Add XpressPyomoSolver

.github/workflows/test.yml

@@ -5,7 +5,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.6, 3.7, 3.8]
 
     steps:
     - name: Check out source code
@@ -19,6 +19,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
+        python -m pip install xpress
         python -m pip install -i https://pypi.gurobi.com gurobipy
         pip install -r requirements.txt
 

docs/customization.md

@@ -4,9 +4,11 @@
 
 ### Selecting the internal MIP solver
 
-By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver, and expects models to be provided using the Pyomo modeling language. Other supported solvers and modeling languages include:
+By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver, and expects models to be provided using the Pyomo modeling language. Supported solvers and modeling languages include:
 
-* `CplexPyomoSolver`: [IBM ILOG CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio) with Pyomo. 
+* `GurobiPyomoSolver`: Gurobi with Pyomo (default).
+* `CplexPyomoSolver`: [IBM ILOG CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio) with Pyomo.
+* `XpressPyomoSolver`: [FICO XPRESS Solver](https://www.fico.com/en/products/fico-xpress-solver) with Pyomo.
 * `GurobiSolver`: Gurobi without any modeling language.
 
 To switch between solvers, provide the desired class using the `solver` argument:
@@ -16,6 +18,22 @@ from miplearn import LearningSolver, CplexPyomoSolver
 solver = LearningSolver(solver=CplexPyomoSolver)
 ```
 
+To configure a particular solver, use the `params` constructor argument, as shown below.
+
+```python
+from miplearn import LearningSolver, GurobiPyomoSolver
+solver = LearningSolver(
+    solver=lambda: GurobiPyomoSolver(
+        params={
+            "TimeLimit": 900,
+            "MIPGap": 1e-3,
+            "NodeLimit": 1000,
+        }
+    ),
+)
+```
+
+
 ## Customizing solver components
 
 `LearningSolver` is composed by a number of individual machine-learning components, each targeting a different part of the solution process. Each component can be individually enabled, disabled or customized. The following components are enabled by default:

miplearn/solvers/pyomo/base.py

@@ -181,18 +181,27 @@ class BasePyomoSolver(InternalSolver):
             if not should_repeat:
                 break
         log = streams[0].getvalue()
-        return {
+        stats = {
             "Lower bound": results["Problem"][0]["Lower bound"],
             "Upper bound": results["Problem"][0]["Upper bound"],
             "Wallclock time": total_wallclock_time,
-            "Nodes": self._extract_node_count(log),
             "Sense": self._obj_sense,
             "Log": log,
-            "Warm start value": self._extract_warm_start_value(log),
         }
+        node_count = self._extract_node_count(log)
+        if node_count is not None:
+            stats["Nodes"] = node_count
+
+        ws_value = self._extract_warm_start_value(log)
+        if ws_value is not None:
+            stats["Warm start value"] = ws_value
+
+        return stats
 
     @staticmethod
     def __extract(log, regexp, default=None):
+        if regexp is None:
+            return default
         value = default
         for line in log.splitlines():
             matches = re.findall(regexp, line)
@@ -208,18 +217,16 @@ class BasePyomoSolver(InternalSolver):
         return value
 
     def _extract_node_count(self, log):
-        return int(self.__extract(log, self._get_node_count_regexp(), default=1))
+        return self.__extract(log, self._get_node_count_regexp())
 
     def get_constraint_ids(self):
         return list(self._cname_to_constr.keys())
 
-    @abstractmethod
     def _get_warm_start_regexp(self):
-        pass
+        return None
 
-    @abstractmethod
     def _get_node_count_regexp(self):
-        pass
+        return None
 
     def extract_constraint(self, cid):
         raise Exception("Not implemented")

miplearn/solvers/pyomo/xpress.py

@@ -0,0 +1,34 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import sys
+import logging
+from io import StringIO
+from pyomo import environ as pe
+from scipy.stats import randint
+
+from .base import BasePyomoSolver
+from .. import RedirectOutput
+
+logger = logging.getLogger(__name__)
+
+
+class XpressPyomoSolver(BasePyomoSolver):
+    """
+    An InternalSolver that uses XPRESS and the Pyomo modeling language.
+
+    Parameters
+    ----------
+    params: dict
+        Dictionary of options to pass to the Pyomo solver. For example,
+        {"Threads": 4} to set the number of threads.
+    """
+
+    def __init__(self, params=None):
+        super().__init__(
+            solver_factory=pe.SolverFactory("xpress_persistent"),
+            params={
+                "randomseed": randint(low=0, high=1000).rvs(),
+            },
+        )

miplearn/solvers/tests/__init__.py

@@ -5,6 +5,7 @@
 from inspect import isclass
 from miplearn import BasePyomoSolver, GurobiSolver, GurobiPyomoSolver
 from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
+from miplearn.solvers.pyomo.xpress import XpressPyomoSolver
 
 
 def _get_instance(solver):
@@ -31,4 +32,4 @@ def _get_instance(solver):
 
 
 def _get_internal_solvers():
-    return [GurobiPyomoSolver, GurobiSolver]
+    return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]

miplearn/solvers/tests/test_internal_solver.py

@@ -43,7 +43,8 @@ def test_internal_solver_warm_starts():
             }
         )
         stats = solver.solve(tee=True)
-        assert stats["Warm start value"] == 725.0
+        if "Warm start value" in stats:
+            assert stats["Warm start value"] == 725.0
 
         solver.set_warm_start(
             {
@@ -56,7 +57,8 @@ def test_internal_solver_warm_starts():
             }
         )
         stats = solver.solve(tee=True)
-        assert stats["Warm start value"] is None
+        if "Warm start value" in stats:
+            assert stats["Warm start value"] is None
 
         solver.fix(
             {
@@ -97,7 +99,6 @@ def test_internal_solver():
         assert stats["Upper bound"] == 1183.0
         assert stats["Sense"] == "max"
         assert isinstance(stats["Wallclock time"], float)
-        assert isinstance(stats["Nodes"], int)
 
         solution = solver.get_solution()
         assert solution["x"][0] == 1.0