From b4e1e775345c35c53d54a5b851d0ca36b85b63cc Mon Sep 17 00:00:00 2001
From: Vincent M <maladiere.vincent@yahoo.fr>
Date: Wed, 2 Oct 2024 12:11:20 +0200
Subject: [PATCH] DOC Replace the MovieLens dataset with the fraud dataset
 (#1053)

---
 CHANGES.rst                                |   4 +
 doc/_static/08_example_aggjoiner.png       | Bin 0 -> 108510 bytes
 doc/_static/08_example_data.png            | Bin 0 -> 39012 bytes
 doc/conf.py                                |   1 +
 doc/reference/downloading_a_dataset.rst    |   1 +
 examples/08_join_aggregation.py            | 532 ++++++++++---------
 examples/FIXME/08_join_aggregation_full.py | 548 +++++++++++++++++++
 pixi.lock                                  | 585 +++++++++++++++++----
 pyproject.toml                             |   3 +-
 skrub/datasets/__init__.py                 |   2 +
 skrub/datasets/_fetching.py                |  43 ++
 skrub/datasets/tests/test_fetching.py      |  38 ++
 12 files changed, 1401 insertions(+), 356 deletions(-)
 create mode 100644 doc/_static/08_example_aggjoiner.png
 create mode 100644 doc/_static/08_example_data.png
 create mode 100644 examples/FIXME/08_join_aggregation_full.py

diff --git a/CHANGES.rst b/CHANGES.rst
index 865dec22d..31a3f63b0 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -44,6 +44,10 @@ Minor changes
   ``pandas.info()`` ) in a table. It can be sorted by each column.
   :pr:`1056` and :pr:`1068` by :user:`Jérôme Dockès <jeromedockes>`.
 
+* The credit fraud dataset is now available with the
+  :func:`fetch_credit_fraud function`.
+  :pr:`1053` by :user:`Vincent Maladiere <Vincent-Maladiere>`.
+
 * Added zero padding for column names in :class:`MinHashEncoder` to improve column ordering consistency.
   :pr:`1069` by :user:`Shreekant Nandiyawar <Shree7676>`.
 
diff --git a/doc/_static/08_example_aggjoiner.png b/doc/_static/08_example_aggjoiner.png
new file mode 100644
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diff --git a/doc/conf.py b/doc/conf.py
index 6b7fb208b..b71d38be0 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -512,6 +512,7 @@ def notebook_modification_function(notebook_content, notebook_filename):
     "Series": "pandas.Series",
     "pandas.Index": "pandas.Index",
     "read_csv": "pandas.read_csv",
+    "pandas.melt": "pandas.melt",
     "pandas.merge": "pandas.merge",
     # Skrub
     "fetch_ken_table_aliases": "skrub.datasets.fetch_ken_table_aliases",
diff --git a/doc/reference/downloading_a_dataset.rst b/doc/reference/downloading_a_dataset.rst
index f471ef70c..3bbb423f5 100644
--- a/doc/reference/downloading_a_dataset.rst
+++ b/doc/reference/downloading_a_dataset.rst
@@ -18,6 +18,7 @@ Downloading a dataset
    fetch_drug_directory
    fetch_world_bank_indicator
    fetch_movielens
+   fetch_credit_fraud
    fetch_ken_table_aliases
    fetch_ken_types
    fetch_ken_embeddings
diff --git a/examples/08_join_aggregation.py b/examples/08_join_aggregation.py
index e26148928..eac307376 100644
--- a/examples/08_join_aggregation.py
+++ b/examples/08_join_aggregation.py
@@ -1,302 +1,342 @@
 """
-Self-aggregation on MovieLens
-=============================
+AggJoiner on a credit fraud dataset
+===================================
 
-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.
+Many problems involve tables whose entities have a one-to-many relationship.
+To simplify aggregate-then-join operations for machine learning, we can include
+the |AggJoiner| in our pipeline.
 
-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 example, we are tackling a fraudulent loan detection use case.
+Because fraud is rare, this dataset is extremely imbalanced, with a prevalence of around
+1.4%.
 
-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 data consists of two distinct entities: e-commerce "baskets", and "products".
+Baskets can be tagged fraudulent (1) or not (0), and are essentially a list of products
+of variable size. Each basket is linked to at least one products, e.g. basket 1 can have
+product 1 and 2.
 
-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.
+.. image:: ../../_static/08_example_data.png
+    :width: 450 px
 
+|
+
+Our aim is to predict which baskets are fraudulent.
+
+The products dataframe can be joined on the baskets dataframe using the ``basket_ID``
+column.
+
+Each product has several attributes:
+
+- a category (marked by the column ``"item"``),
+- a model (``"model"``),
+- a brand (``"make"``),
+- a merchant code (``"goods_code"``),
+- a price per unit (``"cash_price"``),
+- a quantity selected in the basket (``"Nbr_of_prod_purchas"``)
 
 .. |AggJoiner| replace::
      :class:`~skrub.AggJoiner`
 
-.. |AggTarget| replace::
-     :class:`~skrub.AggTarget`
+.. |Joiner| replace::
+     :class:`~skrub.Joiner`
+
+.. |DropCols| replace::
+     :class:`~skrub.DropCols`
 
 .. |TableVectorizer| replace::
      :class:`~skrub.TableVectorizer`
 
-.. |DatetimeEncoder| replace::
-     :class:`~skrub.DatetimeEncoder`
+.. |TableReport| replace::
+     :class:`~skrub.TableReport`
+
+.. |MinHashEncoder| replace::
+     :class:`~skrub.MinHashEncoder`
 
 .. |TargetEncoder| replace::
      :class:`~sklearn.preprocessing.TargetEncoder`
 
 .. |make_pipeline| replace::
-     :class:`~sklearn.pipeline.make_pipeline`
+     :func:`~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`
-"""
-
-###############################################################################
-# The data
-# --------
-#
-# We begin with loading the ratings table from MovieLens.
-# Note that we use the light version (100k rows).
-import pandas as pd
+.. |HGBC| replace::
+     :class:`~sklearn.ensemble.HistGradientBoostingClassifier`
 
-from skrub.datasets import fetch_movielens
+.. |OrdinalEncoder| replace::
+     :class:`~sklearn.preprocessing.OrdinalEncoder`
 
-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")
+.. |TunedThresholdClassifierCV| replace::
+     :class:`~sklearn.model_selection.TunedThresholdClassifierCV`
 
-X = ratings[["userId", "movieId", "timestamp"]]
-y = ratings["rating"]
-X.shape, y.shape
-###############################################################################
-X.head()
+.. |CalibrationDisplay| replace::
+     :class:`~sklearn.calibration.CalibrationDisplay`
 
-###############################################################################
-# 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.
-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()
+.. |pandas.melt| replace::
+     :func:`~pandas.melt`
 
-###############################################################################
-# We can now make a couple of plots and gain some insight on our dataset.
-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
+"""
+# %%
+from skrub import TableReport
+from skrub.datasets import fetch_credit_fraud
 
-daily_volume = X_date_encoded["timestamp_weekday"].value_counts().sort_index()
+bunch = fetch_credit_fraud()
+products, baskets = bunch.products, bunch.baskets
+TableReport(products)
 
-make_barplot(
-    x=daily_volume.index,
-    y=daily_volume.values,
-    title="Daily volume of ratings",
-)
+# %%
+TableReport(baskets)
 
-###############################################################################
-# We also display the distribution of our target ``y``.
-rating_count = y.value_counts().sort_index()
-
-make_barplot(
-    x=rating_count.index,
-    y=rating_count.values,
-    title="Distribution of ratings given to movies",
-)
+# %%
+# Naive aggregation
+# -----------------
+#
+# Let's explore a naive solution first.
+#
+# .. note::
+#
+#    Click :ref:`here<agg-joiner-anchor>` to skip this section and see the AggJoiner
+#    in action!
+#
+#
+# The first idea that comes to mind to merge these two tables is to aggregate the
+# products attributes into lists, using their basket IDs.
+products_grouped = products.groupby("basket_ID").agg(list)
+TableReport(products_grouped)
+
+# %%
+# Then, we can expand all lists into columns, as if we were "flattening" the dataframe.
+# We end up with a products dataframe ready to be joined on the baskets dataframe, using
+# ``"basket_ID"`` as the join key.
+import pandas as pd
 
+products_flatten = []
+for col in products_grouped.columns:
+    cols = [f"{col}{idx}" for idx in range(24)]
+    products_flatten.append(pd.DataFrame(products_grouped[col].to_list(), columns=cols))
+products_flatten = pd.concat(products_flatten, axis=1)
+products_flatten.insert(0, "basket_ID", products_grouped.index)
+TableReport(products_flatten)
 
-###############################################################################
-# AggTarget: aggregate y, then join
-# ---------------------------------
+# %%
+# Look at the "Stats" section of the |TableReport| above. Does anything strike you?
+#
+# Not only did we create 144 columns, but most of these columns are filled with NaN,
+# which is very inefficient for learning!
 #
-# We have just extracted datetime features from timestamps.
+# This is because each basket contains a variable number of products, up to 24, and we
+# created one column for each product attribute, for each position (up to 24) in
+# the dataframe.
 #
-# 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.
+# Moreover, if we wanted to replace text columns with encodings, we would create
+# :math:`d \times 24 \times 2` columns (encoding of dimensionality :math:`d`, for
+# 24 products, for the ``"item"`` and ``"make"`` columns), which would explode the
+# memory usage.
 #
-# 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.
+# .. _agg-joiner-anchor:
 #
-# You can also think of it as a generalization of the |TargetEncoder|, which
-# encodes categorical features based on the target.
+# AggJoiner
+# ---------
+# Let's now see how the |AggJoiner| can help us solve this. We begin with splitting our
+# basket dataset in a training and testing set.
+from sklearn.model_selection import train_test_split
+
+X, y = baskets[["ID"]], baskets["fraud_flag"]
+X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.1)
+X_train.shape, y_train.shape
+
+# %%
+# Before aggregating our product dataframe, we need to vectorize our categorical
+# columns. To do so, we use:
 #
-# 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.
+# - |MinHashEncoder| on "item" and "model" columns, because they both expose typos
+#   and text similarities.
+# - |OrdinalEncoder| on "make" and "goods_code" columns, because they consist in
+#   orthogonal categories.
 #
-# This feature answer questions like
-# *"How many times has this user given a bad, medium or good rate to movies?"*.
-from skrub import AggTarget
-
-agg_target_user = AggTarget(
-    main_key="userId",
-    suffix="_user",
-    operation="hist(3)",
+# We bring this logic into a |TableVectorizer| to vectorize these columns in a
+# single step.
+# See `this example <https://skrub-data.org/stable/auto_examples/01_encodings.html#specializing-the-tablevectorizer-for-histgradientboosting>`_
+# for more details about these encoding choices.
+from sklearn.preprocessing import OrdinalEncoder
+
+from skrub import MinHashEncoder, TableVectorizer
+
+vectorizer = TableVectorizer(
+    high_cardinality=MinHashEncoder(),  # encode ["item", "model"]
+    specific_transformers=[
+        (OrdinalEncoder(), ["make", "goods_code"]),
+    ],
 )
-X_transformed = agg_target_user.fit_transform(X, y)
+products_transformed = vectorizer.fit_transform(products)
+TableReport(products_transformed)
 
-X_transformed.shape
-###############################################################################
-X_transformed.head()
-
-###############################################################################
-# Similarly, we join on ``movieId`` instead of ``userId``.
+# %%
+# Our objective is now to aggregate this vectorized product dataframe by
+# ``"basket_ID"``, then to merge it on the baskets dataframe, still on
+# the ``"basket_ID"``.
 #
-# This feature answer questions like
-# *"How many times has this movie received a bad, medium or good rate from users?"*.
-agg_target_movie = AggTarget(
-    main_key="movieId",
-    suffix="_movie",
-    operation="hist(3)",
-)
-X_transformed = agg_target_movie.fit_transform(X, y)
-X_transformed.shape
-###############################################################################
-X_transformed.head()
-
-###############################################################################
-# Chaining everything together in a pipeline
-# ------------------------------------------
+# .. image:: ../../_static/08_example_aggjoiner.png
+#    :width: 900
 #
-# 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|.
-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
-
-###############################################################################
-# Hyper-parameters tuning and cross validation
-# --------------------------------------------
+# |
+#
+# |AggJoiner| can help us achieve exactly this. We need to pass the product dataframe as
+# an auxiliary table argument to |AggJoiner| in ``__init__``. The ``aux_key`` argument
+# represent both the columns used to groupby on, and the columns used to join on.
 #
-# 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.
+# The basket dataframe is our main table, and we indicate the columns to join on with
+# ``main_key``. Note that we pass the main table during ``fit``, and we discuss the
+# limitations of this design in the conclusion at the bottom of this notebook.
 #
-# Note that you need the name of the pipeline elements to assign them
-# hyper-parameters search.
+# The minimum ("min") is the most appropriate operation to aggregate encodings from
+# |MinHashEncoder|, for reasons that are out of the scope of this notebook.
 #
-# You can lookup the name of the pipeline elements by doing:
-list(pipeline.named_steps)
+from skrub import AggJoiner
+from skrub import _selectors as s
+
+# Skrub selectors allow us to select columns using regexes, which reduces
+# the boilerplate.
+minhash_cols_query = s.glob("item*") | s.glob("model*")
+minhash_cols = s.select(products_transformed, minhash_cols_query).columns
+
+agg_joiner = AggJoiner(
+    aux_table=products_transformed,
+    aux_key="basket_ID",
+    main_key="ID",
+    cols=minhash_cols,
+    operations=["min"],
+)
+baskets_products = agg_joiner.fit_transform(baskets)
+TableReport(baskets_products)
 
-###############################################################################
-# Alternatively, you can use scikit-learn |Pipeline| to name your transformers:
-# ``Pipeline([("agg_target_user", agg_target_user), ...])``
+# %%
+# Now that we understand how to use the |AggJoiner|, we can now assemble our pipeline by
+# chaining two |AggJoiner| together:
 #
-# We now perform the grid search over the ``AggTarget`` transformers to find the
-# operation maximizing our validation score.
-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
-
-###############################################################################
-# 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``.
+# - the first one to deal with the |MinHashEncoder| vectors as we just saw
+# - the second one to deal with the all the other columns
 #
-# 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.
-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)
+# For the second |AggJoiner|, we use the mean, standard deviation, minimum and maximum
+# operations to extract a representative summary of each distribution.
+#
+# |DropCols| is another skrub transformer which removes the "ID" column, which doesn't
+# bring any information after the joining operation.
+from scipy.stats import loguniform, randint
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.pipeline import make_pipeline
 
+from skrub import DropCols
+
+model = make_pipeline(
+    AggJoiner(
+        aux_table=products_transformed,
+        aux_key="basket_ID",
+        main_key="ID",
+        cols=minhash_cols,
+        operations=["min"],
+    ),
+    AggJoiner(
+        aux_table=products_transformed,
+        aux_key="basket_ID",
+        main_key="ID",
+        cols=["make", "goods_code", "cash_price", "Nbr_of_prod_purchas"],
+        operations=["sum", "mean", "std", "min", "max"],
+    ),
+    DropCols(["ID"]),
+    HistGradientBoostingClassifier(),
+)
+model
 
-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))
+# %%
+# We tune the hyper-parameters of the |HGBC| to get a good performance.
+from time import time
 
-results.insert(0, "naive mean estimator", all_baseline_r2)
+from sklearn.model_selection import RandomizedSearchCV
 
-# 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()
+param_distributions = dict(
+    histgradientboostingclassifier__learning_rate=loguniform(1e-3, 1),
+    histgradientboostingclassifier__max_depth=randint(3, 9),
+    histgradientboostingclassifier__max_leaf_nodes=[None, 10, 30, 60, 90],
+    histgradientboostingclassifier__max_iter=randint(50, 500),
+)
 
-###############################################################################
-# The naive estimator has a lower performance than our pipeline, which means
-# that our extracted features brought some predictive power.
+tic = time()
+search = RandomizedSearchCV(
+    model,
+    param_distributions,
+    scoring="neg_log_loss",
+    refit=False,
+    n_iter=10,
+    cv=3,
+    verbose=1,
+).fit(X_train, y_train)
+print(f"This operation took {time() - tic:.1f}s")
+# %%
+# The best hyper parameters are:
+
+pd.Series(search.best_params_)
+
+# %%
+# To benchmark our performance, we plot the log loss of our model on the test set
+# against the log loss of a dummy model that always output the observed probability of
+# the two classes.
+#
+# As this dataset is extremely imbalanced, this dummy model should be a good baseline.
+#
+# The vertical bar represents one standard deviation around the mean of the cross
+# validation log-loss.
+import seaborn as sns
+from matplotlib import pyplot as plt
+from sklearn.dummy import DummyClassifier
+from sklearn.metrics import log_loss
+
+results = search.cv_results_
+best_idx = search.best_index_
+log_loss_model_mean = -results["mean_test_score"][best_idx]
+log_loss_model_std = results["std_test_score"][best_idx]
+
+dummy = DummyClassifier(strategy="prior").fit(X_train, y_train)
+y_proba_dummy = dummy.predict_proba(X_test)
+log_loss_dummy = log_loss(y_true=y_test, y_pred=y_proba_dummy)
+
+fig, ax = plt.subplots()
+ax.bar(
+    height=[log_loss_model_mean, log_loss_dummy],
+    x=["AggJoiner model", "Dummy"],
+    color=["C0", "C4"],
+)
+for container in ax.containers:
+    ax.bar_label(container, padding=4)
+
+ax.vlines(
+    x="AggJoiner model",
+    ymin=log_loss_model_mean - log_loss_model_std,
+    ymax=log_loss_model_mean + log_loss_model_std,
+    linestyle="-",
+    linewidth=1,
+    color="k",
+)
+sns.despine()
+ax.set_title("Log loss (lower is better)")
+
+# %%
+# Conclusion
+# ----------
+# With |AggJoiner|, you can bring the aggregation and joining operations within a
+# sklearn pipeline, and train models more efficiently.
+#
+# One known limitation of both the |AggJoiner| and |Joiner| is that the auxiliary data
+# to join is passed during the ``__init__`` method instead of the ``fit`` method, and
+# is therefore fixed once the model has been trained.
+# This limitation causes two main issues:
+#
+# 1. **Bigger model serialization:** Since the dataset has to be pickled along with
+# the model, it can result in a massive file size on disk.
 #
-# 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|).
+# 2. **Inflexibility with new, unseen data in a production environment:** To use new
+# auxiliary data, you would need to replace the auxiliary table in the |AggJoiner| that
+# was used during ``fit`` with the updated data, which is a rather hacky approach.
 #
-# 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).
+# These limitations will be addressed later in skrub.
diff --git a/examples/FIXME/08_join_aggregation_full.py b/examples/FIXME/08_join_aggregation_full.py
new file mode 100644
index 000000000..e1e363e6f
--- /dev/null
+++ b/examples/FIXME/08_join_aggregation_full.py
@@ -0,0 +1,548 @@
+"""
+AggJoiner on a credit fraud dataset
+===================================
+
+In this example, we are tackling a fraudulent loan detection use case.
+Because fraud is rare, this dataset is extremely imbalanced, with a prevalence of around
+1.4%.
+
+Instead of focusing on arbitrary metrics like accuracy, we will derive a cost function
+based on (questionable) assumptions about the data. In a real-world scenario, we would
+need to consult with a domain expert within the company to develop a realistic utility
+function.
+
+The data consists of two distinct concepts: a "basket," which can be tagged as fraud (1)
+or not (0), and a list of "products." Each product has several attributes:
+
+- a category (marked by the column ``"item"``),
+- a model (``"model"``),
+- a brand (``"make"``),
+- a merchant code (``"goods_code"``),
+- a price per unit (``"cash_price"``),
+- a quantity selected in the basket (``"Nbr_of_prod_purchas"``)
+
+Since the number of products in each basket varies, the creators of this dataset have
+chosen to join all products and their attributes with their respective basket. They have
+arbitrarily decided to cut off the basket at the 24th product. However, since most
+baskets contain only one or two products, a large proportion of the columns are empty.
+Therefore, the dataset is very sparse, which is challenging from a machine learning
+perspective and also inefficient in terms of memory usage.
+
+.. |AggJoiner| replace::
+     :class:`~skrub.AggJoiner`
+
+.. |Joiner| replace::
+     :class:`~skrub.Joiner`
+
+.. |TableVectorizer| replace::
+     :class:`~skrub.TableVectorizer`
+
+.. |MinHashEncoder| replace::
+     :class:`~skrub.MinHashEncoder`
+
+.. |TargetEncoder| replace::
+     :class:`~sklearn.preprocessing.TargetEncoder`
+
+.. |make_pipeline| replace::
+     :func:`~sklearn.pipeline.make_pipeline`
+
+.. |Pipeline| replace::
+     :class:`~sklearn.pipeline.Pipeline`
+
+.. |HGBC| replace::
+     :class:`~sklearn.ensemble.HistGradientBoostingClassifier`
+
+.. |TunedThresholdClassifierCV| replace::
+     :class:`~sklearn.model_selection.TunedThresholdClassifierCV`
+
+.. |CalibrationDisplay| replace::
+     :class:`~sklearn.calibration.CalibrationDisplay`
+
+.. |pandas.melt| replace::
+     :func:`~pandas.melt`
+
+"""
+
+# %%
+# The data
+# --------
+#
+# We begin with loading the table from figshare. It has around 100k rows.
+from skrub.datasets import fetch_figshare
+
+X = fetch_figshare("48931237").X
+
+# %%
+# The total price is the sum of the price per unit of each product in the basket,
+# multiplied by their quantity. This will also allow us to define a utility function
+# later, in addition of being a useful feature for the learner.
+import numpy as np
+import pandas as pd
+
+from skrub import TableReport
+
+
+def total_price(X):
+    total_price = pd.Series(np.zeros(X.shape[0]), index=X.index, name="total_price")
+    max_item = 24
+    for idx in range(1, max_item + 1):
+        total_price += X[f"cash_price{idx}"].fillna(0) * X[
+            f"Nbr_of_prod_purchas{idx}"
+        ].fillna(0)
+
+    return total_price
+
+
+X["total_price"] = total_price(X)
+TableReport(X)
+
+# %%
+# Metrics
+# -------
+#
+# To consider the problem from a business perspective, we define our utility function
+# by the cost matrix in the function ``credit_gain_score``. False positive and false
+# negative predictions incur a negative gain.
+#
+# Ultimately, we want to maximize this metric. To do so, we can train our learner to
+# minimize a proper scoring rule like the log loss.
+import sklearn
+from sklearn.metrics import log_loss, make_scorer
+
+
+def credit_gain_score(y_true, y_pred, amount):
+    """Define our utility function.
+
+    These numbers are entirely made-up, don't try this at home!
+    """
+    mask_tn = (y_true == 0) & (y_pred == 0)
+    mask_fp = (y_true == 0) & (y_pred == 1)
+    mask_fn = (y_true == 1) & (y_pred == 0)
+
+    # Refusing a fraud yields 0 €
+    fraudulent_refuse = 0
+
+    # Accepting a fraud costs its whole amount
+    fraudulent_accept = -amount[mask_fn].sum()
+
+    # Refusing a legitimate basket transactions cost 5 €
+    legitimate_refuse = mask_fp.sum() * -5
+
+    # Accepting a legitimate basket transaction yields 7% of its amount
+    legitimate_accept = (amount[mask_tn] * 0.07).sum()
+
+    return fraudulent_refuse + fraudulent_accept + legitimate_refuse + legitimate_accept
+
+
+def get_results(model, X_test, y_test, threshold, amount, time_to_fit):
+    y_proba = model.predict_proba(X_test)[:, 1]
+    return {
+        "log_loss": log_loss(y_test, y_proba),
+        "gain_score": credit_gain_score(y_test, y_proba > threshold, amount),
+        "y_proba": y_proba,
+        "y_test": y_test,
+        "time_to_fit": time_to_fit,
+    }
+
+
+sklearn.set_config(enable_metadata_routing=True)
+gain_score = make_scorer(credit_gain_score).set_score_request(amount=True)
+
+results = dict()
+
+# %%
+# Dummy model
+# -----------
+#
+# We first evaluate the performance of a dummy model that always predict the negative
+# class (i.e. all transactions are legit).
+# This is a good sanity check to make sure our model actually learns something useful.
+from time import time
+
+from sklearn.dummy import DummyClassifier
+from sklearn.model_selection import train_test_split
+
+target_col = "fraud_flag"
+X_ = X.drop(columns=[target_col])
+y_ = X[target_col]
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X_,
+    y_,
+    test_size=0.1,
+    stratify=y_,
+    random_state=0,
+)
+
+tic = time()
+dummy_negative = DummyClassifier(strategy="constant", constant=0).fit(X_train, y_train)
+time_to_fit = time() - tic
+
+results["Dummy Negative"] = get_results(
+    dummy_negative,
+    X_test,
+    y_test,
+    threshold=0.5,
+    amount=X_test["total_price"],
+    time_to_fit=time_to_fit,
+)
+
+# %%
+# Low effort estimator
+# --------------------
+#
+# Next, we use the |TableVectorizer| and a |HGBC| to create a very simple baseline model
+# that uses the sparse dataset directly. Note that due to the large number of high
+# cardinality columns, we can't use an multi-dimensional encoder like the
+# |MinHashEncoder|, because the number of columns would then explode.
+#
+# Instead, we encode our categories with a |TargetEncoder|.
+#
+# We also further split the training set into a training and validation set for
+# post-training tuning in the post-training phase below.
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import TargetEncoder
+
+from skrub import TableVectorizer
+
+X_train_, X_val, y_train_, y_val = train_test_split(
+    X_train, y_train, test_size=0.1, stratify=y_train, random_state=0
+)
+
+low_effort = make_pipeline(
+    TableVectorizer(
+        high_cardinality=TargetEncoder(),
+    ),
+    HistGradientBoostingClassifier(),
+)
+
+tic = time()
+low_effort.fit(X_train_, y_train_)
+time_to_fit = time() - tic
+
+# %%
+# To maximise our utility function, we have to find the best classification threshold to
+# replace the default at 0.5. |TunedThresholdClassifierCV| is a scikit-learn
+# meta-estimator that is designed for this exact purpose.
+# More details in this `example from scikit-learn <https://scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sensitive_learning.html#sphx-glr-auto-examples-model-selection-plot-cost-sensitive-learning-py>`_.
+#
+# We give it our trained model, and fit it on the validation dataset instead of the
+# training dataset to avoid overfitting. Notice that the scoring method is the utility
+# function, to which we pass the amount in ``fit``
+from sklearn.model_selection import TunedThresholdClassifierCV
+
+low_effort_tuned = TunedThresholdClassifierCV(
+    low_effort, cv="prefit", scoring=gain_score, refit=False
+).fit(X_val, y_val, amount=X_val["total_price"])
+
+results["Low effort"] = get_results(
+    low_effort,
+    X_test,
+    y_test,
+    threshold=low_effort_tuned.best_threshold_,
+    amount=X_test["total_price"],
+    time_to_fit=time_to_fit,
+)
+
+# %%
+# We define some plotting functions to display our results.
+import seaborn as sns
+from matplotlib import pyplot as plt
+from sklearn.calibration import CalibrationDisplay
+
+
+def plot_gain_tradeoff(results):
+    """Scatter plot of the score gain (y) vs the fit time (x) for each model."""
+
+    rows = []
+    for estimator_name, result in results.items():
+        result["estimator_name"] = estimator_name
+        rows.append(result)
+    df = pd.DataFrame(rows)
+
+    names = df["estimator_name"].values
+    palette = dict(zip(names, sns.color_palette("colorblind", n_colors=len(names))))
+
+    fig, ax = plt.subplots(figsize=(5, 4), dpi=100)
+    sns.scatterplot(
+        df,
+        x="time_to_fit",
+        y="gain_score",
+        hue="estimator_name",
+        style="estimator_name",
+        ax=ax,
+        palette=palette,
+        s=200,
+    )
+    ax.grid()
+
+    ticks = df["time_to_fit"].round(3).tolist()
+    labels = [f"{tick}s" for tick in ticks]
+    ax.set_xticks(ticks, labels)
+
+    ticks = df["gain_score"].round().tolist()
+    ticks.insert(1, 650_000)
+    labels = [f"{tick:,} €" for tick in ticks]
+
+    ax.set_yticks(ticks, labels)
+    ax.set_ylabel("Gain score")
+    ax.set_xlabel("Time to fit")
+    ax.set_title("Gain score vs Time to fit")
+    plt.tight_layout()
+
+
+def plot_calibration_curve(results):
+    """Plot a calibration curve and the log-loss."""
+
+    estimator_names = list(results)
+    palette = dict(
+        zip(
+            estimator_names,
+            sns.color_palette("colorblind", n_colors=len(estimator_names)),
+        )
+    )
+    fig, ax = plt.subplots(figsize=(6, 4), dpi=100)
+    for name, result in results.items():
+        log_loss = str(round(result["log_loss"], 4))
+        label = f"{name}, {'log_loss: ' + log_loss}"
+        CalibrationDisplay.from_predictions(
+            y_true=result["y_test"],
+            y_prob=result["y_proba"],
+            strategy="quantile",
+            label=label,
+            ax=ax,
+            color=palette[name],
+            n_bins=15,
+        )
+    ax.set_xlim([-0.001, 0.13])
+    ax.set_ylim([-0.001, 0.13])
+    ax.set_title("Calibration curve")
+
+
+# %%
+# We see below that the low effort classifier significantly improves our gains compared
+# to the dummy baseline. The former is of course slower to train than the latter.
+
+plot_gain_tradeoff(results)
+
+
+# %%
+# We also evaluate the calibration of both models. As very few classes are
+# positive, we can expect all probabilities to be close to 0. We have to
+# zoom on it, and use the "quantile" strategy of |CalibrationDisplay| in order to create
+# bins containing an equal number of samples.
+
+plot_calibration_curve(results)
+
+
+# %%
+# Agg-Joiner based estimator
+# --------------------------
+#
+# We first need to split the dataframe between a dataframe representing baskets and a
+# dataframe representing products. In other words, we need to revert the join operation
+# performed by the creator of this dataset. Conceptually, this is close to a
+# |pandas.melt| operation
+#
+# Note that we don't keep the product ordering information, which is probably not an
+# important feature here.
+
+
+def get_columns_at(idx, cols_2_idx):
+    """Small helper that give the position of each of the columns of the idx-th \
+        product."""
+    cols = [
+        "ID",
+        target_col,
+        f"item{idx}",
+        f"cash_price{idx}",
+        f"make{idx}",
+        f"model{idx}",
+        f"goods_code{idx}",
+        f"Nbr_of_prod_purchas{idx}",
+    ]
+    return [cols_2_idx[col] for col in cols]
+
+
+def melt_multi_columns(X):
+    """Create a dataframe where each product is a row."""
+    products = []
+    cols_2_idx = dict(zip(X.columns, range(X.shape[1])))
+    for row in X.values:
+        n_products = min(row[cols_2_idx["Nb_of_items"]], 24)
+        for idx in range(1, n_products + 1):
+            cols = get_columns_at(idx, cols_2_idx)
+            products.append(row[cols])
+
+    cols = [
+        "ID",
+        target_col,
+        "item",
+        "cash_price",
+        "make",
+        "model",
+        "goods_code",
+        "Nbr_of_prod_purchas",
+    ]
+
+    products = pd.DataFrame(products, columns=cols)
+
+    for col in ["make", "model"]:
+        products[col] = products[col].fillna("None")
+
+    return products
+
+
+X_train_[target_col] = y_train_
+X_val[target_col] = y_val
+X_test[target_col] = y_test
+
+baskets_train = X_train_[["ID", "total_price", target_col]]
+baskets_val = X_val[["ID", "total_price", target_col]]
+baskets_test = X_test[["ID", "total_price", target_col]]
+
+products = melt_multi_columns(X)
+
+TableReport(products)
+
+# %%
+# We have to aggregate the products dataframe before joining it back to the basket
+# dataframe. Prior to that, we need to apply some preprocessing to deal with
+# the high cardinality columns. Since these columns have some morphological variations
+# and typos, we use the |MinHashEncoder|.
+#
+# ``goods_code`` is slightly different, as it represents some merchant IDs, which
+# co-occurs for different products. Therefore, we encode it with a |TargetEncoder| as
+# we previously did.
+#
+# To later perform the joiner operation, we must keep the basket ``ID`` with
+# ``"passthrough"``.
+from skrub import MinHashEncoder
+
+
+def get_X_y(data):
+    return data.drop(columns=[target_col]), data[target_col]
+
+
+tic = time()
+vectorizer = TableVectorizer(
+    high_cardinality=MinHashEncoder(),  # applied on ["item", "model", "make"]
+    specific_transformers=[
+        (TargetEncoder(), ["goods_code"]),
+        ("passthrough", ["ID"]),
+    ],
+)
+
+products_transformed = vectorizer.fit_transform(*get_X_y(products))
+time_to_fit = time() - tic
+
+TableReport(products_transformed)
+
+# %%
+# Let's now detail how to leverage |AggJoiner| here. We have just encoded each product
+# attributes, and now we need to somehow aggregate these product encodings into their
+# respective baskets.
+#
+# By aggregating instead of concatenating, we obtain an invariant number of columns,
+# and we remove the sparsity of the dataset.
+#
+# But which aggregation operation should we choose? Since the |MinHashEncoder| hashes
+# ngrams with different hashing functions and return their minimum, it makes sense to
+# aggregate different product encodings using their **minimum** for each dimension.
+# You can view MinHash minimums as activations.
+#
+# For numeric columns and columns encoded with the |TargetEncoder|, we take the mean,
+# standard deviation, minimum and maximum to extract a representative summary of each
+# distribution.
+#
+# We can apply these two sets of operations by chaining together two |AggJoiner| in
+# a |Pipeline| using |make_pipeline|. We also make use of skrub selectors to select
+# columns with the ``glob`` syntax.
+#
+# We need to pass the product dataframe as an auxiliary table argument to AggJoiner
+# in ``__init__``. The basket dataframe is our main table, and we pass it during
+# ``fit``. We discuss the limitations of this design in the conclusion at the bottom
+# of this notebook.
+#
+# Let's display the output of this preprocessing pipeline.
+
+from sklearn.pipeline import make_pipeline
+
+from skrub import AggJoiner
+from skrub import _selectors as s
+
+minhash_cols = "ID" | s.glob("item_*") | s.glob("model_*") | s.glob("make_*")
+single_cols = ["ID", "goods_code", "Nbr_of_prod_purchas", "cash_price"]
+
+pipe_agg_joiner = make_pipeline(
+    AggJoiner(
+        aux_table=s.select(products_transformed, minhash_cols),
+        key="ID",
+        operations=["min"],
+    ),
+    AggJoiner(
+        aux_table=s.select(products_transformed, single_cols),
+        key="ID",
+        operations=["mean", "sum", "std", "min", "max"],
+    ),
+)
+basket_train_transformed = pipe_agg_joiner.fit_transform(baskets_train)
+
+TableReport(basket_train_transformed)
+
+# %%
+# Now that we get a sense of how the |AggJoiner| can help us, we complete this pipeline
+# with a |HGBC| and evaluate our final model.
+
+tic = time()
+agg_join_estimator = make_pipeline(
+    pipe_agg_joiner,
+    HistGradientBoostingClassifier(),
+).fit(*get_X_y(baskets_train))
+time_to_fit += time() - tic
+
+agg_join_tuned = TunedThresholdClassifierCV(
+    agg_join_estimator, cv="prefit", scoring=gain_score, refit=False
+).fit(*get_X_y(baskets_val), amount=baskets_val["total_price"])
+
+results["Agg Joiner"] = get_results(
+    agg_join_tuned,
+    *get_X_y(baskets_test),
+    threshold=agg_join_tuned.best_threshold_,
+    amount=baskets_test["total_price"],
+    time_to_fit=time_to_fit,
+)
+# %%
+# Not only did we improve the gains, but this operation is also much faster than the
+# naive low effort!
+
+plot_gain_tradeoff(results)
+
+# %%
+# We see that the agg-joiner model is slighly more calibrated, with a lower (better)
+# log loss.
+
+plot_calibration_curve(results)
+
+# %%
+# Conclusion
+# ----------
+#
+# Many problems involve tables where IDs have a one-to-many relationship. To simplify
+# aggregate-then-join operations for machine learning, we can include the |AggJoiner|
+# in our pipeline.
+#
+# One known limitation of both the |AggJoiner| and |Joiner| is that the auxiliary data
+# to join is passed during the ``__init__`` method instead of the ``fit`` method, and
+# is therefore fixed once the model has been trained.
+# This limitation causes two main issues:
+#
+# 1. **Inefficient model serialization:** Since the dataset has to be pickled along with
+# the model, it can result in a massive file size on disk.
+#
+# 2. **Inflexibility with new, unseen data in a production environment:** To use new
+# auxiliary data, you would need to replace the auxiliary table in the AggJoiner that
+# was used during ``fit`` with the updated data, which is a rather hacky approach.
+#
+# These limitations will be addresssed later in skrub.
diff --git a/pixi.lock b/pixi.lock
index 423522acd..fcb8f57e1 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1873,11 +1873,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he02047a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-17.0.0-had3b6fe_16_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-17.0.0-h5888daf_16_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-17.0.0-h5888daf_16_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-17.0.0-hf54134d_16_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_h5888daf_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-17.0.0-hef0f6b3_17_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-17.0.0-h5888daf_17_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-17.0.0-h5888daf_17_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-17.0.0-he882d9a_17_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-24_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda
@@ -1906,9 +1906,9 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.1.0-h77fa898_1.conda
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+  sha256: 1facd5aa7140031be0f68733ab5e413ea1505da40548e27a173b2407046f36b5
+  md5: 05fecc4ae5930dc548327980a4bc7a83
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=11.0
   - aws-crt-cpp >=0.28.3,<0.28.4.0a0
   - aws-sdk-cpp >=1.11.407,<1.11.408.0a0
   - azure-core-cpp >=1.13.0,<1.13.1.0a0
@@ -12637,17 +12848,15 @@ packages:
   - azure-storage-blobs-cpp >=12.12.0,<12.12.1.0a0
   - azure-storage-files-datalake-cpp >=12.11.0,<12.11.1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240116.2,<20240117.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libgcc >=13
+  - libcxx >=17
   - libgoogle-cloud >=2.29.0,<2.30.0a0
   - libgoogle-cloud-storage >=2.29.0,<2.30.0a0
   - libre2-11 >=2023.9.1,<2024.0a0
-  - libstdcxx >=13
   - libutf8proc >=2.8.0,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
@@ -12656,25 +12865,25 @@ packages:
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
-  - parquet-cpp <0.0a0
-  - arrow-cpp <0.0a0
   - apache-arrow-proc =*=cpu
+  - arrow-cpp <0.0a0
+  - parquet-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 8495428
-  timestamp: 1726669963852
+  size: 5318871
+  timestamp: 1726669928492
 - kind: conda
   name: libarrow
   version: 17.0.0
-  build: hc6a7651_16_cpu
-  build_number: 16
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-17.0.0-hc6a7651_16_cpu.conda
-  sha256: 1facd5aa7140031be0f68733ab5e413ea1505da40548e27a173b2407046f36b5
-  md5: 05fecc4ae5930dc548327980a4bc7a83
+  build: hef0f6b3_17_cpu
+  build_number: 17
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-17.0.0-hef0f6b3_17_cpu.conda
+  sha256: 923c17caa8d6b56d2bd0bf53640601959b00647a48debd73164b2301715181bf
+  md5: ae0b3234c958c6071f6523fcde7afe99
   depends:
-  - __osx >=11.0
+  - __glibc >=2.17,<3.0.a0
   - aws-crt-cpp >=0.28.3,<0.28.4.0a0
   - aws-sdk-cpp >=1.11.407,<1.11.408.0a0
   - azure-core-cpp >=1.13.0,<1.13.1.0a0
@@ -12682,15 +12891,17 @@ packages:
   - azure-storage-blobs-cpp >=12.12.0,<12.12.1.0a0
   - azure-storage-files-datalake-cpp >=12.11.0,<12.11.1.0a0
   - bzip2 >=1.0.8,<2.0a0
+  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
-  - libabseil >=20240116.2,<20240117.0a0
+  - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=17
+  - libgcc >=13
   - libgoogle-cloud >=2.29.0,<2.30.0a0
   - libgoogle-cloud-storage >=2.29.0,<2.30.0a0
   - libre2-11 >=2023.9.1,<2024.0a0
+  - libstdcxx >=13
   - libutf8proc >=2.8.0,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
@@ -12699,14 +12910,13 @@ packages:
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
+  - parquet-cpp <0.0a0
   - apache-arrow-proc =*=cpu
   - arrow-cpp <0.0a0
-  - parquet-cpp <0.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 5318871
-  timestamp: 1726669928492
+  size: 8530218
+  timestamp: 1727706025186
 - kind: conda
   name: libarrow-acero
   version: 15.0.2
@@ -12785,22 +12995,21 @@ packages:
 - kind: conda
   name: libarrow-acero
   version: 17.0.0
-  build: h5888daf_16_cpu
-  build_number: 16
+  build: h5888daf_17_cpu
+  build_number: 17
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-17.0.0-h5888daf_16_cpu.conda
-  sha256: 0ff4c712c7c61e60708c6ef4f8158200059e0f63c25d0a54c8e4cca7bd153d86
-  md5: 18f796aae018a26a20ac51d19de69115
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-17.0.0-h5888daf_17_cpu.conda
+  sha256: 6c95ff9332ccade12f839d44f370bed74c0b144fcbad10dab0f1791a4f2a27a8
+  md5: bc27c1f44562481b579f5ceae1a3e51e
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libarrow 17.0.0 had3b6fe_16_cpu
+  - libarrow 17.0.0 hef0f6b3_17_cpu
   - libgcc >=13
   - libstdcxx >=13
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 608267
-  timestamp: 1726669999941
+  size: 608175
+  timestamp: 1727706066011
 - kind: conda
   name: libarrow-acero
   version: 17.0.0
@@ -12942,24 +13151,23 @@ packages:
 - kind: conda
   name: libarrow-dataset
   version: 17.0.0
-  build: h5888daf_16_cpu
-  build_number: 16
+  build: h5888daf_17_cpu
+  build_number: 17
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-17.0.0-h5888daf_16_cpu.conda
-  sha256: e500e0154cf3ebb41bed3bdf41bd0ff5e0a6b7527a46ba755c05e59c8036e442
-  md5: 5400efd6bf101674e0ce170906a0f7cb
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-17.0.0-h5888daf_17_cpu.conda
+  sha256: 0da4757d75ad0cd838810ab8af0ff52a0edd5d78e55aafa218f001413b93eb11
+  md5: b45dc76a8a9a23a421b8f166d7e16684
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libarrow 17.0.0 had3b6fe_16_cpu
-  - libarrow-acero 17.0.0 h5888daf_16_cpu
+  - libarrow 17.0.0 hef0f6b3_17_cpu
+  - libarrow-acero 17.0.0 h5888daf_17_cpu
   - libgcc >=13
-  - libparquet 17.0.0 h39682fd_16_cpu
+  - libparquet 17.0.0 h39682fd_17_cpu
   - libstdcxx >=13
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 585061
-  timestamp: 1726670063965
+  size: 583849
+  timestamp: 1727706140203
 - kind: conda
   name: libarrow-dataset
   version: 17.0.0
@@ -13454,27 +13662,26 @@ packages:
 - kind: conda
   name: libarrow-substrait
   version: 17.0.0
-  build: hf54134d_16_cpu
-  build_number: 16
+  build: he882d9a_17_cpu
+  build_number: 17
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-17.0.0-hf54134d_16_cpu.conda
-  sha256: 53f3d5f12c9ea557f33a4e1cf9067ce2dbb4211eff0a095574eeb7f0528bc044
-  md5: 1cbc3fb1ee28c99e5f8c52920a7717a3
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-17.0.0-he882d9a_17_cpu.conda
+  sha256: 3346fb0097fb9c5e2509487ab950cddb388d0458e3a7594ba9989bfdea8868c1
+  md5: 3c3084245ba8384c07eb4504c1961497
   depends:
   - __glibc >=2.17,<3.0.a0
   - libabseil * cxx17*
-  - libabseil >=20240116.2,<20240117.0a0
-  - libarrow 17.0.0 had3b6fe_16_cpu
-  - libarrow-acero 17.0.0 h5888daf_16_cpu
-  - libarrow-dataset 17.0.0 h5888daf_16_cpu
+  - libabseil >=20240722.0,<20240723.0a0
+  - libarrow 17.0.0 hef0f6b3_17_cpu
+  - libarrow-acero 17.0.0 h5888daf_17_cpu
+  - libarrow-dataset 17.0.0 h5888daf_17_cpu
   - libgcc >=13
-  - libprotobuf >=4.25.3,<4.25.4.0a0
+  - libprotobuf >=5.27.5,<5.27.6.0a0
   - libstdcxx >=13
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 550960
-  timestamp: 1726670093831
+  size: 515946
+  timestamp: 1727706175064
 - kind: conda
   name: libasprintf
   version: 0.22.5
@@ -15080,6 +15287,32 @@ packages:
   purls: []
   size: 1241649
   timestamp: 1725640926284
+- kind: conda
+  name: libgoogle-cloud
+  version: 2.29.0
+  build: h438788a_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.29.0-h438788a_1.conda
+  sha256: cf5c97fb1a270a072faae6decd7e74681e7ead99a1cec6325c8d7a7213bcb2d1
+  md5: 3d27459264de681a74c0aebbbd3ecd8f
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libabseil * cxx17*
+  - libabseil >=20240722.0,<20240723.0a0
+  - libcurl >=8.10.1,<9.0a0
+  - libgcc >=13
+  - libgrpc >=1.65.5,<1.66.0a0
+  - libprotobuf >=5.27.5,<5.27.6.0a0
+  - libstdcxx >=13
+  - openssl >=3.3.2,<4.0a0
+  constrains:
+  - libgoogle-cloud 2.29.0 *_1
+  license: Apache-2.0
+  license_family: Apache
+  purls: []
+  size: 1200532
+  timestamp: 1727245497586
 - kind: conda
   name: libgoogle-cloud
   version: 2.29.0
@@ -15175,6 +15408,30 @@ packages:
   purls: []
   size: 781655
   timestamp: 1725641060970
+- kind: conda
+  name: libgoogle-cloud-storage
+  version: 2.29.0
+  build: h0121fbd_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.29.0-h0121fbd_1.conda
+  sha256: 78e22048ab9bb554c4269f5e2a4ab9baae2c0f490418e0cdecd04e5c59130805
+  md5: ea93fded95ddff7798e28954c446e22f
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libabseil
+  - libcrc32c >=1.1.2,<1.2.0a0
+  - libcurl
+  - libgcc >=13
+  - libgoogle-cloud 2.29.0 h438788a_1
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  - openssl
+  license: Apache-2.0
+  license_family: Apache
+  purls: []
+  size: 781418
+  timestamp: 1727245657213
 - kind: conda
   name: libgoogle-cloud-storage
   version: 2.29.0
@@ -15364,6 +15621,33 @@ packages:
   purls: []
   size: 5016525
   timestamp: 1713392846329
+- kind: conda
+  name: libgrpc
+  version: 1.65.5
+  build: hf5c653b_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.65.5-hf5c653b_0.conda
+  sha256: d279abd46262e817c7a00aeb4df9b5ed4de38130130b248e2c50875e982f30fa
+  md5: 3b0048cabc6815a4d8874a0240519d32
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - c-ares >=1.32.3,<2.0a0
+  - libabseil * cxx17*
+  - libabseil >=20240722.0,<20240723.0a0
+  - libgcc >=13
+  - libprotobuf >=5.27.5,<5.27.6.0a0
+  - libre2-11 >=2023.9.1,<2024.0a0
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  - openssl >=3.3.2,<4.0a0
+  - re2
+  constrains:
+  - grpc-cpp =1.65.5
+  license: Apache-2.0
+  license_family: APACHE
+  purls: []
+  size: 7229891
+  timestamp: 1727200905306
 - kind: conda
   name: libhwloc
   version: 2.11.1
@@ -16142,24 +16426,23 @@ packages:
 - kind: conda
   name: libparquet
   version: 17.0.0
-  build: h39682fd_16_cpu
-  build_number: 16
+  build: h39682fd_17_cpu
+  build_number: 17
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libparquet-17.0.0-h39682fd_16_cpu.conda
-  sha256: 09bc64111e5e1e9f5fee78efdd62592e01c681943fe6e91b369f6580dc8726c4
-  md5: dd1fee2da0659103080fdd74004656df
+  url: https://conda.anaconda.org/conda-forge/linux-64/libparquet-17.0.0-h39682fd_17_cpu.conda
+  sha256: c94d49b69a3023cf37cff4e0ede610c45576c0dea5876f0550b210e6d354b092
+  md5: dee1563caf816cb8f8f7e84622876487
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libarrow 17.0.0 had3b6fe_16_cpu
+  - libarrow 17.0.0 hef0f6b3_17_cpu
   - libgcc >=13
   - libstdcxx >=13
   - libthrift >=0.20.0,<0.20.1.0a0
   - openssl >=3.3.2,<4.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 1186069
-  timestamp: 1726670048098
+  size: 1188999
+  timestamp: 1727706121710
 - kind: conda
   name: libparquet
   version: 17.0.0
@@ -16399,6 +16682,27 @@ packages:
   purls: []
   size: 2883090
   timestamp: 1727161327039
+- kind: conda
+  name: libprotobuf
+  version: 5.27.5
+  build: h5b01275_2
+  build_number: 2
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.27.5-h5b01275_2.conda
+  sha256: 79ac9726cd0a1cb1ba335f7fc7ccac5f679a66d71d9553ca88a805b8787d55ce
+  md5: 66ed3107adbdfc25ba70454ba11e6d1e
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libabseil * cxx17*
+  - libabseil >=20240722.0,<20240723.0a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 2940269
+  timestamp: 1727424395109
 - kind: conda
   name: libre2-11
   version: 2023.09.01
@@ -16461,6 +16765,28 @@ packages:
   purls: []
   size: 184017
   timestamp: 1708947106275
+- kind: conda
+  name: libre2-11
+  version: 2023.09.01
+  build: hbbce691_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2023.09.01-hbbce691_3.conda
+  sha256: 239ca2319645308633ed773bda7ff1f153390ac84ee4e94955e0ed5be7e78967
+  md5: f7f3ff4fff310fcac18769ce3f46e40a
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libabseil * cxx17*
+  - libabseil >=20240722.0,<20240723.0a0
+  - libgcc >=13
+  - libstdcxx >=13
+  constrains:
+  - re2 2023.09.01.*
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 241063
+  timestamp: 1727157471101
 - kind: conda
   name: libre2-11
   version: 2023.09.01
@@ -19362,6 +19688,30 @@ packages:
   purls: []
   size: 1066349
   timestamp: 1723760593232
+- kind: conda
+  name: orc
+  version: 2.0.2
+  build: h690cf93_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.2-h690cf93_1.conda
+  sha256: ce023f259ffd93b4678cc582fc4b15a8a991a7b8edd9def8b6838bf7e7962bec
+  md5: 0044701dd48af57d3d5467a704ef9ebd
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libprotobuf >=5.27.5,<5.27.6.0a0
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  - lz4-c >=1.9.3,<1.10.0a0
+  - snappy >=1.2.1,<1.3.0a0
+  - tzdata
+  - zstd >=1.5.6,<1.6.0a0
+  license: Apache-2.0
+  license_family: Apache
+  purls: []
+  size: 1184634
+  timestamp: 1727242386732
 - kind: conda
   name: orc
   version: 2.0.2
@@ -19711,7 +20061,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/pandas?source=compressed-mapping
+  - pkg:pypi/pandas?source=hash-mapping
   size: 14470437
   timestamp: 1726878887799
 - kind: conda
@@ -23354,6 +23704,22 @@ packages:
   purls: []
   size: 26770
   timestamp: 1708947220914
+- kind: conda
+  name: re2
+  version: 2023.09.01
+  build: h77b4e00_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/re2-2023.09.01-h77b4e00_3.conda
+  sha256: f3cd9d8c39b2b39da67bbf6630c807e5019dce496b21aea104f97b2264b5474a
+  md5: 173a62ebf031d6d53462f8f657c800bb
+  depends:
+  - libre2-11 2023.09.01 hbbce691_3
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 26605
+  timestamp: 1727157480972
 - kind: conda
   name: re2
   version: 2023.09.01
@@ -24615,7 +24981,7 @@ packages:
   name: skrub
   version: 0.4.dev0
   path: .
-  sha256: b7c67ca1782219d797b575bcda5068fa98117f9e47ffa9f926dc09a4ae4e026b
+  sha256: b05795442e9930d7741094828e9df85834daa7798eea5a23ba0d3b5316312389
   requires_dist:
   - numpy>=1.23.5
   - packaging>=23.1
@@ -24637,6 +25003,7 @@ packages:
   - sphinx-gallery ; extra == 'doc'
   - sphinxext-opengraph ; extra == 'doc'
   - statsmodels ; extra == 'doc'
+  - pyarrow ; extra == 'doc'
   - black==23.3.0 ; extra == 'lint'
   - ruff==0.4.8 ; extra == 'lint'
   - pre-commit ; extra == 'lint'
diff --git a/pyproject.toml b/pyproject.toml
index 28fc95e2c..340cacc06 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,6 +54,7 @@ doc = [
     "sphinx-gallery",
     "sphinxext-opengraph",
     "statsmodels",
+    "pyarrow",
 ]
 lint = [
     "black==23.3.0",
@@ -166,7 +167,7 @@ test = { cmd = "pytest -vsl --cov=skrub --cov-report=xml skrub" }
 [tool.pixi.environments]
 lint = ["lint"]
 doc = ["optional", "doc"]
-test = ["test"]
+test = ["optional", "test"]
 dev = ["dev", "optional", "doc", "lint", "test"]
 ci-py309-min-deps = ["py309", "min-dependencies", "test"]
 ci-py309-min-optional-deps = ["py309", "min-dependencies", "min-optional-dependencies", "test"]
diff --git a/skrub/datasets/__init__.py b/skrub/datasets/__init__.py
index d64775d3d..ea393dad3 100644
--- a/skrub/datasets/__init__.py
+++ b/skrub/datasets/__init__.py
@@ -1,6 +1,7 @@
 from ._fetching import (
     DatasetAll,
     DatasetInfoOnly,
+    fetch_credit_fraud,
     fetch_drug_directory,
     fetch_employee_salaries,
     fetch_figshare,
@@ -32,6 +33,7 @@
     "fetch_traffic_violations",
     "fetch_world_bank_indicator",
     "fetch_figshare",
+    "fetch_credit_fraud",
     "fetch_movielens",
     "get_data_dir",
     "make_deduplication_data",
diff --git a/skrub/datasets/_fetching.py b/skrub/datasets/_fetching.py
index 8e4b18691..cd8667c4d 100644
--- a/skrub/datasets/_fetching.py
+++ b/skrub/datasets/_fetching.py
@@ -25,6 +25,7 @@
 from sklearn import __version__ as sklearn_version
 from sklearn.datasets import fetch_openml
 from sklearn.datasets._base import _sha256
+from sklearn.utils import Bunch
 from sklearn.utils.fixes import parse_version
 
 from skrub._utils import import_optional_dependency
@@ -1096,3 +1097,45 @@ def fetch_movielens(
         load_dataframe=load_dataframe,
         data_directory=data_directory,
     )
+
+
+def fetch_credit_fraud(load_dataframe=True, data_directory=None):
+    """Fetch the credit fraud dataset from figshare.
+
+    This is an imbalanced binary classification use-case. This dataset consists in
+    two tables:
+
+    - baskets, containing the binary fraud target label
+    - products
+
+    Baskets contain at least one product each, so aggregation then joining operations
+    are required to build a design matrix.
+
+    More details on \
+        `Figshare <https://figshare.com/articles/dataset/bnp_fraud_parquet/26892673>`_
+
+    Parameters
+    ----------
+    load_dataframe : bool, default=True
+        Whether or not to load the dataset in memory after download.
+
+    data_directory : str, default=None
+        The directory to which the dataset will be written during the download.
+        If None, the directory is set to ~/skrub_data.
+    """
+    dataset_name_to_id = {
+        "products": "49176205",
+        "baskets": "49176202",
+    }
+    bunch = Bunch()
+    for dataset_name, figshare_id in dataset_name_to_id.items():
+        dataset = fetch_figshare(
+            figshare_id,
+            load_dataframe=load_dataframe,
+            data_directory=data_directory,
+        )
+        bunch[dataset_name] = dataset.X
+        bunch[f"source_{dataset_name}"] = dataset.source
+        bunch[f"path_{dataset_name}"] = dataset.path
+
+    return bunch
diff --git a/skrub/datasets/tests/test_fetching.py b/skrub/datasets/tests/test_fetching.py
index 6539ca2db..8cfac5661 100644
--- a/skrub/datasets/tests/test_fetching.py
+++ b/skrub/datasets/tests/test_fetching.py
@@ -5,6 +5,7 @@
 
 import pandas as pd
 import pytest
+from pandas.testing import assert_frame_equal
 
 from skrub.datasets import _fetching
 
@@ -219,3 +220,40 @@ def test_fetch_movielens():
             )
             mock_urlretrieve.assert_not_called()
             assert disk_loaded_info == returned_info
+
+
+def test_fetch_credit_fraud():
+    pytest.importorskip("pyarrow")
+    with TemporaryDirectory() as temp_dir:
+        try:
+            # Valid call
+            bunch = _fetching.fetch_credit_fraud(
+                data_directory=temp_dir,
+            )
+
+        except (ConnectionError, URLError):
+            pytest.skip(
+                "Exception: Skipping this test because we encountered an "
+                "issue probably related to an Internet connection problem. "
+            )
+            return
+
+        assert (
+            bunch.source_products == "https://ndownloader.figshare.com/files/49176205"
+        )
+        assert bunch.source_baskets == "https://ndownloader.figshare.com/files/49176202"
+
+        assert_frame_equal(bunch.products, pd.read_parquet(bunch.path_products))
+        assert_frame_equal(bunch.baskets, pd.read_parquet(bunch.path_baskets))
+
+        # Now that we have verified the file is on disk, we want to test
+        # whether calling the function again reads it from disk (it should)
+        # or queries the network again (it shouldn't).
+        with mock.patch("urllib.request.urlretrieve") as mock_urlretrieve:
+            # Same valid call as above
+            disk_bunch = _fetching.fetch_credit_fraud(
+                data_directory=temp_dir,
+            )
+            mock_urlretrieve.assert_not_called()
+            assert_frame_equal(bunch.products, disk_bunch.products)
+            assert_frame_equal(bunch.baskets, disk_bunch.baskets)