assignment1/Assignment_1_improvement.ipynb

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    "colab": {
      "name": "Assignment 1_improvement.ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyMrNXSYeLj+G5mvhxIiwMEj",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/venomouscyanide/grl/blob/master/assignment1/Assignment_1_improvement.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pw2JsINVpz2Y"
      },
      "source": [
        "# Changelog\n",
        "This is a fork of https://colab.research.google.com/drive/1ZIoa5VfQzVayf5igY2qdOtcok3sC1RXl with modifications made to the accuracy function\n",
        "\n",
        "Changes can be summarized as:\n",
        "- Target tensor dimension containing [Harmonic centrality](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.harmonic_centrality.html#networkx.algorithms.centrality.harmonic_centrality) value is now normalized by (num_nodes - 1)\n",
        "- Accuracy is now replaced with 8 different mean squared error columns + one overall average error column(average of the 8 MSE errors values)\n",
        "\n",
        "    The pseudocode for calculating MSE is as follows:\n",
        "    ```\n",
        "    prediction = model(data)\n",
        "    for dim in range(8):\n",
        "        MSE_for_stat = MSE(prediction[:, dim], target[:, dim]) # gives 8-d tensor\n",
        "    ```\n",
        "    Repeat the above for 3 seeds and take average of the 3 8-d tensors(tensor containing 8 MSE errors captured).\n",
        "    \n",
        "    Capture each value in the averaged 8-d tensor(average of 3 random seed runs) in a dataframe with column name being the name of the statistic.\n",
        "    \n",
        "    Finally, take average of the 8 values in the averaged out tensor and capture this floating value and call it \"Average Error across 8-d\"\n",
        "\n",
        "    _Note: Loss function still remains mean squared error across all training samples_\n",
        "    \n",
        "    **Example for 2 rows(each row is the output after running 3 seeds for a given set of hyperparameters)**:\n",
        "    \n",
        "    <small>Average Error across 8-d is nothing but the mean of the 8 values\n",
        "    <a href=\"https://drive.google.com/uc?export=view&id=1cii-SVRBcCxOyirYVWI6Y7Y7Ypxrd2ug\">\n",
        "    <img src=\"https://drive.google.com/uc?export=view&id=1cii-SVRBcCxOyirYVWI6Y7Y7Ypxrd2ug\"\n",
        "    style=\"width: 500px; max-width: 100%; height: auto\"\n",
        "    title=\"Click for the larger version.\" />\n",
        "</a>\n",
        "\n",
        "- Remove testing over downstream tasks, just focus on the training([MSE](https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html)) error values for each learnt statistic\n",
        "    \n",
        "- Bug fix random ordering for target tensor\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_W5wShvfGFrz",
        "outputId": "f28b87e4-502e-461b-bb7b-f5c4422a7a20"
      },
      "source": [
        "# Install torch geometric\n",
        "!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html\n",
        "!pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html\n",
        "!pip install torch-geometric"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html\n",
            "Collecting torch-scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-1.9.0%2Bcu111/torch_scatter-2.0.9-cp37-cp37m-linux_x86_64.whl (10.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.4 MB 4.3 MB/s \n",
            "\u001b[?25hInstalling collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.0.9\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html\n",
            "Collecting torch-sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-1.9.0%2Bcu111/torch_sparse-0.6.12-cp37-cp37m-linux_x86_64.whl (3.7 MB)\n",
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            "Installing collected packages: torch-sparse\n",
            "Successfully installed torch-sparse-0.6.12\n",
            "Collecting torch-geometric\n",
            "  Downloading torch_geometric-2.0.1.tar.gz (308 kB)\n",
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            "Collecting rdflib\n",
            "  Downloading rdflib-6.0.2-py3-none-any.whl (407 kB)\n",
            "\u001b[K     |████████████████████████████████| 407 kB 27.7 MB/s \n",
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            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
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            "  Downloading isodate-0.6.0-py2.py3-none-any.whl (45 kB)\n",
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            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->torch-geometric) (1.0.1)\n",
            "Building wheels for collected packages: torch-geometric\n",
            "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-2.0.1-py3-none-any.whl size=513822 sha256=33f9e611e1289475d0765ea54cba94212d2461bcdc0d1b16f7b641cf1f06ddb4\n",
            "  Stored in directory: /root/.cache/pip/wheels/78/3d/42/20589db73c66b5109fb93a0c5743edfd6ab5ca820a52afacfc\n",
            "Successfully built torch-geometric\n",
            "Installing collected packages: isodate, yacs, rdflib, torch-geometric\n",
            "Successfully installed isodate-0.6.0 rdflib-6.0.2 torch-geometric-2.0.1 yacs-0.1.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WnlSpv_PFney"
      },
      "source": [
        "# All imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3k5kLCCyFqF8"
      },
      "source": [
        "import time\n",
        "from copy import deepcopy\n",
        "import itertools\n",
        "import random\n",
        "from typing import List, Union\n",
        "from cachetools import Cache\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch_geometric.data import Data, Dataset\n",
        "from torch_geometric.datasets import *\n",
        "from torch_geometric.nn import GCNConv\n",
        "from torch.nn import Linear\n",
        "import networkx as nx\n",
        "from networkx.algorithms.centrality import *\n",
        "from networkx.algorithms.cluster import *\n",
        "from torch_geometric.utils import to_networkx\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuFFVsi80Xcn"
      },
      "source": [
        "Creating a cache to store the target tensors containing node statistics. Calculating N node statistics(8 in my experiments) multiple times can be compute heavy for graphs with high number of nodes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "191padYrvUVs"
      },
      "source": [
        "cache = Cache(maxsize=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0uxyVzMIpqeW"
      },
      "source": [
        "## Increase max rows and cols for dataframes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJXS2sfLnItq"
      },
      "source": [
        "pd.set_option('max_rows', 99999)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0huTdrXRnKzA"
      },
      "source": [
        "pd.set_option('max_colwidth', 400)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6HTjZy5Fu41"
      },
      "source": [
        "# Utility functions\n",
        "A few utility functions to help understand the datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCxPx1FMFuNG"
      },
      "source": [
        "def visualize(h: Union[torch.tensor, nx.Graph], color, epoch: int = None, loss=None):\n",
        "    \"\"\"\n",
        "        Helps visualize the graph in a 2d space\n",
        "    \"\"\"\n",
        "    # Taken from https://colab.research.google.com/drive/1CILdAekIkIh-AX2EXwZ3ZsZ6VcCbwc0t?usp=sharing\n",
        "    plt.figure(figsize=(7, 7))\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "\n",
        "    if torch.is_tensor(h):\n",
        "        h = h.detach().cpu().numpy()\n",
        "        plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap=\"Set2\")\n",
        "        if epoch is not None and loss is not None:\n",
        "            plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)\n",
        "    else:\n",
        "        nx.draw_networkx(h, pos=None, with_labels=False, node_color=color, cmap=\"Dark2\")\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def show_dataset_as_networkx_graph(data: Dataset):\n",
        "    \"\"\"\n",
        "        Convert PyG dataset into nx graph for visualization\n",
        "    \"\"\"\n",
        "    G = to_networkx(data, to_undirected=False)\n",
        "    visualize(G, color=data.y)\n",
        "\n",
        "\n",
        "def show_dataset_stats(dataset: Dataset):\n",
        "    \"\"\"\n",
        "        Show the stats of the dataset\n",
        "    \"\"\"\n",
        "    # Taken from https://colab.research.google.com/drive/1CILdAekIkIh-AX2EXwZ3ZsZ6VcCbwc0t?usp=sharing\n",
        "    data = dataset[0]\n",
        "    print(f'Dataset: {dataset}:')\n",
        "    print('======================')\n",
        "    print(f'Number of graphs: {len(dataset)}')\n",
        "    print(f'Number of features: {dataset.num_features}')\n",
        "    print(f'Number of classes: {dataset.num_classes}')\n",
        "    print(f'Number of nodes: {data.num_nodes}')\n",
        "    print(f'Number of edges: {data.num_edges}')\n",
        "    print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
        "    print(f'Number of training nodes: {data.train_mask.sum()}')\n",
        "    print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}')\n",
        "    print(f'Contains isolated nodes: {data.has_isolated_nodes()}')\n",
        "    print(f'Contains self-loops: {data.has_self_loops()}')\n",
        "    print(f'Is undirected: {data.is_undirected()}')\n",
        "    print('======================')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dn2Z0glKLy1O"
      },
      "source": [
        "# GCN model\n",
        "A simple GCN model with 3 layers. Outputs a tensor that is  num_nodes * 8 dimensions. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKhfj06IL0k3"
      },
      "source": [
        "class GCN(torch.nn.Module):\n",
        "    # TODO: multiple regressor heads?\n",
        "    # reference: https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html\n",
        "\n",
        "    def __init__(self, dataset: Data):\n",
        "        super().__init__()\n",
        "\n",
        "        self.conv1 = GCNConv(dataset.num_node_features, 32)\n",
        "        self.conv2 = GCNConv(32, 16)\n",
        "        self.conv3 = GCNConv(16, Hyperparameters.EMBEDDINGS_SIZE)\n",
        "\n",
        "        self.optimizer = torch.optim.Adam(self.parameters(), lr=Hyperparameters.LEARNING_RATE,\n",
        "                                          weight_decay=Hyperparameters.WEIGHT_DECAY)\n",
        "        self.criterion = torch.nn.MSELoss()\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        x = self.conv1(x, edge_index)\n",
        "\n",
        "        x = F.relu(x)\n",
        "        x = self.conv2(x, edge_index)\n",
        "\n",
        "        x = F.relu(x)\n",
        "        x = self.conv3(x, edge_index)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def train_helper(self, target: torch.tensor, epoch: int, viz_training: bool, data):\n",
        "        self.optimizer.zero_grad()\n",
        "        h = self(data.x, data.edge_index)\n",
        "        loss = self.criterion(h[data.train_mask], target[data.train_mask])\n",
        "        loss.backward()\n",
        "        if viz_training and epoch % 50 == 0:\n",
        "            print(f\"epoch: {epoch}, loss: {loss}\")\n",
        "        self.optimizer.step()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoPGY-YwL5-2"
      },
      "source": [
        "# Class for capturing node statistics in a tensor\n",
        "Calculate the node features based on nx APIs. I capture 8 different statistical properties for each node in the graph. The captured features are then converted into a tensor to be used during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tg-z6vsFL1kG"
      },
      "source": [
        "class CreateNodeFeatureTensor:\n",
        "    def create(self, data: Dataset) -> torch.tensor:\n",
        "        \"\"\"\n",
        "            For the nodes in the dataset, create a tensor of num_nodes * 8, where each node has 8 dimensions.\n",
        "            Each dimension corresponds to a node statistic.\n",
        "        \"\"\"\n",
        "        dataset_id = str(data)\n",
        "        if type(cache.get(dataset_id)) == torch.Tensor:\n",
        "            return cache.get(dataset_id)\n",
        "        nx_graph = to_networkx(data)\n",
        "        # features captured\n",
        "        feature_functions: List[str] = Hyperparameters.ALL_STATISTICS_CAPTURED\n",
        "\n",
        "        # ref for globals()[\"function_name\"](): https://stackoverflow.com/a/834451\n",
        "        feature_values: List[List[float]] = [\n",
        "            list(globals()[feature](nx_graph).values()) for feature in feature_functions\n",
        "        ]\n",
        "\n",
        "        # norm harmonic centrality as values are larger than other stats/unnormalized\n",
        "        self._norm_harmonic_centrality_values(feature_values, nx_graph)\n",
        "\n",
        "        features = [list(node_embedding) for node_embedding in zip(*feature_values)]\n",
        "        target_tensor = torch.tensor(features, dtype=torch.float)\n",
        "        cache[dataset_id] = target_tensor\n",
        "        return target_tensor\n",
        "\n",
        "    def _norm_harmonic_centrality_values(self, feature_values, nx_graph):\n",
        "        \"\"\"\n",
        "        normalize harmonic centrality values\n",
        "        normalized harmonic centrality(node) = sum(1 / distance from node to every other node excluding itself) / (number of nodes - 1)\n",
        "        ref: https://neo4j.com/docs/graph-data-science/current/algorithms/harmonic-centrality/\n",
        "        \"\"\"\n",
        "\n",
        "        normalization_val = nx_graph.number_of_nodes() - 1\n",
        "        idx_of_hc_values = Hyperparameters.ALL_STATISTICS_CAPTURED.index(\"harmonic_centrality\")\n",
        "        feature_values[idx_of_hc_values] = [hc / normalization_val for hc in feature_values[idx_of_hc_values]]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXHZras_MHTN"
      },
      "source": [
        "# Setting the hyperparameters\n",
        "This class contains the hyperparameter configurations for training. It also contains a method to overwrite the default parameters to perform automated analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzzFN0xaMOul"
      },
      "source": [
        "class Hyperparameters:\n",
        "    # Ref for centrality APIs: https://networkx.org/documentation/stable/reference/algorithms/centrality.html\n",
        "    # Ref for clustering APIs: https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.cluster.clustering.html\n",
        "    # I just focus on learning centrality/clustering related info\n",
        "    # target tensor size is 8\n",
        "    ALL_STATISTICS_CAPTURED: List[str] = ['degree_centrality', 'in_degree_centrality',\n",
        "                                          'eigenvector_centrality', 'harmonic_centrality',\n",
        "                                          'closeness_centrality', 'betweenness_centrality', 'load_centrality',\n",
        "                                          'clustering']\n",
        "    # upto 8 dimensional vector captured for each node.\n",
        "    # Each dimension corresponds to a centrality measure in the same order as they are listed above\n",
        "    # NOTE! This class contains the default values. During automated training and eval, these are overwritten to the automated running config.\n",
        "    EMBEDDINGS_SIZE: int = 8\n",
        "    LEARNING_RATE: float = 0.01\n",
        "    WEIGHT_DECAY: float = 1e-5\n",
        "    TRAINING_RATIO: float = 0.70\n",
        "    DATASET: str = \"KarateClub\"\n",
        "    EPOCHS: int = 1000\n",
        "\n",
        "    @staticmethod\n",
        "    def override_defaults(*args):\n",
        "        \"\"\"\n",
        "            this method allows you to override the defaults for automated testing on different configs.\n",
        "        \"\"\"\n",
        "        Hyperparameters.EMBEDDINGS_SIZE = args[0]\n",
        "        Hyperparameters.LEARNING_RATE = args[1]\n",
        "        Hyperparameters.WEIGHT_DECAY = args[2]\n",
        "        Hyperparameters.TRAINING_RATIO = args[3]\n",
        "        Hyperparameters.DATASET = args[4]\n",
        "        Hyperparameters.EPOCHS = args[5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_upICmn4MXvd"
      },
      "source": [
        "# Class to help train the model on the dataset of choice\n",
        "The TrainAndEvaluate class will run training on the Hyperparameters set in Hyperparameters. It also has an evaluate() endpoint to return the MSE error after training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-YlRcuYMYdg"
      },
      "source": [
        "class TrainAndEvaluate:\n",
        "    \"\"\"\n",
        "        This class is responsible for two things:\n",
        "            1) Run the training for N epochs on a dataset\n",
        "            2) Report the error after each run. Error is a 8-d tensor which captures the MSE across each statistic(column-wise error)\n",
        "    \"\"\"\n",
        "    def __init__(self, data: Dataset):\n",
        "        self.data = data\n",
        "        self.target = CreateNodeFeatureTensor().create(data)\n",
        "\n",
        "    def train_helper(self, viz_data: bool = False, viz_training: bool = False):\n",
        "        if viz_data:\n",
        "            show_dataset_as_networkx_graph(self.data)\n",
        "        self._set_train_test_mask()\n",
        "        model = GCN(self.data)\n",
        "        for epoch in range(Hyperparameters.EPOCHS):\n",
        "            model.train_helper(self.target, epoch, viz_training, self.data)\n",
        "        return model\n",
        "\n",
        "    def evaluate(self, trained_model: GCN) -> torch.tensor:\n",
        "        trained_model.eval()\n",
        "        out_tensor = trained_model(self.data.x, self.data.edge_index)\n",
        "        return self._get_accuracy(out_tensor)\n",
        "\n",
        "    def _set_train_test_mask(self):\n",
        "        # set train and test mask according to how you set the training ratio hyperparameter\n",
        "        set_of_all_indices = set(range(len(self.data.x)))\n",
        "\n",
        "        train_mask_indices = torch.randperm(len(self.data.x))[:int(Hyperparameters.TRAINING_RATIO * len(self.data.x))]\n",
        "        set_of_training_indices = set(train_mask_indices.detach().tolist())\n",
        "        test_mask_indices = torch.tensor(list(set_of_all_indices.difference(set_of_training_indices)), dtype=int)\n",
        "\n",
        "        train_mask = torch.tensor([False for _ in range(len(self.data.x))], dtype=bool)\n",
        "        test_mask = torch.tensor([False for _ in range(len(self.data.x))], dtype=bool)\n",
        "\n",
        "        train_mask[train_mask_indices] = True\n",
        "        test_mask[test_mask_indices] = True\n",
        "\n",
        "        # sanity check\n",
        "        assert len(set_of_training_indices.intersection(set(test_mask_indices.detach().tolist()))) == 0 \\\n",
        "            , \"train and test sets are not mutually exclusive\"\n",
        "\n",
        "        # overwrite the test and train boolean masks\n",
        "        self.data.test_mask = test_mask\n",
        "        self.data.train_mask = train_mask\n",
        "\n",
        "    def _get_accuracy(self, out: torch.tensor) -> torch.tensor:\n",
        "        # for some reason cosine sim is giving high similarity no matter what\n",
        "        # cosine_sim = torch.nn.functional.cosine_similarity(x1=out[self.data.test_mask],\n",
        "        #                                                    x2=self.target[self.data.test_mask], dim=1)\n",
        "        # define a custom accuracy metric\n",
        "        # all the output values must be within the threshold defined in Hyperparameters.ERROR_THRESHOLD\n",
        "\n",
        "        ## ~~~~START OF OLD ACCURACY CODE~~~\n",
        "        # diff_tensor = torch.abs(out[self.data.test_mask] - self.target[self.data.test_mask])\n",
        "        # loss = torch.nn.MSELoss()\n",
        "        # for dim in range(Hyperparameters.EMBEDDINGS_SIZE):\n",
        "        #     expectation_for_statistic = self.target[self.data.test_mask][dim]\n",
        "        #     prediction_for_statistic = out[self.data.test_mask][dim]\n",
        "        #     with torch.no_grad():\n",
        "        #         loss(prediction_for_statistic, expectation_for_statistic)\n",
        "        # within_threshold = torch.le(diff_tensor, Hyperparameters.ERROR_THRESHOLD)\n",
        "        # numerator = int(torch.count_nonzero(within_threshold == True))\n",
        "        # denominator = diff_tensor.shape[0] * diff_tensor.shape[1]\n",
        "        # accuracy = round(numerator / denominator * 100, 4)\n",
        "        ## ~~~~END OF OLD ACCURACY CODE~~~\n",
        "\n",
        "        \"\"\"\n",
        "        Calculate the MSE across each statistic(column) and return the 8-d tensor which contains the MSE across each statistic\n",
        "        \"\"\"\n",
        "        loss = torch.nn.MSELoss()\n",
        "        accuracy = []\n",
        "        for dim in range(Hyperparameters.EMBEDDINGS_SIZE):\n",
        "            expectation_for_statistic = self.target[self.data.test_mask][:, dim]\n",
        "            prediction_for_statistic = out[self.data.test_mask][:, dim]\n",
        "            with torch.no_grad():\n",
        "                accuracy.append(float(loss(prediction_for_statistic, expectation_for_statistic)))\n",
        "        return torch.tensor(accuracy)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AauJ1YuMewZ"
      },
      "source": [
        "# Class to automate the training and to summarize the results into a dataframe\n",
        "A class to automate the training on different configurations and capture the result in a dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_A9SZn_FMj94"
      },
      "source": [
        "class TrainAndCaptureResults:\n",
        "    \"\"\"\n",
        "        Helps run different combinations of hyperparameters as captured in HyperParameterCombinations.\n",
        "        For each combination, take the average MSE error over 3 predefined random seeds and add to dataframe. This helps see error across each statistic\n",
        "        Also, take average of the 8-d dimensional error values to get one error value and call it Average Error across 8-d. This helps understand the overall error.\n",
        "        At the end of execution, you will have a N row dataframe, where N = number of combinations.\n",
        "        The dataframe is ordered in ascending order of Average Error across 8-d to find out the best combination.\n",
        "    \"\"\"\n",
        "class TrainAndCaptureResults:\n",
        "    def __init__(self):\n",
        "        self.seeds: List[int] = [7, 13, 666]\n",
        "        self.data = pd.DataFrame(\n",
        "            columns=[\"Embedding Size\", \"Learning Rate\", \"Weight Decay\", \"Training Ratio\", \"Dataset\", \"Epochs\",\n",
        "                     *[f'{stat}() error' for stat in Hyperparameters.ALL_STATISTICS_CAPTURED],\n",
        "                     f\"Average Error across {Hyperparameters.EMBEDDINGS_SIZE}-d\"])\n",
        "\n",
        "    def _setup(self) -> List:\n",
        "        combinations = [HyperParameterCombinations.ES, HyperParameterCombinations.LR, HyperParameterCombinations.WD,\n",
        "                        HyperParameterCombinations.TR, HyperParameterCombinations.DATASET,\n",
        "                        HyperParameterCombinations.EPOCHS]\n",
        "        return combinations\n",
        "\n",
        "    def run(self) -> pd.DataFrame:\n",
        "        # Update the accuracy to MSE error for finer grained analysis of each statistic\n",
        "        errors = []\n",
        "        all_combinations = list(itertools.product(*self._setup()))\n",
        "        total_combinations = len(all_combinations)\n",
        "        print(f\"Total combinations to run exp on: {total_combinations}\")\n",
        "        for index, combination in enumerate(all_combinations):\n",
        "            print(f\"Running combination: {index + 1} of {total_combinations}\")\n",
        "            combination = list(combination)\n",
        "            data = combination[4]\n",
        "            error_tensor_sum = torch.tensor([0.0 for _ in range(Hyperparameters.EMBEDDINGS_SIZE)])\n",
        "            Hyperparameters.override_defaults(*combination)\n",
        "            for seed in self.seeds:\n",
        "                torch.manual_seed(seed)\n",
        "                train_and_eval = TrainAndEvaluate(data[0])\n",
        "                trained_model = train_and_eval.train_helper(False)\n",
        "                error_tensor_sum += train_and_eval.evaluate(trained_model)\n",
        "            avg_mse = torch.div(error_tensor_sum, 3.0)\n",
        "            avg_mse_across_8d = torch.mean(avg_mse)\n",
        "            errors.append(avg_mse_across_8d)\n",
        "            combination.extend([float(x) for x in avg_mse])\n",
        "            combination.append(float(avg_mse_across_8d))\n",
        "            combination[4] = str(combination[4])\n",
        "            self.data.loc[len(self.data)] = combination\n",
        "        least_error = min(errors)\n",
        "        print(f\"Least avg error across 8-d: {least_error}\")\n",
        "        self.data.sort_values(by=f\"Average Error across {Hyperparameters.EMBEDDINGS_SIZE}-d\", ascending=True,\n",
        "                              inplace=True)\n",
        "        return self.data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2NujRlkMmtR"
      },
      "source": [
        "# Experiments\n",
        "Conduct different experiments on small/medium sized graphs to see the accuracy after training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ix1iQ9o2Mqgf"
      },
      "source": [
        "## Experiment on KarateClub dataset\n",
        "Before each experiment it is vital to define the class HyperParameterCombinations which will contain all the values on which the experiments are to be conducted on. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CaNeHGIMs7x",
        "outputId": "b04959fe-660f-4346-bb09-c23f259466c6"
      },
      "source": [
        "show_dataset_stats(KarateClub())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset: KarateClub():\n",
            "======================\n",
            "Number of graphs: 1\n",
            "Number of features: 34\n",
            "Number of classes: 4\n",
            "Number of nodes: 34\n",
            "Number of edges: 156\n",
            "Average node degree: 4.59\n",
            "Number of training nodes: 4\n",
            "Training node label rate: 0.12\n",
            "Contains isolated nodes: False\n",
            "Contains self-loops: False\n",
            "Is undirected: True\n",
            "======================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "VDNN8_r9UjNP",
        "outputId": "9ca84565-4d46-4147-d7a8-1f578d6b11c4"
      },
      "source": [
        "show_dataset_as_networkx_graph(KarateClub()[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNPnL-eWM2Ai"
      },
      "source": [
        "class HyperParameterCombinations:\n",
        "    \"\"\"\n",
        "    To be manually configured\n",
        "    \"\"\"\n",
        "    ES = [8]\n",
        "    LR = [0.01, 0.001]\n",
        "    WD = [1e-5, 1e-3]\n",
        "    TR = [x / 10 for x in range(1, 9)]\n",
        "    DATASET = [KarateClub()]\n",
        "    EPOCHS = [250, 500, 1000]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nF-Ah--UMv72",
        "outputId": "bfe550fd-b0d7-4f77-aaa8-7d5735aaad49"
      },
      "source": [
        "df = TrainAndCaptureResults().run()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total combinations to run exp on: 96\n",
            "Running combination: 1 of 96\n",
            "Running combination: 2 of 96\n",
            "Running combination: 3 of 96\n",
            "Running combination: 4 of 96\n",
            "Running combination: 5 of 96\n",
            "Running combination: 6 of 96\n",
            "Running combination: 7 of 96\n",
            "Running combination: 8 of 96\n",
            "Running combination: 9 of 96\n",
            "Running combination: 10 of 96\n",
            "Running combination: 11 of 96\n",
            "Running combination: 12 of 96\n",
            "Running combination: 13 of 96\n",
            "Running combination: 14 of 96\n",
            "Running combination: 15 of 96\n",
            "Running combination: 16 of 96\n",
            "Running combination: 17 of 96\n",
            "Running combination: 18 of 96\n",
            "Running combination: 19 of 96\n",
            "Running combination: 20 of 96\n",
            "Running combination: 21 of 96\n",
            "Running combination: 22 of 96\n",
            "Running combination: 23 of 96\n",
            "Running combination: 24 of 96\n",
            "Running combination: 25 of 96\n",
            "Running combination: 26 of 96\n",
            "Running combination: 27 of 96\n",
            "Running combination: 28 of 96\n",
            "Running combination: 29 of 96\n",
            "Running combination: 30 of 96\n",
            "Running combination: 31 of 96\n",
            "Running combination: 32 of 96\n",
            "Running combination: 33 of 96\n",
            "Running combination: 34 of 96\n",
            "Running combination: 35 of 96\n",
            "Running combination: 36 of 96\n",
            "Running combination: 37 of 96\n",
            "Running combination: 38 of 96\n",
            "Running combination: 39 of 96\n",
            "Running combination: 40 of 96\n",
            "Running combination: 41 of 96\n",
            "Running combination: 42 of 96\n",
            "Running combination: 43 of 96\n",
            "Running combination: 44 of 96\n",
            "Running combination: 45 of 96\n",
            "Running combination: 46 of 96\n",
            "Running combination: 47 of 96\n",
            "Running combination: 48 of 96\n",
            "Running combination: 49 of 96\n",
            "Running combination: 50 of 96\n",
            "Running combination: 51 of 96\n",
            "Running combination: 52 of 96\n",
            "Running combination: 53 of 96\n",
            "Running combination: 54 of 96\n",
            "Running combination: 55 of 96\n",
            "Running combination: 56 of 96\n",
            "Running combination: 57 of 96\n",
            "Running combination: 58 of 96\n",
            "Running combination: 59 of 96\n",
            "Running combination: 60 of 96\n",
            "Running combination: 61 of 96\n",
            "Running combination: 62 of 96\n",
            "Running combination: 63 of 96\n",
            "Running combination: 64 of 96\n",
            "Running combination: 65 of 96\n",
            "Running combination: 66 of 96\n",
            "Running combination: 67 of 96\n",
            "Running combination: 68 of 96\n",
            "Running combination: 69 of 96\n",
            "Running combination: 70 of 96\n",
            "Running combination: 71 of 96\n",
            "Running combination: 72 of 96\n",
            "Running combination: 73 of 96\n",
            "Running combination: 74 of 96\n",
            "Running combination: 75 of 96\n",
            "Running combination: 76 of 96\n",
            "Running combination: 77 of 96\n",
            "Running combination: 78 of 96\n",
            "Running combination: 79 of 96\n",
            "Running combination: 80 of 96\n",
            "Running combination: 81 of 96\n",
            "Running combination: 82 of 96\n",
            "Running combination: 83 of 96\n",
            "Running combination: 84 of 96\n",
            "Running combination: 85 of 96\n",
            "Running combination: 86 of 96\n",
            "Running combination: 87 of 96\n",
            "Running combination: 88 of 96\n",
            "Running combination: 89 of 96\n",
            "Running combination: 90 of 96\n",
            "Running combination: 91 of 96\n",
            "Running combination: 92 of 96\n",
            "Running combination: 93 of 96\n",
            "Running combination: 94 of 96\n",
            "Running combination: 95 of 96\n",
            "Running combination: 96 of 96\n",
            "Least avg error across 8-d: 0.01032735500484705\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lo0DdHwyM9pI",
        "outputId": "af7baf59-223a-4a09-8b45-fc8b49b50d66"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Embedding Size</th>\n",
              "      <th>Learning Rate</th>\n",
              "      <th>Weight Decay</th>\n",
              "      <th>Training Ratio</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>Epochs</th>\n",
              "      <th>degree_centrality() error</th>\n",
              "      <th>in_degree_centrality() error</th>\n",
              "      <th>eigenvector_centrality() error</th>\n",
              "      <th>harmonic_centrality() error</th>\n",
              "      <th>closeness_centrality() error</th>\n",
              "      <th>betweenness_centrality() error</th>\n",
              "      <th>load_centrality() error</th>\n",
              "      <th>clustering() error</th>\n",
              "      <th>Average Error across 8-d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001361</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>0.001823</td>\n",
              "      <td>0.001731</td>\n",
              "      <td>0.002234</td>\n",
              "      <td>0.003981</td>\n",
              "      <td>0.003905</td>\n",
              "      <td>0.067242</td>\n",
              "      <td>0.010327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001263</td>\n",
              "      <td>0.000315</td>\n",
              "      <td>0.001623</td>\n",
              "      <td>0.001550</td>\n",
              "      <td>0.001974</td>\n",
              "      <td>0.003981</td>\n",
              "      <td>0.003896</td>\n",
              "      <td>0.068031</td>\n",
              "      <td>0.010329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001274</td>\n",
              "      <td>0.000322</td>\n",
              "      <td>0.001738</td>\n",
              "      <td>0.001483</td>\n",
              "      <td>0.001966</td>\n",
              "      <td>0.004066</td>\n",
              "      <td>0.003999</td>\n",
              "      <td>0.068680</td>\n",
              "      <td>0.010441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001198</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>0.001606</td>\n",
              "      <td>0.001389</td>\n",
              "      <td>0.001818</td>\n",
              "      <td>0.004085</td>\n",
              "      <td>0.003998</td>\n",
              "      <td>0.071765</td>\n",
              "      <td>0.010770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001414</td>\n",
              "      <td>0.000353</td>\n",
              "      <td>0.001028</td>\n",
              "      <td>0.000655</td>\n",
              "      <td>0.001187</td>\n",
              "      <td>0.006269</td>\n",
              "      <td>0.006131</td>\n",
              "      <td>0.077555</td>\n",
              "      <td>0.011824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001506</td>\n",
              "      <td>0.000377</td>\n",
              "      <td>0.001870</td>\n",
              "      <td>0.001994</td>\n",
              "      <td>0.002697</td>\n",
              "      <td>0.003533</td>\n",
              "      <td>0.003465</td>\n",
              "      <td>0.079583</td>\n",
              "      <td>0.011878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.001524</td>\n",
              "      <td>0.000324</td>\n",
              "      <td>0.001574</td>\n",
              "      <td>0.001348</td>\n",
              "      <td>0.001799</td>\n",
              "      <td>0.004042</td>\n",
              "      <td>0.003922</td>\n",
              "      <td>0.081417</td>\n",
              "      <td>0.011994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.001475</td>\n",
              "      <td>0.000323</td>\n",
              "      <td>0.001614</td>\n",
              "      <td>0.001269</td>\n",
              "      <td>0.001692</td>\n",
              "      <td>0.004152</td>\n",
              "      <td>0.004038</td>\n",
              "      <td>0.082284</td>\n",
              "      <td>0.012106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001487</td>\n",
              "      <td>0.000370</td>\n",
              "      <td>0.001107</td>\n",
              "      <td>0.000678</td>\n",
              "      <td>0.001243</td>\n",
              "      <td>0.006212</td>\n",
              "      <td>0.006096</td>\n",
              "      <td>0.079703</td>\n",
              "      <td>0.012112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001357</td>\n",
              "      <td>0.000341</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001848</td>\n",
              "      <td>0.002523</td>\n",
              "      <td>0.003516</td>\n",
              "      <td>0.003442</td>\n",
              "      <td>0.088214</td>\n",
              "      <td>0.012868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.000385</td>\n",
              "      <td>0.001046</td>\n",
              "      <td>0.000648</td>\n",
              "      <td>0.001189</td>\n",
              "      <td>0.006512</td>\n",
              "      <td>0.006321</td>\n",
              "      <td>0.087206</td>\n",
              "      <td>0.013158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.002224</td>\n",
              "      <td>0.000557</td>\n",
              "      <td>0.002691</td>\n",
              "      <td>0.002547</td>\n",
              "      <td>0.003216</td>\n",
              "      <td>0.002927</td>\n",
              "      <td>0.002863</td>\n",
              "      <td>0.100537</td>\n",
              "      <td>0.014695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.001522</td>\n",
              "      <td>0.000331</td>\n",
              "      <td>0.001600</td>\n",
              "      <td>0.001549</td>\n",
              "      <td>0.002201</td>\n",
              "      <td>0.003514</td>\n",
              "      <td>0.003400</td>\n",
              "      <td>0.104108</td>\n",
              "      <td>0.014778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.003440</td>\n",
              "      <td>0.000359</td>\n",
              "      <td>0.001559</td>\n",
              "      <td>0.001387</td>\n",
              "      <td>0.001733</td>\n",
              "      <td>0.004079</td>\n",
              "      <td>0.004216</td>\n",
              "      <td>0.104472</td>\n",
              "      <td>0.015156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.003540</td>\n",
              "      <td>0.000358</td>\n",
              "      <td>0.001613</td>\n",
              "      <td>0.001293</td>\n",
              "      <td>0.001554</td>\n",
              "      <td>0.004269</td>\n",
              "      <td>0.004403</td>\n",
              "      <td>0.108021</td>\n",
              "      <td>0.015631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.002135</td>\n",
              "      <td>0.000530</td>\n",
              "      <td>0.002559</td>\n",
              "      <td>0.002488</td>\n",
              "      <td>0.003179</td>\n",
              "      <td>0.002919</td>\n",
              "      <td>0.002854</td>\n",
              "      <td>0.108731</td>\n",
              "      <td>0.015674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.004383</td>\n",
              "      <td>0.000493</td>\n",
              "      <td>0.001108</td>\n",
              "      <td>0.001230</td>\n",
              "      <td>0.001523</td>\n",
              "      <td>0.006620</td>\n",
              "      <td>0.006921</td>\n",
              "      <td>0.112775</td>\n",
              "      <td>0.016882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.002179</td>\n",
              "      <td>0.000530</td>\n",
              "      <td>0.002496</td>\n",
              "      <td>0.002336</td>\n",
              "      <td>0.002909</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>0.002879</td>\n",
              "      <td>0.126855</td>\n",
              "      <td>0.017894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.003954</td>\n",
              "      <td>0.000418</td>\n",
              "      <td>0.001612</td>\n",
              "      <td>0.001232</td>\n",
              "      <td>0.001796</td>\n",
              "      <td>0.003594</td>\n",
              "      <td>0.003738</td>\n",
              "      <td>0.128930</td>\n",
              "      <td>0.018159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.007339</td>\n",
              "      <td>0.000852</td>\n",
              "      <td>0.001058</td>\n",
              "      <td>0.002617</td>\n",
              "      <td>0.003633</td>\n",
              "      <td>0.004353</td>\n",
              "      <td>0.005066</td>\n",
              "      <td>0.125420</td>\n",
              "      <td>0.018792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.009863</td>\n",
              "      <td>0.001033</td>\n",
              "      <td>0.001953</td>\n",
              "      <td>0.002735</td>\n",
              "      <td>0.002695</td>\n",
              "      <td>0.004485</td>\n",
              "      <td>0.005495</td>\n",
              "      <td>0.128670</td>\n",
              "      <td>0.019616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.003714</td>\n",
              "      <td>0.000929</td>\n",
              "      <td>0.002136</td>\n",
              "      <td>0.001962</td>\n",
              "      <td>0.002669</td>\n",
              "      <td>0.003332</td>\n",
              "      <td>0.003189</td>\n",
              "      <td>0.156472</td>\n",
              "      <td>0.021800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.007790</td>\n",
              "      <td>0.001074</td>\n",
              "      <td>0.001497</td>\n",
              "      <td>0.002007</td>\n",
              "      <td>0.002959</td>\n",
              "      <td>0.004130</td>\n",
              "      <td>0.004747</td>\n",
              "      <td>0.151040</td>\n",
              "      <td>0.021905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.010385</td>\n",
              "      <td>0.001177</td>\n",
              "      <td>0.002204</td>\n",
              "      <td>0.002513</td>\n",
              "      <td>0.002260</td>\n",
              "      <td>0.005108</td>\n",
              "      <td>0.006242</td>\n",
              "      <td>0.145750</td>\n",
              "      <td>0.021955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.010323</td>\n",
              "      <td>0.001117</td>\n",
              "      <td>0.001993</td>\n",
              "      <td>0.002482</td>\n",
              "      <td>0.002770</td>\n",
              "      <td>0.003969</td>\n",
              "      <td>0.005122</td>\n",
              "      <td>0.148984</td>\n",
              "      <td>0.022095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.003910</td>\n",
              "      <td>0.000468</td>\n",
              "      <td>0.002155</td>\n",
              "      <td>0.002254</td>\n",
              "      <td>0.002439</td>\n",
              "      <td>0.002988</td>\n",
              "      <td>0.003232</td>\n",
              "      <td>0.159491</td>\n",
              "      <td>0.022117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.005267</td>\n",
              "      <td>0.000697</td>\n",
              "      <td>0.000411</td>\n",
              "      <td>0.000823</td>\n",
              "      <td>0.001471</td>\n",
              "      <td>0.007372</td>\n",
              "      <td>0.006465</td>\n",
              "      <td>0.156197</td>\n",
              "      <td>0.022338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.008297</td>\n",
              "      <td>0.001338</td>\n",
              "      <td>0.001170</td>\n",
              "      <td>0.002757</td>\n",
              "      <td>0.002889</td>\n",
              "      <td>0.005799</td>\n",
              "      <td>0.006087</td>\n",
              "      <td>0.154052</td>\n",
              "      <td>0.022799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.003648</td>\n",
              "      <td>0.000892</td>\n",
              "      <td>0.002092</td>\n",
              "      <td>0.001955</td>\n",
              "      <td>0.002668</td>\n",
              "      <td>0.003325</td>\n",
              "      <td>0.003166</td>\n",
              "      <td>0.171877</td>\n",
              "      <td>0.023703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.003399</td>\n",
              "      <td>0.000416</td>\n",
              "      <td>0.000852</td>\n",
              "      <td>0.000669</td>\n",
              "      <td>0.001547</td>\n",
              "      <td>0.002920</td>\n",
              "      <td>0.002989</td>\n",
              "      <td>0.177344</td>\n",
              "      <td>0.023767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.010282</td>\n",
              "      <td>0.002099</td>\n",
              "      <td>0.001830</td>\n",
              "      <td>0.001907</td>\n",
              "      <td>0.004649</td>\n",
              "      <td>0.003552</td>\n",
              "      <td>0.004503</td>\n",
              "      <td>0.164061</td>\n",
              "      <td>0.024110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.012695</td>\n",
              "      <td>0.002109</td>\n",
              "      <td>0.001201</td>\n",
              "      <td>0.003152</td>\n",
              "      <td>0.003341</td>\n",
              "      <td>0.008731</td>\n",
              "      <td>0.009715</td>\n",
              "      <td>0.154781</td>\n",
              "      <td>0.024466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.012540</td>\n",
              "      <td>0.001677</td>\n",
              "      <td>0.001615</td>\n",
              "      <td>0.002839</td>\n",
              "      <td>0.002703</td>\n",
              "      <td>0.007915</td>\n",
              "      <td>0.009711</td>\n",
              "      <td>0.158401</td>\n",
              "      <td>0.024675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.002974</td>\n",
              "      <td>0.000450</td>\n",
              "      <td>0.000678</td>\n",
              "      <td>0.001077</td>\n",
              "      <td>0.001544</td>\n",
              "      <td>0.003611</td>\n",
              "      <td>0.003636</td>\n",
              "      <td>0.183755</td>\n",
              "      <td>0.024715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.008312</td>\n",
              "      <td>0.001096</td>\n",
              "      <td>0.002161</td>\n",
              "      <td>0.003137</td>\n",
              "      <td>0.002974</td>\n",
              "      <td>0.003245</td>\n",
              "      <td>0.004478</td>\n",
              "      <td>0.176380</td>\n",
              "      <td>0.025223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.018035</td>\n",
              "      <td>0.003197</td>\n",
              "      <td>0.002386</td>\n",
              "      <td>0.003793</td>\n",
              "      <td>0.004113</td>\n",
              "      <td>0.005136</td>\n",
              "      <td>0.007309</td>\n",
              "      <td>0.166571</td>\n",
              "      <td>0.026318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.004695</td>\n",
              "      <td>0.000964</td>\n",
              "      <td>0.002021</td>\n",
              "      <td>0.001988</td>\n",
              "      <td>0.002681</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>0.003123</td>\n",
              "      <td>0.193323</td>\n",
              "      <td>0.026518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.020333</td>\n",
              "      <td>0.003501</td>\n",
              "      <td>0.002812</td>\n",
              "      <td>0.003834</td>\n",
              "      <td>0.003773</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.008208</td>\n",
              "      <td>0.166757</td>\n",
              "      <td>0.026890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.016774</td>\n",
              "      <td>0.003341</td>\n",
              "      <td>0.002689</td>\n",
              "      <td>0.004007</td>\n",
              "      <td>0.003871</td>\n",
              "      <td>0.004308</td>\n",
              "      <td>0.006271</td>\n",
              "      <td>0.179558</td>\n",
              "      <td>0.027602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001807</td>\n",
              "      <td>0.000454</td>\n",
              "      <td>0.000630</td>\n",
              "      <td>0.000873</td>\n",
              "      <td>0.001611</td>\n",
              "      <td>0.002706</td>\n",
              "      <td>0.002597</td>\n",
              "      <td>0.210365</td>\n",
              "      <td>0.027630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.003760</td>\n",
              "      <td>0.000444</td>\n",
              "      <td>0.000544</td>\n",
              "      <td>0.001121</td>\n",
              "      <td>0.001752</td>\n",
              "      <td>0.005243</td>\n",
              "      <td>0.004540</td>\n",
              "      <td>0.207831</td>\n",
              "      <td>0.028154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.018779</td>\n",
              "      <td>0.002995</td>\n",
              "      <td>0.002143</td>\n",
              "      <td>0.005752</td>\n",
              "      <td>0.005146</td>\n",
              "      <td>0.005842</td>\n",
              "      <td>0.007381</td>\n",
              "      <td>0.177225</td>\n",
              "      <td>0.028158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.017220</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>0.002549</td>\n",
              "      <td>0.005452</td>\n",
              "      <td>0.005007</td>\n",
              "      <td>0.005185</td>\n",
              "      <td>0.007220</td>\n",
              "      <td>0.184168</td>\n",
              "      <td>0.028792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.013761</td>\n",
              "      <td>0.002501</td>\n",
              "      <td>0.003650</td>\n",
              "      <td>0.001168</td>\n",
              "      <td>0.006333</td>\n",
              "      <td>0.003260</td>\n",
              "      <td>0.004251</td>\n",
              "      <td>0.195939</td>\n",
              "      <td>0.028858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.019280</td>\n",
              "      <td>0.003335</td>\n",
              "      <td>0.003299</td>\n",
              "      <td>0.003649</td>\n",
              "      <td>0.003367</td>\n",
              "      <td>0.006553</td>\n",
              "      <td>0.008894</td>\n",
              "      <td>0.182836</td>\n",
              "      <td>0.028902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001977</td>\n",
              "      <td>0.000378</td>\n",
              "      <td>0.000268</td>\n",
              "      <td>0.001495</td>\n",
              "      <td>0.002157</td>\n",
              "      <td>0.004661</td>\n",
              "      <td>0.004309</td>\n",
              "      <td>0.217250</td>\n",
              "      <td>0.029062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001584</td>\n",
              "      <td>0.001421</td>\n",
              "      <td>0.000531</td>\n",
              "      <td>0.001592</td>\n",
              "      <td>0.001606</td>\n",
              "      <td>0.003237</td>\n",
              "      <td>0.003492</td>\n",
              "      <td>0.220328</td>\n",
              "      <td>0.029224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001136</td>\n",
              "      <td>0.000302</td>\n",
              "      <td>0.000179</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>0.002009</td>\n",
              "      <td>0.004099</td>\n",
              "      <td>0.003951</td>\n",
              "      <td>0.223929</td>\n",
              "      <td>0.029614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001029</td>\n",
              "      <td>0.000276</td>\n",
              "      <td>0.000223</td>\n",
              "      <td>0.000617</td>\n",
              "      <td>0.001221</td>\n",
              "      <td>0.003255</td>\n",
              "      <td>0.003164</td>\n",
              "      <td>0.229652</td>\n",
              "      <td>0.029930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001737</td>\n",
              "      <td>0.000346</td>\n",
              "      <td>0.000333</td>\n",
              "      <td>0.000892</td>\n",
              "      <td>0.001625</td>\n",
              "      <td>0.003338</td>\n",
              "      <td>0.003355</td>\n",
              "      <td>0.230630</td>\n",
              "      <td>0.030282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.010070</td>\n",
              "      <td>0.001376</td>\n",
              "      <td>0.002015</td>\n",
              "      <td>0.002655</td>\n",
              "      <td>0.003482</td>\n",
              "      <td>0.003408</td>\n",
              "      <td>0.003525</td>\n",
              "      <td>0.215730</td>\n",
              "      <td>0.030283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.023244</td>\n",
              "      <td>0.004458</td>\n",
              "      <td>0.003087</td>\n",
              "      <td>0.003858</td>\n",
              "      <td>0.004147</td>\n",
              "      <td>0.009715</td>\n",
              "      <td>0.012883</td>\n",
              "      <td>0.183153</td>\n",
              "      <td>0.030568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.003600</td>\n",
              "      <td>0.000461</td>\n",
              "      <td>0.000509</td>\n",
              "      <td>0.001237</td>\n",
              "      <td>0.001693</td>\n",
              "      <td>0.003395</td>\n",
              "      <td>0.003428</td>\n",
              "      <td>0.234213</td>\n",
              "      <td>0.031067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.017862</td>\n",
              "      <td>0.003658</td>\n",
              "      <td>0.002338</td>\n",
              "      <td>0.005741</td>\n",
              "      <td>0.004251</td>\n",
              "      <td>0.008321</td>\n",
              "      <td>0.008916</td>\n",
              "      <td>0.198018</td>\n",
              "      <td>0.031138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.008958</td>\n",
              "      <td>0.000874</td>\n",
              "      <td>0.001923</td>\n",
              "      <td>0.001254</td>\n",
              "      <td>0.001424</td>\n",
              "      <td>0.003654</td>\n",
              "      <td>0.003765</td>\n",
              "      <td>0.233613</td>\n",
              "      <td>0.031933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.009960</td>\n",
              "      <td>0.001392</td>\n",
              "      <td>0.001329</td>\n",
              "      <td>0.001319</td>\n",
              "      <td>0.001492</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.003959</td>\n",
              "      <td>0.232325</td>\n",
              "      <td>0.031946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.012869</td>\n",
              "      <td>0.001730</td>\n",
              "      <td>0.001155</td>\n",
              "      <td>0.001198</td>\n",
              "      <td>0.001327</td>\n",
              "      <td>0.007103</td>\n",
              "      <td>0.007252</td>\n",
              "      <td>0.231003</td>\n",
              "      <td>0.032955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.017278</td>\n",
              "      <td>0.002658</td>\n",
              "      <td>0.002605</td>\n",
              "      <td>0.003859</td>\n",
              "      <td>0.004895</td>\n",
              "      <td>0.003945</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>0.223922</td>\n",
              "      <td>0.032978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.020879</td>\n",
              "      <td>0.004810</td>\n",
              "      <td>0.001908</td>\n",
              "      <td>0.005105</td>\n",
              "      <td>0.005439</td>\n",
              "      <td>0.011663</td>\n",
              "      <td>0.013227</td>\n",
              "      <td>0.203594</td>\n",
              "      <td>0.033328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001376</td>\n",
              "      <td>0.000406</td>\n",
              "      <td>0.000398</td>\n",
              "      <td>0.000899</td>\n",
              "      <td>0.001674</td>\n",
              "      <td>0.002840</td>\n",
              "      <td>0.002742</td>\n",
              "      <td>0.262825</td>\n",
              "      <td>0.034145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.017580</td>\n",
              "      <td>0.003146</td>\n",
              "      <td>0.002756</td>\n",
              "      <td>0.009589</td>\n",
              "      <td>0.004823</td>\n",
              "      <td>0.004301</td>\n",
              "      <td>0.006330</td>\n",
              "      <td>0.226297</td>\n",
              "      <td>0.034353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.001846</td>\n",
              "      <td>0.000386</td>\n",
              "      <td>0.000444</td>\n",
              "      <td>0.001094</td>\n",
              "      <td>0.001971</td>\n",
              "      <td>0.003114</td>\n",
              "      <td>0.002961</td>\n",
              "      <td>0.263047</td>\n",
              "      <td>0.034358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.009968</td>\n",
              "      <td>0.001184</td>\n",
              "      <td>0.001199</td>\n",
              "      <td>0.001196</td>\n",
              "      <td>0.001432</td>\n",
              "      <td>0.004879</td>\n",
              "      <td>0.004541</td>\n",
              "      <td>0.254540</td>\n",
              "      <td>0.034867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.017666</td>\n",
              "      <td>0.004553</td>\n",
              "      <td>0.003336</td>\n",
              "      <td>0.002645</td>\n",
              "      <td>0.004377</td>\n",
              "      <td>0.004277</td>\n",
              "      <td>0.004276</td>\n",
              "      <td>0.238159</td>\n",
              "      <td>0.034911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001857</td>\n",
              "      <td>0.000564</td>\n",
              "      <td>0.000382</td>\n",
              "      <td>0.002029</td>\n",
              "      <td>0.003118</td>\n",
              "      <td>0.006667</td>\n",
              "      <td>0.006514</td>\n",
              "      <td>0.266280</td>\n",
              "      <td>0.035926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.004543</td>\n",
              "      <td>0.000953</td>\n",
              "      <td>0.000921</td>\n",
              "      <td>0.002039</td>\n",
              "      <td>0.002957</td>\n",
              "      <td>0.006960</td>\n",
              "      <td>0.006675</td>\n",
              "      <td>0.263480</td>\n",
              "      <td>0.036066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.026151</td>\n",
              "      <td>0.005158</td>\n",
              "      <td>0.003597</td>\n",
              "      <td>0.005391</td>\n",
              "      <td>0.006824</td>\n",
              "      <td>0.005379</td>\n",
              "      <td>0.006729</td>\n",
              "      <td>0.232792</td>\n",
              "      <td>0.036503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.019407</td>\n",
              "      <td>0.004322</td>\n",
              "      <td>0.003198</td>\n",
              "      <td>0.003254</td>\n",
              "      <td>0.005214</td>\n",
              "      <td>0.004211</td>\n",
              "      <td>0.004220</td>\n",
              "      <td>0.255822</td>\n",
              "      <td>0.037456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.006267</td>\n",
              "      <td>0.001178</td>\n",
              "      <td>0.000596</td>\n",
              "      <td>0.000676</td>\n",
              "      <td>0.001131</td>\n",
              "      <td>0.003465</td>\n",
              "      <td>0.003236</td>\n",
              "      <td>0.289135</td>\n",
              "      <td>0.038211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.010452</td>\n",
              "      <td>0.001974</td>\n",
              "      <td>0.001165</td>\n",
              "      <td>0.001061</td>\n",
              "      <td>0.002096</td>\n",
              "      <td>0.003439</td>\n",
              "      <td>0.003355</td>\n",
              "      <td>0.310911</td>\n",
              "      <td>0.041807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.016369</td>\n",
              "      <td>0.002670</td>\n",
              "      <td>0.002488</td>\n",
              "      <td>0.001504</td>\n",
              "      <td>0.002954</td>\n",
              "      <td>0.003791</td>\n",
              "      <td>0.003469</td>\n",
              "      <td>0.323505</td>\n",
              "      <td>0.044594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.037904</td>\n",
              "      <td>0.009754</td>\n",
              "      <td>0.007677</td>\n",
              "      <td>0.005713</td>\n",
              "      <td>0.007806</td>\n",
              "      <td>0.007761</td>\n",
              "      <td>0.007454</td>\n",
              "      <td>0.277562</td>\n",
              "      <td>0.045204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.021752</td>\n",
              "      <td>0.005397</td>\n",
              "      <td>0.004143</td>\n",
              "      <td>0.002727</td>\n",
              "      <td>0.004039</td>\n",
              "      <td>0.003524</td>\n",
              "      <td>0.004334</td>\n",
              "      <td>0.320077</td>\n",
              "      <td>0.045749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.018333</td>\n",
              "      <td>0.004718</td>\n",
              "      <td>0.003197</td>\n",
              "      <td>0.002516</td>\n",
              "      <td>0.003845</td>\n",
              "      <td>0.004302</td>\n",
              "      <td>0.004095</td>\n",
              "      <td>0.329485</td>\n",
              "      <td>0.046312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.026301</td>\n",
              "      <td>0.006058</td>\n",
              "      <td>0.002966</td>\n",
              "      <td>0.008359</td>\n",
              "      <td>0.008002</td>\n",
              "      <td>0.006272</td>\n",
              "      <td>0.006736</td>\n",
              "      <td>0.309814</td>\n",
              "      <td>0.046813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.039712</td>\n",
              "      <td>0.009940</td>\n",
              "      <td>0.004990</td>\n",
              "      <td>0.006968</td>\n",
              "      <td>0.007006</td>\n",
              "      <td>0.006279</td>\n",
              "      <td>0.008488</td>\n",
              "      <td>0.292852</td>\n",
              "      <td>0.047029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.029736</td>\n",
              "      <td>0.006912</td>\n",
              "      <td>0.004099</td>\n",
              "      <td>0.006843</td>\n",
              "      <td>0.006476</td>\n",
              "      <td>0.004582</td>\n",
              "      <td>0.005807</td>\n",
              "      <td>0.315362</td>\n",
              "      <td>0.047477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.020130</td>\n",
              "      <td>0.005385</td>\n",
              "      <td>0.003542</td>\n",
              "      <td>0.003023</td>\n",
              "      <td>0.003919</td>\n",
              "      <td>0.004124</td>\n",
              "      <td>0.003911</td>\n",
              "      <td>0.336225</td>\n",
              "      <td>0.047532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.044570</td>\n",
              "      <td>0.008642</td>\n",
              "      <td>0.005257</td>\n",
              "      <td>0.007914</td>\n",
              "      <td>0.007475</td>\n",
              "      <td>0.008115</td>\n",
              "      <td>0.008440</td>\n",
              "      <td>0.307253</td>\n",
              "      <td>0.049708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.023161</td>\n",
              "      <td>0.005361</td>\n",
              "      <td>0.003701</td>\n",
              "      <td>0.004866</td>\n",
              "      <td>0.005239</td>\n",
              "      <td>0.004180</td>\n",
              "      <td>0.004438</td>\n",
              "      <td>0.349710</td>\n",
              "      <td>0.050082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.018009</td>\n",
              "      <td>0.002197</td>\n",
              "      <td>0.001751</td>\n",
              "      <td>0.004075</td>\n",
              "      <td>0.004416</td>\n",
              "      <td>0.004082</td>\n",
              "      <td>0.003851</td>\n",
              "      <td>0.374613</td>\n",
              "      <td>0.051624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.034430</td>\n",
              "      <td>0.009863</td>\n",
              "      <td>0.007979</td>\n",
              "      <td>0.005731</td>\n",
              "      <td>0.008931</td>\n",
              "      <td>0.007673</td>\n",
              "      <td>0.007036</td>\n",
              "      <td>0.343086</td>\n",
              "      <td>0.053091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.032575</td>\n",
              "      <td>0.010031</td>\n",
              "      <td>0.008185</td>\n",
              "      <td>0.006301</td>\n",
              "      <td>0.009889</td>\n",
              "      <td>0.006807</td>\n",
              "      <td>0.006918</td>\n",
              "      <td>0.365904</td>\n",
              "      <td>0.055826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.034932</td>\n",
              "      <td>0.008015</td>\n",
              "      <td>0.006562</td>\n",
              "      <td>0.006302</td>\n",
              "      <td>0.008959</td>\n",
              "      <td>0.006979</td>\n",
              "      <td>0.008672</td>\n",
              "      <td>0.371869</td>\n",
              "      <td>0.056536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.033110</td>\n",
              "      <td>0.008824</td>\n",
              "      <td>0.006854</td>\n",
              "      <td>0.006875</td>\n",
              "      <td>0.010024</td>\n",
              "      <td>0.007254</td>\n",
              "      <td>0.008321</td>\n",
              "      <td>0.386259</td>\n",
              "      <td>0.058440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.038210</td>\n",
              "      <td>0.009070</td>\n",
              "      <td>0.010748</td>\n",
              "      <td>0.007837</td>\n",
              "      <td>0.009535</td>\n",
              "      <td>0.005522</td>\n",
              "      <td>0.007819</td>\n",
              "      <td>0.389192</td>\n",
              "      <td>0.059742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.022071</td>\n",
              "      <td>0.002595</td>\n",
              "      <td>0.003222</td>\n",
              "      <td>0.004870</td>\n",
              "      <td>0.005635</td>\n",
              "      <td>0.004390</td>\n",
              "      <td>0.003910</td>\n",
              "      <td>0.445724</td>\n",
              "      <td>0.061552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.039086</td>\n",
              "      <td>0.009236</td>\n",
              "      <td>0.010904</td>\n",
              "      <td>0.007465</td>\n",
              "      <td>0.009159</td>\n",
              "      <td>0.005865</td>\n",
              "      <td>0.008766</td>\n",
              "      <td>0.409253</td>\n",
              "      <td>0.062467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.038322</td>\n",
              "      <td>0.008431</td>\n",
              "      <td>0.005292</td>\n",
              "      <td>0.016653</td>\n",
              "      <td>0.012375</td>\n",
              "      <td>0.006296</td>\n",
              "      <td>0.008568</td>\n",
              "      <td>0.404062</td>\n",
              "      <td>0.062500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.041810</td>\n",
              "      <td>0.009750</td>\n",
              "      <td>0.012203</td>\n",
              "      <td>0.009082</td>\n",
              "      <td>0.010068</td>\n",
              "      <td>0.006324</td>\n",
              "      <td>0.009739</td>\n",
              "      <td>0.438675</td>\n",
              "      <td>0.067206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.040089</td>\n",
              "      <td>0.009661</td>\n",
              "      <td>0.004590</td>\n",
              "      <td>0.012438</td>\n",
              "      <td>0.012042</td>\n",
              "      <td>0.008991</td>\n",
              "      <td>0.009639</td>\n",
              "      <td>0.501077</td>\n",
              "      <td>0.074816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.031300</td>\n",
              "      <td>0.006271</td>\n",
              "      <td>0.003652</td>\n",
              "      <td>0.012938</td>\n",
              "      <td>0.011334</td>\n",
              "      <td>0.005440</td>\n",
              "      <td>0.006245</td>\n",
              "      <td>0.521548</td>\n",
              "      <td>0.074841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.030733</td>\n",
              "      <td>0.006153</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.012773</td>\n",
              "      <td>0.011809</td>\n",
              "      <td>0.005044</td>\n",
              "      <td>0.006448</td>\n",
              "      <td>0.527287</td>\n",
              "      <td>0.075605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.037801</td>\n",
              "      <td>0.009028</td>\n",
              "      <td>0.007051</td>\n",
              "      <td>0.009085</td>\n",
              "      <td>0.011471</td>\n",
              "      <td>0.007163</td>\n",
              "      <td>0.008627</td>\n",
              "      <td>0.559551</td>\n",
              "      <td>0.081222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.037464</td>\n",
              "      <td>0.008763</td>\n",
              "      <td>0.006715</td>\n",
              "      <td>0.011333</td>\n",
              "      <td>0.012534</td>\n",
              "      <td>0.007350</td>\n",
              "      <td>0.008386</td>\n",
              "      <td>0.593216</td>\n",
              "      <td>0.085720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>KarateClub()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.024997</td>\n",
              "      <td>0.004098</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>0.007541</td>\n",
              "      <td>0.007500</td>\n",
              "      <td>0.004903</td>\n",
              "      <td>0.004446</td>\n",
              "      <td>0.635349</td>\n",
              "      <td>0.086668</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Embedding Size  Learning Rate  ...  clustering() error  Average Error across 8-d\n",
              "41              8          0.010  ...            0.067242                  0.010327\n",
              "40              8          0.010  ...            0.068031                  0.010329\n",
              "44              8          0.010  ...            0.068680                  0.010441\n",
              "43              8          0.010  ...            0.071765                  0.010770\n",
              "46              8          0.010  ...            0.077555                  0.011824\n",
              "38              8          0.010  ...            0.079583                  0.011878\n",
              "39              8          0.010  ...            0.081417                  0.011994\n",
              "42              8          0.010  ...            0.082284                  0.012106\n",
              "47              8          0.010  ...            0.079703                  0.012112\n",
              "37              8          0.010  ...            0.088214                  0.012868\n",
              "45              8          0.010  ...            0.087206                  0.013158\n",
              "35              8          0.010  ...            0.100537                  0.014695\n",
              "36              8          0.010  ...            0.104108                  0.014778\n",
              "89              8          0.001  ...            0.104472                  0.015156\n",
              "92              8          0.001  ...            0.108021                  0.015631\n",
              "34              8          0.010  ...            0.108731                  0.015674\n",
              "95              8          0.001  ...            0.112775                  0.016882\n",
              "33              8          0.010  ...            0.126855                  0.017894\n",
              "86              8          0.001  ...            0.128930                  0.018159\n",
              "64              8          0.001  ...            0.125420                  0.018792\n",
              "88              8          0.001  ...            0.128670                  0.019616\n",
              "32              8          0.010  ...            0.156472                  0.021800\n",
              "61              8          0.001  ...            0.151040                  0.021905\n",
              "91              8          0.001  ...            0.145750                  0.021955\n",
              "85              8          0.001  ...            0.148984                  0.022095\n",
              "83              8          0.001  ...            0.159491                  0.022117\n",
              "21              8          0.010  ...            0.156197                  0.022338\n",
              "67              8          0.001  ...            0.154052                  0.022799\n",
              "31              8          0.010  ...            0.171877                  0.023703\n",
              "9               8          0.010  ...            0.177344                  0.023767\n",
              "58              8          0.001  ...            0.164061                  0.024110\n",
              "70              8          0.001  ...            0.154781                  0.024466\n",
              "94              8          0.001  ...            0.158401                  0.024675\n",
              "15              8          0.010  ...            0.183755                  0.024715\n",
              "82              8          0.001  ...            0.176380                  0.025223\n",
              "84              8          0.001  ...            0.166571                  0.026318\n",
              "30              8          0.010  ...            0.193323                  0.026518\n",
              "87              8          0.001  ...            0.166757                  0.026890\n",
              "81              8          0.001  ...            0.179558                  0.027602\n",
              "10              8          0.010  ...            0.210365                  0.027630\n",
              "18              8          0.010  ...            0.207831                  0.028154\n",
              "63              8          0.001  ...            0.177225                  0.028158\n",
              "60              8          0.001  ...            0.184168                  0.028792\n",
              "59              8          0.001  ...            0.195939                  0.028858\n",
              "90              8          0.001  ...            0.182836                  0.028902\n",
              "19              8          0.010  ...            0.217250                  0.029062\n",
              "11              8          0.010  ...            0.220328                  0.029224\n",
              "20              8          0.010  ...            0.223929                  0.029614\n",
              "17              8          0.010  ...            0.229652                  0.029930\n",
              "16              8          0.010  ...            0.230630                  0.030282\n",
              "80              8          0.001  ...            0.215730                  0.030283\n",
              "93              8          0.001  ...            0.183153                  0.030568\n",
              "12              8          0.010  ...            0.234213                  0.031067\n",
              "66              8          0.001  ...            0.198018                  0.031138\n",
              "65              8          0.001  ...            0.233613                  0.031933\n",
              "62              8          0.001  ...            0.232325                  0.031946\n",
              "71              8          0.001  ...            0.231003                  0.032955\n",
              "79              8          0.001  ...            0.223922                  0.032978\n",
              "69              8          0.001  ...            0.203594                  0.033328\n",
              "14              8          0.010  ...            0.262825                  0.034145\n",
              "57              8          0.001  ...            0.226297                  0.034353\n",
              "13              8          0.010  ...            0.263047                  0.034358\n",
              "68              8          0.001  ...            0.254540                  0.034867\n",
              "29              8          0.010  ...            0.238159                  0.034911\n",
              "23              8          0.010  ...            0.266280                  0.035926\n",
              "22              8          0.010  ...            0.263480                  0.036066\n",
              "78              8          0.001  ...            0.232792                  0.036503\n",
              "55              8          0.001  ...            0.255822                  0.037456\n",
              "8               8          0.010  ...            0.289135                  0.038211\n",
              "7               8          0.010  ...            0.310911                  0.041807\n",
              "6               8          0.010  ...            0.323505                  0.044594\n",
              "26              8          0.010  ...            0.277562                  0.045204\n",
              "56              8          0.001  ...            0.320077                  0.045749\n",
              "28              8          0.010  ...            0.329485                  0.046312\n",
              "54              8          0.001  ...            0.309814                  0.046813\n",
              "75              8          0.001  ...            0.292852                  0.047029\n",
              "76              8          0.001  ...            0.315362                  0.047477\n",
              "27              8          0.010  ...            0.336225                  0.047532\n",
              "72              8          0.001  ...            0.307253                  0.049708\n",
              "77              8          0.001  ...            0.349710                  0.050082\n",
              "5               8          0.010  ...            0.374613                  0.051624\n",
              "25              8          0.010  ...            0.343086                  0.053091\n",
              "24              8          0.010  ...            0.365904                  0.055826\n",
              "73              8          0.001  ...            0.371869                  0.056536\n",
              "74              8          0.001  ...            0.386259                  0.058440\n",
              "2               8          0.010  ...            0.389192                  0.059742\n",
              "4               8          0.010  ...            0.445724                  0.061552\n",
              "1               8          0.010  ...            0.409253                  0.062467\n",
              "51              8          0.001  ...            0.404062                  0.062500\n",
              "0               8          0.010  ...            0.438675                  0.067206\n",
              "48              8          0.001  ...            0.501077                  0.074816\n",
              "53              8          0.001  ...            0.521548                  0.074841\n",
              "52              8          0.001  ...            0.527287                  0.075605\n",
              "50              8          0.001  ...            0.559551                  0.081222\n",
              "49              8          0.001  ...            0.593216                  0.085720\n",
              "3               8          0.010  ...            0.635349                  0.086668\n",
              "\n",
              "[96 rows x 15 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfaaIrHsBHlR"
      },
      "source": [
        "Highest MSE is contributed by clustering() when compared to others"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YunFxWl0NAgS"
      },
      "source": [
        "## Experiment on the Cora Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrTk9goSNK4u",
        "outputId": "b25d4719-6ce1-427b-9258-5f196e8720ae"
      },
      "source": [
        "show_dataset_stats(Planetoid(root=\"delete_me/\", name=\"Cora\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset: Cora():\n",
            "======================\n",
            "Number of graphs: 1\n",
            "Number of features: 1433\n",
            "Number of classes: 7\n",
            "Number of nodes: 2708\n",
            "Number of edges: 10556\n",
            "Average node degree: 3.90\n",
            "Number of training nodes: 140\n",
            "Training node label rate: 0.05\n",
            "Contains isolated nodes: False\n",
            "Contains self-loops: False\n",
            "Is undirected: True\n",
            "======================\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Processing...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "ddA0B68hUvM-",
        "outputId": "cb6aa2c8-4a50-4671-d765-345422876aef"
      },
      "source": [
        "show_dataset_as_networkx_graph(Planetoid(root=\"delete_me/\", name=\"Cora\")[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0CadQeWWNMwJ"
      },
      "source": [
        "class HyperParameterCombinations:\n",
        "    \"\"\"\n",
        "    To be manually configured\n",
        "    \"\"\"\n",
        "    ES = [8]\n",
        "    LR = [0.01, 0.001]\n",
        "    WD = [1e-5, 1e-3]\n",
        "    TR = [x / 10 for x in range(1, 9)]\n",
        "    DATASET = [Planetoid(root=\"delete_me/\", name=\"Cora\")]\n",
        "    EPOCHS = [250, 500, 1000]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-2S3hXWNVNi",
        "outputId": "de92f526-1c73-4f25-b2d4-4f576569ffc1"
      },
      "source": [
        "df = TrainAndCaptureResults().run()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total combinations to run exp on: 96\n",
            "Running combination: 1 of 96\n",
            "Running combination: 2 of 96\n",
            "Running combination: 3 of 96\n",
            "Running combination: 4 of 96\n",
            "Running combination: 5 of 96\n",
            "Running combination: 6 of 96\n",
            "Running combination: 7 of 96\n",
            "Running combination: 8 of 96\n",
            "Running combination: 9 of 96\n",
            "Running combination: 10 of 96\n",
            "Running combination: 11 of 96\n",
            "Running combination: 12 of 96\n",
            "Running combination: 13 of 96\n",
            "Running combination: 14 of 96\n",
            "Running combination: 15 of 96\n",
            "Running combination: 16 of 96\n",
            "Running combination: 17 of 96\n",
            "Running combination: 18 of 96\n",
            "Running combination: 19 of 96\n",
            "Running combination: 20 of 96\n",
            "Running combination: 21 of 96\n",
            "Running combination: 22 of 96\n",
            "Running combination: 23 of 96\n",
            "Running combination: 24 of 96\n",
            "Running combination: 25 of 96\n",
            "Running combination: 26 of 96\n",
            "Running combination: 27 of 96\n",
            "Running combination: 28 of 96\n",
            "Running combination: 29 of 96\n",
            "Running combination: 30 of 96\n",
            "Running combination: 31 of 96\n",
            "Running combination: 32 of 96\n",
            "Running combination: 33 of 96\n",
            "Running combination: 34 of 96\n",
            "Running combination: 35 of 96\n",
            "Running combination: 36 of 96\n",
            "Running combination: 37 of 96\n",
            "Running combination: 38 of 96\n",
            "Running combination: 39 of 96\n",
            "Running combination: 40 of 96\n",
            "Running combination: 41 of 96\n",
            "Running combination: 42 of 96\n",
            "Running combination: 43 of 96\n",
            "Running combination: 44 of 96\n",
            "Running combination: 45 of 96\n",
            "Running combination: 46 of 96\n",
            "Running combination: 47 of 96\n",
            "Running combination: 48 of 96\n",
            "Running combination: 49 of 96\n",
            "Running combination: 50 of 96\n",
            "Running combination: 51 of 96\n",
            "Running combination: 52 of 96\n",
            "Running combination: 53 of 96\n",
            "Running combination: 54 of 96\n",
            "Running combination: 55 of 96\n",
            "Running combination: 56 of 96\n",
            "Running combination: 57 of 96\n",
            "Running combination: 58 of 96\n",
            "Running combination: 59 of 96\n",
            "Running combination: 60 of 96\n",
            "Running combination: 61 of 96\n",
            "Running combination: 62 of 96\n",
            "Running combination: 63 of 96\n",
            "Running combination: 64 of 96\n",
            "Running combination: 65 of 96\n",
            "Running combination: 66 of 96\n",
            "Running combination: 67 of 96\n",
            "Running combination: 68 of 96\n",
            "Running combination: 69 of 96\n",
            "Running combination: 70 of 96\n",
            "Running combination: 71 of 96\n",
            "Running combination: 72 of 96\n",
            "Running combination: 73 of 96\n",
            "Running combination: 74 of 96\n",
            "Running combination: 75 of 96\n",
            "Running combination: 76 of 96\n",
            "Running combination: 77 of 96\n",
            "Running combination: 78 of 96\n",
            "Running combination: 79 of 96\n",
            "Running combination: 80 of 96\n",
            "Running combination: 81 of 96\n",
            "Running combination: 82 of 96\n",
            "Running combination: 83 of 96\n",
            "Running combination: 84 of 96\n",
            "Running combination: 85 of 96\n",
            "Running combination: 86 of 96\n",
            "Running combination: 87 of 96\n",
            "Running combination: 88 of 96\n",
            "Running combination: 89 of 96\n",
            "Running combination: 90 of 96\n",
            "Running combination: 91 of 96\n",
            "Running combination: 92 of 96\n",
            "Running combination: 93 of 96\n",
            "Running combination: 94 of 96\n",
            "Running combination: 95 of 96\n",
            "Running combination: 96 of 96\n",
            "Least avg error across 8-d: 0.009604694321751595\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wJVenyoXNWiY",
        "outputId": "7764b3c7-6c40-4328-ad4c-ca29110165ba"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Embedding Size</th>\n",
              "      <th>Learning Rate</th>\n",
              "      <th>Weight Decay</th>\n",
              "      <th>Training Ratio</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>Epochs</th>\n",
              "      <th>degree_centrality() error</th>\n",
              "      <th>in_degree_centrality() error</th>\n",
              "      <th>eigenvector_centrality() error</th>\n",
              "      <th>harmonic_centrality() error</th>\n",
              "      <th>closeness_centrality() error</th>\n",
              "      <th>betweenness_centrality() error</th>\n",
              "      <th>load_centrality() error</th>\n",
              "      <th>clustering() error</th>\n",
              "      <th>Average Error across 8-d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>8.385121e-07</td>\n",
              "      <td>0.000189</td>\n",
              "      <td>0.002887</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.071410</td>\n",
              "      <td>0.009605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.667316e-06</td>\n",
              "      <td>0.000188</td>\n",
              "      <td>0.002781</td>\n",
              "      <td>0.002246</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.071758</td>\n",
              "      <td>0.009628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.604436e-07</td>\n",
              "      <td>0.000190</td>\n",
              "      <td>0.002882</td>\n",
              "      <td>0.002298</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.072598</td>\n",
              "      <td>0.009752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>1.424306e-05</td>\n",
              "      <td>0.000183</td>\n",
              "      <td>0.002308</td>\n",
              "      <td>0.001907</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.074192</td>\n",
              "      <td>0.009837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>8.008699e-07</td>\n",
              "      <td>0.000189</td>\n",
              "      <td>0.002801</td>\n",
              "      <td>0.002244</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.073554</td>\n",
              "      <td>0.009855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.709010e-06</td>\n",
              "      <td>0.000192</td>\n",
              "      <td>0.002835</td>\n",
              "      <td>0.002293</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.073683</td>\n",
              "      <td>0.009882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>9.385030e-07</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>0.002964</td>\n",
              "      <td>0.002364</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.073707</td>\n",
              "      <td>0.009910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>8.586690e-07</td>\n",
              "      <td>0.000195</td>\n",
              "      <td>0.002954</td>\n",
              "      <td>0.002359</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.073774</td>\n",
              "      <td>0.009917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>8.706573e-07</td>\n",
              "      <td>0.000195</td>\n",
              "      <td>0.002866</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.074980</td>\n",
              "      <td>0.010049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>1.405754e-05</td>\n",
              "      <td>0.000186</td>\n",
              "      <td>0.002398</td>\n",
              "      <td>0.001982</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.076068</td>\n",
              "      <td>0.010093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>8.725281e-07</td>\n",
              "      <td>0.000202</td>\n",
              "      <td>0.002730</td>\n",
              "      <td>0.002163</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.077536</td>\n",
              "      <td>0.010335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.726343e-06</td>\n",
              "      <td>0.000199</td>\n",
              "      <td>0.002557</td>\n",
              "      <td>0.002059</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.078040</td>\n",
              "      <td>0.010363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>8.319486e-05</td>\n",
              "      <td>0.000201</td>\n",
              "      <td>0.002126</td>\n",
              "      <td>0.001710</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000140</td>\n",
              "      <td>0.078971</td>\n",
              "      <td>0.010421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>7.765184e-07</td>\n",
              "      <td>0.000203</td>\n",
              "      <td>0.002626</td>\n",
              "      <td>0.002096</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.078944</td>\n",
              "      <td>0.010490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.762928e-07</td>\n",
              "      <td>0.000206</td>\n",
              "      <td>0.002727</td>\n",
              "      <td>0.002164</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.078943</td>\n",
              "      <td>0.010511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>1.447126e-05</td>\n",
              "      <td>0.000193</td>\n",
              "      <td>0.002152</td>\n",
              "      <td>0.001771</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>0.080425</td>\n",
              "      <td>0.010580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000076</td>\n",
              "      <td>8.249227e-05</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.002249</td>\n",
              "      <td>0.001836</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.080881</td>\n",
              "      <td>0.010691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.960672e-06</td>\n",
              "      <td>0.000204</td>\n",
              "      <td>0.002513</td>\n",
              "      <td>0.002030</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.080849</td>\n",
              "      <td>0.010705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>9.905716e-07</td>\n",
              "      <td>0.000208</td>\n",
              "      <td>0.002734</td>\n",
              "      <td>0.002166</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.080993</td>\n",
              "      <td>0.010768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>8.737002e-07</td>\n",
              "      <td>0.000213</td>\n",
              "      <td>0.002728</td>\n",
              "      <td>0.002165</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.081048</td>\n",
              "      <td>0.010775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>1.527619e-05</td>\n",
              "      <td>0.000202</td>\n",
              "      <td>0.002144</td>\n",
              "      <td>0.001760</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.083261</td>\n",
              "      <td>0.010933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>8.305322e-07</td>\n",
              "      <td>0.000212</td>\n",
              "      <td>0.002592</td>\n",
              "      <td>0.002070</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.083387</td>\n",
              "      <td>0.011038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>8.130733e-05</td>\n",
              "      <td>0.000212</td>\n",
              "      <td>0.002049</td>\n",
              "      <td>0.001676</td>\n",
              "      <td>0.000055</td>\n",
              "      <td>0.000133</td>\n",
              "      <td>0.084867</td>\n",
              "      <td>0.011143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.455739e-06</td>\n",
              "      <td>0.000297</td>\n",
              "      <td>0.002757</td>\n",
              "      <td>0.002187</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.084207</td>\n",
              "      <td>0.011190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.560317e-06</td>\n",
              "      <td>0.000296</td>\n",
              "      <td>0.002769</td>\n",
              "      <td>0.002193</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.084358</td>\n",
              "      <td>0.011211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.268344e-06</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>0.002601</td>\n",
              "      <td>0.002076</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.084742</td>\n",
              "      <td>0.011222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>2.046703e-06</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.002528</td>\n",
              "      <td>0.002044</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.084906</td>\n",
              "      <td>0.011229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>1.673247e-05</td>\n",
              "      <td>0.000283</td>\n",
              "      <td>0.002155</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>0.000033</td>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.086906</td>\n",
              "      <td>0.011407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000073</td>\n",
              "      <td>8.309353e-05</td>\n",
              "      <td>0.000218</td>\n",
              "      <td>0.002061</td>\n",
              "      <td>0.001684</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.000133</td>\n",
              "      <td>0.087577</td>\n",
              "      <td>0.011485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>8.802008e-05</td>\n",
              "      <td>0.000315</td>\n",
              "      <td>0.002080</td>\n",
              "      <td>0.001724</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.000153</td>\n",
              "      <td>0.090381</td>\n",
              "      <td>0.011862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000371</td>\n",
              "      <td>2.478781e-04</td>\n",
              "      <td>0.000260</td>\n",
              "      <td>0.001570</td>\n",
              "      <td>0.001039</td>\n",
              "      <td>0.000336</td>\n",
              "      <td>0.000156</td>\n",
              "      <td>0.091426</td>\n",
              "      <td>0.011926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>6.095396e-05</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>0.000896</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000073</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.095051</td>\n",
              "      <td>0.012138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>1.134550e-06</td>\n",
              "      <td>0.000290</td>\n",
              "      <td>0.002563</td>\n",
              "      <td>0.002049</td>\n",
              "      <td>0.000028</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.095652</td>\n",
              "      <td>0.012577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>1.301242e-06</td>\n",
              "      <td>0.000297</td>\n",
              "      <td>0.002781</td>\n",
              "      <td>0.002206</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.095270</td>\n",
              "      <td>0.012577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.404693e-06</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>0.002785</td>\n",
              "      <td>0.002207</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.000028</td>\n",
              "      <td>0.096244</td>\n",
              "      <td>0.012700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.958935e-06</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.002493</td>\n",
              "      <td>0.002018</td>\n",
              "      <td>0.000028</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.096990</td>\n",
              "      <td>0.012731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000392</td>\n",
              "      <td>2.765953e-04</td>\n",
              "      <td>0.000344</td>\n",
              "      <td>0.001828</td>\n",
              "      <td>0.001240</td>\n",
              "      <td>0.000394</td>\n",
              "      <td>0.000195</td>\n",
              "      <td>0.098161</td>\n",
              "      <td>0.012854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>1.626658e-05</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>0.002185</td>\n",
              "      <td>0.001789</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.098768</td>\n",
              "      <td>0.012894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>8.661483e-05</td>\n",
              "      <td>0.000296</td>\n",
              "      <td>0.002138</td>\n",
              "      <td>0.001741</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>0.099794</td>\n",
              "      <td>0.013043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000356</td>\n",
              "      <td>2.877117e-04</td>\n",
              "      <td>0.000324</td>\n",
              "      <td>0.001946</td>\n",
              "      <td>0.001338</td>\n",
              "      <td>0.000442</td>\n",
              "      <td>0.000217</td>\n",
              "      <td>0.108340</td>\n",
              "      <td>0.014157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.593523e-06</td>\n",
              "      <td>0.000348</td>\n",
              "      <td>0.002786</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.000033</td>\n",
              "      <td>0.108694</td>\n",
              "      <td>0.014264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.389055e-06</td>\n",
              "      <td>0.000336</td>\n",
              "      <td>0.002598</td>\n",
              "      <td>0.002078</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.109572</td>\n",
              "      <td>0.014332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>1.957591e-05</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>0.000738</td>\n",
              "      <td>0.000596</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>0.113362</td>\n",
              "      <td>0.014369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>1.694517e-06</td>\n",
              "      <td>0.000352</td>\n",
              "      <td>0.002811</td>\n",
              "      <td>0.002228</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.000033</td>\n",
              "      <td>0.110569</td>\n",
              "      <td>0.014505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>8.483376e-05</td>\n",
              "      <td>0.000363</td>\n",
              "      <td>0.002223</td>\n",
              "      <td>0.001812</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>0.111616</td>\n",
              "      <td>0.014546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>2.615803e-06</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>0.002443</td>\n",
              "      <td>0.001983</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.112194</td>\n",
              "      <td>0.014629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>1.911597e-05</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>0.002174</td>\n",
              "      <td>0.001779</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.113540</td>\n",
              "      <td>0.014745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000055</td>\n",
              "      <td>6.314823e-05</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0.001086</td>\n",
              "      <td>0.000907</td>\n",
              "      <td>0.000083</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.115879</td>\n",
              "      <td>0.014786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000161</td>\n",
              "      <td>9.534471e-05</td>\n",
              "      <td>0.000144</td>\n",
              "      <td>0.000849</td>\n",
              "      <td>0.000627</td>\n",
              "      <td>0.000173</td>\n",
              "      <td>0.000088</td>\n",
              "      <td>0.117496</td>\n",
              "      <td>0.014954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>6.423166e-05</td>\n",
              "      <td>0.000175</td>\n",
              "      <td>0.001013</td>\n",
              "      <td>0.000811</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.120226</td>\n",
              "      <td>0.015310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000068</td>\n",
              "      <td>6.086177e-05</td>\n",
              "      <td>0.000204</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000824</td>\n",
              "      <td>0.000073</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.123261</td>\n",
              "      <td>0.015698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000316</td>\n",
              "      <td>3.143607e-04</td>\n",
              "      <td>0.000324</td>\n",
              "      <td>0.002155</td>\n",
              "      <td>0.001495</td>\n",
              "      <td>0.000432</td>\n",
              "      <td>0.000308</td>\n",
              "      <td>0.121725</td>\n",
              "      <td>0.015884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.372932e-06</td>\n",
              "      <td>0.000364</td>\n",
              "      <td>0.002367</td>\n",
              "      <td>0.001896</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.126546</td>\n",
              "      <td>0.016405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>1.373591e-06</td>\n",
              "      <td>0.000355</td>\n",
              "      <td>0.002540</td>\n",
              "      <td>0.002041</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.126535</td>\n",
              "      <td>0.016443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>2.060485e-06</td>\n",
              "      <td>0.000135</td>\n",
              "      <td>0.000480</td>\n",
              "      <td>0.000392</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.132670</td>\n",
              "      <td>0.016718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>1.402876e-06</td>\n",
              "      <td>0.000369</td>\n",
              "      <td>0.002454</td>\n",
              "      <td>0.001960</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.129084</td>\n",
              "      <td>0.016742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000143</td>\n",
              "      <td>1.276263e-04</td>\n",
              "      <td>0.000173</td>\n",
              "      <td>0.001055</td>\n",
              "      <td>0.000794</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.133441</td>\n",
              "      <td>0.017001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000133</td>\n",
              "      <td>6.886968e-05</td>\n",
              "      <td>0.000321</td>\n",
              "      <td>0.002290</td>\n",
              "      <td>0.001767</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>0.133414</td>\n",
              "      <td>0.017271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>2.457425e-06</td>\n",
              "      <td>0.000356</td>\n",
              "      <td>0.002153</td>\n",
              "      <td>0.001746</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.134541</td>\n",
              "      <td>0.017358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>1.586078e-05</td>\n",
              "      <td>0.000348</td>\n",
              "      <td>0.002086</td>\n",
              "      <td>0.001677</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>0.137687</td>\n",
              "      <td>0.017740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>4.468356e-05</td>\n",
              "      <td>0.000127</td>\n",
              "      <td>0.000626</td>\n",
              "      <td>0.000508</td>\n",
              "      <td>0.000091</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.146445</td>\n",
              "      <td>0.018498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>1.564193e-05</td>\n",
              "      <td>0.000125</td>\n",
              "      <td>0.000775</td>\n",
              "      <td>0.000619</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.147176</td>\n",
              "      <td>0.018600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>2.026312e-05</td>\n",
              "      <td>0.000145</td>\n",
              "      <td>0.000774</td>\n",
              "      <td>0.000618</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>0.148272</td>\n",
              "      <td>0.018742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000158</td>\n",
              "      <td>1.645125e-04</td>\n",
              "      <td>0.000210</td>\n",
              "      <td>0.001149</td>\n",
              "      <td>0.000853</td>\n",
              "      <td>0.000193</td>\n",
              "      <td>0.000149</td>\n",
              "      <td>0.147429</td>\n",
              "      <td>0.018788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>1.489855e-05</td>\n",
              "      <td>0.000152</td>\n",
              "      <td>0.000904</td>\n",
              "      <td>0.000728</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>0.150467</td>\n",
              "      <td>0.019047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000493</td>\n",
              "      <td>3.284470e-04</td>\n",
              "      <td>0.000487</td>\n",
              "      <td>0.002337</td>\n",
              "      <td>0.001844</td>\n",
              "      <td>0.000518</td>\n",
              "      <td>0.000370</td>\n",
              "      <td>0.147908</td>\n",
              "      <td>0.019286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000077</td>\n",
              "      <td>1.126515e-04</td>\n",
              "      <td>0.000276</td>\n",
              "      <td>0.001285</td>\n",
              "      <td>0.001080</td>\n",
              "      <td>0.000095</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.158502</td>\n",
              "      <td>0.020187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000077</td>\n",
              "      <td>6.442435e-05</td>\n",
              "      <td>0.000132</td>\n",
              "      <td>0.000777</td>\n",
              "      <td>0.000666</td>\n",
              "      <td>0.000081</td>\n",
              "      <td>0.000083</td>\n",
              "      <td>0.176375</td>\n",
              "      <td>0.022282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>1.892706e-04</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>0.001244</td>\n",
              "      <td>0.000959</td>\n",
              "      <td>0.000220</td>\n",
              "      <td>0.000171</td>\n",
              "      <td>0.176301</td>\n",
              "      <td>0.022436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>6.306119e-05</td>\n",
              "      <td>0.000270</td>\n",
              "      <td>0.001348</td>\n",
              "      <td>0.001105</td>\n",
              "      <td>0.000084</td>\n",
              "      <td>0.000058</td>\n",
              "      <td>0.185798</td>\n",
              "      <td>0.023601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>1.473678e-05</td>\n",
              "      <td>0.000243</td>\n",
              "      <td>0.001033</td>\n",
              "      <td>0.000860</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.188794</td>\n",
              "      <td>0.023886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>2.674575e-06</td>\n",
              "      <td>0.000167</td>\n",
              "      <td>0.000749</td>\n",
              "      <td>0.000601</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.189596</td>\n",
              "      <td>0.023900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000432</td>\n",
              "      <td>3.881313e-04</td>\n",
              "      <td>0.000514</td>\n",
              "      <td>0.002771</td>\n",
              "      <td>0.002133</td>\n",
              "      <td>0.000537</td>\n",
              "      <td>0.000452</td>\n",
              "      <td>0.184173</td>\n",
              "      <td>0.023925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>9.073721e-05</td>\n",
              "      <td>0.000168</td>\n",
              "      <td>0.000818</td>\n",
              "      <td>0.000664</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.189417</td>\n",
              "      <td>0.023931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>2.838964e-06</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>0.001017</td>\n",
              "      <td>0.000812</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.193364</td>\n",
              "      <td>0.024431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>3.518088e-05</td>\n",
              "      <td>0.000313</td>\n",
              "      <td>0.002285</td>\n",
              "      <td>0.001749</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.199825</td>\n",
              "      <td>0.025553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>6.687432e-06</td>\n",
              "      <td>0.000389</td>\n",
              "      <td>0.001836</td>\n",
              "      <td>0.001474</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>0.201092</td>\n",
              "      <td>0.025619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000062</td>\n",
              "      <td>5.894955e-05</td>\n",
              "      <td>0.000287</td>\n",
              "      <td>0.001737</td>\n",
              "      <td>0.001372</td>\n",
              "      <td>0.000088</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.204310</td>\n",
              "      <td>0.025998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>2.937905e-06</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.000612</td>\n",
              "      <td>0.000508</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.207295</td>\n",
              "      <td>0.026076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>1.399786e-05</td>\n",
              "      <td>0.000289</td>\n",
              "      <td>0.002017</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.207224</td>\n",
              "      <td>0.026412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>1.648578e-05</td>\n",
              "      <td>0.000233</td>\n",
              "      <td>0.001255</td>\n",
              "      <td>0.001033</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.212203</td>\n",
              "      <td>0.026860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000184</td>\n",
              "      <td>1.879223e-04</td>\n",
              "      <td>0.000307</td>\n",
              "      <td>0.001620</td>\n",
              "      <td>0.001248</td>\n",
              "      <td>0.000243</td>\n",
              "      <td>0.000209</td>\n",
              "      <td>0.211477</td>\n",
              "      <td>0.026935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>4.216543e-06</td>\n",
              "      <td>0.000215</td>\n",
              "      <td>0.000955</td>\n",
              "      <td>0.000790</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>0.214240</td>\n",
              "      <td>0.027039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>1.042364e-04</td>\n",
              "      <td>0.000221</td>\n",
              "      <td>0.000938</td>\n",
              "      <td>0.000773</td>\n",
              "      <td>0.000127</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.230341</td>\n",
              "      <td>0.029090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000635</td>\n",
              "      <td>4.115888e-04</td>\n",
              "      <td>0.000560</td>\n",
              "      <td>0.003235</td>\n",
              "      <td>0.002607</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.000616</td>\n",
              "      <td>0.227951</td>\n",
              "      <td>0.029592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>3.581906e-06</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.001071</td>\n",
              "      <td>0.000880</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.000041</td>\n",
              "      <td>0.236340</td>\n",
              "      <td>0.029829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000747</td>\n",
              "      <td>6.681966e-04</td>\n",
              "      <td>0.000703</td>\n",
              "      <td>0.004046</td>\n",
              "      <td>0.003824</td>\n",
              "      <td>0.000808</td>\n",
              "      <td>0.000934</td>\n",
              "      <td>0.227549</td>\n",
              "      <td>0.029910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000033</td>\n",
              "      <td>1.615564e-05</td>\n",
              "      <td>0.000238</td>\n",
              "      <td>0.001440</td>\n",
              "      <td>0.001177</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.248663</td>\n",
              "      <td>0.031460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>3.201048e-06</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.001469</td>\n",
              "      <td>0.001203</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.263614</td>\n",
              "      <td>0.033336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000467</td>\n",
              "      <td>4.311437e-04</td>\n",
              "      <td>0.000561</td>\n",
              "      <td>0.003728</td>\n",
              "      <td>0.003206</td>\n",
              "      <td>0.000491</td>\n",
              "      <td>0.000582</td>\n",
              "      <td>0.259996</td>\n",
              "      <td>0.033683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000248</td>\n",
              "      <td>2.316848e-04</td>\n",
              "      <td>0.000374</td>\n",
              "      <td>0.002032</td>\n",
              "      <td>0.001507</td>\n",
              "      <td>0.000313</td>\n",
              "      <td>0.000276</td>\n",
              "      <td>0.266789</td>\n",
              "      <td>0.033971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>1.051605e-04</td>\n",
              "      <td>0.000278</td>\n",
              "      <td>0.001173</td>\n",
              "      <td>0.000938</td>\n",
              "      <td>0.000127</td>\n",
              "      <td>0.000129</td>\n",
              "      <td>0.270740</td>\n",
              "      <td>0.034200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000241</td>\n",
              "      <td>2.330784e-04</td>\n",
              "      <td>0.000455</td>\n",
              "      <td>0.003227</td>\n",
              "      <td>0.002625</td>\n",
              "      <td>0.000268</td>\n",
              "      <td>0.000332</td>\n",
              "      <td>0.281849</td>\n",
              "      <td>0.036154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>2.572337e-04</td>\n",
              "      <td>0.000475</td>\n",
              "      <td>0.002541</td>\n",
              "      <td>0.001896</td>\n",
              "      <td>0.000421</td>\n",
              "      <td>0.000384</td>\n",
              "      <td>0.323941</td>\n",
              "      <td>0.041281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000148</td>\n",
              "      <td>1.223068e-04</td>\n",
              "      <td>0.000327</td>\n",
              "      <td>0.001429</td>\n",
              "      <td>0.001125</td>\n",
              "      <td>0.000184</td>\n",
              "      <td>0.000168</td>\n",
              "      <td>0.336868</td>\n",
              "      <td>0.042546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Cora()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000170</td>\n",
              "      <td>1.374683e-04</td>\n",
              "      <td>0.000387</td>\n",
              "      <td>0.001980</td>\n",
              "      <td>0.001453</td>\n",
              "      <td>0.000214</td>\n",
              "      <td>0.000216</td>\n",
              "      <td>0.394194</td>\n",
              "      <td>0.049844</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Embedding Size  Learning Rate  ...  clustering() error  Average Error across 8-d\n",
              "47              8          0.010  ...            0.071410                  0.009605\n",
              "95              8          0.001  ...            0.071758                  0.009628\n",
              "46              8          0.010  ...            0.072598                  0.009752\n",
              "94              8          0.001  ...            0.074192                  0.009837\n",
              "45              8          0.010  ...            0.073554                  0.009855\n",
              "92              8          0.001  ...            0.073683                  0.009882\n",
              "44              8          0.010  ...            0.073707                  0.009910\n",
              "43              8          0.010  ...            0.073774                  0.009917\n",
              "42              8          0.010  ...            0.074980                  0.010049\n",
              "91              8          0.001  ...            0.076068                  0.010093\n",
              "41              8          0.010  ...            0.077536                  0.010335\n",
              "89              8          0.001  ...            0.078040                  0.010363\n",
              "93              8          0.001  ...            0.078971                  0.010421\n",
              "39              8          0.010  ...            0.078944                  0.010490\n",
              "40              8          0.010  ...            0.078943                  0.010511\n",
              "88              8          0.001  ...            0.080425                  0.010580\n",
              "90              8          0.001  ...            0.080881                  0.010691\n",
              "86              8          0.001  ...            0.080849                  0.010705\n",
              "38              8          0.010  ...            0.080993                  0.010768\n",
              "37              8          0.010  ...            0.081048                  0.010775\n",
              "85              8          0.001  ...            0.083261                  0.010933\n",
              "36              8          0.010  ...            0.083387                  0.011038\n",
              "87              8          0.001  ...            0.084867                  0.011143\n",
              "34              8          0.010  ...            0.084207                  0.011190\n",
              "35              8          0.010  ...            0.084358                  0.011211\n",
              "33              8          0.010  ...            0.084742                  0.011222\n",
              "83              8          0.001  ...            0.084906                  0.011229\n",
              "82              8          0.001  ...            0.086906                  0.011407\n",
              "84              8          0.001  ...            0.087577                  0.011485\n",
              "81              8          0.001  ...            0.090381                  0.011862\n",
              "69              8          0.001  ...            0.091426                  0.011926\n",
              "21              8          0.010  ...            0.095051                  0.012138\n",
              "30              8          0.010  ...            0.095652                  0.012577\n",
              "31              8          0.010  ...            0.095270                  0.012577\n",
              "32              8          0.010  ...            0.096244                  0.012700\n",
              "80              8          0.001  ...            0.096990                  0.012731\n",
              "66              8          0.001  ...            0.098161                  0.012854\n",
              "79              8          0.001  ...            0.098768                  0.012894\n",
              "78              8          0.001  ...            0.099794                  0.013043\n",
              "63              8          0.001  ...            0.108340                  0.014157\n",
              "28              8          0.010  ...            0.108694                  0.014264\n",
              "27              8          0.010  ...            0.109572                  0.014332\n",
              "22              8          0.010  ...            0.113362                  0.014369\n",
              "29              8          0.010  ...            0.110569                  0.014505\n",
              "75              8          0.001  ...            0.111616                  0.014546\n",
              "77              8          0.001  ...            0.112194                  0.014629\n",
              "76              8          0.001  ...            0.113540                  0.014745\n",
              "18              8          0.010  ...            0.115879                  0.014786\n",
              "70              8          0.001  ...            0.117496                  0.014954\n",
              "15              8          0.010  ...            0.120226                  0.015310\n",
              "12              8          0.010  ...            0.123261                  0.015698\n",
              "60              8          0.001  ...            0.121725                  0.015884\n",
              "25              8          0.010  ...            0.126546                  0.016405\n",
              "24              8          0.010  ...            0.126535                  0.016443\n",
              "23              8          0.010  ...            0.132670                  0.016718\n",
              "26              8          0.010  ...            0.129084                  0.016742\n",
              "67              8          0.001  ...            0.133441                  0.017001\n",
              "72              8          0.001  ...            0.133414                  0.017271\n",
              "74              8          0.001  ...            0.134541                  0.017358\n",
              "73              8          0.001  ...            0.137687                  0.017740\n",
              "71              8          0.001  ...            0.146445                  0.018498\n",
              "19              8          0.010  ...            0.147176                  0.018600\n",
              "16              8          0.010  ...            0.148272                  0.018742\n",
              "64              8          0.001  ...            0.147429                  0.018788\n",
              "13              8          0.010  ...            0.150467                  0.019047\n",
              "57              8          0.001  ...            0.147908                  0.019286\n",
              "9               8          0.010  ...            0.158502                  0.020187\n",
              "68              8          0.001  ...            0.176375                  0.022282\n",
              "61              8          0.001  ...            0.176301                  0.022436\n",
              "6               8          0.010  ...            0.185798                  0.023601\n",
              "10              8          0.010  ...            0.188794                  0.023886\n",
              "14              8          0.010  ...            0.189596                  0.023900\n",
              "54              8          0.001  ...            0.184173                  0.023925\n",
              "65              8          0.001  ...            0.189417                  0.023931\n",
              "17              8          0.010  ...            0.193364                  0.024431\n",
              "0               8          0.010  ...            0.199825                  0.025553\n",
              "2               8          0.010  ...            0.201092                  0.025619\n",
              "3               8          0.010  ...            0.204310                  0.025998\n",
              "20              8          0.010  ...            0.207295                  0.026076\n",
              "1               8          0.010  ...            0.207224                  0.026412\n",
              "7               8          0.010  ...            0.212203                  0.026860\n",
              "58              8          0.001  ...            0.211477                  0.026935\n",
              "11              8          0.010  ...            0.214240                  0.027039\n",
              "62              8          0.001  ...            0.230341                  0.029090\n",
              "51              8          0.001  ...            0.227951                  0.029592\n",
              "8               8          0.010  ...            0.236340                  0.029829\n",
              "48              8          0.001  ...            0.227549                  0.029910\n",
              "4               8          0.010  ...            0.248663                  0.031460\n",
              "5               8          0.010  ...            0.263614                  0.033336\n",
              "49              8          0.001  ...            0.259996                  0.033683\n",
              "55              8          0.001  ...            0.266789                  0.033971\n",
              "59              8          0.001  ...            0.270740                  0.034200\n",
              "50              8          0.001  ...            0.281849                  0.036154\n",
              "52              8          0.001  ...            0.323941                  0.041281\n",
              "56              8          0.001  ...            0.336868                  0.042546\n",
              "53              8          0.001  ...            0.394194                  0.049844\n",
              "\n",
              "[96 rows x 15 columns]"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0THoHE-zNDxJ"
      },
      "source": [
        "## Experiment on Citeceer Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "SIid8HSzNRJR",
        "outputId": "2289489d-7dbc-4888-957d-2e8eb4e6ac51"
      },
      "source": [
        "show_dataset_stats(Planetoid(root=\"delete_me/\", name=\"CiteSeer\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.x\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.tx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.allx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.y\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ty\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ally\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.graph\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.test.index\n",
            "Processing...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset: CiteSeer():\n",
            "======================\n",
            "Number of graphs: 1\n",
            "Number of features: 3703\n",
            "Number of classes: 6\n",
            "Number of nodes: 3327\n",
            "Number of edges: 9104\n",
            "Average node degree: 2.74\n",
            "Number of training nodes: 120\n",
            "Training node label rate: 0.04\n",
            "Contains isolated nodes: True\n",
            "Contains self-loops: False\n",
            "Is undirected: True\n",
            "======================\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "bz78EkypV_lQ",
        "outputId": "6386eef2-9401-48fb-be02-26470a0cda30"
      },
      "source": [
        "show_dataset_as_networkx_graph(Planetoid(root=\"delete_me/\", name=\"CiteSeer\")[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "93SLdBjINTru"
      },
      "source": [
        "class HyperParameterCombinations:\n",
        "    \"\"\"\n",
        "    To be manually configured\n",
        "    \"\"\"\n",
        "    ES = [8]\n",
        "    LR = [0.01, 0.001]\n",
        "    WD = [1e-5, 1e-3]\n",
        "    TR = [x / 10 for x in range(1, 9)]\n",
        "    DATASET = [Planetoid(root=\"delete_me/\", name=\"CiteSeer\")]\n",
        "    EPOCHS = [250, 500, 1000]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2BPydSGUNXUr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f35c71d3-b575-466a-d5f3-fbabceae374d"
      },
      "source": [
        "df = TrainAndCaptureResults().run()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total combinations to run exp on: 96\n",
            "Running combination: 1 of 96\n",
            "Running combination: 2 of 96\n",
            "Running combination: 3 of 96\n",
            "Running combination: 4 of 96\n",
            "Running combination: 5 of 96\n",
            "Running combination: 6 of 96\n",
            "Running combination: 7 of 96\n",
            "Running combination: 8 of 96\n",
            "Running combination: 9 of 96\n",
            "Running combination: 10 of 96\n",
            "Running combination: 11 of 96\n",
            "Running combination: 12 of 96\n",
            "Running combination: 13 of 96\n",
            "Running combination: 14 of 96\n",
            "Running combination: 15 of 96\n",
            "Running combination: 16 of 96\n",
            "Running combination: 17 of 96\n",
            "Running combination: 18 of 96\n",
            "Running combination: 19 of 96\n",
            "Running combination: 20 of 96\n",
            "Running combination: 21 of 96\n",
            "Running combination: 22 of 96\n",
            "Running combination: 23 of 96\n",
            "Running combination: 24 of 96\n",
            "Running combination: 25 of 96\n",
            "Running combination: 26 of 96\n",
            "Running combination: 27 of 96\n",
            "Running combination: 28 of 96\n",
            "Running combination: 29 of 96\n",
            "Running combination: 30 of 96\n",
            "Running combination: 31 of 96\n",
            "Running combination: 32 of 96\n",
            "Running combination: 33 of 96\n",
            "Running combination: 34 of 96\n",
            "Running combination: 35 of 96\n",
            "Running combination: 36 of 96\n",
            "Running combination: 37 of 96\n",
            "Running combination: 38 of 96\n",
            "Running combination: 39 of 96\n",
            "Running combination: 40 of 96\n",
            "Running combination: 41 of 96\n",
            "Running combination: 42 of 96\n",
            "Running combination: 43 of 96\n",
            "Running combination: 44 of 96\n",
            "Running combination: 45 of 96\n",
            "Running combination: 46 of 96\n",
            "Running combination: 47 of 96\n",
            "Running combination: 48 of 96\n",
            "Running combination: 49 of 96\n",
            "Running combination: 50 of 96\n",
            "Running combination: 51 of 96\n",
            "Running combination: 52 of 96\n",
            "Running combination: 53 of 96\n",
            "Running combination: 54 of 96\n",
            "Running combination: 55 of 96\n",
            "Running combination: 56 of 96\n",
            "Running combination: 57 of 96\n",
            "Running combination: 58 of 96\n",
            "Running combination: 59 of 96\n",
            "Running combination: 60 of 96\n",
            "Running combination: 61 of 96\n",
            "Running combination: 62 of 96\n",
            "Running combination: 63 of 96\n",
            "Running combination: 64 of 96\n",
            "Running combination: 65 of 96\n",
            "Running combination: 66 of 96\n",
            "Running combination: 67 of 96\n",
            "Running combination: 68 of 96\n",
            "Running combination: 69 of 96\n",
            "Running combination: 70 of 96\n",
            "Running combination: 71 of 96\n",
            "Running combination: 72 of 96\n",
            "Running combination: 73 of 96\n",
            "Running combination: 74 of 96\n",
            "Running combination: 75 of 96\n",
            "Running combination: 76 of 96\n",
            "Running combination: 77 of 96\n",
            "Running combination: 78 of 96\n",
            "Running combination: 79 of 96\n",
            "Running combination: 80 of 96\n",
            "Running combination: 81 of 96\n",
            "Running combination: 82 of 96\n",
            "Running combination: 83 of 96\n",
            "Running combination: 84 of 96\n",
            "Running combination: 85 of 96\n",
            "Running combination: 86 of 96\n",
            "Running combination: 87 of 96\n",
            "Running combination: 88 of 96\n",
            "Running combination: 89 of 96\n",
            "Running combination: 90 of 96\n",
            "Running combination: 91 of 96\n",
            "Running combination: 92 of 96\n",
            "Running combination: 93 of 96\n",
            "Running combination: 94 of 96\n",
            "Running combination: 95 of 96\n",
            "Running combination: 96 of 96\n",
            "Least avg error across 8-d: 0.007654709275811911\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APqOMYpLNXqX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5054331a-caf1-4dc1-a5d6-8ac273a27bf9"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Embedding Size</th>\n",
              "      <th>Learning Rate</th>\n",
              "      <th>Weight Decay</th>\n",
              "      <th>Training Ratio</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>Epochs</th>\n",
              "      <th>degree_centrality() error</th>\n",
              "      <th>in_degree_centrality() error</th>\n",
              "      <th>eigenvector_centrality() error</th>\n",
              "      <th>harmonic_centrality() error</th>\n",
              "      <th>closeness_centrality() error</th>\n",
              "      <th>betweenness_centrality() error</th>\n",
              "      <th>load_centrality() error</th>\n",
              "      <th>clustering() error</th>\n",
              "      <th>Average Error across 8-d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.419012e-07</td>\n",
              "      <td>0.000237</td>\n",
              "      <td>0.001617</td>\n",
              "      <td>0.001182</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.058174</td>\n",
              "      <td>0.007655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.359871e-07</td>\n",
              "      <td>0.000238</td>\n",
              "      <td>0.001616</td>\n",
              "      <td>0.001181</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.058348</td>\n",
              "      <td>0.007677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>2.963424e-06</td>\n",
              "      <td>0.000228</td>\n",
              "      <td>0.001469</td>\n",
              "      <td>0.001114</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.059345</td>\n",
              "      <td>0.007777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.544429e-07</td>\n",
              "      <td>0.000209</td>\n",
              "      <td>0.001568</td>\n",
              "      <td>0.001150</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.059767</td>\n",
              "      <td>0.007840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>6.098127e-07</td>\n",
              "      <td>0.000239</td>\n",
              "      <td>0.001629</td>\n",
              "      <td>0.001192</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.060035</td>\n",
              "      <td>0.007891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.937083e-07</td>\n",
              "      <td>0.000238</td>\n",
              "      <td>0.001618</td>\n",
              "      <td>0.001185</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.060442</td>\n",
              "      <td>0.007939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>2.760685e-05</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.001098</td>\n",
              "      <td>0.000858</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.061206</td>\n",
              "      <td>0.007947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>2.780981e-06</td>\n",
              "      <td>0.000208</td>\n",
              "      <td>0.001431</td>\n",
              "      <td>0.001083</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.060808</td>\n",
              "      <td>0.007949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.200854e-07</td>\n",
              "      <td>0.000208</td>\n",
              "      <td>0.001560</td>\n",
              "      <td>0.001145</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.060654</td>\n",
              "      <td>0.007950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.686294e-07</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>0.001632</td>\n",
              "      <td>0.001191</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.062106</td>\n",
              "      <td>0.008151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.777376e-07</td>\n",
              "      <td>0.000208</td>\n",
              "      <td>0.001573</td>\n",
              "      <td>0.001155</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.062280</td>\n",
              "      <td>0.008156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.923810e-07</td>\n",
              "      <td>0.000209</td>\n",
              "      <td>0.001581</td>\n",
              "      <td>0.001162</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.062495</td>\n",
              "      <td>0.008185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>2.652281e-05</td>\n",
              "      <td>0.000197</td>\n",
              "      <td>0.001080</td>\n",
              "      <td>0.000840</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.063196</td>\n",
              "      <td>0.008192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>4.935532e-06</td>\n",
              "      <td>0.000113</td>\n",
              "      <td>0.000716</td>\n",
              "      <td>0.000568</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.064170</td>\n",
              "      <td>0.008204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>6.208587e-07</td>\n",
              "      <td>0.000253</td>\n",
              "      <td>0.001644</td>\n",
              "      <td>0.001200</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.062673</td>\n",
              "      <td>0.008225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>3.448876e-06</td>\n",
              "      <td>0.000245</td>\n",
              "      <td>0.001478</td>\n",
              "      <td>0.001119</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.063131</td>\n",
              "      <td>0.008254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.383860e-07</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.001623</td>\n",
              "      <td>0.001185</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.063042</td>\n",
              "      <td>0.008266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>2.987740e-05</td>\n",
              "      <td>0.000217</td>\n",
              "      <td>0.001116</td>\n",
              "      <td>0.000855</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>0.064127</td>\n",
              "      <td>0.008318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>2.928159e-06</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.000491</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.065368</td>\n",
              "      <td>0.008335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>6.092164e-07</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.001633</td>\n",
              "      <td>0.001194</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.064848</td>\n",
              "      <td>0.008494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000087</td>\n",
              "      <td>1.013346e-04</td>\n",
              "      <td>0.000168</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.000365</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000140</td>\n",
              "      <td>0.067183</td>\n",
              "      <td>0.008581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>3.004271e-05</td>\n",
              "      <td>0.000218</td>\n",
              "      <td>0.001121</td>\n",
              "      <td>0.000872</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.067781</td>\n",
              "      <td>0.008777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>6.580896e-07</td>\n",
              "      <td>0.000285</td>\n",
              "      <td>0.001645</td>\n",
              "      <td>0.001198</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.067059</td>\n",
              "      <td>0.008777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>3.318433e-06</td>\n",
              "      <td>0.000267</td>\n",
              "      <td>0.001489</td>\n",
              "      <td>0.001129</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.067637</td>\n",
              "      <td>0.008823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>6.690923e-07</td>\n",
              "      <td>0.000286</td>\n",
              "      <td>0.001641</td>\n",
              "      <td>0.001195</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.067646</td>\n",
              "      <td>0.008850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.547960e-07</td>\n",
              "      <td>0.000286</td>\n",
              "      <td>0.001650</td>\n",
              "      <td>0.001203</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.067676</td>\n",
              "      <td>0.008856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>1.209526e-04</td>\n",
              "      <td>0.000183</td>\n",
              "      <td>0.000575</td>\n",
              "      <td>0.000407</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>0.069796</td>\n",
              "      <td>0.008934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.682352e-07</td>\n",
              "      <td>0.000285</td>\n",
              "      <td>0.001657</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.068775</td>\n",
              "      <td>0.008995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>3.030679e-06</td>\n",
              "      <td>0.000153</td>\n",
              "      <td>0.000771</td>\n",
              "      <td>0.000555</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.071884</td>\n",
              "      <td>0.009180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000053</td>\n",
              "      <td>3.544688e-05</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.000283</td>\n",
              "      <td>0.000186</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>0.075500</td>\n",
              "      <td>0.009538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>3.393042e-05</td>\n",
              "      <td>0.000213</td>\n",
              "      <td>0.001133</td>\n",
              "      <td>0.000875</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>0.074385</td>\n",
              "      <td>0.009608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>3.893690e-06</td>\n",
              "      <td>0.000261</td>\n",
              "      <td>0.001497</td>\n",
              "      <td>0.001138</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>0.075760</td>\n",
              "      <td>0.009840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>6.738218e-07</td>\n",
              "      <td>0.000280</td>\n",
              "      <td>0.001671</td>\n",
              "      <td>0.001217</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.075758</td>\n",
              "      <td>0.009869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.872350e-07</td>\n",
              "      <td>0.000278</td>\n",
              "      <td>0.001677</td>\n",
              "      <td>0.001224</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.075809</td>\n",
              "      <td>0.009878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>2.197732e-06</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>0.000837</td>\n",
              "      <td>0.000614</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.077368</td>\n",
              "      <td>0.009881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>6.616385e-07</td>\n",
              "      <td>0.000281</td>\n",
              "      <td>0.001666</td>\n",
              "      <td>0.001214</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.076006</td>\n",
              "      <td>0.009899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.505351e-07</td>\n",
              "      <td>0.000279</td>\n",
              "      <td>0.001673</td>\n",
              "      <td>0.001220</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.076361</td>\n",
              "      <td>0.009945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>1.613531e-06</td>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.000359</td>\n",
              "      <td>0.000262</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.080219</td>\n",
              "      <td>0.010124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>6.362674e-07</td>\n",
              "      <td>0.000263</td>\n",
              "      <td>0.001682</td>\n",
              "      <td>0.001226</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.079030</td>\n",
              "      <td>0.010279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.064892e-07</td>\n",
              "      <td>0.000260</td>\n",
              "      <td>0.001690</td>\n",
              "      <td>0.001233</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.080129</td>\n",
              "      <td>0.010418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000058</td>\n",
              "      <td>3.548450e-05</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>0.001147</td>\n",
              "      <td>0.000882</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000115</td>\n",
              "      <td>0.082663</td>\n",
              "      <td>0.010641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>1.527470e-06</td>\n",
              "      <td>0.000168</td>\n",
              "      <td>0.000693</td>\n",
              "      <td>0.000512</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.083730</td>\n",
              "      <td>0.010642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>4.097474e-06</td>\n",
              "      <td>0.000241</td>\n",
              "      <td>0.001482</td>\n",
              "      <td>0.001133</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>0.084424</td>\n",
              "      <td>0.010917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>5.977854e-07</td>\n",
              "      <td>0.000261</td>\n",
              "      <td>0.001684</td>\n",
              "      <td>0.001228</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.084208</td>\n",
              "      <td>0.010926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>5.596335e-05</td>\n",
              "      <td>0.000114</td>\n",
              "      <td>0.000348</td>\n",
              "      <td>0.000240</td>\n",
              "      <td>0.000083</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.086478</td>\n",
              "      <td>0.010936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000125</td>\n",
              "      <td>1.847032e-04</td>\n",
              "      <td>0.000225</td>\n",
              "      <td>0.000778</td>\n",
              "      <td>0.000485</td>\n",
              "      <td>0.000169</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.085872</td>\n",
              "      <td>0.011006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.749221e-07</td>\n",
              "      <td>0.000258</td>\n",
              "      <td>0.001692</td>\n",
              "      <td>0.001237</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.085083</td>\n",
              "      <td>0.011038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.8</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>9.357432e-06</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.000229</td>\n",
              "      <td>0.000164</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>0.091501</td>\n",
              "      <td>0.011510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>7.720951e-06</td>\n",
              "      <td>0.000149</td>\n",
              "      <td>0.000892</td>\n",
              "      <td>0.000638</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>0.093569</td>\n",
              "      <td>0.011917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.408410e-07</td>\n",
              "      <td>0.000279</td>\n",
              "      <td>0.001696</td>\n",
              "      <td>0.001236</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.094136</td>\n",
              "      <td>0.012172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>3.708578e-05</td>\n",
              "      <td>0.000220</td>\n",
              "      <td>0.001157</td>\n",
              "      <td>0.000903</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.000125</td>\n",
              "      <td>0.095557</td>\n",
              "      <td>0.012261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>4.371446e-06</td>\n",
              "      <td>0.000254</td>\n",
              "      <td>0.001436</td>\n",
              "      <td>0.001114</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.098823</td>\n",
              "      <td>0.012712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>9.623326e-07</td>\n",
              "      <td>0.000277</td>\n",
              "      <td>0.001710</td>\n",
              "      <td>0.001249</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.098430</td>\n",
              "      <td>0.012713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>6.710813e-07</td>\n",
              "      <td>0.000277</td>\n",
              "      <td>0.001704</td>\n",
              "      <td>0.001242</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.098451</td>\n",
              "      <td>0.012713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>8.763911e-07</td>\n",
              "      <td>0.000279</td>\n",
              "      <td>0.001711</td>\n",
              "      <td>0.001248</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.098547</td>\n",
              "      <td>0.012727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>9.070404e-07</td>\n",
              "      <td>0.000292</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.001249</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.101349</td>\n",
              "      <td>0.013079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000152</td>\n",
              "      <td>2.236881e-04</td>\n",
              "      <td>0.000256</td>\n",
              "      <td>0.000873</td>\n",
              "      <td>0.000538</td>\n",
              "      <td>0.000214</td>\n",
              "      <td>0.000290</td>\n",
              "      <td>0.102488</td>\n",
              "      <td>0.013129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.036560e-06</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>0.001720</td>\n",
              "      <td>0.001256</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.102087</td>\n",
              "      <td>0.013174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>9.656188e-07</td>\n",
              "      <td>0.000292</td>\n",
              "      <td>0.001712</td>\n",
              "      <td>0.001250</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.104736</td>\n",
              "      <td>0.013503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>3.379197e-05</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.001229</td>\n",
              "      <td>0.000929</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.106946</td>\n",
              "      <td>0.013698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>8.157316e-06</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>0.000812</td>\n",
              "      <td>0.000564</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.107930</td>\n",
              "      <td>0.013703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.730121e-07</td>\n",
              "      <td>0.000290</td>\n",
              "      <td>0.001708</td>\n",
              "      <td>0.001246</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.108889</td>\n",
              "      <td>0.014020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.7</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>1.423774e-05</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.000202</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.111776</td>\n",
              "      <td>0.014061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>3.969659e-06</td>\n",
              "      <td>0.000277</td>\n",
              "      <td>0.001356</td>\n",
              "      <td>0.001057</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.109877</td>\n",
              "      <td>0.014080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>8.435280e-06</td>\n",
              "      <td>0.000141</td>\n",
              "      <td>0.000636</td>\n",
              "      <td>0.000467</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.119366</td>\n",
              "      <td>0.015088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>8.048195e-05</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>0.000497</td>\n",
              "      <td>0.000310</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.000125</td>\n",
              "      <td>0.120699</td>\n",
              "      <td>0.015256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000183</td>\n",
              "      <td>2.445622e-04</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.001108</td>\n",
              "      <td>0.000713</td>\n",
              "      <td>0.000222</td>\n",
              "      <td>0.000296</td>\n",
              "      <td>0.122235</td>\n",
              "      <td>0.015663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>7.046730e-06</td>\n",
              "      <td>0.000164</td>\n",
              "      <td>0.001183</td>\n",
              "      <td>0.000828</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.129518</td>\n",
              "      <td>0.016479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>1.699396e-05</td>\n",
              "      <td>0.000239</td>\n",
              "      <td>0.001837</td>\n",
              "      <td>0.001314</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>0.000074</td>\n",
              "      <td>0.139782</td>\n",
              "      <td>0.017915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000240</td>\n",
              "      <td>3.304539e-04</td>\n",
              "      <td>0.000420</td>\n",
              "      <td>0.001202</td>\n",
              "      <td>0.000792</td>\n",
              "      <td>0.000285</td>\n",
              "      <td>0.000408</td>\n",
              "      <td>0.140255</td>\n",
              "      <td>0.017991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>4.823167e-06</td>\n",
              "      <td>0.000218</td>\n",
              "      <td>0.000649</td>\n",
              "      <td>0.000467</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.145937</td>\n",
              "      <td>0.018419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>8.298784e-07</td>\n",
              "      <td>0.000134</td>\n",
              "      <td>0.000485</td>\n",
              "      <td>0.000379</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.149745</td>\n",
              "      <td>0.018847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>6.671387e-06</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001375</td>\n",
              "      <td>0.000975</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.148845</td>\n",
              "      <td>0.018935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>9.636366e-06</td>\n",
              "      <td>0.000242</td>\n",
              "      <td>0.001407</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.000076</td>\n",
              "      <td>0.148946</td>\n",
              "      <td>0.018963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000384</td>\n",
              "      <td>5.830477e-04</td>\n",
              "      <td>0.000729</td>\n",
              "      <td>0.002143</td>\n",
              "      <td>0.001472</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>0.000671</td>\n",
              "      <td>0.149939</td>\n",
              "      <td>0.019535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>8.236210e-05</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.000552</td>\n",
              "      <td>0.000388</td>\n",
              "      <td>0.000151</td>\n",
              "      <td>0.000151</td>\n",
              "      <td>0.156477</td>\n",
              "      <td>0.019762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>250</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>4.576362e-04</td>\n",
              "      <td>0.000494</td>\n",
              "      <td>0.001567</td>\n",
              "      <td>0.000972</td>\n",
              "      <td>0.000290</td>\n",
              "      <td>0.000437</td>\n",
              "      <td>0.154248</td>\n",
              "      <td>0.019846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>5.902490e-06</td>\n",
              "      <td>0.000229</td>\n",
              "      <td>0.001601</td>\n",
              "      <td>0.001172</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.159426</td>\n",
              "      <td>0.020311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.441904e-06</td>\n",
              "      <td>0.000186</td>\n",
              "      <td>0.001426</td>\n",
              "      <td>0.001105</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.161989</td>\n",
              "      <td>0.020594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>3.658534e-06</td>\n",
              "      <td>0.000149</td>\n",
              "      <td>0.000744</td>\n",
              "      <td>0.000590</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.174578</td>\n",
              "      <td>0.022015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000191</td>\n",
              "      <td>2.959502e-04</td>\n",
              "      <td>0.000436</td>\n",
              "      <td>0.001812</td>\n",
              "      <td>0.001277</td>\n",
              "      <td>0.000195</td>\n",
              "      <td>0.000373</td>\n",
              "      <td>0.176308</td>\n",
              "      <td>0.022611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.6</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>1.873715e-05</td>\n",
              "      <td>0.000148</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>0.180404</td>\n",
              "      <td>0.022675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>1.061830e-06</td>\n",
              "      <td>0.000177</td>\n",
              "      <td>0.000603</td>\n",
              "      <td>0.000457</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.183967</td>\n",
              "      <td>0.023155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000123</td>\n",
              "      <td>9.550009e-05</td>\n",
              "      <td>0.000213</td>\n",
              "      <td>0.000751</td>\n",
              "      <td>0.000539</td>\n",
              "      <td>0.000139</td>\n",
              "      <td>0.000176</td>\n",
              "      <td>0.186209</td>\n",
              "      <td>0.023531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>2.461377e-06</td>\n",
              "      <td>0.000236</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>0.000999</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.189489</td>\n",
              "      <td>0.024015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>5.693425e-06</td>\n",
              "      <td>0.000140</td>\n",
              "      <td>0.001365</td>\n",
              "      <td>0.001037</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.000052</td>\n",
              "      <td>0.192976</td>\n",
              "      <td>0.024450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000087</td>\n",
              "      <td>1.331440e-04</td>\n",
              "      <td>0.000302</td>\n",
              "      <td>0.001476</td>\n",
              "      <td>0.001118</td>\n",
              "      <td>0.000106</td>\n",
              "      <td>0.000199</td>\n",
              "      <td>0.198708</td>\n",
              "      <td>0.025266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.159875e-06</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>0.001432</td>\n",
              "      <td>0.001088</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.203188</td>\n",
              "      <td>0.025743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>7.298695e-07</td>\n",
              "      <td>0.000155</td>\n",
              "      <td>0.000539</td>\n",
              "      <td>0.000418</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.210150</td>\n",
              "      <td>0.026412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>1.594676e-04</td>\n",
              "      <td>0.000337</td>\n",
              "      <td>0.001288</td>\n",
              "      <td>0.000911</td>\n",
              "      <td>0.000151</td>\n",
              "      <td>0.000250</td>\n",
              "      <td>0.214971</td>\n",
              "      <td>0.027275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.5</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>2.365850e-05</td>\n",
              "      <td>0.000184</td>\n",
              "      <td>0.000412</td>\n",
              "      <td>0.000324</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>0.000076</td>\n",
              "      <td>0.223861</td>\n",
              "      <td>0.028126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>500</td>\n",
              "      <td>0.000135</td>\n",
              "      <td>1.386783e-04</td>\n",
              "      <td>0.000243</td>\n",
              "      <td>0.000962</td>\n",
              "      <td>0.000686</td>\n",
              "      <td>0.000169</td>\n",
              "      <td>0.000241</td>\n",
              "      <td>0.233529</td>\n",
              "      <td>0.029513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>1.192097e-06</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>0.000842</td>\n",
              "      <td>0.000653</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.236084</td>\n",
              "      <td>0.029719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.4</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>3.451128e-05</td>\n",
              "      <td>0.000173</td>\n",
              "      <td>0.000589</td>\n",
              "      <td>0.000418</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>0.251565</td>\n",
              "      <td>0.031627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.2</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>6.142606e-05</td>\n",
              "      <td>0.000245</td>\n",
              "      <td>0.001090</td>\n",
              "      <td>0.000787</td>\n",
              "      <td>0.000077</td>\n",
              "      <td>0.000128</td>\n",
              "      <td>0.256475</td>\n",
              "      <td>0.032366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>CiteSeer()</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000082</td>\n",
              "      <td>5.041217e-05</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.000825</td>\n",
              "      <td>0.000605</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000135</td>\n",
              "      <td>0.307213</td>\n",
              "      <td>0.038653</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Embedding Size  Learning Rate  ...  clustering() error  Average Error across 8-d\n",
              "92              8          0.001  ...            0.058174                  0.007655\n",
              "44              8          0.010  ...            0.058348                  0.007677\n",
              "91              8          0.001  ...            0.059345                  0.007777\n",
              "95              8          0.001  ...            0.059767                  0.007840\n",
              "42              8          0.010  ...            0.060035                  0.007891\n",
              "43              8          0.010  ...            0.060442                  0.007939\n",
              "90              8          0.001  ...            0.061206                  0.007947\n",
              "94              8          0.001  ...            0.060808                  0.007949\n",
              "47              8          0.010  ...            0.060654                  0.007950\n",
              "89              8          0.001  ...            0.062106                  0.008151\n",
              "46              8          0.010  ...            0.062280                  0.008156\n",
              "45              8          0.010  ...            0.062495                  0.008185\n",
              "93              8          0.001  ...            0.063196                  0.008192\n",
              "22              8          0.010  ...            0.064170                  0.008204\n",
              "40              8          0.010  ...            0.062673                  0.008225\n",
              "88              8          0.001  ...            0.063131                  0.008254\n",
              "41              8          0.010  ...            0.063042                  0.008266\n",
              "87              8          0.001  ...            0.064127                  0.008318\n",
              "21              8          0.010  ...            0.065368                  0.008335\n",
              "39              8          0.010  ...            0.064848                  0.008494\n",
              "69              8          0.001  ...            0.067183                  0.008581\n",
              "84              8          0.001  ...            0.067781                  0.008777\n",
              "86              8          0.001  ...            0.067059                  0.008777\n",
              "85              8          0.001  ...            0.067637                  0.008823\n",
              "38              8          0.010  ...            0.067646                  0.008850\n",
              "37              8          0.010  ...            0.067676                  0.008856\n",
              "66              8          0.001  ...            0.069796                  0.008934\n",
              "36              8          0.010  ...            0.068775                  0.008995\n",
              "18              8          0.010  ...            0.071884                  0.009180\n",
              "70              8          0.001  ...            0.075500                  0.009538\n",
              "81              8          0.001  ...            0.074385                  0.009608\n",
              "82              8          0.001  ...            0.075760                  0.009840\n",
              "83              8          0.001  ...            0.075758                  0.009869\n",
              "33              8          0.010  ...            0.075809                  0.009878\n",
              "19              8          0.010  ...            0.077368                  0.009881\n",
              "35              8          0.010  ...            0.076006                  0.009899\n",
              "34              8          0.010  ...            0.076361                  0.009945\n",
              "23              8          0.010  ...            0.080219                  0.010124\n",
              "32              8          0.010  ...            0.079030                  0.010279\n",
              "31              8          0.010  ...            0.080129                  0.010418\n",
              "78              8          0.001  ...            0.082663                  0.010641\n",
              "20              8          0.010  ...            0.083730                  0.010642\n",
              "79              8          0.001  ...            0.084424                  0.010917\n",
              "80              8          0.001  ...            0.084208                  0.010926\n",
              "67              8          0.001  ...            0.086478                  0.010936\n",
              "63              8          0.001  ...            0.085872                  0.011006\n",
              "30              8          0.010  ...            0.085083                  0.011038\n",
              "71              8          0.001  ...            0.091501                  0.011510\n",
              "15              8          0.010  ...            0.093569                  0.011917\n",
              "29              8          0.010  ...            0.094136                  0.012172\n",
              "75              8          0.001  ...            0.095557                  0.012261\n",
              "76              8          0.001  ...            0.098823                  0.012712\n",
              "27              8          0.010  ...            0.098430                  0.012713\n",
              "77              8          0.001  ...            0.098451                  0.012713\n",
              "28              8          0.010  ...            0.098547                  0.012727\n",
              "26              8          0.010  ...            0.101349                  0.013079\n",
              "60              8          0.001  ...            0.102488                  0.013129\n",
              "24              8          0.010  ...            0.102087                  0.013174\n",
              "25              8          0.010  ...            0.104736                  0.013503\n",
              "72              8          0.001  ...            0.106946                  0.013698\n",
              "12              8          0.010  ...            0.107930                  0.013703\n",
              "74              8          0.001  ...            0.108889                  0.014020\n",
              "68              8          0.001  ...            0.111776                  0.014061\n",
              "73              8          0.001  ...            0.109877                  0.014080\n",
              "16              8          0.010  ...            0.119366                  0.015088\n",
              "64              8          0.001  ...            0.120699                  0.015256\n",
              "57              8          0.001  ...            0.122235                  0.015663\n",
              "9               8          0.010  ...            0.129518                  0.016479\n",
              "0               8          0.010  ...            0.139782                  0.017915\n",
              "54              8          0.001  ...            0.140255                  0.017991\n",
              "13              8          0.010  ...            0.145937                  0.018419\n",
              "17              8          0.010  ...            0.149745                  0.018847\n",
              "6               8          0.010  ...            0.148845                  0.018935\n",
              "3               8          0.010  ...            0.148946                  0.018963\n",
              "48              8          0.001  ...            0.149939                  0.019535\n",
              "61              8          0.001  ...            0.156477                  0.019762\n",
              "51              8          0.001  ...            0.154248                  0.019846\n",
              "1               8          0.010  ...            0.159426                  0.020311\n",
              "2               8          0.010  ...            0.161989                  0.020594\n",
              "10              8          0.010  ...            0.174578                  0.022015\n",
              "49              8          0.001  ...            0.176308                  0.022611\n",
              "65              8          0.001  ...            0.180404                  0.022675\n",
              "14              8          0.010  ...            0.183967                  0.023155\n",
              "58              8          0.001  ...            0.186209                  0.023531\n",
              "4               8          0.010  ...            0.189489                  0.024015\n",
              "7               8          0.010  ...            0.192976                  0.024450\n",
              "50              8          0.001  ...            0.198708                  0.025266\n",
              "5               8          0.010  ...            0.203188                  0.025743\n",
              "11              8          0.010  ...            0.210150                  0.026412\n",
              "52              8          0.001  ...            0.214971                  0.027275\n",
              "62              8          0.001  ...            0.223861                  0.028126\n",
              "55              8          0.001  ...            0.233529                  0.029513\n",
              "8               8          0.010  ...            0.236084                  0.029719\n",
              "59              8          0.001  ...            0.251565                  0.031627\n",
              "53              8          0.001  ...            0.256475                  0.032366\n",
              "56              8          0.001  ...            0.307213                  0.038653\n",
              "\n",
              "[96 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixz_rUFID7k9"
      },
      "source": [
        "Among all the errors seen, the largest value across the three datasets is for clustering()"
      ]
    }
  ]
}