fix examples

examples/courses.md

@@ -106,18 +106,18 @@ print(divide(fairpyx.algorithms.almost_egalitarian_with_donation, instance=rando
 ```
 
 ```
-{'s1': ['c1', 'c3'], 's2': ['c1', 'c2'], 's3': ['c1', 'c2'], 's4':
-['c1', 'c3'], 's5': ['c1', 'c2'], 's6': ['c2'], 's7': ['c2'], 's8':
-['c3'], 's9': ['c3'], 's10': ['c3']}
-{'s1': ['c3'], 's2': ['c2'], 's3': ['c2'], 's4': ['c3'], 's5': ['c2'],
-'s6': ['c1', 'c2'], 's7': ['c1', 'c2'], 's8': ['c1', 'c3'], 's9':
-['c1', 'c3'], 's10': ['c1', 'c3']}
-{'s1': ['c1', 'c2', 'c3'], 's2': ['c1', 'c2', 'c3'], 's3': ['c1',
-'c2', 'c3'], 's4': ['c2', 'c3'], 's5': ['c2', 'c3'], 's6': ['c1'],
-'s7': ['c1'], 's8': [], 's9': [], 's10': []}
-{'s1': ['c3'], 's2': ['c1', 'c2'], 's3': ['c1'], 's4': ['c3'], 's5':
-['c3'], 's6': ['c1', 'c2'], 's7': ['c2', 'c3'], 's8': ['c2'], 's9':
-['c1', 'c3'], 's10': ['c1', 'c2']}
+{'s1': ['c2', 'c3'], 's2': ['c2', 'c3'], 's3': ['c1', 'c3'], 's4':
+['c1', 'c3'], 's5': ['c1', 'c2'], 's6': ['c1', 'c3'], 's7': ['c1',
+'c2'], 's8': ['c1', 'c2'], 's9': ['c1', 'c3'], 's10': ['c3']}
+{'s1': ['c2'], 's2': ['c1', 'c2'], 's3': ['c1', 'c3'], 's4': ['c1',
+'c3'], 's5': ['c1', 'c2'], 's6': ['c1', 'c3'], 's7': ['c2', 'c3'],
+'s8': ['c2', 'c3'], 's9': ['c1', 'c3'], 's10': ['c1', 'c3']}
+{'s1': ['c2', 'c3'], 's2': ['c2', 'c3'], 's3': ['c2', 'c3'], 's4':
+['c1', 'c2', 'c3'], 's5': ['c1', 'c2', 'c3'], 's6': ['c1', 'c3'],
+'s7': ['c1', 'c3'], 's8': ['c1'], 's9': ['c1'], 's10': ['c1']}
+{'s1': ['c2'], 's2': ['c3'], 's3': ['c1', 'c3'], 's4': ['c1', 'c3'],
+'s5': ['c1', 'c2'], 's6': ['c1', 'c2', 'c3'], 's7': ['c1', 'c2'],
+'s8': ['c1', 'c3'], 's9': ['c1', 'c3'], 's10': ['c2', 'c3']}
 ```
 
 

examples/input_formats.md

@@ -68,12 +68,12 @@ print(allocation)
 ```
 
 ```
-[[33 29 31 68 28 55 87 85 51  9 62 29]
- [47 57 24 82  9 53 49  4 35 94 25 39]
- [ 9 98 13 23 21 52  1 14 33 50 60 70]
- [29 52 68 85 20 98 85 75 93 38 36 19]
- [72 23 23 34 36 71 29 13 38  2 69 91]]
-{0: [6, 7], 1: [3, 9], 2: [1, 10], 3: [2, 8], 4: [0, 4, 5, 11]}
+[[11 51 26 40 92 22 25  9 26 44 52 98]
+ [58 55  4 57 48  3 24 24 51 84 97  2]
+ [ 6  5 31 66 74 95 11 21 81 28 98 28]
+ [ 9 50  8  9 37 24 42 22 90 15 36 11]
+ [43 73 76 60 30 20  4 11  1 54 68 86]]
+{0: [4], 1: [0, 7, 9], 2: [5, 10], 3: [6, 8], 4: [1, 2, 3, 11]}
 ```
 
 
@@ -89,8 +89,7 @@ print(allocation)
 ```
 
 ```
-No leafs - removing edge (2, 11) with minimum weight 0.17
-{0: [6, 7], 1: [3, 9], 2: [1, 10], 3: [5, 8], 4: [0, 11]}
+{0: [4, 11], 1: [0, 9], 2: [5, 10], 3: [6, 8], 4: [1, 2]}
 ```
 
 
@@ -104,8 +103,7 @@ print(allocation)
 ```
 
 ```
-No leafs - removing edge (0, 6) with minimum weight 0.15
-{0: [7], 1: [3, 9], 2: [1, 5, 10], 3: [6, 8], 4: [11]}
+{0: [3], 1: [0, 9], 2: [4, 5, 10], 3: [1, 8], 4: [11]}
 ```
 
 
@@ -119,8 +117,7 @@ print(allocation)
 ```
 
 ```
-No leafs - removing edge (2, 11) with minimum weight 0.17
-{0: [6, 7], 1: [3, 9], 2: [1, 10], 3: [5, 8], 4: [0, 11]}
+{0: [4, 11], 1: [0, 9], 2: [5, 10], 3: [6, 8], 4: [1, 2]}
 ```
 
 
@@ -134,9 +131,8 @@ print(allocation)
 ```
 
 ```
-No leafs - removing edge (0, 10) with minimum weight 0.05
-{0: [3, 6, 7, 8], 1: [0, 1, 3, 9], 2: [1, 9, 10, 11], 3: [5, 6, 7, 8],
-4: [0, 5, 10, 11]}
+{0: [1, 4, 9, 11], 1: [0, 3, 9, 10], 2: [4, 5, 8, 10], 3: [1, 5, 6,
+8], 4: [0, 2, 3, 11]}
 ```
 
 
@@ -150,8 +146,8 @@ print(allocation)
 ```
 
 ```
-{0: [3, 6, 7], 1: [1, 3, 9], 2: [1, 9, 10, 11], 3: [2, 5, 7, 8], 4:
-[0, 4, 5, 11]}
+{0: [1, 4, 11], 1: [0, 3, 7, 9], 2: [3, 5, 10], 3: [5, 6, 7, 8], 4:
+[1, 2, 9, 11]}
 ```
 
 

fairpyx/algorithms/almost_egalitarian.py

@@ -282,8 +282,8 @@ def remove_agent_from_graph(agent):
         edge_with_min_weight = min(fractional_allocation_graph.edges(), key=lambda edge:fractional_allocation_graph.weight(edge[0],edge[1]))
         agent_min_weight,item_min_weight = edge_with_min_weight
         min_weight = fractional_allocation_graph.weight(agent_min_weight,item_min_weight)
-        logger.warning("No leafs - removing edge %s with minimum weight %g", edge_with_min_weight, min_weight)
-        explanation_logger.info("There are no leaf nodes, but the edge %s has minimum weight %g, so it is removed.", edge_with_min_weight, min_weight, agents=agent_min_weight)
+        # logger.warning("No leafs - removing edge %s with minimum weight %g", edge_with_min_weight, min_weight)
+        explanation_logger.warning("There are no leaf nodes, but the edge %s has minimum weight %g, so it is removed.", edge_with_min_weight, min_weight, agents=agent_min_weight)
         remove_edge_from_graph(agent_min_weight,item_min_weight)
 
     iterated_maximum_matching(alloc)  # Avoid waste

fairpyx/explanations.py

@@ -54,6 +54,9 @@ class ExplanationLogger:
     def __init__(self, language='en'):
         self.language=language
 
+    def warning(self, message:str, *args, agents=None):
+        pass
+
     def info(self, message:str, *args, agents=None):
         pass
 
@@ -117,6 +120,12 @@ def info(self, message:str, *args, agents=None):
         else:                                          # to one agent
             self.logger.info(str(agents)+": "+message.strip(), *args)
 
+    def warning(self, message:str, *args, agents=None):
+        if agents is None or not is_individual_agent(agents):  # to all agents
+            self.logger.warning(message, *args)
+        else:                                          # to one agent
+            self.logger.warning(str(agents)+": "+message.strip(), *args)
+
 
 class ConsoleExplanationLogger(SingleExplanationLogger):
     """
@@ -160,6 +169,16 @@ def info(self, message:str, *args, agents=None):
             for agent in agents:
                 self.map_agent_to_logger[agent].info(message, *args)
 
+    def warning(self, message:str, *args, agents=None):
+        if agents is None:
+            for agent,logger in self.map_agent_to_logger.items():
+                logger.warning(message, *args)
+        elif is_individual_agent(agents):
+            self.map_agent_to_logger[agents].warning(message, *args)
+        else:
+            for agent in agents:
+                self.map_agent_to_logger[agent].warning(message, *args)
+
 
 
 class FilesExplanationLogger(ExplanationLoggerPerAgent):