fixing broken crossreferences

Author: rcap107

doc/modules/data_ops/basics/control_flow.rst

@@ -168,9 +168,9 @@ Finally, there are other situations where using :func:`deferred` can be helpful:
 
 .. rubric:: Examples
 
-- See :ref:`sphx_glr_auto_examples_data_ops_1110_data_ops_intro.py` for an introductory
+- See :ref:`sphx_glr_auto_tutorials_1110_data_ops_intro.py` for an introductory
   example on how to use skrub DataOps on a single dataframe.
-- See :ref:`sphx_glr_auto_examples_data_ops_1120_multiple_tables.py` for an example
+- See :ref:`sphx_glr_auto_examples_02_data_ops_1120_multiple_tables.py` for an example
   of how skrub DataOps can be used to process multiple tables using dataframe APIs.
-- See :ref:`sphx_glr_auto_examples_data_ops_1130_choices.py` for an example of
+- See :ref:`sphx_glr_auto_examples_02_data_ops_1130_choices.py` for an example of
   hyper-parameter tuning using skrub DataOps.

doc/modules/data_ops/ml_pipeline/applying_different_transformers.rst

@@ -150,4 +150,4 @@ to obtain the final result:
 
 More info on advanced column selection and manipulation be found in
 :ref:`user_guide_selectors` and example
-:ref:`sphx_glr_auto_examples_0090_apply_to_cols.py`.
+:ref:`sphx_glr_auto_examples_0010_apply_to_cols.py`.

doc/modules/data_ops/ml_pipeline/subsampling_data.rst

@@ -26,4 +26,4 @@ set to ``True`` to force using the subsampling when we call them. Note that
 even if we set ``keep_subsampling=True``, subsampling is not applied when using
 ``predict``.
 
-See more details in a :ref:`full example <sphx_glr_auto_examples_data_ops_1140_subsampling.py>`.
+See more details in a :ref:`full example <sphx_glr_auto_examples_02_data_ops_1140_subsampling.py>`.

doc/modules/data_ops/validation/exporting_data_ops.rst

@@ -62,5 +62,5 @@ or in a different environment:
 >>> loaded_learner.fit({"orders": new_orders_df})
 SkrubLearner(data_op=<Apply TableVectorizer>)
 
-See :ref:`sphx_glr_auto_examples_data_ops_1150_use_case.py` for an example of how
+See :ref:`sphx_glr_auto_examples_02_data_ops_1150_use_case.py` for an example of how
 to use the learner in a microservice.

doc/modules/data_ops/validation/hyperparameter_tuning.rst

@@ -162,7 +162,7 @@ search respectively), or with the ``choose`` parameter of
 :meth:`.skb.make_learner() <DataOp.skb.make_learner>`.
 
 A full example of how to use hyperparameter search is available in
-:ref:`sphx_glr_auto_examples_data_ops_1130_choices.py`, and a full example using
+:ref:`sphx_glr_auto_examples_02_data_ops_1130_choices.py`, and a full example using
 Optuna is in :ref:`example_optuna_choices`.
 
 |

doc/modules/joining_tables/assembling.rst

@@ -71,4 +71,4 @@ in the right table (the table to be added). This is done by estimating the value
 that the missing rows would have by training a machine learning model on the data
 we have access to.
 
-This transformer is explored in more detail in :ref:`this example <sphx_glr_auto_examples_0080_interpolation_join.py>`.
+This transformer is explored in more detail in :ref:`this example <sphx_glr_auto_examples_03_joining_0080_interpolation_join.py>`.

doc/tutorials/0000_getting_started.py

@@ -112,7 +112,8 @@
 # To handle rich tabular data and feed it to a machine learning model, the
 # pipeline returned by |tabular_pipeline| preprocesses and encodes
 # strings, categories and dates using the |TableVectorizer|.
-# See its documentation or :ref:`sphx_glr_auto_examples_0010_encodings.py` for
+# See its documentation or
+# :ref:`sphx_glr_auto_examples_01_encoding_0010_encodings.py` for
 # more details. An overview of the chosen defaults is available in
 # :ref:`user_guide_tabular_pipeline`.
 
@@ -190,7 +191,8 @@
 # which uses pre-trained language models retrieved from the HuggingFace hub to
 # create meaningful text embeddings.
 # See :ref:`user_guide_encoders_index` for more details on all the categorical encoders
-# provided by skrub, and :ref:`sphx_glr_auto_examples_0010_encodings.py` for a
+# provided by skrub, and
+# :ref:`sphx_glr_auto_examples_01_encoding_0010_encodings.py` for a
 # comparison between the different methods.
 #
 

examples/03_joining/0070_join_aggregation.py

@@ -162,7 +162,7 @@
 #
 # 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>`_
+# See :ref:`this example <sphx_glr_auto_examples_01_encoding_0010_encodings.py>`
 # for more details about these encoding choices.
 from sklearn.preprocessing import OrdinalEncoder
 

skrub/_table_vectorizer.py

@@ -593,7 +593,7 @@ class TableVectorizer(TransformerMixin, SkrubBaseEstimator):
         ``specific_transformers``.
         Consider wrapping the ``TableVectorizer`` in  :class:`~skrub.ApplyToCols`
         to select or exclude specific columns from the processing. Alternatively,
-        the :ref:`skrub Data Ops <user_guide_data_ops>` allow for more complex
+        the :ref:`skrub Data Ops <user_guide_data_ops_index>` allow for more complex
         pre-processing.
 
     drop_null_fraction : float or None, default=1.0