stable_marriage_CP.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OptimizationExpert/Pyomo/blob/main/stable_marriage_CP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvJL0bOSIm84"
      },
      "source": [
        "Developed by Alireza Soroudi\n",
        "alireza.soroudi@gmail.com\n",
        "\n",
        "Linkedin News letter : https://www.linkedin.com/newsletters/optimization-in-open-source-6874020019009859585/\n",
        "\n",
        "Git hub: https://github.com/OptimizationExpert/Pyomo\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_3yuwV8XtMF",
        "outputId": "f64291d8-3313-4775-e44d-d3cff51b301b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ortools in /usr/local/lib/python3.10/dist-packages (9.10.4067)\n",
            "Requirement already satisfied: absl-py>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (2.1.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from ortools) (1.25.2)\n",
            "Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (2.0.3)\n",
            "Requirement already satisfied: protobuf>=5.26.1 in /usr/local/lib/python3.10/dist-packages (from ortools) (5.27.1)\n",
            "Requirement already satisfied: immutabledict>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (4.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0.0->ortools) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install ortools\n",
        "from ortools.sat.python import cp_model # CP-SAT solver\n",
        "import numpy as np\n",
        "from random import randint\n",
        "import random\n",
        "from math import ceil\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt # Data visualization\n",
        "from matplotlib.patches import Rectangle\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "AxZSoOv2-NEu"
      },
      "outputs": [],
      "source": [
        "KOLORS = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\",\n",
        "            \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\",\n",
        "            \"#aec7e8\", \"#ffbb78\", \"#98df8a\", \"#ff9896\", \"#c5b0d5\",\n",
        "            \"#c49c94\", \"#f7b6d2\", \"#c7c7c7\", \"#dbdb8d\", \"#9edae5\",\n",
        "            \"#8c6d31\", \"#9c9ede\", \"#637939\", \"#e7cb94\", \"#b5cf6b\",\n",
        "            \"#cedb9c\", \"#c7c7c7\", \"#bd9e39\", \"#e7969c\", \"#7b4173\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xH4VfXnamQQ",
        "outputId": "7e2f72f9-c51c-4f94-fc02-92c7a771f0a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('w1', 'm1'): 3,\n",
              " ('w1', 'm2'): 1,\n",
              " ('w1', 'm3'): 4,\n",
              " ('w1', 'm4'): 2,\n",
              " ('w1', 'm5'): 5,\n",
              " ('w2', 'm1'): 3,\n",
              " ('w2', 'm2'): 2,\n",
              " ('w2', 'm3'): 5,\n",
              " ('w2', 'm4'): 1,\n",
              " ('w2', 'm5'): 4,\n",
              " ('w3', 'm1'): 4,\n",
              " ('w3', 'm2'): 3,\n",
              " ('w3', 'm3'): 2,\n",
              " ('w3', 'm4'): 1,\n",
              " ('w3', 'm5'): 5,\n",
              " ('w4', 'm1'): 3,\n",
              " ('w4', 'm2'): 5,\n",
              " ('w4', 'm3'): 4,\n",
              " ('w4', 'm4'): 2,\n",
              " ('w4', 'm5'): 1,\n",
              " ('w5', 'm1'): 2,\n",
              " ('w5', 'm2'): 4,\n",
              " ('w5', 'm3'): 5,\n",
              " ('w5', 'm4'): 1,\n",
              " ('w5', 'm5'): 3,\n",
              " ('m1', 'w1'): 3,\n",
              " ('m1', 'w2'): 4,\n",
              " ('m1', 'w3'): 1,\n",
              " ('m1', 'w4'): 2,\n",
              " ('m1', 'w5'): 5,\n",
              " ('m2', 'w1'): 3,\n",
              " ('m2', 'w2'): 5,\n",
              " ('m2', 'w3'): 4,\n",
              " ('m2', 'w4'): 1,\n",
              " ('m2', 'w5'): 2,\n",
              " ('m3', 'w1'): 5,\n",
              " ('m3', 'w2'): 1,\n",
              " ('m3', 'w3'): 4,\n",
              " ('m3', 'w4'): 2,\n",
              " ('m3', 'w5'): 3,\n",
              " ('m4', 'w1'): 5,\n",
              " ('m4', 'w2'): 2,\n",
              " ('m4', 'w3'): 4,\n",
              " ('m4', 'w4'): 3,\n",
              " ('m4', 'w5'): 1,\n",
              " ('m5', 'w1'): 1,\n",
              " ('m5', 'w2'): 5,\n",
              " ('m5', 'w3'): 4,\n",
              " ('m5', 'w4'): 3,\n",
              " ('m5', 'w5'): 2}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "Nw, Nm = 5, 5\n",
        "\n",
        "women = [f\"w{w}\" for w in range(1,1+Nw) ]\n",
        "men = [f\"m{m}\" for m in range(1,1+Nm) ]\n",
        "\n",
        "ranking = [r for r in range(1,1+Nw)]\n",
        "data = {}\n",
        "for w in women:\n",
        "  ranking = [r for r in range(1,1+Nw)]\n",
        "  random.shuffle(ranking)\n",
        "  for indx in range(Nm):\n",
        "    data[w,men[indx]]= ranking[indx]\n",
        "\n",
        "for m in men:\n",
        "  ranking = [r for r in range(1,1+Nw)]\n",
        "  random.shuffle(ranking)\n",
        "  for indx in range(Nw):\n",
        "    data[m,women[indx]]= ranking[indx]\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "men = []\n",
        "men=[\"Adam\",\"Bob\",\"Charlie\",\"Dave\",\"Edgar\"]\n",
        "women=[\"Alice\",\"Barbara\",\"Claire\",\"Doris\",\"Elsie\"]\n",
        "menRankWomen=[[5,1,2,4,3],\n",
        "[4,1,3,2,5],\n",
        "[5,3,2,4,1],\n",
        "[1,5,4,3,2],\n",
        "[4,3,2,1,5]];\n",
        "womenRankMen=[[5,1,2,4,3],\n",
        "[4,1,3,2,5],\n",
        "[5,3,2,4,1],\n",
        "[1,5,4,3,2],\n",
        "[4,3,2,1,5]];\n",
        "random.shuffle(womenRankMen)\n",
        "random.shuffle(menRankWomen)\n",
        "\n",
        "\n",
        "data = {}\n",
        "for w in women:\n",
        "  w_index = women.index(w)\n",
        "  ranking = womenRankMen[w_index]\n",
        "  for indx in range(Nm):\n",
        "    data[w,men[indx]]= ranking[indx]\n",
        "\n",
        "for m in men:\n",
        "  m_index = men.index(m)\n",
        "  ranking = menRankWomen[m_index]\n",
        "  for indx in range(Nw):\n",
        "    data[m,women[indx]]= ranking[indx]\n",
        "\n",
        "data\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tap5AbgRnxDK",
        "outputId": "63d93d32-4cdb-45ff-f053-693b1a79d0d8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('Alice', 'Adam'): 4,\n",
              " ('Alice', 'Bob'): 1,\n",
              " ('Alice', 'Charlie'): 3,\n",
              " ('Alice', 'Dave'): 2,\n",
              " ('Alice', 'Edgar'): 5,\n",
              " ('Barbara', 'Adam'): 5,\n",
              " ('Barbara', 'Bob'): 3,\n",
              " ('Barbara', 'Charlie'): 2,\n",
              " ('Barbara', 'Dave'): 4,\n",
              " ('Barbara', 'Edgar'): 1,\n",
              " ('Claire', 'Adam'): 5,\n",
              " ('Claire', 'Bob'): 1,\n",
              " ('Claire', 'Charlie'): 2,\n",
              " ('Claire', 'Dave'): 4,\n",
              " ('Claire', 'Edgar'): 3,\n",
              " ('Doris', 'Adam'): 1,\n",
              " ('Doris', 'Bob'): 5,\n",
              " ('Doris', 'Charlie'): 4,\n",
              " ('Doris', 'Dave'): 3,\n",
              " ('Doris', 'Edgar'): 2,\n",
              " ('Elsie', 'Adam'): 4,\n",
              " ('Elsie', 'Bob'): 3,\n",
              " ('Elsie', 'Charlie'): 2,\n",
              " ('Elsie', 'Dave'): 1,\n",
              " ('Elsie', 'Edgar'): 5,\n",
              " ('Adam', 'Alice'): 4,\n",
              " ('Adam', 'Barbara'): 3,\n",
              " ('Adam', 'Claire'): 2,\n",
              " ('Adam', 'Doris'): 1,\n",
              " ('Adam', 'Elsie'): 5,\n",
              " ('Bob', 'Alice'): 4,\n",
              " ('Bob', 'Barbara'): 1,\n",
              " ('Bob', 'Claire'): 3,\n",
              " ('Bob', 'Doris'): 2,\n",
              " ('Bob', 'Elsie'): 5,\n",
              " ('Charlie', 'Alice'): 5,\n",
              " ('Charlie', 'Barbara'): 3,\n",
              " ('Charlie', 'Claire'): 2,\n",
              " ('Charlie', 'Doris'): 4,\n",
              " ('Charlie', 'Elsie'): 1,\n",
              " ('Dave', 'Alice'): 1,\n",
              " ('Dave', 'Barbara'): 5,\n",
              " ('Dave', 'Claire'): 4,\n",
              " ('Dave', 'Doris'): 3,\n",
              " ('Dave', 'Elsie'): 2,\n",
              " ('Edgar', 'Alice'): 5,\n",
              " ('Edgar', 'Barbara'): 1,\n",
              " ('Edgar', 'Claire'): 2,\n",
              " ('Edgar', 'Doris'): 4,\n",
              " ('Edgar', 'Elsie'): 3}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RApX4xchjMHD"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "menRankWomen = [[5, 3, 2, 4, 1],\n",
        " [5, 1, 2, 4, 3],\n",
        " [1, 5, 4, 3, 2],\n",
        " [4, 1, 3, 2, 5],\n",
        " [4, 3, 2, 1, 5]]\n",
        "\n",
        "womenRankMen = [[5, 1, 2, 4, 3],\n",
        " [4, 3, 2, 1, 5],\n",
        " [4, 1, 3, 2, 5],\n",
        " [5, 3, 2, 4, 1],\n",
        " [1, 5, 4, 3, 2]]\n"
      ],
      "metadata": {
        "id": "53Z0mCJisWQ2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib as mpl\n",
        "\n",
        "harvest = np.array(menRankWomen)\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "im = ax.imshow(harvest)\n",
        "\n",
        "# Show all ticks and label them with the respective list entries\n",
        "ax.set_yticks(np.arange(len(men)), labels=men)\n",
        "ax.set_xticks(np.arange(len(women)), labels=women)\n",
        "\n",
        "# Rotate the tick labels and set their alignment.\n",
        "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "         rotation_mode=\"anchor\")\n",
        "\n",
        "# Loop over data dimensions and create text annotations.\n",
        "for i in range(len(men)):\n",
        "    for j in range(len(women)):\n",
        "        text = ax.text(j, i, harvest[i, j],\n",
        "                       ha=\"center\", va=\"center\", color=\"w\")\n",
        "\n",
        "ax.set_title(\"Men preferences for women\")\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "VLWyMPj4PNGf",
        "outputId": "e890a7f1-db09-469b-9743-6fa2ff1e7e5a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "harvest = np.array(womenRankMen)\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "im = ax.imshow(harvest)\n",
        "\n",
        "# Show all ticks and label them with the respective list entries\n",
        "ax.set_xticks(np.arange(len(men)), labels=men)\n",
        "ax.set_yticks(np.arange(len(women)), labels=women)\n",
        "\n",
        "# Rotate the tick labels and set their alignment.\n",
        "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "         rotation_mode=\"anchor\")\n",
        "\n",
        "# Loop over data dimensions and create text annotations.\n",
        "for i in range(len(men)):\n",
        "    for j in range(len(women)):\n",
        "        text = ax.text(j, i, harvest[i, j],\n",
        "                       ha=\"center\", va=\"center\", color=\"w\")\n",
        "\n",
        "ax.set_title(\"Women preferences for men\")\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "IAc5XP7YPM9D",
        "outputId": "9572997a-dde3-4817-bebd-af61df61092d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "yU46bQJO2WvI"
      },
      "outputs": [],
      "source": [
        "\n",
        "class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback):\n",
        "    \"\"\"Print intermediate solutions.\"\"\"\n",
        "    def __init__(self, variables, men, women):\n",
        "        cp_model.CpSolverSolutionCallback.__init__(self)\n",
        "        self.__variables = variables\n",
        "        self.__solution_count = 0\n",
        "        self.__men = men\n",
        "        self.__women = women\n",
        "    def on_solution_callback(self):\n",
        "        self.__solution_count += 1\n",
        "        plt.figure(figsize=(6,6))\n",
        "\n",
        "        men_satisfaction = sum(self.Value(v)*data[m,w] for (m,w),v in self.__variables.items())\n",
        "        women_satisfaction = sum(self.Value(v)*data[w,m] for (m,w),v in self.__variables.items())\n",
        "        if self.BestObjectiveBound() == self.objective_value:\n",
        "          print(self.__solution_count , [(m,w) for (m,w),v in self.__variables.items() if self.Value(v)>0])\n",
        "          print(f\"M(OF) = {men_satisfaction} , W(OF) = {women_satisfaction} \")\n",
        "          for (m,w),v in self.__variables.items():\n",
        "            if self.Value(v)>0:\n",
        "\n",
        "              x0,y0 = 1 , self.__men.index(m)\n",
        "              x1,y1 = 4 , self.__women.index(w)\n",
        "              plt.plot([x0,x1],[y0,y1], lw= 2)\n",
        "              plt.text(x0-0.5,y0, s= m)\n",
        "              plt.text(x1+0.2,y1, s= w)\n",
        "              plt.scatter(x0,y0, s= 100, zorder=2)\n",
        "              plt.scatter(x1,y1, s= 100, zorder=2)\n",
        "          plt.axis(\"off\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def solution_count(self):\n",
        "        return self.__solution_count\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "yyF-IzKMcyrj",
        "outputId": "9f67f713-2834-44a0-8e92-3342b5e30bb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 [('Adam', 'Elsie'), ('Bob', 'Claire'), ('Charlie', 'Doris'), ('Dave', 'Barbara'), ('Edgar', 'Alice')]\n",
            "M(OF) = 22 , W(OF) = 18 \n",
            "OPTIMAL\n",
            "40.0\n",
            "Adam Elsie 5 4\n",
            "Bob Claire 3 1\n",
            "Charlie Doris 4 4\n",
            "Dave Barbara 5 4\n",
            "Edgar Alice 5 5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nAdam - Alice\\nBob - Doris\\nCharlie - Barbara\\nDave - Claire\\nEdgar - Elsie\\n\\nfor m in men:\\n  print(m,[data[m,w] for w in women])\\nfor w in women:\\n  print(w,[data[m,w] for m in men])\\n\\n\\nStable marriages\\nAdam - Alice\\nBob - Doris\\nCharlie - Barbara\\nDave - Claire\\nEdgar - Elsie\\n\\n\\n[Adam-Alice,Bob-Claire,Charlie-Doris,Dave-Barbara,Edgar-Elsie]\\n[Adam-Alice,Bob-Doris,Charlie-Barbara,Dave-Claire,Edgar-Elsie]\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = cp_model.CpModel()\n",
        "solver = cp_model.CpSolver()\n",
        "U = {(m,w):model.NewBoolVar(f\"match_{m}_{w}\")  for m in men for w in women}\n",
        "\n",
        "for m in men:\n",
        "  expressions = [U[m,w] for w in women]\n",
        "  model.AddExactlyOne(expressions)\n",
        "\n",
        "for w in women:\n",
        "  expressions = [U[m,w] for m in men]\n",
        "  model.AddExactlyOne(expressions)\n",
        "\n",
        "#\"\"\"\n",
        "for (m,w),v in U.items():\n",
        "  # for men\n",
        "  for w2 in women:\n",
        "    if data[m,w2]>data[m,w] and w!=w2:\n",
        "      model.Add(data[w2,m]<=sum([data[w2,m2]*U[m2,w2] for m2 in men if m!=m2])).OnlyEnforceIf(v)\n",
        "  # for women\n",
        "  for m2 in men:\n",
        "    if data[w,m2]>data[w,m] and m!=m2:\n",
        "      model.Add(data[m2,w]<=sum([data[m2,w2]*U[m2,w2] for w2 in women if w!=w2])).OnlyEnforceIf(v)\n",
        "#\"\"\"\n",
        "\n",
        "expressions = [v*(data[m,w] + data[w,m]) for (m,w),v in U.items()]\n",
        "#model.Minimize(sum(expressions))\n",
        "model.Maximize(sum(expressions))\n",
        "\n",
        "solver.parameters.max_time_in_seconds = 60\n",
        "\n",
        "solver.parameters.enumerate_all_solutions = False\n",
        "solution_printer = VarArraySolutionPrinter(U, men, women)\n",
        "\n",
        "\n",
        "status = solver.Solve(model, solution_printer)\n",
        "\n",
        "if status == cp_model.OPTIMAL:\n",
        "  print(\"OPTIMAL\")\n",
        "elif status == cp_model.FEASIBLE:\n",
        "  print(\"FEASIBLE\")\n",
        "elif status == cp_model.INFEASIBLE:\n",
        "  print(\"INFEASIBLE\")\n",
        "  print(\"FEASIBLE\")\n",
        "elif status == cp_model.UNKNOWN:\n",
        "  print(\"UNKNOWN\")\n",
        "print(solver.ObjectiveValue())\n",
        "\n",
        "for (m,w),v in U.items():\n",
        "  if solver.value(v):\n",
        "    print(m,w, data[m,w], data[w,m])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}