notebooks/03 Training and Testing.ipynb

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    {
      "cell_type": "markdown",
      "id": "e2ff5745",
      "metadata": {
        "id": "e2ff5745"
      },
      "source": [
        "## Install (Colab Only)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "357d9957",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:27.529983566Z",
          "start_time": "2023-07-03T16:15:27.526086345Z"
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        "id": "357d9957",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9faaa1a8-13c3-433c-8e11-1be9e6565080"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: ply, gurobipy, Pyomo, ppft, pox, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, pathos, nvidia-cusolver-cu12, pyepo\n",
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          ]
        }
      ],
      "source": [
        "# install\n",
        "!pip install pyepo"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7f3af8df",
      "metadata": {
        "id": "7f3af8df"
      },
      "source": [
        "# Training and Testing\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "6a0834bb",
      "metadata": {
        "id": "6a0834bb"
      },
      "outputs": [],
      "source": [
        "# set random seed\n",
        "import random\n",
        "random.seed(42)\n",
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "import torch\n",
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "65247802",
      "metadata": {
        "id": "65247802"
      },
      "source": [
        "In this tutorial, we will train and test end-to-end predict-then-optimize for different approaches."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb28d718",
      "metadata": {
        "id": "cb28d718"
      },
      "source": [
        "## 1 Problem Example: Shortest Path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "753fb690",
      "metadata": {
        "id": "753fb690"
      },
      "source": [
        "Consider a 5x5 grid network again. The figure shows that each node has top, bottom, left, and right neighbors. We aim to find the shortest path from left top to right bottom."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b439ecf",
      "metadata": {
        "id": "5b439ecf"
      },
      "source": [
        "<img src=\"https://github.com/khalil-research/PyEPO/blob/main/images/shortestpath.png?raw=1\" width=\"500\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e8ecd83",
      "metadata": {
        "id": "0e8ecd83"
      },
      "source": [
        "The weighted graph includes 25 nodes and 40 edges. The weights of the edges are the costs of the path. We load the synthetic dataset where the costs $\\mathbf{c}$ are unknown and can be predicted from features $\\mathbf{x}$.\n",
        "\n",
        "``pyepo.data.shortestpath.genData`` allows us to generate data with various size, polynomial degree, and noise. We can learn more about the dataset [here](https://github.com/khalil-research/PyEPO/blob/main/notebooks/02%20Optimization%20Dataset.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "ae4ba391",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:30.969643569Z",
          "start_time": "2023-07-03T16:15:30.903584585Z"
        },
        "id": "ae4ba391",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3cac1d6-c0d8-402b-d1d6-eb384d470791"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Auto-Sklearn cannot be imported.\n"
          ]
        }
      ],
      "source": [
        "import pyepo\n",
        "# generate data\n",
        "grid = (5,5) # grid size\n",
        "num_data = 1000 # number of training data\n",
        "num_feat = 5 # size of feature\n",
        "deg = 4 # polynomial degree\n",
        "e = 0.5 # noise width\n",
        "feats, costs = pyepo.data.shortestpath.genData(num_data+1000, num_feat, grid, deg, e, seed=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8210263f",
      "metadata": {
        "id": "8210263f"
      },
      "source": [
        "Thus, we can use ``optGrbModel`` to build a linear model with the Gurobi solver. For more details on ``optModel``, you can read tutorial [01 Optimization Model\n",
        "](https://github.com/khalil-research/PyEPO/blob/main/notebooks/01%20Optimization%20Model.ipynb) and [documentation](https://khalil-research.github.io/PyEPO/build/html/content/examples/model.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8d71af1c",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:34.212005726Z",
          "start_time": "2023-07-03T16:15:34.202712580Z"
        },
        "id": "8d71af1c"
      },
      "outputs": [],
      "source": [
        "# build optModel\n",
        "from pyepo.model.grb import optGrbModel\n",
        "\n",
        "class shortestPathModel(optGrbModel):\n",
        "\n",
        "    def __init__(self):\n",
        "        self.grid = (5,5)\n",
        "        self.arcs = self._getArcs()\n",
        "        super().__init__()\n",
        "\n",
        "    def _getArcs(self):\n",
        "        \"\"\"\n",
        "        A helper method to get list of arcs for grid network\n",
        "\n",
        "        Returns:\n",
        "            list: arcs\n",
        "        \"\"\"\n",
        "        arcs = []\n",
        "        for i in range(self.grid[0]):\n",
        "            # edges on rows\n",
        "            for j in range(self.grid[1] - 1):\n",
        "                v = i * self.grid[1] + j\n",
        "                arcs.append((v, v + 1))\n",
        "            # edges in columns\n",
        "            if i == self.grid[0] - 1:\n",
        "                continue\n",
        "            for j in range(self.grid[1]):\n",
        "                v = i * self.grid[1] + j\n",
        "                arcs.append((v, v + self.grid[1]))\n",
        "        return arcs\n",
        "\n",
        "    def _getModel(self):\n",
        "        \"\"\"\n",
        "        A method to build Gurobi model\n",
        "\n",
        "        Returns:\n",
        "            tuple: optimization model and variables\n",
        "        \"\"\"\n",
        "        import gurobipy as gp\n",
        "        from gurobipy import GRB\n",
        "        # ceate a model\n",
        "        m = gp.Model(\"shortest path\")\n",
        "        # varibles\n",
        "        x = m.addVars(self.arcs, name=\"x\")\n",
        "        # sense\n",
        "        m.modelSense = GRB.MINIMIZE\n",
        "        # flow conservation constraints\n",
        "        for i in range(self.grid[0]):\n",
        "            for j in range(self.grid[1]):\n",
        "                v = i * self.grid[1] + j\n",
        "                expr = 0\n",
        "                for e in self.arcs:\n",
        "                    # flow in\n",
        "                    if v == e[1]:\n",
        "                        expr += x[e]\n",
        "                    # flow out\n",
        "                    elif v == e[0]:\n",
        "                        expr -= x[e]\n",
        "                # source\n",
        "                if i == 0 and j == 0:\n",
        "                    m.addConstr(expr == -1)\n",
        "                # sink\n",
        "                elif i == self.grid[0] - 1 and j == self.grid[0] - 1:\n",
        "                    m.addConstr(expr == 1)\n",
        "                # transition\n",
        "                else:\n",
        "                    m.addConstr(expr == 0)\n",
        "        return m, x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "29f84eea",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:38.795439387Z",
          "start_time": "2023-07-03T16:15:38.731606990Z"
        },
        "id": "29f84eea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fa7e80a-f393-45e2-b41c-48266b247514"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restricted license - for non-production use only - expires 2025-11-24\n"
          ]
        }
      ],
      "source": [
        "optmodel = shortestPathModel()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56225c58",
      "metadata": {
        "id": "56225c58"
      },
      "source": [
        "## 2 Dataset and Data Loader"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ebed5419",
      "metadata": {
        "id": "ebed5419"
      },
      "source": [
        "PyTorch provides two data primitives: ``Dataset`` and ``DataLoader``, which allow you to use pre-loaded datasets as well as your own data. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset to enable easy access to the samples. The PyEPO module ``optDataset`` stores features $\\mathbf{x}$ and costs $\\mathbf{c}$, then computes the corresponding optimal solutions $\\mathbf{w}^*$ and objective values $\\mathbf{z}^*$.\n",
        "\n",
        "More details on ``optDataset`` is [here](https://github.com/khalil-research/PyEPO/blob/main/notebooks/02%20Optimization%20Dataset.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "9988b779",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:42.978633537Z",
          "start_time": "2023-07-03T16:15:42.964467854Z"
        },
        "id": "9988b779"
      },
      "outputs": [],
      "source": [
        "# split train test data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, c_train, c_test = train_test_split(feats, costs, test_size=1000, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "cfcd58dd",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:49.609217956Z",
          "start_time": "2023-07-03T16:15:45.044754480Z"
        },
        "id": "cfcd58dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "789686e4-aa57-4c8e-fb87-7b31d7a951d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizing for optDataset...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:06<00:00, 155.01it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizing for optDataset...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:03<00:00, 269.51it/s]\n"
          ]
        }
      ],
      "source": [
        "# get optDataset\n",
        "dataset_train = pyepo.data.dataset.optDataset(optmodel, x_train, c_train)\n",
        "dataset_test = pyepo.data.dataset.optDataset(optmodel, x_test, c_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "4fc4bb17",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:53.145587193Z",
          "start_time": "2023-07-03T16:15:53.135680577Z"
        },
        "id": "4fc4bb17"
      },
      "outputs": [],
      "source": [
        "# set data loader\n",
        "from torch.utils.data import DataLoader\n",
        "batch_size = 32\n",
        "loader_train = DataLoader(dataset_train, batch_size=batch_size, shuffle=True)\n",
        "loader_test = DataLoader(dataset_test, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7cb4edf9",
      "metadata": {
        "id": "7cb4edf9"
      },
      "source": [
        "## 3 Linear Regression on PyTorch"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f17b53ca",
      "metadata": {
        "id": "f17b53ca"
      },
      "source": [
        "PyTorch is an open-source machine learning library primarily used for developing and training deep learning models such as neural networks. It is developed by Facebook's AI Research lab and is based on the Torch library. PyTorch provides a flexible and intuitive interface for creating and training models.\n",
        "\n",
        "In PyTorch, the ``nn.Module`` is a base class for all neural network modules in the library. It provides a convenient way to organize the layers of a model, and to define the forward pass of the model.\n",
        "\n",
        "Here, we build the simplest PyTorch model, linear regression."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "f7e847e0",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:57.092548254Z",
          "start_time": "2023-07-03T16:15:57.070646496Z"
        },
        "id": "f7e847e0"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "\n",
        "# build linear model\n",
        "class LinearRegression(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(LinearRegression, self).__init__()\n",
        "        self.linear = nn.Linear(num_feat, (grid[0]-1)*grid[1]+(grid[1]-1)*grid[0])\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.linear(x)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1571557e",
      "metadata": {
        "id": "1571557e"
      },
      "source": [
        "Then, we can initialize the predictor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "3f162584",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:02.503758931Z",
          "start_time": "2023-07-03T16:16:02.452869940Z"
        },
        "id": "3f162584"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62407a29",
      "metadata": {
        "id": "62407a29"
      },
      "source": [
        "## 4 Training and Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eebb2ef2",
      "metadata": {
        "id": "eebb2ef2"
      },
      "source": [
        "The core capability of PyEPO is to build optimization models with GurobiPy, Pyomo, or any other solvers and algorithms, then embed the optimization model into an artificial neural network for the end-to-end training. For this purpose, PyEPO implements **SPO+ loss** and **differentiable Black-Box optimizer**, **differentiable perturbed optimizer**, **Fenchel-Young loss with Perturbation**, **Noise Contrastive Estimation**, and **Learning to Rank** as PyTorch autograd modules.\n",
        "\n",
        "We will train and test the above aproaches."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67152388",
      "metadata": {
        "id": "67152388"
      },
      "source": [
        "### 4.1 Introduction to Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3731def8",
      "metadata": {
        "id": "3731def8"
      },
      "source": [
        "``pyepo.metric.regret`` is to evaluate model performance. Regret (also called SPO loss) $l_{Regret}(\\hat{\\mathbf{c}}, \\mathbf{c}) = \\mathbf{c}^T \\mathbf{w}^*_{\\hat{\\mathbf{c}}} - z^*_{\\mathbf{c}}$ aims to measure the error in decision-making. It evaluates the distance between the objective value of the solution from predicted cost $\\hat{\\mathbf{c}}$ and the true optimal objective value $z^*_{c}$."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "01a18cb5",
      "metadata": {
        "id": "01a18cb5"
      },
      "source": [
        "To calculate regret, ``pyepo.metric.regret`` requires:\n",
        "- ``predmodel``: a regression neural network for cost prediction\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``dataloader``: PyTorch dataloader from optDataset to evaluate\n",
        "\n",
        "The following code block is an example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "d92d811a",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:10.429107030Z",
          "start_time": "2023-07-03T16:16:08.916477331Z"
        },
        "id": "d92d811a"
      },
      "outputs": [],
      "source": [
        "import pyepo\n",
        "regret = pyepo.metric.regret(reg, optmodel, loader_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc33c815",
      "metadata": {
        "id": "dc33c815"
      },
      "source": [
        "### 4.2 Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3860d36b",
      "metadata": {
        "id": "3860d36b"
      },
      "source": [
        "Define function to train model with different methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "5f4d57e9",
      "metadata": {
        "id": "5f4d57e9"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "\n",
        "# train model\n",
        "def trainModel(reg, loss_func, method_name, num_epochs=20, lr=1e-2):\n",
        "    # set adam optimizer\n",
        "    optimizer = torch.optim.Adam(reg.parameters(), lr=lr)\n",
        "    # train mode\n",
        "    reg.train()\n",
        "    # init log\n",
        "    loss_log = []\n",
        "    loss_log_regret = [pyepo.metric.regret(reg, optmodel, loader_test)]\n",
        "    # init elpased time\n",
        "    elapsed = 0\n",
        "    for epoch in range(num_epochs):\n",
        "        # start timing\n",
        "        tick = time.time()\n",
        "        # load data\n",
        "        for i, data in enumerate(loader_train):\n",
        "            x, c, w, z = data\n",
        "            # cuda\n",
        "            if torch.cuda.is_available():\n",
        "                x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "            # forward pass\n",
        "            cp = reg(x)\n",
        "            if method_name == \"spo+\":\n",
        "                loss = loss_func(cp, c, w, z)\n",
        "            if method_name in [\"ptb\", \"pfy\", \"imle\", \"aimle\", \"nce\", \"cmap\"]:\n",
        "                loss = loss_func(cp, w)\n",
        "            if method_name in [\"dbb\", \"nid\"]:\n",
        "                loss = loss_func(cp, c, z)\n",
        "            if method_name == \"ltr\":\n",
        "                loss = loss_func(cp, c)\n",
        "            # backward pass\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            # record time\n",
        "            tock = time.time()\n",
        "            elapsed += tock - tick\n",
        "            # log\n",
        "            loss_log.append(loss.item())\n",
        "        regret = pyepo.metric.regret(reg, optmodel, loader_test)\n",
        "        loss_log_regret.append(regret)\n",
        "        print(\"Epoch {:2},  Loss: {:9.4f},  Regret: {:7.4f}%\".format(epoch+1, loss.item(), regret*100))\n",
        "    print(\"Total Elapsed Time: {:.2f} Sec.\".format(elapsed))\n",
        "    return loss_log, loss_log_regret"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c5d241e8",
      "metadata": {
        "id": "c5d241e8"
      },
      "source": [
        "Define functions that visualize the learning curves."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "0449b18a",
      "metadata": {
        "id": "0449b18a"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "def visLearningCurve(loss_log, loss_log_regret):\n",
        "    # create figure and subplots\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,4))\n",
        "\n",
        "    # draw plot for training loss\n",
        "    ax1.plot(loss_log, color=\"c\", lw=2)\n",
        "    ax1.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
        "    ax1.set_xlabel(\"Iters\", fontsize=16)\n",
        "    ax1.set_ylabel(\"Loss\", fontsize=16)\n",
        "    ax1.set_title(\"Learning Curve on Training Set\", fontsize=16)\n",
        "\n",
        "    # draw plot for regret on test\n",
        "    ax2.plot(loss_log_regret, color=\"royalblue\", ls=\"--\", alpha=0.7, lw=2)\n",
        "    ax2.set_xticks(range(0, len(loss_log_regret), 2))\n",
        "    ax2.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
        "    ax2.set_ylim(0, 0.5)\n",
        "    ax2.set_xlabel(\"Epochs\", fontsize=16)\n",
        "    ax2.set_ylabel(\"Regret\", fontsize=16)\n",
        "    ax2.set_title(\"Learning Curve on Test Set\", fontsize=16)\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aaacfd38",
      "metadata": {
        "id": "aaacfd38"
      },
      "source": [
        "### 4.3 Smart Predict-then-Optimize Loss+"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cba77671",
      "metadata": {
        "id": "cba77671"
      },
      "source": [
        "[Elmachtoub, A. N., & Grigas, P. (2022). Smart “predict, then optimize”. Management Science, 68(1), 9-26.](https://doi.org/10.1287/mnsc.2020.3922)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "905f8b07",
      "metadata": {
        "id": "905f8b07"
      },
      "source": [
        "SPO+ Loss is a surrogate loss function of SPO Loss (Regret), which measures the decision error of optimization problem. For SPO/SPO+ Loss, the objective function is linear and constraints are known and fixed, but the cost vector need to be predicted from contextual data. The SPO+ Loss is convex with non-zero subgradient. Thus, allows us to design an algorithm based on stochastic gradient descent."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c03249b",
      "metadata": {
        "id": "0c03249b"
      },
      "source": [
        "First of all, we initialize the predictor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "1586bcfe",
      "metadata": {
        "id": "1586bcfe"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23d3f88e",
      "metadata": {
        "id": "23d3f88e"
      },
      "source": [
        "``pyepo.func.SPOPlus`` allows us to set a SPO+ loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "94f93c65",
      "metadata": {
        "id": "94f93c65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9401c68a-ec17-44ce-cb19-07b765470132"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "# init SPO+ loss\n",
        "spop = pyepo.func.SPOPlus(optmodel, processes=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6e32619",
      "metadata": {
        "id": "a6e32619"
      },
      "source": [
        "#### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89fe3311",
      "metadata": {
        "id": "89fe3311"
      },
      "source": [
        "Then, we can train a linear predictor with SPO+ loss to predict unknown cost coefficients and make decisions.\n",
        "\n",
        "To compute SPO+ loss, ``spop`` requires $\\mathbf{\\hat{c}}$, $\\mathbf{c}$, $\\mathbf{w}^*_{\\mathbf{c}}$, and $\\mathbf{z}^*_{\\mathbf{c}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "25c259af",
      "metadata": {
        "id": "25c259af",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f45ea7d-523e-4b34-de2f-322c5112b840"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    2.3026,  Regret: 11.0720%\n",
            "Epoch  2,  Loss:    1.3455,  Regret:  8.2444%\n",
            "Epoch  3,  Loss:    1.6723,  Regret:  7.4430%\n",
            "Epoch  4,  Loss:    1.1063,  Regret:  7.0685%\n",
            "Epoch  5,  Loss:    1.0820,  Regret:  7.1374%\n",
            "Epoch  6,  Loss:    1.3178,  Regret:  7.3276%\n",
            "Epoch  7,  Loss:    1.5822,  Regret:  7.2296%\n",
            "Epoch  8,  Loss:    0.7266,  Regret:  7.3548%\n",
            "Epoch  9,  Loss:    0.5989,  Regret:  7.4255%\n",
            "Epoch 10,  Loss:    0.9826,  Regret:  7.6219%\n",
            "Epoch 11,  Loss:    0.7405,  Regret:  7.1146%\n",
            "Epoch 12,  Loss:    0.8847,  Regret:  7.6052%\n",
            "Epoch 13,  Loss:    0.9451,  Regret:  7.2431%\n",
            "Epoch 14,  Loss:    0.5897,  Regret:  7.4689%\n",
            "Epoch 15,  Loss:    1.5206,  Regret:  7.3123%\n",
            "Epoch 16,  Loss:    0.5411,  Regret:  7.3580%\n",
            "Epoch 17,  Loss:    0.6211,  Regret:  6.9684%\n",
            "Epoch 18,  Loss:    1.0540,  Regret:  7.5942%\n",
            "Epoch 19,  Loss:    1.4707,  Regret:  7.1119%\n",
            "Epoch 20,  Loss:    0.9712,  Regret:  7.4135%\n",
            "Total Elapsed Time: 1460.98 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=spop, method_name=\"spo+\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ed7c5ab",
      "metadata": {
        "id": "4ed7c5ab"
      },
      "source": [
        "#### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "dde8491a",
      "metadata": {
        "id": "dde8491a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "d45a2abc-f22e-4016-b5d7-503a57ebee29"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3dee3614",
      "metadata": {
        "id": "3dee3614"
      },
      "source": [
        "### 4.4 Perturbed Methods"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89a1a70b",
      "metadata": {
        "id": "89a1a70b"
      },
      "source": [
        "[Berthet, Q., Blondel, M., Teboul, O., Cuturi, M., Vert, J. P., & Bach, F. (2020). Learning with differentiable pertubed optimizers. Advances in neural information processing systems, 33, 9508-9519.](https://papers.nips.cc/paper/2020/hash/6bb56208f672af0dd65451f869fedfd9-Abstract.html)\n",
        "\n",
        "[Niepert, M., Minervini, P., & Franceschi, L. (2021). Implicit MLE: backpropagating through discrete exponential family distributions. Advances in Neural Information Processing Systems, 34, 14567-14579.](https://proceedings.neurips.cc/paper_files/paper/2021/hash/7a430339c10c642c4b2251756fd1b484-Abstract.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2d230790",
      "metadata": {
        "id": "2d230790"
      },
      "source": [
        "#### 4.4.1 Differentiable Perturbed Optimizer"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1dc891ad",
      "metadata": {
        "id": "1dc891ad"
      },
      "source": [
        "Differentiable perturbed optimizer uses Monte-Carlo samples to estimate solutions, in which randomly perturbed costs are sampled to optimize. For the perturbed optimizer, the cost vector needs to be predicted from contextual data and are perturbed with Gaussian noise. The perturbed optimizer is differentiable in its inputs with non-zero Jacobian, thus allowing us to design an algorithm based on stochastic gradient descent."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5f505f2",
      "metadata": {
        "id": "e5f505f2"
      },
      "source": [
        "First of all, we initialize the predictor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "97df2499",
      "metadata": {
        "id": "97df2499"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf545748",
      "metadata": {
        "id": "bf545748"
      },
      "source": [
        "``pyepo.func.perturbedOpt`` allows us to set a SPO+ loss for training, which requires parameters:\n",
        "- ``optmodel``: a PyEPO optimization model\n",
        "- ``n_samples``: number of Monte-Carlo samples\n",
        "- ``sigma``:  the amplitude of the perturbation for costs\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of the cores\n",
        "- ``seed``: random state seed for perturbations"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2993c33a",
      "metadata": {
        "id": "2993c33a"
      },
      "source": [
        "Unlike SPO+, PTB is not a loss function so we need to define a loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "036ff330",
      "metadata": {
        "id": "036ff330",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72b3de27-8304-4a22-abd5-cdf4814d7945"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "# init ptb solver\n",
        "ptb = pyepo.func.perturbedOpt(optmodel, n_samples=3, sigma=1.0, processes=2)\n",
        "# set loss\n",
        "l1 = nn.L1Loss()\n",
        "\n",
        "# loss function\n",
        "def ptbl1(cp, w):\n",
        "    # perturbed optimizer\n",
        "    we = ptb(cp)\n",
        "    # loss\n",
        "    loss = l1(we, w)\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5adad0e",
      "metadata": {
        "id": "e5adad0e"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "303620e3",
      "metadata": {
        "id": "303620e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7366c785-dad8-48b0-a48e-26ee3418ea0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.2833,  Regret: 47.3833%\n",
            "Epoch  2,  Loss:    0.2687,  Regret: 40.2273%\n",
            "Epoch  3,  Loss:    0.2333,  Regret: 32.5097%\n",
            "Epoch  4,  Loss:    0.2562,  Regret: 27.4511%\n",
            "Epoch  5,  Loss:    0.1833,  Regret: 25.5623%\n",
            "Epoch  6,  Loss:    0.2354,  Regret: 24.1541%\n",
            "Epoch  7,  Loss:    0.2521,  Regret: 22.5062%\n",
            "Epoch  8,  Loss:    0.2167,  Regret: 22.0307%\n",
            "Epoch  9,  Loss:    0.1958,  Regret: 19.8910%\n",
            "Epoch 10,  Loss:    0.1917,  Regret: 19.3317%\n",
            "Epoch 11,  Loss:    0.1875,  Regret: 18.1897%\n",
            "Epoch 12,  Loss:    0.2062,  Regret: 16.5771%\n",
            "Epoch 13,  Loss:    0.1521,  Regret: 14.7945%\n",
            "Epoch 14,  Loss:    0.2125,  Regret: 13.4177%\n",
            "Epoch 15,  Loss:    0.2063,  Regret: 12.4954%\n",
            "Epoch 16,  Loss:    0.1958,  Regret: 11.7265%\n",
            "Epoch 17,  Loss:    0.1792,  Regret: 11.0796%\n",
            "Epoch 18,  Loss:    0.1771,  Regret: 10.4451%\n",
            "Epoch 19,  Loss:    0.2083,  Regret:  9.9791%\n",
            "Epoch 20,  Loss:    0.2083,  Regret:  9.4419%\n",
            "Total Elapsed Time: 2648.78 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=ptbl1, method_name=\"ptb\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0bb34fe",
      "metadata": {
        "id": "c0bb34fe"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "f4223d18",
      "metadata": {
        "id": "f4223d18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "7230c0f9-f7d1-4677-ae7f-791f18af18b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e0b7d864",
      "metadata": {
        "id": "e0b7d864"
      },
      "source": [
        "#### 4.4.2 Perturbed Fenchel-Young Loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d918b9e",
      "metadata": {
        "id": "3d918b9e"
      },
      "source": [
        "Perturbed Fenchel-Young loss (PYFL) uses perturbation techniques with Monte-Carlo samples. The use of the loss improves the algorithmic by the specific expression of the gradients of the loss. For the perturbed optimizer, the cost vector need to be predicted from contextual data and are perturbed with Gaussian noise. The Fenchel-Young loss allows to directly optimize a loss between the features and solutions with less computation. Thus, allows us to design an algorithm based on stochastic gradient descent."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b20cf56",
      "metadata": {
        "id": "5b20cf56"
      },
      "source": [
        "First of all, we initialize the predictor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "8353c159",
      "metadata": {
        "id": "8353c159"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89ad1b86",
      "metadata": {
        "id": "89ad1b86"
      },
      "source": [
        "``pyepo.func.perturbedFenchelYoung`` allows us to set a Fenchel-Young loss for training, which requires parameters:\n",
        "- ``optmodel``: a PyEPO optimization model\n",
        "- ``n_samples``: number of Monte-Carlo samples\n",
        "- ``sigma``:  the amplitude of the perturbation for costs\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of the cores\n",
        "- ``seed``: random state seed for perturbations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "8e1ba873",
      "metadata": {
        "id": "8e1ba873",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79437bfc-9aa0-4a48-8cb6-32cf2f5e3a4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "# init pfyl loss\n",
        "pfy = pyepo.func.perturbedFenchelYoung(optmodel, n_samples=3, sigma=1.0, processes=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95ababe4",
      "metadata": {
        "id": "95ababe4"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b6deb950",
      "metadata": {
        "id": "b6deb950"
      },
      "source": [
        "Then, we can train a linear predictor with Fenchel-Young loss to predict unknown cost coefficients and make decisions.\n",
        "\n",
        "To compute Fenchel-Young loss, ``pfy`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{w}^*_{\\mathbf{c}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "cc50c3d8",
      "metadata": {
        "id": "cc50c3d8",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93e5e9b7-537c-4066-f210-580543a63353"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    4.9167,  Regret: 10.3286%\n",
            "Epoch  2,  Loss:    4.6944,  Regret:  8.3012%\n",
            "Epoch  3,  Loss:    3.8889,  Regret:  7.7923%\n",
            "Epoch  4,  Loss:    5.3611,  Regret:  7.3980%\n",
            "Epoch  5,  Loss:    4.9167,  Regret:  7.3597%\n",
            "Epoch  6,  Loss:    3.6944,  Regret:  7.0844%\n",
            "Epoch  7,  Loss:    2.6667,  Regret:  7.1614%\n",
            "Epoch  8,  Loss:    2.1389,  Regret:  7.1099%\n",
            "Epoch  9,  Loss:    5.8889,  Regret:  7.2623%\n",
            "Epoch 10,  Loss:    5.8056,  Regret:  7.3090%\n",
            "Epoch 11,  Loss:    3.5278,  Regret:  7.1150%\n",
            "Epoch 12,  Loss:    3.6944,  Regret:  6.9674%\n",
            "Epoch 13,  Loss:    4.3056,  Regret:  7.2117%\n",
            "Epoch 14,  Loss:    3.3333,  Regret:  7.4110%\n",
            "Epoch 15,  Loss:    4.3056,  Regret:  7.4661%\n",
            "Epoch 16,  Loss:    4.1944,  Regret:  7.4546%\n",
            "Epoch 17,  Loss:    5.4167,  Regret:  7.3243%\n",
            "Epoch 18,  Loss:    4.1389,  Regret:  7.2192%\n",
            "Epoch 19,  Loss:    2.6111,  Regret:  7.2079%\n",
            "Epoch 20,  Loss:    5.2222,  Regret:  7.3841%\n",
            "Total Elapsed Time: 2606.85 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=pfy, method_name=\"pfy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e663292c",
      "metadata": {
        "id": "e663292c"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "0fd40036",
      "metadata": {
        "id": "0fd40036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "d7b3e9c9-b744-46f3-cba3-047e88d7c292"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4.4.3 Implicit Maximum Likelihood Estimator"
      ],
      "metadata": {
        "id": "IK-MVo0s5xET"
      },
      "id": "IK-MVo0s5xET"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Implicit Maximum Likelihood Estimator (I-MLE) use the perturb-and-MAP framework. They sample noise from a Sum-of-Gamma distribution and interpolate the loss function to approximate finite difference."
      ],
      "metadata": {
        "id": "tRJDHUEH5zoN"
      },
      "id": "tRJDHUEH5zoN"
    },
    {
      "cell_type": "markdown",
      "source": [
        "First of all, we initialize the predictor."
      ],
      "metadata": {
        "id": "yWi85vkE5_EE"
      },
      "id": "yWi85vkE5_EE"
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ],
      "metadata": {
        "id": "FDhj882n5z0s"
      },
      "id": "FDhj882n5z0s",
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "``pyepo.func.implicitMLE`` allows us to set a Maximum Likelihood loss for training, which requires parameters:\n",
        "- ``optmodel``: a PyEPO optimization model\n",
        "- ``n_samples``: number of Monte-Carlo samples\n",
        "- ``sigma``:  the amplitude of the perturbation for costs\n",
        "- ``lambd``: a hyperparameter for differentiable block-box to control interpolation degree\n",
        "- ``distribution``: noise distribution\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of the cores\n",
        "- ``seed``: random state seed for perturbations"
      ],
      "metadata": {
        "id": "drLpLVGO6gP_"
      },
      "id": "drLpLVGO6gP_"
    },
    {
      "cell_type": "code",
      "source": [
        "# init i-mle solver\n",
        "imle = pyepo.func.implicitMLE(optmodel, n_samples=10, sigma=1.0, lambd=10, processes=2)\n",
        "# set loss\n",
        "l1 = nn.L1Loss()\n",
        "\n",
        "# loss function\n",
        "def ilmel1(cp, w):\n",
        "    # perturbed optimizer\n",
        "    we = imle(cp)\n",
        "    # loss\n",
        "    loss = l1(we, w)\n",
        "    return loss"
      ],
      "metadata": {
        "id": "YiRPpUYx6Dtl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8464ef00-9b20-4564-d279-2af174438e37"
      },
      "id": "YiRPpUYx6Dtl",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Training"
      ],
      "metadata": {
        "id": "c6SV0HSi7mKJ"
      },
      "id": "c6SV0HSi7mKJ"
    },
    {
      "cell_type": "code",
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=ilmel1, method_name=\"imle\")"
      ],
      "metadata": {
        "id": "-M2cSiLF7odY",
        "outputId": "76acb309-13be-4646-8e75-1869b08f31aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "-M2cSiLF7odY",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.3206,  Regret: 38.0452%\n",
            "Epoch  2,  Loss:    0.2494,  Regret: 31.7271%\n",
            "Epoch  3,  Loss:    0.2631,  Regret: 23.8160%\n",
            "Epoch  4,  Loss:    0.2138,  Regret: 20.0830%\n",
            "Epoch  5,  Loss:    0.2119,  Regret: 19.5366%\n",
            "Epoch  6,  Loss:    0.1569,  Regret: 18.4293%\n",
            "Epoch  7,  Loss:    0.1688,  Regret: 17.4331%\n",
            "Epoch  8,  Loss:    0.1425,  Regret: 16.0938%\n",
            "Epoch  9,  Loss:    0.2062,  Regret: 15.0838%\n",
            "Epoch 10,  Loss:    0.1581,  Regret: 14.2379%\n",
            "Epoch 11,  Loss:    0.1550,  Regret: 15.3800%\n",
            "Epoch 12,  Loss:    0.0750,  Regret: 15.0740%\n",
            "Epoch 13,  Loss:    0.1887,  Regret: 15.2037%\n",
            "Epoch 14,  Loss:    0.1844,  Regret: 14.6371%\n",
            "Epoch 15,  Loss:    0.1488,  Regret: 12.8213%\n",
            "Epoch 16,  Loss:    0.1875,  Regret: 12.1834%\n",
            "Epoch 17,  Loss:    0.1431,  Regret: 11.9381%\n",
            "Epoch 18,  Loss:    0.1144,  Regret: 11.5124%\n",
            "Epoch 19,  Loss:    0.1088,  Regret: 10.9112%\n",
            "Epoch 20,  Loss:    0.1906,  Regret: 11.0749%\n",
            "Total Elapsed Time: 13463.34 Sec.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Learning Curve"
      ],
      "metadata": {
        "id": "MT6Obx6176Qn"
      },
      "id": "MT6Obx6176Qn"
    },
    {
      "cell_type": "code",
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ],
      "metadata": {
        "id": "Uhc4ZUO37r8v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "45962724-ce2d-4570-c5c6-476e789803b9"
      },
      "id": "Uhc4ZUO37r8v",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.4.4 Adaptive Implicit Maximum Likelihood Estimator"
      ],
      "metadata": {
        "id": "E4b9DOrQZzpL"
      },
      "id": "E4b9DOrQZzpL"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adaptive Implicit Maximum Likelihood Estimator (AI-MLE) use the adaptive interpolation step and the perturb-and-MAP framework. They sample noise from a Sum-of-Gamma distribution and interpolate the loss function to approximate finite difference."
      ],
      "metadata": {
        "id": "RMN_FjetaEQk"
      },
      "id": "RMN_FjetaEQk"
    },
    {
      "cell_type": "markdown",
      "source": [
        "First of all, we initialize the predictor."
      ],
      "metadata": {
        "id": "k89xeam7aIn0"
      },
      "id": "k89xeam7aIn0"
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ],
      "metadata": {
        "id": "caArAOgjZ9-z"
      },
      "id": "caArAOgjZ9-z",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "``pyepo.func.implicitMLE`` allows us to set a Maximum Likelihood loss for training, which requires parameters:\n",
        "- ``optmodel``: a PyEPO optimization model\n",
        "- ``n_samples``: number of Monte-Carlo samples\n",
        "- ``sigma``:  the amplitude of the perturbation for costs\n",
        "- ``distribution``: noise distribution\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of the cores\n",
        "- ``seed``: random state seed for perturbations"
      ],
      "metadata": {
        "id": "Nkri9IMWaMYU"
      },
      "id": "Nkri9IMWaMYU"
    },
    {
      "cell_type": "code",
      "source": [
        "# init i-mle solver\n",
        "aimle = pyepo.func.adaptiveImplicitMLE(optmodel, n_samples=10, sigma=1.0, processes=2)\n",
        "\n",
        "# set loss\n",
        "l1 = nn.L1Loss()\n",
        "\n",
        "# loss function\n",
        "def ilmel1(cp, w):\n",
        "    # perturbed optimizer\n",
        "    we = aimle(cp)\n",
        "    # loss\n",
        "    loss = l1(we, w)\n",
        "    return loss"
      ],
      "metadata": {
        "id": "6M6AR36XZ-HD",
        "outputId": "680da772-0fab-46a1-dc21-25ce58ce9307",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "6M6AR36XZ-HD",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Training"
      ],
      "metadata": {
        "id": "AIOwW4VVa66b"
      },
      "id": "AIOwW4VVa66b"
    },
    {
      "cell_type": "code",
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=ilmel1, method_name=\"aimle\")"
      ],
      "metadata": {
        "id": "CqiPkxGYZ-N2",
        "outputId": "6d4804cc-48ef-438f-b153-8d07ed794597",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "CqiPkxGYZ-N2",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.2894,  Regret: 33.9973%\n",
            "Epoch  2,  Loss:    0.1613,  Regret: 20.9949%\n",
            "Epoch  3,  Loss:    0.1556,  Regret: 13.4601%\n",
            "Epoch  4,  Loss:    0.1781,  Regret: 10.4737%\n",
            "Epoch  5,  Loss:    0.1906,  Regret:  8.9431%\n",
            "Epoch  6,  Loss:    0.1456,  Regret:  8.0974%\n",
            "Epoch  7,  Loss:    0.1575,  Regret:  7.6704%\n",
            "Epoch  8,  Loss:    0.1463,  Regret:  7.6688%\n",
            "Epoch  9,  Loss:    0.1750,  Regret:  7.5612%\n",
            "Epoch 10,  Loss:    0.1744,  Regret:  7.5239%\n",
            "Epoch 11,  Loss:    0.1931,  Regret:  7.1727%\n",
            "Epoch 12,  Loss:    0.1500,  Regret:  7.5141%\n",
            "Epoch 13,  Loss:    0.1562,  Regret:  7.5043%\n",
            "Epoch 14,  Loss:    0.1219,  Regret:  7.2785%\n",
            "Epoch 15,  Loss:    0.1756,  Regret:  7.3347%\n",
            "Epoch 16,  Loss:    0.1494,  Regret:  7.3263%\n",
            "Epoch 17,  Loss:    0.2125,  Regret:  7.4210%\n",
            "Epoch 18,  Loss:    0.1638,  Regret:  7.5295%\n",
            "Epoch 19,  Loss:    0.1150,  Regret:  7.1987%\n",
            "Epoch 20,  Loss:    0.1312,  Regret:  7.3199%\n",
            "Total Elapsed Time: 13554.00 Sec.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Learning Curve"
      ],
      "metadata": {
        "id": "VbQmHccna28i"
      },
      "id": "VbQmHccna28i"
    },
    {
      "cell_type": "code",
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ],
      "metadata": {
        "id": "0oIXPmaDa3KR",
        "outputId": "40ea1383-40cc-4712-f24f-5081ccb841f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "id": "0oIXPmaDa3KR",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3098e8bc",
      "metadata": {
        "id": "3098e8bc"
      },
      "source": [
        "### 4.5 Black-Box Methods"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWtP5Ih4doL0"
      },
      "source": [
        "#### 4.5.1 Differentiable Black-Box Optimizer"
      ],
      "id": "qWtP5Ih4doL0"
    },
    {
      "cell_type": "markdown",
      "id": "e36b38d3",
      "metadata": {
        "id": "e36b38d3"
      },
      "source": [
        "[Pogančić, M. V., Paulus, A., Musil, V., Martius, G., & Rolinek, M. (2020). Differentiation of blackbox combinatorial solvers. In International Conference on Learning Representations.](https://arxiv.org/abs/1912.02175)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4360852",
      "metadata": {
        "id": "e4360852"
      },
      "source": [
        "Differentiable black-box optimizer estimates gradients from interpolation, replacing the zero gradients. For differentiable block-box, the objective function is linear and constraints are known and fixed, but the cost vector need to be predicted from contextual data."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "012b0fe5",
      "metadata": {
        "id": "012b0fe5"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "96ce18e8",
      "metadata": {
        "id": "96ce18e8"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6e91a091",
      "metadata": {
        "id": "6e91a091"
      },
      "source": [
        "``pyepo.func.blackboxOpt`` allows us to set a differentiable solver, which requires parameters:\n",
        "- ``optmodel``: a PyEPO optimization model\n",
        "- ``lambd``: a parameter to contral interpolation degree\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of the cores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "8cbb1806",
      "metadata": {
        "id": "8cbb1806",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a01dde70-40a3-49a0-d685-4fceb79879f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "# init dbb solver\n",
        "dbb = pyepo.func.blackboxOpt(optmodel, lambd=20, processes=2)\n",
        "# set loss\n",
        "l1 = nn.L1Loss()\n",
        "\n",
        "# loss function\n",
        "def dbbl1(cp, c, z):\n",
        "    # black-box optimizer\n",
        "    wp = dbb(cp)\n",
        "    # objective value\n",
        "    zp = (wp * c).sum(1).view(-1, 1)\n",
        "    # loss\n",
        "    loss = l1(zp, z)\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c4053880",
      "metadata": {
        "id": "c4053880"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "6acc5d3f",
      "metadata": {
        "id": "6acc5d3f",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "213cfa14-674c-4a60-962b-94b730f10821"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.9439,  Regret: 23.8580%\n",
            "Epoch  2,  Loss:    0.8881,  Regret: 17.5521%\n",
            "Epoch  3,  Loss:    0.4325,  Regret: 16.2228%\n",
            "Epoch  4,  Loss:    0.6169,  Regret: 14.6881%\n",
            "Epoch  5,  Loss:    0.4001,  Regret: 12.2015%\n",
            "Epoch  6,  Loss:    0.3536,  Regret: 11.7734%\n",
            "Epoch  7,  Loss:    0.1634,  Regret: 11.3525%\n",
            "Epoch  8,  Loss:    0.5177,  Regret: 10.5717%\n",
            "Epoch  9,  Loss:    0.5378,  Regret: 11.0589%\n",
            "Epoch 10,  Loss:    0.3728,  Regret: 14.4662%\n",
            "Epoch 11,  Loss:    0.3031,  Regret: 15.9604%\n",
            "Epoch 12,  Loss:    0.5441,  Regret: 14.8836%\n",
            "Epoch 13,  Loss:    1.3928,  Regret: 16.6957%\n",
            "Epoch 14,  Loss:    0.8396,  Regret: 21.2838%\n",
            "Epoch 15,  Loss:    0.6382,  Regret: 14.1804%\n",
            "Epoch 16,  Loss:    0.4505,  Regret: 15.9162%\n",
            "Epoch 17,  Loss:    1.0361,  Regret: 16.4918%\n",
            "Epoch 18,  Loss:    0.6433,  Regret: 15.9511%\n",
            "Epoch 19,  Loss:    0.4326,  Regret: 15.9264%\n",
            "Epoch 20,  Loss:    1.0221,  Regret: 14.8649%\n",
            "Total Elapsed Time: 2760.76 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=dbbl1, method_name=\"dbb\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63fe2239",
      "metadata": {
        "id": "63fe2239"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "ae895239",
      "metadata": {
        "id": "ae895239",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "cbce9d07-9564-4032-f9e2-b0f9b2b1b641"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1BOfVSxd1Pl"
      },
      "source": [
        "#### 4.5.2 Negative Identity Backpropagation (NID)"
      ],
      "id": "N1BOfVSxd1Pl"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15lg0xVrd1Ps"
      },
      "source": [
        "[Sahoo, S. S., Paulus, A., Vlastelica, M., Musil, V., Kuleshov, V., & Martius, G. (2022). Backpropagation through combinatorial algorithms: Identity with projection works. arXiv preprint arXiv:2205.15213.](https://arxiv.org/abs/2205.15213)"
      ],
      "id": "15lg0xVrd1Ps"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZ8jtTHkd1Ps"
      },
      "source": [
        "Negative Identity Backpropagation (NID) treats the solver as a negative identity mapping during the backward pass, which is equivalent to DBB with certain hyperparameter. It is hyperparameter-free and does not require any additional computationally expensive call to the solver on the backward pass."
      ],
      "id": "qZ8jtTHkd1Ps"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YfO0smjd1Ps"
      },
      "source": [
        "First of all, we initialize the predictor"
      ],
      "id": "3YfO0smjd1Ps"
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "kP9IVVpUd1Ps"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ],
      "id": "kP9IVVpUd1Ps"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkNDsLbmd1Ps"
      },
      "source": [
        "``pyepo.func.blackboxOpt`` allows us to set a differentiable solver, which requires parameters:\n",
        "- ``optmodel``: a PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of the cores"
      ],
      "id": "kkNDsLbmd1Ps"
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "MdJz4Oi2d1Ps",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a05586c8-effd-4cc1-a7a5-837c9e770bb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "# init dbb solver\n",
        "nid = pyepo.func.negativeIdentity(optmodel, processes=2)\n",
        "# set loss\n",
        "l1 = nn.L1Loss()\n",
        "\n",
        "# loss function\n",
        "def nidl1(cp, c, z):\n",
        "    # black-box optimizer\n",
        "    wp = nid(cp)\n",
        "    # objective value\n",
        "    zp = (wp * c).sum(1).view(-1, 1)\n",
        "    # loss\n",
        "    loss = l1(zp, z)\n",
        "    return loss"
      ],
      "id": "MdJz4Oi2d1Ps"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZqOwcRsd1Pt"
      },
      "source": [
        "##### Training"
      ],
      "id": "FZqOwcRsd1Pt"
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "scrolled": false,
        "id": "rXKg0nkVd1Pt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20b4ad50-0e13-4618-9cdd-47c18d20629d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    1.8785,  Regret: 38.5151%\n",
            "Epoch  2,  Loss:    0.8931,  Regret: 26.6921%\n",
            "Epoch  3,  Loss:    0.4992,  Regret: 20.7440%\n",
            "Epoch  4,  Loss:    0.3404,  Regret: 16.8463%\n",
            "Epoch  5,  Loss:    0.1710,  Regret: 13.9922%\n",
            "Epoch  6,  Loss:    0.6588,  Regret: 12.8433%\n",
            "Epoch  7,  Loss:    0.4963,  Regret: 12.0467%\n",
            "Epoch  8,  Loss:    0.1946,  Regret: 11.4351%\n",
            "Epoch  9,  Loss:    1.1988,  Regret: 10.8277%\n",
            "Epoch 10,  Loss:    0.2047,  Regret: 10.7434%\n",
            "Epoch 11,  Loss:    0.4176,  Regret: 10.6128%\n",
            "Epoch 12,  Loss:    0.2643,  Regret: 10.3937%\n",
            "Epoch 13,  Loss:    0.2228,  Regret: 10.0930%\n",
            "Epoch 14,  Loss:    0.3808,  Regret:  9.8150%\n",
            "Epoch 15,  Loss:    0.3091,  Regret:  9.7413%\n",
            "Epoch 16,  Loss:    0.5195,  Regret:  9.6280%\n",
            "Epoch 17,  Loss:    0.3558,  Regret:  9.4822%\n",
            "Epoch 18,  Loss:    0.1835,  Regret:  9.5340%\n",
            "Epoch 19,  Loss:    0.3043,  Regret:  9.5290%\n",
            "Epoch 20,  Loss:    0.1938,  Regret:  9.3862%\n",
            "Total Elapsed Time: 1344.91 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=nidl1, method_name=\"nid\")"
      ],
      "id": "rXKg0nkVd1Pt"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtIcL5EXd1Pt"
      },
      "source": [
        "##### Learning Curve"
      ],
      "id": "xtIcL5EXd1Pt"
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "MbHqlmbVd1Pt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "319dd15f-b8e1-4aa7-eec0-86a1e7e3cf08"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ],
      "id": "MbHqlmbVd1Pt"
    },
    {
      "cell_type": "markdown",
      "id": "0e74c152",
      "metadata": {
        "id": "0e74c152"
      },
      "source": [
        "### 4.6 Contrastive Estimation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8b9a1d00",
      "metadata": {
        "id": "8b9a1d00"
      },
      "source": [
        "[Mulamba, M., Mandi, J., Diligenti, M., Lombardi, M., Bucarey, V., & Guns, T. (2021). Contrastive losses and solution caching for predict-and-optimize. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence.](https://www.ijcai.org/proceedings/2021/390)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fdca6a1",
      "metadata": {
        "id": "7fdca6a1"
      },
      "source": [
        "#### 4.6.1 Noise Contrastive Estimation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7eaab10b",
      "metadata": {
        "id": "7eaab10b"
      },
      "source": [
        "It uses a noise contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. For the NCE, the cost vector needs to be predicted from contextual data and maximizes the separation of the probability of the optimal solution."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b407567",
      "metadata": {
        "id": "5b407567"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "b447a5f7",
      "metadata": {
        "id": "b447a5f7"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "841c0a44",
      "metadata": {
        "id": "841c0a44"
      },
      "source": [
        "``pyepo.func.NCE`` allows us to use a noise contrastive estimiation loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores\n",
        "- ``solve_ratio``: the ratio of new solutions computed during training\n",
        "- ``dataset``: a dataset to initialize the solution pool with. Usually this is simply the training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "19fc8c2b",
      "metadata": {
        "id": "19fc8c2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e2c5968-c29c-4235-f55d-8ab42c26384e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "nce = pyepo.func.NCE(optmodel, processes=2, solve_ratio=0.05, dataset=dataset_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b831428",
      "metadata": {
        "id": "0b831428"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e15a116",
      "metadata": {
        "id": "4e15a116"
      },
      "source": [
        "To compute noise contrastive estimation loss, ``nce`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{w}^*_{\\mathbf{c}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "b863fea1",
      "metadata": {
        "id": "b863fea1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1363357-23ae-45a0-ed8e-b6377d374a96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:   -3.3355,  Regret: 19.8765%\n",
            "Epoch  2,  Loss:   -5.8450,  Regret: 13.5880%\n",
            "Epoch  3,  Loss:   -7.2441,  Regret: 12.3784%\n",
            "Epoch  4,  Loss:  -11.2381,  Regret: 11.8383%\n",
            "Epoch  5,  Loss:  -23.8893,  Regret: 11.3297%\n",
            "Epoch  6,  Loss:  -23.3409,  Regret: 11.1223%\n",
            "Epoch  7,  Loss:  -26.8991,  Regret: 10.9389%\n",
            "Epoch  8,  Loss:  -21.9243,  Regret: 10.9396%\n",
            "Epoch  9,  Loss:  -36.1788,  Regret: 10.8408%\n",
            "Epoch 10,  Loss:  -38.1990,  Regret: 10.3816%\n",
            "Epoch 11,  Loss:  -42.9615,  Regret: 10.5025%\n",
            "Epoch 12,  Loss:  -44.3681,  Regret: 10.5010%\n",
            "Epoch 13,  Loss:  -45.9899,  Regret: 10.6922%\n",
            "Epoch 14,  Loss:  -47.0421,  Regret: 10.5530%\n",
            "Epoch 15,  Loss:  -37.7189,  Regret: 10.5179%\n",
            "Epoch 16,  Loss:  -73.2137,  Regret: 10.6224%\n",
            "Epoch 17,  Loss:  -63.8391,  Regret: 10.5547%\n",
            "Epoch 18,  Loss:  -49.0079,  Regret: 10.5204%\n",
            "Epoch 19,  Loss:  -88.0546,  Regret: 10.5354%\n",
            "Epoch 20,  Loss:  -72.4117,  Regret: 10.4534%\n",
            "Total Elapsed Time: 111.28 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=nce, method_name=\"nce\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8f879771",
      "metadata": {
        "id": "8f879771"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "8c8ad61e",
      "metadata": {
        "id": "8c8ad61e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "07202de9-9da0-4a1f-f16a-3e92c041c8c3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "75e35693",
      "metadata": {
        "id": "75e35693"
      },
      "source": [
        "#### 4.6.2 Maximum A Posterior"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4aa00bb7",
      "metadata": {
        "id": "4aa00bb7"
      },
      "source": [
        "An autograd module for Maximum A Posterior contrastive estimation as surrogate loss functions, which is a efficient self-contrastive algorithm. For the MAP, the cost vector needs to be predicted from contextual data and maximizes the separation of the probability of the optimal solution."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b09586e6",
      "metadata": {
        "id": "b09586e6"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "52152357",
      "metadata": {
        "id": "52152357"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8319d359",
      "metadata": {
        "id": "8319d359"
      },
      "source": [
        "``pyepo.func.contrastiveMAP`` allows us to use a noise contrastive estimiation loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores\n",
        "- ``solve_ratio``: the ratio of new solutions computed during training\n",
        "- ``dataset``: a dataset to initialize the solution pool with. Usually this is simply the training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "0b960707",
      "metadata": {
        "id": "0b960707",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f5fa180-a166-4b52-d486-ad069faed758"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "cmap = pyepo.func.contrastiveMAP(optmodel, processes=2, solve_ratio=0.05, dataset=dataset_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b060aa52",
      "metadata": {
        "id": "b060aa52"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6929f837",
      "metadata": {
        "id": "6929f837"
      },
      "source": [
        "To compute Maximum A Posterior contrastive estimation loss, ``cmap`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{w}^*_{\\mathbf{c}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "3ef81b1a",
      "metadata": {
        "id": "3ef81b1a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95ef91fe-fc05-483b-b233-e6a41d5efb54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.4324,  Regret: 12.7274%\n",
            "Epoch  2,  Loss:    0.0347,  Regret:  9.9825%\n",
            "Epoch  3,  Loss:    0.1383,  Regret: 11.4524%\n",
            "Epoch  4,  Loss:    0.0260,  Regret:  9.7139%\n",
            "Epoch  5,  Loss:    0.0090,  Regret: 12.2812%\n",
            "Epoch  6,  Loss:    0.1580,  Regret: 12.4865%\n",
            "Epoch  7,  Loss:    0.0753,  Regret: 12.4794%\n",
            "Epoch  8,  Loss:    0.0573,  Regret: 15.7053%\n",
            "Epoch  9,  Loss:    0.1604,  Regret: 14.5669%\n",
            "Epoch 10,  Loss:    0.0433,  Regret: 11.8005%\n",
            "Epoch 11,  Loss:    0.1057,  Regret: 13.0185%\n",
            "Epoch 12,  Loss:    0.0797,  Regret: 16.0303%\n",
            "Epoch 13,  Loss:    0.0812,  Regret: 13.6553%\n",
            "Epoch 14,  Loss:    0.0548,  Regret: 13.0333%\n",
            "Epoch 15,  Loss:    0.0540,  Regret: 15.0368%\n",
            "Epoch 16,  Loss:    0.0413,  Regret: 11.7389%\n",
            "Epoch 17,  Loss:    0.0360,  Regret: 15.3481%\n",
            "Epoch 18,  Loss:    0.0806,  Regret: 15.4118%\n",
            "Epoch 19,  Loss:    0.1240,  Regret: 14.8577%\n",
            "Epoch 20,  Loss:    0.0852,  Regret: 14.2020%\n",
            "Total Elapsed Time: 94.21 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=cmap, method_name=\"cmap\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "108fd0a4",
      "metadata": {
        "id": "108fd0a4"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "4196c11a",
      "metadata": {
        "id": "4196c11a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "81a5c016-c3ae-4c21-dd5d-bf1692a64019"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6abb4845",
      "metadata": {
        "id": "6abb4845"
      },
      "source": [
        "### 4.7 Learning To Rank"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5cd0621d",
      "metadata": {
        "id": "5cd0621d"
      },
      "source": [
        "[Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Decision-focused learning: through the lens of learning to rank. Proceedings of the 39th International Conference on Machine Learning.](https://proceedings.mlr.press/v162/mandi22a.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6bbc75cb",
      "metadata": {
        "id": "6bbc75cb"
      },
      "source": [
        "#### 4.7.1 Listwise Learning To Rank"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2327a93b",
      "metadata": {
        "id": "2327a93b"
      },
      "source": [
        "A autograd module for listwise learning to rank, where the goal is to learn an objective function that ranks a pool of feasible solutions correctly. For the listwise LTR, the cost vector needs to be predicted from contextual data, and the loss measures the scores of the whole ranked lists."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4c8046eb",
      "metadata": {
        "id": "4c8046eb"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "602505a8",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:24.267717164Z",
          "start_time": "2023-07-03T16:16:18.452617457Z"
        },
        "id": "602505a8"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89c56904",
      "metadata": {
        "id": "89c56904"
      },
      "source": [
        "``pyepo.func.listwiseLTR`` allows us to use a listwise learning to rank loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores\n",
        "- ``solve_ratio``: the ratio of new solutions computed during training\n",
        "- ``dataset``: a dataset to initialize the solution pool with. Usually this is simply the training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "6b33d585",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:27.478976953Z",
          "start_time": "2023-07-03T16:16:27.473935240Z"
        },
        "id": "6b33d585",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21a0b243-f20e-4f49-8de6-486b9a03fe30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "lsltr = pyepo.func.listwiseLTR(optmodel, processes=2, solve_ratio=0.05, dataset=dataset_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e8d837cc",
      "metadata": {
        "id": "e8d837cc"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "32cb0d1d",
      "metadata": {
        "id": "32cb0d1d"
      },
      "source": [
        "To compute learning-to-rank loss, ``lsltr`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{c}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "fa65886a",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:17:01.763318176Z",
          "start_time": "2023-07-03T16:16:32.516958317Z"
        },
        "id": "fa65886a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ffb0d59-d498-4d54-b392-66dd2b2dcfa6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.0520,  Regret: 15.8786%\n",
            "Epoch  2,  Loss:    0.0572,  Regret:  8.6204%\n",
            "Epoch  3,  Loss:    0.0595,  Regret:  9.4602%\n",
            "Epoch  4,  Loss:    0.0579,  Regret:  9.5452%\n",
            "Epoch  5,  Loss:    0.0599,  Regret:  9.6820%\n",
            "Epoch  6,  Loss:    0.0546,  Regret:  9.4775%\n",
            "Epoch  7,  Loss:    0.0553,  Regret:  9.2227%\n",
            "Epoch  8,  Loss:    0.0568,  Regret:  9.7939%\n",
            "Epoch  9,  Loss:    0.0576,  Regret:  9.3171%\n",
            "Epoch 10,  Loss:    0.0581,  Regret:  9.6342%\n",
            "Epoch 11,  Loss:    0.0591,  Regret:  9.4784%\n",
            "Epoch 12,  Loss:    0.0593,  Regret:  9.3956%\n",
            "Epoch 13,  Loss:    0.0608,  Regret:  9.7072%\n",
            "Epoch 14,  Loss:    0.0572,  Regret:  9.1933%\n",
            "Epoch 15,  Loss:    0.0603,  Regret:  9.0706%\n",
            "Epoch 16,  Loss:    0.0560,  Regret:  9.4318%\n",
            "Epoch 17,  Loss:    0.0537,  Regret:  9.5584%\n",
            "Epoch 18,  Loss:    0.0577,  Regret:  9.7192%\n",
            "Epoch 19,  Loss:    0.0571,  Regret: 10.4206%\n",
            "Epoch 20,  Loss:    0.0635,  Regret:  9.7530%\n",
            "Total Elapsed Time: 117.42 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=lsltr, method_name=\"ltr\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f0dd4c7",
      "metadata": {
        "id": "3f0dd4c7"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "23f5ec24",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:17:46.308808781Z",
          "start_time": "2023-07-03T16:17:45.655774057Z"
        },
        "id": "23f5ec24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "2ddd5407-59f4-44cd-d641-135cfb0ea846"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf527d34",
      "metadata": {
        "id": "bf527d34"
      },
      "source": [
        "#### 4.7.2 Pairwise Learning To Rank"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7477432c",
      "metadata": {
        "id": "7477432c"
      },
      "source": [
        "An autograd module for pairwise learning to rank, where the goal is to learn an objective function that ranks a pool of feasible solutions correctly. For the pairwise LTR, the cost vector needs to be predicted from contextual data, and the loss learns the relative ordering of pairs of items."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ea1cde2b",
      "metadata": {
        "id": "ea1cde2b"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "af18ca38",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:24.267717164Z",
          "start_time": "2023-07-03T16:16:18.452617457Z"
        },
        "id": "af18ca38"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27d7caf5",
      "metadata": {
        "id": "27d7caf5"
      },
      "source": [
        "``pyepo.func.pairwiseLTR`` allows us to use a listwise learning to rank loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores\n",
        "- ``solve_ratio``: the ratio of new solutions computed during training\n",
        "- ``dataset``: a dataset to initialize the solution pool with. Usually this is simply the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "72ff1275",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:27.478976953Z",
          "start_time": "2023-07-03T16:16:27.473935240Z"
        },
        "id": "72ff1275",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41872963-06cd-439b-de17-d495ac387e0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "prltr = pyepo.func.pairwiseLTR(optmodel, processes=2, solve_ratio=0.05, dataset=dataset_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2eb36db3",
      "metadata": {
        "id": "2eb36db3"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4850ca91",
      "metadata": {
        "id": "4850ca91"
      },
      "source": [
        "To compute learning-to-rank loss, ``prltr`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{c}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "120e76ad",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:17:01.763318176Z",
          "start_time": "2023-07-03T16:16:32.516958317Z"
        },
        "id": "120e76ad",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7b7b809-b45c-4d64-a4c7-8f8a0ec81b42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.0270,  Regret: 16.1443%\n",
            "Epoch  2,  Loss:    0.0904,  Regret: 11.6068%\n",
            "Epoch  3,  Loss:    0.0200,  Regret:  9.2222%\n",
            "Epoch  4,  Loss:    0.0553,  Regret:  8.6771%\n",
            "Epoch  5,  Loss:    0.0237,  Regret:  8.4503%\n",
            "Epoch  6,  Loss:    0.0171,  Regret:  8.7090%\n",
            "Epoch  7,  Loss:    0.0132,  Regret:  7.9820%\n",
            "Epoch  8,  Loss:    0.0022,  Regret:  8.6320%\n",
            "Epoch  9,  Loss:    0.0011,  Regret:  9.2666%\n",
            "Epoch 10,  Loss:    0.0217,  Regret:  8.9509%\n",
            "Epoch 11,  Loss:    0.0053,  Regret:  9.4807%\n",
            "Epoch 12,  Loss:    0.0047,  Regret:  9.5872%\n",
            "Epoch 13,  Loss:    0.0051,  Regret: 10.6936%\n",
            "Epoch 14,  Loss:    0.0067,  Regret: 11.9149%\n",
            "Epoch 15,  Loss:    0.0114,  Regret: 10.7558%\n",
            "Epoch 16,  Loss:    0.0001,  Regret: 13.5582%\n",
            "Epoch 17,  Loss:    0.0020,  Regret:  8.4199%\n",
            "Epoch 18,  Loss:    0.0067,  Regret: 10.9543%\n",
            "Epoch 19,  Loss:    0.0019,  Regret:  9.9811%\n",
            "Epoch 20,  Loss:    0.0065,  Regret: 10.9216%\n",
            "Total Elapsed Time: 327.31 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=prltr, method_name=\"ltr\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ddef1bb",
      "metadata": {
        "id": "9ddef1bb"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "a99fb844",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:17:46.308808781Z",
          "start_time": "2023-07-03T16:17:45.655774057Z"
        },
        "id": "a99fb844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "8db8dcee-baad-40ca-f808-6c8a55cd155a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb42db37",
      "metadata": {
        "id": "cb42db37"
      },
      "source": [
        "#### 4.7.3 Pointwise Learning To Rank"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab4fef25",
      "metadata": {
        "id": "ab4fef25"
      },
      "source": [
        "An autograd module for pointwise learning to rank, where the goal is to learn an objective function that ranks a pool of feasible solutions correctly. For the pointwise LTR, the cost vector needs to be predicted from contextual data, and calculates the ranking scores of the items."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b5081d9",
      "metadata": {
        "id": "7b5081d9"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "b478099f",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:24.267717164Z",
          "start_time": "2023-07-03T16:16:18.452617457Z"
        },
        "id": "b478099f"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "05dea9c7",
      "metadata": {
        "id": "05dea9c7"
      },
      "source": [
        "``pyepo.func.pointwiseLTR`` allows us to use a listwise learning to rank loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores\n",
        "- ``solve_ratio``: the ratio of new solutions computed during training\n",
        "- ``dataset``: a dataset to initialize the solution pool with. Usually this is simply the training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "6680ac57",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:27.478976953Z",
          "start_time": "2023-07-03T16:16:27.473935240Z"
        },
        "id": "6680ac57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bce762f6-09bc-434c-b334-6c6deaaacfd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "ptltr = pyepo.func.pointwiseLTR(optmodel, processes=2, solve_ratio=0.05, dataset=dataset_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03e921d1",
      "metadata": {
        "id": "03e921d1"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c51f232",
      "metadata": {
        "id": "1c51f232"
      },
      "source": [
        "To compute learning-to-rank loss, ``ptltr`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{c}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "543a0a05",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:17:01.763318176Z",
          "start_time": "2023-07-03T16:16:32.516958317Z"
        },
        "id": "543a0a05",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2586fb07-62b1-4ea0-89d2-58b8972d209e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:   13.8106,  Regret: 47.0799%\n",
            "Epoch  2,  Loss:   13.7507,  Regret: 24.3963%\n",
            "Epoch  3,  Loss:    1.9341,  Regret: 15.3132%\n",
            "Epoch  4,  Loss:    1.6020,  Regret: 12.6421%\n",
            "Epoch  5,  Loss:    1.2931,  Regret: 11.0526%\n",
            "Epoch  6,  Loss:    1.1071,  Regret:  9.9174%\n",
            "Epoch  7,  Loss:    1.2312,  Regret:  9.4479%\n",
            "Epoch  8,  Loss:    1.7714,  Regret:  9.0156%\n",
            "Epoch  9,  Loss:    3.0600,  Regret:  8.6596%\n",
            "Epoch 10,  Loss:    1.9459,  Regret:  8.5895%\n",
            "Epoch 11,  Loss:    1.6136,  Regret:  8.2903%\n",
            "Epoch 12,  Loss:    1.4074,  Regret:  8.6503%\n",
            "Epoch 13,  Loss:    1.8967,  Regret:  8.4661%\n",
            "Epoch 14,  Loss:    2.3907,  Regret:  8.5811%\n",
            "Epoch 15,  Loss:    6.7833,  Regret:  8.5820%\n",
            "Epoch 16,  Loss:    1.1277,  Regret:  8.2011%\n",
            "Epoch 17,  Loss:    3.8611,  Regret:  8.6099%\n",
            "Epoch 18,  Loss:    1.6858,  Regret:  8.4453%\n",
            "Epoch 19,  Loss:    3.1378,  Regret:  8.5085%\n",
            "Epoch 20,  Loss:    3.6354,  Regret:  8.4110%\n",
            "Total Elapsed Time: 78.74 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=ptltr, method_name=\"ltr\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6bc9a55",
      "metadata": {
        "id": "c6bc9a55"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "35f1b5b7",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:17:46.308808781Z",
          "start_time": "2023-07-03T16:17:45.655774057Z"
        },
        "id": "35f1b5b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "5a984813-59c9-479e-aa5b-6305e9bea57f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "a36e3916",
      "metadata": {
        "id": "a36e3916"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}