DOC Minor changes to DOCS to address sphinx warnings (scikit-learn#12999)

Author: thomasjpfan

doc/modules/clustering.rst

@@ -1311,24 +1311,24 @@ more broadly common names.
  Machine Learning Research 3: 583–617.
  `doi:10.1162/153244303321897735 <http://strehl.com/download/strehl-jmlr02.pdf>`_.
 
- * [VEB2009] Vinh, Epps, and Bailey, (2009). "Information theoretic measures
+ * `Wikipedia entry for the (normalized) Mutual Information
+ <https://en.wikipedia.org/wiki/Mutual_Information>`_
+
+ * `Wikipedia entry for the Adjusted Mutual Information
+ <https://en.wikipedia.org/wiki/Adjusted_Mutual_Information>`_
+ 
+ .. [VEB2009] Vinh, Epps, and Bailey, (2009). "Information theoretic measures
  for clusterings comparison". Proceedings of the 26th Annual International
  Conference on Machine Learning - ICML '09.
  `doi:10.1145/1553374.1553511 <https://dl.acm.org/citation.cfm?doid=1553374.1553511>`_.
  ISBN 9781605585161.
 
- * [VEB2010] Vinh, Epps, and Bailey, (2010). "Information Theoretic Measures for
+ .. [VEB2010] Vinh, Epps, and Bailey, (2010). "Information Theoretic Measures for
  Clusterings Comparison: Variants, Properties, Normalization and
  Correction for Chance". JMLR
  <http://jmlr.csail.mit.edu/papers/volume11/vinh10a/vinh10a.pdf>
-
- * `Wikipedia entry for the (normalized) Mutual Information
- <https://en.wikipedia.org/wiki/Mutual_Information>`_
-
- * `Wikipedia entry for the Adjusted Mutual Information
- <https://en.wikipedia.org/wiki/Adjusted_Mutual_Information>`_
  
- * [YAT2016] Yang, Algesheimer, and Tessone, (2016). "A comparative analysis of
+ .. [YAT2016] Yang, Algesheimer, and Tessone, (2016). "A comparative analysis of
  community
  detection algorithms on artificial networks". Scientific Reports 6: 30750.
  `doi:10.1038/srep30750 <https://www.nature.com/articles/srep30750>`_.
@@ -1655,7 +1655,8 @@ Drawbacks
 .. _calinski_harabasz_index:
 
 Calinski-Harabasz Index
-----------------------
+-----------------------
+
 If the ground truth labels are not known, the Calinski-Harabasz index
 (:func:`sklearn.metrics.calinski_harabasz_score`) - also known as the Variance 
 Ratio Criterion - can be used to evaluate the model, where a higher 

doc/modules/ensemble.rst

@@ -1053,10 +1053,10 @@ Vector Machine, a Decision Tree, and a K-nearest neighbor classifier::
  :align: center
  :scale: 75%
 
-Using the `VotingClassifier` with `GridSearch`
-----------------------------------------------
+Using the `VotingClassifier` with `GridSearchCV`
+------------------------------------------------
 
-The `VotingClassifier` can also be used together with `GridSearch` in order
+The `VotingClassifier` can also be used together with `GridSearchCV` in order
 to tune the hyperparameters of the individual estimators::
 
  >>> from sklearn.model_selection import GridSearchCV

doc/whats_new/v0.20.rst

@@ -2,7 +2,7 @@
 
 .. currentmodule:: sklearn
 
- .. _changes_0_20_3:
+.. _changes_0_20_3:
 
 Version 0.20.3
 ==============

doc/whats_new/v0.21.rst

@@ -52,7 +52,7 @@ Support for Python 3.4 and below has been officially dropped.
  :user:`Adrin Jalali <adrinjalali>`, and :user:`Erich Schubert <kno10>`.
 
 :mod:`sklearn.datasets`
- ......................
+.......................
 
  - |Fix| Added support for 64-bit group IDs and pointers in SVMLight files
  :class:`datasets.svmlight_format` :issue:`10727` by
@@ -168,7 +168,7 @@ Support for Python 3.4 and below has been officially dropped.
  to add flexibility in identifying the best
  estimator. An example for this interface has been added.
  :issue:`11354` by :user:`Wenhao Zhang <wenhaoz@ucla.edu>`,
- `Joel Nothman`_ and `Adrin Jalali`_
+ `Joel Nothman`_ and :user:`Adrin Jalali <adrinjalali>`.
 
 - |Enhancement| Classes :class:`~model_selection.GridSearchCV`,
  :class:`~model_selection.RandomizedSearchCV`, and methods

examples/covariance/plot_robust_vs_empirical_covariance.py

@@ -6,7 +6,7 @@
 The usual covariance maximum likelihood estimate is very sensitive to the
 presence of outliers in the data set. In such a case, it would be better to
 use a robust estimator of covariance to guarantee that the estimation is
-resistant to "erroneous" observations in the data set.
+resistant to "erroneous" observations in the data set. [1]_, [2]_
 
 Minimum Covariance Determinant Estimator
 ----------------------------------------
@@ -23,7 +23,7 @@
 we end up with robust estimates of the data set location and covariance.
 
 The Minimum Covariance Determinant estimator (MCD) has been introduced by
-P.J.Rousseuw in [1]_.
+P.J.Rousseuw in [3]_.
 
 Evaluation
 ----------
@@ -42,14 +42,14 @@
 
 References
 ----------
-.. [1] P. J. Rousseeuw. Least median of squares regression. Journal of American
- Statistical Ass., 79:871, 1984.
-.. [2] Johanna Hardin, David M Rocke. The distribution of robust distances.
+.. [1] Johanna Hardin, David M Rocke. The distribution of robust distances.
  Journal of Computational and Graphical Statistics. December 1, 2005,
  14(4): 928-946.
-.. [3] Zoubir A., Koivunen V., Chakhchoukh Y. and Muma M. (2012). Robust
+.. [2] Zoubir A., Koivunen V., Chakhchoukh Y. and Muma M. (2012). Robust
  estimation in signal processing: A tutorial-style treatment of
  fundamental concepts. IEEE Signal Processing Magazine 29(4), 61-80.
+.. [3] P. J. Rousseeuw. Least median of squares regression. Journal of American
+ Statistical Ass., 79:871, 1984.
 
 """
 print(__doc__)

sklearn/covariance/robust_covariance.py

@@ -601,14 +601,14 @@ class MinCovDet(EmpiricalCovariance):
  References
  ----------
 
- .. [Rouseeuw1984] `P. J. Rousseeuw. Least median of squares regression.
- J. Am Stat Ass, 79:871, 1984.`
- .. [Rousseeuw] `A Fast Algorithm for the Minimum Covariance Determinant
+ .. [Rouseeuw1984] P. J. Rousseeuw. Least median of squares regression.
+ J. Am Stat Ass, 79:871, 1984.
+ .. [Rousseeuw] A Fast Algorithm for the Minimum Covariance Determinant
  Estimator, 1999, American Statistical Association and the American
- Society for Quality, TECHNOMETRICS`
- .. [ButlerDavies] `R. W. Butler, P. L. Davies and M. Jhun,
+ Society for Quality, TECHNOMETRICS
+ .. [ButlerDavies] R. W. Butler, P. L. Davies and M. Jhun,
  Asymptotics For The Minimum Covariance Determinant Estimator,
- The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400`
+ The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400
 
  """
  _nonrobust_covariance = staticmethod(empirical_covariance)
@@ -685,9 +685,9 @@ def correct_covariance(self, data):
  References
  ----------
 
- .. [RVD] `A Fast Algorithm for the Minimum Covariance
+ .. [RVD] A Fast Algorithm for the Minimum Covariance
  Determinant Estimator, 1999, American Statistical Association
- and the American Society for Quality, TECHNOMETRICS`
+ and the American Society for Quality, TECHNOMETRICS
 
  Returns
  -------
@@ -726,9 +726,9 @@ def reweight_covariance(self, data):
  References
  ----------
 
- .. [RVDriessen] `A Fast Algorithm for the Minimum Covariance
+ .. [RVDriessen] A Fast Algorithm for the Minimum Covariance
  Determinant Estimator, 1999, American Statistical Association
- and the American Society for Quality, TECHNOMETRICS`
+ and the American Society for Quality, TECHNOMETRICS
 
  Returns
  -------

sklearn/decomposition/fastica_.py

@@ -259,9 +259,9 @@ def my_g(x):
  faster for Fortran-ordered input.
 
  Implemented using FastICA:
- `A. Hyvarinen and E. Oja, Independent Component Analysis:
+ *A. Hyvarinen and E. Oja, Independent Component Analysis:
  Algorithms and Applications, Neural Networks, 13(4-5), 2000,
- pp. 411-430`
+ pp. 411-430*
 
  """
  random_state = check_random_state(random_state)
@@ -455,9 +455,9 @@ def my_g(x):
  Notes
  -----
  Implementation based on
- `A. Hyvarinen and E. Oja, Independent Component Analysis:
+ *A. Hyvarinen and E. Oja, Independent Component Analysis:
  Algorithms and Applications, Neural Networks, 13(4-5), 2000,
- pp. 411-430`
+ pp. 411-430*
 
  """
  def __init__(self, n_components=None, algorithm='parallel', whiten=True,

sklearn/decomposition/incremental_pca.py

@@ -117,22 +117,22 @@ class IncrementalPCA(_BasePCA):
  Notes
  -----
  Implements the incremental PCA model from:
- `D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual
+ *D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual
  Tracking, International Journal of Computer Vision, Volume 77, Issue 1-3,
- pp. 125-141, May 2008.`
+ pp. 125-141, May 2008.*
  See https://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf
 
  This model is an extension of the Sequential Karhunen-Loeve Transform from:
- `A. Levy and M. Lindenbaum, Sequential Karhunen-Loeve Basis Extraction and
+ *A. Levy and M. Lindenbaum, Sequential Karhunen-Loeve Basis Extraction and
  its Application to Images, IEEE Transactions on Image Processing, Volume 9,
- Number 8, pp. 1371-1374, August 2000.`
+ Number 8, pp. 1371-1374, August 2000.*
  See https://www.cs.technion.ac.il/~mic/doc/skl-ip.pdf
 
  We have specifically abstained from an optimization used by authors of both
  papers, a QR decomposition used in specific situations to reduce the
  algorithmic complexity of the SVD. The source for this technique is
- `Matrix Computations, Third Edition, G. Holub and C. Van Loan, Chapter 5,
- section 5.4.4, pp 252-253.`. This technique has been omitted because it is
+ *Matrix Computations, Third Edition, G. Holub and C. Van Loan, Chapter 5,
+ section 5.4.4, pp 252-253.*. This technique has been omitted because it is
  advantageous only when decomposing a matrix with ``n_samples`` (rows)
  >= 5/3 * ``n_features`` (columns), and hurts the readability of the
  implemented algorithm. This would be a good opportunity for future
@@ -141,11 +141,11 @@ class IncrementalPCA(_BasePCA):
  References
  ----------
  D. Ross, J. Lim, R. Lin, M. Yang. Incremental Learning for Robust Visual
-  Tracking, International Journal of Computer Vision, Volume 77,
-  Issue 1-3, pp. 125-141, May 2008.
+ Tracking, International Journal of Computer Vision, Volume 77,
+ Issue 1-3, pp. 125-141, May 2008.
 
  G. Golub and C. Van Loan. Matrix Computations, Third Edition, Chapter 5,
-  Section 5.4.4, pp. 252-253.
+ Section 5.4.4, pp. 252-253.
 
  See also
  --------

sklearn/decomposition/pca.py

@@ -246,8 +246,8 @@ class PCA(_BasePCA):
 
  References
  ----------
- For n_components == 'mle', this class uses the method of `Minka, T. P.
- "Automatic choice of dimensionality for PCA". In NIPS, pp. 598-604`
+ For n_components == 'mle', this class uses the method of *Minka, T. P.
+ "Automatic choice of dimensionality for PCA". In NIPS, pp. 598-604*
 
  Implements the probabilistic PCA model from:
  `Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal
@@ -259,13 +259,13 @@ class PCA(_BasePCA):
  For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.
 
  For svd_solver == 'randomized', see:
- `Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).
+ *Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).
  "Finding structure with randomness: Probabilistic algorithms for
  constructing approximate matrix decompositions".
- SIAM review, 53(2), 217-288.` and also
- `Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).
+ SIAM review, 53(2), 217-288.* and also
+ *Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).
  "A randomized algorithm for the decomposition of matrices".
- Applied and Computational Harmonic Analysis, 30(1), 47-68.`
+ Applied and Computational Harmonic Analysis, 30(1), 47-68.*
 
 
  Examples

sklearn/feature_extraction/text.py

@@ -1205,12 +1205,12 @@ class TfidfTransformer(BaseEstimator, TransformerMixin):
  References
  ----------
 
- .. [Yates2011] `R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern
- Information Retrieval. Addison Wesley, pp. 68-74.`
+ .. [Yates2011] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern
+ Information Retrieval. Addison Wesley, pp. 68-74.
 
- .. [MRS2008] `C.D. Manning, P. Raghavan and H. Schütze (2008).
+ .. [MRS2008] C.D. Manning, P. Raghavan and H. Schütze (2008).
  Introduction to Information Retrieval. Cambridge University
- Press, pp. 118-120.`
+ Press, pp. 118-120.
  """
 
  def __init__(self, norm='l2', use_idf=True, smooth_idf=True,