✨ Parsing tspwp problems

tests/test_complete.py

@@ -1,11 +1,13 @@
 """Test that all existing instances are complete graphs"""
 
 import networkx as nx
-from tspwplib.dataset import load_tsplib_dataset
+import tsplib95
+from tspwplib.utils import build_path_to_tsplib_instance
 
 
 def test_tsplib_is_complete(tsplib_root, instance_name):
     """Test each instance of TSPLIB95 is a complete graph"""
-    problem = load_tsplib_dataset(tsplib_root, instance_name)
+    filepath = build_path_to_tsplib_instance(tsplib_root, instance_name)
+    problem = tsplib95.load(filepath)
     graph = problem.get_graph()
     assert nx.complete_graph(graph)

tests/test_profits_problem.py

@@ -0,0 +1,34 @@
+"""Test the TSP with Profits Problem class"""
+
+import math
+import networkx as nx
+from tspwplib.problem import ProfitsProblem
+from tspwplib.types import OptimalSolutionTSP
+from tspwplib.utils import build_path_to_oplib_instance
+
+
+def test_parse_profits_problem(oplib_root, generation, instance_name, alpha):
+    """Test an OP instance can be parsed"""
+    filepath = build_path_to_oplib_instance(
+        oplib_root, generation, instance_name, alpha=alpha
+    )
+    assert "COST_LIMIT" in ProfitsProblem.fields_by_keyword
+
+    problem = ProfitsProblem.load(filepath)
+    assert problem.is_complete()
+    graph = problem.get_graph()
+    assert nx.complete_graph(graph)
+
+
+def test_get_cost_limit(oplib_root, generation, instance_name, alpha):
+    """Test we can get the cost limit"""
+    filepath = build_path_to_oplib_instance(
+        oplib_root, generation, instance_name, alpha=alpha
+    )
+    problem = ProfitsProblem.load(filepath)
+    # total_cost = sum([problem.get_weight(source, target) for source, target in problem.get_edges()])
+
+    expected_cost_limit = math.ceil(
+        OptimalSolutionTSP[instance_name] * (alpha.value / 100.0)
+    )
+    assert problem.get_cost_limit() == expected_cost_limit

tspwplib/problem.py

@@ -0,0 +1,40 @@
+"""Functions and classes for datasets"""
+
+from typing import Dict
+import tsplib95
+
+
+class ProfitsProblem(tsplib95.models.StandardProblem):
+    """TSP with Profits Problem"""
+
+    # Maximum distance of the total route in a OP.
+    cost_limit = tsplib95.fields.IntegerField("COST_LIMIT")
+    # The scores of the nodes of a OP are given in the form (per line)
+    node_score = tsplib95.fields.DemandsField("NODE_SCORE_SECTION")
+    # The optimal solution to the TSP
+    tspsol = tsplib95.fields.IntegerField("TSPSOL")
+    # TODO add profit limit for PcTSP
+
+    def get_cost_limit(self) -> int:
+        """Get the cost limit for a TSP with Profits problem
+
+        Returns:
+            Cost limit
+        """
+        return self.cost_limit
+
+    def get_node_score(self) -> Dict[int, int]:
+        """Get the node scores (profits)
+
+        Returns:
+            Mapping from node to node score (profit)
+        """
+        # TODO
+
+    def get_tsp_optimal_value(self) -> int:
+        """Get the value of the optimal solution to TSP
+
+        Returns:
+            TSP optimal value
+        """
+        return self.tspsol

tspwplib/types.py

@@ -1,6 +1,6 @@
 """Type hinting and names"""
 
-from enum import Enum
+from enum import Enum, IntEnum
 
 
 class InstanceName(str, Enum):
@@ -25,3 +25,120 @@ class Alpha(Enum):
     """Ratio between profit/cost limit and profit/cost of TSP solution"""
 
     fifty = 50
+
+
+class OptimalSolutionTSP(IntEnum):
+    """Value of optimal solutions to TSP instances"""
+
+    a280 = 2579
+    ali535 = 202339
+    att48 = 10628
+    att532 = 27686
+    bayg29 = 1610
+    bays29 = 2020
+    berlin52 = 7542
+    bier127 = 118282
+    brazil58 = 25395
+    brd14051 = 469385
+    brg180 = 1950
+    burma14 = 3323
+    ch130 = 6110
+    ch150 = 6528
+    d198 = 15780
+    d493 = 35002
+    d657 = 48912
+    d1291 = 50801
+    d1655 = 62128
+    d2103 = 80450
+    d15112 = 1573084
+    d18512 = 645238
+    dantzig42 = 699
+    dsj1000 = 18659688  # (EUC_2D)
+    # dsj1000 = 18660188 # (CEIL_2D)    # NOTE breaks keys
+    eil51 = 426
+    eil76 = 538
+    eil101 = 629
+    fl417 = 11861
+    fl1400 = 20127
+    fl1577 = 22249
+    fl3795 = 28772
+    fnl4461 = 182566
+    fri26 = 937
+    gil262 = 2378
+    gr17 = 2085
+    gr21 = 2707
+    gr24 = 1272
+    gr48 = 5046
+    gr96 = 55209
+    gr120 = 6942
+    gr137 = 69853
+    gr202 = 40160
+    gr229 = 134602
+    gr431 = 171414
+    gr666 = 294358
+    hk48 = 11461
+    kroA100 = 21282
+    kroB100 = 22141
+    kroC100 = 20749
+    kroD100 = 21294
+    kroE100 = 22068
+    kroA150 = 26524
+    kroB150 = 26130
+    kroA200 = 29368
+    kroB200 = 29437
+    lin105 = 14379
+    lin318 = 42029
+    linhp318 = 41345
+    nrw1379 = 56638
+    p654 = 34643
+    pa561 = 2763
+    pcb442 = 50778
+    pcb1173 = 56892
+    pcb3038 = 137694
+    pla7397 = 23260728
+    pla33810 = 66048945
+    pla85900 = 142382641
+    pr76 = 108159
+    pr107 = 44303
+    pr124 = 59030
+    pr136 = 96772
+    pr144 = 58537
+    pr152 = 73682
+    pr226 = 80369
+    pr264 = 49135
+    pr299 = 48191
+    pr439 = 107217
+    pr1002 = 259045
+    pr2392 = 378032
+    rat99 = 1211
+    rat195 = 2323
+    rat575 = 6773
+    rat783 = 8806
+    rd100 = 7910
+    rd400 = 15281
+    rl1304 = 252948
+    rl1323 = 270199
+    rl1889 = 316536
+    rl5915 = 565530
+    rl5934 = 556045
+    rl11849 = 923288
+    si175 = 21407
+    si535 = 48450
+    si1032 = 92650
+    st70 = 675
+    swiss42 = 1273
+    ts225 = 126643
+    tsp225 = 3916
+    u159 = 42080
+    u574 = 36905
+    u724 = 41910
+    u1060 = 224094
+    u1432 = 152970
+    u1817 = 57201
+    u2152 = 64253
+    u2319 = 234256
+    ulysses16 = 6859
+    ulysses22 = 7013
+    usa13509 = 19982859
+    vm1084 = 239297
+    vm1748 = 336556