Repository: skrub-data/skrub
Pull request #1314: Add skrub pipeline user guide
Author: login: Vincent-Maladiere
Labels: name: documentation
description: Add or improve the documentation
name: data_ops
description: Something related to the skrub DataOps
Created: 2025-04-11T16:29:27Z | Merged: 2025-04-23T16:25:15Z

Address https://github.com/skrub-data/skrub/issues/1297, toward https://github.com/skrub-data/skrub/issues/1293


Diff:
diff --git a/doc/_static/sklearn_pipeline.svg b/doc/_static/sklearn_pipeline.svg
index b2273ddb0..688f20449 100644
--- a/doc/_static/sklearn_pipeline.svg
+++ b/doc/_static/sklearn_pipeline.svg
@@ -1,6 +1,4 @@
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diff --git a/doc/documentation.rst b/doc/documentation.rst
index 3cd6574f1..384804293 100644
--- a/doc/documentation.rst
+++ b/doc/documentation.rst
@@ -41,3 +41,4 @@ essential to prevent data leakage and ensure generalization.
    encoding
    assembling
    cleaning
+   skrub_pipeline
diff --git a/doc/skrub_pipeline.rst b/doc/skrub_pipeline.rst
new file mode 100644
index 000000000..26b072756
--- /dev/null
+++ b/doc/skrub_pipeline.rst
@@ -0,0 +1,353 @@
+.. _skrub_pipeline:
+
+========================================================
+Skrub Pipeline: Cross-validate or tune any data pipeline
+========================================================
+
+.. currentmodule:: skrub
+
+Introduction
+~~~~~~~~~~~~
+
+Skrub provides an easy way to build complex, flexible machine learning pipelines.
+There are several problems that are not easily addressed with standard scikit-learn
+tools such as :class:`~sklearn.pipeline.Pipeline` and
+:class:`~sklearn.compose.ColumnTransformer`, and for which a Skrub pipeline offers a
+solution:
+
+- **Multiple tables**: When there are several tables of different shapes (for example,
+  "Customers", "Orders", and "Products" tables), standard scikit-learn estimators fall
+  short, as they expect a single design matrix ``X`` and a target array ``y``, with one
+  row per observation.
+- **DataFrame wrangling**: Performing typical DataFrame operations such as projections,
+  joins, and aggregations—leveraging the powerful APIs of pandas or polars—is not
+  easily expressed in standard pipelines.
+- **Iterative development**: Building a pipeline step by step while inspecting
+  intermediate results allows for a short feedback loop and early discovery of errors.
+- **Hyperparameter tuning**: Choices of estimators, hyperparameters, and even the
+  pipeline architecture can be guided by validation scores. Specifying a grid of
+  hyperparameters separately from the model (as in
+  :class:`~sklearn.model_selection.GridSearchCV`) becomes difficult in complex
+  pipelines.
+
+What is the difference with scikit-learn :class:`~sklearn.pipeline.Pipeline`?
+=============================================================================
+
+Scikit-learn pipelines represent a linear sequence of transformations on one
+table with a fixed number of rows.
+
+.. image:: _static/sklearn_pipeline.svg
+    :width: 500
+
+Skrub expressions, on the other hand, can manipulate any number of variables.
+The transformation they perform is not a linear sequence but any Directed
+Acyclic Graph of computations.
+
+.. image:: _static/skrub_expressions.svg
+
+What is the difference with orchestrators like Apache Airflow?
+==============================================================
+
+Skrub pipelines are not an [orchestrator](https://huyenchip.com/2021/09/13/data-science-infrastructure.html#workflow)
+and do not offer capabilities for scheduling runs or provisioning resources and
+environments. Instead, they are a generalization of scikit-learn pipelines, which can still be used within an orchestrator.
+
+.. _expressions:
+
+Skrub expressions
+~~~~~~~~~~~~~~~~~
+
+Skrub pipelines are built using special objects that represent intermediate results in
+a computation. These objects record the operations performed on them—such as applying
+operators or calling methods—allowing the entire computation graph to be retrieved
+later as a machine learning estimator that can be fitted and applied to unseen data.
+
+Because these Skrub objects encapsulate computations that can be evaluated to produce
+results, we call them **expressions**.
+
+The simplest expressions are **variables**, which represent inputs to our machine
+learning estimator—such as "products" or "customers" tables or dataframes.
+
+These variables can be combined using operators and function calls to build more
+complex expressions. The estimator is constructed implicitly as we apply these
+operations, rather than by specifying an explicit list of transformations.
+
+We start by declaring inputs:
+
+.. code:: python
+
+    import skrub
+
+    a = skrub.var("a")
+    b = skrub.var("b")
+
+We then apply transformations, which we can finally evaluate, by passing a dictionary
+mapping input name to values:
+
+.. code:: python
+
+    c = a + b
+    c.skb.eval({"a": 10, "b": 6})
+    # 16
+
+As shown above, the special ``.skb`` attribute allows to interact with the expression
+object itself, and ``.skb.eval()`` evaluate an expression.
+
+Access to any other attribute is simply added as a new operation in the computation
+graph:
+
+.. code:: python
+
+    d = c.capitalize()
+    d.skb.eval({"a": "hello, ", "b": "world!"})
+    # Hello world!
+
+Finally, we can get an estimator that can be fitted and applied to data.
+
+.. code:: python
+
+    estimator = c.skb.get_estimator()
+    estimator.fit_transform({"a": 10, "b": 7})
+    # 17
+
+Previews
+~~~~~~~~
+
+As we saw above, we can call ``.skb.eval()`` with a dictionary of bindings to compute
+the result of a pipeline. However, to make interactive development easier without
+having to call ``.skb.eval()`` repeatedly, Skrub provides a way to preview the result
+of an expression. When creating a variable, if we pass a value along with its name,
+Skrub will use that value to compute and preview the result of the expression.
+
+.. code:: python
+
+   a = skrub.var("a", 10)
+   b = skrub.var("b", 6)
+   c = a + b
+   c  # we don't need to call .skb.eval anymore!
+   # 16
+
+Note that example values are immutable throughout the pipeline. This means that
+to change the value of a variable, we need to create the pipeline again with
+the new value.
+
+Composing expressions
+~~~~~~~~~~~~~~~~~~~~~
+
+Suppose we want to process dataframes that look like this:
+
+.. code:: python
+
+    import pandas as pd
+
+    orders_df = pd.DataFrame(
+        {
+            "item": ["pen", "cup", "pen", "fork"],
+            "price": [1.5, None, 1.5, 2.2],
+            "qty": [1, 1, 2, 4],
+        }
+    )
+
+We can create a skrub variable to represent that input:
+
+.. code:: python
+
+    orders = skrub.var("orders", orders_df)
+
+Because we know that a dataframe will be provided as input to the computation, we
+can manipulate ``orders`` as if it were a regular dataframe.
+
+We can access its attributes:
+
+.. code:: python
+
+    orders.columns
+    # Index([item, price, qty])
+
+    orders["item"].iloc[1:]
+    # 1    cup
+    # 2    pen
+    # 3    fork
+    # Name: item, dtype: object
+
+We can apply operators:
+
+.. code:: python
+
+    orders["price"] * orders["qty"]
+    # 0    1.5
+    # 1    NaN
+    # 2    3.0
+    # 3    8.8
+    # dtype: float64
+
+We can call methods:
+
+.. code:: python
+
+    orders.assign(total=orders["price"] * orders["qty"])
+    #    item  price  qty  total
+    # 0   pen    1.5    1    1.5
+    # 1   cup    NaN    1    NaN
+    # 2   pen    1.5    2    3.0
+    # 3  fork    2.2    4    8.8
+
+It's important to note that the original ``orders`` pipeline is not modified by the
+operations in the previous cells. Instead, each cell creates a new expression that
+represents the result of the operation.
+
+This behavior is similar to how Pandas and Polars dataframes work: when we call
+a method on a dataframe, it returns a new dataframe representing the result, rather
+than modifying the original in place. However, unlike Pandas—where in-place
+modifications are possible—Skrub does not allow in-place updates.
+
+Applying machine-learning estimators
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+As mentioned above, in addition to those usual operations, the expressions
+have a special attribute: ``.skb``, which gives access to the methods and objects
+provided by Skrub. A particularly important one is
+``.skb.apply()``, which allows to scikit-learn estimators to the pipeline.
+
+.. code:: python
+
+    orders.skb.apply(skrub.TableVectorizer())
+    # <Apply TableVectorizer>
+    # Result:
+    # ―――――――
+    #    item_cup  item_fork  item_pen  price  qty
+    # 0       0.0        0.0       1.0    1.5  1.0
+    # 1       1.0        0.0       0.0    NaN  1.0
+    # 2       0.0        0.0       1.0    1.5  2.0
+    # 3       0.0        1.0       0.0    2.2  4.0
+
+It is also possible to apply a transformer to a subset of the columns:
+
+.. code:: python
+
+    vectorized_orders = orders.skb.apply(
+        skrub.StringEncoder(n_components=3), cols="item"
+    )
+    vectorized_orders
+    # <Apply StringEncoder>
+    # Result:
+    # ―――――――
+    #          item_0        item_1        item_2  price  qty
+    # 0  1.000000e+00  4.260130e-08 -2.691092e-09    1.5    1
+    # 1 -1.703285e-07  9.999984e-01  1.792236e-03    NaN    1
+    # 2  1.000000e+00  4.260130e-08 -2.691092e-09    1.5    2
+    # 3  9.629613e-10 -1.792075e-03  9.999982e-01    2.2    4
+
+Again, the crucial point is that when we apply such operations, the returned value
+encapsulates the entire computation that produces the result we see. We're not just
+interested in the output for the example values we provided—we're building a machine
+learning estimator that can be fitted and applied to unseen data.
+
+We can retrieve the estimator, fit it on the data we initially provided, and
+then apply it to new data:
+
+.. code:: python
+
+    estimator = vectorized_orders.skb.get_estimator(fitted=True)
+    new_orders = pd.DataFrame({"item": ["fork"], "price": [2.2], "qty": [5]})
+    estimator.transform({"orders": new_orders})
+    #          item_0    item_1    item_2  price  qty
+    # 0  3.247730e-09 -0.126657  0.991947    2.2    5
+
+
+Deferred evaluation
+~~~~~~~~~~~~~~~~~~~
+
+Expressions represent computations that haven't been executed yet and will only be
+triggered when we call something like ``eval()``, or when we retrieve the estimator
+and call ``fit()``.
+
+This means we can't use standard Python control flow statements—such as ``if``,
+``for``, ``with``, etc.—with expressions, because those constructs would execute
+immediately.
+
+.. code:: python
+
+    for column in orders.columns:
+        pass
+    # TypeError: This object is an expression that will be evaluated later, when your
+    # pipeline runs. So it is not possible to eagerly iterate over it now.
+
+We get an error because the ``for`` statement tries to iterate immediately
+over the columns. However, ``orders.columns`` is not an actual list of
+columns: it is a Skrub expression that will produce a list of columns, later,
+when we run the computation.
+
+This remains true even if we have provided a value for ``orders`` and we can
+see a result for that value:
+
+.. code:: python
+
+    orders.columns
+    # Index([item, price, qty])
+
+So we must delay the execution of the ``for`` statement until the computation
+actually runs and ``orders.columns`` has been evaluated.
+
+We can achieve this by defining a function that contains the control flow logic
+we need, and decorating it with ``skrub.deferred``. This decorator defers the execution
+of the function: when we call it, it doesn't run immediately. Instead, it returns
+a Skrub expression that wraps the function call. The original function is only
+executed when the expression is evaluated.
+
+.. code:: python
+
+    @skrub.deferred
+    def with_upper_columns(df):
+        new_columns = [c.upper() for c in df.columns]
+        return df.set_axis(new_columns, axis="columns")
+
+    with_upper_columns(orders)
+    # <Call 'with_upper_columns'>
+    # Result:
+    # ―――――――
+    #    ITEM  PRICE  QTY
+    # 0   pen    1.5    1
+    # 1   cup    NaN    1
+    # 2   pen    1.5    2
+    # 3  fork    2.2    4
+
+``skrub.deferred`` is useful not only for our own functions, but also when we
+need to call module-level functions from a library. For example, to delay the
+loading of a CSV file, we could write something like:
+
+.. code:: python
+
+    csv_path = skrub.var("csv_path")
+    data = skrub.deferred(pd.read_csv)(csv_path)
+
+For the same reason (we are building a computation graph, not
+immediately computing a single result), any transformation that we have must
+not modify its input, but leave it unchanged and return a new value.
+
+Think of the transformers in a scikit-learn pipeline: each computes a new
+result without modifying its input.
+
+.. code:: python
+
+    orders['total'] = orders['price'] * orders['qty']
+    # TypeError: Do not modify an expression in-place. Instead, use a function that
+    # returns a new value.This is necessary to allow chaining several steps in a
+    # sequence of transformations.
+    # For example if df is a pandas DataFrame:
+    # df = df.assign(new_col=...) instead of df['new_col'] = ...
+
+Finally, there are other situations where using ``deferred`` can be helpful:
+
+- When we have many nodes in our graph and want to collapse a sequence of steps into
+  a single function call that appears as a single node.
+- When certain steps need to be deferred until the full computation runs, because they
+  depend on the runtime environment, or on objects that cannot be pickled with the
+  rest of the computation graph (for example, opening and reading a file).
+
+.. rubric:: Examples
+
+- See :ref:`example_expressions_intro` for an example of skrub pipelines using
+  expressions on dataframes.
+- See :ref:`example_tuning_pipelines` for an example of hyper-parameter tuning using
+  skrub pipelines.
diff --git a/examples/expressions/10_expressions_intro.py b/examples/expressions/10_expressions_intro.py
index 1a8a52483..ddbf84133 100644
--- a/examples/expressions/10_expressions_intro.py
+++ b/examples/expressions/10_expressions_intro.py
@@ -1,35 +1,11 @@
 """
+.. _example_expressions_intro:
+
 Building complex tabular pipelines
 ==================================
 
-Skrub provides an easy way to build complex, flexible machine-learning
-pipelines. There are several problems that are not easily addressed with the
-standard scikit-learn tools such as the ``Pipeline`` and ``ColumnTransformer``
-and for which a skrub pipeline is a solution:
-
-- **Multiple tables:** when there are several tables of different shapes (for example,
-  "Customers", "Orders" and "Products" tables). Not suitable for scikit-learn
-  estimators, expecting a single design matrix ``X`` and array of targets ``y``
-  with one row per observation.
-
-- **DataFrame wrangling:** performing typical dataframe operations
-  such as projections, joins and aggregations leveraging the powerful APIs of
-  ``pandas`` or ``polars``.
-
-- **Iterative development:** building a pipeline step by step, while
-  inspecting the intermediate results so that the feedback loop is short and
-  errors are discovered early.
-
-- **Hyperparameter tuning:** choices of estimators, hyperparameters,
-  and even the architecture of the pipeline can be informed by validation scores.
-  Specifying the grid of hyperparameters separately from the model (as in
-  ``GridSearchCV``) is difficult for complex pipelines.
-
-In this example, we show a pipeline to handle a dataset with 2 tables.
-Despite being very simple, this pipeline would be difficult to implement,
-validate and deploy correctly without skrub.
-We show the script with minimal comments to motivate the tools that are
-explained in detail in subsequent examples.
+In this example, we show a simple pipeline handling a dataset with 2 tables, which
+would be difficult to implement, validate, and deploy correctly without skrub.
 """
 
 # %%
@@ -99,7 +75,7 @@
 # storing fitted estimators, splitting the input data and cross-validation, and
 # hyper-parameter tuning.
 #
-# Moreover, we might need some pandas code to perform the aggregation and join.
+# Moreover, we might need some Pandas code to perform the aggregation and join.
 # Again, as this transformation is not in a scikit-learn estimator, it is error-prone.
 # The difficulty is that we have to keep track of it ourselves to apply it later
 # to unseen data, and we cannot tune any choices (like the choice of the
@@ -257,6 +233,12 @@
 )
 search.results_
 
+# %%
+# We can also run a cross validation, using the first choices defined in the ``choose``
+# objects:
+
+predictions.skb.cross_validate(scoring="roc_auc", verbose=1, n_jobs=4)
+
 # %%
 # We can also display a parallel coordinates plot of the results.
 #
@@ -265,7 +247,7 @@
 # scores, and training and scoring computation durations.
 # Different columns show the hyperparameter values.
 #
-# By clicking and dragging the mouse on any column, we can restrict the
+# By **clicking and dragging the mouse** on any column, we can restrict the
 # set of lines we see. This allows quickly inspecting which hyperparameters are
 # important, which values perform best, and potential trade-offs between the quality
 # of predictions and computation time.
@@ -277,11 +259,31 @@
 # It seems here that using the LSA as an encoder brings better test scores,
 # but at the expense of training and scoring time.
 #
+# Serializing
+# -----------
+# We would usually save this model in a binary file, but to avoid accessing the
+# filesystem with this example notebook, we serialize the model in memory instead.
+import pickle
+
+saved_model = pickle.dumps(search.best_estimator_)
+
+# %%
+# Let's say we got some new data, and we want to use the model we just saved
+# to make predictions on them:
+new_data = skrub.datasets.fetch_credit_fraud(split="test")
+new_baskets = new_data.baskets[["ID"]]
+new_products = new_data.products
+
+# %%
+# Our estimator expects the same variable names as the training pipeline, which is why
+# we pass a dictionary that contains new dataframes and the same variable:
+loaded_model = pickle.loads(saved_model)
+loaded_model.predict({"baskets": new_baskets, "products": new_products})
 
 # %%
 # Conclusion
 # ----------
 #
-# If you are curious to know more on how to
-# build your own complex, multi-table pipelines with easy hyperparameter
-# tuning, please see the next examples for an in-depth tutorial.
+# If you are curious to know more on how to build your own complex, multi-table
+# pipelines with easy hyperparameter tuning, please see the next examples for an
+# in-depth tutorial.
diff --git a/examples/expressions/13_choices.py b/examples/expressions/11_choices.py
similarity index 99%
rename from examples/expressions/13_choices.py
rename to examples/expressions/11_choices.py
index 1154c4fc2..08ab0338b 100644
--- a/examples/expressions/13_choices.py
+++ b/examples/expressions/11_choices.py
@@ -15,7 +15,7 @@
 We want to tune those choices by trying several options and keeping those that
 give the best performance on a validation set.
 
-Skrub `expressions <10_expressions.html>`_ provide a convenient way to specify
+Skrub :ref:`expressions <skrub_pipeline>` provide a convenient way to specify
 the range of possible values, by inserting it directly in place of the actual
 value. For example we can write:
 
diff --git a/examples/expressions/11_expressions_explained.py b/examples/expressions/11_expressions_explained.py
deleted file mode 100644
index 75a922f87..000000000
--- a/examples/expressions/11_expressions_explained.py
+++ /dev/null
@@ -1,418 +0,0 @@
-# ruff: noqa: E501
-"""
-Skrub expressions
-=================
-
-Skrub provides special objects that represent intermediate results in a
-computation. They record the different operations we perform on them (such as
-applying operators or calling methods), which allows to later retrieve the
-whole computation graph as a machine-learning estimator that can be fitted and
-applied to unseen data.
-
-Because those Skrub objects encapsulate a computation, which can be evaluated
-to produce a result, we call them "expressions".
-
-The simplest expressions are variables, which represent inputs to our
-machine-learning estimator such as a ``"products"`` or a ``"customers"`` table.
-Those variables can be combined with operators and function calls to build up
-more complex expressions. The estimator is being constructed implicitly when we
-apply those operations, rather than by providing an explicit list of
-transformations.
-"""
-
-# %%
-# We start by declaring inputs with ``skrub.var``.
-
-# %%
-import skrub
-
-a = skrub.var("a")
-b = skrub.var("b")
-
-# %%
-# We then apply transformations, composing more complex expressions.
-
-# %%
-c = a + b
-c
-
-# %%
-# We can evaluate the expression.
-
-# %%
-c.skb.eval({"a": 10, "b": 6})
-
-# %%
-c.skb.eval({"a": 2, "b": 3})
-
-# %%
-# As shown above, the special ``.skb`` attribute provides access to skrub
-# functionality to interact with the expression object itself. Access to any other
-# attribute is simply added as a new operation in the computation
-# graph:
-
-# %%
-d = c.capitalize()
-d
-
-# %%
-d.skb.eval({"a": "hello, ", "b": "world!"})
-
-# %%
-# Finally, we can get an estimator that can be fitted and applied to data.
-
-# %%
-estimator = c.skb.get_estimator()
-estimator.fit_transform({"a": 10, "b": 7})
-
-
-# %%
-# Handling multiple inputs
-# ------------------------
-#
-# One advantage of the approach outlined above is that we can build models that
-# handle and transform multiple tables (and other kinds of inputs).
-#
-# Scikit-learn pipelines represent a linear sequence of transformations on one
-# table with a fixed number of rows.
-#
-# .. image:: ../../_static/sklearn_pipeline.svg
-#     :width: 500
-#
-# Skrub expressions, on the other hand, can manipulate any number of variables.
-# The transformation they perform is not a linear sequence but any Directed
-# Acyclic Graph of computations.
-#
-# .. image:: ../../_static/skrub_expressions.svg
-
-
-# %%
-# Previews of the results
-# -----------------------
-#
-# As we saw above, we can call ``eval()`` with a dictionary of bindings for the
-# variables in our expression in order to compute the result of the pipeline.
-# However, this is an operation that we want to do very often during development:
-# to avoid having to call ``eval()`` repeatedly, skrub provides a way to
-# preview the result of the expression as we work on it.
-#
-# When we create a variable, we can pass a value in addition to its name. If a value
-# is provided, skrub will use it to compute the result of the expression on the
-# value. This makes
-# the development more interactive, allows catching errors early, and can
-# provide better help and tab-completion in an interactive Python shell.
-#
-# Moreover, those values can be used to obtain a fitted estimator or
-# cross-validation scores as we will see.
-#
-# Note that example values are immutable throughout the pipeline. This means that
-# to change the value of a variable, we need to create the pipeline again with
-# the new value.
-#
-# To provide an example value to ``var("a")``, we can write:
-
-# %%
-a = skrub.var("a", 10)  # note the value 10
-b = skrub.var("b", 6)
-c = a + b
-c
-
-# %%
-# In the display above, you can still see the graph by clicking on the dropdown
-# ``▶ Show graph``.
-#
-# Seeing results for the values we provided does *not* change the fact that we
-# are building a computation graph that we want to reuse, not just computing
-# the result for a fixed input. We can think of the displayed result as a
-# preview of the output on one example dataset.
-
-# %%
-c.skb.eval({"a": 3, "b": 2})
-
-
-# %%
-# In what follows we will always provide values for our variables. When
-# building your own estimator we recommend you do so as well, because running
-# the estimator on example data every step of the way makes development much
-# easier by catching errors as soon as the operation is added, rather than
-# later when fitting the full estimator.
-
-# %%
-# Composing expressions
-# ---------------------
-#
-# The simplest expressions are variables created with ``skrub.var``. Complex
-# expressions are constructed by applying functions or operators to other
-# expressions.
-#
-# So what operations are allowed? Remember that when we apply an operation to
-# an expression, it is implicitly added as a step in our computation. When we
-# run the computation, those operations are replayed on the actual data we
-# provide.
-#
-# So we can use any operation that is valid for the types we will pass to run
-# the computation (with some caveats detailed below). If we know we will pass a
-# pandas DataFrame as the ``"products"`` table, we can use any of the methods
-# of pandas DataFrames. If instead we know we will pass a numpy array, we use
-# the methods of numpy arrays, etc.
-#
-# More operations (e.g. applying a scikit-learn estimator) are available
-# through the special ``skb`` attribute, as we will see later.
-#
-# Basic operations
-# ~~~~~~~~~~~~~~~~
-#
-# Suppose we want to process dataframes that look like this:
-
-# %%
-import pandas as pd
-
-orders_df = pd.DataFrame(
-    {
-        "item": ["pen", "cup", "pen", "fork"],
-        "price": [1.5, None, 1.5, 2.2],
-        "qty": [1, 1, 2, 4],
-    }
-)
-orders_df
-
-# %%
-# We can create a skrub variable to represent that input
-
-# %%
-orders = skrub.var("orders", orders_df)
-orders
-
-# %%
-# Because we know we will feed a DataFrame to the computation, we manipulate
-# ``products`` as if it were a DataFrame.
-#
-# We can access its attributes:
-
-# %%
-orders.columns
-
-# %%
-# Access the "items" column, indexing, slicing:
-
-# %%
-orders["item"].iloc[1:]
-
-# %%
-# Applying operators:
-
-# %%
-orders["price"] * orders["qty"]
-
-# %%
-# Calling methods:
-
-# %%
-orders.assign(total=orders["price"] * orders["qty"])
-
-# %%
-# It is important to note that the original ``orders`` pipeline is not modified
-# by the previous cells. Instead, in each cell a new expression is created that
-# represents the result of the operation.
-#
-# This is similar to how pandas and polars
-# DataFrames work: when we call a method on a DataFrame, it returns a new
-# DataFrame that represents the result of the operation, rather than modifying
-# the original DataFrame in place. However, while in pandas it is possible to
-# work on a DataFrame in place, skrub does not allow this.
-#
-# We can check this by looking at the graph of ``orders`` (which remains unmodified),
-# and ``new_orders`` (which instead contains the new steps):
-# %%
-orders
-
-# %%
-new_orders = orders.assign(total=orders["price"] * orders["qty"])
-new_orders
-
-# %%
-# Applying machine-learning estimators
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#
-# As mentioned above, in addition to those usual operations, the expressions
-# have a special attribute: ``.skb``, which gives access to the methods and objects
-# provided by skrub. A particularly important one is
-# ``.skb.apply()``, which allows to scikit-learn estimators to the pipeline.
-
-# %%
-orders.skb.apply(skrub.TableVectorizer())
-
-# %%
-# It is also possible to apply a transformer to a subset of the columns:
-
-# %%
-orders.skb.apply(skrub.StringEncoder(n_components=3), cols="item")
-
-# %%
-# Again, the crucial part is that when applying such operations, the return
-# value encapsulates the whole computation that produces the result we see. We are
-# not interested in a single result for the example values we provided, but in
-# the ability to retrieve a machine-learning estimator that we can fit and then
-# apply to unseen data.
-
-# %%
-vectorized_orders = orders.assign(total=orders["price"] * orders["qty"]).skb.apply(
-    skrub.TableVectorizer()
-)
-vectorized_orders
-
-# We can use ``.skb.get_estimator(fitted=True)`` to retrieve the estimator and
-# fit it on the data we provided.
-# %%
-estimator = vectorized_orders.skb.get_estimator(fitted=True)
-
-# %%
-# We can now use the fitted estimator to transform new data.
-new_orders = pd.DataFrame({"item": ["fork"], "price": [2.2], "qty": [5]})
-estimator.transform({"orders": new_orders})
-
-# %%
-# Deferred evaluation
-# ------------------
-#
-# Expressions represent a computation that has not yet been executed, and will
-# be executed when we trigger it, for example by calling ``eval()`` or getting
-# the estimator and calling ``fit()``.
-#
-# This means that we cannot use usual Python control flow statements such as
-# ``if``, ``for``, ``with`` etc. with expressions, because those constructs would
-# execute immediately.
-
-# %%
-# For example, we cannot do this::
-#
-#     for column in orders.columns:
-#         pass
-#
-# .. code-block:: none
-#
-#     TypeError: This object is an expression that will be evaluated later, when your pipeline runs. So it is not possible to eagerly iterate over it now.
-#
-# We get an error because the ``for`` statement tries to iterate immediately
-# over the columns. However, ``orders.columns`` is not an actual list of
-# columns: it is a skrub expression that will produce a list of columns, later,
-# when we run the computation.
-#
-# .. image:: ../../_static/skrub_3d_2.svg
-#   :width: 200
-#
-# This remains true even if we have provided a value for ``orders`` and we can
-# see a result for that value:
-
-# %%
-orders.columns
-
-# %%
-# The "result" we see is an *example* result that the computation produces for
-# the data we provided. But we want to fit our estimator and apply it many
-# times to different datasets, for which it will return a new object every
-# time. So even if we see a preview of the output on the data we provided,
-# ``orders.columns`` still represents a future computation that remains to be
-# evaluated.
-#
-# So we must delay the execution of the ``for`` statement until the computation
-# actually runs and ``orders.columns`` has been evaluated.
-#
-# We can do this by wrapping it in a function and using ``skrub.deferred``.
-# Suppose we want to convert the columns to upper case, and our first attempt
-# runs into the problem we just described::
-#
-#     COLUMNS = [c.upper() for c in orders.columns]
-#
-# .. code-block:: none
-#
-#     TypeError: This object is an expression that will be evaluated later, when your pipeline runs. So it is not possible to eagerly iterate over it now.
-
-# %%
-# We define a function that contains the control flow statement we need. Then,
-# we apply the ``skrub.deferred`` decorator to it. What this does is deferring
-# the calls to our function. Now when we call it, instead of running
-# immediately, it returns a skrub expression that wraps the function call. The
-# original function is actually called when we evaluate the expression.
-
-
-# %%
-@skrub.deferred
-def with_upper_columns(df):
-    new_columns = [c.upper() for c in df.columns]
-    return df.set_axis(new_columns, axis="columns")
-
-
-with_upper_columns(orders)
-
-# %%
-# If you unfold the dropdown ``▶ Show graph`` above, you can
-# see that a call to our function has been added to the computation graph.
-#
-# .. image:: ../../_static/skrub_3d_3.svg
-#   :width: 200
-#
-# When the computation runs, ``orders`` will be evaluated first and the result (an
-# actual dataframe) will be passed as the ``df`` argument to our function.
-#
-# .. image:: ../../_static/skrub_3d_4.svg
-#   :width: 400
-
-# %%
-# ``skrub.deferred`` is useful for our own functions but also when we need to
-# call module-level functions from a library. For example to delay loading of a
-# csv file we could use something like:
-
-# %%
-csv_path = skrub.var("csv_path")
-data = skrub.deferred(pd.read_csv)(csv_path)
-
-# %%
-# Also note that for the same reason (we are building a computation graph, not
-# immediately computing a single result), any transformation that we have must
-# not modify its input, but leave it unchanged and return a new value.
-#
-# Think of the transformers in a scikit-learn pipeline: each computes a new
-# result without modifying its input.
-
-# %%
-# This would raise an error::
-#
-#     orders['total'] = orders['price'] * orders['qty']
-#
-# .. code-block:: none
-#
-#     TypeError: Do not modify an expression in-place. Instead, use a function that returns a new value.This is necessary to allow chaining several steps in a sequence of transformations.
-#     For example if df is a pandas DataFrame:
-#     df = df.assign(new_col=...) instead of df['new_col'] = ...
-#
-# In this case the error message suggests a solution: use ``assign()`` instead
-# of item assignment. Luckily, we are mostly focussed on pandas and polars
-# dataframes and they provide an API supporting this functional (returning
-# transformed values without modifying the inputs) style for most operations
-# (even more so polars than pandas).
-#
-# When we do need assignments or in-place transformations, we can put them in a
-# ``deferred`` function. But remember to make a (shallow) copy and return the new value.
-#
-
-
-# %%
-@skrub.deferred
-def add_total(df):
-    new_df = df.copy(deep=False)  # Creates a new dataframe but does not copy any data
-    new_df["total"] = new_df["price"] * new_df["qty"]
-    return new_df
-
-
-add_total(orders)
-
-# %%
-# Finally, there are other occasions where we may need to use ``deferred``:
-#
-# - we have many nodes in our graph, and we want to collapse a sequence of
-#   steps into a single function call, that will appear as a single node
-# - we have steps that need to be deferred until the full computation runs,
-#   because they depend on the runtime environment or on objects that cannot be
-#   pickled together with the rest of the computation graph -- for example
-#   opening and reading a file.
diff --git a/examples/expressions/12_scikit_learn_estimators_in_expressions.py b/examples/expressions/12_scikit_learn_estimators_in_expressions.py
deleted file mode 100644
index 30ae9cc33..000000000
--- a/examples/expressions/12_scikit_learn_estimators_in_expressions.py
+++ /dev/null
@@ -1,292 +0,0 @@
-"""
-Supervised estimators and cross-validation
-------------------------------------------
-
-We can use ``.skb.apply()`` to add scikit-learn transformers to the computation,
-but also to add a supervised learner like ``HistGradientBoostingClassifier``
-or ``LogisticRegression``.
-
-When we have built a full estimator, we want to run cross-validation to
-measure its performance. We may also want to tune hyperparameters (shown in
-the next example).
-
-Skrub can run the cross-validation for us. In order to do so, it needs to
-split the dataset into training and testing sets. For this to happen, we need
-to tell skrub which items in our pipeline constitute the feature matrix ``X``
-and the targets (labels, outputs) ``y``.
-
-Indeed, an estimator built from a skrub expression can accept inputs that are
-not yet neatly organized into correctly-aligned ``X`` and ``y`` matrices.
-There may be some steps (such as loading data, performing joins or
-aggregations, separating features and targets from a single source) that need
-to be performed in order to construct the design matrix the targets.
-
-To indicate which intermediate results to split, we call ``.skb.mark_as_X()``
-and ``.skb.mark_as_y()`` on the appropriate objects:
-"""
-
-# %%
-from sklearn.ensemble import HistGradientBoostingRegressor
-
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_employee_salaries()
-
-full_data = skrub.var("data", dataset.employee_salaries)
-
-employees = full_data.drop(columns="current_annual_salary").skb.mark_as_X()
-salaries = full_data["current_annual_salary"].skb.mark_as_y()
-
-vectorizer = skrub.TableVectorizer(
-    high_cardinality=skrub.MinHashEncoder(n_components=8)
-)
-predictions = employees.skb.apply(vectorizer).skb.apply(
-    HistGradientBoostingRegressor(), y=salaries
-)
-predictions
-
-# %%
-# Note how ``apply`` works for supervised estimators: we pass the targets
-# (another skrub expression) as the argument for the parameter ``y``.
-#
-# If you unfold the dropdown to see the computation graph, you will see the
-# inner nodes that have been marked as ``X`` and ``y``: these nodes are highlighted
-# in blue and red, respectively. Furthermore, the ``data`` is an input variable,
-# so it is marked with a double box.
-#
-# To perform
-# cross-validation, skrub first runs all the prior steps until it has computed
-# ``X`` and ``y``. Then, it splits those (with the usual scikit-learn
-# cross-validation tools, as is done in ``sklearn.cross_validate``) and runs
-# the rest of the computation inside of the cross-validation loop.
-#
-
-# %%
-predictions.skb.cross_validate()
-
-
-# %%
-# In the simplest case where we have ``X`` and ``y`` available right from the
-# start, we can indicate that simply by creating the input variables with
-# ``skrub.X()`` and ``skrub.y()`` instead of ``skrub.var()``: ``skrub.X()`` is
-# syntactic sugar for ``skrub.var("X").skb.mark_as_X()``.
-
-# %%
-# The construction of ``X`` and ``y`` must be done before splitting the samples
-# into cross-validation folds; it happens outside of the cross-validation loop.
-#
-# This means that any step we perform in this part of the computation has
-# access to the full data (training and test sets). We should not use
-# estimators that learn from the data before reaching the cross-validation
-# loop, or we might obtain optimistic scores due to data leakage.
-# Join operations that involve aggregations are not safe either, because they
-# may involve information from the test set. Finally, no
-# choices or hyperparameters can be tuned in this part of the computation
-# (tuning is discussed in more detail in the next example).
-#
-# Therefore, we should build ``X`` and ``y`` at the very start of the
-# computation and use ``mark_as_X()`` and ``mark_as_y()`` as soon as possible
-# -- as soon as we have separate ``X`` and ``y`` tables that are aligned and
-# have one row per sample. In particular, we should use ``mark_as_X()`` before
-# doing any feature extraction and selection.
-#
-# Skrub will let us apply transformers before reaching ``mark_as_X`` because
-# there are special cases where we know it is safe and faster to do so, but in
-# general we should be careful and remember to separate features and targets
-# and then use ``mark_as_X()`` as soon as possible.
-
-
-# %%
-# A full example
-# --------------
-#
-# Now we know enough about skrub expressions to create a full estimator for a
-# realistic example and evaluate its performance.
-#
-# We finally come back to the credit fraud dataset from the previous example.
-
-# %%
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_credit_fraud()
-
-# %%
-# The ``baskets`` are orders on a e-commerce website. Note that in the drop-down
-# menu, ``"baskets"`` is both an input variable and a table that is
-# marked as ``X``, and therefore it is colored blue and has a double box.
-
-# %%
-baskets = skrub.var("baskets", dataset.baskets[["ID"]]).skb.mark_as_X()
-baskets
-
-# %%
-# Each basket is associated with a fraud flag. The ``"fraud_flag"`` column in the
-# ``baskets`` table is red as it is marked as ``y``.
-
-# %%
-fraud_flags = skrub.var("fraud_flags", dataset.baskets["fraud_flag"]).skb.mark_as_y()
-fraud_flags
-
-# %%
-# Each basket contains one or several products. The ``"basket_ID"`` column
-# in the ``"products"`` refers to the ``"ID"`` column in the ``"baskets"``
-# table. Given the information we have about a basket, we want to predict if
-# its purchase was fraudulent.
-
-# %%
-products = skrub.var("products", dataset.products)
-products
-
-# %%
-# The ``"baskets"`` table itself contains no
-# information: we have to bring in features extracted from the corresponding
-# products. To do so, we must first vectorize the products lines, so that we
-# can aggregate the extracted numeric features, and attach the resulting vector
-# to the corresponding ``"basket_ID"`` and thus the corresponding fraud flag.
-#
-# We do the vectorization with a ``TableVectorizer``. When we run the
-# cross-validation, the ``"baskets"`` table (which we marked as X) will be
-# split in train and test sets. To fit our ``TableVectorizer``, we want to use
-# only products that belong to a basket in the train set, not one in the test
-# set. So we do a semi-join to exclude any products that do not belong to a
-# basket that can be found in the ``"baskets"`` table currently being
-# processed.
-
-# %%
-
-# Note: using deferred or even defining a function is completely optional here,
-# because the computation it contains involves no control flow or assignments.
-# we only do it to simplify the computation graph by collapsing several
-# operations into a single function call.
-
-
-@skrub.deferred
-def filter_products(products, baskets):
-    return products[products["basket_ID"].isin(baskets["ID"])]
-
-
-kept_products = filter_products(products, baskets)
-
-# %%
-# We saw before that a transformer can be restricted to a subset of columns in
-# a dataframe. The ``cols`` argument can be a column name, a list of column
-# names, or a skrub selector. Skrub selectors are similar to Polars or Ibis
-# selectors: they can be used to select columns by name (including glob and
-# regex patterns), dtype or other criteria, and they can be combined with the
-# same operators as Python sets. Here we have a very simple selection to make:
-# we want to vectorize all columns in the ``"products"`` table, _except_ the
-# ``"basket_ID"`` column, which we will need for joining. So we can just use the
-# ``exclude_cols`` parameter.
-
-# %%
-vectorizer = skrub.TableVectorizer(high_cardinality=skrub.StringEncoder(n_components=8))
-vectorized_products = products.skb.apply(vectorizer, exclude_cols="basket_ID")
-
-
-# %%
-# Now that we vectorized the product rows, we can aggregate them by basket ID
-# before joining on the ``"baskets"`` table.
-
-# %%
-
-# Here, deferred is still optional and is used to simplify the display of the
-# computation graph. By commenting it out, you will be able to see all the steps
-# that are performed in ``join_baskets_products`` to reach the final result.
-
-
-@skrub.deferred
-def join_baskets_products(baskets, vectorized_products):
-    aggregated_products = (
-        vectorized_products.groupby("basket_ID").agg("mean").reset_index()
-    )
-    joined = baskets.merge(
-        aggregated_products, left_on="ID", right_on="basket_ID"
-    ).drop(columns=["ID", "basket_ID"])
-    return joined
-
-
-features = join_baskets_products(baskets, vectorized_products)
-features
-
-# %%
-# We can now add the supervised learner, the final step in our computation graph.
-
-# %%
-from sklearn.ensemble import HistGradientBoostingClassifier
-
-predictions = features.skb.apply(HistGradientBoostingClassifier(), y=fraud_flags)
-predictions
-
-# %%
-# Finally, we can evaluate our estimator.
-
-# %%
-cv_results = predictions.skb.cross_validate(scoring="roc_auc", verbose=1, n_jobs=4)
-cv_results
-
-# %%
-cv_results["test_score"]
-
-# %%
-# If we are happy with the cross-validation scores, we might want to
-# fit the estimator on the data we have, and save the estimator object
-# so that we can use it later, say for next week's transactions.
-
-# %%
-estimator = predictions.skb.get_estimator(fitted=True)
-estimator
-
-# %%
-# Normally we would save it in a file; here we simulate that by pickling the
-# model into a string so that our example notebook does not need to access the
-# filesystem.
-
-# %%
-import pickle
-
-saved_model = pickle.dumps(estimator)
-
-# %%
-# Let us say we got some new data, and we want to use the model we just saved
-# to make predictions on it.
-#
-
-# %%
-new_data = skrub.datasets.fetch_credit_fraud(split="test")
-new_baskets = new_data.baskets[["ID"]]
-new_products = new_data.products
-new_products
-
-# %%
-# We can then load the saved model to make a prediction on the new data. To do so,
-# we need to pass the new data so that the same transformations can be applied to
-# it.
-# Note that the loaded estimator will expect the same input variables as the
-# original pipeline, with the same names: this is why we pass a dictionary that
-# contains the new dataframes and the same variable names (``baskets`` and
-# ``products``) that we used when we built the original estimator.
-
-# %%
-loaded_model = pickle.loads(saved_model)
-loaded_model.predict({"baskets": new_baskets, "products": new_products})
-
-# %%
-# Conclusion
-# ----------
-# In this example we have seen how to build a full estimator with skrub expressions,
-# including a supervised learner, and how to evaluate it with cross-validation.
-# The example included complex data preparation and aggregation steps that involved
-# joining dataframes: thanks to the skrub expressions, we could build a full
-# pipeline that ensures that no leakage occurs, and we were able to obtain a
-# new estimator that we could save and use later.
-#
-# This is not all there is to the skrub expressions: in the next example we will
-# go over hyperparameter tuning, which expressions simplify a lot.
-#
-# A few more advanced features have not been shown and remain for more
-# specialized examples, for example:
-#
-# - naming nodes, passing the value for any node with the inputs
-# - ``.skb.applied_estimator``
-# - ``.skb.concat_horizontal``, ``.skb.drop`` and ``.skb.select``, more skrub selectors
-# - ``.skb.freeze_after_fit`` (niche / very advanced)


Reviews:
user: login: jeromedockes
body: I think this is an improvement, thanks a lot @Vincent-Maladiere ! :rocket: I think once the conflicts and links are good we could merge and iterate in other prs
state: APPROVED
author_association: MEMBER
submitted_at: 2025-04-16T16:17:12Z
user: login: GaelVaroquaux
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-04-17T07:58:56Z
user: login: Vincent-Maladiere
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-04-18T07:46:32Z
user: login: Vincent-Maladiere
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-04-22T14:54:59Z
user: login: Vincent-Maladiere
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-04-22T14:56:25Z
user: login: Vincent-Maladiere
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-04-22T15:23:37Z

Review comments:
diff_hunk: @@ -0,0 +1,353 @@
+.. _skrub_pipeline:
+
+===================================================
+Skrub Pipeline: flexible machine learning pipelines
+===================================================
+
+.. currentmodule:: skrub
+
+Introduction
+~~~~~~~~~~~~
+
+Skrub provides an easy way to build complex, flexible machine learning pipelines.
+There are several problems that are not easily addressed with standard scikit-learn
+tools such as :class:`~sklearn.pipeline.Pipeline` and
+:class:`~sklearn.compose.ColumnTransformer`, and for which a Skrub pipeline offers a
+solution:
+
+- **Multiple tables**: When there are several tables of different shapes (for example,
+  "Customers", "Orders", and "Products" tables), standard scikit-learn estimators fall
+  short, as they expect a single design matrix ``X`` and a target array ``y``, with one
+  row per observation.
+- **DataFrame wrangling**: Performing typical DataFrame operations such as projections,
+  joins, and aggregations—leveraging the powerful APIs of pandas or polars—is not
+  easily expressed in standard pipelines.
+- **Iterative development**: Building a pipeline step by step while inspecting
+  intermediate results allows for a short feedback loop and early discovery of errors.
+- **Hyperparameter tuning**: Choices of estimators, hyperparameters, and even the
+  pipeline architecture can be guided by validation scores. Specifying a grid of
+  hyperparameters separately from the model (as in
+  :class:`~sklearn.model_selection.GridSearchCV`) becomes difficult in complex
+  pipelines.
+
+What is the difference with scikit-learn :class:`~sklearn.pipeline.Pipeline`?
+=============================================================================
+
+Scikit-learn pipelines represent a linear sequence of transformations on one
+table with a fixed number of rows.
+
+.. image:: ../../_static/sklearn_pipeline.svg
path: doc/skrub_pipeline.rst
user: login: jeromedockes
body: maybe we need 1 less level of `../` now?
created_at: 2025-04-16T16:10:11Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,353 @@
+.. _skrub_pipeline:
+
+===================================================
+Skrub Pipeline: flexible machine learning pipelines
+===================================================
+
+.. currentmodule:: skrub
+
+Introduction
+~~~~~~~~~~~~
+
+Skrub provides an easy way to build complex, flexible machine learning pipelines.
+There are several problems that are not easily addressed with standard scikit-learn
+tools such as :class:`~sklearn.pipeline.Pipeline` and
+:class:`~sklearn.compose.ColumnTransformer`, and for which a Skrub pipeline offers a
+solution:
+
+- **Multiple tables**: When there are several tables of different shapes (for example,
+  "Customers", "Orders", and "Products" tables), standard scikit-learn estimators fall
+  short, as they expect a single design matrix ``X`` and a target array ``y``, with one
+  row per observation.
+- **DataFrame wrangling**: Performing typical DataFrame operations such as projections,
+  joins, and aggregations—leveraging the powerful APIs of pandas or polars—is not
+  easily expressed in standard pipelines.
+- **Iterative development**: Building a pipeline step by step while inspecting
+  intermediate results allows for a short feedback loop and early discovery of errors.
+- **Hyperparameter tuning**: Choices of estimators, hyperparameters, and even the
+  pipeline architecture can be guided by validation scores. Specifying a grid of
+  hyperparameters separately from the model (as in
+  :class:`~sklearn.model_selection.GridSearchCV`) becomes difficult in complex
+  pipelines.
+
+What is the difference with scikit-learn :class:`~sklearn.pipeline.Pipeline`?
+=============================================================================
+
+Scikit-learn pipelines represent a linear sequence of transformations on one
+table with a fixed number of rows.
+
+.. image:: ../../_static/sklearn_pipeline.svg
+    :width: 500
+
+Skrub expressions, on the other hand, can manipulate any number of variables.
+The transformation they perform is not a linear sequence but any Directed
+Acyclic Graph of computations.
+
+.. image:: ../../_static/skrub_expressions.svg
+
+What is the difference with orchestrators like Apache Airflow?
path: doc/skrub_pipeline.rst
user: login: jeromedockes
body: those 2 "what is the difference" sectiosn are very useful :+1: 
created_at: 2025-04-16T16:10:47Z
author_association: MEMBER
diff_hunk: @@ -1,292 +0,0 @@
-"""
-Supervised estimators and cross-validation
-------------------------------------------
-
-We can use ``.skb.apply()`` to add scikit-learn transformers to the computation,
-but also to add a supervised learner like ``HistGradientBoostingClassifier``
-or ``LogisticRegression``.
-
-When we have built a full estimator, we want to run cross-validation to
-measure its performance. We may also want to tune hyperparameters (shown in
-the next example).
-
-Skrub can run the cross-validation for us. In order to do so, it needs to
-split the dataset into training and testing sets. For this to happen, we need
-to tell skrub which items in our pipeline constitute the feature matrix ``X``
-and the targets (labels, outputs) ``y``.
-
-Indeed, an estimator built from a skrub expression can accept inputs that are
-not yet neatly organized into correctly-aligned ``X`` and ``y`` matrices.
-There may be some steps (such as loading data, performing joins or
-aggregations, separating features and targets from a single source) that need
-to be performed in order to construct the design matrix the targets.
-
-To indicate which intermediate results to split, we call ``.skb.mark_as_X()``
-and ``.skb.mark_as_y()`` on the appropriate objects:
-"""
-
-# %%
-from sklearn.ensemble import HistGradientBoostingRegressor
-
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_employee_salaries()
-
-full_data = skrub.var("data", dataset.employee_salaries)
-
-employees = full_data.drop(columns="current_annual_salary").skb.mark_as_X()
-salaries = full_data["current_annual_salary"].skb.mark_as_y()
-
-vectorizer = skrub.TableVectorizer(
-    high_cardinality=skrub.MinHashEncoder(n_components=8)
-)
-predictions = employees.skb.apply(vectorizer).skb.apply(
-    HistGradientBoostingRegressor(), y=salaries
-)
-predictions
-
-# %%
-# Note how ``apply`` works for supervised estimators: we pass the targets
-# (another skrub expression) as the argument for the parameter ``y``.
-#
-# If you unfold the dropdown to see the computation graph, you will see the
-# inner nodes that have been marked as ``X`` and ``y``: these nodes are highlighted
-# in blue and red, respectively. Furthermore, the ``data`` is an input variable,
-# so it is marked with a double box.
-#
-# To perform
path: examples/expressions/12_scikit_learn_estimators_in_expressions.py
user: login: jeromedockes
body: I think there may be a bit of explanation about mark_as_x etc to rescue out of this section and move elswhere but I'm not sure
created_at: 2025-04-16T16:13:46Z
author_association: MEMBER
diff_hunk: @@ -1,292 +0,0 @@
-"""
-Supervised estimators and cross-validation
-------------------------------------------
-
-We can use ``.skb.apply()`` to add scikit-learn transformers to the computation,
-but also to add a supervised learner like ``HistGradientBoostingClassifier``
-or ``LogisticRegression``.
-
-When we have built a full estimator, we want to run cross-validation to
-measure its performance. We may also want to tune hyperparameters (shown in
-the next example).
-
-Skrub can run the cross-validation for us. In order to do so, it needs to
-split the dataset into training and testing sets. For this to happen, we need
-to tell skrub which items in our pipeline constitute the feature matrix ``X``
-and the targets (labels, outputs) ``y``.
-
-Indeed, an estimator built from a skrub expression can accept inputs that are
-not yet neatly organized into correctly-aligned ``X`` and ``y`` matrices.
-There may be some steps (such as loading data, performing joins or
-aggregations, separating features and targets from a single source) that need
-to be performed in order to construct the design matrix the targets.
-
-To indicate which intermediate results to split, we call ``.skb.mark_as_X()``
-and ``.skb.mark_as_y()`` on the appropriate objects:
-"""
-
-# %%
-from sklearn.ensemble import HistGradientBoostingRegressor
-
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_employee_salaries()
-
-full_data = skrub.var("data", dataset.employee_salaries)
-
-employees = full_data.drop(columns="current_annual_salary").skb.mark_as_X()
-salaries = full_data["current_annual_salary"].skb.mark_as_y()
-
-vectorizer = skrub.TableVectorizer(
-    high_cardinality=skrub.MinHashEncoder(n_components=8)
-)
-predictions = employees.skb.apply(vectorizer).skb.apply(
-    HistGradientBoostingRegressor(), y=salaries
-)
-predictions
-
-# %%
-# Note how ``apply`` works for supervised estimators: we pass the targets
-# (another skrub expression) as the argument for the parameter ``y``.
-#
-# If you unfold the dropdown to see the computation graph, you will see the
-# inner nodes that have been marked as ``X`` and ``y``: these nodes are highlighted
-# in blue and red, respectively. Furthermore, the ``data`` is an input variable,
-# so it is marked with a double box.
-#
-# To perform
-# cross-validation, skrub first runs all the prior steps until it has computed
-# ``X`` and ``y``. Then, it splits those (with the usual scikit-learn
-# cross-validation tools, as is done in ``sklearn.cross_validate``) and runs
-# the rest of the computation inside of the cross-validation loop.
-#
-
-# %%
-predictions.skb.cross_validate()
-
-
-# %%
-# In the simplest case where we have ``X`` and ``y`` available right from the
-# start, we can indicate that simply by creating the input variables with
-# ``skrub.X()`` and ``skrub.y()`` instead of ``skrub.var()``: ``skrub.X()`` is
-# syntactic sugar for ``skrub.var("X").skb.mark_as_X()``.
-
-# %%
-# The construction of ``X`` and ``y`` must be done before splitting the samples
-# into cross-validation folds; it happens outside of the cross-validation loop.
-#
-# This means that any step we perform in this part of the computation has
-# access to the full data (training and test sets). We should not use
-# estimators that learn from the data before reaching the cross-validation
-# loop, or we might obtain optimistic scores due to data leakage.
-# Join operations that involve aggregations are not safe either, because they
-# may involve information from the test set. Finally, no
-# choices or hyperparameters can be tuned in this part of the computation
-# (tuning is discussed in more detail in the next example).
-#
-# Therefore, we should build ``X`` and ``y`` at the very start of the
-# computation and use ``mark_as_X()`` and ``mark_as_y()`` as soon as possible
-# -- as soon as we have separate ``X`` and ``y`` tables that are aligned and
-# have one row per sample. In particular, we should use ``mark_as_X()`` before
-# doing any feature extraction and selection.
-#
-# Skrub will let us apply transformers before reaching ``mark_as_X`` because
-# there are special cases where we know it is safe and faster to do so, but in
-# general we should be careful and remember to separate features and targets
-# and then use ``mark_as_X()`` as soon as possible.
-
-
-# %%
-# A full example
-# --------------
-#
-# Now we know enough about skrub expressions to create a full estimator for a
-# realistic example and evaluate its performance.
-#
-# We finally come back to the credit fraud dataset from the previous example.
-
-# %%
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_credit_fraud()
-
-# %%
-# The ``baskets`` are orders on a e-commerce website. Note that in the drop-down
-# menu, ``"baskets"`` is both an input variable and a table that is
-# marked as ``X``, and therefore it is colored blue and has a double box.
-
-# %%
-baskets = skrub.var("baskets", dataset.baskets[["ID"]]).skb.mark_as_X()
-baskets
-
-# %%
-# Each basket is associated with a fraud flag. The ``"fraud_flag"`` column in the
-# ``baskets`` table is red as it is marked as ``y``.
-
-# %%
-fraud_flags = skrub.var("fraud_flags", dataset.baskets["fraud_flag"]).skb.mark_as_y()
-fraud_flags
-
-# %%
-# Each basket contains one or several products. The ``"basket_ID"`` column
-# in the ``"products"`` refers to the ``"ID"`` column in the ``"baskets"``
-# table. Given the information we have about a basket, we want to predict if
-# its purchase was fraudulent.
-
-# %%
-products = skrub.var("products", dataset.products)
-products
-
-# %%
-# The ``"baskets"`` table itself contains no
-# information: we have to bring in features extracted from the corresponding
-# products. To do so, we must first vectorize the products lines, so that we
-# can aggregate the extracted numeric features, and attach the resulting vector
-# to the corresponding ``"basket_ID"`` and thus the corresponding fraud flag.
-#
-# We do the vectorization with a ``TableVectorizer``. When we run the
-# cross-validation, the ``"baskets"`` table (which we marked as X) will be
-# split in train and test sets. To fit our ``TableVectorizer``, we want to use
-# only products that belong to a basket in the train set, not one in the test
-# set. So we do a semi-join to exclude any products that do not belong to a
-# basket that can be found in the ``"baskets"`` table currently being
-# processed.
-
-# %%
-
-# Note: using deferred or even defining a function is completely optional here,
-# because the computation it contains involves no control flow or assignments.
-# we only do it to simplify the computation graph by collapsing several
-# operations into a single function call.
-
-
-@skrub.deferred
path: examples/expressions/12_scikit_learn_estimators_in_expressions.py
user: login: jeromedockes
body: I agree this is redundant with the example 10. I wonder if there is another place we could illustrate the recommended practice of grouping steps together in `deferred` functions to simplify the graph avoid potential issues with pickling etc
created_at: 2025-04-16T16:15:13Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,353 @@
+.. _skrub_pipeline:
+
+===================================================
+Skrub Pipeline: flexible machine learning pipelines
path: doc/skrub_pipeline.rst
user: login: GaelVaroquaux
body: I hate to be that person, but that title is too generic: it does not make us guess what is different to other pipelines.

The cool thing about the skrub pipeline is that it applies to any wrangling tool.

Maybe "Cross-validate or tune any wrangling pipeline"
created_at: 2025-04-17T07:58:56Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,353 @@
+.. _skrub_pipeline:
+
+===================================================
+Skrub Pipeline: flexible machine learning pipelines
path: doc/skrub_pipeline.rst
user: login: Vincent-Maladiere
body: Agreed, was looking for something better. Let's use your suggestion
created_at: 2025-04-18T07:46:31Z
author_association: MEMBER
diff_hunk: @@ -1,292 +0,0 @@
-"""
-Supervised estimators and cross-validation
-------------------------------------------
-
-We can use ``.skb.apply()`` to add scikit-learn transformers to the computation,
-but also to add a supervised learner like ``HistGradientBoostingClassifier``
-or ``LogisticRegression``.
-
-When we have built a full estimator, we want to run cross-validation to
-measure its performance. We may also want to tune hyperparameters (shown in
-the next example).
-
-Skrub can run the cross-validation for us. In order to do so, it needs to
-split the dataset into training and testing sets. For this to happen, we need
-to tell skrub which items in our pipeline constitute the feature matrix ``X``
-and the targets (labels, outputs) ``y``.
-
-Indeed, an estimator built from a skrub expression can accept inputs that are
-not yet neatly organized into correctly-aligned ``X`` and ``y`` matrices.
-There may be some steps (such as loading data, performing joins or
-aggregations, separating features and targets from a single source) that need
-to be performed in order to construct the design matrix the targets.
-
-To indicate which intermediate results to split, we call ``.skb.mark_as_X()``
-and ``.skb.mark_as_y()`` on the appropriate objects:
-"""
-
-# %%
-from sklearn.ensemble import HistGradientBoostingRegressor
-
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_employee_salaries()
-
-full_data = skrub.var("data", dataset.employee_salaries)
-
-employees = full_data.drop(columns="current_annual_salary").skb.mark_as_X()
-salaries = full_data["current_annual_salary"].skb.mark_as_y()
-
-vectorizer = skrub.TableVectorizer(
-    high_cardinality=skrub.MinHashEncoder(n_components=8)
-)
-predictions = employees.skb.apply(vectorizer).skb.apply(
-    HistGradientBoostingRegressor(), y=salaries
-)
-predictions
-
-# %%
-# Note how ``apply`` works for supervised estimators: we pass the targets
-# (another skrub expression) as the argument for the parameter ``y``.
-#
-# If you unfold the dropdown to see the computation graph, you will see the
-# inner nodes that have been marked as ``X`` and ``y``: these nodes are highlighted
-# in blue and red, respectively. Furthermore, the ``data`` is an input variable,
-# so it is marked with a double box.
-#
-# To perform
path: examples/expressions/12_scikit_learn_estimators_in_expressions.py
user: login: Vincent-Maladiere
body: That depends on the role we want the User Guide to play. I tried to keep it as light as possible so that readers understand the main expression concepts, without diving straight into all the details (the example showcases these details better IMO because they are built around complete use-cases). For instance, if I add `mark_as_X` in the User Guide, I should also add the `choose_from` mechanism, etc. Does that make sense?
created_at: 2025-04-22T14:54:59Z
author_association: MEMBER
diff_hunk: @@ -1,292 +0,0 @@
-"""
-Supervised estimators and cross-validation
-------------------------------------------
-
-We can use ``.skb.apply()`` to add scikit-learn transformers to the computation,
-but also to add a supervised learner like ``HistGradientBoostingClassifier``
-or ``LogisticRegression``.
-
-When we have built a full estimator, we want to run cross-validation to
-measure its performance. We may also want to tune hyperparameters (shown in
-the next example).
-
-Skrub can run the cross-validation for us. In order to do so, it needs to
-split the dataset into training and testing sets. For this to happen, we need
-to tell skrub which items in our pipeline constitute the feature matrix ``X``
-and the targets (labels, outputs) ``y``.
-
-Indeed, an estimator built from a skrub expression can accept inputs that are
-not yet neatly organized into correctly-aligned ``X`` and ``y`` matrices.
-There may be some steps (such as loading data, performing joins or
-aggregations, separating features and targets from a single source) that need
-to be performed in order to construct the design matrix the targets.
-
-To indicate which intermediate results to split, we call ``.skb.mark_as_X()``
-and ``.skb.mark_as_y()`` on the appropriate objects:
-"""
-
-# %%
-from sklearn.ensemble import HistGradientBoostingRegressor
-
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_employee_salaries()
-
-full_data = skrub.var("data", dataset.employee_salaries)
-
-employees = full_data.drop(columns="current_annual_salary").skb.mark_as_X()
-salaries = full_data["current_annual_salary"].skb.mark_as_y()
-
-vectorizer = skrub.TableVectorizer(
-    high_cardinality=skrub.MinHashEncoder(n_components=8)
-)
-predictions = employees.skb.apply(vectorizer).skb.apply(
-    HistGradientBoostingRegressor(), y=salaries
-)
-predictions
-
-# %%
-# Note how ``apply`` works for supervised estimators: we pass the targets
-# (another skrub expression) as the argument for the parameter ``y``.
-#
-# If you unfold the dropdown to see the computation graph, you will see the
-# inner nodes that have been marked as ``X`` and ``y``: these nodes are highlighted
-# in blue and red, respectively. Furthermore, the ``data`` is an input variable,
-# so it is marked with a double box.
-#
-# To perform
-# cross-validation, skrub first runs all the prior steps until it has computed
-# ``X`` and ``y``. Then, it splits those (with the usual scikit-learn
-# cross-validation tools, as is done in ``sklearn.cross_validate``) and runs
-# the rest of the computation inside of the cross-validation loop.
-#
-
-# %%
-predictions.skb.cross_validate()
-
-
-# %%
-# In the simplest case where we have ``X`` and ``y`` available right from the
-# start, we can indicate that simply by creating the input variables with
-# ``skrub.X()`` and ``skrub.y()`` instead of ``skrub.var()``: ``skrub.X()`` is
-# syntactic sugar for ``skrub.var("X").skb.mark_as_X()``.
-
-# %%
-# The construction of ``X`` and ``y`` must be done before splitting the samples
-# into cross-validation folds; it happens outside of the cross-validation loop.
-#
-# This means that any step we perform in this part of the computation has
-# access to the full data (training and test sets). We should not use
-# estimators that learn from the data before reaching the cross-validation
-# loop, or we might obtain optimistic scores due to data leakage.
-# Join operations that involve aggregations are not safe either, because they
-# may involve information from the test set. Finally, no
-# choices or hyperparameters can be tuned in this part of the computation
-# (tuning is discussed in more detail in the next example).
-#
-# Therefore, we should build ``X`` and ``y`` at the very start of the
-# computation and use ``mark_as_X()`` and ``mark_as_y()`` as soon as possible
-# -- as soon as we have separate ``X`` and ``y`` tables that are aligned and
-# have one row per sample. In particular, we should use ``mark_as_X()`` before
-# doing any feature extraction and selection.
-#
-# Skrub will let us apply transformers before reaching ``mark_as_X`` because
-# there are special cases where we know it is safe and faster to do so, but in
-# general we should be careful and remember to separate features and targets
-# and then use ``mark_as_X()`` as soon as possible.
-
-
-# %%
-# A full example
-# --------------
-#
-# Now we know enough about skrub expressions to create a full estimator for a
-# realistic example and evaluate its performance.
-#
-# We finally come back to the credit fraud dataset from the previous example.
-
-# %%
-import skrub.datasets
-
-dataset = skrub.datasets.fetch_credit_fraud()
-
-# %%
-# The ``baskets`` are orders on a e-commerce website. Note that in the drop-down
-# menu, ``"baskets"`` is both an input variable and a table that is
-# marked as ``X``, and therefore it is colored blue and has a double box.
-
-# %%
-baskets = skrub.var("baskets", dataset.baskets[["ID"]]).skb.mark_as_X()
-baskets
-
-# %%
-# Each basket is associated with a fraud flag. The ``"fraud_flag"`` column in the
-# ``baskets`` table is red as it is marked as ``y``.
-
-# %%
-fraud_flags = skrub.var("fraud_flags", dataset.baskets["fraud_flag"]).skb.mark_as_y()
-fraud_flags
-
-# %%
-# Each basket contains one or several products. The ``"basket_ID"`` column
-# in the ``"products"`` refers to the ``"ID"`` column in the ``"baskets"``
-# table. Given the information we have about a basket, we want to predict if
-# its purchase was fraudulent.
-
-# %%
-products = skrub.var("products", dataset.products)
-products
-
-# %%
-# The ``"baskets"`` table itself contains no
-# information: we have to bring in features extracted from the corresponding
-# products. To do so, we must first vectorize the products lines, so that we
-# can aggregate the extracted numeric features, and attach the resulting vector
-# to the corresponding ``"basket_ID"`` and thus the corresponding fraud flag.
-#
-# We do the vectorization with a ``TableVectorizer``. When we run the
-# cross-validation, the ``"baskets"`` table (which we marked as X) will be
-# split in train and test sets. To fit our ``TableVectorizer``, we want to use
-# only products that belong to a basket in the train set, not one in the test
-# set. So we do a semi-join to exclude any products that do not belong to a
-# basket that can be found in the ``"baskets"`` table currently being
-# processed.
-
-# %%
-
-# Note: using deferred or even defining a function is completely optional here,
-# because the computation it contains involves no control flow or assignments.
-# we only do it to simplify the computation graph by collapsing several
-# operations into a single function call.
-
-
-@skrub.deferred
path: examples/expressions/12_scikit_learn_estimators_in_expressions.py
user: login: Vincent-Maladiere
body: We have an entry about deferred functions in the user guide, what do you think about it?
created_at: 2025-04-22T14:56:25Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,353 @@
+.. _skrub_pipeline:
+
+===================================================
+Skrub Pipeline: flexible machine learning pipelines
path: doc/skrub_pipeline.rst
user: login: Vincent-Maladiere
body: What about "Cross-validate or tune any **data** pipeline", since I'm not sure most readers will be familiar with wrangling pipelines
created_at: 2025-04-22T15:23:37Z
author_association: MEMBER

PR comments:
user: login: Vincent-Maladiere
created_at: 2025-04-22T15:24:22Z
body: The last TODO is to update the images, once we agree with the content
author_association: MEMBER
user: login: jeromedockes
created_at: 2025-04-23T16:25:11Z
body: thanks a lot @Vincent-Maladiere  that is super useful!!
author_association: MEMBER