Remove obsolete benchmark files

miplearn/problems/knapsack.py

@@ -13,38 +13,6 @@ from scipy.stats.distributions import rv_frozen
 from miplearn.instance.base import Instance
 
 
-class ChallengeA:
-    """
-    - 250 variables, 10 constraints, fixed weights
-    - w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
-    - K = 500, u ~ U(0., 1.)
-    - alpha = 0.25
-    """
-
-    def __init__(
-        self,
-        seed: int = 42,
-        n_training_instances: int = 500,
-        n_test_instances: int = 50,
-    ) -> None:
-        np.random.seed(seed)
-        self.gen = MultiKnapsackGenerator(
-            n=randint(low=250, high=251),
-            m=randint(low=10, high=11),
-            w=uniform(loc=0.0, scale=1000.0),
-            K=uniform(loc=500.0, scale=0.0),
-            u=uniform(loc=0.0, scale=1.0),
-            alpha=uniform(loc=0.25, scale=0.0),
-            fix_w=True,
-            w_jitter=uniform(loc=0.95, scale=0.1),
-        )
-        np.random.seed(seed + 1)
-        self.training_instances = self.gen.generate(n_training_instances)
-
-        np.random.seed(seed + 2)
-        self.test_instances = self.gen.generate(n_test_instances)
-
-
 class MultiKnapsackInstance(Instance):
     """Representation of the Multidimensional 0-1 Knapsack Problem.
 
@@ -93,19 +61,6 @@ class MultiKnapsackInstance(Instance):
 
         return model
 
-    @overrides
-    def get_instance_features(self) -> np.ndarray:
-        return np.array([float(np.mean(self.prices))] + list(self.capacities))
-
-    @overrides
-    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
-        features = []
-        for i in range(len(self.weights)):
-            f = [self.prices[i]]
-            f.extend(self.weights[:, i])
-            features.append(f)
-        return np.array(features)
-
 
 # noinspection PyPep8Naming
 class MultiKnapsackGenerator:

miplearn/problems/stab.py

@@ -1,6 +1,7 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+
 from typing import List, Dict
 
 import networkx as nx
@@ -14,28 +15,6 @@ from scipy.stats.distributions import rv_frozen
 from miplearn.instance.base import Instance
 
 
-class ChallengeA:
-    def __init__(
-        self,
-        seed: int = 42,
-        n_training_instances: int = 500,
-        n_test_instances: int = 50,
-    ) -> None:
-        np.random.seed(seed)
-        self.generator = MaxWeightStableSetGenerator(
-            w=uniform(loc=100.0, scale=50.0),
-            n=randint(low=200, high=201),
-            p=uniform(loc=0.05, scale=0.0),
-            fix_graph=True,
-        )
-
-        np.random.seed(seed + 1)
-        self.training_instances = self.generator.generate(n_training_instances)
-
-        np.random.seed(seed + 2)
-        self.test_instances = self.generator.generate(n_test_instances)
-
-
 class MaxWeightStableSetInstance(Instance):
     """An instance of the Maximum-Weight Stable Set Problem.
 
@@ -65,30 +44,6 @@ class MaxWeightStableSetInstance(Instance):
             model.clique_eqs.add(sum(model.x[v] for v in clique) <= 1)
         return model
 
-    @overrides
-    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
-        features = []
-        assert len(names) == len(self.nodes)
-        for i, v1 in enumerate(self.nodes):
-            assert names[i] == f"x[{v1}]".encode()
-            neighbor_weights = [0.0] * 15
-            neighbor_degrees = [100.0] * 15
-            for v2 in self.graph.neighbors(v1):
-                neighbor_weights += [self.weights[v2] / self.weights[v1]]
-                neighbor_degrees += [self.graph.degree(v2) / self.graph.degree(v1)]
-            neighbor_weights.sort(reverse=True)
-            neighbor_degrees.sort()
-            f = []
-            f += neighbor_weights[:5]
-            f += neighbor_degrees[:5]
-            f += [self.graph.degree(v1)]
-            features.append(f)
-        return np.array(features)
-
-    @overrides
-    def get_variable_categories(self, names: np.ndarray) -> np.ndarray:
-        return np.array(["default" for _ in names], dtype="S")
-
 
 class MaxWeightStableSetGenerator:
     """Random instance generator for the Maximum-Weight Stable Set Problem.

miplearn/problems/tsp.py

@@ -17,30 +17,6 @@ from miplearn.solvers.pyomo.base import BasePyomoSolver
 from miplearn.types import ConstraintName
 
 
-class ChallengeA:
-    def __init__(
-        self,
-        seed: int = 42,
-        n_training_instances: int = 500,
-        n_test_instances: int = 50,
-    ) -> None:
-        np.random.seed(seed)
-        self.generator = TravelingSalesmanGenerator(
-            x=uniform(loc=0.0, scale=1000.0),
-            y=uniform(loc=0.0, scale=1000.0),
-            n=randint(low=350, high=351),
-            gamma=uniform(loc=0.95, scale=0.1),
-            fix_cities=True,
-            round=True,
-        )
-
-        np.random.seed(seed + 1)
-        self.training_instances = self.generator.generate(n_training_instances)
-
-        np.random.seed(seed + 2)
-        self.test_instances = self.generator.generate(n_test_instances)
-
-
 class TravelingSalesmanInstance(Instance):
     """An instance ot the Traveling Salesman Problem.