add missing dois, fix bug in baldwin

docs/source/conf.py

@@ -22,7 +22,7 @@
 author = 'Wes Holliday and Eric Pacuit'
 
 # The full version, including alpha/beta/rc tags
-release = '1.1.0'
+release = '1.1.2'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/margin_based_methods.md

@@ -1,7 +1,7 @@
 Margin Methods
 =======================================
 
-Suppose that $\mathbf{P}$ is a profile.   We write $\mathcal{M}(\mathbf{P})$ for the margin graph of $\mathbf{P}$. 
+Suppose that $\mathbf{P}$ is a profile.  We write $\mathcal{M}(\mathbf{P})$ for the margin graph of $\mathbf{P}$. 
 
 A voting method $F$ is **margin-based** if it satisfies the following invariance property: For all $\mathbf{P}, \mathbf{P}'$, if $\mathcal{M}(\mathbf{P})= \mathcal{M}(\mathbf{P}')$, then $F(\mathbf{P}) = F(\mathbf{P}')$. 
 

docs/source/profiles_with_ties.md

@@ -69,7 +69,7 @@ Use the `ProfileWithTies` class to create a profile in which voters may submit s
 
 ```{eval-rst}
 
-.. info::
+.. note::
     The ``Ranking`` class used to the represent the ballots in a ``ProfileWithTies`` is described on the [ballots](ballots) page.
 
 ```

paper/paper.bib

@@ -6,7 +6,8 @@ @book{Arrow1963
 	Edition = {2nd},
 	Publisher = {John Wiley \& Sons, Inc.},
 	Title = {Social Choice and Individual Values},
-	Year = {1963}}
+	Year = {1963}, 
+	doi = {https://doi.org/10.12987/9780300186987}}
 	
 	@incollection{Aspremont2002,
 	Address = {Amsterdam},
@@ -53,7 +54,7 @@ @incollection{Brams2002
 	Publisher = {North-Holland},
 	Title = {Voting Procedures},
 	Volume = {1},
-	doi = {10.1016/s1574-0110(02)80008-x},
+	doi = {https://doi.org/10.1016/S1574-0110(02)80008-X},
 	Year = {2002}}
 	
 		@book{Brams2007,
@@ -62,7 +63,8 @@ @book{Brams2007
 	Publisher = {Springer},
 	Title = {Approval Voting},
 	address = {New York},
-	Year = {2007}}
+	Year = {2007},
+	doi = {https://doi.org/10.1007/978-0-387-49896-6}}
 
 	@book{Balinski2010,
 	Author = {Michel Balinski and Rida Laraki},
@@ -140,7 +142,8 @@ @article{Gibbard1977
 	Pages = {665-681},
 	Title = {Manipulation of Schemes that Mix Voting with Chance},
 	Volume = {45},
-	Year = {1977}}
+	Year = {1977}, 
+	doi = {https://doi.org/10.2307/1911681}}
 	
 	@article{Green-Armytage2016,
   title={Statistical evaluation of voting rules},
@@ -218,6 +221,7 @@ @book{Nurmi1987
 	Publisher = {D. Reidel},
 	Title = {Comparing Voting Systems},
 	Year = {1987},
+	doi = {https://doi.org/10.1007/978-94-009-3985-1}
 	}
 	
 	@book{Nurmi1999,

pref_voting/__init__.py

@@ -1 +1 @@
-__version__ = '1.1.0'
+__version__ = '1.1.2'

pref_voting/iterative_methods.py

@@ -919,25 +919,22 @@ def baldwin(profile, curr_cands = None):
         prof.display()
         baldwin.display(prof)
     """
-    all_num_cands = profile.num_cands   
+    all_num_cands = profile.num_cands  
     candidates = profile.candidates if curr_cands is None else curr_cands
-
     rcounts = profile._rcounts # get all the ranking data
-    rankings = profile._rankings if curr_cands is None else _find_updated_profile(profile._rankings, np.array([c for c in profile.candidates if c not in curr_cands]), all_num_cands)
-
-    cands_to_ignore = np.empty(0) if curr_cands is None else np.array([c for c in profile.candidates if c not in curr_cands])
-
+    rankings = profile._rankings if curr_cands is None else _find_updated_profile(profile._rankings, np.array([c for c in profile.candidates if c not in candidates]), all_num_cands)
+    num_cands = len(candidates)
+    cands_to_ignore = np.empty(0) 
     borda_scores = {c: _borda_score(rankings, rcounts, len(candidates), c) for c in candidates}
 
     min_borda_score = min(list(borda_scores.values()))
-
     last_place_borda_scores = [c for c in candidates 
                                if c in borda_scores.keys() and borda_scores[c] == min_borda_score]
 
     cands_to_ignore = np.concatenate((cands_to_ignore, last_place_borda_scores), axis=None)
 
     winners = list()
-    if cands_to_ignore.shape[0] ==  all_num_cands: # all candidates have lowest Borda score
+    if cands_to_ignore.shape[0] ==  num_cands: # all candidates have lowest Borda score
         winners = sorted(last_place_borda_scores)
     else: # remove the candidates with lowest Borda score
         num_cands = len(candidates)
@@ -951,7 +948,7 @@ def baldwin(profile, curr_cands = None):
 
         cands_to_ignore = np.concatenate((cands_to_ignore, last_place_borda_scores), axis=None)
 
-        if cands_to_ignore.shape[0] == all_num_cands: # removed all remaining candidates
+        if cands_to_ignore.shape[0] == num_cands: # removed all remaining candidates
             winners = sorted(last_place_borda_scores)
         elif num_cands - cands_to_ignore.shape[0] ==  1: # only one candidate remains
             winners = sorted([c for c in candidates if c not in cands_to_ignore])
@@ -994,21 +991,18 @@ def baldwin_tb(profile, curr_cands = None, tie_breaker=None):
     # the tie_breaker is any linear order (i.e., list) of the candidates
     tb = tie_breaker if tie_breaker is not None else list(range(profile.num_cands))
 
-    all_num_cands = profile.num_cands   
+    all_num_cands = profile.num_cands  
     candidates = profile.candidates if curr_cands is None else curr_cands
-
     rcounts = profile._rcounts # get all the ranking data
-    rankings = profile._rankings if curr_cands is None else _find_updated_profile(profile._rankings, np.array([c for c in profile.candidates if c not in curr_cands]), all_num_cands)
-
-    cands_to_ignore = np.empty(0) if curr_cands is None else np.array([c for c in profile.candidates if c not in curr_cands])
-
-    borda_scores = {c: _borda_score(rankings, rcounts, len(candidates), c) for c in candidates}
+    rankings = profile._rankings if curr_cands is None else _find_updated_profile(profile._rankings, np.array([c for c in profile.candidates if c not in candidates]), all_num_cands)
+    num_cands = len(candidates)
+    cands_to_ignore = np.empty(0) 
+    borda_scores = {c: _borda_score(rankings, rcounts, num_cands, c) for c in candidates}
 
     min_borda_score = min(list(borda_scores.values()))
-
     last_place_borda_scores = [c for c in candidates 
                                if c in borda_scores.keys() and borda_scores[c] == min_borda_score]
-
+            
     cand_to_remove = last_place_borda_scores[0]
     for c in last_place_borda_scores[1:]: 
         if tb.index(c) < tb.index(cand_to_remove):
@@ -1037,7 +1031,7 @@ def baldwin_tb(profile, curr_cands = None, tie_breaker=None):
 
         cands_to_ignore = np.concatenate((cands_to_ignore, np.array([cand_to_remove])), axis=None)
 
-        if cands_to_ignore.shape[0] == all_num_cands: # removed all remaining candidates
+        if cands_to_ignore.shape[0] == num_cands: # removed all remaining candidates
             winners = sorted(last_place_borda_scores)
         elif num_cands - cands_to_ignore.shape[0] ==  1: # only one candidate remains
             winners = sorted([c for c in candidates if c not in cands_to_ignore])

pref_voting/margin_based_methods.py

@@ -613,34 +613,9 @@ def _ranked_pairs_from_stacks(edata, curr_cands = None):
         A sorted list of candidates. 
 
     .. seealso::
-
-        :meth:`pref_voting.margin_based_methods.ranked_pairs`, :meth:`pref_voting.margin_based_methods.is_stack`
-
-    :Example: 
-
-    .. plot::  margin_graphs_examples/mg_ex_bp_rp.py
-        :context: reset  
-        :include-source: True
-
-
-    .. code-block:: 
-
-        from pref_voting.margin_based_methods import ranked_pairs, ranked_pairs_from_stacks
-
-        ranked_pairs.display(mg)
-        ranked_pairs_from_stacks.display(mg)
-
-
-    .. exec_code:: 
-        :hide_code:
-
-        from pref_voting.weighted_majority_graphs import MarginGraph
-        from pref_voting.margin_based_methods import ranked_pairs, ranked_pairs_from_stacks
         
-        mg = MarginGraph([0, 1, 2, 3], [(0, 2, 3), (1, 0, 5), (2, 1, 5), (2, 3, 1), (3, 0, 3), (3, 1, 1)])
+        :meth:`pref_voting.margin_based_methods.is_stack`
 
-        ranked_pairs.display(mg)
-        ranked_pairs_from_stacks.display(mg)
 
     """    
 
@@ -658,6 +633,14 @@ def _ranked_pairs_basic(
     curr_cands = None, 
     strength_function = None):   
     """An implementation of Ranked Pairs that uses a basic algorithm. 
+
+    Args:
+        edata (Profile, ProfileWithTies, MarginGraph): Any election data that has a `margin` method. 
+        curr_cands (List[int], optional): If set, then find the winners for the profile restricted to the candidates in ``curr_cands``
+
+    Returns: 
+        A sorted list of candidates. 
+
     """
     candidates = edata.candidates if curr_cands is None else curr_cands 
     cidx_to_cand = {cidx: c for cidx, c in enumerate(candidates)}  
@@ -752,7 +735,10 @@ def ranked_pairs(
 
 
 @vm(name="Ranked Pairs")
-def ranked_pairs_with_test(edata, curr_cands = None, strength_function = None):   
+def ranked_pairs_with_test(
+    edata, 
+    curr_cands=None, 
+    strength_function=None):   
     """Find the Ranked Pairs winners, but include a test to determined if it will take too long to compute the Ranked Pairs winners. If the calculation of the winners will take too long, return None.
 
     .. important::
@@ -905,18 +891,17 @@ def ranked_pairs_tb(
 
     .. seealso::
 
-        :meth:`pref_voting.margin_based_methods.ranked_pairs`, :meth:`pref_voting.margin_based_methods.ranked_pairs_with_test`, :meth:`pref_voting.margin_based_methods.ranked_pairs_from_stacks`
+        :meth:`pref_voting.margin_based_methods.ranked_pairs`, :meth:`pref_voting.margin_based_methods.ranked_pairs_with_test`
 
     .. exec_code::
 
         from pref_voting.profiles import Profile
-        from pref_voting.margin_based_methods import ranked_pairs_from_stacks, ranked_pairs_tb, ranked_pairs_zt
+        from pref_voting.margin_based_methods import ranked_pairs_tb, ranked_pairs_zt
 
         prof = Profile([[2, 3, 1, 0], [0, 3, 1, 2], [1, 3, 2, 0], [2, 1, 3, 0]], [1, 1, 1, 1])
 
         prof.display()
 
-        ranked_pairs_from_stacks.display(prof)
         ranked_pairs_tb.display(prof)
         ranked_pairs_tb.display(prof, tie_breaker = [3, 2, 1, 0])
         ranked_pairs_zt.display(prof)
@@ -972,18 +957,17 @@ def ranked_pairs_zt(
 
     .. seealso::
 
-        :meth:`pref_voting.margin_based_methods.ranked_pairs`, :meth:`pref_voting.margin_based_methods.ranked_pairs_with_test`, :meth:`pref_voting.margin_based_methods.ranked_pairs_from_stacks`
+        :meth:`pref_voting.margin_based_methods.ranked_pairs`, :meth:`pref_voting.margin_based_methods.ranked_pairs_with_test`
 
     .. exec_code::
 
         from pref_voting.profiles import Profile
-        from pref_voting.margin_based_methods import ranked_pairs_from_stacks, ranked_pairs_tb, ranked_pairs_zt
+        from pref_voting.margin_based_methods import ranked_pairs_tb, ranked_pairs_zt
 
         prof = Profile([[2, 3, 1, 0], [0, 3, 1, 2], [1, 3, 2, 0], [2, 1, 3, 0]], [1, 1, 1, 1])
 
         prof.display()
 
-        ranked_pairs_from_stacks.display(prof)
         ranked_pairs_tb.display(prof)
         ranked_pairs_tb.display(prof, tie_breaker = [3, 2, 1, 0])
         ranked_pairs_zt.display(prof)
@@ -1327,7 +1311,7 @@ def simple_stable_voting(
 
     .. seealso::
 
-        :meth:`pref_voting.margin_based_methods.simple_stable_voting_faster`, :meth:`pref_voting.margin_based_methods.stable_voting`
+        :meth:`pref_voting.margin_based_methods.stable_voting`
 
     :Example: 
 
@@ -1478,7 +1462,7 @@ def stable_voting(
 
     .. seealso::
 
-        :meth:`pref_voting.margin_based_methods.simple_stable_faster`, :meth:`pref_voting.margin_based_methods.simple_stable_voting`
+        :meth:`pref_voting.margin_based_methods.simple_stable_voting`
 
 
     :Example: 
@@ -1634,9 +1618,7 @@ def distance_to_margin_graph(edata, rel, exp = 1, curr_cands = None):
     #river,
     #river_with_test, 
     simple_stable_voting,
-    #simple_stable_voting_faster,
     stable_voting,
-    #stable_voting_faster,
     essential,
     weighted_covering,
     loss_trimmer

pref_voting/tests/conftest.py

@@ -30,3 +30,7 @@ def profile_with_ties():
         {0:1, 1:1, 2:2}, 
         {0:2, 1:2, 2:1}],
         rcounts=[2, 1])
+
+@pytest.fixture
+def profile_single_voter():
+    return Profile([[0, 1, 2, 3]])