Add PuLP example

.github/workflows/python.yaml

@@ -1,22 +1,21 @@
 name: Test Python
-on: [push]
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [opened, synchronize, reopened]
 jobs:
   build:
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ['3.10']
+    defaults:
+      run:
+        shell: bash -el {0}  # Essential for Conda environment activation
     steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+    - uses: actions/checkout@v7
+    - uses: conda-incubator/setup-miniconda@v4
       with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install jsonschema
-    - name: Test
-      run: |
-        cd python
-        python mof.py
+        activate-environment: mathoptformat
+        environment-file: python/environment.yml
+    - run: python -m pytest ./python/run_pulp.py
+    - run: python ./python/mof.py

.gitignore

@@ -1 +1,2 @@
 .vscode
+python/__pycache__

README.md

@@ -1,7 +1,8 @@
 # MathOptFormat
 
-This repository describes a file-format for mathematical optimization problems
-called _MathOptFormat_ with the file extension `.mof.json`.
+This [repository](https://github.com/jump-dev/MathOptFormat) describes a
+file-format for mathematical optimization problems called _MathOptFormat_, with
+the file extension `.mof.json`.
 
 MathOptFormat is rigidly defined by the [JSON schema](http://json-schema.org/)
 available at
@@ -23,10 +24,11 @@ structure for mathematical optimization problems. INFORMS Journal on Computing.
 
 ## Implementations
 
-- Julia
+- The [MathOptInterface.jl](https://github.com/jump-dev/MathOptInterface.jl)
+  package supports reading and writing MathOptFormat files.
 
-  - The [MathOptInterface.jl](https://github.com/jump-dev/MathOptInterface.jl) package
-    supports reading and writing MathOptFormat files.
+- Pedagogical Python code for parsing MathOptFormat files into
+  [PuLP](https://coin-or.github.io/pulp/) is available in the [python directory](https://github.com/jump-dev/MathOptFormat/tree/master/python).
 
 ## Standard form
 

python/environment.yml

@@ -0,0 +1,8 @@
+name: mathoptformat
+dependencies:
+  - python=3.10
+  - pytest
+  - pip
+  - pip:
+    - pulp==3.*
+    - jsonschema

python/mof.py

@@ -1,9 +1,14 @@
+# Copyright (c) 2020: Oscar Dowson and contributors
+#
+# Use of this source code is governed by an MIT-style license that can be found
+# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
+
 import json
 import jsonschema
 import os
 
-
-SCHEMA_FILENAME = '../schemas/mof.1.schema.json'
+ROOT = os.path.dirname(os.path.abspath(__file__)) + "/../"
+SCHEMA_FILENAME = ROOT + 'schemas/mof.1.schema.json'
 
 def validate(filename):
     with open(filename, 'r', encoding='utf-8') as io:
@@ -88,8 +93,8 @@ def summarize_nonlinear(schema):
 ### Validate all the files in the examples directory.
 ###
 
-for filename in os.listdir('../examples'):
-    validate(os.path.join('../examples', filename))
+for filename in os.listdir(ROOT + 'examples'):
+    validate(os.path.join(ROOT + 'examples', filename))
 
 ###
 ### Summarize the schema for the README table.

python/run_pulp.py

@@ -0,0 +1,120 @@
+# Copyright (c) 2020: Oscar Dowson and contributors
+#
+# Use of this source code is governed by an MIT-style license that can be found
+# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
+
+import json
+import pulp
+import pytest
+import os
+
+ROOT = os.path.dirname(os.path.abspath(__file__)) + "/../"
+
+class UnsupportedObjective(Exception):
+    def __init__(self, F):
+        self.F = F
+        return
+
+class UnsupportedConstraint(Exception):
+    def __init__(self, F, S):
+        self.F, self.S = F, S
+        return
+
+def parse_func(f, var_by_name):
+    match f["type"]:
+        case "Variable":
+            return var_by_name[f["name"]]
+        case "ScalarAffineFunction":
+            expr = pulp.LpAffineExpression()
+            for term in f.get("terms", []):
+                expr += term["coefficient"] * var_by_name[term["variable"]]
+            if "constant" in f:
+                expr += f["constant"]
+            return expr
+        case _:
+            return None
+
+def add_variable_constraint(prob, f, s, var_by_name):
+    x = var_by_name[f["name"]]
+    match s["type"]:
+        case "LessThan":
+            x.upBound = s["upper"]
+        case "GreaterThan":
+            x.lowBound = s["lower"]
+        case "EqualTo":
+            x.lowBound = x.upBound = s["value"]
+        case "Interval":
+            x.lowBound, x.upBound = s["lower"], s["upper"]
+        case "ZeroOne":
+            x.cat = pulp.LpBinary
+        case "Integer":
+            x.cat = pulp.LpInteger
+        case _:
+            raise UnsupportedConstraint(f["type"], s["type"])
+    return
+
+def add_constraint(prob, c, var_by_name):
+    f, s = c["function"], c["set"]
+    match f["type"]:
+        case "Variable":
+            add_variable_constraint(prob, f, s, var_by_name)
+        case _:
+            expr = parse_func(f, var_by_name)
+            if expr is None:
+                raise UnsupportedConstraint(f["type"], s["type"])
+            match s["type"]:
+                case "LessThan":
+                    prob += (expr <= s["upper"])
+                case "GreaterThan":
+                    prob += (expr >= s["lower"])
+                case "EqualTo":
+                    prob += (expr == s["value"])
+                case _:
+                    raise UnsupportedConstraint(f["type"], s["type"])
+    return
+
+def read_from_file(filename):
+    with open(filename, "r") as f:
+        data = json.load(f)
+    return read_from_dict(data)
+
+def read_from_dict(data):
+    name = data.get("name", "")
+    obj = data.get("objective", {})
+    is_max = obj.get("sense", "MIN_SENSE") == "MAX_SENSE"
+    sense = pulp.LpMaximize if is_max else pulp.LpMinimize
+    prob = pulp.LpProblem(name=name, sense=sense)
+    prob.var_by_name = {}
+    for x in data.get("variables", []):
+        name = x["name"]
+        prob.var_by_name[name] = prob.add_variable(name, None, None)
+    if "function" in obj:
+        obj_f = parse_func(obj["function"], prob.var_by_name)
+        if obj_f is None:
+            raise UnsupportedObjective(obj["function"]["type"])
+        prob += obj_f
+    for c in data.get("constraints", []):
+        add_constraint(prob, c, prob.var_by_name)
+    return prob
+
+def solve_from_file(filename):
+    prob = read_from_file(filename)
+    print(prob)
+    prob.solve()
+    return {
+        "status": pulp.LpStatus[prob.status],
+        "primal": {k: v.value() for (k, v) in prob.var_by_name.items()}
+    }
+
+# Usage:
+
+def test_success():
+    ret = solve_from_file(ROOT + 'examples/milp.mof.json')
+    assert ret["status"] == "Optimal"
+    assert ret["primal"] == {'x': 0.0, 'y': 1.0}
+    return
+
+def test_failure():
+    with pytest.raises(UnsupportedObjective):
+        solve_from_file(ROOT + 'examples/nlp.mof.json')
+    return