Strict doc building

.github/workflows/docs.yml

@@ -17,6 +17,10 @@ jobs:
       run: |
         cd doc
         make html
+    - name: Check links
+      run: |
+        cd doc
+        make linkcheck
     - name: Pull latest gh-pages
       if: (contains(github.ref, 'develop') || contains(github.ref, 'master')) && github.event_name == 'push'
       run: |

autosklearn/__init__.py

@@ -15,7 +15,7 @@
 if os.name != 'posix':
     raise ValueError(
         'Detected unsupported operating system: %s. Please check '
-        'the compability information of auto-sklearn: http://automl.github.io'
+        'the compability information of auto-sklearn: https://automl.github.io'
         '/auto-sklearn/stable/installation.html#windows-osx-compability' %
         sys.platform
     )

autosklearn/automl.py

@@ -999,7 +999,7 @@ def fit_pipeline(
             attributes will be automatically One-Hot encoded. The values
             used for a categorical attribute must be integers, obtained for
             example by `sklearn.preprocessing.LabelEncoder
-            <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
+            <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
 
         Returns
         -------

autosklearn/estimators.py

@@ -390,7 +390,7 @@ def fit_pipeline(
             attributes will be automatically One-Hot encoded. The values
             used for a categorical attribute must be integers, obtained for
             example by `sklearn.preprocessing.LabelEncoder
-            <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
+            <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
 
         Returns
         -------
@@ -635,7 +635,7 @@ def fit(self, X, y,
             attributes will be automatically One-Hot encoded. The values
             used for a categorical attribute must be integers, obtained for
             example by `sklearn.preprocessing.LabelEncoder
-            <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
+            <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
 
         dataset_name : str, optional (default=None)
             Create nicer output. If None, a string will be determined by the

autosklearn/pipeline/components/base.py

@@ -115,8 +115,8 @@ def fit(self, X, y):
         Notes
         -----
         Please see the `scikit-learn API documentation
-        <http://scikit-learn.org/dev/developers/index.html#apis-of-scikit
-        -learn-objects>`_ for further information."""
+        <https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects>`_
+        for further information."""
         raise NotImplementedError()
 
     def set_hyperparameters(self, configuration, init_params=None):
@@ -208,8 +208,8 @@ def predict(self, X):
         Notes
         -----
         Please see the `scikit-learn API documentation
-        <http://scikit-learn.org/dev/developers/index.html#apis-of-scikit
-        -learn-objects>`_ for further information."""
+        <https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects>`_
+        for further information."""
         raise NotImplementedError()
 
     def predict_proba(self, X):
@@ -260,8 +260,8 @@ def transform(self, X):
         Notes
         -----
         Please see the `scikit-learn API documentation
-        <http://scikit-learn.org/dev/developers/index.html#apis-of-scikit
-        -learn-objects>`_ for further information."""
+        <https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects>`_
+        for further information."""
         raise NotImplementedError()
 
     def get_preprocessor(self):
@@ -301,8 +301,8 @@ def predict(self, X):
         Notes
         -----
         Please see the `scikit-learn API documentation
-        <http://scikit-learn.org/dev/developers/index.html#apis-of-scikit
-        -learn-objects>`_ for further information."""
+        <https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects>`_
+        for further information."""
         raise NotImplementedError()
 
     def get_estimator(self):

autosklearn/pipeline/implementations/util.py

@@ -11,7 +11,7 @@ def softmax(df):
         return np.transpose(np.array((1 - ppositive, ppositive)))
     else:
         # Compute the Softmax like it is described here:
-        # http://www.iro.umontreal.ca/~bengioy/dlbook/numerical.html
+        # https://www.deeplearningbook.org/contents/numerical.html
         tmp = df - np.max(df, axis=1).reshape((-1, 1))
         tmp = np.exp(tmp)
         return tmp / np.sum(tmp, axis=1).reshape((-1, 1))

doc/api.rst

@@ -45,7 +45,7 @@ Note: The default ``autosklearn.metrics.f1``, ``autosklearn.metrics.precision``
 built-in metrics are applicable only for binary classification. In order to apply them on multilabel and multiclass
 classification, please use the corresponding metrics with an appropriate averaging mechanism, such as ``autosklearn.metrics.f1_macro``.
 For more information about how these metrics are used, please read
-`this scikit-learn documentation <http://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures>`_.
+`this scikit-learn documentation <https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures>`_.
 
 .. autoclass:: autosklearn.metrics.accuracy
 

doc/conf.py

@@ -254,7 +254,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+# html_static_path = ['_static']
 
 # Add any extra paths that contain custom files (such as robots.txt or
 # .htaccess) here, relative to this directory. These files are copied
@@ -384,3 +384,7 @@
 # "init"
 # Only the __init__ method’s docstring is inserted.
 autoclass_content = 'both'
+
+
+def setup(app):
+    app.warningiserror = True

doc/extending.rst

@@ -128,7 +128,7 @@ you have to implement
 and
 :meth:`AutoSklearnClassificationAlgorithm.predict() <autosklearn.pipeline.components.base.AutoSklearnClassificationAlgorithm.predict>`
 . These are an implementation of the `scikit-learn predictor API
-<http://scikit-learn.org/stable/developers/index.html#apis-of-scikit-learn-objects>`_.
+<https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects>`_.
 
 Regression
 ==========
@@ -139,7 +139,7 @@ you have to implement
 and
 :meth:`AutoSklearnRegressionAlgorithm.predict() <autosklearn.pipeline.components.base.AutoSklearnRegressionAlgorithm.predict>`
 . These are an implementation of the `scikit-learn predictor API
-<http://scikit-learn.org/stable/developers/index.html#apis-of-scikit-learn-objects>`_.
+<https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects>`_.
 
 Feature Preprocessing
 =====================
@@ -150,4 +150,4 @@ you have to implement
 and
 :meth:`AutoSklearnPreprocessingAlgorithm.transform() <autosklearn.pipeline.components.base.AutoSklearnPreprocessingAlgorithm.transform>`
 . These are an implementation of the `scikit-learn predictor API
-<http://scikit-learn.org/stable/developers/index.html#apis-of-scikit-learn-objects>`_.
+<https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects>`_.

doc/faq.rst

@@ -1,6 +1,6 @@
 :orphan:
 
-.. _manual:
+.. _faq:
 
 ===
 FAQ
@@ -83,11 +83,11 @@ following:
 
 1. Check if you can use a pre-compiled version of the pyrfr to avoid compiling it yourself. We
    provide pre-compiled versions of the pyrfr on `pypi <https://pypi.org/project/pyrfr/#files>`_.
-3. Check if the dependencies specified under :ref:`installation` are correctly installed,
+2. Check if the dependencies specified under :ref:`installation` are correctly installed,
    especially that you have ``swig`` and a ``C++`` compiler. If you are using an older version of
    the pyrfr (``<=0.8.0``) the dependency on SWIG as stricter and you actually need SWIG3 to
    compile the pyrfr.
-2. If you are not yet using Conda, consider using it; it simplifies installation of the correct
+3. If you are not yet using Conda, consider using it; it simplifies installation of the correct
    dependencies.
 4. Install correct build dependencies before installing the pyrfr, you can check the following
    github issues for suggestions: `1025 <https://github.com/automl/auto-sklearn/issues/1025>`_,
@@ -167,9 +167,9 @@ open an issue for that.
 Can the preprocessing be disabled
 ---------------------------------
 
-Feature preprocessing can be disabled as discussed und :ref:`Restricting the searchspace`. Other
-preprocessing steps such as one hot encoding, missing feature imputation and normalization cannot
-yet be disabled, but we're working on that.
+Feature preprocessing can be disabled as discussed in the example
+:ref:`restricting_the_searchspace`. Other preprocessing steps such as one hot encoding, missing
+feature imputation and normalization cannot yet be disabled, but we're working on that.
 
 Usage
 =====
@@ -178,9 +178,9 @@ Only use interpretable models
 -----------------------------
 
 Auto-sklearn can be restricted to only use interpretable models and preprocessing algorithms.
-Please see the section :ref:`Restricting the searchspace` to learn how to restrict the models
-which are searched over or see
-`this example <examples/40_advanced/example_interpretable_models.html>`_.
+Please see the Section :ref:`restricting_the_searchspace` to learn how to restrict the models
+which are searched over or see the Example
+:ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py`.
 
 We don't provide a judgement which of the models are interpretable as this is very much up to the
 specific use case, but would like to note that decision trees and linear models usually most

doc/index.rst

@@ -20,7 +20,7 @@ replacement for a scikit-learn estimator:
 hyperparameter tuning. It leverages recent advantages in *Bayesian
 optimization*, *meta-learning* and *ensemble construction*. Learn more about
 the technology behind *auto-sklearn* by reading our paper published at
-`NIPS 2015 <http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf>`_
+`NIPS 2015 <https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf>`_
 .
 
 .. topic:: NEW: Auto-sklearn 2.0
@@ -91,7 +91,7 @@ reference to the following paper:
         pages = {2962--2970},
         year = {2015},
         publisher = {Curran Associates, Inc.},
-        url = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
+        url = {https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
      }
 
 If you are using Auto-sklearn 2.0, please also cite

doc/installation.rst

@@ -40,7 +40,7 @@ Then install *auto-sklearn*:
     pip3 install auto-sklearn
 
 We recommend installing *auto-sklearn* into a
-`virtual environment <http://docs.python-guide.org/en/latest/dev/virtualenvs/>`_
+`virtual environment <https://docs.python-guide.org/dev/virtualenvs/>`_
 or an
 `Anaconda environment <https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html>`_.