template/colab.ipynb

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      "metadata": {
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      "source": [
        "# AMPL Model Colaboratory Template\n",
        "[![colab.ipynb](https://img.shields.io/badge/github-%23121011.svg?logo=github)](https://github.com/ampl/colab.ampl.com/blob/master/template/colab.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ampl/colab.ampl.com/blob/master/template/colab.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/ampl/colab.ampl.com/blob/master/template/colab.ipynb) [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/ampl/colab.ampl.com/blob/master/template/colab.ipynb) [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ampl/colab.ampl.com/blob/master/template/colab.ipynb) [![Hits](https://h.ampl.com/https://github.com/ampl/colab.ampl.com/blob/master/template/colab.ipynb)](https://colab.ampl.com)\n",
        "\n",
        "Description: Basic notebook template for the AMPL Colab repository\n",
        "\n",
        "Tags: amplpy, template, highlights\n",
        "\n",
        "Notebook author: Filipe Brandão <<fdabrandao@gmail.com>>"
      ]
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    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "rQ50dBwL7KbX"
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      "outputs": [],
      "source": [
        "# Install dependencies\n",
        "%pip install -q amplpy pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
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      "source": [
        "# Google Colab & Kaggle integration\n",
        "from amplpy import AMPL, ampl_notebook\n",
        "\n",
        "ampl = ampl_notebook(\n",
        "    modules=[\"highs\"],  # modules to install\n",
        "    license_uuid=\"default\",  # license to use\n",
        ")  # instantiate AMPL object and register magics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twrd0pLo7KbZ"
      },
      "source": [
        "### Use `%%ampl_eval` to evaluate AMPL commands and declarations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "aEtyesqs7KbZ"
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      "outputs": [],
      "source": [
        "%%ampl_eval\n",
        "set NUTR;\n",
        "set FOOD;\n",
        "\n",
        "param cost {FOOD} > 0;\n",
        "param f_min {FOOD} >= 0;\n",
        "param f_max {j in FOOD} >= f_min[j];\n",
        "\n",
        "param n_min {NUTR} >= 0;\n",
        "param n_max {i in NUTR} >= n_min[i];\n",
        "\n",
        "param amt {NUTR,FOOD} >= 0;\n",
        "\n",
        "var Buy {j in FOOD} >= f_min[j], <= f_max[j];\n",
        "\n",
        "minimize Total_Cost:  sum {j in FOOD} cost[j] * Buy[j];\n",
        "\n",
        "subject to Diet {i in NUTR}:\n",
        "   n_min[i] <= sum {j in FOOD} amt[i,j] * Buy[j] <= n_max[i];"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "jCyp8qRS7Kba"
      },
      "source": [
        "### Load data directly from Python data structures using [amplpy](https://amplpy.readthedocs.io/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "jYDiBDD_7Kba"
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      "source": [
        "foods = [\"BEEF\", \"CHK\", \"FISH\", \"HAM\", \"MCH\", \"MTL\", \"SPG\", \"TUR\"]\n",
        "nutrients = [\"A\", \"C\", \"B1\", \"B2\", \"NA\", \"CAL\"]\n",
        "ampl.set[\"FOOD\"] = foods\n",
        "ampl.param[\"cost\"] = [3.59, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]\n",
        "ampl.param[\"f_min\"] = [2, 2, 2, 2, 2, 2, 2, 2]\n",
        "ampl.param[\"f_max\"] = [10, 10, 10, 10, 10, 10, 10, 10]\n",
        "ampl.set[\"NUTR\"] = nutrients\n",
        "ampl.param[\"n_min\"] = [700, 700, 700, 700, 0, 16000]\n",
        "ampl.param[\"n_max\"] = [20000, 20000, 20000, 20000, 50000, 24000]\n",
        "amounts = [\n",
        "    [60, 8, 8, 40, 15, 70, 25, 60],\n",
        "    [20, 0, 10, 40, 35, 30, 50, 20],\n",
        "    [10, 20, 15, 35, 15, 15, 25, 15],\n",
        "    [15, 20, 10, 10, 15, 15, 15, 10],\n",
        "    [928, 2180, 945, 278, 1182, 896, 1329, 1397],\n",
        "    [295, 770, 440, 430, 315, 400, 379, 450],\n",
        "]\n",
        "ampl.param[\"amt\"] = {\n",
        "    (nutrient, food): amounts[i][j]\n",
        "    for i, nutrient in enumerate(nutrients)\n",
        "    for j, food in enumerate(foods)\n",
        "}"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "uftSUdTh9eZY"
      },
      "source": [
        "# Solve with HiGHS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6pPuSOi8nPH",
        "outputId": "096f397f-7680-4a1e-f3f3-e5126edfc931"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HiGHS 1.3.0: tech:outlev=1\n",
            "Running HiGHS 1.3.0 [date: 2022-12-14, git hash: fbd9bd3-dirty]\n",
            "Copyright (c) 2022 ERGO-Code under MIT licence terms\n",
            "Presolving model\n",
            "6 rows, 8 cols, 47 nonzeros\n",
            "6 rows, 8 cols, 47 nonzeros\n",
            "Presolve : Reductions: rows 6(-0); columns 8(-0); elements 47(-0) - Not reduced\n",
            "Problem not reduced by presolve: solving the LP\n",
            "Using EKK dual simplex solver - serial\n",
            "  Iteration        Objective     Infeasibilities num(sum)\n",
            "          0     3.9440062313e+01 Pr: 5(9372.5) 0s\n",
            "          4     1.1998975894e+02 Pr: 0(0) 0s\n",
            "Model   status      : Optimal\n",
            "Simplex   iterations: 4\n",
            "Objective value     :  1.1998975894e+02\n",
            "HiGHS run time      :          0.00\n",
            "HiGHS 1.3.0: optimal solution; objective 119.9897589\n",
            "4 simplex iterations\n",
            "0 barrier iterations\n",
            "absmipgap=119.99, relmipgap=inf\n"
          ]
        }
      ],
      "source": [
        "ampl.option[\"solver\"] = \"highs\"\n",
        "ampl.option[\"highs_options\"] = \"outlev=1\"\n",
        "ampl.solve()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6Xb28Bg7ODqV"
      },
      "source": [
        "# Retrieve solution as a pandas dataframe"
      ]
    },
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      "execution_count": 11,
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              "        Buy.val\n",
              "BEEF   5.226933\n",
              "CHK    2.000000\n",
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              "MCH   10.000000\n",
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