Merge branch 'main' into dop-better-error-message-dispatch

Author: rcap107

CHANGES.rst

@@ -9,6 +9,11 @@ Release history
 Ongoing Development
 ===================
 
+New features
+------------
+- The :meth:`DataOp.skb.full_report` now displays the time each node took to
+  evaluate. :pr:`1596` by :user:`Jérôme Dockès <jeromedockes>`.
+
 Changes
 -------
 - Ken embeddings are now deprecated, the functions :func:`datasets.get_ken_embeddings`,
@@ -28,9 +33,24 @@ Bugfixes
   was displayed). :pr:`1590` by :user:`Jérôme Dockès <jeromedockes>`.
 - Fixed an error that occurred when using ``.skb.concat`` with a pandas dataframe
   with column names that aren't strings. :pr:`1594` by :user:`Riccardo Cappuzzo<rcap107>`.
+- Fixed the range from which :func:`choose_float` and :func:`choose_int` sample
+  values when ``log=False`` and ``n_steps`` is ``None``. It was between ``low``
+  and ``low + high``, now it is between ``low`` and ``high``. :pr:`1603` by
+  :user:`Jérôme Dockès <jeromedockes>`.
 - DataOp hyperparameter search would raise an error when doing classification
   and using the ``scoring`` parameter, when the dataop contained no variables.
   Fixed in :pr:`1601` by :user:`Jérôme Dockès <jeromedockes>`.
+- :class:`SkrubLearner` used to do a prediction on the train set during
+  ``fit()``, this has been fixed.
+  :pr:`1610` by :user:`Jérôme Dockès <jeromedockes>`.
+- :class:`DataOp` would raise errors when containing subclasses of list, tuple
+  or dict that cannot be initialized with an instance of the builtin type (such
+  as classes created by ``collections.namedtuple``), this has been fixed.
+  DataOps now only recurse into the builtin collections to evaluate their items
+  (not into their subclasses). If you need the items evaluated (ie if they
+  contain DataOps or Choices), store them in one of the builtin collections.
+  :pr:`1612` by :user:`Jérôme Dockès <jeromedockes>`.
+
 
 Release 0.6.1
 ===================

doc/conf.py

@@ -105,6 +105,16 @@
 
 extensions.append("sphinx_autosummary_accessors")
 
+try:
+    import sphinx_sitemap  # noqa: F401
+
+    extensions.append("sphinx_sitemap")
+    html_baseurl = "https://skrub-data.org/stable/"
+    sitemap_url_scheme = "{link}"
+except ImportError:
+    print("ERROR: sphinx_sitemap import failed")
+
+
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 #
@@ -133,6 +143,10 @@
 # The full version, including alpha/beta/rc tags.
 release = version
 
+# Adding noindex to dev pages so that search engines do not index them
+if "dev" in release:
+    html_meta = {"robots": "noindex"}
+
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
 #

doc/userguide_data_cleaning.rst

@@ -22,10 +22,10 @@ Cleaning dataframes and parsing datatypes
 ... })
 >>> df_clean = Cleaner().fit_transform(df)
 >>> df_clean
-      id       date
-  0   1 2024-05-05
-  1   2 2024-05-06
-  2   3 2024-05-07
+   id        date
+0   1  2024-05-05
+1   2  2024-05-06
+2   3  2024-05-07
 
 The |Cleaner| converts data types and Nan values in dataframes to ease downstream preprocessing. It includes:
 

doc/userguide_data_ops_validation.rst

@@ -2,12 +2,12 @@
 
 .. _userguide_data_ops_validation:
 
-=====================================
+======================================
 Tuning and validating Skrub DataOps plans
-=====================================
+======================================
 
 To evaluate the prediction performance of our plan, we can fit it on a training
-dataset, then obtaining prediction on an unseen, test dataset.
+dataset, then obtain predictions on an unseen, test dataset.
 
 In scikit-learn, we pass to estimators and pipelines an ``X`` and ``y`` matrix
 with one row per observation from the start. Therefore, we can split the
@@ -59,19 +59,18 @@ Now we can add our supervised estimator:
 <Apply Ridge>
 Result:
 ―――――――
-         target
-0    182.673354
-1     90.998607
-2    166.113476
-3    156.034880
-4    133.659575
-..          ...
-437  180.323365
-438  135.798908
-439  139.855630
-440  182.645829
-441   83.564413
-[442 rows x 1 columns]
+0      182.673354
+1       90.998607
+2      166.113476
+3      156.034880
+4      133.659575
+          ...
+437    180.323365
+438    135.798908
+439    139.855630
+440    182.645829
+441     83.564413
+Name: target, Length: 442, dtype: float64
 
 
 Once a pipeline is defined and the ``X`` and ``y`` nodes are identified, skrub

doc/userguide_tablereport.rst

@@ -22,7 +22,7 @@ Usage
 The command ``TableReport(df).open()`` opens the report in a browser window.
 
 A demo of the |TableReport|
-Pre-computed examples of of the |TableReport| are available
+Pre-computed examples of the |TableReport| are available
 `here <https://skrub-data.org/skrub-reports/examples/index.html>`_, and you can
 try it out on your data `here <https://skrub-data.org/skrub-reports/index.html>`_.
 

doc/userguide_tablevectorizer.rst

@@ -82,8 +82,9 @@ problems, but may not beat properly tuned ad-hoc pipelines.
 
 .. list-table:: Parameter values choice of :class:`TableVectorizer` when using the :func:`tabular_pipeline` function
    :header-rows: 1
+   :widths: 25 25 25 25
 
-   * -
+   * - Parameter
      - ``RandomForest`` models
      - ``HistGradientBoosting`` models
      - Linear models and others