ilp_avg

prtpy/VERSION

@@ -1 +1 @@
-0.8.3
+0.8.4

prtpy/__init__.py

@@ -20,6 +20,7 @@ class partitioning:
 
     from prtpy.partitioning.integer_programming import optimal as ilp
     from prtpy.partitioning.integer_programming import optimal as integer_programming
+    from prtpy.partitioning.integer_programming_avg import optimal as ilp_avg
 
     from prtpy.partitioning.greedy import greedy
     from prtpy.partitioning.greedy import greedy as lpt

prtpy/partitioning/complete_greedy.py

@@ -23,7 +23,8 @@
 
 
 def anytime(
-    binner: Binner, numbins: int, items: List[int], relative_value: List[int] = None,
+    binner: Binner, numbins: int, items: List[int], 
+    entitlements: List[int] = None,
     objective: obj.Objective = obj.MinimizeDifference,
     use_lower_bound: bool = True,
     # Prune branches whose lower bound (= optimistic value) is at least as large as the current minimum.
@@ -64,10 +65,14 @@ def anytime(
     Bin #0: [46, 10], sum=56.0
     Bin #1: [27, 16, 13], sum=56.0
     Bin #2: [39, 26], sum=65.0
+
+    # Minimize the distance to the average - equal rights:
     >>> printbins(anytime(BinnerKeepingContents(), 3, walter_numbers, objective=obj.MinimizeDistAvg))
     Bin #0: [39, 16], sum=55.0
     Bin #1: [46, 13], sum=59.0
     Bin #2: [27, 26, 10], sum=63.0
+
+    # Minimize the distance to the average - different rights:
     >>> printbins(anytime(BinnerKeepingContents(), 3, walter_numbers,[0.2,0.4,0.4], objective=obj.MinimizeDistAvg))
     Bin #0: [27, 10], sum=37.0
     Bin #1: [39, 16, 13], sum=68.0
@@ -171,9 +176,9 @@ def anytime(
     # we add a sum to each bin in order to equal them out to the bin with the highest relative value
     # (at the end of the algorithm we will remove these sums).
     first_bins = binner.new_bins(numbins)
-    if (relative_value):
+    if (entitlements):
         for i in range(numbins):
-            binner.add_item_to_bin(first_bins, (max(relative_value) * sum(items) - relative_value[i] * sum(items)), i)
+            binner.add_item_to_bin(first_bins, (max(entitlements) * sum(items) - entitlements[i] * sum(items)), i)
     first_vertex = (first_bins, 0)
     stack: List[Tuple[BinsArray, int]] = [first_vertex]
     if use_set_of_seen_states:
@@ -250,13 +255,13 @@ def anytime(
                         new_smallest_sum = current_sums[0]
                     fast_lower_bound = -(new_smallest_sum + sum_of_remaining_items)
                 elif objective == obj.MinimizeDistAvg:
-                    if relative_value:
+                    if entitlements:
                         fast_lower_bound = 0
                         for i in range (numbins):
-                            # For each bin: we take off the sum that we added in the beginning of the algorithm (max(relative_value) * sum(items) - relative_value[i] * sum(items))
-                            # Then we check if the difference between the bin's sum and the relative AVG for bin i: (sum(items)*relative_value[i])
+                            # For each bin: we take off the sum that we added in the beginning of the algorithm (max(entitlements) * sum(items) - entitlements[i] * sum(items))
+                            # Then we check if the difference between the bin's sum and the relative AVG for bin i: (sum(items)*entitlements[i])
                             # is positive and contributes to our final difference or negative and we will not add anything to our difference.
-                            fast_lower_bound = fast_lower_bound + max((current_sums[i]-(max(relative_value) * sum(items) - relative_value[i] * sum(items)))-sum(items)*relative_value[i],0)
+                            fast_lower_bound = fast_lower_bound + max((current_sums[i]-(max(entitlements) * sum(items) - entitlements[i] * sum(items)))-sum(items)*entitlements[i],0)
                     else:
                         fast_lower_bound = 0
                         avg = sum(items) / numbins
@@ -269,7 +274,7 @@ def anytime(
                     continue
 
             new_bins = binner.add_item_to_bin(binner.copy_bins(current_bins), next_item, bin_index)
-            if not relative_value:
+            if not entitlements:
                 binner.sort_by_ascending_sum(new_bins)
             new_sums = tuple(binner.sums(new_bins))
 
@@ -298,7 +303,7 @@ def anytime(
                 times_heuristic_3_activated)
 
 
-    if (relative_value):
+    if (entitlements):
         # For each bin we remove the value that we added in the beginning of the algorithm.
         for i in range(numbins):
             binner.remove_item_from_bin(best_bins, i, 0)

prtpy/partitioning/integer_programming.py

@@ -24,7 +24,7 @@ def optimal(
     copies=1,
     time_limit=inf,
     additional_constraints:Callable=lambda sums:[],
-    weights:List[float]=None,
+    entitlements:List[float]=None,
     verbose=0,
     solver_name = mip.CBC, # passed to MIP. See https://docs.python-mip.com/en/latest/quickstart.html#creating-models. 
     # solver_name = mip.GRB, # passed to MIP. See https://docs.python-mip.com/en/latest/quickstart.html#creating-models. 
@@ -42,7 +42,7 @@ def optimal(
     :param copies: how many copies there are of each item. Default: 1.
     :param time_limit: stop the computation after this number of seconds have passed.
     :param additional_constraints: a function that accepts the list of sums in ascending order, and returns a list of possible additional constraints on the sums.
-    :param weights: if given, must be of size bins.num. Divides each sum by its weight before applying the objective function.
+    :param entitlements: if given, must be of size bins.num. Divides each sum by its weight before applying the objective function.
     :param solver_name: passed to MIP. See https://docs.python-mip.com/en/latest/quickstart.html#creating-models
     :param model_filename: if not None, the MIP model will be written into this file, for debugging. NOTE: The extension should be either ".lp" or ".mps" (it indicates the output format)
     :param solution_filename: if not None, the solution will be written into this file, for debugging.
@@ -76,11 +76,11 @@ def optimal(
     array([56., 56., 65.])
 
     >>> items = [11.1, 11, 11, 11, 22]
-    >>> optimal(BinnerKeepingSums(), 2, items, objective=obj.MaximizeSmallestSum, weights=[1,1])
+    >>> optimal(BinnerKeepingSums(), 2, items, objective=obj.MaximizeSmallestSum, entitlements=[1,1])
     array([33. , 33.1])
-    >>> optimal(BinnerKeepingSums(), 2, items, objective=obj.MaximizeSmallestSum, weights=[1,2])
+    >>> optimal(BinnerKeepingSums(), 2, items, objective=obj.MaximizeSmallestSum, entitlements=[1,2])
     array([22. , 44.1])
-    >>> optimal(BinnerKeepingSums(), 2, items, objective=obj.MaximizeSmallestSum, weights=[10,2])
+    >>> optimal(BinnerKeepingSums(), 2, items, objective=obj.MaximizeSmallestSum, entitlements=[10,2])
     array([11.1, 55. ])
 
     >>> from prtpy import partition
@@ -103,21 +103,25 @@ def optimal(
     Bin #3: [62, 187], sum=249.0
     Bin #4: [93, 158], sum=251.0
     """
+    if objective == obj.MinimizeDistAvg:
+        from prtpy.partitioning.integer_programming_avg import optimal as optimal_avg
+        return optimal_avg(binner, numbins, items, entitlements=entitlements, copies=copies, time_limit=time_limit, verbose=verbose, solver_name=solver_name, model_filename=model_filename, solution_filename=solution_filename)
+
     ibins = range(numbins)
     items = list(items)
     iitems = range(len(items))
     if isinstance(copies, Number):
         copies = {iitem: copies for iitem in iitems}
-    if weights is None:
-        weights = numbins*[1]
+    if entitlements is None:
+        entitlements = numbins*[1]
 
     model = mip.Model(name = '', solver_name=solver_name)
     counts: dict = {
         iitem: [model.add_var(var_type=mip.INTEGER, name=f'item{iitem}_in_bin{ibin}') for ibin in ibins] 
         for iitem in iitems
     }  # counts[i][j] is a variable that represents how many times item i appears in bin j.
     bin_sums = [
-        sum([counts[iitem][ibin] * binner.valueof(items[iitem]) for iitem in iitems])/weights[ibin] 
+        sum([counts[iitem][ibin] * binner.valueof(items[iitem]) for iitem in iitems])/entitlements[ibin] 
         for ibin in ibins
     ]  # bin_sums[j] is a variable-expression that represents the sum of values in bin j.
 

prtpy/partitioning/integer_programming_avg.py

@@ -15,7 +15,7 @@
 import mip
 
 def optimal(
-    binner: Binner, numbins: int, items: List[any], relative_values: List[any] = None,
+    binner: Binner, numbins: int, items: List[any], entitlements: List[any] = None,
     copies=1,
     time_limit=inf,
     verbose=0,
@@ -29,7 +29,7 @@ def optimal(
 
     :param numbins: number of bins.
     :param items: list of items.
-    :param relative_values: list of relative values that sum up to 1 for the bins if there are any.
+    :param entitlements: list of relative values that sum up to 1 for the bins if there are any.
     :param copies: how many copies there are of each item. Default: 1.
     :param time_limit: stop the computation after this number of seconds have passed.
     :param valueof: a function that maps an item from the list `items` to a number representing its value.
@@ -101,16 +101,11 @@ def optimal(
         for i in range (len(items)):
             sum_items = sum_items + items[i] * copies[i]
 
-    if relative_values:
-        z_js = [
-            bin_sums[ibin] - sum_items * relative_values[ibin]
-            for ibin in ibins
-        ]
-    else:
-        z_js = [
-            bin_sums[ibin] - sum_items / len(ibins)
-            for ibin in ibins
-        ]
+    effective_entitlements = entitlements or [1. / numbins for ibin in ibins]
+    z_js = [
+        bin_sums[ibin] - sum_items * effective_entitlements[ibin]
+        for ibin in ibins
+    ]
 
     t_js = [
         model.add_var(var_type=mip.INTEGER) for ibin in ibins
@@ -147,7 +142,7 @@ def optimal(
             count_item_in_bin = int(counts[iitem][ibin].x)
             for _ in range(count_item_in_bin):
                 binner.add_item_to_bin(output, items[iitem], ibin)
-    if not relative_values:
+    if not entitlements:
         binner.sort_by_ascending_sum(output)
 
     if solution_filename is not None:
@@ -163,5 +158,4 @@ def optimal(
 
 if __name__ == "__main__":
     import doctest, logging
-    (failures, tests) = doctest.testmod(report=True, optionflags=doctest.FAIL_FAST)
-    print("{} failures, {} tests".format(failures, tests))
+    print(doctest.testmod(report=True, optionflags=doctest.FAIL_FAST))