Copy_of_HW_5.ipynb

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            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/southjohn64/data_mine/blob/main/Copy_of_HW_5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4X6zuhyulUc"
      },
      "source": [
        "# Homework Assignment  5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qMUllf7IXEfR",
        "outputId": "4b0b67c0-a5e2-44bd-eb1b-3c7111d8a7aa"
      },
      "source": [
        "!pip install turicreate"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting turicreate\n",
            "  Downloading turicreate-6.4.1-cp37-cp37m-manylinux1_x86_64.whl (92.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 92.0 MB 1.2 MB/s \n",
            "\u001b[?25hCollecting resampy==0.2.1\n",
            "  Downloading resampy-0.2.1.tar.gz (322 kB)\n",
            "\u001b[K     |████████████████████████████████| 322 kB 58.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from turicreate) (1.15.0)\n",
            "Requirement already satisfied: decorator>=4.0.9 in /usr/local/lib/python3.7/dist-packages (from turicreate) (4.4.2)\n",
            "Collecting prettytable==0.7.2\n",
            "  Downloading prettytable-0.7.2.zip (28 kB)\n",
            "Requirement already satisfied: pillow>=5.2.0 in /usr/local/lib/python3.7/dist-packages (from turicreate) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from turicreate) (1.19.5)\n",
            "Requirement already satisfied: requests>=2.9.1 in /usr/local/lib/python3.7/dist-packages (from turicreate) (2.23.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from turicreate) (1.4.1)\n",
            "Collecting numba<0.51.0\n",
            "  Downloading numba-0.50.1-cp37-cp37m-manylinux2014_x86_64.whl (3.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.6 MB 54.2 MB/s \n",
            "\u001b[?25hCollecting tensorflow<2.1.0,>=2.0.0\n",
            "  Downloading tensorflow-2.0.4-cp37-cp37m-manylinux2010_x86_64.whl (86.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 86.4 MB 72 kB/s \n",
            "\u001b[?25hCollecting coremltools==3.3\n",
            "  Downloading coremltools-3.3-cp37-none-manylinux1_x86_64.whl (3.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.5 MB 38.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.23.2 in /usr/local/lib/python3.7/dist-packages (from turicreate) (1.1.5)\n",
            "Requirement already satisfied: protobuf>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from coremltools==3.3->turicreate) (3.17.3)\n",
            "Collecting llvmlite<0.34,>=0.33.0.dev0\n",
            "  Downloading llvmlite-0.33.0-cp37-cp37m-manylinux1_x86_64.whl (18.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 18.3 MB 1.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba<0.51.0->turicreate) (57.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23.2->turicreate) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23.2->turicreate) (2018.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.9.1->turicreate) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.9.1->turicreate) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.9.1->turicreate) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.9.1->turicreate) (2.10)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (0.2.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (0.8.1)\n",
            "Collecting tensorflow-estimator<2.1.0,>=2.0.0\n",
            "  Downloading tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449 kB)\n",
            "\u001b[K     |████████████████████████████████| 449 kB 61.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (1.1.2)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading gast-0.2.2.tar.gz (10 kB)\n",
            "Collecting h5py<=2.10.0\n",
            "  Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9 MB 50.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (1.13.3)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (0.12.0)\n",
            "Collecting tensorboard<2.1.0,>=2.0.0\n",
            "  Downloading tensorboard-2.0.2-py3-none-any.whl (3.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8 MB 48.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (1.1.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (0.37.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (3.3.0)\n",
            "Collecting keras-applications>=1.0.8\n",
            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 7.2 MB/s \n",
            "\u001b[?25hCollecting numpy\n",
            "  Downloading numpy-1.18.5-cp37-cp37m-manylinux1_x86_64.whl (20.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 20.1 MB 1.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.1.0,>=2.0.0->turicreate) (1.42.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (1.35.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (0.4.6)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (3.3.6)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (1.0.1)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (4.2.4)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (4.7.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (4.8.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (3.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (3.10.0.2)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow<2.1.0,>=2.0.0->turicreate) (3.1.1)\n",
            "Building wheels for collected packages: prettytable, resampy, gast\n",
            "  Building wheel for prettytable (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prettytable: filename=prettytable-0.7.2-py3-none-any.whl size=13714 sha256=1d7ed8523a154cb1d9e5afa12b699cfd102a448346e46f3d16696a2594c1264e\n",
            "  Stored in directory: /root/.cache/pip/wheels/b2/7f/f6/f180315b584f00445045ff1699b550fa895d09471337ce21c6\n",
            "  Building wheel for resampy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for resampy: filename=resampy-0.2.1-py3-none-any.whl size=320860 sha256=bac60a76020aa7004ccca0b05328b770c7a20159b6119b64cc3151e821d27dac\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/74/53/d5ceb7c5ee7a168c7d106041863e71ac3273f4a4677743a284\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7554 sha256=f298bf5c6430307b10c9a69d07d775bce5ec4d69c791bed74cf19b29774b40e3\n",
            "  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3\n",
            "Successfully built prettytable resampy gast\n",
            "Installing collected packages: numpy, llvmlite, h5py, tensorflow-estimator, tensorboard, numba, keras-applications, gast, tensorflow, resampy, prettytable, coremltools, turicreate\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.19.5\n",
            "    Uninstalling numpy-1.19.5:\n",
            "      Successfully uninstalled numpy-1.19.5\n",
            "  Attempting uninstall: llvmlite\n",
            "    Found existing installation: llvmlite 0.34.0\n",
            "    Uninstalling llvmlite-0.34.0:\n",
            "      Successfully uninstalled llvmlite-0.34.0\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.1.0\n",
            "    Uninstalling h5py-3.1.0:\n",
            "      Successfully uninstalled h5py-3.1.0\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.7.0\n",
            "    Uninstalling tensorflow-estimator-2.7.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.7.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.7.0\n",
            "    Uninstalling tensorboard-2.7.0:\n",
            "      Successfully uninstalled tensorboard-2.7.0\n",
            "  Attempting uninstall: numba\n",
            "    Found existing installation: numba 0.51.2\n",
            "    Uninstalling numba-0.51.2:\n",
            "      Successfully uninstalled numba-0.51.2\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.4.0\n",
            "    Uninstalling gast-0.4.0:\n",
            "      Successfully uninstalled gast-0.4.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.7.0\n",
            "    Uninstalling tensorflow-2.7.0:\n",
            "      Successfully uninstalled tensorflow-2.7.0\n",
            "  Attempting uninstall: resampy\n",
            "    Found existing installation: resampy 0.2.2\n",
            "    Uninstalling resampy-0.2.2:\n",
            "      Successfully uninstalled resampy-0.2.2\n",
            "  Attempting uninstall: prettytable\n",
            "    Found existing installation: prettytable 2.4.0\n",
            "    Uninstalling prettytable-2.4.0:\n",
            "      Successfully uninstalled prettytable-2.4.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-probability 0.15.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\n",
            "librosa 0.8.1 requires resampy>=0.2.2, but you have resampy 0.2.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed coremltools-3.3 gast-0.2.2 h5py-2.10.0 keras-applications-1.0.8 llvmlite-0.33.0 numba-0.50.1 numpy-1.18.5 prettytable-0.7.2 resampy-0.2.1 tensorboard-2.0.2 tensorflow-2.0.4 tensorflow-estimator-2.0.1 turicreate-6.4.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsD_l_cGXPoo"
      },
      "source": [
        "# features of link to sframe\n",
        "import networkx as nx\n",
        "import turicreate as tc "
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euP-mSdtulUg"
      },
      "source": [
        "**Question 1:** Select a directed network, and develop a simple\n",
        "    link prediction classifier based on the network's directed topology (40pt). \n",
        "    Use node or link embedding features and try to improve the classification (20pt)\n",
        "    \n",
        "**Bonus:** Evaluate the classifier's ability to predict future links (10pt)\n",
        "    \n",
        "** Note:** You can use [Reddit community networks](http://dynamics.cs.washington.edu/data.html), or search\n",
        "for networks using [The Colorado Index of Complex Networks](https://icon.colorado.edu/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqqlq23sulUi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8476ff9-4fea-49d4-e38e-f8926816645e"
      },
      "source": [
        "# read data\n",
        "'FAA Preferred Routes (2010)'\n",
        "'https://downloads.skewed.de/mirror/konect.cc/files/download.tsv.maayan-faa.tar.bz2'\n",
        "!mkdir ./datasets/\n",
        "!mkdir ./datasets/flight\n",
        "!wget https://downloads.skewed.de/mirror/konect.cc/files/download.tsv.maayan-faa.tar.bz2 -O ./datasets/flight/flight.tar.bz2\n",
        "!tar -xf ./datasets/flight/flight.tar.bz2 -C ./datasets/flight/\n",
        "!ls ./datasets/flight/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-23 13:52:27--  https://downloads.skewed.de/mirror/konect.cc/files/download.tsv.maayan-faa.tar.bz2\n",
            "Resolving downloads.skewed.de (downloads.skewed.de)... 49.12.93.194\n",
            "Connecting to downloads.skewed.de (downloads.skewed.de)|49.12.93.194|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9549 (9.3K) [application/x-bzip2]\n",
            "Saving to: ‘./datasets/flight/flight.tar.bz2’\n",
            "\n",
            "\r          ./dataset   0%[                    ]       0  --.-KB/s               \r./datasets/flight/f 100%[===================>]   9.33K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-11-23 13:52:27 (184 MB/s) - ‘./datasets/flight/flight.tar.bz2’ saved [9549/9549]\n",
            "\n",
            "flight.tar.bz2\tmaayan-faa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szL5ePobXNYz",
        "outputId": "37e1196a-74b6-4885-d5c1-4e762ae43865"
      },
      "source": [
        "#parsing data\n",
        "from_array = []\n",
        "to_array = []\n",
        "graph_dic = {'from_node': from_array, 'to_node': to_array}\n",
        "\n",
        "filename = '/content/datasets/flight/maayan-faa/out.maayan-faa'\n",
        "line_num = 0\n",
        "with open(filename) as file:\n",
        "    for line in file:\n",
        "        if line_num > 0:\n",
        "            from_array.append(line.rstrip().split(' ')[0])\n",
        "            to_array.append(line.rstrip().split(' ')[1])\n",
        "            \n",
        "        line_num += 1\n",
        "\n",
        "print('from_array: ', len(from_array))\n",
        "print('to_array: ', len(to_array))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "from_array:  2615\n",
            "to_array:  2615\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ZM6m3NJ0XSCW",
        "outputId": "19512464-7810-4290-a7df-906a4e50167e"
      },
      "source": [
        "sf = tc.SFrame(graph_dic)\n",
        "sf"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
              "    <tr>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">from_node</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">to_node</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
              "    </tr>\n",
              "</table>\n",
              "[2615 rows x 2 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
              "</div>"
            ],
            "text/plain": [
              "Columns:\n",
              "\tfrom_node\tstr\n",
              "\tto_node\tstr\n",
              "\n",
              "Rows: 2615\n",
              "\n",
              "Data:\n",
              "+-----------+---------+\n",
              "| from_node | to_node |\n",
              "+-----------+---------+\n",
              "|     1     |    2    |\n",
              "|     2     |    3    |\n",
              "|     3     |    4    |\n",
              "|     4     |    5    |\n",
              "|     1     |    6    |\n",
              "|     6     |    7    |\n",
              "|     7     |    8    |\n",
              "|     8     |    9    |\n",
              "|     6     |    10   |\n",
              "|     10    |    11   |\n",
              "+-----------+---------+\n",
              "[2615 rows x 2 columns]\n",
              "Note: Only the head of the SFrame is printed.\n",
              "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrfoJzF5XTjJ",
        "outputId": "b5951d4c-4548-469d-f81a-ace94dce5ae6"
      },
      "source": [
        "directed_graph_nx = nx.DiGraph()\n",
        "directed_graph_nx.add_edges_from([(r['from_node'],r['to_node']) for r in sf])\n",
        "print(nx.info(directed_graph_nx))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DiGraph with 1226 nodes and 2615 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "id": "aC6lmIwxXVNm",
        "outputId": "e30869d9-3bfd-44e7-b17c-5c8e5bdb6bdf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "l = [n for n,degree in dict(directed_graph_nx.degree()).items() if degree > 10]\n",
        "h = directed_graph_nx.subgraph(l)\n",
        "plt.figure(figsize=(10,10))\n",
        "#nx.write_gml(h, \"./datasets/enron/maildir/network.gml\")\n",
        "nx.draw_spring(h, with_labels=True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ygb9vfzsXaCQ"
      },
      "source": [
        "import operator\n",
        "def common_friends(g,u,v):\n",
        "    if not g.has_node(u) or not g.has_node(v):\n",
        "        return None\n",
        "    return set(g.neighbors(u)) & set(g.neighbors(v))\n",
        "\n",
        "def find_friends_of_friends(g, v):\n",
        "    \"Returns a set with all the friends of friends of v that are not friends of v\"\n",
        "    friends_set = set(g.neighbors(v))\n",
        "    friends_of_friends_set = set()\n",
        "    for f in friends_set:\n",
        "        friends_of_friends_set |= set(g.neighbors(f))\n",
        "    friends_of_friends_set -= friends_set\n",
        "    friends_of_friends_set -= {v}\n",
        "    return friends_of_friends_set\n",
        "\n",
        "\n",
        "def rank_friends_of_friends(g, v, top_n):\n",
        "    rank_dict = {}\n",
        "    vertices_set = find_friends_of_friends(g, v)\n",
        "    for u in vertices_set:\n",
        "        rank_dict[u] = len(common_friends(g, v, u))\n",
        "    sorted_d = sorted(rank_dict.items(), key=operator.itemgetter(1), reverse=True)[:top_n]\n",
        "    sorted_d = [a for a in sorted_d if a[1]>0 ]\n",
        "\n",
        "    return sorted_d"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4KRkj8IXc4u",
        "outputId": "cbe2ce12-f8d1-4f2f-d2b1-f3f044527838"
      },
      "source": [
        "# i showing only nodes with rank above 0 and only topN node\n",
        "rank_friends_of_friends(directed_graph_nx, '1', top_n=10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('43', 3),\n",
              " ('3', 3),\n",
              " ('134', 2),\n",
              " ('49', 2),\n",
              " ('21', 1),\n",
              " ('51', 1),\n",
              " ('7', 1),\n",
              " ('124', 1),\n",
              " ('144', 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fh_ktgEZbDuv",
        "outputId": "9d835755-d428-4f10-cb87-098bef7abe00"
      },
      "source": [
        "!pip install tqdm\n",
        "from tqdm import tqdm_notebook as tqdm "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.62.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpmUP3QvbMlC",
        "outputId": "48f08193-0227-448a-e1f8-c881feed7b22"
      },
      "source": [
        "g = nx.Graph() # Creating Undirected Graph\n",
        "# adding all nodes and vertices at once\n",
        "g.add_edges_from([(r['from_node'],r['to_node']) for r in sf])\n",
        "print(nx.info(g))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph with 1226 nodes and 2410 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oWX-vVwbOJ1"
      },
      "source": [
        "# This is for predicting missing links scenario\n",
        "import random \n",
        "\n",
        "def select_random_existing_links(g, n):\n",
        "    l =list(g.edges())\n",
        "    random.shuffle(l)\n",
        "    return l[:n]\n",
        "\n",
        "def select_random_non_existing_links(g, n):\n",
        "    select_links = set()\n",
        "    v_list = list(g.nodes())\n",
        "    while len(select_links) < n:\n",
        "        u = random.choice(v_list)\n",
        "        v = random.choice(v_list)\n",
        "        if g.has_edge(u,v) or (u,v) in select_links and u == v:\n",
        "            continue\n",
        "        else:\n",
        "            select_links.add((u,v))\n",
        "    return list(select_links)\n",
        "            \n",
        "    \n",
        "postive_links = select_random_existing_links(directed_graph_nx,1000)\n",
        "negative_links = select_random_non_existing_links(directed_graph_nx,1000)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285,
          "referenced_widgets": [
            "d916acc5a12343148948c4b7611df513",
            "31e38a0451f9444c8d2e3ead6d6a0d97",
            "8d3c57ef0eed46c2b4ee49d0c1e03729",
            "b060d862fef5470c8c912a53e3bb9ace",
            "388cb7ef154f4a16b44ffba27ee4c56f",
            "126d3b3ece754a638022112147b5a75d",
            "d6cbc4de45d74ada8c671bba522ac955",
            "a96381d3a785486281fac9843dd6bcb1",
            "0d7a229877cf47a4b41ed148a7487489",
            "ca0401b3268b49739704f461c17e7747",
            "01aae51b170e472a91710c7df719f5fc",
            "7474ce09496540a2bcf8ba03a61637a9",
            "790a35e89fe54ab48a4ca4cfefdb72a2",
            "a2294395496840329559b368d7b86210",
            "8d3bf791ee554bd1a2647da1d811fb5a",
            "88dc217fb2754b21845d0b7a78b4056f",
            "994125fc862748d2a7770f9834ed4c75",
            "9e757a15b20949b4a64d07972186aa2f",
            "56d52fb4be2a4146ad1c44bd54faa556",
            "a99882e5ebb143b194cdf92e4e1482b4",
            "6e770af8227a440d8c23198a10df8362",
            "99d176c4ac0a4ecb9ad4079b9f73ee90"
          ]
        },
        "id": "VouEkGOgbSdw",
        "outputId": "2e897f2c-f381-4718-b3f1-dd7ab77e8c20"
      },
      "source": [
        "def friends_measure(g, u,v):\n",
        "    s = 0\n",
        "    for x in find_friends_of_friends(g,u):\n",
        "        for y in find_friends_of_friends(g,v):\n",
        "            if g.has_edge(x,y) or x == y:\n",
        "                s += 1\n",
        "    return s\n",
        "\n",
        "def total_friends(g, u, v):   \n",
        "    return len(set(g.neighbors(v)) | set(g.neighbors(u)))\n",
        "\n",
        "def get_features_dict(g,u, v):\n",
        "    removed_edge = False\n",
        "    if g.has_edge(u,v):\n",
        "        g.remove_edge(u,v) #  remove edge before feature calculation\n",
        "        removed_edge = True\n",
        "    try:\n",
        "        d = {\n",
        "            'degree_v': len(list(g.neighbors(v))),\n",
        "            'degree_u': len(list(g.neighbors(u))),\n",
        "            'total_friends': total_friends(g,u,v),\n",
        "            'common_friends': len(common_friends(g,u,v)),\n",
        "            'jaccard_coefficient': list(nx.jaccard_coefficient(g,[(u,v)]))[0][2],\n",
        "            'adamic_adar_index': list(nx.adamic_adar_index(g,[(u,v)]))[0][2],\n",
        "            'friends_measure': friends_measure(g, u,v)\n",
        "        }\n",
        "    except:\n",
        "        if removed_edge:\n",
        "            g.add_edge(u,v) # return the edge back to the graph\n",
        "        return None\n",
        "\n",
        "    \n",
        "    if removed_edge:\n",
        "        g.add_edge(u,v) # return the edge back to the graph\n",
        "    return d\n",
        "\n",
        "links_features = []\n",
        "\n",
        "for i in tqdm(range(len(postive_links))):    \n",
        "    u,v = postive_links[i]\n",
        "    d = get_features_dict(g, u,v )\n",
        "    if d is None:\n",
        "        continue\n",
        "    d['class'] = True\n",
        "    links_features.append(d)\n",
        "\n",
        "for i in tqdm(range(len(negative_links))):    \n",
        "    u,v = negative_links[i]\n",
        "    d = get_features_dict(g, u,v )\n",
        "    if d is None:\n",
        "        continue\n",
        "    d['class'] = False\n",
        "    links_features.append(d)\n",
        "links_features[0]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:39: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d916acc5a12343148948c4b7611df513",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:47: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7474ce09496540a2bcf8ba03a61637a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'adamic_adar_index': 0,\n",
              " 'class': True,\n",
              " 'common_friends': 0,\n",
              " 'degree_u': 2,\n",
              " 'degree_v': 0,\n",
              " 'friends_measure': 0,\n",
              " 'jaccard_coefficient': 0.0,\n",
              " 'total_friends': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "9dkwZOZwbXpY",
        "outputId": "8472e3af-8138-4d06-c49d-322fa1e0abe3"
      },
      "source": [
        "import turicreate as tc\n",
        "features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure', 'class' ]\n",
        "data_dict = {f:[] for f in features_list}\n",
        " \n",
        "\n",
        "for d in links_features:\n",
        "    for feature in features_list:\n",
        "        data_dict[feature].append(d[feature])\n",
        "sf = tc.SFrame(data_dict)\n",
        "sf"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
              "    <tr>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">adamic_adar_index</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">class</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">common_friends</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">degree_u</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">degree_v</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">friends_measure</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">jaccard_coefficient</th>\n",
              "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">total_friends</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">31</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">19</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.7702466315385705</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">17</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">264</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1111111111111111</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">71</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
              "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13</td>\n",
              "    </tr>\n",
              "</table>\n",
              "[2000 rows x 8 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
              "</div>"
            ],
            "text/plain": [
              "Columns:\n",
              "\tadamic_adar_index\tfloat\n",
              "\tclass\tint\n",
              "\tcommon_friends\tint\n",
              "\tdegree_u\tint\n",
              "\tdegree_v\tint\n",
              "\tfriends_measure\tint\n",
              "\tjaccard_coefficient\tfloat\n",
              "\ttotal_friends\tint\n",
              "\n",
              "Rows: 2000\n",
              "\n",
              "Data:\n",
              "+--------------------+-------+----------------+----------+----------+-----------------+\n",
              "| adamic_adar_index  | class | common_friends | degree_u | degree_v | friends_measure |\n",
              "+--------------------+-------+----------------+----------+----------+-----------------+\n",
              "|        0.0         |   1   |       0        |    2     |    0     |        0        |\n",
              "|        0.0         |   1   |       0        |    1     |    2     |        10       |\n",
              "|        0.0         |   1   |       0        |    2     |    2     |        2        |\n",
              "|        0.0         |   1   |       0        |    2     |    9     |        31       |\n",
              "|        0.0         |   1   |       0        |    9     |    6     |        8        |\n",
              "|        0.0         |   1   |       0        |    19    |    1     |        4        |\n",
              "|        0.0         |   1   |       0        |    5     |    2     |        3        |\n",
              "| 1.7702466315385705 |   1   |       4        |    23    |    17    |       264       |\n",
              "|        0.0         |   1   |       0        |    2     |    12    |        71       |\n",
              "|        0.0         |   1   |       0        |    1     |    12    |        5        |\n",
              "+--------------------+-------+----------------+----------+----------+-----------------+\n",
              "+---------------------+---------------+\n",
              "| jaccard_coefficient | total_friends |\n",
              "+---------------------+---------------+\n",
              "|         0.0         |       2       |\n",
              "|         0.0         |       3       |\n",
              "|         0.0         |       4       |\n",
              "|         0.0         |       11      |\n",
              "|         0.0         |       15      |\n",
              "|         0.0         |       20      |\n",
              "|         0.0         |       7       |\n",
              "|  0.1111111111111111 |       36      |\n",
              "|         0.0         |       14      |\n",
              "|         0.0         |       13      |\n",
              "+---------------------+---------------+\n",
              "[2000 rows x 8 columns]\n",
              "Note: Only the head of the SFrame is printed.\n",
              "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "V9gzUesnbeA4",
        "outputId": "2b8b927d-a279-47f1-8e50-a0005f5c9745"
      },
      "source": [
        "test, train  = sf.random_split(0.2)\n",
        "print(f\"Train size {len(train)}\\nTest size: {len(test)}\" )\n",
        "features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure' ]\n",
        "cls = tc.classifier.create(train,features=features_list, target=\"class\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train size 1602\n",
            "Test size: 398\n",
            "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
            "          You can set ``validation_set=None`` to disable validation tracking.\n",
            "\n",
            "PROGRESS: The following methods are available for this type of problem.\n",
            "PROGRESS: BoostedTreesClassifier, RandomForestClassifier, DecisionTreeClassifier, SVMClassifier, LogisticClassifier\n",
            "PROGRESS: The returned model will be chosen according to validation accuracy.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Boosted trees classifier:</pre>"
            ],
            "text/plain": [
              "Boosted trees classifier:"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>--------------------------------------------------------</pre>"
            ],
            "text/plain": [
              "--------------------------------------------------------"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of examples          : 1521</pre>"
            ],
            "text/plain": [
              "Number of examples          : 1521"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of classes           : 2</pre>"
            ],
            "text/plain": [
              "Number of classes           : 2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of feature columns   : 7</pre>"
            ],
            "text/plain": [
              "Number of feature columns   : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of unpacked features : 7</pre>"
            ],
            "text/plain": [
              "Number of unpacked features : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |</pre>"
            ],
            "text/plain": [
              "| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 1         | 0.005319     | 0.842209          | 0.864198            | 0.558009          | 0.551221            |</pre>"
            ],
            "text/plain": [
              "| 1         | 0.005319     | 0.842209          | 0.864198            | 0.558009          | 0.551221            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 2         | 0.012522     | 0.850756          | 0.864198            | 0.479800          | 0.471785            |</pre>"
            ],
            "text/plain": [
              "| 2         | 0.012522     | 0.850756          | 0.864198            | 0.479800          | 0.471785            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 3         | 0.016659     | 0.855358          | 0.864198            | 0.429677          | 0.422130            |</pre>"
            ],
            "text/plain": [
              "| 3         | 0.016659     | 0.855358          | 0.864198            | 0.429677          | 0.422130            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 4         | 0.020598     | 0.854701          | 0.864198            | 0.395349          | 0.388180            |</pre>"
            ],
            "text/plain": [
              "| 4         | 0.020598     | 0.854701          | 0.864198            | 0.395349          | 0.388180            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 5         | 0.025478     | 0.855358          | 0.876543            | 0.370809          | 0.363702            |</pre>"
            ],
            "text/plain": [
              "| 5         | 0.025478     | 0.855358          | 0.876543            | 0.370809          | 0.363702            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 10        | 0.047482     | 0.867850          | 0.888889            | 0.311643          | 0.339765            |</pre>"
            ],
            "text/plain": [
              "| 10        | 0.047482     | 0.867850          | 0.888889            | 0.311643          | 0.339765            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Random forest classifier:</pre>"
            ],
            "text/plain": [
              "Random forest classifier:"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>--------------------------------------------------------</pre>"
            ],
            "text/plain": [
              "--------------------------------------------------------"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of examples          : 1521</pre>"
            ],
            "text/plain": [
              "Number of examples          : 1521"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of classes           : 2</pre>"
            ],
            "text/plain": [
              "Number of classes           : 2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of feature columns   : 7</pre>"
            ],
            "text/plain": [
              "Number of feature columns   : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of unpacked features : 7</pre>"
            ],
            "text/plain": [
              "Number of unpacked features : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |</pre>"
            ],
            "text/plain": [
              "| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 1         | 0.007101     | 0.840894          | 0.864198            | 0.398231          | 0.377782            |</pre>"
            ],
            "text/plain": [
              "| 1         | 0.007101     | 0.840894          | 0.864198            | 0.398231          | 0.377782            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 2         | 0.010489     | 0.844181          | 0.888889            | 0.395640          | 0.369326            |</pre>"
            ],
            "text/plain": [
              "| 2         | 0.010489     | 0.844181          | 0.888889            | 0.395640          | 0.369326            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 3         | 0.014632     | 0.844839          | 0.888889            | 0.393079          | 0.367766            |</pre>"
            ],
            "text/plain": [
              "| 3         | 0.014632     | 0.844839          | 0.888889            | 0.393079          | 0.367766            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 4         | 0.018046     | 0.849441          | 0.876543            | 0.402409          | 0.373595            |</pre>"
            ],
            "text/plain": [
              "| 4         | 0.018046     | 0.849441          | 0.876543            | 0.402409          | 0.373595            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 5         | 0.022535     | 0.846811          | 0.876543            | 0.406821          | 0.378656            |</pre>"
            ],
            "text/plain": [
              "| 5         | 0.022535     | 0.846811          | 0.876543            | 0.406821          | 0.378656            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 10        | 0.039307     | 0.846154          | 0.876543            | 0.412907          | 0.376959            |</pre>"
            ],
            "text/plain": [
              "| 10        | 0.039307     | 0.846154          | 0.876543            | 0.412907          | 0.376959            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Decision tree classifier:</pre>"
            ],
            "text/plain": [
              "Decision tree classifier:"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>--------------------------------------------------------</pre>"
            ],
            "text/plain": [
              "--------------------------------------------------------"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of examples          : 1521</pre>"
            ],
            "text/plain": [
              "Number of examples          : 1521"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of classes           : 2</pre>"
            ],
            "text/plain": [
              "Number of classes           : 2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of feature columns   : 7</pre>"
            ],
            "text/plain": [
              "Number of feature columns   : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of unpacked features : 7</pre>"
            ],
            "text/plain": [
              "Number of unpacked features : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |</pre>"
            ],
            "text/plain": [
              "| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 1         | 0.004852     | 0.842209          | 0.864198            | 0.392924          | 0.385028            |</pre>"
            ],
            "text/plain": [
              "| 1         | 0.004852     | 0.842209          | 0.864198            | 0.392924          | 0.385028            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>SVM:</pre>"
            ],
            "text/plain": [
              "SVM:"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>--------------------------------------------------------</pre>"
            ],
            "text/plain": [
              "--------------------------------------------------------"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of examples          : 1521</pre>"
            ],
            "text/plain": [
              "Number of examples          : 1521"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of classes           : 2</pre>"
            ],
            "text/plain": [
              "Number of classes           : 2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of feature columns   : 7</pre>"
            ],
            "text/plain": [
              "Number of feature columns   : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of unpacked features : 7</pre>"
            ],
            "text/plain": [
              "Number of unpacked features : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of coefficients    : 8</pre>"
            ],
            "text/plain": [
              "Number of coefficients    : 8"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Starting L-BFGS</pre>"
            ],
            "text/plain": [
              "Starting L-BFGS"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>--------------------------------------------------------</pre>"
            ],
            "text/plain": [
              "--------------------------------------------------------"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+----------+-----------+--------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |</pre>"
            ],
            "text/plain": [
              "| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+----------+-----------+--------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 0         | 2        | 1.000000  | 0.002593     | 0.497041          | 0.555556            |</pre>"
            ],
            "text/plain": [
              "| 0         | 2        | 1.000000  | 0.002593     | 0.497041          | 0.555556            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 1         | 5        | 5.000000  | 0.008171     | 0.670611          | 0.654321            |</pre>"
            ],
            "text/plain": [
              "| 1         | 5        | 5.000000  | 0.008171     | 0.670611          | 0.654321            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 2         | 6        | 5.000000  | 0.010044     | 0.581854          | 0.580247            |</pre>"
            ],
            "text/plain": [
              "| 2         | 6        | 5.000000  | 0.010044     | 0.581854          | 0.580247            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 3         | 11       | 1.404453  | 0.018108     | 0.672584          | 0.654321            |</pre>"
            ],
            "text/plain": [
              "| 3         | 11       | 1.404453  | 0.018108     | 0.672584          | 0.654321            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 4         | 16       | 0.802442  | 0.024864     | 0.700197          | 0.728395            |</pre>"
            ],
            "text/plain": [
              "| 4         | 16       | 0.802442  | 0.024864     | 0.700197          | 0.728395            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 9         | 29       | 0.838234  | 0.052943     | 0.717291          | 0.728395            |</pre>"
            ],
            "text/plain": [
              "| 9         | 29       | 0.838234  | 0.052943     | 0.717291          | 0.728395            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+----------+-----------+--------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
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          "data": {
            "text/html": [
              "<pre>Logistic regression:</pre>"
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            "text/plain": [
              "Logistic regression:"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>--------------------------------------------------------</pre>"
            ],
            "text/plain": [
              "--------------------------------------------------------"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of examples          : 1521</pre>"
            ],
            "text/plain": [
              "Number of examples          : 1521"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of classes           : 2</pre>"
            ],
            "text/plain": [
              "Number of classes           : 2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of feature columns   : 7</pre>"
            ],
            "text/plain": [
              "Number of feature columns   : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of unpacked features : 7</pre>"
            ],
            "text/plain": [
              "Number of unpacked features : 7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Number of coefficients      : 8</pre>"
            ],
            "text/plain": [
              "Number of coefficients      : 8"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>Starting Newton Method</pre>"
            ],
            "text/plain": [
              "Starting Newton Method"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>--------------------------------------------------------</pre>"
            ],
            "text/plain": [
              "--------------------------------------------------------"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+----------+--------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| Iteration | Passes   | Elapsed Time | Training Accuracy | Validation Accuracy |</pre>"
            ],
            "text/plain": [
              "| Iteration | Passes   | Elapsed Time | Training Accuracy | Validation Accuracy |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+----------+--------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 1         | 2        | 0.005857     | 0.719264          | 0.765432            |</pre>"
            ],
            "text/plain": [
              "| 1         | 2        | 0.005857     | 0.719264          | 0.765432            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 2         | 3        | 0.012911     | 0.722551          | 0.753086            |</pre>"
            ],
            "text/plain": [
              "| 2         | 3        | 0.012911     | 0.722551          | 0.753086            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 3         | 4        | 0.020242     | 0.725181          | 0.765432            |</pre>"
            ],
            "text/plain": [
              "| 3         | 4        | 0.020242     | 0.725181          | 0.765432            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 4         | 5        | 0.024552     | 0.726496          | 0.765432            |</pre>"
            ],
            "text/plain": [
              "| 4         | 5        | 0.024552     | 0.726496          | 0.765432            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 5         | 6        | 0.030518     | 0.725838          | 0.765432            |</pre>"
            ],
            "text/plain": [
              "| 5         | 6        | 0.030518     | 0.725838          | 0.765432            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>| 6         | 7        | 0.039202     | 0.725838          | 0.765432            |</pre>"
            ],
            "text/plain": [
              "| 6         | 7        | 0.039202     | 0.725838          | 0.765432            |"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
            ],
            "text/plain": [
              "+-----------+----------+--------------+-------------------+---------------------+"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre>SUCCESS: Optimal solution found.</pre>"
            ],
            "text/plain": [
              "SUCCESS: Optimal solution found."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre></pre>"
            ],
            "text/plain": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PROGRESS: Model selection based on validation accuracy:\n",
            "PROGRESS: ---------------------------------------------\n",
            "PROGRESS: BoostedTreesClassifier          : 0.8888888888888888\n",
            "PROGRESS: RandomForestClassifier          : 0.8765432098765432\n",
            "PROGRESS: DecisionTreeClassifier          : 0.8641975308641975\n",
            "PROGRESS: SVMClassifier                   : 0.7283950617283951\n",
            "PROGRESS: LogisticClassifier              : 0.7654320987654321\n",
            "PROGRESS: ---------------------------------------------\n",
            "PROGRESS: Selecting BoostedTreesClassifier based on validation set performance.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bz6zKXGfbwfC",
        "outputId": "703a078c-485b-4f63-9311-473bb367c840"
      },
      "source": [
        "results = cls.evaluate(test)\n",
        "results"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.7989949748743719,\n",
              " 'auc': 0.8708744728668469,\n",
              " 'confusion_matrix': Columns:\n",
              " \ttarget_label\tint\n",
              " \tpredicted_label\tint\n",
              " \tcount\tint\n",
              " \n",
              " Rows: 4\n",
              " \n",
              " Data:\n",
              " +--------------+-----------------+-------+\n",
              " | target_label | predicted_label | count |\n",
              " +--------------+-----------------+-------+\n",
              " |      1       |        0        |   39  |\n",
              " |      1       |        1        |  160  |\n",
              " |      0       |        1        |   41  |\n",
              " |      0       |        0        |  158  |\n",
              " +--------------+-----------------+-------+\n",
              " [4 rows x 3 columns],\n",
              " 'f1_score': 0.8,\n",
              " 'log_loss': 0.4489007722529617,\n",
              " 'precision': 0.7960199004975125,\n",
              " 'recall': 0.8040201005025126,\n",
              " 'roc_curve': Columns:\n",
              " \tthreshold\tfloat\n",
              " \tfpr\tfloat\n",
              " \ttpr\tfloat\n",
              " \tp\tint\n",
              " \tn\tint\n",
              " \n",
              " Rows: 1001\n",
              " \n",
              " Data:\n",
              " +-----------+-----+-----+-----+-----+\n",
              " | threshold | fpr | tpr |  p  |  n  |\n",
              " +-----------+-----+-----+-----+-----+\n",
              " |    0.0    | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.001   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.002   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.003   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.004   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.005   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.006   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.007   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.008   | 1.0 | 1.0 | 199 | 199 |\n",
              " |   0.009   | 1.0 | 1.0 | 199 | 199 |\n",
              " +-----------+-----+-----+-----+-----+\n",
              " [1001 rows x 5 columns]\n",
              " Note: Only the head of the SFrame is printed.\n",
              " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Oa7kJbsdbrlx",
        "outputId": "91706219-899e-4f94-d972-1a5692c12d3d"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.plot(results['roc_curve']['fpr'],\n",
        "         results['roc_curve']['tpr'],\n",
        "         label='ROC Curve (AUC = %0.5f)' %results['auc'])\n",
        "\n",
        "plt.xlim([-0.02, 1.0])\n",
        "plt.ylim([0., 1.02])\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('ROC curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu2Usey1cDcq",
        "outputId": "79ce2f3d-32ae-4ce7-a1e9-dad45757d8f3"
      },
      "source": [
        "!pip install karateclub"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting karateclub\n",
            "  Downloading karateclub-1.2.2.tar.gz (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 716 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from karateclub) (1.18.5)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from karateclub) (2.6.3)\n",
            "Requirement already satisfied: decorator==4.4.2 in /usr/local/lib/python3.7/dist-packages (from karateclub) (4.4.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from karateclub) (4.62.3)\n",
            "Requirement already satisfied: python-louvain in /usr/local/lib/python3.7/dist-packages (from karateclub) (0.15)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from karateclub) (1.0.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from karateclub) (1.4.1)\n",
            "Collecting pygsp\n",
            "  Downloading PyGSP-0.5.1-py2.py3-none-any.whl (1.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8 MB 15.2 MB/s \n",
            "\u001b[?25hCollecting gensim>=4.0.0\n",
            "  Downloading gensim-4.1.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (24.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 24.1 MB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from karateclub) (1.1.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from karateclub) (1.15.0)\n",
            "Collecting python-Levenshtein\n",
            "  Downloading python-Levenshtein-0.12.2.tar.gz (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 6.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from gensim>=4.0.0->karateclub) (5.2.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->karateclub) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->karateclub) (2.8.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from python-Levenshtein->karateclub) (57.4.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->karateclub) (3.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->karateclub) (1.1.0)\n",
            "Building wheels for collected packages: karateclub, python-Levenshtein\n",
            "  Building wheel for karateclub (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for karateclub: filename=karateclub-1.2.2-py3-none-any.whl size=97744 sha256=d735fc168bd6e1900e82e00692f1b5ae9b61b4493e15f28aa3232e2aad2c10eb\n",
            "  Stored in directory: /root/.cache/pip/wheels/14/4b/99/abc777e42da28bc43b6959267f4f1c5eee06814a5c608a9ed2\n",
            "  Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149866 sha256=38ab3fe160d678ce8a2d00425b4a9b5b44466f9675434ed72c6c8a4850fc2c81\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/5f/ca/7c4367734892581bb5ff896f15027a932c551080b2abd3e00d\n",
            "Successfully built karateclub python-Levenshtein\n",
            "Installing collected packages: python-Levenshtein, pygsp, gensim, karateclub\n",
            "  Attempting uninstall: gensim\n",
            "    Found existing installation: gensim 3.6.0\n",
            "    Uninstalling gensim-3.6.0:\n",
            "      Successfully uninstalled gensim-3.6.0\n",
            "Successfully installed gensim-4.1.2 karateclub-1.2.2 pygsp-0.5.1 python-Levenshtein-0.12.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "DwXvMOU2cIYC",
        "outputId": "cda6e0d7-f0b9-460e-c11d-388e5daa54be"
      },
      "source": [
        "train_df = train.to_dataframe()\n",
        "train_df"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>adamic_adar_index</th>\n",
              "      <th>class</th>\n",
              "      <th>common_friends</th>\n",
              "      <th>degree_u</th>\n",
              "      <th>degree_v</th>\n",
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              "      <th>jaccard_coefficient</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1600</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>121</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1601</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1602 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      adamic_adar_index  class  ...  jaccard_coefficient  total_friends\n",
              "0                   0.0      1  ...                  0.0              3\n",
              "1                   0.0      1  ...                  0.0              4\n",
              "2                   0.0      1  ...                  0.0             15\n",
              "3                   0.0      1  ...                  0.0             20\n",
              "4                   0.0      1  ...                  0.0              7\n",
              "...                 ...    ...  ...                  ...            ...\n",
              "1597                0.0      0  ...                  0.0             11\n",
              "1598                0.0      0  ...                  0.0              4\n",
              "1599                0.0      0  ...                  0.0             10\n",
              "1600                0.0      0  ...                  0.0             15\n",
              "1601                0.0      0  ...                  0.0             10\n",
              "\n",
              "[1602 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "mEeHMjrLc3td",
        "outputId": "7701a4cf-77a5-4497-fee4-3feafecd0ff9"
      },
      "source": [
        "import karateclub\n",
        "\n",
        "model = karateclub.DeepWalk()\n",
        "\n",
        "model.fit(train_df)\n",
        "embedding = model.get_embedding()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-cb39180fd988>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkarateclub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDeepWalk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0membedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/karateclub/node_embedding/neighbourhood/deepwalk.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, graph)\u001b[0m\n\u001b[1;32m     56\u001b[0m         \"\"\"\n\u001b[1;32m     57\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_seed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0mwalker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomWalker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalk_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalk_number\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mwalker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_walks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/karateclub/estimator.py\u001b[0m in \u001b[0;36m_check_graph\u001b[0;34m(self, graph)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;34m\"\"\"Check the Karate Club assumptions about the graph.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_integrity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/karateclub/estimator.py\u001b[0m in \u001b[0;36m_check_indexing\u001b[0;34m(graph)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;34m\"\"\"Checking the consecutive numeric indexing.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0mnumeric_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_of_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0mnode_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5139\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5140\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5141\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5143\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'number_of_nodes'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLGS1MiDulUo"
      },
      "source": [
        "**Question 2:** Use three different centrality algorithms to identify managers at Enron.\n",
        "    Evaluate the precision@10 of the algorithms, i.e. how many managers attained the top-10 highest ranks (30pt). Draw \n",
        "    the network and visualize one of the centrality measures (10pt)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQzWpvcfulUq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}