CS224W_Colab_5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuXWJLEm2UWS"
      },
      "source": [
        "# **CS224W - Colab 5**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gzsP50bF6Gb"
      },
      "source": [
        "In this Colab we will experiment on scaling up GNNs using PyTorch Geometric, DeepSNAP and NetworkX. As we have **canceled** the Colab 5 assignment, this notebook will be a tutorial and you do not need to submit it on Gradescope.\n",
        "\n",
        "At first, we will use PyTorch Geometric `NeighborSampler` to scale up the training and testing on OGB `arxiv` dataset.\n",
        "\n",
        "Then, using the DeepSNAP and NetworkX, we will implement a simplified version of `NeighborSampler` and run experiments with different smapling ratios on the Cora graph.\n",
        "\n",
        "At last, we will partition the Cora graph into clusters by using different partition algorithms and then train the models in the way of vanilla Cluster-GCN.\n",
        "\n",
        "**Note**: Make sure to **sequentially run all the cells in each section**, so that the intermediate variables / packages will carry over to the next cell"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSaetj53YnT6"
      },
      "source": [
        "# Device\n",
        "You might need to use GPU for this Colab.\n",
        "\n",
        "Please click `Runtime` and then `Change runtime type`. Then set the `hardware accelerator` to **GPU**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67gOQITlCNQi"
      },
      "source": [
        "# Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "J_m9l6OYCQZP"
      },
      "outputs": [],
      "source": [
        "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html\n",
        "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html\n",
        "!pip install -q torch-geometric\n",
        "!pip install -q ogb\n",
        "!pip install -q git+https://github.com/snap-stanford/deepsnap.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "PRfgbfTjCRD_",
        "outputId": "d8409be3-ed15-4a4e-c7c9-102c0c783bf4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import torch_geometric\n",
        "torch_geometric.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SxkYLgxAOxz7"
      },
      "source": [
        "# 1 PyTorch Geometric Neighbor Sampling\n",
        "\n",
        "Neighbor Sampling, originally proposed in **GraphSAGE** ([Hamilton et al. (2017)](https://arxiv.org/abs/1706.02216)), is a representative method to scale up GNNs. As what we have learned in lecture, only a K-hop neighborhood nodes will be loaded into GPU for each time training. To further reduce the cost, we can sample a subset of neighborhood nodes for GNNs to aggregate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kho6SHUVO1ny"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "X1WJLGKsOx_k"
      },
      "outputs": [],
      "source": [
        "import copy\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torch_geometric.transforms as T\n",
        "\n",
        "from torch_geometric.nn import SAGEConv\n",
        "from torch_geometric.data import NeighborSampler\n",
        "from ogb.nodeproppred import PygNodePropPredDataset, Evaluator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKqZWqRbO7km"
      },
      "source": [
        "## Neighbor Sampler\n",
        "\n",
        "PyTorch Geometric has implemented the Neighbor Sampling method as the [NeighborSampler](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.NeighborSampler) in `torch_geometric.data`. Following is an example that uses the Neighbor Sampling method on training the OGB `arxiv` dataset.\n",
        "\n",
        "If you are interested in memory-efficient aggregations, please refer to PyG's [Memory-Efficient Aggregations](https://pytorch-geometric.readthedocs.io/en/latest/notes/sparse_tensor.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWlyStlRO6_u",
        "outputId": "64dded5b-4952-4187-90f1-cb476c602bff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch_geometric/deprecation.py:12: UserWarning: 'data.NeighborSampler' is deprecated, use 'loader.NeighborSampler' instead\n",
            "  warnings.warn(out)\n"
          ]
        }
      ],
      "source": [
        "dataset_name = 'ogbn-arxiv'\n",
        "dataset = PygNodePropPredDataset(name=dataset_name,\n",
        "                                 transform=T.ToSparseTensor())\n",
        "data = dataset[0]\n",
        "data.adj_t = data.adj_t.to_symmetric()\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "print('Device: {}'.format(device))\n",
        "\n",
        "data = data.to(device)\n",
        "split_idx = dataset.get_idx_split()\n",
        "train_idx = split_idx['train'].to(device)\n",
        "\n",
        "# Construct the training dataloader for training data\n",
        "# Sample 10 neighbors for each node in the first layer and 5 for the second layer\n",
        "train_loader = NeighborSampler(data.adj_t, node_idx=train_idx,\n",
        "                               sizes=[10, 5], batch_size=4096,\n",
        "                               shuffle=True, num_workers=2)\n",
        "\n",
        "# Specify size as -1 to include all neighbors\n",
        "all_loader = NeighborSampler(data.adj_t, node_idx=None, sizes=[-1],\n",
        "                                  batch_size=4096, shuffle=False,\n",
        "                                  num_workers=2)\n",
        "evaluator = Evaluator(name='ogbn-arxiv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjdkIcFpRYyl"
      },
      "source": [
        "## GNN Model\n",
        "\n",
        "After creating the `NeighborSampler`, we also need to modify the model to let it support the mini-batch training.\n",
        "\n",
        "The `forward` function will take the node feature `x` and a list of three-element tuples `adjs`. Each element in `adjs` contains following elements:\n",
        "* `edge_index`: The edge index tensor between source and destination nodes, which forms a bipartite grpah.\n",
        "* `e_id`: The indices of the edges in the original graph.\n",
        "* `size`: The shape of the bipartite graph, in (*number of source nodes*, *number of destination nodes*) format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "gRBJS_5qRWbu"
      },
      "outputs": [],
      "source": [
        "class SAGE(torch.nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim, num_layers,\n",
        "                 dropout):\n",
        "        super(SAGE, self).__init__()\n",
        "\n",
        "        self.convs = torch.nn.ModuleList()\n",
        "        self.bns = torch.nn.ModuleList()\n",
        "\n",
        "        self.convs.append(SAGEConv(input_dim, hidden_dim))\n",
        "        self.bns.append(torch.nn.BatchNorm1d(hidden_dim))\n",
        "\n",
        "        for i in range(num_layers - 2):\n",
        "            self.convs.append(\n",
        "                SAGEConv(hidden_dim, hidden_dim))\n",
        "            self.bns.append(torch.nn.BatchNorm1d(hidden_dim))\n",
        "        self.convs.append(SAGEConv(hidden_dim, output_dim))\n",
        "\n",
        "        self.softmax = torch.nn.LogSoftmax(dim=1)\n",
        "\n",
        "        self.dropout = dropout\n",
        "\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        for conv in self.convs:\n",
        "            conv.reset_parameters()\n",
        "        for bn in self.bns:\n",
        "            bn.reset_parameters()\n",
        "\n",
        "    def forward(self, x, adjs, mode=\"batch\"):\n",
        "        if mode == \"batch\":\n",
        "            for i, (edge_index, _, size) in enumerate(adjs):\n",
        "                # Extract target node features\n",
        "                x_target = x[:size[1]]\n",
        "\n",
        "                # Update x for next layer reuse\n",
        "                x = self.convs[i]((x, x_target), edge_index)\n",
        "                if i != self.num_layers - 1:\n",
        "                    x = self.bns[i](x)\n",
        "                    x = F.relu(x)\n",
        "                    x = F.dropout(x, p=self.dropout, training=self.training)\n",
        "        else:\n",
        "            for i, conv in enumerate(self.convs):\n",
        "                x = conv(x, adjs)\n",
        "                if i != self.num_layers - 1:\n",
        "                    x = self.bns[i](x)\n",
        "                    x = F.relu(x)\n",
        "                    x = F.dropout(x, p=self.dropout, training=self.training)\n",
        "        return self.softmax(x)\n",
        "    \n",
        "    def inference(self, x_all, all_loader):\n",
        "        # This function will be called in test\n",
        "        for i in range(self.num_layers):\n",
        "            xs = []\n",
        "            for batch_size, n_id, adj in all_loader:\n",
        "                edge_index, _, size = adj.to(device)\n",
        "                x = x_all[n_id].to(device)\n",
        "                x_target = x[:size[1]]\n",
        "                x = self.convs[i]((x, x_target), edge_index)\n",
        "                if i != self.num_layers - 1:\n",
        "                    x = self.bns[i](x)\n",
        "                    x = F.relu(x)\n",
        "                    x = F.dropout(x, p=self.dropout, training=self.training)\n",
        "                \n",
        "                # Append the node embeddings to xs\n",
        "                xs.append(x.cpu())\n",
        "            \n",
        "            # Concat all embeddings into one tensor\n",
        "            x_all = torch.cat(xs, dim=0)\n",
        "\n",
        "        return x_all"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7cfm7K3wRqqY"
      },
      "source": [
        "## Training and Testing\n",
        "\n",
        "Now lets implement the training and testing functions.\n",
        "\n",
        "In both training and testing, we need to sample batch from the dataloader.\n",
        "\n",
        "Each batch in the `NeighborSampler` dataloader holds three elements:\n",
        "* `batch_size`: The batch size specified in the dataloader.\n",
        "* `n_id`: All nodes (in index format) used in the adjacency matrices.\n",
        "* `adjs`: The three-element tuples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "-JN0-_QCRn8N"
      },
      "outputs": [],
      "source": [
        "def train(model, data, train_loader, train_idx, optimizer, loss_fn, mode=\"batch\"):\n",
        "    model.train()\n",
        "\n",
        "    total_loss = 0\n",
        "    if mode == \"batch\":\n",
        "        for batch_size, n_id, adjs in train_loader:\n",
        "            # Move all adj sparse tensors to GPU\n",
        "            adjs = [adj.to(device) for adj in adjs]\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Index on the node features\n",
        "            out = model(data.x[n_id], adjs)\n",
        "            train_label = data.y[n_id[:batch_size]].squeeze(-1)\n",
        "            loss = loss_fn(out, train_label)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            total_loss += loss.item()\n",
        "    else:\n",
        "        optimizer.zero_grad()\n",
        "        out = model(data.x, data.adj_t, mode=mode)[train_idx]\n",
        "        train_label = data.y.squeeze(1)[train_idx]\n",
        "        loss = loss_fn(out, train_label)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss = loss.item()\n",
        "\n",
        "    return total_loss\n",
        "\n",
        "@torch.no_grad()\n",
        "def test(model, data, all_loader, split_idx, evaluator, mode=\"batch\"):\n",
        "    model.eval()\n",
        "\n",
        "    if mode == \"batch\":\n",
        "        out = model.inference(data.x, all_loader)\n",
        "    else:\n",
        "        out = model(data.x, data.adj_t, mode=\"all\")\n",
        "\n",
        "    y_true = data.y.cpu()\n",
        "    y_pred = out.argmax(dim=-1, keepdim=True)\n",
        "\n",
        "    train_acc = evaluator.eval({\n",
        "        'y_true': y_true[split_idx['train']],\n",
        "        'y_pred': y_pred[split_idx['train']],\n",
        "    })['acc']\n",
        "    valid_acc = evaluator.eval({\n",
        "        'y_true': y_true[split_idx['valid']],\n",
        "        'y_pred': y_pred[split_idx['valid']],\n",
        "    })['acc']\n",
        "    test_acc = evaluator.eval({\n",
        "        'y_true': y_true[split_idx['test']],\n",
        "        'y_pred': y_pred[split_idx['test']],\n",
        "    })['acc']\n",
        "\n",
        "    return train_acc, valid_acc, test_acc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AiehZ8OiR2q9"
      },
      "source": [
        "## Mini-batch Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "zFaI2eCARy0v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc476b21-3dca-49a3-a5aa-27b486644f91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01, Loss: 40.4106, Train: 62.81%, Valid: 63.95% Test: 63.55%\n",
            "Epoch: 02, Loss: 28.6407, Train: 67.42%, Valid: 67.51% Test: 67.01%\n",
            "Epoch: 03, Loss: 26.6660, Train: 69.14%, Valid: 68.73% Test: 67.89%\n",
            "Epoch: 04, Loss: 25.6303, Train: 69.99%, Valid: 69.29% Test: 68.55%\n",
            "Epoch: 05, Loss: 24.9138, Train: 70.71%, Valid: 69.04% Test: 67.49%\n",
            "Epoch: 06, Loss: 24.5037, Train: 70.99%, Valid: 69.58% Test: 68.34%\n",
            "Epoch: 07, Loss: 24.2329, Train: 71.23%, Valid: 70.21% Test: 69.92%\n",
            "Epoch: 08, Loss: 23.9834, Train: 71.77%, Valid: 70.16% Test: 69.29%\n",
            "Epoch: 09, Loss: 23.6174, Train: 71.96%, Valid: 69.18% Test: 67.07%\n",
            "Epoch: 10, Loss: 23.5219, Train: 72.14%, Valid: 69.94% Test: 69.66%\n",
            "Epoch: 11, Loss: 23.4016, Train: 72.44%, Valid: 69.36% Test: 67.97%\n",
            "Epoch: 12, Loss: 23.1631, Train: 72.48%, Valid: 70.49% Test: 69.94%\n",
            "Epoch: 13, Loss: 22.9472, Train: 72.97%, Valid: 70.44% Test: 69.45%\n",
            "Epoch: 14, Loss: 23.0102, Train: 73.06%, Valid: 69.79% Test: 68.29%\n",
            "Epoch: 15, Loss: 22.7917, Train: 73.28%, Valid: 70.46% Test: 69.72%\n",
            "Epoch: 16, Loss: 22.6442, Train: 73.44%, Valid: 70.47% Test: 70.01%\n",
            "Epoch: 17, Loss: 22.5529, Train: 73.68%, Valid: 70.48% Test: 69.31%\n",
            "Epoch: 18, Loss: 22.5606, Train: 73.76%, Valid: 70.94% Test: 70.27%\n",
            "Epoch: 19, Loss: 22.3264, Train: 74.12%, Valid: 70.70% Test: 69.66%\n",
            "Epoch: 20, Loss: 22.3014, Train: 73.99%, Valid: 70.67% Test: 70.09%\n",
            "Epoch: 21, Loss: 22.1858, Train: 74.02%, Valid: 70.56% Test: 69.56%\n",
            "Epoch: 22, Loss: 22.1650, Train: 74.10%, Valid: 70.66% Test: 69.50%\n",
            "Epoch: 23, Loss: 21.9695, Train: 74.53%, Valid: 70.56% Test: 69.26%\n",
            "Epoch: 24, Loss: 21.9209, Train: 74.30%, Valid: 70.39% Test: 69.06%\n",
            "Epoch: 25, Loss: 22.0599, Train: 74.44%, Valid: 70.09% Test: 68.09%\n",
            "Epoch: 26, Loss: 21.8873, Train: 74.42%, Valid: 70.30% Test: 68.82%\n",
            "Epoch: 27, Loss: 21.6834, Train: 74.78%, Valid: 70.84% Test: 69.68%\n",
            "Epoch: 28, Loss: 21.7917, Train: 74.78%, Valid: 70.51% Test: 69.07%\n",
            "Epoch: 29, Loss: 21.7244, Train: 74.88%, Valid: 70.82% Test: 69.38%\n",
            "Epoch: 30, Loss: 21.6257, Train: 75.09%, Valid: 70.87% Test: 70.06%\n",
            "Epoch: 31, Loss: 21.6147, Train: 75.11%, Valid: 70.74% Test: 69.51%\n",
            "Epoch: 32, Loss: 21.5091, Train: 75.14%, Valid: 70.91% Test: 69.78%\n",
            "Epoch: 33, Loss: 21.3751, Train: 74.99%, Valid: 70.87% Test: 69.58%\n",
            "Epoch: 34, Loss: 21.3993, Train: 74.98%, Valid: 70.50% Test: 68.76%\n",
            "Epoch: 35, Loss: 21.4488, Train: 75.14%, Valid: 69.91% Test: 67.82%\n",
            "Epoch: 36, Loss: 21.3961, Train: 75.37%, Valid: 70.43% Test: 68.71%\n",
            "Epoch: 37, Loss: 21.3226, Train: 75.57%, Valid: 70.48% Test: 69.07%\n",
            "Epoch: 38, Loss: 21.2963, Train: 75.52%, Valid: 70.17% Test: 68.45%\n",
            "Epoch: 39, Loss: 21.3384, Train: 75.49%, Valid: 70.87% Test: 69.85%\n",
            "Epoch: 40, Loss: 21.2850, Train: 75.63%, Valid: 70.53% Test: 69.18%\n",
            "Epoch: 41, Loss: 21.2885, Train: 75.39%, Valid: 70.89% Test: 70.02%\n",
            "Epoch: 42, Loss: 21.1324, Train: 75.55%, Valid: 70.44% Test: 69.14%\n",
            "Epoch: 43, Loss: 21.1952, Train: 76.01%, Valid: 71.09% Test: 69.98%\n",
            "Epoch: 44, Loss: 21.1296, Train: 75.42%, Valid: 69.37% Test: 67.53%\n",
            "Epoch: 45, Loss: 21.0208, Train: 75.74%, Valid: 70.67% Test: 69.54%\n",
            "Epoch: 46, Loss: 21.0589, Train: 75.78%, Valid: 70.79% Test: 69.84%\n",
            "Epoch: 47, Loss: 20.9526, Train: 75.89%, Valid: 70.52% Test: 68.59%\n",
            "Epoch: 48, Loss: 20.9792, Train: 76.01%, Valid: 70.72% Test: 69.45%\n",
            "Epoch: 49, Loss: 20.9735, Train: 75.91%, Valid: 70.72% Test: 69.03%\n",
            "Epoch: 50, Loss: 20.9495, Train: 76.03%, Valid: 70.69% Test: 69.29%\n",
            "Epoch: 51, Loss: 20.8580, Train: 76.01%, Valid: 71.02% Test: 69.68%\n",
            "Epoch: 52, Loss: 20.8708, Train: 76.00%, Valid: 70.92% Test: 69.87%\n",
            "Epoch: 53, Loss: 20.8413, Train: 76.27%, Valid: 70.93% Test: 69.53%\n",
            "Epoch: 54, Loss: 20.8145, Train: 76.17%, Valid: 70.08% Test: 68.05%\n",
            "Epoch: 55, Loss: 20.8147, Train: 76.05%, Valid: 71.06% Test: 70.37%\n",
            "Epoch: 56, Loss: 20.9122, Train: 76.24%, Valid: 71.08% Test: 70.07%\n",
            "Epoch: 57, Loss: 20.9032, Train: 76.29%, Valid: 71.12% Test: 70.22%\n",
            "Epoch: 58, Loss: 20.7368, Train: 76.52%, Valid: 70.57% Test: 68.89%\n",
            "Epoch: 59, Loss: 20.7918, Train: 76.31%, Valid: 70.87% Test: 69.95%\n",
            "Epoch: 60, Loss: 20.7001, Train: 76.25%, Valid: 70.95% Test: 69.27%\n",
            "Epoch: 61, Loss: 20.7096, Train: 76.45%, Valid: 70.89% Test: 69.90%\n",
            "Epoch: 62, Loss: 20.6686, Train: 76.47%, Valid: 70.96% Test: 70.13%\n",
            "Epoch: 63, Loss: 20.6192, Train: 76.34%, Valid: 70.30% Test: 68.51%\n",
            "Epoch: 64, Loss: 20.5413, Train: 76.45%, Valid: 70.90% Test: 69.94%\n",
            "Epoch: 65, Loss: 20.7420, Train: 76.63%, Valid: 70.64% Test: 69.42%\n",
            "Epoch: 66, Loss: 20.6520, Train: 76.78%, Valid: 70.94% Test: 69.82%\n",
            "Epoch: 67, Loss: 20.5603, Train: 76.53%, Valid: 70.66% Test: 69.13%\n",
            "Epoch: 68, Loss: 20.6379, Train: 76.52%, Valid: 71.00% Test: 69.44%\n",
            "Epoch: 69, Loss: 20.6227, Train: 76.49%, Valid: 70.57% Test: 69.22%\n",
            "Epoch: 70, Loss: 20.5798, Train: 76.75%, Valid: 70.84% Test: 69.96%\n",
            "Epoch: 71, Loss: 20.6557, Train: 76.63%, Valid: 70.32% Test: 68.42%\n",
            "Epoch: 72, Loss: 20.5961, Train: 76.82%, Valid: 70.96% Test: 70.27%\n",
            "Epoch: 73, Loss: 20.5800, Train: 76.64%, Valid: 70.82% Test: 69.40%\n",
            "Epoch: 74, Loss: 20.3920, Train: 76.73%, Valid: 70.54% Test: 69.26%\n",
            "Epoch: 75, Loss: 20.4306, Train: 76.95%, Valid: 71.19% Test: 69.83%\n",
            "Epoch: 76, Loss: 20.5140, Train: 76.75%, Valid: 70.66% Test: 69.40%\n",
            "Epoch: 77, Loss: 20.5267, Train: 76.75%, Valid: 71.14% Test: 70.15%\n",
            "Epoch: 78, Loss: 20.3382, Train: 77.02%, Valid: 70.69% Test: 69.70%\n",
            "Epoch: 79, Loss: 20.4624, Train: 76.96%, Valid: 71.02% Test: 69.78%\n",
            "Epoch: 80, Loss: 20.4776, Train: 77.03%, Valid: 71.14% Test: 70.15%\n",
            "Epoch: 81, Loss: 20.5429, Train: 77.08%, Valid: 70.97% Test: 70.41%\n",
            "Epoch: 82, Loss: 20.3174, Train: 76.91%, Valid: 70.81% Test: 69.73%\n",
            "Epoch: 83, Loss: 20.2969, Train: 76.94%, Valid: 70.77% Test: 69.60%\n",
            "Epoch: 84, Loss: 20.3159, Train: 76.97%, Valid: 70.99% Test: 70.06%\n",
            "Epoch: 85, Loss: 20.3311, Train: 76.97%, Valid: 70.95% Test: 69.99%\n",
            "Epoch: 86, Loss: 20.2909, Train: 77.06%, Valid: 71.07% Test: 69.82%\n",
            "Epoch: 87, Loss: 20.3032, Train: 77.04%, Valid: 70.34% Test: 68.54%\n",
            "Epoch: 88, Loss: 20.2745, Train: 77.07%, Valid: 70.98% Test: 70.22%\n",
            "Epoch: 89, Loss: 20.3087, Train: 77.18%, Valid: 71.17% Test: 70.39%\n",
            "Epoch: 90, Loss: 20.3073, Train: 77.06%, Valid: 70.90% Test: 69.80%\n",
            "Epoch: 91, Loss: 20.2871, Train: 77.29%, Valid: 71.23% Test: 70.56%\n",
            "Epoch: 92, Loss: 20.2542, Train: 77.35%, Valid: 70.97% Test: 69.72%\n",
            "Epoch: 93, Loss: 20.1974, Train: 77.25%, Valid: 71.10% Test: 70.45%\n",
            "Epoch: 94, Loss: 20.2835, Train: 77.27%, Valid: 71.02% Test: 69.77%\n",
            "Epoch: 95, Loss: 20.1831, Train: 77.26%, Valid: 70.55% Test: 69.34%\n",
            "Epoch: 96, Loss: 20.2377, Train: 77.20%, Valid: 70.80% Test: 69.72%\n",
            "Epoch: 97, Loss: 20.2219, Train: 77.04%, Valid: 70.77% Test: 69.53%\n",
            "Epoch: 98, Loss: 20.1598, Train: 77.31%, Valid: 70.67% Test: 69.10%\n",
            "Epoch: 99, Loss: 20.1638, Train: 77.31%, Valid: 71.02% Test: 70.06%\n",
            "Epoch: 100, Loss: 19.9536, Train: 77.44%, Valid: 70.82% Test: 69.82%\n",
            "Best model: Train: 77.29%, Valid: 71.23% Test: 70.56%\n"
          ]
        }
      ],
      "source": [
        "args = {\n",
        "    'device': device,\n",
        "    'num_layers': 2,\n",
        "    'hidden_dim': 128,\n",
        "    'dropout': 0.5,\n",
        "    'lr': 0.01,\n",
        "    'epochs': 100,\n",
        "}\n",
        "\n",
        "batch_model = SAGE(data.num_features, args['hidden_dim'],\n",
        "            dataset.num_classes, args['num_layers'],\n",
        "            args['dropout']).to(device)\n",
        "batch_model.reset_parameters()\n",
        "\n",
        "optimizer = torch.optim.Adam(batch_model.parameters(), lr=args['lr'])\n",
        "loss_fn = F.nll_loss\n",
        "\n",
        "best_batch_model = None\n",
        "best_valid_acc = 0\n",
        "\n",
        "batch_results = []\n",
        "\n",
        "for epoch in range(1, 1 + args[\"epochs\"]):\n",
        "    loss = train(batch_model, data, train_loader, train_idx, optimizer, loss_fn, mode=\"batch\")\n",
        "    result = test(batch_model, data, all_loader, split_idx, evaluator, mode=\"batch\")\n",
        "    batch_results.append(result)\n",
        "    train_acc, valid_acc, test_acc = result\n",
        "    if valid_acc > best_valid_acc:\n",
        "        best_valid_acc = valid_acc\n",
        "        best_batch_model = copy.deepcopy(batch_model)\n",
        "    print(f'Epoch: {epoch:02d}, '\n",
        "          f'Loss: {loss:.4f}, '\n",
        "          f'Train: {100 * train_acc:.2f}%, '\n",
        "          f'Valid: {100 * valid_acc:.2f}% '\n",
        "          f'Test: {100 * test_acc:.2f}%')\n",
        "best_result = test(best_batch_model, data, all_loader, split_idx, evaluator, mode=\"batch\")\n",
        "train_acc, valid_acc, test_acc = best_result\n",
        "print(f'Best model: '\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * valid_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OyqW-1pSMLW"
      },
      "source": [
        "## Full-batch Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "mU5eAviTSFMO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63ce7eba-5f22-4878-ded0-5ce28f91c51b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01, Loss: 3.9309, Train: 20.46%, Valid: 11.87% Test: 10.24%\n",
            "Epoch: 02, Loss: 2.5975, Train: 32.19%, Valid: 38.03% Test: 40.45%\n",
            "Epoch: 03, Loss: 2.2114, Train: 35.24%, Valid: 41.53% Test: 44.06%\n",
            "Epoch: 04, Loss: 2.0342, Train: 36.99%, Valid: 43.38% Test: 46.10%\n",
            "Epoch: 05, Loss: 1.8995, Train: 40.10%, Valid: 45.99% Test: 49.05%\n",
            "Epoch: 06, Loss: 1.7957, Train: 43.23%, Valid: 48.44% Test: 51.43%\n",
            "Epoch: 07, Loss: 1.7145, Train: 46.64%, Valid: 51.07% Test: 54.00%\n",
            "Epoch: 08, Loss: 1.6451, Train: 49.66%, Valid: 53.99% Test: 56.35%\n",
            "Epoch: 09, Loss: 1.5837, Train: 52.09%, Valid: 56.28% Test: 58.30%\n",
            "Epoch: 10, Loss: 1.5299, Train: 54.10%, Valid: 58.22% Test: 59.88%\n",
            "Epoch: 11, Loss: 1.4896, Train: 55.69%, Valid: 59.64% Test: 60.85%\n",
            "Epoch: 12, Loss: 1.4586, Train: 56.95%, Valid: 60.83% Test: 61.69%\n",
            "Epoch: 13, Loss: 1.4298, Train: 58.29%, Valid: 61.66% Test: 62.20%\n",
            "Epoch: 14, Loss: 1.3998, Train: 59.54%, Valid: 62.42% Test: 62.78%\n",
            "Epoch: 15, Loss: 1.3794, Train: 60.65%, Valid: 63.07% Test: 63.26%\n",
            "Epoch: 16, Loss: 1.3627, Train: 61.43%, Valid: 63.62% Test: 63.82%\n",
            "Epoch: 17, Loss: 1.3389, Train: 62.03%, Valid: 63.93% Test: 64.23%\n",
            "Epoch: 18, Loss: 1.3247, Train: 62.34%, Valid: 64.12% Test: 64.48%\n",
            "Epoch: 19, Loss: 1.3152, Train: 62.50%, Valid: 64.18% Test: 64.63%\n",
            "Epoch: 20, Loss: 1.3019, Train: 62.72%, Valid: 64.43% Test: 64.84%\n",
            "Epoch: 21, Loss: 1.2842, Train: 63.10%, Valid: 64.60% Test: 65.12%\n",
            "Epoch: 22, Loss: 1.2738, Train: 63.54%, Valid: 64.94% Test: 65.35%\n",
            "Epoch: 23, Loss: 1.2622, Train: 63.92%, Valid: 65.26% Test: 65.54%\n",
            "Epoch: 24, Loss: 1.2515, Train: 64.25%, Valid: 65.36% Test: 65.53%\n",
            "Epoch: 25, Loss: 1.2386, Train: 64.61%, Valid: 65.57% Test: 65.65%\n",
            "Epoch: 26, Loss: 1.2260, Train: 64.96%, Valid: 65.81% Test: 65.77%\n",
            "Epoch: 27, Loss: 1.2183, Train: 65.26%, Valid: 65.98% Test: 66.04%\n",
            "Epoch: 28, Loss: 1.2122, Train: 65.52%, Valid: 66.28% Test: 66.27%\n",
            "Epoch: 29, Loss: 1.2022, Train: 65.82%, Valid: 66.55% Test: 66.53%\n",
            "Epoch: 30, Loss: 1.1972, Train: 66.11%, Valid: 66.81% Test: 66.72%\n",
            "Epoch: 31, Loss: 1.1878, Train: 66.35%, Valid: 66.97% Test: 66.80%\n",
            "Epoch: 32, Loss: 1.1830, Train: 66.58%, Valid: 67.22% Test: 66.87%\n",
            "Epoch: 33, Loss: 1.1732, Train: 66.74%, Valid: 67.22% Test: 66.91%\n",
            "Epoch: 34, Loss: 1.1669, Train: 66.93%, Valid: 67.30% Test: 66.99%\n",
            "Epoch: 35, Loss: 1.1648, Train: 67.07%, Valid: 67.45% Test: 67.10%\n",
            "Epoch: 36, Loss: 1.1597, Train: 67.22%, Valid: 67.53% Test: 67.20%\n",
            "Epoch: 37, Loss: 1.1538, Train: 67.39%, Valid: 67.58% Test: 67.34%\n",
            "Epoch: 38, Loss: 1.1454, Train: 67.59%, Valid: 67.75% Test: 67.46%\n",
            "Epoch: 39, Loss: 1.1390, Train: 67.77%, Valid: 67.85% Test: 67.58%\n",
            "Epoch: 40, Loss: 1.1332, Train: 67.94%, Valid: 67.94% Test: 67.56%\n",
            "Epoch: 41, Loss: 1.1296, Train: 68.09%, Valid: 68.07% Test: 67.65%\n",
            "Epoch: 42, Loss: 1.1259, Train: 68.23%, Valid: 68.22% Test: 67.72%\n",
            "Epoch: 43, Loss: 1.1209, Train: 68.36%, Valid: 68.40% Test: 67.76%\n",
            "Epoch: 44, Loss: 1.1139, Train: 68.48%, Valid: 68.51% Test: 67.85%\n",
            "Epoch: 45, Loss: 1.1110, Train: 68.64%, Valid: 68.62% Test: 67.93%\n",
            "Epoch: 46, Loss: 1.1082, Train: 68.73%, Valid: 68.72% Test: 67.95%\n",
            "Epoch: 47, Loss: 1.1048, Train: 68.85%, Valid: 68.73% Test: 67.92%\n",
            "Epoch: 48, Loss: 1.1021, Train: 69.01%, Valid: 68.69% Test: 67.90%\n",
            "Epoch: 49, Loss: 1.0997, Train: 69.14%, Valid: 68.74% Test: 67.82%\n",
            "Epoch: 50, Loss: 1.0948, Train: 69.22%, Valid: 68.75% Test: 67.75%\n",
            "Epoch: 51, Loss: 1.0895, Train: 69.28%, Valid: 68.86% Test: 67.81%\n",
            "Epoch: 52, Loss: 1.0855, Train: 69.38%, Valid: 68.84% Test: 67.89%\n",
            "Epoch: 53, Loss: 1.0853, Train: 69.44%, Valid: 68.84% Test: 67.95%\n",
            "Epoch: 54, Loss: 1.0809, Train: 69.51%, Valid: 68.97% Test: 68.01%\n",
            "Epoch: 55, Loss: 1.0809, Train: 69.66%, Valid: 69.09% Test: 68.13%\n",
            "Epoch: 56, Loss: 1.0753, Train: 69.78%, Valid: 69.17% Test: 68.15%\n",
            "Epoch: 57, Loss: 1.0700, Train: 69.92%, Valid: 69.19% Test: 68.29%\n",
            "Epoch: 58, Loss: 1.0712, Train: 70.05%, Valid: 69.26% Test: 68.31%\n",
            "Epoch: 59, Loss: 1.0659, Train: 70.16%, Valid: 69.31% Test: 68.35%\n",
            "Epoch: 60, Loss: 1.0656, Train: 70.24%, Valid: 69.31% Test: 68.39%\n",
            "Epoch: 61, Loss: 1.0591, Train: 70.30%, Valid: 69.39% Test: 68.38%\n",
            "Epoch: 62, Loss: 1.0553, Train: 70.38%, Valid: 69.42% Test: 68.45%\n",
            "Epoch: 63, Loss: 1.0541, Train: 70.47%, Valid: 69.45% Test: 68.47%\n",
            "Epoch: 64, Loss: 1.0526, Train: 70.55%, Valid: 69.47% Test: 68.34%\n",
            "Epoch: 65, Loss: 1.0514, Train: 70.55%, Valid: 69.46% Test: 68.30%\n",
            "Epoch: 66, Loss: 1.0487, Train: 70.55%, Valid: 69.49% Test: 68.34%\n",
            "Epoch: 67, Loss: 1.0465, Train: 70.65%, Valid: 69.58% Test: 68.37%\n",
            "Epoch: 68, Loss: 1.0442, Train: 70.75%, Valid: 69.56% Test: 68.51%\n",
            "Epoch: 69, Loss: 1.0439, Train: 70.84%, Valid: 69.67% Test: 68.54%\n",
            "Epoch: 70, Loss: 1.0386, Train: 70.92%, Valid: 69.74% Test: 68.57%\n",
            "Epoch: 71, Loss: 1.0370, Train: 70.95%, Valid: 69.73% Test: 68.57%\n",
            "Epoch: 72, Loss: 1.0374, Train: 70.93%, Valid: 69.73% Test: 68.62%\n",
            "Epoch: 73, Loss: 1.0276, Train: 70.97%, Valid: 69.80% Test: 68.71%\n",
            "Epoch: 74, Loss: 1.0308, Train: 71.03%, Valid: 69.95% Test: 68.92%\n",
            "Epoch: 75, Loss: 1.0278, Train: 71.12%, Valid: 70.07% Test: 68.96%\n",
            "Epoch: 76, Loss: 1.0274, Train: 71.16%, Valid: 70.10% Test: 68.92%\n",
            "Epoch: 77, Loss: 1.0231, Train: 71.14%, Valid: 69.98% Test: 68.72%\n",
            "Epoch: 78, Loss: 1.0209, Train: 71.15%, Valid: 69.85% Test: 68.57%\n",
            "Epoch: 79, Loss: 1.0187, Train: 71.18%, Valid: 69.67% Test: 68.31%\n",
            "Epoch: 80, Loss: 1.0185, Train: 71.21%, Valid: 69.54% Test: 68.11%\n",
            "Epoch: 81, Loss: 1.0124, Train: 71.31%, Valid: 69.60% Test: 68.15%\n",
            "Epoch: 82, Loss: 1.0131, Train: 71.37%, Valid: 69.69% Test: 68.32%\n",
            "Epoch: 83, Loss: 1.0131, Train: 71.39%, Valid: 69.74% Test: 68.30%\n",
            "Epoch: 84, Loss: 1.0098, Train: 71.36%, Valid: 69.73% Test: 68.21%\n",
            "Epoch: 85, Loss: 1.0084, Train: 71.32%, Valid: 69.63% Test: 67.97%\n",
            "Epoch: 86, Loss: 1.0090, Train: 71.36%, Valid: 69.55% Test: 67.87%\n",
            "Epoch: 87, Loss: 1.0045, Train: 71.47%, Valid: 69.59% Test: 67.99%\n",
            "Epoch: 88, Loss: 1.0055, Train: 71.54%, Valid: 69.74% Test: 68.24%\n",
            "Epoch: 89, Loss: 1.0032, Train: 71.58%, Valid: 69.86% Test: 68.48%\n",
            "Epoch: 90, Loss: 1.0025, Train: 71.61%, Valid: 69.77% Test: 68.28%\n",
            "Epoch: 91, Loss: 1.0002, Train: 71.67%, Valid: 69.64% Test: 67.99%\n",
            "Epoch: 92, Loss: 0.9975, Train: 71.71%, Valid: 69.53% Test: 67.80%\n",
            "Epoch: 93, Loss: 0.9951, Train: 71.80%, Valid: 69.63% Test: 67.84%\n",
            "Epoch: 94, Loss: 0.9933, Train: 71.86%, Valid: 69.80% Test: 68.08%\n",
            "Epoch: 95, Loss: 0.9925, Train: 71.95%, Valid: 69.84% Test: 68.32%\n",
            "Epoch: 96, Loss: 0.9900, Train: 72.01%, Valid: 69.92% Test: 68.43%\n",
            "Epoch: 97, Loss: 0.9891, Train: 72.07%, Valid: 69.82% Test: 68.20%\n",
            "Epoch: 98, Loss: 0.9865, Train: 72.09%, Valid: 69.76% Test: 68.10%\n",
            "Epoch: 99, Loss: 0.9855, Train: 72.16%, Valid: 69.85% Test: 68.22%\n",
            "Epoch: 100, Loss: 0.9855, Train: 72.21%, Valid: 69.97% Test: 68.56%\n",
            "Best model: Train: 71.16%, Valid: 70.10% Test: 68.92%\n"
          ]
        }
      ],
      "source": [
        "# Use the same parameters for a full-batch training\n",
        "args = {\n",
        "    'device': device,\n",
        "    'num_layers': 2,\n",
        "    'hidden_dim': 128,\n",
        "    'dropout': 0.5,\n",
        "    'lr': 0.01,\n",
        "    'epochs': 100,\n",
        "}\n",
        "\n",
        "all_model = SAGE(data.num_features, args['hidden_dim'],\n",
        "            dataset.num_classes, args['num_layers'],\n",
        "            args['dropout']).to(device)\n",
        "all_model.reset_parameters()\n",
        "\n",
        "optimizer = torch.optim.Adam(all_model.parameters(), lr=args['lr'])\n",
        "loss_fn = F.nll_loss\n",
        "\n",
        "best_all_model = None\n",
        "best_valid_acc = 0\n",
        "\n",
        "all_results = []\n",
        "\n",
        "for epoch in range(1, 1 + args[\"epochs\"]):\n",
        "    loss = train(all_model, data, train_loader, train_idx, optimizer, loss_fn, mode=\"all\")\n",
        "    result = test(all_model, data, all_loader, split_idx, evaluator, mode=\"all\")\n",
        "    all_results.append(result)\n",
        "    train_acc, valid_acc, test_acc = result\n",
        "    if valid_acc > best_valid_acc:\n",
        "        best_valid_acc = valid_acc\n",
        "        best_all_model = copy.deepcopy(all_model)\n",
        "    print(f'Epoch: {epoch:02d}, '\n",
        "          f'Loss: {loss:.4f}, '\n",
        "          f'Train: {100 * train_acc:.2f}%, '\n",
        "          f'Valid: {100 * valid_acc:.2f}% '\n",
        "          f'Test: {100 * test_acc:.2f}%')\n",
        "best_result = test(best_all_model, data, all_loader, split_idx, evaluator, mode=\"all\")\n",
        "train_acc, valid_acc, test_acc = best_result\n",
        "print(f'Best model: '\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * valid_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrECcOQQSZo1"
      },
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "sh_qvSG1SV63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "0e729140-96e6-4dce-b213-51805d25077b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "batch_results = np.array(batch_results)\n",
        "all_results = np.array(all_results)\n",
        "\n",
        "x = np.arange(1, 101)\n",
        "\n",
        "plt.figure(figsize=(9, 7))\n",
        "\n",
        "plt.plot(x, batch_results[:, 1], label=\"Batch Validation\")\n",
        "plt.plot(x, batch_results[:, 2], label=\"Batch Test\")\n",
        "plt.plot(x, all_results[:, 1], label=\"All Validation\")\n",
        "plt.plot(x, all_results[:, 2], label=\"All Test\")\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFb2OAvOSn_O"
      },
      "source": [
        "# 2 Neighbor Sampling with Different Ratios\n",
        "\n",
        "Now we will implement a simplified version of Neighbor Sampling by using DeepSNAP and NetworkX, and train models with different neighborhood sampling ratios.\n",
        "\n",
        "To make the experiments faster, we will use the Cora graph here."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9U0F7bnSz9u"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PUF4on-fSxcq"
      },
      "outputs": [],
      "source": [
        "import copy\n",
        "import torch\n",
        "import random\n",
        "import numpy as np\n",
        "import networkx as nx\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from torch_geometric.nn import SAGEConv\n",
        "from torch.utils.data import DataLoader\n",
        "from torch_geometric.datasets import Planetoid\n",
        "from torch.nn import Sequential, Linear, ReLU\n",
        "from deepsnap.dataset import GraphDataset\n",
        "from deepsnap.graph import Graph\n",
        "\n",
        "pyg_dataset = Planetoid('./tmp', \"Cora\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qw6k-KdFTEYw"
      },
      "source": [
        "## GNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "PvUlNi2TS09i"
      },
      "outputs": [],
      "source": [
        "class GNN(torch.nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim, args):\n",
        "        super(GNN, self).__init__()\n",
        "        self.dropout = args['dropout']\n",
        "        self.num_layers = args['num_layers']\n",
        "\n",
        "        self.convs = nn.ModuleList()\n",
        "        self.bns = nn.ModuleList()\n",
        "\n",
        "        self.convs.append(SAGEConv(input_dim, hidden_dim))\n",
        "        self.bns.append(nn.BatchNorm1d(hidden_dim))\n",
        "\n",
        "        for l in range(self.num_layers - 2):\n",
        "            self.convs.append(SAGEConv(hidden_dim, hidden_dim))\n",
        "            self.bns.append(nn.BatchNorm1d(hidden_dim))\n",
        "        self.convs.append(SAGEConv(hidden_dim, hidden_dim))\n",
        "\n",
        "        self.post_mp = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, data, mode=\"batch\"):\n",
        "        if mode == \"batch\":\n",
        "            edge_indices, x = data\n",
        "            for i in range(len(self.convs) - 1):\n",
        "                edge_index = edge_indices[i]\n",
        "                x = self.convs[i](x, edge_index)\n",
        "                x = self.bns[i](x)\n",
        "                x = F.relu(x)\n",
        "                x = F.dropout(x, p=self.dropout, training=self.training)\n",
        "            x = self.convs[-1](x, edge_indices[len(self.convs) - 1])\n",
        "        else:\n",
        "            x, edge_index = data.node_feature, data.edge_index\n",
        "            for i in range(len(self.convs) - 1):\n",
        "                x = self.convs[i](x, edge_index)\n",
        "                x = self.bns[i](x)\n",
        "                x = F.relu(x)\n",
        "                x = F.dropout(x, p=self.dropout, training=self.training)\n",
        "            x = self.convs[-1](x, edge_index)\n",
        "        x = self.post_mp(x)\n",
        "        x = F.log_softmax(x, dim=1)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ulp1A3evcJ-I"
      },
      "source": [
        "## Neighbor Sampling\n",
        "\n",
        "Here we implement functions that will sample neighbors by using DeepSNAP and NetworkX.\n",
        "\n",
        "Notice that node classification task on Cora is a semi-supervised classification task, here we keep all the labeled training nodes (140 nodes) by setting the last ratio to 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "LI4qHkE4cQOh"
      },
      "outputs": [],
      "source": [
        "def sample_neighbors(nodes, G, ratio, all_nodes):\n",
        "    # This fuction takes a set of nodes, a NetworkX graph G and neighbor sampling ratio.\n",
        "    # It will return sampled neighbors (unioned with input nodes) and edges between \n",
        "    neighbors = set()\n",
        "    edges = []\n",
        "    for node in nodes:\n",
        "        neighbors_list = list(nx.neighbors(G, node))\n",
        "\n",
        "        # We only sample the (ratio * number of neighbors) neighbors\n",
        "        num = int(len(neighbors_list) * ratio)\n",
        "        if num > 0:\n",
        "            # Random shuffle the neighbors\n",
        "            random.shuffle(neighbors_list)\n",
        "            neighbors_list = neighbors_list[:num]\n",
        "            for neighbor in neighbors_list:\n",
        "                # Add neighbors\n",
        "                neighbors.add(neighbor)\n",
        "                edges.append((neighbor, node))\n",
        "    return neighbors, neighbors.union(all_nodes), edges\n",
        "\n",
        "def nodes_to_tensor(nodes):\n",
        "    # This function transform a set of nodes to node index tensor\n",
        "    node_label_index = torch.tensor(list(nodes), dtype=torch.long)\n",
        "    return node_label_index\n",
        "\n",
        "def edges_to_tensor(edges):\n",
        "    # This function transform a set of edges to edge index tensor\n",
        "    edge_index = torch.tensor(list(edges), dtype=torch.long)\n",
        "    edge_index = torch.cat([edge_index, torch.flip(edge_index, [1])], dim=0)\n",
        "    edge_index = edge_index.permute(1, 0)\n",
        "    return edge_index\n",
        "\n",
        "def relable(nodes, labeled_nodes, edges_list):\n",
        "    # Relable the nodes, labeled_nodes and edges_list\n",
        "    relabled_edges_list = []\n",
        "    sorted_nodes = sorted(nodes)\n",
        "    node_mapping = {node : i for i, node in enumerate(sorted_nodes)}\n",
        "    for orig_edges in edges_list:\n",
        "        relabeled_edges = []\n",
        "        for edge in orig_edges:\n",
        "            relabeled_edges.append((node_mapping[edge[0]], node_mapping[edge[1]]))\n",
        "        relabled_edges_list.append(relabeled_edges)\n",
        "    relabeled_labeled_nodes = [node_mapping[node] for node in labeled_nodes]\n",
        "    relabeled_nodes = [node_mapping[node] for node in nodes]\n",
        "    return relabled_edges_list, relabeled_nodes, relabeled_labeled_nodes\n",
        "\n",
        "def neighbor_sampling(graph, K=2, ratios=(0.1, 0.1, 0.1)):\n",
        "    # This function takes a DeepSNAP graph, K the number of GNN layers, and neighbor \n",
        "    # sampling ratios for each layer. This function returns relabeled node feature, \n",
        "    # edge indices and node_label_index\n",
        "\n",
        "    assert K + 1 == len(ratios)\n",
        "\n",
        "    labeled_nodes = graph.node_label_index.tolist()\n",
        "    random.shuffle(labeled_nodes)\n",
        "    num = int(len(labeled_nodes) * ratios[-1])\n",
        "    if num > 0:\n",
        "        labeled_nodes = labeled_nodes[:num]\n",
        "    nodes_list = [set(labeled_nodes)]\n",
        "    edges_list = []\n",
        "    all_nodes = labeled_nodes\n",
        "    for k in range(K):\n",
        "        # Get nodes and edges from the previous layer\n",
        "        nodes, all_nodes, edges = \\\n",
        "            sample_neighbors(nodes_list[-1], graph.G, ratios[len(ratios) - k - 2], all_nodes)\n",
        "        nodes_list.append(nodes)\n",
        "        edges_list.append(edges)\n",
        "    \n",
        "    # Reverse the lists\n",
        "    nodes_list.reverse()\n",
        "    edges_list.reverse()\n",
        "\n",
        "    relabled_edges_list, relabeled_all_nodes, relabeled_labeled_nodes = \\\n",
        "        relable(all_nodes, labeled_nodes, edges_list)\n",
        "\n",
        "    node_index = nodes_to_tensor(relabeled_all_nodes)\n",
        "    # All node features that will be used\n",
        "    node_feature = graph.node_feature[node_index]\n",
        "    edge_indices = [edges_to_tensor(edges) for edges in relabled_edges_list]\n",
        "    node_label_index = nodes_to_tensor(relabeled_labeled_nodes)\n",
        "    log = \"Sampled {} nodes, {} edges, {} labeled nodes\"\n",
        "    print(log.format(node_feature.shape[0], edge_indices[0].shape[1] // 2, node_label_index.shape[0]))\n",
        "    return node_feature, edge_indices, node_label_index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooy6Hcf7TIhI"
      },
      "source": [
        "## Training and Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "iSmZhpzPTGPY"
      },
      "outputs": [],
      "source": [
        "def train(train_graphs, val_graphs, args, model, optimizer, mode=\"batch\"):\n",
        "    best_val = 0\n",
        "    best_model = None\n",
        "    accs = []\n",
        "    graph_train = train_graphs[0]\n",
        "    graph_train.to(args['device'])\n",
        "    for epoch in range(1, 1 + args['epochs']):\n",
        "        model.train()\n",
        "        optimizer.zero_grad()\n",
        "        if mode == \"batch\":\n",
        "            node_feature, edge_indices, node_label_index = neighbor_sampling(graph_train, args['num_layers'], args['ratios'])\n",
        "            node_feature = node_feature.to(args['device'])\n",
        "            node_label_index = node_label_index.to(args['device'])\n",
        "            for i in range(len(edge_indices)):\n",
        "                edge_indices[i] = edge_indices[i].to(args['device'])\n",
        "            pred = model([edge_indices, node_feature])\n",
        "            pred = pred[node_label_index]\n",
        "            label = graph_train.node_label[node_label_index]\n",
        "        elif mode == \"community\":\n",
        "            graph = random.choice(train_graphs)\n",
        "            graph = graph.to(args['device'])\n",
        "            pred = model(graph, mode=\"all\")\n",
        "            pred = pred[graph.node_label_index]\n",
        "            label = graph.node_label[graph.node_label_index]\n",
        "        else:\n",
        "            pred = model(graph_train, mode=\"all\")\n",
        "            label = graph_train.node_label\n",
        "            pred = pred[graph_train.node_label_index]\n",
        "        loss = F.nll_loss(pred, label)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_acc, val_acc, test_acc = test(val_graphs, model)\n",
        "        accs.append((train_acc, val_acc, test_acc))\n",
        "        if val_acc > best_val:\n",
        "            best_val = val_acc\n",
        "            best_model = copy.deepcopy(model)\n",
        "        print(f'Epoch: {epoch:02d}, '\n",
        "              f'Loss: {loss:.4f}, '\n",
        "              f'Train: {100 * train_acc:.2f}%, '\n",
        "              f'Valid: {100 * val_acc:.2f}% '\n",
        "              f'Test: {100 * test_acc:.2f}%')\n",
        "    return best_model, accs\n",
        "\n",
        "def test(graphs, model):\n",
        "    model.eval()\n",
        "    accs = []\n",
        "    for graph in graphs:\n",
        "        graph = graph.to(args['device'])\n",
        "        pred = model(graph, mode=\"all\")\n",
        "        label = graph.node_label\n",
        "        pred = pred[graph.node_label_index].max(1)[1]\n",
        "        acc = pred.eq(label).sum().item()\n",
        "        acc /= len(label)\n",
        "        accs.append(acc)\n",
        "    return accs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "HV7i0v0ETKzf"
      },
      "outputs": [],
      "source": [
        "args = {\n",
        "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
        "    'dropout': 0.5,\n",
        "    'num_layers': 2,\n",
        "    'hidden_size': 64,\n",
        "    'lr': 0.005,\n",
        "    'epochs': 50,\n",
        "    'ratios': (0.8, 0.8, 1),\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLpRYKbnTQnj"
      },
      "source": [
        "## Full-Batch Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "pMGGjbJBTOo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e246f901-f47c-4177-d021-c89d6e8ed366"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index fields: test_mask ignored.\n",
            "Index fields: val_mask ignored.\n",
            "Index fields: train_mask ignored.\n",
            "Epoch: 01, Loss: 1.9746, Train: 51.43%, Valid: 19.00% Test: 21.60%\n",
            "Epoch: 02, Loss: 1.1768, Train: 73.57%, Valid: 29.00% Test: 31.30%\n",
            "Epoch: 03, Loss: 0.6920, Train: 95.00%, Valid: 36.80% Test: 36.40%\n",
            "Epoch: 04, Loss: 0.3667, Train: 100.00%, Valid: 44.80% Test: 43.10%\n",
            "Epoch: 05, Loss: 0.1795, Train: 100.00%, Valid: 51.20% Test: 49.70%\n",
            "Epoch: 06, Loss: 0.0829, Train: 100.00%, Valid: 55.40% Test: 54.90%\n",
            "Epoch: 07, Loss: 0.0399, Train: 100.00%, Valid: 59.00% Test: 59.10%\n",
            "Epoch: 08, Loss: 0.0217, Train: 100.00%, Valid: 61.60% Test: 62.80%\n",
            "Epoch: 09, Loss: 0.0140, Train: 100.00%, Valid: 63.40% Test: 65.70%\n",
            "Epoch: 10, Loss: 0.0086, Train: 100.00%, Valid: 66.60% Test: 68.40%\n",
            "Epoch: 11, Loss: 0.0055, Train: 100.00%, Valid: 67.20% Test: 69.40%\n",
            "Epoch: 12, Loss: 0.0024, Train: 100.00%, Valid: 68.60% Test: 70.20%\n",
            "Epoch: 13, Loss: 0.0031, Train: 100.00%, Valid: 69.60% Test: 70.90%\n",
            "Epoch: 14, Loss: 0.0050, Train: 100.00%, Valid: 70.40% Test: 71.20%\n",
            "Epoch: 15, Loss: 0.0007, Train: 100.00%, Valid: 70.80% Test: 71.10%\n",
            "Epoch: 16, Loss: 0.0007, Train: 100.00%, Valid: 71.40% Test: 71.60%\n",
            "Epoch: 17, Loss: 0.0008, Train: 100.00%, Valid: 72.00% Test: 71.90%\n",
            "Epoch: 18, Loss: 0.0007, Train: 100.00%, Valid: 73.00% Test: 72.30%\n",
            "Epoch: 19, Loss: 0.0003, Train: 100.00%, Valid: 73.00% Test: 72.40%\n",
            "Epoch: 20, Loss: 0.0004, Train: 100.00%, Valid: 72.80% Test: 72.60%\n",
            "Epoch: 21, Loss: 0.0002, Train: 100.00%, Valid: 72.80% Test: 73.60%\n",
            "Epoch: 22, Loss: 0.0009, Train: 100.00%, Valid: 73.00% Test: 73.60%\n",
            "Epoch: 23, Loss: 0.0001, Train: 100.00%, Valid: 73.00% Test: 73.90%\n",
            "Epoch: 24, Loss: 0.0001, Train: 100.00%, Valid: 73.00% Test: 74.10%\n",
            "Epoch: 25, Loss: 0.0001, Train: 100.00%, Valid: 73.00% Test: 74.30%\n",
            "Epoch: 26, Loss: 0.0001, Train: 100.00%, Valid: 73.00% Test: 74.40%\n",
            "Epoch: 27, Loss: 0.0001, Train: 100.00%, Valid: 73.00% Test: 74.30%\n",
            "Epoch: 28, Loss: 0.0000, Train: 100.00%, Valid: 73.20% Test: 74.40%\n",
            "Epoch: 29, Loss: 0.0000, Train: 100.00%, Valid: 73.20% Test: 74.40%\n",
            "Epoch: 30, Loss: 0.0003, Train: 100.00%, Valid: 73.00% Test: 74.30%\n",
            "Epoch: 31, Loss: 0.0002, Train: 100.00%, Valid: 72.80% Test: 74.40%\n",
            "Epoch: 32, Loss: 0.0008, Train: 100.00%, Valid: 72.60% Test: 74.50%\n",
            "Epoch: 33, Loss: 0.0000, Train: 100.00%, Valid: 72.60% Test: 74.50%\n",
            "Epoch: 34, Loss: 0.0001, Train: 100.00%, Valid: 72.60% Test: 74.60%\n",
            "Epoch: 35, Loss: 0.0001, Train: 100.00%, Valid: 72.60% Test: 74.70%\n",
            "Epoch: 36, Loss: 0.0000, Train: 100.00%, Valid: 72.40% Test: 74.70%\n",
            "Epoch: 37, Loss: 0.0000, Train: 100.00%, Valid: 72.40% Test: 74.70%\n",
            "Epoch: 38, Loss: 0.0000, Train: 100.00%, Valid: 72.40% Test: 74.70%\n",
            "Epoch: 39, Loss: 0.0000, Train: 100.00%, Valid: 72.40% Test: 74.70%\n",
            "Epoch: 40, Loss: 0.0000, Train: 100.00%, Valid: 72.40% Test: 74.70%\n",
            "Epoch: 41, Loss: 0.0001, Train: 100.00%, Valid: 72.40% Test: 74.70%\n",
            "Epoch: 42, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 74.70%\n",
            "Epoch: 43, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 74.80%\n",
            "Epoch: 44, Loss: 0.0003, Train: 100.00%, Valid: 72.00% Test: 74.80%\n",
            "Epoch: 45, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 74.70%\n",
            "Epoch: 46, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 74.70%\n",
            "Epoch: 47, Loss: 0.0004, Train: 100.00%, Valid: 72.00% Test: 74.70%\n",
            "Epoch: 48, Loss: 0.0001, Train: 100.00%, Valid: 71.80% Test: 74.70%\n",
            "Epoch: 49, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 74.60%\n",
            "Epoch: 50, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 74.60%\n",
            "Best model: Train: 100.00%, Valid: 73.20% Test: 74.40%\n"
          ]
        }
      ],
      "source": [
        "graphs_train, graphs_val, graphs_test = \\\n",
        "    GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n",
        "\n",
        "graph_train = graphs_train[0]\n",
        "graph_val = graphs_val[0]\n",
        "graph_test = graphs_test[0]\n",
        "\n",
        "model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
        "graphs = [graph_train, graph_val, graph_test]\n",
        "all_best_model, all_accs = train(graphs, graphs, args, model, optimizer, mode=\"all\")\n",
        "train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], all_best_model)\n",
        "print('Best model:',\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * val_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWkGiwB6Thr4"
      },
      "source": [
        "## Sampling with Ratios 0.8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "yWusJ9u3Tfhv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a7e43a0-fc54-42f0-ae15-ddab13e5dbd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index fields: test_mask ignored.\n",
            "Index fields: val_mask ignored.\n",
            "Index fields: train_mask ignored.\n",
            "Sampled 1328 nodes, 1997 edges, 140 labeled nodes\n",
            "Epoch: 01, Loss: 2.0073, Train: 32.86%, Valid: 13.20% Test: 14.10%\n",
            "Sampled 1314 nodes, 1971 edges, 140 labeled nodes\n",
            "Epoch: 02, Loss: 1.6474, Train: 55.71%, Valid: 16.80% Test: 18.30%\n",
            "Sampled 1294 nodes, 1949 edges, 140 labeled nodes\n",
            "Epoch: 03, Loss: 1.3858, Train: 73.57%, Valid: 26.80% Test: 27.50%\n",
            "Sampled 1315 nodes, 2022 edges, 140 labeled nodes\n",
            "Epoch: 04, Loss: 1.0821, Train: 87.14%, Valid: 31.40% Test: 31.70%\n",
            "Sampled 1307 nodes, 2012 edges, 140 labeled nodes\n",
            "Epoch: 05, Loss: 0.8320, Train: 92.14%, Valid: 35.20% Test: 34.60%\n",
            "Sampled 1289 nodes, 1968 edges, 140 labeled nodes\n",
            "Epoch: 06, Loss: 0.6978, Train: 96.43%, Valid: 39.00% Test: 38.60%\n",
            "Sampled 1302 nodes, 1976 edges, 140 labeled nodes\n",
            "Epoch: 07, Loss: 0.4461, Train: 98.57%, Valid: 41.80% Test: 41.60%\n",
            "Sampled 1308 nodes, 1995 edges, 140 labeled nodes\n",
            "Epoch: 08, Loss: 0.3699, Train: 99.29%, Valid: 45.00% Test: 43.50%\n",
            "Sampled 1331 nodes, 2003 edges, 140 labeled nodes\n",
            "Epoch: 09, Loss: 0.3088, Train: 99.29%, Valid: 45.00% Test: 44.70%\n",
            "Sampled 1374 nodes, 2113 edges, 140 labeled nodes\n",
            "Epoch: 10, Loss: 0.2136, Train: 99.29%, Valid: 46.20% Test: 47.10%\n",
            "Sampled 1327 nodes, 2000 edges, 140 labeled nodes\n",
            "Epoch: 11, Loss: 0.1975, Train: 99.29%, Valid: 47.20% Test: 49.30%\n",
            "Sampled 1293 nodes, 1959 edges, 140 labeled nodes\n",
            "Epoch: 12, Loss: 0.1391, Train: 100.00%, Valid: 48.20% Test: 50.40%\n",
            "Sampled 1330 nodes, 2020 edges, 140 labeled nodes\n",
            "Epoch: 13, Loss: 0.0852, Train: 100.00%, Valid: 48.20% Test: 50.50%\n",
            "Sampled 1325 nodes, 1968 edges, 140 labeled nodes\n",
            "Epoch: 14, Loss: 0.0898, Train: 100.00%, Valid: 49.40% Test: 51.10%\n",
            "Sampled 1323 nodes, 2011 edges, 140 labeled nodes\n",
            "Epoch: 15, Loss: 0.0636, Train: 100.00%, Valid: 49.80% Test: 51.00%\n",
            "Sampled 1329 nodes, 2032 edges, 140 labeled nodes\n",
            "Epoch: 16, Loss: 0.0675, Train: 100.00%, Valid: 49.00% Test: 50.90%\n",
            "Sampled 1325 nodes, 1990 edges, 140 labeled nodes\n",
            "Epoch: 17, Loss: 0.0634, Train: 100.00%, Valid: 48.80% Test: 50.80%\n",
            "Sampled 1287 nodes, 1887 edges, 140 labeled nodes\n",
            "Epoch: 18, Loss: 0.0667, Train: 100.00%, Valid: 49.60% Test: 51.00%\n",
            "Sampled 1339 nodes, 2021 edges, 140 labeled nodes\n",
            "Epoch: 19, Loss: 0.0595, Train: 100.00%, Valid: 48.60% Test: 51.10%\n",
            "Sampled 1284 nodes, 1935 edges, 140 labeled nodes\n",
            "Epoch: 20, Loss: 0.0342, Train: 100.00%, Valid: 48.20% Test: 51.30%\n",
            "Sampled 1303 nodes, 1971 edges, 140 labeled nodes\n",
            "Epoch: 21, Loss: 0.0302, Train: 100.00%, Valid: 48.00% Test: 51.30%\n",
            "Sampled 1330 nodes, 1993 edges, 140 labeled nodes\n",
            "Epoch: 22, Loss: 0.0336, Train: 100.00%, Valid: 47.80% Test: 51.50%\n",
            "Sampled 1334 nodes, 2006 edges, 140 labeled nodes\n",
            "Epoch: 23, Loss: 0.0250, Train: 100.00%, Valid: 47.80% Test: 51.50%\n",
            "Sampled 1332 nodes, 2034 edges, 140 labeled nodes\n",
            "Epoch: 24, Loss: 0.0384, Train: 100.00%, Valid: 47.40% Test: 51.20%\n",
            "Sampled 1308 nodes, 1994 edges, 140 labeled nodes\n",
            "Epoch: 25, Loss: 0.0148, Train: 100.00%, Valid: 47.40% Test: 51.30%\n",
            "Sampled 1365 nodes, 2058 edges, 140 labeled nodes\n",
            "Epoch: 26, Loss: 0.0173, Train: 100.00%, Valid: 47.20% Test: 51.30%\n",
            "Sampled 1313 nodes, 1998 edges, 140 labeled nodes\n",
            "Epoch: 27, Loss: 0.0088, Train: 100.00%, Valid: 47.20% Test: 51.30%\n",
            "Sampled 1321 nodes, 1972 edges, 140 labeled nodes\n",
            "Epoch: 28, Loss: 0.0174, Train: 100.00%, Valid: 47.40% Test: 51.30%\n",
            "Sampled 1336 nodes, 2005 edges, 140 labeled nodes\n",
            "Epoch: 29, Loss: 0.0279, Train: 100.00%, Valid: 47.60% Test: 51.20%\n",
            "Sampled 1327 nodes, 2031 edges, 140 labeled nodes\n",
            "Epoch: 30, Loss: 0.0072, Train: 100.00%, Valid: 47.40% Test: 51.20%\n",
            "Sampled 1304 nodes, 1972 edges, 140 labeled nodes\n",
            "Epoch: 31, Loss: 0.0253, Train: 100.00%, Valid: 48.80% Test: 51.60%\n",
            "Sampled 1277 nodes, 1944 edges, 140 labeled nodes\n",
            "Epoch: 32, Loss: 0.0138, Train: 100.00%, Valid: 49.00% Test: 52.00%\n",
            "Sampled 1322 nodes, 1992 edges, 140 labeled nodes\n",
            "Epoch: 33, Loss: 0.0143, Train: 100.00%, Valid: 48.60% Test: 52.00%\n",
            "Sampled 1293 nodes, 1990 edges, 140 labeled nodes\n",
            "Epoch: 34, Loss: 0.0109, Train: 100.00%, Valid: 49.00% Test: 51.90%\n",
            "Sampled 1309 nodes, 2010 edges, 140 labeled nodes\n",
            "Epoch: 35, Loss: 0.0324, Train: 100.00%, Valid: 49.00% Test: 51.80%\n",
            "Sampled 1336 nodes, 1984 edges, 140 labeled nodes\n",
            "Epoch: 36, Loss: 0.0306, Train: 100.00%, Valid: 49.80% Test: 51.70%\n",
            "Sampled 1277 nodes, 1988 edges, 140 labeled nodes\n",
            "Epoch: 37, Loss: 0.0210, Train: 100.00%, Valid: 49.20% Test: 52.40%\n",
            "Sampled 1299 nodes, 1966 edges, 140 labeled nodes\n",
            "Epoch: 38, Loss: 0.0096, Train: 100.00%, Valid: 49.00% Test: 52.20%\n",
            "Sampled 1283 nodes, 1948 edges, 140 labeled nodes\n",
            "Epoch: 39, Loss: 0.0038, Train: 100.00%, Valid: 49.00% Test: 52.30%\n",
            "Sampled 1299 nodes, 1966 edges, 140 labeled nodes\n",
            "Epoch: 40, Loss: 0.0073, Train: 100.00%, Valid: 48.40% Test: 52.30%\n",
            "Sampled 1308 nodes, 2005 edges, 140 labeled nodes\n",
            "Epoch: 41, Loss: 0.0077, Train: 100.00%, Valid: 48.00% Test: 52.10%\n",
            "Sampled 1254 nodes, 1868 edges, 140 labeled nodes\n",
            "Epoch: 42, Loss: 0.0142, Train: 100.00%, Valid: 47.60% Test: 51.80%\n",
            "Sampled 1276 nodes, 1961 edges, 140 labeled nodes\n",
            "Epoch: 43, Loss: 0.0195, Train: 100.00%, Valid: 47.60% Test: 51.50%\n",
            "Sampled 1316 nodes, 2043 edges, 140 labeled nodes\n",
            "Epoch: 44, Loss: 0.0039, Train: 100.00%, Valid: 47.40% Test: 51.40%\n",
            "Sampled 1318 nodes, 1969 edges, 140 labeled nodes\n",
            "Epoch: 45, Loss: 0.0112, Train: 100.00%, Valid: 47.60% Test: 51.40%\n",
            "Sampled 1303 nodes, 1928 edges, 140 labeled nodes\n",
            "Epoch: 46, Loss: 0.0017, Train: 100.00%, Valid: 48.20% Test: 52.00%\n",
            "Sampled 1297 nodes, 1937 edges, 140 labeled nodes\n",
            "Epoch: 47, Loss: 0.0154, Train: 100.00%, Valid: 48.20% Test: 51.90%\n",
            "Sampled 1333 nodes, 2020 edges, 140 labeled nodes\n",
            "Epoch: 48, Loss: 0.0041, Train: 100.00%, Valid: 47.60% Test: 51.50%\n",
            "Sampled 1328 nodes, 2027 edges, 140 labeled nodes\n",
            "Epoch: 49, Loss: 0.0259, Train: 100.00%, Valid: 47.60% Test: 51.60%\n",
            "Sampled 1340 nodes, 2023 edges, 140 labeled nodes\n",
            "Epoch: 50, Loss: 0.0111, Train: 100.00%, Valid: 47.80% Test: 52.00%\n",
            "Best model: Train: 100.00%, Valid: 49.80% Test: 51.00%\n"
          ]
        }
      ],
      "source": [
        "args['ratios'] = (0.8, 0.8, 1)\n",
        "\n",
        "graphs_train, graphs_val, graphs_test = \\\n",
        "    GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n",
        "\n",
        "graph_train = graphs_train[0]\n",
        "graph_val = graphs_val[0]\n",
        "graph_test = graphs_test[0]\n",
        "\n",
        "model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
        "graphs = [graph_train, graph_val, graph_test]\n",
        "batch_best_model, batch_accs = train(graphs, graphs, args, model, optimizer)\n",
        "train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], batch_best_model)\n",
        "print('Best model:',\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * val_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_FjkNHDT4c6"
      },
      "source": [
        "## Sampling with Ratios 0.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "booJ6DASTjO4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "789d1a25-93f2-4b2e-db87-bce33210e5fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index fields: test_mask ignored.\n",
            "Index fields: val_mask ignored.\n",
            "Index fields: train_mask ignored.\n",
            "Sampled 407 nodes, 212 edges, 140 labeled nodes\n",
            "Epoch: 01, Loss: 2.0069, Train: 18.57%, Valid: 16.60% Test: 15.70%\n",
            "Sampled 446 nodes, 255 edges, 140 labeled nodes\n",
            "Epoch: 02, Loss: 1.9280, Train: 20.71%, Valid: 21.00% Test: 19.20%\n",
            "Sampled 408 nodes, 226 edges, 140 labeled nodes\n",
            "Epoch: 03, Loss: 1.8744, Train: 25.00%, Valid: 23.60% Test: 23.10%\n",
            "Sampled 441 nodes, 256 edges, 140 labeled nodes\n",
            "Epoch: 04, Loss: 1.8799, Train: 33.57%, Valid: 27.80% Test: 28.60%\n",
            "Sampled 381 nodes, 183 edges, 140 labeled nodes\n",
            "Epoch: 05, Loss: 1.8575, Train: 36.43%, Valid: 28.80% Test: 31.20%\n",
            "Sampled 446 nodes, 262 edges, 140 labeled nodes\n",
            "Epoch: 06, Loss: 1.7968, Train: 40.71%, Valid: 31.00% Test: 32.50%\n",
            "Sampled 395 nodes, 213 edges, 140 labeled nodes\n",
            "Epoch: 07, Loss: 1.6951, Train: 44.29%, Valid: 30.60% Test: 32.10%\n",
            "Sampled 417 nodes, 237 edges, 140 labeled nodes\n",
            "Epoch: 08, Loss: 1.7865, Train: 47.14%, Valid: 30.20% Test: 32.00%\n",
            "Sampled 423 nodes, 235 edges, 140 labeled nodes\n",
            "Epoch: 09, Loss: 1.7997, Train: 50.00%, Valid: 28.20% Test: 32.00%\n",
            "Sampled 389 nodes, 200 edges, 140 labeled nodes\n",
            "Epoch: 10, Loss: 1.7161, Train: 52.14%, Valid: 29.60% Test: 32.60%\n",
            "Sampled 461 nodes, 278 edges, 140 labeled nodes\n",
            "Epoch: 11, Loss: 1.7998, Train: 52.14%, Valid: 29.80% Test: 31.80%\n",
            "Sampled 414 nodes, 213 edges, 140 labeled nodes\n",
            "Epoch: 12, Loss: 1.6850, Train: 53.57%, Valid: 30.00% Test: 32.00%\n",
            "Sampled 423 nodes, 240 edges, 140 labeled nodes\n",
            "Epoch: 13, Loss: 1.6295, Train: 53.57%, Valid: 31.20% Test: 32.70%\n",
            "Sampled 430 nodes, 234 edges, 140 labeled nodes\n",
            "Epoch: 14, Loss: 1.5917, Train: 56.43%, Valid: 32.00% Test: 34.60%\n",
            "Sampled 435 nodes, 243 edges, 140 labeled nodes\n",
            "Epoch: 15, Loss: 1.6462, Train: 58.57%, Valid: 32.20% Test: 35.40%\n",
            "Sampled 417 nodes, 230 edges, 140 labeled nodes\n",
            "Epoch: 16, Loss: 1.6548, Train: 58.57%, Valid: 33.40% Test: 36.70%\n",
            "Sampled 416 nodes, 221 edges, 140 labeled nodes\n",
            "Epoch: 17, Loss: 1.7728, Train: 60.71%, Valid: 35.80% Test: 38.50%\n",
            "Sampled 411 nodes, 230 edges, 140 labeled nodes\n",
            "Epoch: 18, Loss: 1.6532, Train: 62.14%, Valid: 37.00% Test: 39.60%\n",
            "Sampled 446 nodes, 262 edges, 140 labeled nodes\n",
            "Epoch: 19, Loss: 1.8378, Train: 61.43%, Valid: 38.20% Test: 40.10%\n",
            "Sampled 438 nodes, 252 edges, 140 labeled nodes\n",
            "Epoch: 20, Loss: 1.6625, Train: 63.57%, Valid: 38.80% Test: 40.70%\n",
            "Sampled 391 nodes, 191 edges, 140 labeled nodes\n",
            "Epoch: 21, Loss: 1.5996, Train: 63.57%, Valid: 39.80% Test: 41.80%\n",
            "Sampled 447 nodes, 274 edges, 140 labeled nodes\n",
            "Epoch: 22, Loss: 1.6968, Train: 63.57%, Valid: 39.40% Test: 42.10%\n",
            "Sampled 354 nodes, 162 edges, 140 labeled nodes\n",
            "Epoch: 23, Loss: 1.6763, Train: 64.29%, Valid: 41.00% Test: 42.20%\n",
            "Sampled 396 nodes, 221 edges, 140 labeled nodes\n",
            "Epoch: 24, Loss: 1.5415, Train: 63.57%, Valid: 41.80% Test: 41.40%\n",
            "Sampled 443 nodes, 249 edges, 140 labeled nodes\n",
            "Epoch: 25, Loss: 1.5240, Train: 65.71%, Valid: 41.60% Test: 40.60%\n",
            "Sampled 439 nodes, 268 edges, 140 labeled nodes\n",
            "Epoch: 26, Loss: 1.4530, Train: 63.57%, Valid: 40.40% Test: 40.70%\n",
            "Sampled 435 nodes, 244 edges, 140 labeled nodes\n",
            "Epoch: 27, Loss: 1.5747, Train: 62.86%, Valid: 39.20% Test: 39.70%\n",
            "Sampled 427 nodes, 228 edges, 140 labeled nodes\n",
            "Epoch: 28, Loss: 1.6516, Train: 62.86%, Valid: 38.80% Test: 39.70%\n",
            "Sampled 454 nodes, 286 edges, 140 labeled nodes\n",
            "Epoch: 29, Loss: 1.6920, Train: 60.71%, Valid: 38.80% Test: 39.40%\n",
            "Sampled 420 nodes, 237 edges, 140 labeled nodes\n",
            "Epoch: 30, Loss: 1.6049, Train: 60.71%, Valid: 38.80% Test: 39.20%\n",
            "Sampled 440 nodes, 259 edges, 140 labeled nodes\n",
            "Epoch: 31, Loss: 1.6590, Train: 61.43%, Valid: 39.40% Test: 39.80%\n",
            "Sampled 433 nodes, 252 edges, 140 labeled nodes\n",
            "Epoch: 32, Loss: 1.5525, Train: 61.43%, Valid: 40.00% Test: 40.80%\n",
            "Sampled 429 nodes, 252 edges, 140 labeled nodes\n",
            "Epoch: 33, Loss: 1.6449, Train: 60.00%, Valid: 40.20% Test: 41.20%\n",
            "Sampled 398 nodes, 206 edges, 140 labeled nodes\n",
            "Epoch: 34, Loss: 1.5606, Train: 60.71%, Valid: 39.20% Test: 42.10%\n",
            "Sampled 444 nodes, 266 edges, 140 labeled nodes\n",
            "Epoch: 35, Loss: 1.6774, Train: 60.71%, Valid: 39.80% Test: 41.70%\n",
            "Sampled 374 nodes, 186 edges, 140 labeled nodes\n",
            "Epoch: 36, Loss: 1.6591, Train: 61.43%, Valid: 39.20% Test: 41.70%\n",
            "Sampled 394 nodes, 187 edges, 140 labeled nodes\n",
            "Epoch: 37, Loss: 1.5630, Train: 62.86%, Valid: 39.40% Test: 42.30%\n",
            "Sampled 447 nodes, 249 edges, 140 labeled nodes\n",
            "Epoch: 38, Loss: 1.6208, Train: 62.14%, Valid: 39.40% Test: 42.30%\n",
            "Sampled 365 nodes, 176 edges, 140 labeled nodes\n",
            "Epoch: 39, Loss: 1.4734, Train: 62.86%, Valid: 39.80% Test: 43.50%\n",
            "Sampled 419 nodes, 237 edges, 140 labeled nodes\n",
            "Epoch: 40, Loss: 1.5526, Train: 64.29%, Valid: 40.20% Test: 43.80%\n",
            "Sampled 449 nodes, 274 edges, 140 labeled nodes\n",
            "Epoch: 41, Loss: 1.5151, Train: 65.00%, Valid: 40.60% Test: 43.90%\n",
            "Sampled 351 nodes, 159 edges, 140 labeled nodes\n",
            "Epoch: 42, Loss: 1.6257, Train: 65.00%, Valid: 41.40% Test: 44.30%\n",
            "Sampled 389 nodes, 197 edges, 140 labeled nodes\n",
            "Epoch: 43, Loss: 1.4970, Train: 66.43%, Valid: 42.40% Test: 45.40%\n",
            "Sampled 403 nodes, 222 edges, 140 labeled nodes\n",
            "Epoch: 44, Loss: 1.6045, Train: 66.43%, Valid: 42.60% Test: 45.40%\n",
            "Sampled 451 nodes, 257 edges, 140 labeled nodes\n",
            "Epoch: 45, Loss: 1.5353, Train: 66.43%, Valid: 42.80% Test: 45.20%\n",
            "Sampled 402 nodes, 217 edges, 140 labeled nodes\n",
            "Epoch: 46, Loss: 1.5114, Train: 65.71%, Valid: 44.20% Test: 45.60%\n",
            "Sampled 399 nodes, 225 edges, 140 labeled nodes\n",
            "Epoch: 47, Loss: 1.5679, Train: 65.71%, Valid: 44.00% Test: 45.20%\n",
            "Sampled 463 nodes, 279 edges, 140 labeled nodes\n",
            "Epoch: 48, Loss: 1.4956, Train: 65.00%, Valid: 44.20% Test: 45.80%\n",
            "Sampled 431 nodes, 254 edges, 140 labeled nodes\n",
            "Epoch: 49, Loss: 1.5447, Train: 63.57%, Valid: 44.00% Test: 45.60%\n",
            "Sampled 400 nodes, 208 edges, 140 labeled nodes\n",
            "Epoch: 50, Loss: 1.4790, Train: 64.29%, Valid: 43.40% Test: 45.70%\n",
            "Best model: Train: 65.71%, Valid: 44.20% Test: 45.60%\n"
          ]
        }
      ],
      "source": [
        "# Change the ratio to 0.3\n",
        "args['ratios'] = (0.3, 0.3, 1)\n",
        "\n",
        "graphs_train, graphs_val, graphs_test = \\\n",
        "    GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n",
        "\n",
        "graph_train = graphs_train[0]\n",
        "graph_val = graphs_val[0]\n",
        "graph_test = graphs_test[0]\n",
        "\n",
        "model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
        "graphs = [graph_train, graph_val, graph_test]\n",
        "batch_best_model, batch_accs_1 = train(graphs, graphs, args, model, optimizer)\n",
        "train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], batch_best_model)\n",
        "print('Best model:',\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * val_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EePAvNlGUM2K"
      },
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "7etNAkXAT55d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "c7e0aeb1-89f4-4a9e-8004-cb0de0371c88"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAG5CAYAAABLHaTAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3zdZfn/8dd9Vs7J3knbJE3pohMrZbWUDhABReSLgsBXQIEiiiJuGQrqVxBUVASRLSgOwB8uUKADqIwWSltmZ9omaTNPkpMzcsbnc//+OCdp2macpOfknCTX8/E4jyZnXknbnHfucd1Ka40QQgghRLqxpLoAIYQQQoi+SEgRQgghRFqSkCKEEEKItCQhRQghhBBpSUKKEEIIIdKShBQhhBBCpCUJKUKIIVNKVSultFLKFsd9L1NKrRuJuoQQY4uEFCHGOKXUbqVUSClVfMj1b8WCRnVqKjuolmyllFcp9WyqaxFCpA8JKUKMDzXAhd2fKKXmAZmpK+cw5wFB4CNKqfKRfOF4RoOEEKkhIUWI8eEx4JJen18KPNr7DkqpPKXUo0qpZqXUHqXUjUopS+w2q1Lqp0qpFqXULuBjfTz2QaXUfqVUvVLqR0op6xDquxS4F9gC/O8hz32yUuoVpVS7UqpWKXVZ7HqXUupnsVo7lFLrYtctU0rVHfIcu5VSp8U+vlkp9aRS6vdKKQ9wmVLqeKXUq7HX2K+U+rVSytHr8XOUUs8rpdxKqUal1PVKqXKllF8pVdTrfh+Off/sQ/jahRD9kJAixPjwGpCrlJoVCw+fAX5/yH3uAvKAo4ClREPN52K3XQl8HFgALAQ+dchjHwEiwLTYfU4HroinMKXUZGAZ8IfY5ZJDbns2VlsJ8CFgU+zmnwLHAouAQuBbgBnPawLnAE8C+bHXNIDrgGLgJOBU4IuxGnKAF4B/AxNjX+MqrXUDsBY4v9fzfhb4k9Y6HGcdQogBSEgRYvzoHk35CPA+UN99Q6/g8l2tdafWejfwM6JvuhB9I/6F1rpWa+0Gbu312DLgLOCrWmuf1roJuDP2fPH4LLBFa/0e8CdgjlJqQey2i4AXtNZ/1FqHtdatWutNsRGezwPXaq3rtdaG1voVrXUwztd8VWv9tNba1FoHtNZvaq1f01pHYl/7b4kGNYiGswat9c+01l2x78/rsdt+R2zkJ/Y9vJDo91kIkQAyFyvE+PEY8BIwhUOmeoiOINiBPb2u2wNMin08Eag95LZuk2OP3a+U6r7Ocsj9B3IJcD+A1rpeKfUi0emft4BKYGcfjykGnP3cFo+DalNKzQB+TnSUKJPoz8Y3Yzf3VwPA34B7lVJTgJlAh9Z6/TBrEkIcQkZShBgntNZ7iC6gPQv46yE3twBhooGjWxUHRlv2E32z7n1bt1qii16Ltdb5sUuu1nrOYDUppRYB04HvKqUalFINwAnARbEFrbXA1D4e2gJ09XObj16LgmMjHCWH3OfQ499/A3wATNda5wLXA92Jq5boFNhhtNZdwF+IjqZ8FhlFESKhJKQIMb5cDqzQWvt6X6m1Noi+2f6fUionthbkaxxYt/IX4CtKqQqlVAHwnV6P3Q88B/xMKZWrlLIopaYqpZYyuEuB54HZRNebfAiYC7iAM4muFzlNKXW+UsqmlCpSSn1Ia20CDwE/V0pNjC3sPUkplQFsA5xKqY/FFrDeCGQMUkcO4AG8Sqmjgat73fZPYIJS6qtKqYzY9+eEXrc/ClwGfAIJKUIklIQUIcYRrfVOrfUb/dz8ZaKjELuAdcDjRIMARKdj/gNsBjZy+EjMJYADeA9oI7oodcJAtSilnETXutyltW7odakh+mZ/qdZ6L9GRn68DbqKLZo+JPcU3gLeBDbHbfgJYtNYdRBe9PkB0JMgHHLTbpw/fILr+pTP2tf65+watdSfRdTxnAw3AdmB5r9v/S3TB7sbYaJUQIkGU1oeOegohhBgKpdRq4HGt9QOprkWIsURCihBCHAGl1HFEp6wqY6MuQogEkekeIYQYJqXU74j2UPmqBBQhEk9GUoQQQgiRlmQkRQghhBBpadQ1cysuLtbV1dWpLkMIIYQQCfDmm2+2aK0P7WUEjMKQUl1dzRtv9LeDUgghhBCjiVKq3637Mt0jhBBCiLQkIUUIIYQQaUlCihBCCCHSkoQUIYQQQqQlCSlCCCGESEsSUoQQQgiRliSkCCGEECItSUgRQgghRFqSkCKEEEKItCQhRQghhBBpSUKKEEIIIdKShBQhhBBCpKWkhRSl1ENKqSal1Dv93K6UUr9SSu1QSm1RSn04WbUIIYQQYvRJ5kjKI8AZA9x+JjA9dlkJ/CaJtQghhBBilLEl64m11i8ppaoHuMs5wKNaaw28ppTKV0pN0FrvT1ZN6cTwhTE7Q6kuI+1FTJN97QEMM9WVCCHE+FZ5VD6uTMeIvmbSQkocJgG1vT6vi1035kOKDhs0/nIjpkdCSjwyUl2AEEIIas6fxuwPTxjR10xlSImbUmol0SkhqqqqUlzNkfO92YTpCZH3sSlY8+UtGKDdH2JLnYctde1sa+wkYmqyHFbmTszj6Am5OGyyxlsIIVJpzuT8EX/NVIaUeqCy1+cVsesOo7W+D7gPYOHChTr5pSWPNjSdL9XhqMwh++RJKKVSXRIAWmu2NXrZ2ewdwdeE7U2dvPB+I+/UewCoLsrkI4srOG1WGcdOLsBmlXAihBDjVSpDyt+Ba5RSfwJOADrGw3qUwDvNGO4u8s+akvKAEjZMNtS4ef79Rl54v5Fad2DEa1AKPlxVwLfPOJqPzC5lakl2yr8vQggh0kPSQopS6o/AMqBYKVUHfB+wA2it7wWeAc4CdgB+4HPJqiVdaK3pXFuHrcSFc3ZRSmrwdIVZu7WZF95rZM3WJjq7ImTYLJw8rZgvLpvGMRX5WC0jFxKKsx0UZcuUlxBCiMMlc3fPhYPcroEvJev101Fwezvh/T4KPjUdNYJBQGvN/3urnr9urOe1Xa1ETE1RloMz5pRz2uwylkwvJtMxKpYnCSGEGEfknWkEda6txZrrIPNDpSP2mqap+fEz7/PAuhqOKs7i8iVT+MisMhZUFYzoiIkQQggxVBJSRkhwr4fgrg7yPjYFNUI7VcKGybef3MJf36rnskXVfO/js7FIMBFCCDFKSEgZIZ1r61AuG1nHl4/I6wVCBl96fCOrP2jiG6fP4EvLp8mCVCGEEKOKhJQREG7y0/VeKzkrKrFkJP9b3uEPc/nvNvDm3jb+79y5XHzC5KS/phBCCJFoElJGQOeLdSi7hexFE5P+Wo2eLi55cD01LT7uvujDnDVvZLsDCiGEEIkiISXJIu1B/JuayDq+HGt2cs88qGnx8dkHX6fNF+Lhzx3H4mnFSX09IYQQIpkkpCSZd109aE3Okoqkvs479R1c+tB6NPDHlScyv2Lk2xcLIYQQiSQhJYlMfxjf+v1kHlOKrdCZtNd5ZWcLKx99kzyXnccuP56jSrKT9lpCCCHESJGQkkTeV/ejQyY5S5M3ivJOfQeXPbSB6uJMHv38CZTnJS8MCSGEECNJQkqSmCED73/rcR5diL08KymvobXmR/96jxynjT+vPImCrOSueRFCCCFGkhwxmyT+DQ2Y/gg5y5I3irJ2azOv7XJz7WnTJaAIIYQYcySkJIE2TDpfrscxOZeM6rykvIZham599n2mFGdx4fFVSXkNIYQQIpUkpCSBf0sLRnswqWtRnnqzjm2NXr750ZnYrfLXKIQQYuyRd7cE06amc20ttrJMnEcXJuU1AiGDnz+/jQ9V5nPm3JFpsy+EEEKMNAkpCRbe5yXS6Cfn5EmoJB3m99B/a2jwdHH9WbPkPB4hhBBjloSUBIu0dQFgr8hJyvO7fSHuXbuT02aVcfyU5IzUCCGEEOlAQkqCGR0hAGx5ydltc9fq7fhCEb5z5sykPL8QQgiRLiSkJJjREQSbBeVKfAuaPa0+fv/aHi44ropppckZqRFCCCHShYSUBDM8IWx5jqSsFbnjP1uxWSxcd9r0hD+3EEIIkW4kpCSY0RHEmpeR8OfdXNvOP7fs58olUyjNldb3Qgghxj4JKQmWjJCitebHz7xPUZaDlUunJvS5hRBCiHQlISWBtKkxPCGsCV40u2ZrE6/XRNvfZ2fIcUtCCCHGBwkpCWT6wmDohI6kGKbmtmc/kPb3Qgghxh0JKQlkdAQBsOYmLqR0t7//lrS/F0IIMc7Iu14CdfdISdR0TyBk8LPnt7KgKp8zpP29EEKIcUZCSgIZnthISoKmex5+pYZGT5Dvnint74UQQow/ElISyOgIglVhybIf8XN1hQ0efLmGpTNKpP29EEKIcUlCSgIZHSGsuY6EHCz4xBu1tPpCfHGZbDkWQggxPklISaBE9UiJGCa/fWkXC6ryZRRFCCHEuCUhJYESFVL+9fZ+6toCXL10qqxFEUIIMW5JSEkQrTWRjiNv5Ka15jdrdzKtNJvTZpUlqDohhBBi9JGQkiCmPwIR84h7pKzd1swHDZ18YelULAlY2yKEEEKMVhJSEqSnkdsRjqTcu3YnE/KcfOKYiYkoSwghhBi1JKQkiOHpbuQ2/JGUjXvbeL3GzRVLjsJhk78aIYQQ45u8EybIgZGU4YeUe9fuJD/TzmeOq0xUWUIIIcSoJSElQYyOICiwZg9vumdHUyfPvdfIJSdVkyUnHQshhBASUhLF6AhhzXGgrMNb7Hrvi7tw2i1ctqg6sYUJIYQQo5SElAQxPMPvkbKvPcDfNtXzmeOqKMxKzOGEQgghxGgnISVBoo3chhcwHlxXg6nhiiVTElyVEEIIMXpJSEkQoyM0rJGUdn+IP67fyznHTKSiIDMJlQkhhBCjk4SUBDC7IuigMayQ8uire/CHDK5aKgcJCiGEEL1JSEmA4TZy84ciPPzfGk6bVcrM8pxklCaEEEKMWhJSEsDoGF4jt79sqKXNH+YLMooihBBCHEZCSgL0jKQM4dyesGFy/8s1HFddwMLqwmSVJoQQQoxaElIS4EBIiX+6559b9lHfHuDqZTKKIoQQQvRFQkoCGJ4Qlmw7agjn7Ty4roaZZTksn1maxMqEEEKI0UtCSgJEe6TEP9UTCBm8u8/DmfPKUWp4HWqFEEKIsU5CSgIYHcEhTfXsaPKiNRwtO3qEEEKIfklISYDIEBu5fdDgAWBGmYQUIYQQoj8SUo6QGTLQgciQQsq2xk4cNguTi7KSWJkQQggxuklIOUKGp7tHSvzTPVsbvUwvzcZqkfUoQgghRH8kpByhA91mhzCS0tDJTJnqEUIIIQYkIeUIDTWkdPjDNHi6mCGLZoUQQogBSUg5Qj0t8ePc3bOtqRNARlKEEEKIQUhIOUJGRxBLpg2LwxrX/bc2REOKjKQIIYQQA5OQcoSiPVKGtrMnJ8PGxDxnEqsSQgghRj8JKUfI8ISGtrOnoZMZ5TnSaVYIIYQYhISUIzSUlvhaa7Y1dkoTNyGEECIOElKOgI6YmN5w3CGl2RukzR9mZll2kisTQgghRj8JKUdgqI3ctjV4AVk0K4QQQsRDQsoRGGqPlO4ze2T7sRBCCDE4CSlHYKghZVtjJ8XZDoqy498NJIQQQoxXElKOwFAbuW1t9MqiWSGEECJOElKOgNERRGVYsThtg97XNDXbZWePEEIIETcJKUcguv04vlGU+vYA/pDBTFk0K4QQQsRFQsoRiHhCca9H6WmHLyMpQgghRFwkpByBobTE39rYHVKkR4oQQggRDwkpw6QNjdkZf0v8bY2dTMp3keO0J7kyIYQQYmyQkDJMhjcEOv7tx1sbOmU9ihBCCDEEElKGaSg9UsKGya5mn6xHEUIIIYZAQsowDSWk7Gn1ETJMZpbLehQhhBAiXkkNKUqpM5RSW5VSO5RS3+nj9iql1Bql1FtKqS1KqbOSWU8idTdys8WxJmVr95k9MpIihBBCxC1pIUUpZQXuBs4EZgMXKqVmH3K3G4G/aK0XAJ8B7klWPYlmdARRdgvKNXgjt60NHiwKppbISIoQQggRr2SOpBwP7NBa79Jah4A/Aeccch8N5MY+zgP2JbGehIo2cstAKTXofbc2dlJdnIXTbh2ByoQQQoixIZkhZRJQ2+vzuth1vd0M/K9Sqg54BvhyX0+klFqplHpDKfVGc3NzMmodMqMjFPeZPdsavXLysRBCCDFEqV44eyHwiNa6AjgLeEwpdVhNWuv7tNYLtdYLS0pKRrzIvnSPpAymK2ywu1V29gghhBBDlcyQUg9U9vq8InZdb5cDfwHQWr8KOIHiJNaUENrUGJ74GrntaPKiNdIjRQghhBiiZIaUDcB0pdQUpZSD6MLYvx9yn73AqQBKqVlEQ0p6zOcMwPSFwdRxjaR0n9kjIUUIIYQYmqSFFK11BLgG+A/wPtFdPO8qpX6glPpE7G5fB65USm0G/ghcprXWyaopUXp6pMRxbs+2xk4cNguTCzOTXZYQQggxpgy+f/YIaK2fIbogtvd13+v18XvA4mTWkAwHGrnF0SOlsZNpJdnYrKle/iOEEEKMLvLOOQzdjdzime7ZJmf2CCGEEMMiIWUYjI4gWBWWrIFPNPZ0hdnX0SU7e4QQQohhkJAyDEZHEGuuA2UZuJHb9sbuRbPSaVYIIYQYKgkpwxDdfhzPzh45s0cIIYQYLgkpwxBvI7etDR6yHFYm5btGoCohhBBibJGQMkRaayId8TVy29rYyYzynLjO9xFCCCHEwSSkDJHpj0DEHLRHitaarQ2dcmaPEEIIMUwSUoboQI+UgUNKizdEmz8s24+FEEKIYZKQMkSGp7tHysDTPdu6d/bISIoQQggxLBJShqh7JMU2yEhK95k9M2QkRQghhBgWCSlDZHQEwQKWnMFHUoqyHBRnD74LSAghhBCHk5AyREZHCGv24I3ctjZ2Sn8UIYQQ4ghISBmieHqkaK3lzB4hhBDiCElIGaJoSBl4qqe+PYAvZMhIihBCCHEEbKkuYDTRWmN0BHHOKBjwftvkzB4hRIpprTHNAIbhxzC6on/GPjeNAIYRQGOmuszU0hpThzCMQM/3xDD90T8Nf+x6P6YZiuvpLFYnVmsmVosr+qfVhSX2Z/f1yjKyb7sW5Tjw+rE/LVYXVkusPsvAB+WmmoSUIdBBAx0yB53u6T6zZ7qMpAhxRLQ2DnqTVcp64AetxRl3N2etNTr2ZmQYfkwzGNfjVK8f8BZLxrBf76A/zQNvfgdfHwA9eGgwdfjA19H9ePPw1zHNQFy1ioNZLM6D3tCjwcKBYuC/e41JJNTZ6++gC9OMP+CkilJ2LBYHDPL1ARz74cfJyZmT/KJ6kZAyBAcauQ083bO1wcPEPCe5zvROqEKMBK01kUgHoZCbULiVcKiVUKiVUDj6ZzjUSijsxjB8h7zJDvYDXsV+Ezz4t0SlLIeFA9MMoLVxhF+JOvDbscWV8NezWBwoNfiPZKVsB75eSyYWqxObLZeMjLKe3457flvudb/Dfpu2OFHKOtxvxphhsdijox2xv1OlErsKwjQjsRGt6L+NI/93OBQa0wwdFJDN7lG1XgE33iBltw88i5AMElKGwOjobuQ2yEhKo1cWzYoxTWuDYLCRYLCpV/DoJ4SE3Wgd6fN5bLZ8HI4i7PYCHI6iA0PQ1kPeVGPXa20eNBxv9jFCobWB3V4Ye1zvN+wDvxlbrE7UoEvyDv8Bf9Drxt54+nq9w4JCr1DT8/UddF+XBIYxymKxYbHkYLPJe8JwSEgZgp6RlAHO7YkYJjubvJwyo3ikyhIi4bTWhMKtdAVqCQRq6eqqIxCoJdBVR1dXHV1d+9E6fNjjrNYsHPYi7I4inM5J5ObMw+4owuEowmGP/mmPfWy356f9fLgQIrUkpAzBgZDS/3TP7lY/IcOUdvgirWht4na/TG3do7S1vQ7oQe5vHBZC7PYiXK5KcnLmUVp6Fi5nBRkZ5bGRkCIcjuhoghBCJIqElCEwPCEs2XaUrf9h4t0tPgCmFGeNVFlC9CsS6WT//r9SV/8Yfn8NDkcJEyd+CovFOeDjFBYynOW4nBU4nRW4XBVYrZkjVHXihcNhfD5fzyUQCKD1wEENICMjg6ysrJ5LRkb8i2eFEEdOQsoQGB3BAUdRAGrb/ABUFY7eH+hiZJimyfbt29m1a9egb5hKKZxO50FvmN0Xp9OJxXJwcPb7a6ite5T9+/+KYXjJzV3AnNl3Ulp6Rmwlf3RKJxgMHvTm3X3x+/2xmrqAHbHL6BCJRA77ekKhxOywsFqtff4d9HXJzMzEbpfpLCGOhISUITA8oUEXze51+8lyWCnMGjjMiPErEAiwadMm1q9fT1tbG3a7Hat14EWT3YGiLxaLhczMTLKyMsnNrSU37w0yM3ehtQWvdzYez7HU7JrI5k1NwKNEIhH8fj8+nw/D6HungcPhOCz4jBY2m60nKBQUFPQZIFwuV1wjIl1dXX2GuO5LU1PTgN/HQ0diusNLf6FmtH7PhUgWCSlDYHiCOKoGXmtS6w5QWZgpQ8LiME1NTaxfv57NmzcTDoepqqritNNO4+ijjz4spGht9vTOMA0/EcNPsKsFr3c/fn8D/kATwWAz4XAbRqQNU3cAHiyWEIaRjadjGT7fsZhmtKGgo1dmdrlclJeXDzgCYLPJj4Z4dQfI7uDX18Xr9eJ2u6mtre01SnW4kQ4p/Y3O9b44HKPzFy6lFC6XS/49j3LyNxcnHTExfZEBd/YA1Lr9VBXJVI+IMk2Tbdu28frrr1NTU4PVqph/zESOnpmDw9GC1/dbNm6sJWL4o829zK5Yn42uQZ7ZgsNRiMtZhN1RhsMxG7u9kLy8BZSWfLRnSkckX/dUnNPppLCwcND7m6ZJIBA4aGqt++P+RmSSQWt90EjR/v378fl8/Y7YjWZ9jWh1X+KZkus9zdc9EibBZ2TIdzlOhifWI2WANSlaa2rb/CyeJtuPx7NIJEJ9/V527XqTnbv+i9b15Of7OOWULlANaB2krj56X6ezkszMajKcE/vsC3Kgj0ZWTy8Rh6MImy0v4U2nxMiwWCw9b3jpqPd0oNfrJRw+fKv5aKC1PigMdl9aW1sHHdGKx6HBZ6Sn65RS/U4d9rVOrfdarUNH/eINpieffDL5+fnJ+HL6JSElToanu9ts/yMprb4Q/pBBVaFswxzrtDYJBhvp6qrH799Lc/P7uNu2EQjUgm7FkeFDKc2UKdH7OxxlZGfPIDvrNLKyZpCdPYPMzKnYbOn5RiXGL5vNRm5uLrm5uakuJalM08Tv9xOJ9N1osDfDMAZcm+R2u6mrq8M0R+4spO4Q1helVE9w6g4n/QURq9VKRsbAMwTdFi5cOOx6h0tCSpx6us0OMJJS647u7KmUnT1jSnRYfB8ezyY8ns10eDbj8byN1gf/pw8GXRhGARkZs8jJmUpZ6Wzy82eSlTUduz0vRdULIfpisVjIzo7/ENiioqIkVjM83UFroADVeyF5X5d031YvISVOPSMpA4SUvW7ZfpxutNaEw+5op9RALYbRNeCJoNHW6xE8ni29AskmQqGW2PPZ8PmK6OiYQsCfi81WTmnpHCZPXsCUKTPIyZEmfkKIkdEdtIYStkYbCSlxMjpCKLsF5er/W1bXFh16qyiQkDKStNb4/bvw+3cSCNQR6KqlK1DX08LdMPzDfm7DKKGjvQi3+yg6O4uxWquorp7GvLnVTJkyZcTnZ4UQYjyRkBInwxNt5DbQsNjeVj8lORm4HHJQWLKZZpC2tvW0tK6ipWUNXV11PbdZrdm4XBW4XFUUFizC6arA5azE5arEas066Gj7SNhHR0cz7R1NdHpa8PrceDweWppddHYWk5GRz5QpUzj22ClMmTKFwsLCtB4aFUKIsURCSpyMjhCWwbYft/mpLJBFs8kSCrXQ0rKWltbVuN3rMAwfFouTwsLFVE/+Ajk5c3C5KrHZ8vsMEl6vl71762lqauq5tLS09Nr2aaWwcCZlZWWcdFJ0pKSkpERCiRBCpIiElDgZniCOyQOvdt/r9rNwcsEIVTQ++Hy7aGp+lpaWNXg8mwBNRkY55WWfoLj4VAoKTsJq7f8cGr/fz/vvv8+7775LTU1Nz5bDvLw8SktLmTZtGqWlpZSUlFBcXDxqG1cJIcRYJCElDlrraEv8AUZSwobJ/o4u2dmTAIFALY1Nz9DY+E+83vcAyM2Zz5Qp11JSvILs7NkDjm50dXWxdetW3nnnHXbu3IlpmhQWFrJkyZKeUOJ0DnzAnhBCiNSTkBIH0xcGQw+4s2d/exeGqSWkDFNXsIGmxmdobPpXbMQEcnMXMH36jZSWnokzo3zAx4dCIbZt28a7777Ltm3bMAyDvLw8TjzxRObOncuECRNk2kYIIUYZCSlx6Ok2mzdAj5TY6ceVsrMnbqGQm6amaDBpb98AaHKy5zBt6rcoLf0YLlfFoM/R0tLC+vXr2bRpE6FQiOzsbBYuXMjcuXOZNGmSHNgmhBCjmISUOBxoid//dE9PjxQ5t2dQnZ3vUlv3KI2Nf8c0Q2RlTeeoKddSVvZxMjOnDPp40zTZuXMnr7/+Ojt27MBisTB37lwWLFjA5MmTJZgIIcQYISElDkZHd0v8gRu52a2K8lxZ69AX0wzT3PwctXWP0tHxBhaLiwkTPs2kSReRk310XM/R1dXFpk2bWL9+PW63m+zsbJYvX86xxx47ppsZCSHEeCUhJQ6GJwQKrDkDt8SflO/CapF1D72FQq3U7/sT9fWPEww24HJWMX3aDUyY8Cns9vjOBmlubmb9+vVs3ryZUChERUUFy5cvZ9asWXISqRBCjGHyEz4ORkcQS5YdZe1/GqHW7ZdFs710ej+gdu+DNDb9E9MMUVi4hKNn/pCioqUoFV+zO6/Xy/PPP8/mzZuxWq3MnTuX448/nkmTJiW5eiGEEOlAQkocDE9owNOPAWrbApwxSQ6RA2hre423Nl2GxWJnwoTzqaz4LFlZ0+J+vGEYbNiwgTVr1hAOh1m8eDEnnXSSTOkIIcQ4IyElDqYniLWg/7Um3mAEty8kO3sAn28HW96+mszMao798B+x24fW3G7Pnj0888wzNDY2MnXqVM4880yKi4uTVK0QQoh0JiElDoYnhKO6/1GSWjn9GIBgqIVNmy/HYnFwzPwHh8VnuZkAACAASURBVBRQOjs7ef7559myZQu5ubmcf/75zJo1S3qbCCHEOCYhZRA6bGD6IwM2cuveflxZOH7P7TGMAFs2X0ko1MqxH34clyu+dSOGYbB+/XrWrl1LJBJhyZIlLFmyRNrTCyGEkJAymHh6pIz3kRStDd5972t4Ot9m/rx7yc2dP+hjfD4fNTU1vPTSSzQ1NTFt2jTOPPNMioqKRqBiIYQQo4GElEEYHXF0m3X7ycmwkeeyj1RZaWX7jttobn6OGdNvoqTktD7vEwgE2LNnDzU1NdTU1NDU1ARED/q74IILOProo2VqRwghxEEkpAzC8MQauQ0w3VPbFqCyMHNcvsnW1j1Kbe1DVFZcRmXlZT3Xh0Ih9uzZw+7du6mpqWH//v1orbHZbFRVVTFv3jyqq6uZOHEiVmt8W5KFEEKMLxJSBnHg3J6BW+JPLckaqZLSRnPLKrZt+yHFxacxffr1Pdd3dHRw33334fP5sFgsVFRUcMoppzBlyhQqKiqkAZsQYtQINzTge+VV0Dohz+c4agquY45ByfEdcZF3i0EYHUGUw4LK6Pu3fa01tW4/y2eWjHBlqeXxvM0771xLTs4c5s65s6dBm2EYPPXUU4TDYS688EKmTJkii2CFEKOODodxP/oozXffg/b7E/rc1qIispcvI2fFCrJOOgmLa/xuuhiMhJRBGJ4Q1tyMfqdymjuDBCPmuOo2GwjUs3nLlTgchRwz/36s1gNf+0svvcTevXs599xzmTlzZgqrFEKI4fG/8QYNt9xCcPsOslesoOTar2BNQDNJbZoENm/Bu3oVnf/+Dx1PPoXKyCBr0SKyVywnZ/lybNIX6iASUgYRDSkDrUfp3n48PkJKJNLJ5i2XY5pdLFjwGBkZB0aQdu/ezUsvvcT8+fM55phjUlilGE0Mj4f2v/6V/E9+Emt+fqrLEeNYpLWVpjt+SsfTT2OfOJGKe+4mZ8WKhL6Go7KSvI9/DB0K4X/jDTpXr8G7ejXeNWtoUArX/Pm4FixA2QZZq2e3k3X88WQuXIiyj91NGxJSBmF0BMmY0n8jt54eKeOg26zWBu+8ex1+fw0fOuZhsrOm99zm9/t56qmnKCgo4GMf+1gKqxSjSbipidorVxLcupX2J5+k6oEHsJeXp7osMc5o06T9L0/QdOedmH4/RStXUvyFq7BkJu/nunI4yFq0iKxFi9A3XE9w2za8q1fTuWo1bX/84+A1h8O0/uZeLDk5ZJ9yCtkrlpO9ZAnW3PgObh0tJKQMQJsao3OQkRR3AICKgrE/p7hr1y9obV3DzBm3UFi4qOd6rTV/+9vf8Pl8XHHFFWRkDHzOkRAAob172Xv5FURaWyn5+tdovfe37L7oIqoeeJCMo6akujwxTgTefZeGW35A15YtZB5/POXf/x4ZU6eOaA1KKZwzZ+KcOZPiq6+O6zFmIIDv1VfpXLUK75q1eP71L7DZyDxuITnLV5C9YgWOitF/GKuElAGY/jAYetBus2W5GTjtY3sbbWPTs+zecw8TJ5zPpEkXH3Tb+vXr2bp1Kx/96EeZOHFiiioUo0nX+++z98qVEIkw+eGHcB1zDFmLFlF75Ur2XHwxlffdh2ve3FSXKUahSEsL3rVr8b3yKmYwOOB9dTCI75VXsBYUMPGO28n9+MdHTSsJi8tFzooV5KxYgTYMAlu24F29hs7Vq2n88Y9p/PGPyZg5k4KLLyL/U58atbuJJKQM4EAjt4G3H4/1TrNe71bef/9b5OYuYObMmw/6T9zQ0MBzzz3H9OnTOfHEE1NYpRgt/Bs2UHv1F7FkZ1P1u0d6fmt1zZlD9eN/YO/lV7D30kupuPvXZJ10UoqrFelOa01ox46etR2BLVtAa2zl5XGtcSq4+GJKvnzNqJ4mUVYrmQsWkLlgAaVf/xqh3bvpXLMWzzPP0PC979P+1FNM+P73cc6enepSh0xCygC6G7lZBhhJqXP7OfGosdvKPRxuZ/OWq7Bas5k/724slgOBLRQK8cQTT+ByufjkJz85an4DEanTuWoV9dd9DXtFBVUPPoB9woSDbndUVzP58cepveIKaldexcQ77iD3jI+mqFqRrnQ4jP/NjXjXrKZz9RrCtbUAOOfNo+QrXyZ7xalkzJg+bn8mOaqrKfrcZRRedimev/+dxtvvoOZTn44GsmHuVNJap+T7KSFlAIM1cgtGDPZ7usbszh7TjPDOO9cSDDZy7IcfJyOj7KDbn332WVpbW7nkkkvIyhp/zezGq2BNTXQ3wosvYcnJIWfFcrKXLh1062T7U39l/0034Zw7l8rf3outoO9Tsu1lpUz+/WPUXv1F6q+7DqP9+xR85oJkfClilDGDQVrvfwD3Y49hdnREF5+edBJFV1xB9rJl2MtKU11iWlFKkXfOOWQvW0bzL39J2+9/T+e//03Zd79DzplnDho6zFAI/+uvR9e9rH2R6j/9ccQXtktIGYDREQQF1uy+R1L2tXeh9djdfrxz5+2429Yx6+jbyMtbcNBtb7/9Nm+99RZLlizhqKOOSlGFYiRowyCwaROdq1fjXb2GUE0NABkzZxKqrcW7ahUoheuYY8g+NTpH7jjqqIN+ALY++CBNd/yUrEWLqLjrV1gGCbXWvDyqHnyA+q9eR8PNN2O0uSn6whfG7W/GArwvr6Phhz8kvHcvOR/5CLmfOJvsRYsG/bckov+fyr/3PfLOPZeGm2+h/mtfJ+vJJym76SYyphy8SD3S1ob3xRfxrl6Db906TL8flZlJ9uLFmP7AiNcuIWUAhieEJduBsvb9g3HvGD79uKHhb+ytfZCKSZ9l4sRPH3Sb2+3mH//4BxUVFSxbtiw1BYqkMv1+vP/9L97Va/CuXYvR1hbty3DccRRcfDE5y5dhnzQJrTXBDz7oCTDNP/s5zT/7OfbJVeQsX0HOqSvoXLMW90MPkXvWmUy87TZUnB2ILS4XFb++i/033kjzL39FxN1G2Xe/M2oXAIrhCTc20vjjW+n8z39wVFdT9fBDslZpmFzz5lH9lz/T9qc/0fyLX1LziXMouvIKcs88E+/L6/CuXo1/40YwTWylpeR+4mxyVqwg84QTsKRo16aElAEYntCgpx8DVBaOre3HHs/bvP/Bd8nPP57p02846LZIJMJTTz2FUorzzjtPDgdME5HmZmqvuYauLW8n5glj55RYcnPJXrqUnBXLyTr5ZKw5OQfdTSmFc9YsnLNmUfKlLxFuaMC7Zg2dq9fQ9oc/4H7kEQAKLrqQshtuQA3x34uy25lw661YCwpxP/IIvldfIefU08hZsRznvHkSWMYwHYngfuz3tNx1F9owKPnqtRR+/vNY5JiNI6KsVgovvpjc00+n8Y47aLnnN7Tc8xsAMo4+muIvXEX28hU458xOi/9fElIGYHQEsRX1H0Bq3X4cVgtlOc4RrCq5QqEWtrx9NXZ7IfPm3oXFcqCTocfj4YknnqC+vp5Pf/rTFPSzpkCMrJ5+Iy0tFF1xeWK6T1qtZB67kMxjPzyk57OXl1Nw4YUUXHghhteHb906AHI+evqwp2qUxULpt79FxsyZdDz9NK0PPEDrb3+LtbiYnOXLyO4+/8Q5dv4fjnf+jRtpuOUHBLduJWvpKZTfeCOOyspUlzWm2EpKmHT77RScfz7BHTvJOvnktOyrIiFlAIYnNGC32do2PxUFLiyWsTFPbpph3n7ny4TDbo798J9xOA4shNy1axdPPvkk4XCY8847jzlz5qSwUtGtp99IOMzkRx7GlUbHEVizsxK2M0cpRf65nyT/3E9idHTgfeklOlevxvPMs7Q/8STK6SRr8WJyVizH9aEPwSC/AVqysrCXyiLLRNBaY3o8WPP6/1kZr4jbTdPPf07Hk09hmzCBil/fRfapp8papCTKXLiQzIULU11GvySk9EOHDXQgMuB0z163f0wtmt258w7a29czZ/bPyc2dB4Bpmqxbt441a9ZQVFTEBRdcQEnJ+DrxOV31129krLPm5ZF39tnknX02OhTCt2FDTxMr76pVcT9PyXXXUbTySnkDPAJdW7fScPMtBN56i4wZM6KH5K1YgXPu3LinCkJ1ddF28KvX4H/jDQCKrric4quvlkWxQkJKf3oaueX2v1io1h3gQ5Vj40A0j2cLe2sfZtLECykvPweAQCDAX//6V7Zv387cuXM5++yzpeV9muhcvTrab2TixGi/kXHa6Vc5HGQvXkz24sWU3XgDwQ8+ILhj56CP61y9iuY778Rwuyn99rfSYu59NDG8Plruugv373+PNTeXopUrCWzaROv9D9B672+xlhSTs2w52SuWHzYVp02Trnfe6VlsHdy2DQDHtKkUff7z5J3ziXETuMXgJKT0o7uRW38t8TsCYToC4TGxs8c0I7z/wQ04HMVMm/ZtAPbt28df/vIXPB4PZ511Fscdd5z8xpkEhtdHaOcOnLNmxb3rpf2v/y/ab2T2bCrv+22//UbGm96LeAeT+7GzaCwuwf2732G0tzHhRz8a0yfJJorWms7//IfGH99KpLmZ/PPPp/S6r/Z0djXa2/G+/DKdq1bjeeYZ2p94omcqLuukkwhu3Urn2jUYzS2xdU/HUvqdb5OzfDmOyZNT/NWJdCQhpR+DNXKrHUOnH9fWPYzX+x7z5t6N1ZrNG2+8wbPPPktWVhaf//znqaioSHWJY47Wms5//5vGW28j0tSEJSuLrFOWkLNiRfQk037aeQ+134jom7JYKLv+u9gKC2j+5a8w2juY9Is7sbjG1k69RArt2UPDD3+Eb906MmbPouKuXx22Bsqan98zFWeGQvjXb4hO5axZg3fVKiyZmWSdckp0t9iSJRKwxaAkpPTjwHRP37/dHth+PLpDSiBQy65dv6C4+DTy8pbz9NNPs3nzZqZOncr//M//SCfZJAjt3h39Yf/f/5IxexYl111HYONGOteuofPZf/f8htk9v++oqkJrTdMdP8X90EPknHkGE3/yE9mKeYSUUhRffTXWggIabvkBey+/gsrf3JOQBaBjiRkM0nrf/bTefz/Kbqfs+uspuOhClG3gtw+Lw0H2yYvJPnkxZTfdSHjvXmwTJsi/WzEkg4YUpdTZwL+01uYI1JM2DE8Q5bBicfb9LaptizVyKxq9IUVrzQdbb0IpK0cddQOPP/44e/bsYenSpSxduhSLzNMnlNnVdeCHfUYGZTfeSMGFn0FZreSf+0nKu+fqV63Gu3o1Tbf9hKbbfoJj2lTspWX4Xnll2P1GRP8KPvMZrPkF7PvmN9nzv5+l8oEHEtZevXv9hX/jRjAG+RFqsZCzfBmO6uqEvPaRMjo78a59kea77iK8dy+5Z51F6Xe+PaxdUUopmc4RwxLPSMoFwC+UUk8BD2mtP0hyTWlhsEZue91+8lx2cp2jdx67sfEfuN0vM33aTTzzr1fZs2cP5513HvPmzUt1aWOO9+WXafjhj6I/7D/+cUq/9c3DftgriwXX/Pm45s+n9LqvRlvOx3Y9BDZtoviaayj+0hdlbVAS5J7xUax5udR96Rr2XHQRVQ8+MOywYHZ14XvttWi33jVriDQ3x/3Y5p//nKIrr6Bo5cqU9H0J79vXc5qwb8MGCIejXV4fepCsRYtGvB4hlI51lhzwTkrlAhcCnwM08DDwR611Z3LLO9zChQv1G7FtasnUdM8mlN1CyZXz+7z90ofW4/aF+MeXT056LckQDrfz6msfweWspKnpc2zY8CZnnHEGJ554YqpLG1PCDQ003npbtKX3lCmUf++mYbX0TtUJpONN4O13qF25EiwWqu6/L+6j7SNuN961L+Jdsxrvuv+iA4GD118sWjToehejo4OmO3+B5x//wF5ZSflNN5J9yimJ+LL6pbWm6733erZwB99/H4ieott9DpPrQx+SkTuRVEqpN7XWfTZriWtNitbao5R6EnABXwXOBb6plPqV1vquxJWaPgZt5Ob2c/SEnH5vT3fbd9xGJNKB338tGza8yeLFiyWgJFjbn/5M0+23J6SltwSUkeGaN5fJf/gDe6+4nD2fvYScM88Y8HuvtSZUs5vAW29FzzspLyf/3E+SveJUMo8/bkh/35asLCbdcTv5551Hww9+QO3Kq8g5/XTKvvsd7BMmxPUcEbcb75q1BDZvhkFm6M1gEP/6DUQaGsBiwbVgAaXf/AbZy1eQcdSUAR8rxEiJZ03KJ4iOoEwDHgWO11o3KaUygfeAfkOKUuoM4JeAFXhAa31bH/c5H7iZ6AjNZq31RcP4OhJKm3rA6R7T1NS1BfjInLIRriwx2tpeY//+J8jIOIcXnt/B/PnzOfXUU1Nd1pihtab5zl/Qet99ZC1eTPktN+OQHVKjRsZRU6h+/HH2feOb+F56edD720pKKP7iF8lZsZyMWbOOOFBmnXgCRz39/2h9+BFafvMbvOvWUXLNNRR+9n/73CYd3LXrwLTgW2+B1ljy8gY/EM5iwTVvHtnXXkv20lOwFRYeUd1CJEM8IynnAXdqrV/qfaXW2q+Uury/BymlrMDdwEeAOmCDUurvWuv3et1nOvBdYLHWuk0plRZ9qk1fGEzdbyO3xs4uQoY5KrcfG0aQD7beiNVazprVWUydOpVzzjlHFskmiI5EaLjlFtqfeJL8Cy6g/Hs3yVD5KGQvL2fy7x9L2esrh4Piq1ZG+7n86P9ouv12Op5+mvKbv4/rmGMIvPVWz9qR0O7dAGTMnhUNS6euSEhYEiIdxBNSbgb2d3+ilHIBZVrr3VrrgXpQHw/s0Frvij3uT8A5REdful0J3K21bgPQWjcNrfzkMDoGbuS2tzW2s2cUbj/evece/P4a3nv3dMrKKjn//PPlJOMEMYNB9n3jG3Q+/wJFV3+Bkq98Rd4oxBFxVFRQee9v6Fy1iob/+z/2XHQxltxcTI8H7HayTjiBgks+S87y5XFPCQkxmsQTUp4Aei/rNmLXHTfI4yYBtb0+rwNOOOQ+MwCUUv8lOiV0s9b634c+kVJqJbASoKqqKo6Sj8ygjdzaAsDo65Hi9W1nz557aW2ZhtazuOiii6TNfYIYXi91X7oG/+uvU3b99RRe8tlUlyTGkJxTTyXrpJNofeBBwvv2kb1sKVknn4w1OzvVpQmRVPGEFJvWOtT9idY6pJRKVDceGzAdWAZUAC8ppeZprdt730lrfR9wH0R39yTotfs1WEv8vW4/SsGk/NHTnVJrk3ff/Q7hsJX6+sVceun/ki0/4BIi0tpK7ZUr6dq2jYl33E7e2WenuiQxBlkyMyn5ypdTXYYQIyqehQjNscWzACilzgFa4nhcPVDZ6/OK2HW91QF/11qHtdY1wDaioSWljI4QKLBk9x1S6tx+JuQ6cdhGzzqO3Xsew+vdxJ7dJ3DBBVdQKIvkEiJUV8fuiy4iuGsXlffcLQFFCCESKJ532S8A1yul9iqlaoFvA1fF8bgNwHSl1JTYyMtngL8fcp+niY6ioJQqJjr9syvO2pPG8ISw5DhQ1r7XE+x1+0fVVE9XVws7dvyE9vZyVqy4gYnj9MTcROvauo09F16E0d5B1cMPJb2nhRBCjDeDTvdorXcCJyqlsmOfe+N5Yq11RCl1DfAfoutNHtJav6uU+gHwhtb677HbTldKvUd0rcs3tdatw/xaEsbwBPud6oFoS/wl00tGsKIj8/r6m1AqRFXlt5g2bVqqy0l7htcHRmTA+3R9sJW6L38Zi8tF9e8fI2N6ygcAhRBizImrmZtS6mPAHMDZvVtBa/2DwR6ntX4GeOaQ677X62MNfC12SRtGRwhbcd/rTbrCBo2e4KjZ2bN//yZCoefpCizk1BWfGPwB45g2TZp+8hPcv3s0rvs7qqupevAB7JMmJbkyIYQYn+Jp5nYvkAksBx4APgWsT3JdKWV4gmRM7bvbbF3Pzp70XzSrtebNjTfgcNg46aQfy3bYAehQiH3X34Dnn/8k79xzcc46euAH2GzknnmmHDUvhBBJFM9IyiKt9Xyl1Bat9S1KqZ8Bzya7sFQxQwa6y+h/+7F79PRIWb/hD7hcH5DhuIDi4qNSXU7aMv1+6q79Kr6XX6bkuusoWnmlBDohhEgD8YSUrtiffqXURKAVGLNdgwZr5FbbFg0p6d5ttqOjnYaGu3A6czhlyQ2pLidtGe3t1H7hagJbtlD+g1soOP/8VJckhBAiJp6Q8g+lVD5wB7CR6Bk79ye1qhTqaeTWT0v8va1+MmwWSnLStwma1prVq39Cbl4LlZXfx27PSnVJaSnc2EjtFVcQ2r2HSb+4k9zTT091SUIIIXoZMKQopSzAqlhztaeUUv8EnFrrjhGpLgUOdJvtfySlsjAzracD3n33LRwZ/0KpyUyf9r+pLictBXfVsPeKyzE7PFTefz9ZJx7aDFkIIUSqDdgnRWttEj0ksPvz4FgOKNB7uqefkRR3IK3Xo/j9fjZu/ClOp4/5835INGeK3gJvv8Oeiy9GdwWpevR3ElCEECJNxfMOtkopdZ5K56GDBDI9IVSGFUvG4Yfuaa2pc/upLEjfnT3PPfcUZeUbycleTHHx4lSXk3Z8r77K3ksvxZKZSfXjf8A1Z06qSxJCCNGPeELKVUQPFAwqpTxKqU6llCfJdaWM0RHsd6qn3R+mMxhJ226z27dvp9P7ODarwZw53091OWmn84UXqF15FfZJk5j8+OM4qqtTXZIQQogBxNNxNmckCkkXhifU71RPz86eNAwpwWCQ559/jBkztzNx4oVkZU1NdUlpJfD229R/7es4Z8+m8r7fYs3ruw+OEEKI9BFPM7c+DyTRWr+U+HJSL9rILb/P2/amcY+UF154geKSF7FaXEydem2qy0kr4aYm6r50DbaSEip+c48EFCGEGCXi2YL8zV4fO4HjgTeBFUmpKIW0qTE6QwM0cuvuNpteIWXPnj1s2/YP5h9Tx5Qp38ThKE51SWnDDAap+/KXMbxeqv/4ODY5/VkIIUaNeKZ7Djp7XilVCfwiaRWlkOkNg9l/I7d97QHyXHayM+I68mhEhMNh/v73vzFt+iYyMiZQWXlZqktKG1prGr5/M12btzDpV7/EOXNmqksSQggxBMN5t60DZiW6kHRgeAbeftzo6aI81zmSJQ3qtddew2p9g8zMZqZN/TlWa3rVl0ruR35Hx9NPU3zNNdKoTQghRqF41qTcRbTLLER3A32IaOfZMcfoGLiRW2NnkNJ+AkwqeL1e1q1bw7EL3yYnZx5lZWcP/qBxwvvyOpruuIOc00+n+ItXp7ocIYQQwxDPSMobvT6OAH/UWv83SfWk1KAjKR1dTC9Nn/Uea9eupajofaxWD9OnfUcat8UEa2qo/9rXyJg+nYm3/hhlke+LEEKMRvGElCeBLq21AaCUsiqlMrXW/uSWNvIMTwgsYMm2H36bqWn2BilLk5GU5uZm3nxzA4sW7yQ3dwEFBSemuqS0YHR2UvfFL6FsNiruvhtLlpxbJIQQo1VcHWeB3i1WXcALySkntYyOINYcB8pyeHPdVl8Qw9SUpcmalOeff56ysv1YLK1UVV2e6nLSgjYM6r/+dUK1tVT86pc4KialuiQhhBBHIJ6Q4tRae7s/iX2cXntwE2SgRm5NsamgdAgpu3btYtu2bcyYuQens5LSElkUCtB85534XnqZ8htvJPO441JdjhBCiCMUz3SPTyn1Ya31RgCl1LFAILllpYbhCWIv6Tt/NXq6gNSHFNM0ee6555gwIYDWO6iqvAmlDj9naKzQWhPZtw8diQx4P99rr9P6wIPkX/gZCj5zwQhVJ4QQIpniCSlfBZ5QSu0DFFAOjMl3AaMjhHNaQZ+3NfSElNSuSdmyZQsNDQ2celo9ppnLhAmfTmk9yRSsqaHxhz/E98qrcd0/87jjKL/++iRXJYQQYqTE08xtg1LqaKC7E9ZWrXU4uWWNPDNooIMGln4auTV6gigFxdmpCymhUIhVq1ZRVZVJKPQ6k6uuxGYbewtDza4uWu+7j9b7H0BlZFDyta9hLy8b8DHKZiPrlKUo++GLnoUQQoxO8fRJ+RLwB631O7HPC5RSF2qt70l6dSOoZ/txPy3xmzxdFGVlYLembjvrq6++SmdnJ0uXevF0WqiovCRltSSL96WXaPjhjwjX1pJ79tmUfeub2EpKUl2WEEKIFIjnHfdKrXV79yda6zbgyuSVlBo9jdz6HUnporyfADMSOjs7WbduHbNmTcbr+w9lZWfjzChPWT2JFt6/n7qvXEvtyqtQdjtVjzzCpDtul4AihBDjWDxrUqxKKaW11hDtkwL0/U4+ih1o5Nb/dM+EvNQtml27di2GYTB3npuGBj9VlWNj27EOh3E/9nuaf/1rME1KrruOos9dhnKMuX9iQow5YSPMm01vstW9lbyMPAqdhRQ5iyh0FlLgLMBpi/9nZsSMEIgEMExj0PvaLDay7FkodXi7CDG2xBNS/g38WSn129jnVwHPJq+k1DA83S3x+5nu6ezimMr8kSzpwGs3NbFx40aOO24Bbe7bKCxYTE5O+h6fpLWm8/nnCdfvG/iOpknH3/5GcNs2spcto+zGG3BUVIxMkUKIYWnramNd/TrW1q7llX2v4A17+71vpi2TQmchha5CCjIKiOgIgXCAQOTwS9gc2lJHu8VOgbOgJxT1XFwHPp6QNYFJ2ZPItI/JrhnjQjwh5dvASuALsc+3EN3hM6YYHUGU04rFcfh23lDEpMUbStnOnueeew6Hw8Hs2T527mpk1qxbU1JHPLRp0njrbbQ99lhc97dPnEjF3b8m59RTk1yZEOkjbIbZ07GHHR072NG2gx3tO9jZvpOgERz0sVZlxWV34bIdfMm0ZR523UGXfh7jtDmxDHCkhtaane07ebHuRV6se5HNzZsxtUmxq5iPVn+UUypOYUHpArxhL21dbbi73D2X1kBrz8eN/kZsyobL7qIks6TfOm2Wwd+WwkaYtmCv1wq4qemowd3lpsvoOuz+hc5CJmVPOnDJOfDxxKyJozxo6QAAIABJREFU2K2pXXAfMSO82/ou6/evZ33Deuo663DanIf/nfb6O8yyZ1GdW820/GlU5lRitSSuFYUv7GNn+052tO9ge9v2no8fPuNhJudOTtjrxCOe3T2mUup1YCpwPlAMPJXswkbaQI3cmr2pa+S2c+dOduzYwWmnnUZj061kZU2nsPCUEa8jHjoUYt/1N+D55z8pvPQSiq+5BgYZjrW4XCjr2O3zIsY3wzSo99azvT32g75tB9vbt7Pbs5uIGe39Y1EWqnKqmJo/lWx79qDPeehoRHtX+2GjEoYefMqktwxrRr+hYVfHLuq99QDMKpzFyvkrWVaxjFlFsw4KNwXOAipzKof0uommtSYQCeDuctMSaGG/bz/13nrqOuuo99bzbuu7vLDnBSL6QN8lhaI0s5RJ2ZOoyKk4EF6yJ1KRXUFpZmlCAwCAqU22ureyviEaSt5sfBNf2AfAjIIZzCuZR8gI9fx9dvg7oh/3+nvv/TVkWDM4Ku8opuZPZVr+NKYXTGda/jQmZE3omRIztUlXpAt/xH/Yv5dmfzM72qNheUfbDvb5DoyCO61OpuZPZdHERShGfnqt35CilJoBXBi7tAB/BtBaLx+Z0kaW4Qn1f/pxinqkdDduy8/PZ8YMg7ff+YBZR9+WlvOwpt9P3bVfxffyy9F1JSuvTMs6xyqtNb6wL67fxENGCHcw+ttn79963V1uWrta/z97Zx4fRX3//+fsnc0mm2RzXwRIINwBwiGXQcUAKnj36/G1Wqkibb1qqVqPorbFahUsVX9YrNSj/SotggoCCggIckpCIIRw5b6vzbH3zO+PSUJCEhKSTQI6z8djHpvszHw+n9mdnXnN+/3+vN9U2iqpdlQjSmKnbZn15uaLessLfFdN7E0XTrvHTmPYW58gCAIGtaFTK0JXkSSJkoaS5qfO7OpsTlaf5HT16VZP9lGmKOID4rky+kriA+NJCEggzhyHXu29a4skSbhEV/MNqOmmZHfbW9+cznO7tHfzqrBXEB8Qz89G/owro68kzPfCqQD6G0EQMGqNGLVGov2iSSKpzTYe0UNpQyn5dbJwKagroLCukPzafPYW7aW0oRSJc+eiRqUhxKdjy09LC4eqk7koIiInKk+wv2Q/NY4aAOL847h+0PVMCJ/AhPAJBBmCunSsDa4GztSckc+1qpOcrDnJ/uL9fH768+ZtjBojerVe/v7bsTC1RKPSMNA8kDGhY7g14FYGBwwmISCBKL8or/xGusuFLCnHgZ3A9ZIknQQQBOGxPhlVPyCoBTRB7VtKSvsp22x6ejolJSXceuutFBb+BZ0umPDweX06hq7gqa4m78GF2I4cIfyFJQTefnt/D6kNoiSSXZVNha2iXbN3V83MvYkkSTg8jjY3lKabx/mm9FaLrRKn6Ox233q1vtmPH+wTTEJgAupOMhlLSFTZq8irzeO7ou+wuVsnog7UBxJliiLAENDqeFr93cmFsy8wqA0d3nQ6+wxAjtE4WX2yVWxGiE8I8QHx3Db0NhICEhgcMJjBAYPx1fZ+XiNBENCpdejUOsx6c6/3d7mhVqmJMEUQYYpgAm3LZzg9zmYLTEFdAQW1BZTZys79Fl02ym3l3Y6piTJFcVXMVUwIn8DE8IndFn5GrZERwSMYETyi1ftWp1UWylXZnK45jVt0X1hgaXwINAQS6x+LVnXp5Zm60FX5ZuB/gG2CIHwJ/Bv6wdbTR4QuHNPhupJ+qtuzb98+QkNDGRCnZd++HQwa+Bgq1aVRhbkJV3ExuQsW4MrJJWrZ6/hfe2nUEZIkiTPWM+wv2s/e4r3sL95PtaP6gvtoVVoMGkO3/fvtLS7R1SwiKuwVrYRFk+iodlTT4GrA7rF3yXrRUlBYDBYSAhLkYEF9UJdmU2hUmlb7B/kEYdQYe2T5kiSJSnvluafSpqfU2gIq7ZXNF8JITWS7n5Nere/TpzVREpsF4flL042orKGsS9+HSWfiukHXkRCQQHxgPPEB8Yo4uIzRqXUM8B9w0bEXHtHTygLTEb39MOSv82ds6FjGho7t1X76ig4/LUmSPgU+FQTBF5iPnB4/VBCEt4C1kiRt7qMx9jvFVjsalUCQse+mxZaVlVFYWMi1115LXt4/UKkMREff1Wf9dwXH6TPkLrgfscZKzDvv4Dt5Ur+OJ782v9nHu69oH2W2MgDCfcOZET2DSRGTiDZFNz/JtzRvtzGFt1isTislDSVt3u8ufjq/5hkJcf5xmPVmjNoLiyKjxkiAIQCLwYKPxueSc6UJgoDFx4LFx8LokNH9PRwFhT7H23ErCjJdCZytBz4CPhIEIRC4DXnGz49GpJRY7YT66VGp+u7GkJ6ejiAIDE2MIC1tHZGRt6PVtl9XqD+wHckg74EHQBCI/edqfEaM6HynbnCy6iRbcrZQ56rrUETY3DZqnbVU2isBsBgsTAyfyMSIiUwKn0S0X7TXb+otYyna8+83LS2tFk1Lf88kUFBQULhcuCi7U2O22ZWNy4+GUquD0D509YiiSHp6OoMHD6a6ei2S5CI25r4+678z6vfsIf8Xv0QdGEjsqr+ji4vzeh+HSw+zKmMV2/O2A3Q43dJsNMtT9bRGhgYOZVLEJAaZB/W6pUElqJoD9BQUFBQUeof+jRS8TCix2hkc0vnUQG+Rk5NDTU0NV189nYKCZYQEX4PRGNdn/V+I+t27yXtwIbq4OGL+/ne0YaFea1uSJL4t/Ja/H/k7B0sOYtabWTRmEXck3kGAoY8S6YkecNaBQYkpUFDA7QBJAm3/ZdtW+HGjiJQuUGK1M2Wwpc/6S0tLQ6fTYTYfo6y8itjYBX3W94Vw5uSQ/9jj6OLiGPDB+6jN3rmRu0U3W3K2sOrIKrKqsggzhrF4wmJuSbil9ywVkgQ1eVCaCaXHzr2WZ4PbDrFTYOTNMHw+mLwnxNpQWwxVORA9AVT9N81PQaEZtwNObYWM/0LWBvA45fNz4Ax5iUoGjVK2QqFvUERKJ9icHqx2d5+5e5xOJ8eOHWP48OGUlq7GZBqO2Ty+T/q+EJ66OvIW/QIBiH7zb14RKA6Pg3Un1/He0ffIq80jzj+OF6a8wPWDrvd+3IYoQuY6OPm1LEjKjssWkyb8oyE0EQalgMYHMj+DDU/AxsUQNx1G3gLDbgBj13IYXJD6CnksGf+Fs7sACQIHwsQHYOxdihVHoe/xuOHMN3D0v/K5b68Bn0BZqBvMcGYnbF8K2/8k/z5iJ58TLRFJoFZuJQq9g3JmdUJJH+dIycrKwul0Mny4hbz8dOLjn+r3mRySx0PhE7/BefYssatWoYvpeVbJSnslC7csJLMykxGWEbye8jozY2Z6P0JekuD457Dtj7KlxGiBsBGQdBeEDoPQ4bI4OV8YXPU7KDkmX7Qz/gOfPQxfPA6Dr5IFy9C5YPDv+jhs1XD8C7mt09tB8oAlAa5cDIFxcPA92PQUbH0Jku6QBUvIUC9+EAoK5yF6IHePfE4eWw8N5aDzg2HXw4ibGwV7C4uJrQpydsOZHfLy9RL5fZ0fRCeDXwT4BoNvSIsl+Nyr5tJKn6BweaCIlE7o62yzaWlpmM1m1JpDgEBY2PV90u+FKFv+BnXbtxP27DNemWZcUl/Cz7f8nMK6Ql5LeY1rYq/xvhCTJMjeAttegqI0WRDcskq++HbVrRI2XF5m/k5uI+M/cHQtrH0Q1DowR7e9GBuDW1+YSzPl/U5+JZvNA2Jh6sPyOMJHnSsbkHQnFH4Pe1fCoX/C/r/DoJkwaSEkzAJleuPlTXWu7NqrL4P68vNeG/9uqJBdi6HDWwtoc0zH5SUkSW63yWVZlim/VuVAZzlePE7ZmqjxgaFzZKtJ/KyO4098AiHxOnkBqCuDsztlwVL4vewqrS+DjrIe6/ygKxZScxTENVppBky5uIcBhR8cQl+movYGycnJ0oEDB/qsv/VphTz8r+/Z8tgMEsL8erWv2tpaXnvtNaZNm4qP8U8YDBGMG/tBr/bZGTWff0HhE08QcPvthC/5fY/FRJ41j59v+TnVjmr+etVfmRDeNuNjj5Ak2VKx7Q+Qvx8CBkDKkzDqdu+YpEURCg7I1pmagtY3m4by9m8MfhGyKBl5C0SN67SeEXVlcOg92L8KaotkS8uEBTDwSggeogQxXk5UnoaNT0L2prbrdH6tLQ8+gfL3XZoJtYWttwtNhJBEWbiotS3iqDLB3iJJoW+oLHCCBkFnScMEFcRMhCGzQe+liQGSBI7axt9DRQsRVia7OUV3Zw1AWRbk7ZPFjqCGyLEwcLosWmImg06ZUfdDQxCEg5IkJbe3TrGkdEJTSvy+iEnJyMhAkiTi47WcPHWWuAEP9nqfF8KWcZSi3/0On+TxhD/zux4LlOyqbB7c8iBO0cmqa1e1SefcY3J2w9Y/QM4u8I+C65fB2Lu79vTWVVSNF/aYiW3XiaJsEm95YfaLgJhJFxcUawqBGb+BqY/K8QF7/x9sfkZeJ6ggaLB802r5xB00yLvHqdAznA2w63X4drn8vcx8BiKTWlvcLiQ2bVXyzbqlGMnaAN83Vhc3mOXvfeTN8mtIonwu+Ab3zfF1hCDIlg+DP1gGd78dlx3y9zW6lnbC7r/Kn6dKKwfxDpgiu21Dh8v9KOf+DxZFpHRCidWOQavC39D7H1VaWhqRkZE4HDsQBB0hIbN7vc+OcJeVkf+LX6C2BBG9fDmCrmfR/BnlGSz8aiE6lY73Ut8jPjC+850ctXD4X3Diy86fwBxW2eRsCoM5f4ZxP+17i4NKBb4WeSGx5+2ptfJNaOTNUHEKitNbz0Y6/sU5y41aJ7u0AuNau5tavYaAT5AS5NibSJIsJr58UnbxjLwVrn0R/CMvrh2fQDk4NXZy6/fryuTfgl945xa5yxmt4VxgLoCjDnK/g7ON8TC7Xjt37qu0soUxdFhr8R4Qd3nNmKsvb3Sf7YSKk51vr9ZC1Hj5M4qe8ION+VGuVp1QbHUQ5m/o9eDVkpISiouLmT07lZLSVQQHz0Sr7R9frOh0kv+rh/FYrcR99CEaS8+mX+8v3s8vv/4lgYZA3rn2nc7LuVecgn0r4fsPwVkrPyV2lidF4wOzXpTdIj9Ec7BlsLyMuOncey47lJ9oLVyqc2R3VH25HJzbBkGeoWRsR8C0FDVGS+fuApBjKPS96wa9bKg4JYuT7M0QMgx++rnspvAmphDvtne5oDdBwjXyAu2f+3n7IGPNuX20Rjn4PGRYixifYbJgvBQEnr2mdSBySYb8vs4kj7Oz4pYNlbDjFfjmZdAYZIvtwBmyWzhyrPceRiRJHmt9uRyH18cPf4pI6YQSq50wv97/UtLS0lCpVMTE1nP8eDnhYf1T7ViSJIp/vwTb4cNELVuGYdiwHrW3I38Hj29/nChTFCtnrey44qcoyrkZ9r4NJ7fIT0cjboJJD8ozBxTaojVAxGh5OR9RlGMVWsUEnBeoWV8OJUfl/+0XLr54QcyxjTeBFsGewUNA69P9NvsDSWr8zFoEtmp9OnfROBvkJ/tvl4NaD9f+QT5vFRdE79HRue+oPecmKzkmBxKf+hrSPjq3jd7caHFpPF+NXXgIU2tbi3lDQNeEjiTJVt6mc6quFAoOyqKk6LBsDdIYZIvZVc82Coykrp87tmpZ6DQFMG99EXhRFjoDpsjpE7pixXPZ5Ji6VteIxjii+jJoqvD84A6I6LgYb2+giJROKLXaGRnVu3krRFHkyJEjxMfHU1O9CY3GD4tlZq/22RFV779PzX//S/CiRfjPTu1RWxvPbOTpnU8zJGgIb1/zNoGGdmoP2a2Q9i857qLylOyuSXkKxt8Hft0rYa6AbOY2BslLV6Yye1ytAx0bKuUpqhdEgpp8OedMaaYsMpsuZoJKjpMJHSYHL3d2QRdUsoujjVUnpGdix9nQjkA7L9i55f+duRXPD3b1tcCp7VCTKwdnX/ui7IpR6B/0jdOhz3+waag8L3FjJhz9VJ763x1UmrbnqSFAtjicf055nOft2xhXM2OxbGnriavGJwAS58oLNLqMdp2zzmRfZIk9rW/jMQXLcX0RY1pPKTf3PP3ExaKIlAsgSRIlVgfXDOtdS8qZM2eora0lNXUmpWUvExo6B7W67/2Ldd9+S8nSl/GbdQ3Bv/xFj9pac2INL+x5gbGhY1lx9Qr8dOe5BMpPyi6dwx/JLp3oCbI4GT5fyWbZH6i18s21JzdYj0uezdIyg2/pcTmBXmeIngtMXTU1uqCCuyZYXLZzNwlXffvbaI3nLrz+0XJCsvNdX0YLuJ0dW6OqzsozyPwj4aa3IG5a52NT6B+MQRA3VV6akCSoK5EflDrD4zhnfWzvfKg4JVs1DGb53DGFQ9ioczf85nMqGIITQOfbO8fpGwwjbpQXgNoSWTh1hkYnj7G3xtUDFJFyAWodbmwuT68ncktLS0Ov1xMUVEBJaR3hYTf0an/t4czJoeDxX6MfPJjIpUsRehBw9smJT3hhzwtMjZrK6ymv46NpvLGIopwvZN//k19VjYGhEx+E6P7PqqvQQ9TaxhiAoa1jZ7qKs76DPCItbgznP5W2h95PtuK0ibkJOXfTuAQvxgp9jCD0XJhf6viFXfYWaUWkXICSmqbpx71n1XA4HGRmZjJ69GjKyj9HpwslMHBy5zt6keaU94JA9FtvovLt/gV83cl1vLjnRaZFTWP5zOXo1DpZyR/+SLacVJ6WnzJSnobx9172PyAFL6LzlZfAuP4eiYKCwiWCIlIuQIlVNj/3piUlMzMTl8vFiBEDOZvzDTHR9yB0FtXtRZpT3ufkyCnvo6O73dYXp7/g2W+fZVLEJJbNXIau8qwsTNL+JWe2jJ4oZ28dNk9x6SgoKCgodIoiUi5AX9TtSU9PJyAgAJ3+CJLkIqyPXT3NKe+fexbfSe0kKOsim89u5ne7fkdyeDJvDL0X/b/ukmfpqHVyptWJD8jZVhUUFBQUFLqIIlIuQElt79btqamp4fTp01x55ZWUlLyD0TgIP7+RvdJXu/1//gUVK1cScPvtBN5xR7fb2Zq7ld/u+C2jzYNZUV6Lzz/mykGHM38nz9L5seZ2UFBQUOhH6iorEEURo78ZTQ8TcvYXiki5AKVWB34GDUZd73xMR44cASAxMYxjmfsYNPCRPqt47K2U9zvyd/Dr7Y8zHD1vHv4ao95fnu8/6UElyZeCgoJCF5EkifrqKirycnHaGgiKjiEwPBKVuuvu//rqKvKOppN7NJ28jHSqS4qa1+l8jBjNZoz+Ac2vPv5mjGYzGp0et8OOy+HA5bDjstvl1/P+n73ocYIio3rj8DtEESkXoMRq7zVXjyRJpKWlERMTg9O5C5D6zNXjrZT3uzPX8Ni+F0hwOHirogLTjMUweZE8d19BQUHhMkcSRU4e+I68o0forBivIAgYTH4YzU0iwIzRLAsBg6+p1YOgra6WitwcyvNzKc/LoSIvh/K8HOx1ta3aVGs0BEVGY4kZQHDMAIJjB2CJHoA5JBRBpcJWV0v+0SOyKDmaTkV+LgB6oy/Rw0eSlHodWoMPNmsNDTXVNDS+VpcUU5SdRYO1BkkUzzsOFVqDHq3e0Ljo0RgMaA0+QN8XJFZEygUottp7zdVTXFxMWVkZ1113HcUlS/D3T8JojOuVvlrilZT3VWfZ//XTPFKbRpxbZOWAm/G/+zdyLgIFBQWFyxyP20Xmrm/Yv24NlYX5aA0+qDUXvl1Kooijof28PCq1WrZa+PnTUGulvqqyeZ3e6IslOpYhk6Y2ixG90UhFo4Apz8uhIOsYx7/9pnkfjV6PX5CFquIikCQ0ej3RiSMYPuMqYkeOIXTgIFSqzi0wkihir6/D5XCgNciiRK3R9JlFvysoIuUClFodTBrYOzfetLQ01Go1AwfqSUvPZEjCc73ST0s6TXm/6Xdy5tdO1PL3WhW/CA8lSuvPO/NWE2AZ0nuDVlBQUOgjXHY7R7Zu4sDnn1JbUUbIgIFc9/BvGDJ5WpfcLh63G3tdrWy1qKmhwSq/2mrPWTJCB8YTHBPbLEhMQZZ2RUHYoNZFWB0NDc3CpSIvB2t5KcOmzyR2xBjC4xNQay6+DIOgUuHj54/PJeyZV0RKB4iiRGmtnTCz9909oiiSkZFBQkICNTWbEQQ1oWHXeb2f87lgyvsja2DPCki8vsM06i5J5NP6s/ylJp0w3zD+Pvd9gnz6uTS8goKCQg+x1dVy+MvPOfTlZ9hrrUQljmDWz39BXNL4i7IqqDUafAMC8Q1opwRID9EbjUQOSSRyiBcqrF9GKCKlA6oanLg8EmF+3nf3FBUVUVdXR2JiIsUlbxEYOAW9rndv9hdMeV95Bj57VM5jctt7bYpbuUU3X5z+grfS3qKgroAxIWP4y5V/IVgRKAoKCufhcrnIz8/Hbrf391AujCQhih6cNhtOux1VaCSTF/wKndEXjVaLHTh+/Hh/j/IHhcFgIDo6Gq2261YfRaR0QG8mcsvOzgYgPLyeo8fyGTTwUa/30RJ3eTmFHaW897jgP/fLBd5u+XsrgSJKIpvObuLNw29y1nqWYUHDePrqp5keNf2S8lkqKChcOuTn5+Pn50dcXNwlcZ2QJAnR48btdLZeXA4kUQKjAR9fP4wBgWj1fV8zrSWiKNFgdeKyu1GpBAS1gEqtQqUSUKkbl8a/BZWAKEpIHglRlBA9jYsoIXrE5vcRhA73VzW2Lwj0+nclSRIVFRXk5+czcODALu+niJQOaErkFtoLIuXEiRNER0dTXbMZlcpASMgsr/fRkuIXX0K02YhavqxtyvutL8qlw29bDYEDAPlk2pq7lRWHV3Cy+iTxAfEsS1nGVbFXXRIXHQUFhUsXu93eawKlSXCIbg+iJCKJEpIkIokikiTJM1UaX0VRxO1y4XE6EFvMYFFp1Gi0enz85NwhOh8jmot4su8NJEnCaXNTW+lA9IhodGrcbhHJ0Sg0LpImMSKoBZAk3E5RFjQdtCWoBHQGNVqDBp1BjVqj8vr3JwgCFouFsrKyi9pPESkdcC7brHeVdV1dHYWFhaSkTKe09P8RHHw1Go3Jq320xPrlJmo3bSLk8cfRDxrUeuXJr+Hb5XINnRE3IkkSOwt2suL7FWRWZhLnH8efZ/yZ1LhUVEL3Cw4qKCj8uOjuDU5qdMF4XG48bhei24XH5cbtdiG6XHjc7k6nAoMcECqoBDQaHQaTHxqdDo1Oh1qnQ62+tG57bpeHukoHTrsbjVaFf7ARneHcGGXx1dJKIi+SKLWwhrS2kHT0+UtiizZaWFzcLhGn3YOjQb7vqdQCWr0GnY8anV6DWuud6393zotL69u6hGhy94T6edeS0uTqiYyqIS+vkvDw+V5tvyXuqiqKX3wRw4gRWH52X+uVdaWwdiGEDIPUP2F1Wnlq51PsyN9BtCmaP0z7A3MHzkWjUk4RBQWF3kWSJOz1ddRXVeJ2tq50rVKrUWu0aPR69L4m1BotKo0alUqFIKgab8oqaHwVhI5v0pcSoijRUOOgodaJgIAp0ICPn7bN2AWhye3T8z4FlYBaJdCeTpMkCY9bxGX34LR7cDncOBpcAKjUKnQGNUazDo2272rLASiPxx1QUmvH4qtDp/HuR5SdnY3JZMLp3IlGE4AlaLpX229J6dKleGpqiPjDSwgt5/iLIqx9EBxWuPVdTtuKueuLu9hdsJvfJP+G9TetZ97geYpAUVBQ6FUkScJeV0dFfi41JcWAgJ8lmIDwCCzRsYQOHERo3CAs0TEEhEXgZwnGaJaTo+l8jGgNBjQ6PWqtFrVa0yhcBNRqNUlJSYwZM4Zx48axe/fuC46jurqaN998s9PxpqSkcODAgU63W716NQkJCSQkJLB69eq2x9zg4pste7jyqulcM3cac26aScbxw60EyqZNm0hKSiIpKQmTycTQoUNJSkrinnvu6bR/gLfffpt//vOfXdoWZDGk0arx8dNhDvHBEmUiKMIXU5ABrV6Fw+ZGoO/Fn3IX6oBSq93r8Sgej4dTp04xfPhgysreIzx8PipV79RTqN2+nZp16wletAhD4nlT1vb8FU5theteY4ernN9+/Vt0ah3vXPsOyeHJvTIeBQUFhSYkScLR0EB9VQUuhwONToc5LLxNZtbu4uPjw+HDhwH5Zv/UU0/xzTffdLh9k0hZtGhRj/uurKxkyZIlHDhwAEEQGD9+PDfccANm/wBEj0h9tROn3c0Lf3yW53//PPPmX8+GDRtYvHgx27dvb24nNTWV1FQ5VURKSgqvvvoqycmtr88ejwd1B/lbFi5c2KPjEAQBjU6NRqcGPx2SJPWLhUoRKR3QG9lmc3NzcTgcxMRUUlVtIzxsnlfbb8JTW0vx879Hn5BA8MIHW6/MPwhfv4CUeAPvGmD5179kaNBQls9cTqQpslfGo6Cg8ONkyWdHOVZobfWeJIpyvIkoylYPjbYxUVphl9ocHunP8zeM6PIYrFYrgYFy3pK6ujrmz59PVVUVLpeLl156ifnz5/Pkk09y6tQpkpKSmDVrFq+88govv/wyH3zwASqVijlz5rB06VIAPvnkExYtWkR1dTUr317JlCnTWsWLfLrmM66cNhPBqUf0SEy7IoWPP/iUm+ffCsg3f1OgAZ1Bg80uZ6itqakhMrJr19+4uDh+8pOfsGXLFhYvXkxtbS0rV67E6XQSHx/P+++/j9Fo5Pe//z0mk4knnniClJQUJk2axLZt26iurmbVqlVMn35xVvz+cqEpIqUDSqwORkSYvdpmdnZ2oznyAHp9OAEBE7zafhOlr7yKu6yM6BV/bV2Xx26F//wMm18Ez4cGs/HQMmbHzeaFqS/go/HplbEoKCj8sHE0NFBZkNcc0OpxuXDabQCIbndzbRgJCdHtbhYnGq0WVS8FsdpsNpKSkrDb7RQVFbF161ZAztOxdu1a/P39KS8vZ/LkycybN4+lS5eSkZHRbH3ZuHEj69at47s936FCS0lhGdUlDbidHmqrGvg3gsHQAAAgAElEQVT8k6/4attmnnvu96z5cH2rvnNz84kIl4vwaXRqYmNjqKwpxS/IgKAW0OnVqNQqli1bRmpqKk888QSiKHbqkmqJxWLh0KFDAFRUVPDzn/8cgGeeeYZVq1bxq1/9qs0+brebffv2sWHDBpYsWcJXX3118R9sP6CIlHZwe0TK6xxet6ScOHGCuIGhVFV/REz0PXKwl5ep/+47qj/+mKD7f4bPqFHnVkgSfP4YRbWFPDJsIsfztvLIuEe4f+T9l0WQmULv47LbqSkroaa0hNryMhy2hsbKqHZcdgcup6NVdVS3w0FgRCRDp85g4Jjxl20p+B8aVUUFFBw/RkBYBJbYAfiYvJvz3OWwU5h1nNyjaeQdTaf4VHarInXTfvEbKgvyAXhotBFGG5vXqTQaTAGB+Pj5t87X5GVaunv27NnDPffcQ0ZGBpIk8fTTT7Njxw5UKhUFBQWUlJS02X/z5i3ccfvdNFSJSKIdk9EsizBB4MYbb8Jo1nHFtEk8++KTBIQaW+UdMQXosdslAsPldA96owadjwYfv9a/j7feeovXX3+dW265hY8//pj777+/y8LhJz/5SfPfGRkZPPPMM1RXV1NXV9fsIjqfm2++GYDx48dz9uzZLvVzKaCIlHYor3MiSXg1JX5VVRXl5eWMGaPD6XL1SsVjsaGBomeeRTdgACHnK+nDH3Iw+zMeHxCH01HJiqtXMCN6htfHoHBp43LYKcjKxFpaQk1pMTWlJc3CxGatabuDIKDV6RuLj7WojGrQozcayc1II2vPTnQ+RuInTCZxygxiRyU1F2NzNLioKbPh46fDx6SV/dsKXsdlt3Ni77cc2bqZguNHW63zDQwiOGYAluhYuZJuzAAs0THofIwdtNYaj9tFUXYWuRlypd2i7ON43G5UajVhgxOYOP82wuOHNH/ndYKGwIj2XBcCWoMBVS+Kk/a44oorKC8vp6ysjA0bNlBWVsbBgwfRarXExcW1yozrtLux1bqw17lw2t3o9Gp8/HVo9epG64+KoFB/TAEG7G4fPB43Op/Wt9GoqKhWsSX5+fmkpKS0Gdfq1atZvnw5ALfddhsLFizo8jH5tsh3de+99/Lpp58yZswY3nvvvVZ9t0TfmKhOrVbjdru73Fd/o4iUdmjOkeLF6cdNU491usNotAPx8xvptbabKF22DFd+PgM+eB+VocXYCw/zn+3P8FJEGNGmMJZf9QaDzIM6bkjhB4e9ro7Dmz7n0Mb12GrlGAGVWo1/cCj+oWHET5iMOSQM/9Aw+TUkFL2vLxqt7oKWNtHjITcjjeO7d3By3x6O7diKwdeP0EFjkYR4ygvNSJ5z+2v1anz8tLJo8dNhbPzbN0BPQnIYBlP/JtW6nJAkiaLsLDK2byFr9w6cNhuBEVFMv/NeBo2bQG1FORV5OZTnyUXp0r/6ErfT0by/X3AIOkPnbt6a0hJ5P0EgbOBgxs6ZR+yI0UQlDm9X6GRmZqI3+rbTUv9w/PhxPB4PFouFmpoaQkND0Wq1bNu2jZycHCRRQqs2UFNtpbqkAUElcO21s/jza0t56OEF6AwaKisrCQrqWrHZ1NRUnn76aaqqqgDYvHkzf/rTn9psFxkZyTfffENKSgpbt24lISGhW8dXW1tLREQELpeLDz/8kKioqG61c6miiJR2OJfIzXsi5cSJE4SG6qirP8TAuF953cXScOgQVe9/QOCdd2JsGQFuLeKbNXfy+yA/poZN4M9XLcNf5+/VvhUuXeoqKzjwxaekf/UlLruNQeMmkJR6PZboGExBli6Vc78QKrWauDHjiB42hkHjbyNty7cUZu0n98geYAdavR/RIyYSOmgcBlMM9loPDbVO7HVOaivtlOZYsdW6kESJPZ+eYsxVMSRdE4PeqIiVjmioqebYjq1kbP+KivxcNHo9QydPZ+RVs4gaOrz52hIcM4CBSeOb95NEkZrSEsrzc6nIy6EiPxe3y9lRN83EjhpDzIjRxAwbhcHUe4knvUlTTArIYm716tWo1WruuusubrjhBkaNGsW4seMZkjCUyuJ6YqIsTJowmZlzrmDO3Dm8+uqrZJ06RnJyMjqdjrlz5/LHP/6xS30HBQXx7LPPMmGCHHP43HPPNQucBQsWsHDhQpKTk3nnnXd45JFHcLvdGAwGVq5c2a1jffHFF5k0aRIhISFMmjSJ2trabrVzqSJ0JXvfpURycrLUlXnqPeH9PWd5dt1R9j19tVemITudTv785z8zYWINGs1/mTxpC76+3rNkiA4HZ268CcnhYOD69ahNjU8xLht5/0jlJ9oKos2D+Oe8TzBovJ/mX+HSo6qogP3r/8OxHVsRRZHEKTOYMO8WQgZ0vWZGZ3jcInnHKsk+UMKZtHJcDg8+floGjwtl4OgAbNZssvbs4MyhA7hdTkyWYIZeMZ3EK6YTNjih+WYqiRKVRfXs/+IMpw6VoTdqSLomltFXRbfKvPljxFZXS0VuDuX5sjWkPPcsRdnHET0eIoYkMjJlFolTpnfZddMXZGZmMmzYsP4eRru4nR7sDW4c9S48bjmORu+jaeXSUehd2js/BEE4KElSu/kvftxXgA4osTpQqwQsJu8Ezp45cwa3243RmIFeP8KrAgWgfMXfcJ45Q8yqv58TKJKEbe2DPEYJgtbMa7PeUgTKj4CS0yfZt24NJ/Z+i1qjYeRVqSRffxMBYeFe7SfveCVb/5lJXaUDvVFDQnIo8clhRA0JQKVuijkIY+gV03DaGjh1YC/Hd+/g+42fcfDztZjDwkmcMoOhV0wnODYOS5SJ2Q+Moiyvln2fnWHv+tOkfZ3HuNQBjEyJQvsjiGUpyzlD8alsKvLPuWjqqyqb1+uNvlhiBjBu7nxGplyDJTq2H0d7+eB2eXA0uLHXu/C4ZGGiM2gw+uvQGzUtzleFSxFFpLRDidVOiEmPWuUdVZ2dnY3Jz4bTeYLY2Ce90mYT9uPHqXj3Xcy33Ixp6tTm96XtL/NS6U5O+Jn4W8pfiPaL9mq/CpcGbpeLouzj5Gakk3vkMIUnMtH5GJk4/1bGzZmHb0CgV/tzOT3s+e8pjmzPJyDMyNxFo4kdHoT6ApmZdT5Ghk2fybDpM7HX1ZG9fzdZu3eyb90a9q79mKCoGOInTEaj1eFy2DEY7EQOqqX0bCXbV9ez4wM3Rn8VWr1E+OAExs6+gbBB8V49rv7C43ZxYs8uvv/yc4pOZgGg0emxRMcQN3oslsZA1+CYAZiCLMqTfifIBQglPC4Rl1MWJ26nB2iMhwoyoDdqUCvC5LKhV0WKIAizgeWAGvi7JElLO9juFmANMEGSpN715XSBklrvTT+WJIns7GwSEuQgqrDQ67zSbhOlr7yK2mQibPHic28eXcsnh/7K+uAgFo15iOnRvZd6X6Fv8bjdlJzOJu/oEXIz0ijMysTtciIIKsIGDWb6nfcyZtacXglcLD5dw9erM6kuaWD0VdFMvnHwRVs4DCYTo2Zey6iZ19JgrSF777cc372DfevWgCSh1mjQ6g1oDPIsInOoBlutiroqAY1OS3XxLo5+8zWRQ4Yxdvb1JEya2jyr5HKivrqKtC0bSf9qI/XVVQRGRDHz3gcZNDYZ/9DQHscK/RgQPSJul7x4nCJulwe3S2xV6VejU2MK1KM3ai8opBUuXXrt1y0Ighr4GzALyAf2C4KwXpKkY+dt5wc8AuztrbFcLKVWOzFB3vHxlpaWUlNTjb//cfz9J2IweC+ra92ub6n/9ltCn/wtanNj4rmCQxz54lcsDQ1iWuQUHhzTs9TICv2Ly2Gn5Mwpik4cJ+/YEfIzj+JqTJQVEhvH6FlziB05mqjEERh8eyeo0eMW2f/5GQ5tysE3UM/8R5OITuzaTIcLYfQ3M2bWXMbMmovb6USlVjdmHm2NJEnkZ1Wxb/1pik6VYdBkUV2SxhdvvIIpcBWjZ81h9NWzvW416g2KsrP4/svPyNqzC9HjZuDYZMbOvoG40WPxeCQaapyU5dRjq3Niq3Viq3XRUHvub1utE6O/nlFXRhE70oLKS9beSxlRlPC4PLidjYLEJQsS0XNOjAgqeXqwwahBrVWj0anQaFWKK+cHQG8+gkwETkqSdBpAEIR/A/OBY+dt9yLwMvCbXhzLRVFstZMc550LXnZ2Nr6+VYhiAWFh3hMMkihS+pe/oI2KIvDOO+U3rYVU/t+dPB4cSKhvOEtn/BlVLySMU+gdJFGksjCfouwsik5mUXTyBOW5Z5sTZQVGRjN8+kxiR44mevgojP4XnxHZaXNTVdJAQJgRvU/nP//y/Dq+eu8YFfl1JE6JYNptCV3a72K5UCI4QRCISQwiemggORkV7PssmNKcUQREF6LVZLD74w/Z+9//Y+gV0xk7+wbC44d4fXw9wV5fx+lD+/n+y88oPnkCnY8PSdfOZeTVc3A2mMjPquL7Ld9TcsaKKLadyKDRqZqnbPsG6CnPq+OLN9PxDzYwKiWaYVMiLno2lMctUlFQh1+QoU2Ssf5CEqVGEeJptpC4nSKi51yiOEEQUGtV6AwaNDqVLEi0KjmZmuIK+0HSmyIlCshr8X8+MKnlBoIgjANiJEn6QhCEDkWKIAgPAA8AxMb2brCY3eWhusHltRwpJ06cIC6uFEHQEBoy2yttAlg//xxHZiaRr7yCSqcDZwOef/0Pv/WFSq2W969ajlnv3bT+Ct6nPPcsx3fvoCj7OMWnsnHaZCuJzsdIePwQJs6/jYiEIYQPHtIjS0FVcT1HtuVz/LtiXA7ZR28K1BMU6UtQpAlLpC9Bkb4ERvii1akRRYnDW3LZu/40eqOGuQ+NYuCYEK8cc3cRBIG4UcEMGGnhTFo5e9ebqCyMIjR+Br7+J8jev5tjO7fhZwnp1AUkqNX4BVkwh4ZhDg1vzg9jDg3DaA7o0Q3PZbdTcPwouUfTyc1Ip/TMKSRJJDAiiuR5P0VvGkXJGRv/+fMpPC4RQYCQAf4kzYrBHGrE2ChImvLJaPWtrUsej8iZw+Wkb8vj2zUn2bv+NEMnRzA6JZqgyPbdfKJHpCy3joITVeRnVVF0shq3U775W6J8iRoaSPTQQCITAnp9+rckSY3WkCZXjSxKmmbbNKHRqtDq1Wh0WjRaFWqtCrVGpYiRHxn95swV5JzwrwH3dratJEkrgZUgT0HuzXGV1crJjryRbdZms5GXl8u0aacICpqGTtdzEznIU45Lly3DMHw4/tfNBVGETx/ib/Ycvgvw54XJzzLcMtwrfSn0DgVZmexb9wmnD+5DpVYTHBvHsGkziUgYSvjgIQRFRvU4bbgkSuRkVJC+PZ+8Y5WoNAIJyWHEjQrGWm6jorCOysJ6CrLyz90gBPAP9kGtFqgqbmDw2BCuvHPoJfO0DbJYGZQUwsDRwZw8VMq+z85QdGYMIYOTCYnKx9FQ0GlBeY/bTW1FGacO7qOhprrVOo1Oj39IKObQMEyBQRjNARj9zfiYAzD6mTGazRjNAfj4+aNSq3E7nRSeOE7eMVmUFJ/MQvR4UKk1BMfGMyBpNh5PFFUlZjJ2ikAhlmgTI2dEET00kIiEgIuyTqnVKuLHhxI/PpSy3FrSt+dzfHcRR3cUEJ0YyKiUaOJGWagsqif/eBUFJ6opPFGF0y6L06BIX4ZNiSRisJmachsFWVUc3VlI+tZ8WTDF+hE1NJCooYFEDDajM2gQRQl7navR7eTE1vy37I5y1J/LYBqa5KGmzNZ24JKE2y02z7BpPh6NCo1Ohd5XFiNNgqQnYkStVjNq1CgkSUKtVrNixQqmTJnS4fbV1dV89NFHnVZB7qga8fmsXr2al156CZDr6fz0pz9ts83hw4dZuHAhdrsdjUbDm2++ycSJE5vXNzQ0EBMTw5kzZ/D3P5fb6sYbb+SOO+5olRq/JSaTibq6OgoLC3n44YdZs2ZNt45j2bJlPPDAAxiNcujD3Llz+eijjwgICLjgsXub3hQpBUBMi/+jG99rwg8YCWxvPBnDgfWCIMzrz+BZbyZyO3XqFH5+pSBUejUNftWHH+EuLCLyj3+Ub2Tb/sTWs5t4JyyEWxJu4aaEm7zWl4L3kCSJs4cPsm/dGvIzMzD4+TPltrtISr0OHz/vJdhzNLjI3F3EkW8KsJbZ8DXrmDRvEMOnRWL0bys2RI9ITZmNysJ6KgrrqSysp67KzjX3DWfIxLBL9slVUMmia/DYEE7sL2H/52fI2h9GQNhAQmL9CIr0bbQQmfC3GBA6iN9wOexYy0rlEgGlxdSUlTaWDSih9OxpGmqqW9WmaYnBzx+X3YbH5UIQVFhiBhI7+ho8nkhqysxYq1VYqyEw3EjiFY3WiiEB+Ji8I/pCYv24+p5hTLl5MMd2FZLxTQEb3z6CSiMguuXnOXOID/ETwogeGkjUkMA250DynDg8LpHiMzXkZ1VRkFVF2td5fL85F5VKQO+rwVbngnYeDwUBDH469D4amk6T4FEG3C5Pu+NVa1TofTRotGrUOhUajarD76UntKzds2nTJp566im++eabDrevrq7mzTff7FSkdIXKykqWLFnCgQMHEASB8ePHM2/evOZKzE0sXryY559/njlz5rBhwwYWL17cKqW90WgkNTWVtWvXNoucmpoadu3axUcffdTpOCIjI9sVKF1l2bJl3H333c0iZcOGDd1uqyf0pkjZDyQIgjAQWZz8D3Bn00pJkmqA4Kb/BUHYDjzR37N7SqyNlhQvzO45ceIEERF5qFR6QoKv6XF7AJ7qasrffhvf6dPxnTwZTn5Fzrev8ruYGEZYEnlq0lNe6UfBe4geDye+28W+dWsoyzmDyRLMzJ/+nFFXpaI1eMetKIkS5fl1HNtVyPG9xbgdHiIGm5k8fxCDxoZccMqlSq0iMNyXwHBfBo/zynD6FJVaReLkCBImhJG1p5jTh8soPlVD9v5zheM0OhVBEb7N7q3AMON5sz1MaA0mgmMHE3yeR1mSRJz2Bhz1Vhz1VuznvXrcKkSisJYHUleroq4W/IMNDB4vi5KooYH4mr1brPR8fEw6xs+OY+ysWE4fLqcwu5rQOD+ihgTiF9T5OabWqogaIosYbgCXw0PxKVm02BtcrVxQzX/7azEYtW1ERmZmJpbIxiDujU9C8RHvHmz4KJjT7kTRdrFarc0Coa6ujvnz51NVVYXL5eKll15i/vz5PPnkk5w6dYqkpCRmzZrFK6+8wssvv8wHH3yASqVizpw5LF0q9/nJJ5+waNEiqqurWbVqFdOnt549uWnTJmbNmtWcZXbWrFl8+eWX3HHHHa22EwQBq1UuUVFTU0NkZNtJFXfccQdvvvlms0hZu3YtqampiKLI1Vdf3eY4WnL27Fmuv/56MjIysNls3HfffaSlpZGYmIjNds7S9dBDD7F//35sNhu33norS5Ys4Y033qCwsJCZM2cSHBzMtm3biIuL48CBAwQHB/Paa6/x7rvvAnIW3UcffZSzZ88yZ84cpk2bxu7du4mKimLdunX4+HReeuFC9JpIkSTJLQjCL4FNyFOQ35Uk6aggCC8AByRJWn/hFvoHb9XtEUWRk6eyGDs2h+Dgq9FovDPzonzlO4i1tYQ+8Wtw2bB98TiPRUah1pl4LeU19OrevRgqdB2308nRb77mwGf/pbqkiKDIaFIfepRh065EremZ31+SJKqKGyjIkmMMCk5U4ah3o9aoSJgQyuiZMYTEerf67aWOWq1i+LRIhk+TL/ZOm5vKovpGC5Hs2so9WsnxPcU97EmH/HzV/IyFr1lH7MjAZmuFf3DPLszdRdXCFdQTtHo1McODiBnuHRd1X9OUFt9ut1NUVMTWrVsBMBgMrF27Fn9/f8rLy5k8eTLz5s1j6dKlZGRkNFtfNm7cyLp169i7dy9Go5HKynNJ9dxuN/v27WPDhg0sWbKkTeXigoICYmLOORGio6MpKCjgfJYtW0ZqaipPPPEEoiiye/fuNtukpqayYMECKioqsFgs/Pvf/+aXv/xlh8fRkdXzrbfewmg0kpmZSXp6OuPGnXsa+cMf/kBQUBAej4err76a9PR0Hn74YV577TW2bdtGcHBwq7YOHjzIP/7xD/bu3YskSUyaNIkrr7ySwMBAsrOz+de//sU777zD7bffzn/+8x/uvvvuzr6uC9KrMSmSJG0ANpz33nMdbJvSm2PpKiVWOzqNioAeBo8VFBSg151Gpaon3EuuHldBAVUffID5xhsxDB0KW1/iHarJVpt5e8bLRJq8N71ZoWeU5Z7ls9f+SFVRIeGDE5h399PEJ0/udpyJJElYy+3nRElWFQ1Wue6KKUjPwNHBRA8NJHaE5ZKKH+lPdD4awgeZCR/UOoDcVuekptTWKp9GT/Dx02EO9blk3WL9ykVYPLxJS3fPnj17uOeee8jIyECSJJ5++ml27NiBSqWioKCAkpKSNvt/9dVX3Hfffc2ujpbFBW+++WYAxo8fz9mzZ7s9xrfeeovXX3+dW265hY8//pj777+/jeDR6XTMmzePNWvWcMstt/D999+Tmpra4XGEh7efWXrHjh08/PDDAIwePZrRo0c3r/v4449ZuXIlbreboqIijh071mr9+ezatYubbrqpuRLzzTffzM6dO5k3bx4DBw5srpnU08+nicsvC1IvU2K1E+av7/EFJzs7m5DQs6jVflgsV3plbGVvvAGCQMjDv4KyE+R991dWR4Vx3aC5TI2a2nkDCn3C0W++5qu/v4ne15ebn1pC3Jhx3T6fPG6RzN1FfL8lF2tjMKLRXyfPxkhsemo3KDfIi8DHpPNaTIjCpc8VV1xBeXk5ZWVlbNiwgbKyMg4ePIhWqyUuLg673X5R7en1srVarVbjdrvbrI+KimoVW5Kfn09KSkqb7VavXs3y5csBuO2221iwYEG7/d1xxx28+OKLSJLE/Pnz0Wq1vPfeez0+DpBLtrz66qvs37+fwMBA7r333m6100TTZwPy59PSrdRdlCQa51FidXhl+vHJk8cICcknNHQ2KlXPXTD2zExq1n9G0D3/izY8HL54nFctgajVeh4b91iP21foOW6nky0rV/Dlm68TET+E/126nIFJ47slIESPSObuQj58/ju++SgLo5+OGf8zhDt/P4l7X57KtfePYPjUSMwhyhO8gsKFOH78OB6PB4vFQk1NDaGhoWi1WrZt20ZOTg4Afn5+raoHz5o1i3/84x80NDQAtHL3dEZqaiqbN2+mqqqKqqoqNm/eTGpqapvtIiMjm4N5t27dSkJCQrvtpaSkkJ2dzd/+9rfmuJaOjqMjZsyY0Rxsm5GRQXp6OiDH6/j6+mI2mykpKWHjxo3N+5z/mTQxffp0Pv30UxoaGqivr2ft2rVt4nK8iWJJOY+SWjvDwns206K2than8yAqldNrrp7SV/+C2t8fy89/Dukfs7t4P1sjQnlkzIOE+YZ5pQ+F7lNTWsz61/5E6ZlTTJx/K1N/8r/tZk/tDFGUyN5fwv4vzlBTaiN0gB9X3jmU2OFBihhRUOgiTTEpILtKV69ejVqt5q677uKGG25g1KhRJCcnk5iYCIDFYmHq1KmMHDmSOXPm8Morr3D48GGSk5PR6XTMnTuXP/7xj13qOygoiGeffZYJEyYA8NxzzzW7ixYsWMDChQtJTk7mnXfe4ZFHHsHtdmMwGFi5cmW77alUKm699VY+/vhjrrxStsp3dBwd8dBDD3HfffcxbNgwhg0bxvjx4wEYM2YMY8eOJTExkZiYGKa2qP/2wAMPMHv2bCIjI9m2bVvz++PGjePee+9tni69YMECxo4d6xXXTnsIktSraUe8TnJysnTgQO9NABr5/CZuS47m+RtGdLuNQ4cOkZ39a8LD65gx4zvkCgHdp27Xt+QtWEDok7/F8pP5uFZM4NYQEy7/SNbOX6sEy/Yzpw/tZ8OKV0GC2b94nPjkSZ3vdB6SKHH6cBl7PztDVVE9ligTE28YyMAxwYo4UbisyMzMZNiwYf09DIVLlPbOD0EQDkqS1G7SFsWS0oI6h5s6h7vHOVKys9MJshQQHnF3jwVKm/T3m3/LvzUOTgs+vJH8G0Wg9COi6JFTsq/9mNC4wdzw+FMEhLUfuNYRkiRx9kgF+z47TXleHYHhRq5dMIL4caG9kj9CQUFB4XJCESktOJfIrfs3frfbTY11O8EhHq+4eprT37/6KqrSdCoOrebNuAFMjZhESkxKj9tX6B711VVs+Osr5GakM+qqa5l534NodV0/byRJIi+zkr3rz1B61op/iA/X3DuMhInhP4qicQoKCgpdQREpLfBGttmcnByCAk+iVofh75/Uo/E0p78fMQL/1Fmw6ir+GhaBHYnFExcrboB+QBJFju3cxs6P3sNRX0/qQ48yMuXiEvUVnKhi7/rTFJ2swRSkZ+bdiQy9IvyCCdcUFBQUfowoIqUFpc3ZZrsvUk6cOEBAYDGREQ/0WERUvvvuufT3B1dxtDKL/0ZF8L/D7mKQeVCP2la4eHIz0tj+/irKzp4mbFACNz+1hNC4rn8Pxadr2Lv+NPnHqzCa5dk6w6dGotYq4kRBQUGhPRSR0oKeWlIkSaKq+lPCwiSiom7p0Vjqdu6k7K8r8J87B9/hsUgrbuFPUXEEGowsHLOwR20rXBwV+Xns+PBdTh/aj19wCHN/9QSJU2Z0OTFbWW4te9efJiejAh8/LVNvjWfkjCg0up7FKykoKCj80FFESgtKrA58dWpM+u59LCUleQQFpaHRjMfXd3C3x+E4fYaCx3+NfsgQIl56CT5fyOcGNWnYeWHc0/jpflzpzvuL+uoq9qz5iPSvN6HVG5h+572MmzMPja5ricCqiuv5bt1pTn9fht6oYfKNgxiVEo3OoPzsFBQUFLqCYmdugZxttvuunqysd9FqHQwe3P1Kmh6rlfxFixC0WmL+tgJVwbfUZ67n9dBwRlhGMD9+fueNKPQIl9PB3rUf8+6jD3Bk62bGzJrL/W+8w8T5t3ZZoJTmWFnz8kHyMyuZcLtZJ+8AACAASURBVP1A/vcPUxg/O04RKAoKfYBarSYpKYkxY8Ywbty4duvitKSpCnJnpKSk0JUUGKtXryYhIYGEhARWr17d7jaHDx9m8uTJJCUlkZyczL59+1qt37RpE0lJSSQlJWEymRg6dChJSUncc889nfbfxHvvvUdhYWGXt78UUa6YLeiJSBFFNzb7Z7jdEURFdi8NvuTxUPDrJ3Dm5zPgvX+gDQ2CN5/gnYg4yjw2Xp/0FCpB0ZW9RXVxEVl7dnJ4ywbqKsqJnzCZ6XfeS1Bk9EW1U55fy/rlh9EbNdz063FdqkKroKDgPVrW7tm0aRNPPfVUc3bX9mgSKYsWdf8Bs4nKykqWLFnCgQMHEASB8ePHM2/evOZKzE0sXryY559/njlz5rBhwwYWL17cKp1+ampqc6balJQUXn31VZKT200l0iHvvfceI0eObLfC8uWCIlJaUFJrZ3xsYOcbtkN+wXq02mq02ju6HTBb+pfXqN+5k/AlSzAmJ8PWl8ipzeefgbHMGzSPMSFjutWuQsdYy0vJ2rOLrN07KTmdDUD0sJHM/eWviRk+6qLbqyioY92yw2j1am58bKwiUBR+1Ly872WOVx73apuJQYn8duJvu7y91WptFgh1dXXMnz+fqqoqXC4XL730EvPnz+fJJ5/k1KlTJCUlMWvWLF555RVefvllPvjgA1QqFXPmzGHpUrlY4ieffMKiRYuorq5m1apVbVLCb9q0iVmzZjVnmZ01axZffvllc0r7JgRBwGq1AnKa+64KiQ8++IA33ngDp9PJpEmTmi1A999/f7Mw+tnPfkZMTAwHDhzgrrvuwsfHhz179uDj0z/VuXuCIlIakSRJrtvTDUuKJEmcOf02DQ1+jE26vVv916xbR+W77xJ45x0E/uR2KDsBu5bxyqARaAUHj457tFvtKrSlvrpKFiZ7dlKYdQyAsEEJXHn3zxhyxTT8g7tX5r6quJ51yw+jUgvMf2ws/sGX3wVBQeGHQFNafLvdTlFREVu3bgXAYDCwdu1a/P39KS8vZ/LkycybN4+lS5eSkZHRbH3ZuHEj69atY+/evRiNxla1e9xuN/v27WPDhg0sWbKkTeXigoICYmJimv+Pjo6moKCgzRiXLVtGamoqTzzxBKIoduqSAjlb6//93//x7bffotVqWbRoER9++CEjRoygoKCAjIwMQLYMBQQEsGLFim5ZYC4lFJHSSI3NhdMtEtoNkVJdvRe35xTl5TOIvEjXAIAtPZ2iZ5/DOHEiYU89BZIEXzzODj9/vvFU8dj4xwgxhlx0uwqtOXVwH4c2fEre0QwkSSQkNo5p/3MPQ6+YTkB4RI/ari5tYN3r34MkcePj4wgINXpp1AoKly8XY/HwJi3dPXv27OGee+4hIyMDSZJ4+v+3d+dxVVb5A8c/514usoOiuICK5pobCoprUoaoieYyqVkuk9Nom81van6OZb+xNG10mnKsHBtzmYwyS2kaS1Mx1xRQUBTNjUVABNl37r3P7w/wJoFySRCk7/v18sW9z/Oc83yfR7x+7znnOWfhQvbv349OpyMpKYnU1NRK5Xfv3s3s2bNxcCj7d3yjVQRg4sSJAPj6+t7RejUffPABf//735k0aRJbtmzhqaeeqpTw/NyePXuIjIy0rAtUWFiIh4cHwcHBXLp0ieeff55HHnmEkSNH/uK4GhpJUspdvYPZZuPi/klpqR3Nmo5BZ+VjqTeUpl7jyrPPYdOiBZ7vvoMyGCD6M64mHmZRh050cGrFE92fqHFM4iea2czhzzfzw5ef4dayNf4Tp9Bt8DDcvdrVSv056YWE/v0EJqPGo//Tl6atHGulXiHEnRs0aBDp6emkpaWxY8cO0tLSiIyMxGAw4O3tTVFRUY3qa9Kk7P8IvV6P0WistN/T07PC2JIrV64QEBBQ6biNGzfy7rvvAvCb3/yGOXPmVHtuTdOYOXMmy5Ytq7QvOjqanTt3smbNGrZs2cJHH31k5RU1bDIKs1xq+URurWrYkpKXd46MzP0kJXWla9eajWEwFxdz5fnnMeXn4/X++9g0bQqFmRTteoUXvNpTrNPxzoPvYKu37okSUVlxQQGhf1vKD19+Rs8HRzLzb+8z5LHptZag5GYUsf3vJygtNjHuRR/cPZ1qpV4hRO04e/YsJpMJd3d3srOz8fDwwGAwEBYWRnx8PADOzs7k5uZaygQGBrJ+/XoKCgoAKnT3VCcoKIhdu3aRmZlJZmYmu3btsgyAvVmbNm0sg3n37t1L586dq617xIgRbN26lWvXrlniio+PJz09HbPZzKRJk1iyZAnHjx+v8rruRdKSUu6XTuSWkPAvNM2Wa6nd6djR+tlHNU3j6muvUXTyJJ7/WIVd1y5l23cvZrGDmbM6E6uGLZeZZe9A5tVkQlcsISP5Cg/N/j0+QWNrdSmB/KxiQv9+guL8Usb/oS8t2sr8NUI0BDfGpEDZZ+3GjRvR6/VMnz6d4OBgevXqhZ+fH926dQPA3d2dIUOG0LNnT0aPHs2KFSuIiorCz88PW1tbxowZw5tvvmnVuZs1a8aiRYssXTKvvfaapbtozpw5zJ07Fz8/Pz788EPmz5+P0WjEzs6OtWvXVlv3/fffz5IlSxg5ciRmsxmDwcB7772Hvb09s2fPxmw2A1haWmbNmsXcuXPv6YGzStO0+o6hRvz8/DRrnlOvqdV7z7Ny14+cfWMUdgbrZgItKkrh8JEA0tJ6UFoykSeesL5b5vpH67n217/S/IXnaXHjsbcrEWza8igr3JvynM9z/L7P73/JpQggLvo4X7/7FkqnJ/jFBbTr2btW6y/IKWH728fJyyxm3HwfWnV0rdX6hbhXxcbG0r179/oOQzRQVf1+KKUiNU2rcnSvtKSUS80pxs3BYHWCApB4ZQOapnHpYkcefrir1eXMJSWkrV6NU0AAzefNK9toMnJkx3P8rZkbD3sF8Lvev6vpJQjKvjVFfr2N/Zs30LxtO8a//CquHq1q9RxJ5zL5PuQcuRlFBD8vCYoQQtQVSVLKXc0poqWz9V09RmMuSUmfotP1p7jYiS5dulhdtvBEFFpBAW6/mWzpfkg8tJKXbXLo6NCapQ+8JZO2/QKlJcV8t3Y1sQfC6Ow/mFHP/AFbu9pr3ky5WLZAYNK5TBxdbXnk2T606exWa/ULIYSoSJKUctdyimjpan2SkpT0CSZTHsnJPWjVqhmurtZ/m84/dAj0ehz8/QEoyLjE/B83YjbYsmrUehwM8vhqTeVeTyd05VJSL51nyGNP4D9xSq2NP7kWn8PRry6TcLpsgcChv+lMj2FtZIFAIYSoY5KklNPpFF5NrfvWbTaXkJi4EVcXfw4eKGXYMOtbUQDyDx/G3scHvZMTmqaxaMdMLtro+cD//2jrUrOnTjRNIy/jOg6uruhtDDUqWxdKi4tIPneWpHNnaNqqNff5+WNrXzdJV3FBPnHRx7kYeYxLkccwm82Mf+lVOvUfWCv1p1/J49h/LnE5Op0mjjYMmnAfvQK8MDSR5EQIIe4GSVLKbXtmiNXHXk39iuKSVJo0+T2adrlGXT3GzEyKTp+m+fPPAbBu35/ZZcrij+5+DO42sUYxX4u7xP7N64k/eQKdXk/T1p64t21P87btaO7VHve27XFr1Qqdru7+UzWWlnL1/DkSTp8k8fRJUs6fxXTT3AE2Bls69utP18HD6NCvPwbbms9Dc7OsqyllScnxo1yJPY3ZZMLO2YX7fAcw4NHf1MqjxRkp+YR/fZkLkdewtdMzILgDfR5qi629/HMRQoi7ST51a0jTzCQk/Asnp+7Exdnj5ORUo8WbCo4cAU3DacgQ9sftZlX8fxlt1DFz1Bqr68jLuM6hLR8Ts283dg6ODP7NdEzGUtIT40m9dJ4fjxywHGtjsKWZZ1vc27bD0a0phiZNMDSxw6b8p6GJHQa7n17rDQaq6yQpLijgSmwMiWdOkXT2DMaSYlCKlh3uo+/ocbTr2Yc2XbqTlnCZc4cP8OMPB/nx6CEMdvZ08vOn6+AH8O7Tt9qWH5PRSGFuDpkpSVw6Hs6lyGNkJF8BwN2rHb5jJ3BfvwG07tLVqkSsuNBI5tV8CnNLKcwtKfuTU0rBjdfl2wtyS7Cx1eM7uj0+D7fDzrH+W6iEEOLXSJKUGrp+/Xvy88/TrdsK9u45x/3331+jWWbzDh1C5+LC1bZOLPh6Nt1KSlj80HsoQ/XjYUqKCon4z5eE/+dLzEYTvmPGM3DiVOycKk4gVlpUxPWkRNIT40lPjOd6YjyJZ05RlJtbllDUkubtvOk1YiTtevTBq3vPSnF4deuBV7cePDjrd1w5E8PZw/s5f/QwsQf30cTRkc4DBuPh3ZHC3BwKsrMpyMmiIDubwpxsCnKyKcr7aRIind6Gtj160WfkGDr2G4BbS+ue2CkpMhJ3Mp3zEddIOHMds7HiI/eGJnrsnQ3YO9vi7G5HS29nnJvb02NoG+ydZRI9Ie5Fer2eXr16oWkaer2e1atXM3jw4Fsen5WVxSeffFLtKsjWrka8ceNGlixZAsCrr77KzJkzKx2zaNEiQkND0el0eHh4sGHDhgpfeHfu3Mn//m/ZsgIXLlzA09MTe3t7evfuzaZNm257foA1a9bg4ODAjBkzqj22IZN5Umoo8vjjFBYm0LrVR/z7358wZcoUq+cE0DSNCw8+RJPePZk//BLXsy/zqUt/2kzeeNtyZrOJ0/v2cGjLx+RnZtBl0DCGTZtp9X/UFWIwmyktKaa0qIjS4mJKi4swlv8sLS7CVFpabR16g4HWnbri4FrzJ1tMxlLiT0Vx7vABLoQfoaSwEAA7ZxccXFxxcHXFwcXN8tPexRVnd3e8uveiiYN1Y1uMJSbiY65zPiKV+FPXMZaacXRrQidfDzy7NsXBxdaSmBhk8KsQtaohzJPi5OREXl4eUPaf/ZtvvmmZ3bUqcXFxjB071rJA361Yk6RkZGTg5+dnWZHY19eXyMhIy0rMN+Tk5ODi4gLAqlWrOHPmDGvWVN2ifqvzmkwm9Pp76zNM5kmpQ9k50WRlHaVzp1c4c+YSer2e++67z+ryJZcuYbx6lbgJflzIS+Cd7CLaPLbitmXioo/z/ccfkZ4QR+su3Rj3P3+mTZdf/gGgdDps7exr9dHcmtDbGOjYtz8d+/bHWFpKcX4e9s4u6O7wH5qp1ExCbAYXIlK5HJ1OabEJe2cD3Qe3ppNfS1rf54rS1d5ss0KI6l19802KY8/Wap1Nunej1cKFVh+fk5NjSRDy8vIYP348mZmZlJaWsmTJEsaPH8+CBQu4ePEiPj4+BAYGsmLFCt566y0+/vhjdDodo0ePZvny5QB8/vnnPPPMM2RlZbFu3TqGDRtW4Xw7d+4kMDDQMstsYGAg3377LdOmTatw3I0EBSA/P9/qpxG9vb2ZMmUK3333HX/605/Izc1l7dq1lJSU0KlTJ/7973/j4ODAX/7yF5ycnHjppZcICAjA39+fsLCwW8bdUEmSUgMpKV+i1zvQuvVv2LbtIzp06ICtrfVdAvmHypbi/sDme/wKi3ho4Mvg5FHlsZqmseejNUTv+i+uLVsR/IcFdPYfUuUvcl5mEU0cDfdcq4CNwYCNW9PqD7yN0hITp/Zd4cSuBIrySmniYENnPw869W+JZ2c3dHqZb0aIX5sb0+IXFRWRkpLC3r17AbCzs2Pbtm24uLiQnp7OwIEDGTduHMuXLycmJsaycvI333xDaGgoR48excHBocLaPUajkWPHjrFjxw4WL15caeXipKQk2rZta3nv5eVFUlJSlXG+8sorbNq0CVdXV8LCwqy+Pnd3d8v6PNevX+d3vyub/PPVV19l3bp1PP/885XKVBd3QyVJSg1kZR3DzdWPrKwiMjMzb9vHWZX8Q4fIb+XKead8Ps0yoPxm3/LYI1s/IXrXf+k3ZjzDHp+FjaHy4E1N0zjy5UVOfJcAClyb29OsjSPunk40a+1IszaOuLV0QG/T+P6jNpaaOL0/mcid8RTmlNDu/mb0etCLtt2bNcrrFeJeVJMWj9pkb29vSTiOHDnCjBkziImJQdM0Fi5cyP79+9HpdCQlJZGamlqp/O7du5k9ezYO5V3MN1pFACZOLHsK09fXl7i4uDuKc+nSpSxdupRly5axevVqFi9ebFW5KVOmWF7HxMTw6quvkpWVRV5eXpWLGdZ23HeTJClWKi3NJD//R1q1DObHH38EsGrVyhvMJSXkHT3K4R6ljM0roMfgV+AWg2Wjv/uGI1tD6DH8YQJmzKmy9cRs1tgfco7TB5LpNrAVzs3tyUjOIyM5n7hT19HMZWONdDqFWysHS9Li3saJZm0ccWlhj+4e7P4wGc3EHk4hYkcc+VnFeHZxY8DTPWnTSWZ+FUJUNmjQINLT00lLS2PHjh2kpaURGRmJwWDA29uboqKiGtXXpEnZNAp6vR7jTdMt3ODp6cm+ffss769cuUJAQMBt65w+fTpjxoyxOklxdHS0vJ41axbbt2+nT58+bNiwocK5axJ3QyVJipWyssoG67q5DWDv3pO0bNkSNzfr/2MsPBEFRUXEtNex2OgAfZ+s8rjz4UfYs+4DOvT1I/Dp56pMUEwmM3s2xHI+PJV+o9ozcHzHCscZS01kpRaQkZzP9eR8MpLzSY3L4ULkNcsxeoOuLHEpT15utMA4NW1SqysF1xazycy5o1cJ/28cudeLaNXRlYdndcerW7PqCwshfrXOnj2LyWTC3d2d7OxsPDw8MBgMhIWFER8fD4CzszO5uT89TRgYGMjrr7/O9OnTLd09N7em3E5QUBALFy4kMzMTgF27dllWJb7Z+fPnLV90Q0NDLSsy11Rubi6tW7emtLSUzZs34+np+YvqaagkSbFSVlY4Op0tNjadSEz8b40HHcXv3o5ZQe+mubQe+gbYVB7LcuXsaXa8u4JW93Um+MUF6G0q//UYS0zs/DCGuFPXGTThPvoFta90jI1BT3MvZ5p7OVfYXlJkJPNqARnJeZbk5crZDM4dvWo5plkbR8bM64Vri/qfmr+02ERhbgkpF7MJ/+9lsq8V0qKdM8OndaVdj2YNMpkSQtS/G2NSoKxbfOPGjej1eqZPn05wcDC9evXCz8/Pkhi4u7szZMgQevbsyejRo1mxYgVRUVH4+flha2vLmDFjePPNN606d7NmzVi0aBH9+/cH4LXXXrMkOHPmzGHu3Ln4+fmxYMECzp07h06no3379rd8sqc6b7zxBv7+/rRo0QJ/f/8KyVZjII8gWyk8fAI6vT0GmwV8+eWXzJkzBy8vL6vKappG2Mj+5GkFPDRWj9Pzx0FfcYxJemI8n/7fn3BwcWPq63/FwaXyWkAlRUZ2fHCSpB+zGD61Cz2HW3f+6hTll5KRkk96Yh7hX18GYMy8XrSu5S4Uk8lMUW7Z5Gk3ft48iVrZRGo/vTaWmC1l3T0dGRDckQ59mktyIkQD1hAeQRYNlzyCXAeMxjxy807Tvv1cjkeew9HRsUazzH4f8zUtE/NRvkU4DV9SKUHJvZ7OF8v+DxuDLZMWvl5lglKUX8p//hFNWkIuD8+6n67+NZ8j5VbsHA206eRGm05utOvRjK9XR7P9nROMmNmdLv1rfp6i/FKi9ySSmZJfnoiUJR7FBVX3g+r0CnvnsrlLHJxtadrSwTKPib2zAedmdnh2aSqPEAshxK+MJClWyM4+gaaZcHH248KFQzWaZbbUXMruL//GdKC/tzP0nlJhf1FeHl+8+RolBQVM+ctyXD1aVqojP7uYr96NIvtaIaN/35MOfVrUxmVVyc3Dgcn/68c3a07x3bozZF8rxG+Mt1WtF5pZI/ZICke2XaQ4vxS3Vo44OBto7uVkSTjsnW1xuOm1nZOBJg420joihBCiEklSrJCVHY5SenJymlNcXFyjBQW3/riV1qevYbY14zj5z6D/6ZaXlhSzfcXrZF1NZuKfX8fDu2Ol8jnphYS+G0VBTgmPPNebtndhoKido4Fx830I+/gsx/5TNhbkwSe6oTfcOjFLS8jl+5BzpF7OofV9rgyb2oUWbZ1vebwQQghRHUlSrJCVFY6zUw8uXryCXq+nY8fKyURVckty+eDE+6y8rOHa1oDq85hln9lsYseqFSSdi2Xs/D/RrmfvSuWvXs5m59oYSotNjJ/vQ6uOlbuB6oreRseImd1x87Dn6FeXyc0oYvTve2HnVLGrqii/lKOhl4g5kIS9k4ERs7rT1b+VtIwIIYS4Y5KkVMNkKiYnJwovzyeJjk6gTZs2lufNq/OvU//CMTkT51xwfHws3LRS776N/+JC+A88OOtpug766Ukhk8nMpeNpnAxL5OqlHOxdbHn0f/rR3MupqlPUKaUUfmM64NLCnr0bz7L1rxGMfbYPbi0dKnXt9A7wYkBwB5o4yIrBQgghaockKdXIyT2J2VyCk1M/kpOPMWjQIKvKJeUl8fGZj5l/sey946SfVtfMSr3KiZ1f02fkI/QbPQ6AgpwSTh9IImZ/EgXZJbi0sGfobzrTbXBrmtjX719Tl/6tcG5qx441p9j61wgGT+zEmYPJZV07nVx5YGqXSo87CyGEEHdK5g+vRnZWOAAFBa0xm820a9fOqnKrjq9Cp5kZfLYA29bNsb2pXNSu/6KUwn/Cb0iNy2H3+jNsXHiIY/+5THNPJx55tjdPLB5InxFt6z1BuaF1Jzcm/68v9k62hP37LDnXi3h4Vncm/LGfJChCiAZFr9fj4+NDnz596NevH4cPH77t8VlZWbz//vvV1hsQEIA1U2Bs3LiRzp0707lzZzZurHqV+0WLFtG7d298fHwYOXIkycnJFfYXFBTg7u5OTk5Ohe2PPvoon3322S3P7eRU1uqenJzM5MmTf/F1vPPOOxQUFFjejxkzhqysrNuWqQuSpFQjM+sYjo5dSErKBqiwcNStnEo7xY7LO5iZa8R4rQmOAQ9b9pUWFRETtgvPrr7s/DCOrcsjuBSVRo9hnjz+F3+CX/DBu1fzBvm4rWsLByb9yZeA6V2ZvnggXQe2lrEnQogG58baPdHR0Sxbtow///nPtz3e2iTFGhkZGSxevJijR49y7NgxFi9ebJl99mYvv/wyJ0+eJCoqirFjx/L6669X2O/g4EBQUBDbtm2zbMvOzubgwYMEBwdXG0ebNm3YunXrL76OnycpO3bsqNEs67WlYXxNb6DMZiPZ2cdp1WoCEeEJtGjRwrLg1K1omsbKiJW42zgy7fQlrhndcRw61LI/9uA+ivPzSUvuiGvLUoZN6Uy3ga2xbSAtJtWxczTQY1jjmnZZCFE3Dmz5kfTEvFqts3lbJ4Y9Zv0Tljk5OTRtWrbael5eHuPHjyczM5PS0lKWLFnC+PHjWbBgARcvXsTHx4fAwEBWrFjBW2+9xccff4xOp2P06NEsX74cgM8//5xnnnmGrKws1q1bV2n28Z07dxIYGGiZZTYwMJBvv/2WadOmVTjOxcXF8jo/P7/KL3zTpk3j/fffZ+bMmQBs27aNoKAgzGYzI0aMqHQdN4uLi2Ps2LHExMRQWFjI7NmziY6Oplu3bhQWFlqOmzdvHuHh4RQWFjJ58mQWL17MqlWrSE5O5sEHH6R58+aEhYXh7e1NREQEzZs35+233+ajjz4CymbRffHFF4mLi2P06NEMHTqUw4cP4+npSWhoKPb29lb/XVXl3vifsZ7k5cViMuXj6uJLYuJpevbsWW2Z6LRojl87zqJ8MOV6gb4EB39/oCyBObHza+ycWqNv4sXEl/rh6GrdIFwhhBDWuTEtflFRESkpKezduxcAOzs7tm3bhouLC+np6QwcOJBx48axfPlyYmJiLCsnf/PNN4SGhnL06FHL2j03GI1Gjh07xo4dO1i8eDG7d++ucO6kpKQKLe5eXl4kJSVVGecrr7zCpk2bcHV1JSwsrNL+oKAg5syZw/Xr13F3d+fTTz/lueeeu+V13Kpl+4MPPsDBwYHY2FhOnjxJv379LPuWLl1Ks2bNMJlMjBgxgpMnT/LCCy/w9ttvExYWRvPmzSvUFRkZyfr16zl69CiapuHv78/w4cNp2rQp58+fJyQkhA8//JDHHnuML774gieeeOJ2f1XVkiTlNrLKx6MYjR0oLj5u1XiUIylHUEBQeiIZmQOw9/FAX95HeCU2hvSEOGwcAukf5C0JihCiUatJi0dtutHdA3DkyBFmzJhBTEwMmqaxcOFC9u/fj06nIykpidTU1Erld+/ezezZsy0t5zcvLjhx4kQAfH19iYuLu6M4ly5dytKlS1m2bBmrV6+utAqyra0t48aNY+vWrUyaNIkTJ04QFBR0y+to1arqGcL379/PCy+8AEDv3r3p3funKS+2bNnC2rVrMRqNpKSkcObMmQr7f+7gwYNMmDDBshLzxIkTOXDgAOPGjaNDhw6WNZNq4/6AjEm5raysY9jbtyMlpaxpzJokJTzlKN2MGo6uPhRdvILjkMGWfSe++Q86vT3OzXvTN9C6AbhCCCF+uUGDBpGenk5aWhqbN28mLS2NyMhIoqKiaNmyJUVFRTWq78YUFHq9HqOx8lIfnp6eJCYmWt5fuXKl2pWJp0+fzhdffFHlvmnTpvHpp5+ydetWxo8fj8FgqJXrALh8+TIrV65kz549nDx5kkceeeQX1XPDzdNz3Or+1JQkKbegaWaysiNwc+1PQkICzs7O1Q4aKjYVE30tmv75uRQ4BoKm4TS4LEnJSU/jQvgPKJseDHy0K4Ym+tvWJYQQ4s6dPXsWk8mEu7s72dnZeHh4YDAYCAsLIz4+HgBnZ+cKqwcHBgayfv16y8DRm7t7qhMUFMSuXbvIzMwkMzOTXbt2ERQUVOm48+fPW16HhoZaVmT+uYCAAM6fP897771nGddyq+u4lQceeIBPPvkEgJiYGE6ePAmUjddxdHTE1dWV1NRUvvnmG0uZn9+TG4YNG8b27dspKCggPz+fbdu2VRqXU5uku+cW8gsuJu3H9AAAH7BJREFUUlqaiZvbABISEmjXrl21T7JEX4umRDMyoKiUvJQcdC4u2JWPY4n69r9omkbz9oPpNqj13bgEIYT4VboxJgXKxgJu3LgRvV7P9OnTCQ4OplevXvj5+VkSA3d3d4YMGULPnj0ZPXo0K1asICoqCj8/P2xtbRkzZgxvvvmmVedu1qwZixYton///gC89tprlu6iOXPmMHfuXPz8/FiwYAHnzp1Dp9PRvn171qxZU2V9Op2OyZMns2XLFoYPHw5wy+u4lXnz5jF79my6d+9O9+7d8fX1BaBPnz707duXbt260bZtW4YMGWIp8/TTTzNq1CjatGlTYbxMv379mDVrFgMGDLBcU9++fWula6cqStO0Oqm4rvj5+WnWPKd+p64kfcK5c4vo2WM7H3zwBaNHj8a/fADsraw+sZoPo//JgWI3UkNssO/dG69V72IsKeH9383AZGrJ5IX/R9v76379HSGEqA+xsbF07969vsMQDVRVvx9KqUhN0/yqOl66e24hK+sYtrYepKaWJXHWjke5v6SUJk36YLx6FcfyrDQmLIzSojzadB0uCYoQQghhJUlSqqBpGllZ4bi59ScxMRFbW1s8PDxuW6agtICT6afoX1hIflrZiHDHIYPRNI0jX25D6d0Z8dvAuxG+EEII0ShIklKFoqIrFBdfpWn5eJS2bdui199+oGvUtSiMmokBRUXkn0vDtn17bL28+PFoNAVZV/C6fzjNPWX6eCGEEMJakqRUISvrGAB2dr1ITU21qqvn2NVj2AB9bVuRHxltefR4/yefg7Jl5O8m1mXIQgghRKMjSUoVMrPCsbFxJSPDDrBuvZ7wq8foUWLClNMJrbAQx6HDuHj8Mjmpp/DsNhi3lnd/zQMhhBDiXiZJShV+Go9yBaUUXl5etz0+vzSf09dP45+bx7W9yTTp3BnHYcMI2/gFYGbEb6fcncCFEEKIRkSSlJ8pLr5GYWEcbm5+JCQk0Lp1a2xtbW9bJjI1EpNmZkg0lKZm4fHyS5yPTCU7NZwW3j1p0a76lhghhBC1Q6/X4+PjQ58+fejXrx+HDx++7fHWroIcEBCANVNgbNy4kc6dO9O5c2c2btxY5TGLFi2id+/e+Pj4MHLkSJKTkyvs37lzJz4+Pvj4+ODk5ETXrl3x8fFhxowZ1Z7/hg0bNlSq914jScrP3Fivx9nJl6SkJOsePb4ajksxOEY54ODvT5OBg/l+8w7Q8hkyZVJdhyyEEOImN9buiY6OZtmyZfz5z3++7fHWJinWyMjIYPHixRw9epRjx46xePFiMjMzKx338ssvc/LkSaKiohg7diyvv/56hf1BQUFERUVZJpXbvHkzUVFRbNq0yepYGkOSIjPO/kxWdjh6vQN5eU0xGo1WD5r97WET5mKFx0svcSosibzrx3Byb0lHH9+7ELUQQjQ8YRvWci3+Uq3W6dG+Iw/Oetrq43NycmjatCkAeXl5jB8/nszMTEpLS1myZAnjx49nwYIFXLx4ER8fHwIDA1mxYgVvvfUWH3/8MTqdjtGjR7N8+XIAPv/8c5555hmysrJYt25dpSnhd+7cSWBgoGWW2cDAQL799lvLlPY3uLi4WF7n5+dXO6P5DR9//DGrVq2ipKQEf39/S3L11FNPERERgVKK3/72t7Rt25aIiAimT5+Ovb09R44cwd7e3ur71lBIkvIzWVnhuLr0JTGxLPusLknJKckhNS4W/wgzLv6dMXl15tia7WimFPqP/R1KJ41VQghxN92YFr+oqIiUlBT27t0LgJ2dHdu2bcPFxYX09HQGDhzIuHHjWL58OTExMZaVk7/55htCQ0M5evQoDg4OFdbuMRqNHDt2jB07drB48WJ2795d4dxJSUkVHrbw8vIiKSmpyjhfeeUVNm3ahKura4Wp528lNjaWzz77jEOHDmEwGHjmmWfYvHkzPXr0ICkpiZiYGKCsZcjNzY3Vq1ezcuVK/PyqnMz1niBJyk1KS7PIyztHxw7zOXs2gWbNmuHk5HTbMpFXI5l8wIjODC5z5vH16iiK845jY9uEHgEP36XIhRCi4alJi0dtutHdA3DkyBFmzJhBTEwMmqaxcOFC9u/fj06nIykpidTU1Erld+/ezezZs3FwKJuY80arCMDEiWXTSfj6+t7xejVLly5l6dKlLFu2jNWrV7N48eLbHr9nzx4iIyMt6wIVFhbi4eFBcHAwly5d4vnnn+eRRx5h5MiRdxRXQyJf82+SlR0JaLiWr3xsTVfPmchdPHhSw7mLid1HPchISsdc+iM9ho+giYNj3QcthBDilgYNGkR6ejppaWls3ryZtLQ0IiMjiYqKomXLlhQVFdWoviZNmgBlg3ONRmOl/Z6eniQmJlreX7lyBU9Pz9vWOX36dL744otqz61pGjNnzrSMVTl37hx/+ctfaNq0KdHR0QQEBLBmzRrmzJlTo2tqyCRJuUlWVjhK2VJa6klhYaFVSUqbTXsotlXEdP0DyRdyaO55CjDTb8z4ug9YCCHEbZ09exaTyYS7uzvZ2dl4eHhgMBgICwsjPj4eAGdnZ3Jzcy1lAgMDWb9+PQUFBQAVunuqExQUxK5du8jMzCQzM5Ndu3YRFBRU6bjz589bXoeGhla7kjHAiBEj2Lp1K9euXbPEFR8fT3p6OmazmUmTJrFkyRKOHz9e5XXdi6S75yZZWeG4uPTiypWyX4DqkpTUg3vpGptL5NBJ5GR3o8cwPZFfHcQveCLN2tw+cxZCCFE3boxJgbLWh40bN6LX65k+fTrBwcH06tULPz8/S2Lg7u7OkCFD6NmzJ6NHj2bFihWWp2psbW0ZM2YMb775plXnbtasGYsWLbJ0ybz22muW7qI5c+Ywd+5c/Pz8WLBgAefOnUOn09G+fXvWrFlTbd33338/S5YsYeTIkZjNZgwGA++99x729vbMnj0bs9kMwLJlywCYNWsWc+fOvacHzipN0+o7hhrx8/PTrHlOvaZMpgK+39+Xdu1+x6mT93H+/HlefvnlW4641jSNk4+O5oKpC1e9HqVPPyOXL/2XgqxMZv99Dbb2DrUeoxBCNHSxsbF07969vsMQDVRVvx9KqUhN06oc3SvdPeWys0+gaUaalq983K5du9s+Epa7cyfXs1tz1etROtntx6lNNtcuX2T4jDmSoAghhBC1QJKUcmWTuOnQ67uQkZFx264eraSE0x+EEtv1cXJdzjO4fRiHvvyCdj1703XQsFuWE0IIIYT1JEkpl5l1DGfn7iQnl80MeLsk5fy67UR5jCPPkIR763c5nO5FSVEhD82ea/WEPEIIIYS4PUlSKBtfYmvrjrv7gyQkJGBjY0OrVq2qPPb65TT2RdpjoJAvev+TrtcLibmQTb8x43H3qv5pICGEEEJYR57uAZRS9Or5DwD27F6Lp6cnNjaVb01+djFfvR0JZjOFQyPR2RQTn9gOJzdXBk2aerfDFkIIIRo1aUm5SXFxMSkpKbfs6tm34RRFRRqDXU6yq+lJAhJcuVbkzPAZT8tgWSGEEKKW1WmSopQapZQ6p5S6oJRaUMX+/1FKnVFKnVRK7VFKta/LeKqTlJSEpmlVJinJMcnExebQPmkv7s+OIyU9AY9YO9q2MNB18AP1EK0QQohb2b59O0opzp49a9kWFxdHz549Adi3bx9jx46tUGbnzp34+Pjg4+ODk5MTXbt2xcfHhxkzZlh1zjVr1tRolWJRvTrr7lFK6YH3gEDgChCulPpK07QzNx12AvDTNK1AKTUP+Cswpa5iqk5CQgJAhcWhAEqupvL9377DoNzwn/cwR/SJ+J5zA6NixCNDZbCsEEI0MCEhIQwdOpSQkJBq18S5ISgoyDI7bEBAQJWL85lMJvR6fZXl586de2dBi0rqckzKAOCCpmmXAJRSnwLjAUuSomnazcs+/gA8UYfxVCshIYGWLVtiZ2dn2VYSF8fx55eQ4fk4AwYYcA8exvEvX6JzohP9miXh3rfydMdCCCEg6z8XKUnOr9U6bds44hZ8322PycvL4+DBg4SFhREcHGx1knIr3t7eTJkyhe+++44//elP5ObmsnbtWkpKSujUqRP//ve/cXBw4C9/+QtOTk689NJLBAQE4O/vT1hYGFlZWaxbt45hw2SKipqqy+4eTyDxpvdXyrfdylPAN1XtUEo9rZSKUEpFpKWl1WKIPzGZTFy5cqVCV0/h6dNcnv4kPzYdjpOzjn5PDsFsNmHaFYvJTmOwZxZ4yMyKQgjRkISGhjJq1Ci6dOmCu7s7kZGRd1ynu7s7x48fZ+rUqUycOJHw8HCio6Pp3r0769atq7KM0Wjk2LFjvPPOO3ecKP1aNYine5RSTwB+wPCq9muathZYC2XT4tdFDKmpqZSUlFiSlPwfjnLl2We55ulProMnIyZ1RW/Qsf/rT3HJVLj2yMLW2w90VTf7CSHEr111LR51JSQkhPnz5wMwdepUQkJC8PX1vaM6p0z5aSRCTEwMr776KllZWeTl5VW5gCDAxIkTAfD19SUuLu6Ozv9rVZdJShJw8+AOr/JtFSilHgZeAYZrmlZch/Hc1o3xKO3atSPnu+9I/p8/YuPdgfhe02jWxECXAa0oyMkmcuvnpLgX8YjuHLR7tL7CFUIIUYWMjAz27t3LqVOnUEphMplQSrFixYo7qtfR0dHyetasWWzfvp0+ffqwYcMG9u3bV2WZJk2aAKDX6zEajXd0/l+ruuzuCQc6K6U6KKVsganAVzcfoJTqC/wTGKdp2rU6jKVaCQkJuLq6Yt61i6T5L2LXoweFz/+N7OslDBzfEZ1Osf/jjzAVF3OmVwFdjKXQblB9hiyEEOJntm7dypNPPkl8fDxxcXEkJibSoUMHDhw4UGvnyM3NpXXr1pSWlrJ58+Zaq1dUVmdJiqZpRuA5YCcQC2zRNO20Uup1pdS48sNWAE7A50qpKKXUV7eors4lJibSsriEq4tew3HoENqs+ZDIPVdp1dEF797NuXQ8nNPf7+FSVxPdmrmg0xmgTb/6ClcIIUQVQkJCmDBhQoVtkyZNIiQkpNbO8cYbb+Dv78+QIUPo1q1brdUrKlOaVidDPOqMn5+fFhERUat1amYzCcvfIm3rVlqPGEGbN5dyIiyZI19eZMIf+9KstYENLz2DjYM9/+h1mAW4Ms1kB3N212ocQghxr4uNjaV7d3mgQFStqt8PpVSkpml+VR0vM84CGI2oixfxmjyJNm8tp6QUjn8bT7se7rTp3JSwjWspyM7CcbwvZh0MuHoB2g2s76iFEEKIRq1BPN1T35StLV4fvI8yGFBKcWLXZYoLjAx8tCMXI49yZv9e/Cc+xt+zQ/Gya0HH4gQZjyKEEELUMWlJKaeztUUpRX52MdF7E+ncvyXOTRXfffgeLdp5c623Az9m/sh81x4ogLb+9R2yEEII0ahJkvIzETviMBs1BgR3IGzDPynIzmL40/N479QH9G7em6DrqeDeGRyb13eoQgghRKMmScpNstMKOHMgmfuHtuF64inOHAjDf8Jj7CjYz7XCa7zk+z+oK8dkPIoQQghxF0iScpOjX11GZ6PoGeDOd2tX06KdN/eNGsH6mPUEtg+kr84RCjNlPIoQQghxF0iSUi4tMZfz4an0eagtR7/cSGFuDkHP/IE1Mf+k1FzKi/1ehITDZQdLS4oQQjRo27dvRynF2bNnLdvi4uLo2bMnAPv27WPs2LEVyhQUFODu7k5OTk6F7Y8++iifffbZLc/l5OQEQHJyMpMnT67ymICAAKqbPuOdd96hoKDA8n7MmDFkZWXdtkxjJ0lKuaOhl2jiYINbi6vEHghjwKOPke1mYtuFbUztOpV2ds3h0Ltl41GadazvcIUQQtxGSEgIQ4cOrdEkbg4ODgQFBbFt2zbLtuzsbA4ePEhwcHC15du0acPWrVt/UbxQOUnZsWMHbm5uv7i+xkAeQQbMJjNOzezo423Lvk1v0qJ9BwZOfIxnw57H0eDI3D5zIWwpZMbBrP+CUvUdshBCNHjffPMNV69erdU6W7VqxejRo297TF5eHgcPHiQsLIzg4OAarUA8bdo03n//fWbOnAnAtm3bCAoKwmw2M2LECDIzMyktLWXJkiWMHz++Qtm4uDjGjh1LTEwMhYWFzJ49m+joaLp160ZhYaHluHnz5hEeHk5hYSGTJ09m8eLFrFq1iuTkZB588EGaN29OWFgY3t7eRERE0Lx5c95++20++ugjAObMmcOLL75IXFwco0ePZujQoRw+fBhPT09CQ0Oxt7e3+nobOmlJAXR6HQGPdyXt8n8pzM1h1DN/4IfUYxxKPsTve/8e17Tz8MP74Pdb8B5a3+EKIYS4jdDQUEaNGkWXLl1wd3cnMjLS6rJBQUEcP36c69evA/Dpp58ybdo07Ozs2LZtG8ePHycsLIw//vGP3G7G9g8++AAHBwdiY2NZvHhxhRiWLl1KREQEJ0+e5Pvvv+fkyZO88MILtGnThrCwMMLCwirUFRkZyfr16zl69Cg//PADH374ISdOnADg/PnzPPvss5w+fRo3Nze++OKLmtyqBk9aUspdCP+B2IP7GDR5Gu7t2jP365fwcvJiWqdJ8K+Hwbk1PGx9Ni6EEL921bV41JWQkBDmz58PwNSpUwkJCcHX19eqsra2towbN46tW7cyadIkTpw4QVBQEJqmsXDhQvbv349OpyMpKYnU1FRatWpVZT379+/nhRdeAKB379707t3bsm/Lli2sXbsWo9FISkoKZ86cqbD/5w4ePMiECRMsKzFPnDiRAwcOMG7cODp06ICPjw8Avr6+xMXFWXWd9wpJUgCTsZQ969fQon0H/Cc8RujFUM5nnmfl8JXYHlkNabHw+Bawc6nvUIUQQtxGRkYGe/fu5dSpUyilMJlMKKVYsWKF1XVMmzaNN954A03TGD9+PAaDgQ0bNpCWlkZkZCQGgwFvb2+KiopqHN/ly5dZuXIl4eHhNG3alFmzZv2iem5o0qSJ5bVer6/QrdQYSHcPoLcx8OhLrzL6uT9SrJXyjxP/oE+LPoy084L9K6HXY9AlqL7DFEIIUY2tW7fy5JNPEh8fT1xcHImJiXTo0IEDBw5YXUdAQADnz5/nvffeY9q0aUDZAFoPDw8MBgNhYWHEx8ffto4HHniATz75BICYmBhOnjwJQE5ODo6Ojri6upKamso333xjKePs7Exubm6luoYNG8b27dspKCggPz+fbdu2MWzYMKuv514mSUq5lh070aKdNxtObyC9ML1s4ravnitrPRm1vL7DE0IIYYWQkBAmTJhQYdukSZNq9JSPTqdj8uTJXL9+neHDhwMwffp0IiIi6NWrF5s2baJbt263rWPevHnk5eXRvXt3XnvtNUt3U58+fejbty/dunXj8ccfZ8iQIZYyTz/9NKNGjeLBBx+sUFe/fv2YNWsWAwYMwN/fnzlz5tC3b1+rr+depm438Kch8vPz06p71vyXulZwjbHbxvKA1wOsNHjDrldg0jroVfVz70IIISqKjY2le/fu9R2GaKCq+v1QSkVqmuZX1fEyJuUmq0+sxmg2Mr/DBNg0CbqMhp6T6jssIYQQ4ldJunvKncs4x/YL23m82zTa7l0GegOMfVvmRBFCCCHqibSkAJqmsTJiJS5NXPid5gxxByD4XXBpU9+hCSGEEL9akqQARs1Il6ZdGNHCF9dvl4L3MOg3s77DEkIIIX7VJEkBDDoDL/u9BCFTwVQK41ZJN48QQghRz2RMyg0xX8CP38JDr8oCgkIIIUQDIEkKgLEEdi0CT18YOK++oxFCCHGHtm/fjlKKs2fPWrbFxcXRs2dPAPbt28fYsWMrlNm5cyc+Pj74+Pjg5ORE165d8fHxYcaMGVafd8OGDSQnJ9fORQhJUgCwsYUnv4RH14BOX9/RCCGEuEMhISEMHTq0RpO4BQUFERUVRVRUFH5+fmzevJmoqCg2bdpkdR2SpNQuGZNyg4dMPiSEELXpxx/fIDcvtlbrdHbqTpcui257TF5eHgcPHiQsLIzg4GAWL76zxWE//vhjVq1aRUlJCf7+/rz//vsAPPXUU0RERKCU4re//S1t27YlIiKC6dOnY29vz5EjR7C3t7+jc//aSZIihBCiUQkNDWXUqFF06dIFd3d3IiMjrV4F+ediY2P57LPPOHToEAaDgWeeeYbNmzfTo0cPkpKSiImJASArKws3NzdWr17NypUr8fOrcgJVUUOSpAghhKgT1bV41JWQkBDmz58PwNSpUwkJCfnFScqePXuIjIykf//+ABQWFuLh4UFwcDCXLl3i+eef55FHHmHkyJG1Fr/4iSQpQgghGo2MjAz27t3LqVOnUEphMplQSrFixYpfVJ+macycOZNly5ZV2hcdHc3OnTtZs2YNW7Zs4aOPPrrT8MXPyMBZIYQQjcbWrVt58skniY+PJy4ujsTERDp06MCBAwd+UX0jRoxg69atXLt2DShLguLj40lPT8dsNjNp0iSWLFnC8ePHAXB2diY3N7fWrufXTpIUIYQQjUZISAgTJkyosG3SpEk1esrnZvfffz9Llixh5MiR9O7dm8DAQFJSUkhKSiIgIAAfHx+eeOIJS0vLrFmzmDt3Lj4+PhQWFt7x9fzaKU3T6juGGvHz89MiIiLqOwwhhBBViI2NpXt3eVpSVK2q3w+lVKSmaVWONJaWFCGEEEI0SJKkCCGEEKJBkiRFCCFErbrXhhGIu+OX/F5IkiKEEKLW2NnZcf36dUlURAWapnH9+nXs7OxqVE7mSRFCCFFrvLy8uHLlCmlpafUdimhg7Ozs8PLyqlEZSVKEEELUGoPBQIcOHeo7DNFISHePEEIIIRokSVKEEEII0SBJkiKEEEKIBumem3FWKZUGxP+Cos2B9FoOR9ye3PO7T+753Sf3/O6Te3731eU9b69pWouqdtxzScovpZSKuNW0u6JuyD2/++Se331yz+8+ued3X33dc+nuEUIIIUSDJEmKEEIIIRqkX1OSsra+A/gVknt+98k9v/vknt99cs/vvnq557+aMSlCCCGEuLf8mlpShBBCCHEPkSRFCCGEEA1So09SlFKjlFLnlFIXlFIL6juexkop9ZFS6ppSKuambc2UUt8ppc6X/2xanzE2JkqptkqpMKXUGaXUaaXU/PLtcs/rkFLKTil1TCkVXX7fF5dv76CUOlr+OfOZUsq2vmNtTJRSeqXUCaXU1+Xv5X7XMaVUnFLqlFIqSikVUb7trn++NOokRSmlB94DRgP3A9OUUvfXb1SN1gZg1M+2LQD2aJrWGdhT/l7UDiPwR03T7gcGAs+W/27LPa9bxcBDmqb1AXyAUUqpgcBbwN81TesEZAJP1WOMjdF8IPam93K/744HNU3zuWl+lLv++dKokxRgAHBB07RLmqaVAJ8C4+s5pkZJ07T9QMbPNo8HNpa/3gg8eleDasQ0TUvRNO14+etcyj7APZF7Xqe0Mnnlbw3lfzTgIWBr+Xa577VIKeUFPAL8q/y9Qu53fbnrny+NPUnxBBJven+lfJu4O1pqmpZS/voq0LI+g2mslFLeQF/gKHLP61x510MUcA34DrgIZGmaZiw/RD5natc7wJ8Ac/l7d+R+3w0asEspFamUerp8213/fLGp6xMIAWXfQJVS8rx7LVNKOQFfAC9qmpZT9iWzjNzzuqFpmgnwUUq5AduAbvUcUqOllBoLXNM0LVIpFVDf8fzKDNU0LUkp5QF8p5Q6e/POu/X50thbUpKAtje99yrfJu6OVKVUa4Dyn9fqOZ5GRSlloCxB2axp2pflm+We3yWapmUBYcAgwE0pdeNLn3zO1J4hwDilVBxl3fUPAe8i97vOaZqWVP7zGmXJ+ADq4fOlsScp4UDn8pHgtsBU4Kt6junX5CtgZvnrmUBoPcbSqJT3y68DYjVNe/umXXLP65BSqkV5CwpKKXsgkLLxQGHA5PLD5L7XEk3T/qxpmpemad6UfX7v1TRtOnK/65RSylEp5XzjNTASiKEePl8a/YyzSqkxlPVp6oGPNE1bWs8hNUpKqRAggLLlvFOB/wO2A1uAdkA88JimaT8fXCt+AaXUUOAAcIqf+uoXUjYuRe55HVFK9aZswKCesi95WzRNe10p1ZGyb/rNgBPAE5qmFddfpI1PeXfPS5qmjZX7XbfK7++28rc2wCeapi1VSrlzlz9fGn2SIoQQQoh7U2Pv7hFCCCHEPUqSFCGEEEI0SJKkCCGEEKJBkiRFCCGEEA2SJClCCCGEaJAkSRFC1CmllKl8JdUbf2ptUTKllPfNK28LIRoXmRZfCFHXCjVN86nvIIQQ9x5pSRFC1AulVJxS6q9KqVNKqWNKqU7l272VUnuVUieVUnuUUu3Kt7dUSm1TSkWX/xlcXpVeKfWhUuq0UmpX+UywQohGQJIUIURds/9Zd8+Um/Zla5rWC1hN2czQAP8ANmqa1hvYDKwq374K+F7TtD5AP+B0+fbOwHuapvUAsoBJdXw9Qoi7RGacFULUKaVUnqZpTlVsjwMe0jTtUvliiVc1TXNXSqUDrTVNKy3fnqJpWnOlVBrgdfP050opb+A7TdM6l7//X8CgadqSur8yIURdk5YUIUR90m7xuiZuXrPFhIy1E6LRkCRFCFGfptz080j568OUrXgLMJ2yhRQB9gDzAJRSeqWU690KUghRP+QbhxCirtkrpaJuev+tpmk3HkNuqpQ6SVlryLTybc8D65VSLwNpwOzy7fOBtUqppyhrMZkHpNR59EKIeiNjUoQQ9aJ8TIqfpmnp9R2LEKJhku4eIYQQQjRI0pIihBBCiAZJWlKEEEII0SBJkiKEEEKIBkmSFCGEEEI0SJKkCCGEEKJBkiRFCCGEEA3S/wPrr7pF4mYuOQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "batch_results = np.array(batch_accs)\n",
        "batch_results_1 = np.array(batch_accs_1)\n",
        "all_results = np.array(all_accs)\n",
        "\n",
        "x = np.arange(1, 51)\n",
        "\n",
        "plt.figure(figsize=(9, 7))\n",
        "\n",
        "plt.plot(x, batch_results[:, 0], label=\"Batch 0.8 Train\")\n",
        "plt.plot(x, batch_results[:, 1], label=\"Batch 0.8 Validation\")\n",
        "plt.plot(x, batch_results[:, 2], label=\"Batch 0.8 Test\")\n",
        "plt.plot(x, batch_results_1[:, 0], label=\"Batch 0.3 Train\")\n",
        "plt.plot(x, batch_results_1[:, 1], label=\"Batch 0.3 Validation\")\n",
        "plt.plot(x, batch_results_1[:, 2], label=\"Batch 0.3 Test\")\n",
        "plt.plot(x, all_results[:, 0], label=\"All Train\")\n",
        "plt.plot(x, all_results[:, 1], label=\"All Validation\")\n",
        "plt.plot(x, all_results[:, 2], label=\"All Test\")\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkA7-0groq7q"
      },
      "source": [
        "Here all accuracies are evaluated on the full-batch mode."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iee0U8KGURc8"
      },
      "source": [
        "# 3 Cluster Sampling\n",
        "\n",
        "Instead of the neighbor sampling, we can use another approach, subgraph (cluster) sampling, to scale up GNN. This approach is proposed in Cluster-GCN ([Chiang et al. (2019)](https://arxiv.org/abs/1905.07953)).\n",
        "\n",
        "In this section, we will implement vanilla Cluster-GCN and experiment with 3 different community partition algorithms.\n",
        "\n",
        "Notice that this section requires you have run the `Setup`, `GNN Model` and `Training and Testing` cells of the last section."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BXjP79gUYir"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "UGQ_VKp8UOEm"
      },
      "outputs": [],
      "source": [
        "import copy\n",
        "import torch\n",
        "import random\n",
        "import numpy as np\n",
        "import networkx as nx\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import community.community_louvain as community_louvain\n",
        "\n",
        "from torch_geometric.nn import SAGEConv\n",
        "from torch.utils.data import DataLoader\n",
        "from torch_geometric.datasets import Planetoid\n",
        "from torch.nn import Sequential, Linear, ReLU\n",
        "from deepsnap.dataset import GraphDataset\n",
        "from deepsnap.graph import Graph\n",
        "\n",
        "pyg_dataset = Planetoid('./tmp', \"Cora\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "bzMatyCSUaB6"
      },
      "outputs": [],
      "source": [
        "args = {\n",
        "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
        "    'dropout': 0.5,\n",
        "    'num_layers': 2,\n",
        "    'hidden_size': 64,\n",
        "    'lr': 0.005,\n",
        "    'epochs': 150,\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekV-sokSUeLc"
      },
      "source": [
        "## Partition the Graph into Clusters\n",
        "\n",
        "Here we use following three community detection / partition algorithms to partition the graph into different clusters:\n",
        "* [Kernighan–Lin algorithm (bisection)](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection.html)\n",
        "* [Clauset-Newman-Moore greedy modularity maximization](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.modularity_max.greedy_modularity_communities.html#networkx.algorithms.community.modularity_max.greedy_modularity_communities)\n",
        "* [Louvain algorithm](https://python-louvain.readthedocs.io/en/latest/api.html)\n",
        "\n",
        "\n",
        "To make the training more stable, we discard the cluster that has less than 10 nodes.\n",
        "\n",
        "Let's first define these algorithms as DeepSNAP transformation on a graph."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "N8XeT005UcKh"
      },
      "outputs": [],
      "source": [
        "def preprocess(G, node_label_index, method=\"louvain\"):\n",
        "    graphs = []\n",
        "    labeled_nodes = set(node_label_index.tolist())\n",
        "    if method == \"louvain\":\n",
        "        community_mapping = community_louvain.best_partition(G, resolution=10)\n",
        "        communities = {}\n",
        "        for node in community_mapping:\n",
        "            comm = community_mapping[node]\n",
        "            if comm in communities:\n",
        "                communities[comm].add(node)\n",
        "            else:\n",
        "                communities[comm] = set([node])\n",
        "        communities = communities.values()\n",
        "    elif method == \"bisection\":\n",
        "        communities = nx.algorithms.community.kernighan_lin_bisection(G)\n",
        "    elif method == \"greedy\":\n",
        "        communities = nx.algorithms.community.greedy_modularity_communities(G)\n",
        "\n",
        "    for community in communities:\n",
        "        nodes = set(community)\n",
        "        subgraph = G.subgraph(nodes)\n",
        "        # Make sure each subgraph has more than 10 nodes\n",
        "        if subgraph.number_of_nodes() > 10:\n",
        "            node_mapping = {node : i for i, node in enumerate(subgraph.nodes())}\n",
        "            subgraph = nx.relabel_nodes(subgraph, node_mapping)\n",
        "            # Get the id of the training set labeled node in the new graph\n",
        "            train_label_index = []\n",
        "            for node in labeled_nodes:\n",
        "                if node in node_mapping:\n",
        "                    # Append relabeled labeled node index\n",
        "                    train_label_index.append(node_mapping[node])\n",
        "\n",
        "            # Make sure the subgraph contains at least one training set labeled node\n",
        "            if len(train_label_index) > 0:\n",
        "                dg = Graph(subgraph)\n",
        "                # Update node_label_index\n",
        "                dg.node_label_index = torch.tensor(train_label_index, dtype=torch.long)\n",
        "                graphs.append(dg)\n",
        "    return graphs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CYEamCAU-TJ"
      },
      "source": [
        "## Louvain Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "-TrC6ybWU7eO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcf1927a-8eae-40a6-f692-c03138812b5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index fields: test_mask ignored.\n",
            "Index fields: val_mask ignored.\n",
            "Index fields: train_mask ignored.\n",
            "Partition the graph in to 74 communities\n",
            "Each community has 21 nodes in average\n",
            "Each community has 31 edges in average\n"
          ]
        }
      ],
      "source": [
        "graphs_train, graphs_val, graphs_test = \\\n",
        "    GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n",
        "\n",
        "graph_train = graphs_train[0]\n",
        "graph_val = graphs_val[0]\n",
        "graph_test = graphs_test[0]\n",
        "graphs = preprocess(graph_train.G, graph_train.node_label_index, method=\"louvain\")\n",
        "print(\"Partition the graph in to {} communities\".format(len(graphs)))\n",
        "avg_num_nodes = 0\n",
        "avg_num_edges = 0\n",
        "for graph in graphs:\n",
        "    avg_num_nodes += graph.num_nodes\n",
        "    avg_num_edges += graph.num_edges\n",
        "avg_num_nodes = int(avg_num_nodes / len(graphs))\n",
        "avg_num_edges = int(avg_num_edges / len(graphs))\n",
        "print(\"Each community has {} nodes in average\".format(avg_num_nodes))\n",
        "print(\"Each community has {} edges in average\".format(avg_num_edges))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O03uXIuGVIgJ"
      },
      "source": [
        "## Louvain Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "iSbGf5ADVFQq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e5def9e-657d-4ca1-880b-4c90e8d13f6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01, Loss: 1.9603, Train: 14.29%, Valid: 12.20% Test: 13.00%\n",
            "Epoch: 02, Loss: 2.6627, Train: 14.29%, Valid: 12.20% Test: 13.00%\n",
            "Epoch: 03, Loss: 1.7358, Train: 14.29%, Valid: 12.40% Test: 13.00%\n",
            "Epoch: 04, Loss: 1.7722, Train: 16.43%, Valid: 12.40% Test: 13.00%\n",
            "Epoch: 05, Loss: 2.8830, Train: 16.43%, Valid: 12.60% Test: 13.60%\n",
            "Epoch: 06, Loss: 2.9599, Train: 15.71%, Valid: 13.40% Test: 13.70%\n",
            "Epoch: 07, Loss: 2.1967, Train: 15.71%, Valid: 16.00% Test: 12.90%\n",
            "Epoch: 08, Loss: 1.9894, Train: 15.00%, Valid: 15.80% Test: 13.90%\n",
            "Epoch: 09, Loss: 2.6716, Train: 15.00%, Valid: 14.80% Test: 13.40%\n",
            "Epoch: 10, Loss: 1.7193, Train: 17.14%, Valid: 14.60% Test: 14.10%\n",
            "Epoch: 11, Loss: 3.1722, Train: 16.43%, Valid: 14.60% Test: 13.40%\n",
            "Epoch: 12, Loss: 2.0712, Train: 19.29%, Valid: 13.20% Test: 13.80%\n",
            "Epoch: 13, Loss: 0.6594, Train: 20.71%, Valid: 13.00% Test: 14.10%\n",
            "Epoch: 14, Loss: 2.5782, Train: 22.86%, Valid: 13.80% Test: 13.10%\n",
            "Epoch: 15, Loss: 3.3516, Train: 20.71%, Valid: 13.40% Test: 13.60%\n",
            "Epoch: 16, Loss: 2.7207, Train: 19.29%, Valid: 13.40% Test: 13.40%\n",
            "Epoch: 17, Loss: 2.5449, Train: 19.29%, Valid: 13.60% Test: 13.80%\n",
            "Epoch: 18, Loss: 0.0388, Train: 20.00%, Valid: 13.40% Test: 13.60%\n",
            "Epoch: 19, Loss: 2.7950, Train: 20.00%, Valid: 13.00% Test: 13.30%\n",
            "Epoch: 20, Loss: 1.8653, Train: 18.57%, Valid: 13.00% Test: 13.20%\n",
            "Epoch: 21, Loss: 1.5064, Train: 18.57%, Valid: 13.00% Test: 13.30%\n",
            "Epoch: 22, Loss: 2.5990, Train: 20.00%, Valid: 13.80% Test: 12.90%\n",
            "Epoch: 23, Loss: 3.5917, Train: 24.29%, Valid: 14.40% Test: 12.00%\n",
            "Epoch: 24, Loss: 3.1050, Train: 25.00%, Valid: 14.80% Test: 11.40%\n",
            "Epoch: 25, Loss: 2.6107, Train: 24.29%, Valid: 15.20% Test: 13.00%\n",
            "Epoch: 26, Loss: 2.2246, Train: 25.00%, Valid: 14.60% Test: 13.70%\n",
            "Epoch: 27, Loss: 2.4428, Train: 25.00%, Valid: 14.20% Test: 12.40%\n",
            "Epoch: 28, Loss: 1.3594, Train: 24.29%, Valid: 13.80% Test: 12.10%\n",
            "Epoch: 29, Loss: 0.0510, Train: 22.86%, Valid: 13.00% Test: 12.50%\n",
            "Epoch: 30, Loss: 1.3420, Train: 23.57%, Valid: 12.20% Test: 12.40%\n",
            "Epoch: 31, Loss: 2.4383, Train: 24.29%, Valid: 12.40% Test: 12.00%\n",
            "Epoch: 32, Loss: 0.8908, Train: 23.57%, Valid: 11.60% Test: 12.00%\n",
            "Epoch: 33, Loss: 2.6924, Train: 21.43%, Valid: 11.20% Test: 11.90%\n",
            "Epoch: 34, Loss: 2.8488, Train: 24.29%, Valid: 10.40% Test: 11.40%\n",
            "Epoch: 35, Loss: 1.3952, Train: 26.43%, Valid: 10.20% Test: 10.20%\n",
            "Epoch: 36, Loss: 0.2039, Train: 25.71%, Valid: 9.40% Test: 10.40%\n",
            "Epoch: 37, Loss: 2.3123, Train: 27.86%, Valid: 9.60% Test: 10.30%\n",
            "Epoch: 38, Loss: 1.0586, Train: 27.14%, Valid: 9.60% Test: 9.70%\n",
            "Epoch: 39, Loss: 1.8594, Train: 28.57%, Valid: 9.80% Test: 9.40%\n",
            "Epoch: 40, Loss: 1.1361, Train: 29.29%, Valid: 9.40% Test: 9.20%\n",
            "Epoch: 41, Loss: 1.9195, Train: 27.86%, Valid: 9.40% Test: 8.30%\n",
            "Epoch: 42, Loss: 0.8828, Train: 26.43%, Valid: 9.40% Test: 9.10%\n",
            "Epoch: 43, Loss: 2.8343, Train: 25.71%, Valid: 9.20% Test: 9.80%\n",
            "Epoch: 44, Loss: 0.0093, Train: 25.00%, Valid: 10.80% Test: 9.50%\n",
            "Epoch: 45, Loss: 2.5725, Train: 29.29%, Valid: 10.00% Test: 10.30%\n",
            "Epoch: 46, Loss: 1.4978, Train: 27.14%, Valid: 10.80% Test: 11.40%\n",
            "Epoch: 47, Loss: 0.5633, Train: 27.86%, Valid: 12.20% Test: 11.50%\n",
            "Epoch: 48, Loss: 3.9026, Train: 31.43%, Valid: 13.00% Test: 12.00%\n",
            "Epoch: 49, Loss: 3.8620, Train: 32.14%, Valid: 13.60% Test: 11.90%\n",
            "Epoch: 50, Loss: 0.7076, Train: 32.14%, Valid: 13.20% Test: 11.80%\n",
            "Epoch: 51, Loss: 0.0848, Train: 33.57%, Valid: 14.40% Test: 11.70%\n",
            "Epoch: 52, Loss: 2.5751, Train: 32.14%, Valid: 14.80% Test: 11.70%\n",
            "Epoch: 53, Loss: 1.3949, Train: 32.14%, Valid: 13.60% Test: 11.80%\n",
            "Epoch: 54, Loss: 2.6834, Train: 32.86%, Valid: 14.60% Test: 11.80%\n",
            "Epoch: 55, Loss: 1.3349, Train: 36.43%, Valid: 14.80% Test: 11.40%\n",
            "Epoch: 56, Loss: 0.6912, Train: 36.43%, Valid: 14.80% Test: 11.30%\n",
            "Epoch: 57, Loss: 3.9744, Train: 37.14%, Valid: 14.40% Test: 11.80%\n",
            "Epoch: 58, Loss: 1.3683, Train: 36.43%, Valid: 14.60% Test: 12.70%\n",
            "Epoch: 59, Loss: 0.9520, Train: 37.14%, Valid: 15.00% Test: 13.10%\n",
            "Epoch: 60, Loss: 0.4542, Train: 37.14%, Valid: 16.00% Test: 13.00%\n",
            "Epoch: 61, Loss: 0.7867, Train: 36.43%, Valid: 16.60% Test: 13.40%\n",
            "Epoch: 62, Loss: 0.0180, Train: 36.43%, Valid: 17.20% Test: 13.90%\n",
            "Epoch: 63, Loss: 0.1457, Train: 37.86%, Valid: 16.60% Test: 13.90%\n",
            "Epoch: 64, Loss: 2.3968, Train: 36.43%, Valid: 16.60% Test: 13.80%\n",
            "Epoch: 65, Loss: 2.0320, Train: 37.14%, Valid: 17.20% Test: 14.00%\n",
            "Epoch: 66, Loss: 2.3039, Train: 38.57%, Valid: 18.80% Test: 14.00%\n",
            "Epoch: 67, Loss: 0.0681, Train: 37.86%, Valid: 18.80% Test: 14.60%\n",
            "Epoch: 68, Loss: 2.0672, Train: 39.29%, Valid: 18.80% Test: 14.90%\n",
            "Epoch: 69, Loss: 1.9822, Train: 42.14%, Valid: 18.20% Test: 15.30%\n",
            "Epoch: 70, Loss: 0.0261, Train: 40.71%, Valid: 19.00% Test: 15.30%\n",
            "Epoch: 71, Loss: 0.0055, Train: 42.14%, Valid: 18.40% Test: 14.20%\n",
            "Epoch: 72, Loss: 0.0151, Train: 42.14%, Valid: 18.20% Test: 14.40%\n",
            "Epoch: 73, Loss: 0.0650, Train: 42.14%, Valid: 17.20% Test: 14.30%\n",
            "Epoch: 74, Loss: 2.6419, Train: 43.57%, Valid: 18.20% Test: 14.10%\n",
            "Epoch: 75, Loss: 0.0782, Train: 43.57%, Valid: 19.40% Test: 14.10%\n",
            "Epoch: 76, Loss: 1.0515, Train: 44.29%, Valid: 17.80% Test: 13.40%\n",
            "Epoch: 77, Loss: 0.5761, Train: 42.86%, Valid: 17.20% Test: 13.60%\n",
            "Epoch: 78, Loss: 0.6541, Train: 42.86%, Valid: 17.80% Test: 14.40%\n",
            "Epoch: 79, Loss: 2.0343, Train: 43.57%, Valid: 16.60% Test: 14.40%\n",
            "Epoch: 80, Loss: 0.7430, Train: 44.29%, Valid: 16.20% Test: 14.10%\n",
            "Epoch: 81, Loss: 0.1216, Train: 45.00%, Valid: 16.60% Test: 14.20%\n",
            "Epoch: 82, Loss: 0.0100, Train: 43.57%, Valid: 18.00% Test: 15.00%\n",
            "Epoch: 83, Loss: 0.2672, Train: 41.43%, Valid: 16.60% Test: 16.00%\n",
            "Epoch: 84, Loss: 0.8128, Train: 41.43%, Valid: 16.20% Test: 16.30%\n",
            "Epoch: 85, Loss: 2.7033, Train: 41.43%, Valid: 16.20% Test: 16.00%\n",
            "Epoch: 86, Loss: 0.2799, Train: 41.43%, Valid: 15.60% Test: 15.80%\n",
            "Epoch: 87, Loss: 3.4044, Train: 40.71%, Valid: 15.80% Test: 16.00%\n",
            "Epoch: 88, Loss: 0.1123, Train: 40.71%, Valid: 16.20% Test: 16.20%\n",
            "Epoch: 89, Loss: 1.7168, Train: 44.29%, Valid: 16.00% Test: 15.80%\n",
            "Epoch: 90, Loss: 0.0802, Train: 46.43%, Valid: 15.80% Test: 16.00%\n",
            "Epoch: 91, Loss: 0.5748, Train: 47.14%, Valid: 15.60% Test: 15.10%\n",
            "Epoch: 92, Loss: 1.6259, Train: 48.57%, Valid: 15.00% Test: 14.20%\n",
            "Epoch: 93, Loss: 1.1953, Train: 48.57%, Valid: 14.60% Test: 13.90%\n",
            "Epoch: 94, Loss: 0.0816, Train: 48.57%, Valid: 13.80% Test: 14.10%\n",
            "Epoch: 95, Loss: 0.3296, Train: 50.00%, Valid: 12.80% Test: 13.20%\n",
            "Epoch: 96, Loss: 1.1078, Train: 49.29%, Valid: 13.00% Test: 12.90%\n",
            "Epoch: 97, Loss: 1.2204, Train: 47.86%, Valid: 12.80% Test: 13.10%\n",
            "Epoch: 98, Loss: 1.2835, Train: 47.86%, Valid: 13.40% Test: 13.70%\n",
            "Epoch: 99, Loss: 0.6902, Train: 47.14%, Valid: 13.40% Test: 13.10%\n",
            "Epoch: 100, Loss: 1.7779, Train: 46.43%, Valid: 13.40% Test: 13.20%\n",
            "Epoch: 101, Loss: 1.5793, Train: 45.71%, Valid: 13.80% Test: 13.70%\n",
            "Epoch: 102, Loss: 0.4111, Train: 43.57%, Valid: 13.60% Test: 13.30%\n",
            "Epoch: 103, Loss: 2.6169, Train: 43.57%, Valid: 12.80% Test: 13.20%\n",
            "Epoch: 104, Loss: 0.2103, Train: 45.00%, Valid: 12.60% Test: 13.50%\n",
            "Epoch: 105, Loss: 0.2172, Train: 43.57%, Valid: 13.60% Test: 13.80%\n",
            "Epoch: 106, Loss: 0.1813, Train: 43.57%, Valid: 14.40% Test: 13.60%\n",
            "Epoch: 107, Loss: 0.6304, Train: 45.00%, Valid: 14.40% Test: 13.60%\n",
            "Epoch: 108, Loss: 2.6683, Train: 45.71%, Valid: 14.60% Test: 14.00%\n",
            "Epoch: 109, Loss: 1.5813, Train: 46.43%, Valid: 13.40% Test: 13.90%\n",
            "Epoch: 110, Loss: 0.0133, Train: 45.71%, Valid: 13.40% Test: 13.90%\n",
            "Epoch: 111, Loss: 1.9322, Train: 45.71%, Valid: 13.60% Test: 12.90%\n",
            "Epoch: 112, Loss: 0.7061, Train: 45.71%, Valid: 13.20% Test: 12.70%\n",
            "Epoch: 113, Loss: 0.0902, Train: 45.71%, Valid: 13.80% Test: 12.70%\n",
            "Epoch: 114, Loss: 0.0234, Train: 47.14%, Valid: 13.40% Test: 12.70%\n",
            "Epoch: 115, Loss: 0.3897, Train: 49.29%, Valid: 12.20% Test: 11.60%\n",
            "Epoch: 116, Loss: 2.4167, Train: 48.57%, Valid: 12.60% Test: 11.70%\n",
            "Epoch: 117, Loss: 0.7557, Train: 49.29%, Valid: 12.40% Test: 11.80%\n",
            "Epoch: 118, Loss: 1.7275, Train: 50.00%, Valid: 12.20% Test: 11.30%\n",
            "Epoch: 119, Loss: 0.9543, Train: 49.29%, Valid: 12.40% Test: 11.70%\n",
            "Epoch: 120, Loss: 2.2911, Train: 47.86%, Valid: 12.20% Test: 11.90%\n",
            "Epoch: 121, Loss: 1.1708, Train: 48.57%, Valid: 12.20% Test: 11.80%\n",
            "Epoch: 122, Loss: 0.5278, Train: 49.29%, Valid: 12.20% Test: 12.40%\n",
            "Epoch: 123, Loss: 0.2584, Train: 50.71%, Valid: 13.00% Test: 12.00%\n",
            "Epoch: 124, Loss: 1.0425, Train: 52.14%, Valid: 13.20% Test: 12.90%\n",
            "Epoch: 125, Loss: 1.8564, Train: 53.57%, Valid: 13.40% Test: 12.50%\n",
            "Epoch: 126, Loss: 0.0585, Train: 53.57%, Valid: 14.00% Test: 12.50%\n",
            "Epoch: 127, Loss: 0.0218, Train: 55.00%, Valid: 13.40% Test: 12.70%\n",
            "Epoch: 128, Loss: 0.0287, Train: 55.71%, Valid: 14.40% Test: 12.40%\n",
            "Epoch: 129, Loss: 0.8256, Train: 55.00%, Valid: 14.00% Test: 12.90%\n",
            "Epoch: 130, Loss: 0.0234, Train: 55.00%, Valid: 13.80% Test: 13.40%\n",
            "Epoch: 131, Loss: 0.0026, Train: 55.00%, Valid: 13.60% Test: 13.40%\n",
            "Epoch: 132, Loss: 0.0008, Train: 53.57%, Valid: 13.60% Test: 13.40%\n",
            "Epoch: 133, Loss: 0.1888, Train: 54.29%, Valid: 13.00% Test: 14.00%\n",
            "Epoch: 134, Loss: 0.0182, Train: 55.00%, Valid: 13.00% Test: 14.10%\n",
            "Epoch: 135, Loss: 2.3585, Train: 54.29%, Valid: 13.00% Test: 14.30%\n",
            "Epoch: 136, Loss: 1.1898, Train: 55.71%, Valid: 14.00% Test: 15.00%\n",
            "Epoch: 137, Loss: 1.2404, Train: 56.43%, Valid: 14.40% Test: 14.40%\n",
            "Epoch: 138, Loss: 2.2943, Train: 56.43%, Valid: 14.80% Test: 14.50%\n",
            "Epoch: 139, Loss: 0.0623, Train: 58.57%, Valid: 15.40% Test: 15.10%\n",
            "Epoch: 140, Loss: 0.0990, Train: 57.14%, Valid: 15.00% Test: 15.30%\n",
            "Epoch: 141, Loss: 0.1234, Train: 57.14%, Valid: 15.20% Test: 15.20%\n",
            "Epoch: 142, Loss: 0.1512, Train: 57.86%, Valid: 14.80% Test: 14.90%\n",
            "Epoch: 143, Loss: 0.2642, Train: 57.14%, Valid: 14.60% Test: 15.20%\n",
            "Epoch: 144, Loss: 0.1393, Train: 57.86%, Valid: 14.60% Test: 14.70%\n",
            "Epoch: 145, Loss: 1.3726, Train: 57.14%, Valid: 14.00% Test: 14.80%\n",
            "Epoch: 146, Loss: 4.1628, Train: 57.14%, Valid: 14.80% Test: 15.30%\n",
            "Epoch: 147, Loss: 0.0269, Train: 57.14%, Valid: 15.40% Test: 15.40%\n",
            "Epoch: 148, Loss: 0.0000, Train: 56.43%, Valid: 15.40% Test: 14.80%\n",
            "Epoch: 149, Loss: 3.3020, Train: 56.43%, Valid: 15.60% Test: 14.90%\n",
            "Epoch: 150, Loss: 0.0212, Train: 57.86%, Valid: 15.20% Test: 14.30%\n",
            "Best model: Train: 43.57%, Valid: 19.40% Test: 14.10%\n"
          ]
        }
      ],
      "source": [
        "model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
        "louvain_best_model, louvain_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode=\"community\")\n",
        "train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], louvain_best_model)\n",
        "print('Best model:',\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * val_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CvTf0ANVO9U"
      },
      "source": [
        "## Bisection Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "HkV0zlhgVJ7u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d51d02e-6177-4c67-db18-928bf5afa468"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index fields: test_mask ignored.\n",
            "Index fields: val_mask ignored.\n",
            "Index fields: train_mask ignored.\n",
            "Partition the graph in to 2 communities\n",
            "Each community has 1354 nodes in average\n",
            "Each community has 2400 edges in average\n"
          ]
        }
      ],
      "source": [
        "graphs_train, graphs_val, graphs_test = \\\n",
        "    GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n",
        "\n",
        "graph_train = graphs_train[0]\n",
        "graph_val = graphs_val[0]\n",
        "graph_test = graphs_test[0]\n",
        "graphs = preprocess(graph_train.G, graph_train.node_label_index, method=\"bisection\")\n",
        "print(\"Partition the graph in to {} communities\".format(len(graphs)))\n",
        "avg_num_nodes = 0\n",
        "avg_num_edges = 0\n",
        "for graph in graphs:\n",
        "    avg_num_nodes += graph.num_nodes\n",
        "    avg_num_edges += graph.num_edges\n",
        "avg_num_nodes = int(avg_num_nodes / len(graphs))\n",
        "avg_num_edges = int(avg_num_edges / len(graphs))\n",
        "print(\"Each community has {} nodes in average\".format(avg_num_nodes))\n",
        "print(\"Each community has {} edges in average\".format(avg_num_edges))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqMCvP8wVVms"
      },
      "source": [
        "## Bisection Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "k1wgFg1bVRGY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b06718fc-addc-4809-db4b-89bb6b31a3dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01, Loss: 2.0372, Train: 32.86%, Valid: 17.40% Test: 20.20%\n",
            "Epoch: 02, Loss: 1.0818, Train: 42.14%, Valid: 21.40% Test: 25.20%\n",
            "Epoch: 03, Loss: 1.8989, Train: 68.57%, Valid: 32.60% Test: 35.80%\n",
            "Epoch: 04, Loss: 0.3703, Train: 87.14%, Valid: 46.80% Test: 49.50%\n",
            "Epoch: 05, Loss: 0.2813, Train: 95.71%, Valid: 55.20% Test: 60.10%\n",
            "Epoch: 06, Loss: 0.1633, Train: 97.14%, Valid: 62.60% Test: 65.60%\n",
            "Epoch: 07, Loss: 0.0903, Train: 97.14%, Valid: 64.80% Test: 67.80%\n",
            "Epoch: 08, Loss: 0.0629, Train: 97.14%, Valid: 67.00% Test: 69.20%\n",
            "Epoch: 09, Loss: 0.3876, Train: 97.14%, Valid: 67.40% Test: 70.00%\n",
            "Epoch: 10, Loss: 0.0146, Train: 98.57%, Valid: 68.20% Test: 70.50%\n",
            "Epoch: 11, Loss: 0.0136, Train: 99.29%, Valid: 69.00% Test: 72.00%\n",
            "Epoch: 12, Loss: 0.1332, Train: 99.29%, Valid: 70.00% Test: 72.80%\n",
            "Epoch: 13, Loss: 0.0749, Train: 100.00%, Valid: 71.20% Test: 73.20%\n",
            "Epoch: 14, Loss: 0.0555, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 15, Loss: 0.0583, Train: 100.00%, Valid: 72.40% Test: 74.00%\n",
            "Epoch: 16, Loss: 0.0037, Train: 100.00%, Valid: 72.40% Test: 73.60%\n",
            "Epoch: 17, Loss: 0.0198, Train: 100.00%, Valid: 72.40% Test: 74.20%\n",
            "Epoch: 18, Loss: 0.0103, Train: 100.00%, Valid: 72.80% Test: 73.90%\n",
            "Epoch: 19, Loss: 0.0024, Train: 100.00%, Valid: 72.60% Test: 73.80%\n",
            "Epoch: 20, Loss: 0.0024, Train: 100.00%, Valid: 72.40% Test: 73.90%\n",
            "Epoch: 21, Loss: 0.0044, Train: 100.00%, Valid: 72.80% Test: 73.80%\n",
            "Epoch: 22, Loss: 0.0045, Train: 100.00%, Valid: 72.60% Test: 73.90%\n",
            "Epoch: 23, Loss: 0.0028, Train: 100.00%, Valid: 73.20% Test: 74.20%\n",
            "Epoch: 24, Loss: 0.0028, Train: 100.00%, Valid: 73.00% Test: 74.10%\n",
            "Epoch: 25, Loss: 0.0011, Train: 100.00%, Valid: 73.00% Test: 73.60%\n",
            "Epoch: 26, Loss: 0.0026, Train: 100.00%, Valid: 73.20% Test: 73.40%\n",
            "Epoch: 27, Loss: 0.0039, Train: 100.00%, Valid: 72.80% Test: 73.30%\n",
            "Epoch: 28, Loss: 0.0005, Train: 100.00%, Valid: 72.80% Test: 73.50%\n",
            "Epoch: 29, Loss: 0.0003, Train: 100.00%, Valid: 72.80% Test: 73.20%\n",
            "Epoch: 30, Loss: 0.0002, Train: 100.00%, Valid: 72.40% Test: 73.20%\n",
            "Epoch: 31, Loss: 0.0005, Train: 100.00%, Valid: 72.40% Test: 73.40%\n",
            "Epoch: 32, Loss: 0.0029, Train: 100.00%, Valid: 72.20% Test: 73.40%\n",
            "Epoch: 33, Loss: 0.0006, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 34, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 73.30%\n",
            "Epoch: 35, Loss: 0.0004, Train: 100.00%, Valid: 72.00% Test: 73.50%\n",
            "Epoch: 36, Loss: 0.0022, Train: 100.00%, Valid: 72.00% Test: 73.30%\n",
            "Epoch: 37, Loss: 0.0038, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 38, Loss: 0.0005, Train: 100.00%, Valid: 72.00% Test: 73.10%\n",
            "Epoch: 39, Loss: 0.0007, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 40, Loss: 0.0001, Train: 100.00%, Valid: 71.80% Test: 73.30%\n",
            "Epoch: 41, Loss: 0.0004, Train: 100.00%, Valid: 71.40% Test: 73.10%\n",
            "Epoch: 42, Loss: 0.0006, Train: 100.00%, Valid: 71.80% Test: 73.00%\n",
            "Epoch: 43, Loss: 0.0001, Train: 100.00%, Valid: 71.80% Test: 73.20%\n",
            "Epoch: 44, Loss: 0.0004, Train: 100.00%, Valid: 71.80% Test: 73.20%\n",
            "Epoch: 45, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 73.50%\n",
            "Epoch: 46, Loss: 0.0005, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 47, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 48, Loss: 0.0010, Train: 100.00%, Valid: 72.40% Test: 73.80%\n",
            "Epoch: 49, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 50, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 51, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 52, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.60%\n",
            "Epoch: 53, Loss: 0.0002, Train: 100.00%, Valid: 71.80% Test: 73.30%\n",
            "Epoch: 54, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 55, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 73.40%\n",
            "Epoch: 56, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 73.20%\n",
            "Epoch: 57, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.60%\n",
            "Epoch: 58, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 59, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.40%\n",
            "Epoch: 60, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 61, Loss: 0.0007, Train: 100.00%, Valid: 72.20% Test: 73.30%\n",
            "Epoch: 62, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 73.60%\n",
            "Epoch: 63, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 64, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 65, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.40%\n",
            "Epoch: 66, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.40%\n",
            "Epoch: 67, Loss: 0.0013, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 68, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.50%\n",
            "Epoch: 69, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.30%\n",
            "Epoch: 70, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 73.40%\n",
            "Epoch: 71, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.30%\n",
            "Epoch: 72, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 73.40%\n",
            "Epoch: 73, Loss: 0.0005, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 74, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 75, Loss: 0.0003, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 76, Loss: 0.0025, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 77, Loss: 0.0005, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 78, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 79, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 73.60%\n",
            "Epoch: 80, Loss: 0.0002, Train: 100.00%, Valid: 71.80% Test: 73.80%\n",
            "Epoch: 81, Loss: 0.0004, Train: 100.00%, Valid: 72.20% Test: 73.60%\n",
            "Epoch: 82, Loss: 0.0001, Train: 100.00%, Valid: 72.40% Test: 73.60%\n",
            "Epoch: 83, Loss: 0.0002, Train: 100.00%, Valid: 72.60% Test: 73.70%\n",
            "Epoch: 84, Loss: 0.0000, Train: 100.00%, Valid: 72.40% Test: 73.70%\n",
            "Epoch: 85, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 73.70%\n",
            "Epoch: 86, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 87, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 73.60%\n",
            "Epoch: 88, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 89, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.90%\n",
            "Epoch: 90, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 73.90%\n",
            "Epoch: 91, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 92, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 93, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 94, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 95, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 96, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 73.70%\n",
            "Epoch: 97, Loss: 0.0001, Train: 100.00%, Valid: 72.60% Test: 73.70%\n",
            "Epoch: 98, Loss: 0.0001, Train: 100.00%, Valid: 72.60% Test: 73.60%\n",
            "Epoch: 99, Loss: 0.0001, Train: 100.00%, Valid: 72.60% Test: 73.50%\n",
            "Epoch: 100, Loss: 0.0002, Train: 100.00%, Valid: 72.60% Test: 73.80%\n",
            "Epoch: 101, Loss: 0.0001, Train: 100.00%, Valid: 72.60% Test: 73.80%\n",
            "Epoch: 102, Loss: 0.0000, Train: 100.00%, Valid: 72.60% Test: 73.60%\n",
            "Epoch: 103, Loss: 0.0000, Train: 100.00%, Valid: 72.60% Test: 73.80%\n",
            "Epoch: 104, Loss: 0.0002, Train: 100.00%, Valid: 72.60% Test: 73.80%\n",
            "Epoch: 105, Loss: 0.0000, Train: 100.00%, Valid: 72.60% Test: 73.60%\n",
            "Epoch: 106, Loss: 0.0001, Train: 100.00%, Valid: 72.60% Test: 73.70%\n",
            "Epoch: 107, Loss: 0.0005, Train: 100.00%, Valid: 72.60% Test: 73.90%\n",
            "Epoch: 108, Loss: 0.0000, Train: 100.00%, Valid: 72.60% Test: 73.60%\n",
            "Epoch: 109, Loss: 0.0006, Train: 100.00%, Valid: 72.40% Test: 73.70%\n",
            "Epoch: 110, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 73.90%\n",
            "Epoch: 111, Loss: 0.0007, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 112, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 73.90%\n",
            "Epoch: 113, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 114, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 73.90%\n",
            "Epoch: 115, Loss: 0.0005, Train: 100.00%, Valid: 72.00% Test: 74.00%\n",
            "Epoch: 116, Loss: 0.0007, Train: 100.00%, Valid: 72.00% Test: 74.00%\n",
            "Epoch: 117, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 74.00%\n",
            "Epoch: 118, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 74.00%\n",
            "Epoch: 119, Loss: 0.0002, Train: 100.00%, Valid: 72.20% Test: 74.00%\n",
            "Epoch: 120, Loss: 0.0001, Train: 100.00%, Valid: 72.20% Test: 74.00%\n",
            "Epoch: 121, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 74.00%\n",
            "Epoch: 122, Loss: 0.0000, Train: 100.00%, Valid: 72.00% Test: 73.90%\n",
            "Epoch: 123, Loss: 0.0000, Train: 100.00%, Valid: 72.20% Test: 73.80%\n",
            "Epoch: 124, Loss: 0.0012, Train: 100.00%, Valid: 72.00% Test: 73.90%\n",
            "Epoch: 125, Loss: 0.0000, Train: 100.00%, Valid: 71.80% Test: 73.90%\n",
            "Epoch: 126, Loss: 0.0008, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 127, Loss: 0.0000, Train: 100.00%, Valid: 71.80% Test: 73.90%\n",
            "Epoch: 128, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 129, Loss: 0.0001, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 130, Loss: 0.0003, Train: 100.00%, Valid: 71.80% Test: 73.90%\n",
            "Epoch: 131, Loss: 0.0002, Train: 100.00%, Valid: 72.00% Test: 73.80%\n",
            "Epoch: 132, Loss: 0.0000, Train: 100.00%, Valid: 71.80% Test: 73.50%\n",
            "Epoch: 133, Loss: 0.0000, Train: 100.00%, Valid: 71.80% Test: 73.70%\n",
            "Epoch: 134, Loss: 0.0004, Train: 100.00%, Valid: 71.60% Test: 73.40%\n",
            "Epoch: 135, Loss: 0.0001, Train: 100.00%, Valid: 70.80% Test: 73.40%\n",
            "Epoch: 136, Loss: 0.0000, Train: 100.00%, Valid: 71.60% Test: 73.50%\n",
            "Epoch: 137, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.30%\n",
            "Epoch: 138, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 73.30%\n",
            "Epoch: 139, Loss: 0.0002, Train: 100.00%, Valid: 71.00% Test: 73.20%\n",
            "Epoch: 140, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 73.30%\n",
            "Epoch: 141, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 73.20%\n",
            "Epoch: 142, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 73.10%\n",
            "Epoch: 143, Loss: 0.0002, Train: 100.00%, Valid: 70.60% Test: 73.20%\n",
            "Epoch: 144, Loss: 0.0001, Train: 100.00%, Valid: 71.20% Test: 73.20%\n",
            "Epoch: 145, Loss: 0.0000, Train: 100.00%, Valid: 71.40% Test: 73.30%\n",
            "Epoch: 146, Loss: 0.0002, Train: 100.00%, Valid: 71.80% Test: 73.30%\n",
            "Epoch: 147, Loss: 0.0001, Train: 100.00%, Valid: 71.40% Test: 73.30%\n",
            "Epoch: 148, Loss: 0.0005, Train: 100.00%, Valid: 71.20% Test: 73.40%\n",
            "Epoch: 149, Loss: 0.0001, Train: 100.00%, Valid: 71.60% Test: 73.30%\n",
            "Epoch: 150, Loss: 0.0001, Train: 100.00%, Valid: 71.80% Test: 73.30%\n",
            "Best model: Train: 100.00%, Valid: 73.20% Test: 74.20%\n"
          ]
        }
      ],
      "source": [
        "model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
        "bisection_best_model, bisection_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode=\"community\")\n",
        "train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], bisection_best_model)\n",
        "print('Best model:',\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * val_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PROPwoOVcJy"
      },
      "source": [
        "## Greedy Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "h3DVamWqVT92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78b788b4-061b-4465-f0d0-5b3b3d03ee8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index fields: test_mask ignored.\n",
            "Index fields: val_mask ignored.\n",
            "Index fields: train_mask ignored.\n",
            "Partition the graph in to 20 communities\n",
            "Each community has 121 nodes in average\n",
            "Each community has 222 edges in average\n"
          ]
        }
      ],
      "source": [
        "graphs_train, graphs_val, graphs_test = \\\n",
        "    GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n",
        "\n",
        "graph_train = graphs_train[0]\n",
        "graph_val = graphs_val[0]\n",
        "graph_test = graphs_test[0]\n",
        "graphs = preprocess(graph_train.G, graph_train.node_label_index, method=\"greedy\")\n",
        "print(\"Partition the graph in to {} communities\".format(len(graphs)))\n",
        "avg_num_nodes = 0\n",
        "avg_num_edges = 0\n",
        "for graph in graphs:\n",
        "    avg_num_nodes += graph.num_nodes\n",
        "    avg_num_edges += graph.num_edges\n",
        "avg_num_nodes = int(avg_num_nodes / len(graphs))\n",
        "avg_num_edges = int(avg_num_edges / len(graphs))\n",
        "print(\"Each community has {} nodes in average\".format(avg_num_nodes))\n",
        "print(\"Each community has {} edges in average\".format(avg_num_edges))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93pR_-kxVgma"
      },
      "source": [
        "## Greedy Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "lQgQY-jPVd_U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65265842-04e3-4933-f0d4-d49cfdff2637"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01, Loss: 2.1490, Train: 18.57%, Valid: 17.20% Test: 15.00%\n",
            "Epoch: 02, Loss: 2.4786, Train: 15.00%, Valid: 12.20% Test: 9.90%\n",
            "Epoch: 03, Loss: 2.1033, Train: 14.29%, Valid: 11.40% Test: 10.30%\n",
            "Epoch: 04, Loss: 2.8764, Train: 15.71%, Valid: 11.20% Test: 10.30%\n",
            "Epoch: 05, Loss: 2.3939, Train: 16.43%, Valid: 11.20% Test: 10.20%\n",
            "Epoch: 06, Loss: 1.8844, Train: 20.00%, Valid: 11.40% Test: 9.80%\n",
            "Epoch: 07, Loss: 1.0651, Train: 20.00%, Valid: 10.80% Test: 10.50%\n",
            "Epoch: 08, Loss: 2.5194, Train: 20.00%, Valid: 11.60% Test: 12.20%\n",
            "Epoch: 09, Loss: 2.6902, Train: 16.43%, Valid: 13.00% Test: 14.90%\n",
            "Epoch: 10, Loss: 3.4291, Train: 19.29%, Valid: 16.60% Test: 18.60%\n",
            "Epoch: 11, Loss: 0.1227, Train: 20.00%, Valid: 20.20% Test: 19.80%\n",
            "Epoch: 12, Loss: 1.9983, Train: 25.71%, Valid: 21.60% Test: 21.50%\n",
            "Epoch: 13, Loss: 1.4808, Train: 26.43%, Valid: 21.60% Test: 21.60%\n",
            "Epoch: 14, Loss: 2.5617, Train: 26.43%, Valid: 22.20% Test: 22.60%\n",
            "Epoch: 15, Loss: 2.0356, Train: 30.00%, Valid: 21.40% Test: 22.50%\n",
            "Epoch: 16, Loss: 0.4840, Train: 36.43%, Valid: 22.20% Test: 24.60%\n",
            "Epoch: 17, Loss: 2.2662, Train: 42.14%, Valid: 21.40% Test: 25.00%\n",
            "Epoch: 18, Loss: 1.2630, Train: 42.14%, Valid: 21.40% Test: 25.00%\n",
            "Epoch: 19, Loss: 1.8750, Train: 42.14%, Valid: 23.00% Test: 26.70%\n",
            "Epoch: 20, Loss: 1.5618, Train: 46.43%, Valid: 23.60% Test: 27.20%\n",
            "Epoch: 21, Loss: 1.6735, Train: 49.29%, Valid: 24.80% Test: 27.30%\n",
            "Epoch: 22, Loss: 1.9927, Train: 50.71%, Valid: 25.60% Test: 27.90%\n",
            "Epoch: 23, Loss: 1.2866, Train: 50.71%, Valid: 26.60% Test: 28.50%\n",
            "Epoch: 24, Loss: 0.7215, Train: 51.43%, Valid: 26.60% Test: 29.70%\n",
            "Epoch: 25, Loss: 0.2762, Train: 48.57%, Valid: 26.20% Test: 29.90%\n",
            "Epoch: 26, Loss: 0.7091, Train: 46.43%, Valid: 24.00% Test: 27.70%\n",
            "Epoch: 27, Loss: 1.8541, Train: 46.43%, Valid: 24.20% Test: 26.50%\n",
            "Epoch: 28, Loss: 0.3933, Train: 44.29%, Valid: 21.60% Test: 24.30%\n",
            "Epoch: 29, Loss: 2.6417, Train: 43.57%, Valid: 22.20% Test: 23.50%\n",
            "Epoch: 30, Loss: 2.5405, Train: 44.29%, Valid: 22.00% Test: 23.70%\n",
            "Epoch: 31, Loss: 0.6849, Train: 43.57%, Valid: 21.00% Test: 23.20%\n",
            "Epoch: 32, Loss: 1.8898, Train: 45.00%, Valid: 20.20% Test: 22.30%\n",
            "Epoch: 33, Loss: 1.8043, Train: 44.29%, Valid: 19.60% Test: 21.60%\n",
            "Epoch: 34, Loss: 0.3289, Train: 45.71%, Valid: 20.60% Test: 22.80%\n",
            "Epoch: 35, Loss: 0.5485, Train: 44.29%, Valid: 19.00% Test: 21.30%\n",
            "Epoch: 36, Loss: 0.5717, Train: 45.71%, Valid: 19.40% Test: 20.90%\n",
            "Epoch: 37, Loss: 1.6830, Train: 47.14%, Valid: 20.20% Test: 21.90%\n",
            "Epoch: 38, Loss: 1.5820, Train: 50.00%, Valid: 21.80% Test: 22.70%\n",
            "Epoch: 39, Loss: 0.8972, Train: 54.29%, Valid: 21.20% Test: 23.80%\n",
            "Epoch: 40, Loss: 0.0848, Train: 55.71%, Valid: 22.40% Test: 22.90%\n",
            "Epoch: 41, Loss: 2.2101, Train: 57.14%, Valid: 22.20% Test: 24.00%\n",
            "Epoch: 42, Loss: 1.3145, Train: 61.43%, Valid: 20.60% Test: 23.30%\n",
            "Epoch: 43, Loss: 0.7797, Train: 63.57%, Valid: 21.40% Test: 24.00%\n",
            "Epoch: 44, Loss: 0.1900, Train: 62.14%, Valid: 19.60% Test: 22.30%\n",
            "Epoch: 45, Loss: 0.1238, Train: 61.43%, Valid: 18.40% Test: 21.10%\n",
            "Epoch: 46, Loss: 0.0554, Train: 62.14%, Valid: 19.40% Test: 22.10%\n",
            "Epoch: 47, Loss: 0.9096, Train: 64.29%, Valid: 19.00% Test: 22.10%\n",
            "Epoch: 48, Loss: 0.2438, Train: 65.71%, Valid: 19.80% Test: 22.10%\n",
            "Epoch: 49, Loss: 3.5502, Train: 68.57%, Valid: 21.20% Test: 23.60%\n",
            "Epoch: 50, Loss: 0.0421, Train: 67.86%, Valid: 22.00% Test: 23.10%\n",
            "Epoch: 51, Loss: 2.5512, Train: 67.86%, Valid: 23.00% Test: 23.70%\n",
            "Epoch: 52, Loss: 0.4656, Train: 68.57%, Valid: 22.20% Test: 23.90%\n",
            "Epoch: 53, Loss: 0.3373, Train: 70.00%, Valid: 22.20% Test: 24.10%\n",
            "Epoch: 54, Loss: 0.0186, Train: 70.71%, Valid: 21.40% Test: 23.80%\n",
            "Epoch: 55, Loss: 0.5825, Train: 70.71%, Valid: 21.80% Test: 23.80%\n",
            "Epoch: 56, Loss: 0.2332, Train: 70.71%, Valid: 21.40% Test: 23.50%\n",
            "Epoch: 57, Loss: 0.0707, Train: 72.14%, Valid: 21.80% Test: 23.80%\n",
            "Epoch: 58, Loss: 0.2849, Train: 72.14%, Valid: 21.80% Test: 23.00%\n",
            "Epoch: 59, Loss: 0.0453, Train: 72.86%, Valid: 21.40% Test: 22.40%\n",
            "Epoch: 60, Loss: 0.0632, Train: 73.57%, Valid: 21.80% Test: 22.00%\n",
            "Epoch: 61, Loss: 0.2786, Train: 73.57%, Valid: 21.00% Test: 21.30%\n",
            "Epoch: 62, Loss: 0.0409, Train: 73.57%, Valid: 22.20% Test: 21.80%\n",
            "Epoch: 63, Loss: 0.1613, Train: 74.29%, Valid: 22.40% Test: 23.00%\n",
            "Epoch: 64, Loss: 1.8514, Train: 75.00%, Valid: 22.60% Test: 24.20%\n",
            "Epoch: 65, Loss: 0.0773, Train: 75.71%, Valid: 23.20% Test: 24.60%\n",
            "Epoch: 66, Loss: 0.0281, Train: 74.29%, Valid: 22.80% Test: 24.20%\n",
            "Epoch: 67, Loss: 0.3920, Train: 75.00%, Valid: 22.80% Test: 24.80%\n",
            "Epoch: 68, Loss: 0.3694, Train: 77.14%, Valid: 22.00% Test: 23.60%\n",
            "Epoch: 69, Loss: 1.2958, Train: 77.86%, Valid: 23.00% Test: 24.00%\n",
            "Epoch: 70, Loss: 0.0419, Train: 80.00%, Valid: 24.40% Test: 24.80%\n",
            "Epoch: 71, Loss: 0.0242, Train: 82.14%, Valid: 25.20% Test: 26.60%\n",
            "Epoch: 72, Loss: 1.3297, Train: 83.57%, Valid: 25.80% Test: 27.10%\n",
            "Epoch: 73, Loss: 0.1309, Train: 84.29%, Valid: 26.40% Test: 28.10%\n",
            "Epoch: 74, Loss: 0.8318, Train: 85.71%, Valid: 26.80% Test: 28.20%\n",
            "Epoch: 75, Loss: 0.1776, Train: 85.00%, Valid: 26.60% Test: 27.40%\n",
            "Epoch: 76, Loss: 0.1391, Train: 86.43%, Valid: 26.00% Test: 26.60%\n",
            "Epoch: 77, Loss: 0.0057, Train: 86.43%, Valid: 26.40% Test: 26.60%\n",
            "Epoch: 78, Loss: 0.3198, Train: 87.14%, Valid: 25.20% Test: 27.50%\n",
            "Epoch: 79, Loss: 0.0002, Train: 87.86%, Valid: 24.60% Test: 27.10%\n",
            "Epoch: 80, Loss: 0.1230, Train: 87.86%, Valid: 25.60% Test: 27.20%\n",
            "Epoch: 81, Loss: 0.0020, Train: 88.57%, Valid: 25.00% Test: 26.50%\n",
            "Epoch: 82, Loss: 0.1106, Train: 87.86%, Valid: 25.00% Test: 27.00%\n",
            "Epoch: 83, Loss: 0.0003, Train: 87.14%, Valid: 26.20% Test: 27.50%\n",
            "Epoch: 84, Loss: 0.0105, Train: 87.86%, Valid: 25.80% Test: 27.50%\n",
            "Epoch: 85, Loss: 0.0890, Train: 87.86%, Valid: 24.40% Test: 26.30%\n",
            "Epoch: 86, Loss: 0.1089, Train: 87.86%, Valid: 24.40% Test: 26.00%\n",
            "Epoch: 87, Loss: 0.0958, Train: 87.86%, Valid: 24.60% Test: 25.30%\n",
            "Epoch: 88, Loss: 0.0176, Train: 87.14%, Valid: 24.60% Test: 25.30%\n",
            "Epoch: 89, Loss: 0.0038, Train: 87.86%, Valid: 24.60% Test: 25.20%\n",
            "Epoch: 90, Loss: 0.0380, Train: 87.86%, Valid: 25.40% Test: 26.00%\n",
            "Epoch: 91, Loss: 0.1216, Train: 87.86%, Valid: 25.60% Test: 26.50%\n",
            "Epoch: 92, Loss: 0.4670, Train: 87.86%, Valid: 25.00% Test: 26.20%\n",
            "Epoch: 93, Loss: 0.6047, Train: 87.14%, Valid: 25.60% Test: 26.90%\n",
            "Epoch: 94, Loss: 0.0159, Train: 87.14%, Valid: 24.80% Test: 26.10%\n",
            "Epoch: 95, Loss: 0.0001, Train: 87.14%, Valid: 25.40% Test: 26.00%\n",
            "Epoch: 96, Loss: 0.0016, Train: 87.14%, Valid: 24.80% Test: 26.40%\n",
            "Epoch: 97, Loss: 0.0563, Train: 87.14%, Valid: 26.20% Test: 26.00%\n",
            "Epoch: 98, Loss: 0.3746, Train: 87.14%, Valid: 25.60% Test: 26.20%\n",
            "Epoch: 99, Loss: 0.3983, Train: 87.14%, Valid: 26.40% Test: 26.50%\n",
            "Epoch: 100, Loss: 0.2596, Train: 87.14%, Valid: 26.40% Test: 26.70%\n",
            "Epoch: 101, Loss: 0.0660, Train: 87.14%, Valid: 26.00% Test: 26.60%\n",
            "Epoch: 102, Loss: 0.2418, Train: 87.86%, Valid: 25.40% Test: 27.30%\n",
            "Epoch: 103, Loss: 0.4143, Train: 87.86%, Valid: 26.40% Test: 27.30%\n",
            "Epoch: 104, Loss: 0.0015, Train: 87.14%, Valid: 26.40% Test: 27.70%\n",
            "Epoch: 105, Loss: 0.0009, Train: 87.14%, Valid: 26.60% Test: 28.10%\n",
            "Epoch: 106, Loss: 0.0044, Train: 87.14%, Valid: 26.00% Test: 28.60%\n",
            "Epoch: 107, Loss: 0.0000, Train: 87.14%, Valid: 25.80% Test: 28.10%\n",
            "Epoch: 108, Loss: 0.0108, Train: 86.43%, Valid: 25.20% Test: 26.10%\n",
            "Epoch: 109, Loss: 0.0031, Train: 87.14%, Valid: 25.40% Test: 28.10%\n",
            "Epoch: 110, Loss: 0.0019, Train: 87.14%, Valid: 25.60% Test: 28.20%\n",
            "Epoch: 111, Loss: 0.0001, Train: 86.43%, Valid: 24.60% Test: 26.20%\n",
            "Epoch: 112, Loss: 0.1320, Train: 86.43%, Valid: 24.20% Test: 25.70%\n",
            "Epoch: 113, Loss: 0.0025, Train: 87.14%, Valid: 23.60% Test: 24.30%\n",
            "Epoch: 114, Loss: 0.0062, Train: 86.43%, Valid: 23.80% Test: 23.90%\n",
            "Epoch: 115, Loss: 0.0507, Train: 86.43%, Valid: 23.20% Test: 24.30%\n",
            "Epoch: 116, Loss: 0.0471, Train: 87.14%, Valid: 22.80% Test: 24.10%\n",
            "Epoch: 117, Loss: 0.3639, Train: 87.86%, Valid: 22.80% Test: 25.90%\n",
            "Epoch: 118, Loss: 0.0143, Train: 87.86%, Valid: 23.60% Test: 25.90%\n",
            "Epoch: 119, Loss: 0.1115, Train: 87.14%, Valid: 23.20% Test: 26.40%\n",
            "Epoch: 120, Loss: 0.0028, Train: 87.14%, Valid: 25.00% Test: 27.80%\n",
            "Epoch: 121, Loss: 0.0061, Train: 87.14%, Valid: 25.00% Test: 28.00%\n",
            "Epoch: 122, Loss: 0.0993, Train: 86.43%, Valid: 25.40% Test: 28.70%\n",
            "Epoch: 123, Loss: 0.0312, Train: 87.14%, Valid: 25.80% Test: 29.00%\n",
            "Epoch: 124, Loss: 0.0786, Train: 87.14%, Valid: 26.40% Test: 29.20%\n",
            "Epoch: 125, Loss: 0.0375, Train: 87.14%, Valid: 26.80% Test: 29.20%\n",
            "Epoch: 126, Loss: 0.0477, Train: 87.86%, Valid: 27.40% Test: 29.20%\n",
            "Epoch: 127, Loss: 0.0347, Train: 87.86%, Valid: 26.80% Test: 29.70%\n",
            "Epoch: 128, Loss: 0.0008, Train: 87.86%, Valid: 27.80% Test: 29.20%\n",
            "Epoch: 129, Loss: 0.0102, Train: 87.14%, Valid: 27.60% Test: 29.60%\n",
            "Epoch: 130, Loss: 0.0094, Train: 87.14%, Valid: 28.20% Test: 29.50%\n",
            "Epoch: 131, Loss: 0.0034, Train: 86.43%, Valid: 29.00% Test: 29.70%\n",
            "Epoch: 132, Loss: 0.6465, Train: 87.86%, Valid: 29.20% Test: 29.80%\n",
            "Epoch: 133, Loss: 0.0227, Train: 88.57%, Valid: 28.80% Test: 30.10%\n",
            "Epoch: 134, Loss: 0.0002, Train: 88.57%, Valid: 28.80% Test: 30.70%\n",
            "Epoch: 135, Loss: 0.0036, Train: 89.29%, Valid: 27.80% Test: 29.70%\n",
            "Epoch: 136, Loss: 0.0979, Train: 89.29%, Valid: 27.60% Test: 29.80%\n",
            "Epoch: 137, Loss: 0.2617, Train: 90.00%, Valid: 27.40% Test: 30.60%\n",
            "Epoch: 138, Loss: 0.1984, Train: 90.00%, Valid: 27.40% Test: 31.10%\n",
            "Epoch: 139, Loss: 0.0335, Train: 89.29%, Valid: 27.60% Test: 30.40%\n",
            "Epoch: 140, Loss: 0.2096, Train: 89.29%, Valid: 27.60% Test: 30.50%\n",
            "Epoch: 141, Loss: 0.0019, Train: 89.29%, Valid: 28.20% Test: 30.20%\n",
            "Epoch: 142, Loss: 0.0216, Train: 89.29%, Valid: 28.00% Test: 30.20%\n",
            "Epoch: 143, Loss: 0.1433, Train: 89.29%, Valid: 28.00% Test: 30.30%\n",
            "Epoch: 144, Loss: 0.0212, Train: 89.29%, Valid: 28.00% Test: 29.90%\n",
            "Epoch: 145, Loss: 0.1373, Train: 89.29%, Valid: 28.40% Test: 30.00%\n",
            "Epoch: 146, Loss: 0.0846, Train: 89.29%, Valid: 27.80% Test: 30.00%\n",
            "Epoch: 147, Loss: 0.0146, Train: 89.29%, Valid: 28.40% Test: 29.90%\n",
            "Epoch: 148, Loss: 0.0183, Train: 89.29%, Valid: 28.40% Test: 29.70%\n",
            "Epoch: 149, Loss: 0.1058, Train: 89.29%, Valid: 28.60% Test: 30.40%\n",
            "Epoch: 150, Loss: 0.0199, Train: 89.29%, Valid: 28.40% Test: 30.30%\n",
            "Best model: Train: 87.86%, Valid: 29.20% Test: 29.80%\n"
          ]
        }
      ],
      "source": [
        "model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
        "greedy_best_model, greedy_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode=\"community\")\n",
        "train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], greedy_best_model)\n",
        "print('Best model:',\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * val_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5edKKT6Vk1C"
      },
      "source": [
        "## Full-Batch Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "N5tIXxC8ViFD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c75b3be8-8ca0-41cf-cddb-447606e4bc5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index fields: test_mask ignored.\n",
            "Index fields: val_mask ignored.\n",
            "Index fields: train_mask ignored.\n",
            "Epoch: 01, Loss: 2.1153, Train: 42.14%, Valid: 9.20% Test: 10.90%\n",
            "Epoch: 02, Loss: 1.2590, Train: 92.86%, Valid: 28.20% Test: 28.60%\n",
            "Epoch: 03, Loss: 0.7959, Train: 100.00%, Valid: 51.00% Test: 52.10%\n",
            "Epoch: 04, Loss: 0.3934, Train: 100.00%, Valid: 62.00% Test: 63.40%\n",
            "Epoch: 05, Loss: 0.2325, Train: 100.00%, Valid: 67.20% Test: 67.70%\n",
            "Epoch: 06, Loss: 0.1077, Train: 100.00%, Valid: 69.80% Test: 69.90%\n",
            "Epoch: 07, Loss: 0.0552, Train: 100.00%, Valid: 71.00% Test: 71.00%\n",
            "Epoch: 08, Loss: 0.0285, Train: 100.00%, Valid: 71.80% Test: 72.20%\n",
            "Epoch: 09, Loss: 0.0175, Train: 100.00%, Valid: 71.20% Test: 72.50%\n",
            "Epoch: 10, Loss: 0.0105, Train: 100.00%, Valid: 70.20% Test: 72.80%\n",
            "Epoch: 11, Loss: 0.0049, Train: 100.00%, Valid: 70.60% Test: 72.70%\n",
            "Epoch: 12, Loss: 0.0045, Train: 100.00%, Valid: 70.40% Test: 73.20%\n",
            "Epoch: 13, Loss: 0.0016, Train: 100.00%, Valid: 71.00% Test: 73.20%\n",
            "Epoch: 14, Loss: 0.0024, Train: 100.00%, Valid: 71.20% Test: 73.50%\n",
            "Epoch: 15, Loss: 0.0014, Train: 100.00%, Valid: 71.00% Test: 73.20%\n",
            "Epoch: 16, Loss: 0.0032, Train: 100.00%, Valid: 71.40% Test: 73.00%\n",
            "Epoch: 17, Loss: 0.0009, Train: 100.00%, Valid: 71.60% Test: 73.20%\n",
            "Epoch: 18, Loss: 0.0016, Train: 100.00%, Valid: 71.60% Test: 73.50%\n",
            "Epoch: 19, Loss: 0.0004, Train: 100.00%, Valid: 71.40% Test: 73.50%\n",
            "Epoch: 20, Loss: 0.0002, Train: 100.00%, Valid: 71.60% Test: 73.50%\n",
            "Epoch: 21, Loss: 0.0008, Train: 100.00%, Valid: 71.60% Test: 73.50%\n",
            "Epoch: 22, Loss: 0.0002, Train: 100.00%, Valid: 71.40% Test: 73.30%\n",
            "Epoch: 23, Loss: 0.0001, Train: 100.00%, Valid: 71.20% Test: 73.20%\n",
            "Epoch: 24, Loss: 0.0003, Train: 100.00%, Valid: 71.20% Test: 73.10%\n",
            "Epoch: 25, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.10%\n",
            "Epoch: 26, Loss: 0.0007, Train: 100.00%, Valid: 70.60% Test: 73.10%\n",
            "Epoch: 27, Loss: 0.0003, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 28, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 29, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.80%\n",
            "Epoch: 30, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 31, Loss: 0.0002, Train: 100.00%, Valid: 70.40% Test: 73.00%\n",
            "Epoch: 32, Loss: 0.0004, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 33, Loss: 0.0005, Train: 100.00%, Valid: 70.00% Test: 72.90%\n",
            "Epoch: 34, Loss: 0.0002, Train: 100.00%, Valid: 70.00% Test: 73.00%\n",
            "Epoch: 35, Loss: 0.0001, Train: 100.00%, Valid: 70.00% Test: 72.90%\n",
            "Epoch: 36, Loss: 0.0019, Train: 100.00%, Valid: 69.80% Test: 72.80%\n",
            "Epoch: 37, Loss: 0.0002, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 38, Loss: 0.0002, Train: 100.00%, Valid: 69.80% Test: 72.50%\n",
            "Epoch: 39, Loss: 0.0005, Train: 100.00%, Valid: 70.20% Test: 72.40%\n",
            "Epoch: 40, Loss: 0.0012, Train: 100.00%, Valid: 70.20% Test: 72.50%\n",
            "Epoch: 41, Loss: 0.0013, Train: 100.00%, Valid: 70.20% Test: 72.40%\n",
            "Epoch: 42, Loss: 0.0000, Train: 100.00%, Valid: 70.20% Test: 72.60%\n",
            "Epoch: 43, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.60%\n",
            "Epoch: 44, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 45, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 46, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.90%\n",
            "Epoch: 47, Loss: 0.0003, Train: 100.00%, Valid: 70.60% Test: 72.90%\n",
            "Epoch: 48, Loss: 0.0000, Train: 100.00%, Valid: 70.80% Test: 72.80%\n",
            "Epoch: 49, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.70%\n",
            "Epoch: 50, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.70%\n",
            "Epoch: 51, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.70%\n",
            "Epoch: 52, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 72.80%\n",
            "Epoch: 53, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.80%\n",
            "Epoch: 54, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.80%\n",
            "Epoch: 55, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.90%\n",
            "Epoch: 56, Loss: 0.0002, Train: 100.00%, Valid: 70.60% Test: 72.90%\n",
            "Epoch: 57, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 58, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 59, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 60, Loss: 0.0006, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 61, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 62, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 63, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.00%\n",
            "Epoch: 64, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.10%\n",
            "Epoch: 65, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.20%\n",
            "Epoch: 66, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 73.20%\n",
            "Epoch: 67, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 73.20%\n",
            "Epoch: 68, Loss: 0.0003, Train: 100.00%, Valid: 70.60% Test: 73.20%\n",
            "Epoch: 69, Loss: 0.0118, Train: 100.00%, Valid: 70.40% Test: 73.00%\n",
            "Epoch: 70, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 71, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 72.90%\n",
            "Epoch: 72, Loss: 0.0001, Train: 100.00%, Valid: 70.60% Test: 72.80%\n",
            "Epoch: 73, Loss: 0.0000, Train: 100.00%, Valid: 70.60% Test: 72.70%\n",
            "Epoch: 74, Loss: 0.0000, Train: 100.00%, Valid: 71.00% Test: 72.60%\n",
            "Epoch: 75, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.60%\n",
            "Epoch: 76, Loss: 0.0002, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 77, Loss: 0.0001, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 78, Loss: 0.0001, Train: 100.00%, Valid: 70.00% Test: 72.80%\n",
            "Epoch: 79, Loss: 0.0000, Train: 100.00%, Valid: 69.80% Test: 72.70%\n",
            "Epoch: 80, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 81, Loss: 0.0000, Train: 100.00%, Valid: 69.80% Test: 72.60%\n",
            "Epoch: 82, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 83, Loss: 0.0014, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 84, Loss: 0.0000, Train: 100.00%, Valid: 69.80% Test: 72.70%\n",
            "Epoch: 85, Loss: 0.0000, Train: 100.00%, Valid: 69.80% Test: 72.70%\n",
            "Epoch: 86, Loss: 0.0000, Train: 100.00%, Valid: 69.60% Test: 72.80%\n",
            "Epoch: 87, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.80%\n",
            "Epoch: 88, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 89, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 90, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 91, Loss: 0.0002, Train: 100.00%, Valid: 70.00% Test: 72.60%\n",
            "Epoch: 92, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.60%\n",
            "Epoch: 93, Loss: 0.0009, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 94, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.70%\n",
            "Epoch: 95, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 72.80%\n",
            "Epoch: 96, Loss: 0.0000, Train: 100.00%, Valid: 69.60% Test: 72.90%\n",
            "Epoch: 97, Loss: 0.0000, Train: 100.00%, Valid: 69.60% Test: 72.90%\n",
            "Epoch: 98, Loss: 0.0006, Train: 100.00%, Valid: 69.60% Test: 72.80%\n",
            "Epoch: 99, Loss: 0.0000, Train: 100.00%, Valid: 69.20% Test: 72.80%\n",
            "Epoch: 100, Loss: 0.0001, Train: 100.00%, Valid: 69.00% Test: 72.80%\n",
            "Epoch: 101, Loss: 0.0000, Train: 100.00%, Valid: 69.00% Test: 72.80%\n",
            "Epoch: 102, Loss: 0.0001, Train: 100.00%, Valid: 69.00% Test: 72.80%\n",
            "Epoch: 103, Loss: 0.0000, Train: 100.00%, Valid: 69.20% Test: 72.80%\n",
            "Epoch: 104, Loss: 0.0000, Train: 100.00%, Valid: 69.20% Test: 72.80%\n",
            "Epoch: 105, Loss: 0.0000, Train: 100.00%, Valid: 69.00% Test: 72.80%\n",
            "Epoch: 106, Loss: 0.0001, Train: 100.00%, Valid: 69.20% Test: 72.80%\n",
            "Epoch: 107, Loss: 0.0000, Train: 100.00%, Valid: 69.40% Test: 72.80%\n",
            "Epoch: 108, Loss: 0.0003, Train: 100.00%, Valid: 69.40% Test: 72.80%\n",
            "Epoch: 109, Loss: 0.0017, Train: 100.00%, Valid: 69.80% Test: 73.00%\n",
            "Epoch: 110, Loss: 0.0000, Train: 100.00%, Valid: 69.80% Test: 73.00%\n",
            "Epoch: 111, Loss: 0.0001, Train: 100.00%, Valid: 69.60% Test: 73.00%\n",
            "Epoch: 112, Loss: 0.0000, Train: 100.00%, Valid: 69.80% Test: 73.10%\n",
            "Epoch: 113, Loss: 0.0002, Train: 100.00%, Valid: 69.60% Test: 73.30%\n",
            "Epoch: 114, Loss: 0.0001, Train: 100.00%, Valid: 70.00% Test: 73.40%\n",
            "Epoch: 115, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 73.30%\n",
            "Epoch: 116, Loss: 0.0002, Train: 100.00%, Valid: 70.00% Test: 73.20%\n",
            "Epoch: 117, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 73.10%\n",
            "Epoch: 118, Loss: 0.0001, Train: 100.00%, Valid: 70.00% Test: 73.10%\n",
            "Epoch: 119, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 73.10%\n",
            "Epoch: 120, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 73.10%\n",
            "Epoch: 121, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 73.10%\n",
            "Epoch: 122, Loss: 0.0000, Train: 100.00%, Valid: 70.00% Test: 73.10%\n",
            "Epoch: 123, Loss: 0.0004, Train: 100.00%, Valid: 70.00% Test: 73.10%\n",
            "Epoch: 124, Loss: 0.0000, Train: 100.00%, Valid: 70.20% Test: 73.00%\n",
            "Epoch: 125, Loss: 0.0001, Train: 100.00%, Valid: 70.20% Test: 72.90%\n",
            "Epoch: 126, Loss: 0.0000, Train: 100.00%, Valid: 70.20% Test: 72.90%\n",
            "Epoch: 127, Loss: 0.0001, Train: 100.00%, Valid: 70.20% Test: 72.90%\n",
            "Epoch: 128, Loss: 0.0000, Train: 100.00%, Valid: 70.20% Test: 72.90%\n",
            "Epoch: 129, Loss: 0.0000, Train: 100.00%, Valid: 70.20% Test: 72.90%\n",
            "Epoch: 130, Loss: 0.0002, Train: 100.00%, Valid: 70.40% Test: 72.90%\n",
            "Epoch: 131, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 132, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 133, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 134, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 135, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 136, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 137, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 138, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 139, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 140, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 141, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 142, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 143, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 144, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 145, Loss: 0.0002, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 146, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 147, Loss: 0.0002, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 148, Loss: 0.0001, Train: 100.00%, Valid: 70.40% Test: 72.80%\n",
            "Epoch: 149, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.90%\n",
            "Epoch: 150, Loss: 0.0000, Train: 100.00%, Valid: 70.40% Test: 72.90%\n",
            "Best model: Train: 100.00%, Valid: 71.80% Test: 72.20%\n"
          ]
        }
      ],
      "source": [
        "graphs_train, graphs_val, graphs_test = \\\n",
        "    GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n",
        "\n",
        "graph_train = graphs_train[0]\n",
        "graph_val = graphs_val[0]\n",
        "graph_test = graphs_test[0]\n",
        "\n",
        "model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
        "graphs = [graph_train, graph_val, graph_test]\n",
        "all_best_model, all_accs = train(graphs, graphs, args, model, optimizer, mode=\"all\")\n",
        "train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], all_best_model)\n",
        "print('Best model:',\n",
        "      f'Train: {100 * train_acc:.2f}%, '\n",
        "      f'Valid: {100 * val_acc:.2f}% '\n",
        "      f'Test: {100 * test_acc:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RpuETv7Vpx0"
      },
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "PMK33kY5VmF5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "eefda952-a8fc-4732-9f6a-5eee11361151"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "louvain_results = np.array(louvain_accs)\n",
        "bisection_results = np.array(bisection_accs)\n",
        "greedy_results = np.array(greedy_accs)\n",
        "all_results = np.array(all_accs)\n",
        "\n",
        "x = np.arange(1, 151)\n",
        "\n",
        "plt.figure(figsize=(9, 7))\n",
        "\n",
        "plt.plot(x, louvain_results[:, 1], label=\"Louvain Validation\")\n",
        "plt.plot(x, louvain_results[:, 2], label=\"Louvain Test\")\n",
        "plt.plot(x, bisection_results[:, 1], label=\"Bisection Validation\")\n",
        "plt.plot(x, bisection_results[:, 2], label=\"Bisection Test\")\n",
        "plt.plot(x, greedy_results[:, 1], label=\"Greedy Validation\")\n",
        "plt.plot(x, greedy_results[:, 2], label=\"Greedy Test\")\n",
        "plt.plot(x, all_results[:, 1], label=\"All Validation\")\n",
        "plt.plot(x, all_results[:, 2], label=\"All Test\")\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "Copy of CS224W - Colab 5.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}