_layout()




.. image-sg:: /auto_examples/images/sphx_glr_08_join_aggregation_003.png
   :alt: Hyper parameters grid-search results
   :srcset: /auto_examples/images/sphx_glr_08_join_aggregation_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/circleci/project/examples/08_join_aggregation.py:288: UserWarning: The figure layout has changed to tight
      plt.tight_layout()




.. GENERATED FROM PYTHON SOURCE LINES 291-303

The naive estimator has a lower performance than our pipeline, which means
that our extracted features brought some predictive power.

It seems that using the ``"value_counts"`` as an aggregation operator for
|AggTarget| yields better performances than using the mean (which is
equivalent to using the |TargetEncoder|).

Here, the number of bins encoding the target is proportional to the
performance: computing the mean yields a single statistic, whereas histograms
yield a density over a reduced set of bins, and ``"value_counts"`` yields an
exhaustive histogram over all the possible values of ratings
(here 10 different values, from 0.5 to 5).


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

   **Total running time of the script:** (0 minutes 20.361 seconds)


.. _sphx_glr_download_auto_examples_08_join_aggregation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/skrub-data/skrub/0.2.0?urlpath=lab/tree/notebooks/auto_examples/08_join_aggregation.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../lite/lab/?path=auto_examples/08_join_aggregation.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 08_join_aggregation.ipynb <08_join_aggregation.ipynb>`

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

      :download:`Download Python source code: 08_join_aggregation.py <08_join_aggregation.py>`


.. only:: html

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

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