[MRG] Correct a few small doc issues following #280 (#281)

* add docstrings for constraints class, pairs and chunks methods

* fix missing optional values and descriptions, uniformize

* fix indentation problems in docstring and uniformize

* fix more small things

* cosmit

* remove unnecessary line

* missing blank line for pep8

Author: bellet

doc/conf.py

@@ -69,3 +69,14 @@
 # Switch to old behavior with html4, for a good display of references,
 # as described in https://github.com/sphinx-doc/sphinx/issues/6705
 html4_writer = True
+
+
+# Temporary work-around for spacing problem between parameter and parameter
+# type in the doc, see https://github.com/numpy/numpydoc/issues/215. The bug
+# has been fixed in sphinx (https://github.com/sphinx-doc/sphinx/pull/5976) but
+# through a change in sphinx basic.css except rtd_theme does not use basic.css.
+# In an ideal world, this would get fixed in this PR:
+# https://github.com/readthedocs/sphinx_rtd_theme/pull/747/files
+def setup(app):
+    app.add_javascript('js/copybutton.js')
+    app.add_stylesheet("basic.css")

doc/weakly_supervised.rst

@@ -483,7 +483,7 @@ is the off-diagonal L1 norm.
        L1-penalized log-determinant regularization <https://icml.cc/Conferences/2009/papers/46.pdf>`_.
        ICML 2009.
 
-    .. [2] Adapted from https://gist.github.com/kcarnold/5439945
+    .. [2] Code adapted from https://gist.github.com/kcarnold/5439945
 
 .. _rca:
 
@@ -893,6 +893,6 @@ by default, :math:`D_{ld}(\mathbf{\cdot, \cdot})` is the LogDet divergence:
        `Metric Learning from Relative Comparisons by Minimizing Squared
        Residual <http://www.cs.ucla.edu/~weiwang/paper/ICDM12.pdf>`_. ICDM 2012
 
-    .. [2] Adapted from https://gist.github.com/kcarnold/5439917
+    .. [2] Code adapted from https://gist.github.com/kcarnold/5439917
 
 

metric_learn/base_metric.py

@@ -39,7 +39,7 @@ def score_pairs(self, pairs):
 
     Returns
     -------
-    scores: `numpy.ndarray` of shape=(n_pairs,)
+    scores : `numpy.ndarray` of shape=(n_pairs,)
       The score of every pair.
 
     See Also
@@ -69,27 +69,27 @@ def _prepare_inputs(self, X, y=None, type_of_inputs='classic',
 
     Parameters
     ----------
-    input: array-like
+    X : array-like
       The input data array to check.
 
     y : array-like
       The input labels array to check.
 
-    type_of_inputs: `str` {'classic', 'tuples'}
+    type_of_inputs : `str` {'classic', 'tuples'}
       The type of inputs to check. If 'classic', the input should be
       a 2D array-like of points or a 1D array like of indicators of points. If
       'tuples', the input should be a 3D array-like of tuples or a 2D
       array-like of indicators of tuples.
 
-    **kwargs: dict
+    **kwargs : dict
       Arguments to pass to check_input.
 
     Returns
     -------
     X : `numpy.ndarray`
       The checked input data array.
 
-    y: `numpy.ndarray` (optional)
+    y : `numpy.ndarray` (optional)
       The checked input labels array.
     """
     self._check_preprocessor()
@@ -203,7 +203,7 @@ def score_pairs(self, pairs):
 
     Returns
     -------
-    scores: `numpy.ndarray` of shape=(n_pairs,)
+    scores : `numpy.ndarray` of shape=(n_pairs,)
       The learned Mahalanobis distance for every pair.
 
     See Also
@@ -271,7 +271,7 @@ def metric_fun(u, v, squared=False):
 
       Returns
       -------
-      distance: float
+      distance : float
         The distance between u and v according to the new metric.
       """
       u = validate_vector(u)

metric_learn/constraints.py

@@ -20,12 +20,12 @@ class Constraints(object):
   Parameters
   ----------
   partial_labels : `numpy.ndarray` of ints, shape=(n_samples,)
-      Array of labels, with -1 indicating unknown label.
+    Array of labels, with -1 indicating unknown label.
 
   Attributes
   ----------
   partial_labels : `numpy.ndarray` of ints, shape=(n_samples,)
-      Array of labels, with -1 indicating unknown label.
+    Array of labels, with -1 indicating unknown label.
   """
 
   def __init__(self, partial_labels):
@@ -46,26 +46,29 @@ def positive_negative_pairs(self, num_constraints, same_length=False,
 
     Parameters
     ----------
-      num_constraints : int
-        Number of positive and negative constraints to generate.
-      same_length : bool, optional (default=False)
-        If True, forces the number of positive and negative pairs to be
-        equal by ignoring some pairs from the larger set.
-      random_state : int or numpy.RandomState or None, optional (default=None)
-        A pseudo random number generator object or a seed for it if int.
+    num_constraints : int
+      Number of positive and negative constraints to generate.
+
+    same_length : bool, optional (default=False)
+      If True, forces the number of positive and negative pairs to be
+      equal by ignoring some pairs from the larger set.
+
+    random_state : int or numpy.RandomState or None, optional (default=None)
+      A pseudo random number generator object or a seed for it if int.
+
     Returns
     -------
     a : array-like, shape=(n_constraints,)
-    1D array of indicators for the left elements of positive pairs.
+      1D array of indicators for the left elements of positive pairs.
 
     b : array-like, shape=(n_constraints,)
-    1D array of indicators for the right elements of positive pairs.
+      1D array of indicators for the right elements of positive pairs.
 
     c : array-like, shape=(n_constraints,)
-    1D array of indicators for the left elements of negative pairs.
+      1D array of indicators for the left elements of negative pairs.
 
     d : array-like, shape=(n_constraints,)
-    1D array of indicators for the right elements of negative pairs.
+      1D array of indicators for the right elements of negative pairs.
     """
     random_state = check_random_state(random_state)
     a, b = self._pairs(num_constraints, same_label=True,

metric_learn/itml.py

@@ -211,9 +211,9 @@ class ITML(_BaseITML, _PairsClassifierMixin):
 
   References
   ----------
-  .. [1] `Information-theoretic Metric Learning
+  .. [1] Jason V. Davis, et al. `Information-theoretic Metric Learning
          <http://www.prateekjain.org/publications/all_papers\
-/DavisKJSD07_ICML.pdf>`_ Jason V. Davis, et al.
+          /DavisKJSD07_ICML.pdf>`_. ICML 2007.
   """
 
   def fit(self, pairs, y, bounds=None, calibration_params=None):
@@ -229,8 +229,10 @@ def fit(self, pairs, y, bounds=None, calibration_params=None):
       3D Array of pairs with each row corresponding to two points,
       or 2D array of indices of pairs if the metric learner uses a
       preprocessor.
+
     y: array-like, of shape (n_constraints,)
       Labels of constraints. Should be -1 for dissimilar pair, 1 for similar.
+
     bounds : array-like of two numbers
       Bounds on similarity, aside slack variables, s.t.
       ``d(a, b) < bounds_[0]`` for all given pairs of similar points ``a``
@@ -239,6 +241,7 @@ def fit(self, pairs, y, bounds=None, calibration_params=None):
       If not provided at initialization, bounds_[0] and bounds_[1] will be
       set to the 5th and 95th percentile of the pairwise distances among all
       points present in the input `pairs`.
+
     calibration_params : `dict` or `None`
       Dictionary of parameters to give to `calibrate_threshold` for the
       threshold calibration step done at the end of `fit`. If `None` is
@@ -280,7 +283,7 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
       `num_labeled` was deprecated in version 0.5.0 and will
       be removed in 0.6.0.
 
-  num_constraints: int, optional (default=None)
+  num_constraints : int, optional (default=None)
     Number of constraints to generate. If None, default to `20 *
     num_classes**2`.
 

metric_learn/lfda.py

@@ -39,10 +39,16 @@ class LFDA(MahalanobisMixin, TransformerMixin):
     defaults to min(7, n_features - 1).
 
   embedding_type : str, optional (default: 'weighted')
-    Type of metric in the embedding space
-      'weighted'        - weighted eigenvectors
-      'orthonormalized' - orthonormalized
-      'plain'           - raw eigenvectors
+    Type of metric in the embedding space.
+
+    'weighted'
+      weighted eigenvectors
+
+    'orthonormalized'
+      orthonormalized
+
+    'plain'
+      raw eigenvectors
 
   preprocessor : array-like, shape=(n_samples, n_features) or callable
     The preprocessor to call to get tuples from indices. If array-like,
@@ -67,13 +73,14 @@ class LFDA(MahalanobisMixin, TransformerMixin):
 
   References
   ------------------
-  .. [1] `Dimensionality Reduction of Multimodal Labeled Data by Local Fisher
-         Discriminant Analysis <http://www.ms.k.u-tokyo.ac.jp/2007/LFDA.pdf>`_
-         Masashi Sugiyama.
-
-  .. [2] `Local Fisher Discriminant Analysis on Beer Style Clustering
-         <https://gastrograph.com/resources/whitepapers/local-fisher\
--discriminant-analysis-on-beer-style-clustering.html#>`_ Yuan Tang.
+  .. [1] Masashi Sugiyama. `Dimensionality Reduction of Multimodal Labeled
+         Data by Local Fisher Discriminant Analysis
+         <http://www.ms.k.u-tokyo.ac.jp/2007/LFDA.pdf>`_. JMLR 2007.
+
+  .. [2] Yuan Tang. `Local Fisher Discriminant Analysis on Beer Style
+        Clustering
+        <https://gastrograph.com/resources/whitepapers/local-fisher\
+        -discriminant-analysis-on-beer-style-clustering.html#>`_.
   '''
 
   def __init__(self, n_components=None, num_dims='deprecated',

metric_learn/lmnn.py

@@ -137,10 +137,11 @@ class LMNN(MahalanobisMixin, TransformerMixin):
 
   References
   ----------
-  .. [1] `Distance Metric Learning for Large Margin Nearest Neighbor
-         Classification <http://papers.nips.cc/paper/2795-distance-metric\
--learning-for-large-margin-nearest-neighbor-classification>`_
-         Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul
+  .. [1] K. Q. Weinberger, J. Blitzer, L. K. Saul. `Distance Metric
+         Learning for Large Margin Nearest Neighbor Classification
+         <http://papers.nips.cc/paper/2795-distance-metric\
+         -learning-for-large-margin-nearest-neighbor-classification>`_. NIPS
+         2005.
   """
 
   def __init__(self, init=None, k=3, min_iter=50, max_iter=1000,

metric_learn/lsml.py

@@ -208,7 +208,7 @@ class LSML(_BaseLSML, _QuadrupletsClassifierMixin):
          Squared Residual
          <http://www.cs.ucla.edu/~weiwang/paper/ICDM12.pdf>`_. ICDM 2012.
 
-  .. [2] Adapted from https://gist.github.com/kcarnold/5439917
+  .. [2] Code adapted from https://gist.github.com/kcarnold/5439917
 
   See Also
   --------

metric_learn/mlkr.py

@@ -73,15 +73,15 @@ class MLKR(MahalanobisMixin, TransformerMixin):
       :meth:`fit` and n_features_a must be less than or equal to that.
       If ``n_components`` is not None, n_features_a must match it.
 
-  A0: Not used.
+  A0 : Not used.
     .. deprecated:: 0.5.0
       `A0` was deprecated in version 0.5.0 and will
       be removed in 0.6.0. Use 'init' instead.
 
-  tol: float, optional (default=None)
+  tol : float, optional (default=None)
     Convergence tolerance for the optimization.
 
-  max_iter: int, optional (default=1000)
+  max_iter : int, optional (default=1000)
     Cap on number of conjugate gradient iterations.
 
   verbose : bool, optional (default=False)
@@ -118,9 +118,9 @@ class MLKR(MahalanobisMixin, TransformerMixin):
 
   References
   ----------
-  .. [1] `Information-theoretic Metric Learning
-     <http://machinelearning.wustl.edu/\
-mlpapers/paper_files/icml2007_DavisKJSD07.pdf>`_ Jason V. Davis, et al.
+  .. [1] K.Q. Weinberger and G. Tesauto. `Metric Learning for Kernel
+         Regression <http://proceedings.mlr.press/v2/weinberger07a\
+         /weinberger07a.pdf>`_. AISTATS 2007.
   """
 
   def __init__(self, n_components=None, num_dims='deprecated', init=None,

metric_learn/mmc.py

@@ -383,10 +383,6 @@ class MMC(_BaseMMC, _PairsClassifierMixin):
       An SPD matrix of shape (n_features, n_features), that will
       be used as such to initialize the metric.
 
-  preprocessor : array-like, shape=(n_samples, n_features) or callable
-    The preprocessor to call to get tuples from indices. If array-like,
-    tuples will be gotten like this: X[indices].
-
   A0 : Not used.
     .. deprecated:: 0.5.0
       `A0` was deprecated in version 0.5.0 and will
@@ -442,10 +438,11 @@ class MMC(_BaseMMC, _PairsClassifierMixin):
 
   References
   ----------
-  .. [1] `Distance metric learning with application to clustering with
-         side-information <http://papers.nips.cc/paper/2164-distance-metric-\
-learning-with-application-to-clustering-with-side-information.pdf>`_
-         Xing, Jordan, Russell, Ng.
+  .. [1] Xing, Jordan, Russell, Ng. `Distance metric learning with application
+         to clustering with side-information
+         <http://papers.nips.cc/paper/2164-distance-metric-\
+         learning-with-application-to-clustering-with-side-information.pdf>`_.
+         NIPS 2002.
 
   See Also
   --------
@@ -538,10 +535,6 @@ class MMC_Supervised(_BaseMMC, TransformerMixin):
       A numpy array of shape (n_features, n_features), that will
       be used as such to initialize the metric.
 
-  preprocessor : array-like, shape=(n_samples, n_features) or callable
-    The preprocessor to call to get tuples from indices. If array-like,
-    tuples will be gotten like this: X[indices].
-
   A0 : Not used.
     .. deprecated:: 0.5.0
       `A0` was deprecated in version 0.5.0 and will

metric_learn/nca.py

@@ -123,7 +123,7 @@ class NCA(MahalanobisMixin, TransformerMixin):
   .. [1] J. Goldberger, G. Hinton, S. Roweis, R. Salakhutdinov. `Neighbourhood
          Components Analysis
          <http://www.cs.nyu.edu/~roweis/papers/ncanips.pdf>`_.
-         Advances in Neural Information Processing Systems. 17, 513-520, 2005.
+         NIPS 2005.
 
   .. [2] Wikipedia entry on `Neighborhood Components Analysis
          <https://en.wikipedia.org/wiki/Neighbourhood_components_analysis>`_

metric_learn/rca.py

@@ -72,10 +72,10 @@ class RCA(MahalanobisMixin, TransformerMixin):
 
   References
   ------------------
-  .. [1] `Adjustment learning and relevant component analysis
-         <http://citeseerx.ist.\
-psu.edu/viewdoc/download?doi=10.1.1.19.2871&rep=rep1&type=pdf>`_ Noam
-         Shental, et al.
+  .. [1] Noam Shental, et al. `Adjustment learning and relevant component
+         analysis <http://citeseerx.ist.\
+         psu.edu/viewdoc/download?doi=10.1.1.19.2871&rep=rep1&type=pdf>`_ .
+         ECCV 2002.
 
 
   Attributes

metric_learn/sdml.py

@@ -211,14 +211,12 @@ class SDML(_BaseSDML, _PairsClassifierMixin):
 
   References
   ----------
+  .. [1] Qi et al. `An efficient sparse metric learning in high-dimensional
+         space via L1-penalized log-determinant regularization
+         <http://www.machinelearning.org/archive/icml2009/papers/46.pdf>`_.
+         ICML 2009.
 
-  .. [1] Qi et al.
-         An efficient sparse metric learning in high-dimensional space via
-         L1-penalized log-determinant regularization. ICML 2009.
-         http://lms.comp.nus.edu.sg/sites/default/files/publication\
--attachments/icml09-guojun.pdf
-
-  .. [2] Adapted from https://gist.github.com/kcarnold/5439945
+  .. [2] Code adapted from https://gist.github.com/kcarnold/5439945
   """
 
   def fit(self, pairs, y, calibration_params=None):