FB_EDA.ipynb

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    "colab": {
      "name": "Copy of FB_EDA.ipynb",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnysF8x_ORlO",
        "colab_type": "text"
      },
      "source": [
        "<p style=\"font-size:32px;text-align:center\"> <b>Social network Graph Link Prediction - Facebook Challenge</b> </p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkoerGBXORlS",
        "colab_type": "text"
      },
      "source": [
        "### Problem statement: \n",
        "Given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3QpE6i9ORlU",
        "colab_type": "text"
      },
      "source": [
        "### Data Overview\n",
        "Taken data from facebook's recruting challenge on kaggle https://www.kaggle.com/c/FacebookRecruiting  \n",
        "data contains two columns source and destination eac edge in graph \n",
        "    - Data columns (total 2 columns):  \n",
        "    - source_node         int64  \n",
        "    - destination_node    int64  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHsD-3qlORlV",
        "colab_type": "text"
      },
      "source": [
        "### Mapping the problem into supervised learning problem:\n",
        "- Generated training samples of good and bad links from given directed graph and for each link got some features like no of followers, is he followed back, page rank, katz score, adar index, some svd fetures of adj matrix, some weight features etc. and trained ml model based on these features to predict link. \n",
        "- Some reference papers and videos :  \n",
        "    - https://www.cs.cornell.edu/home/kleinber/link-pred.pdf\n",
        "    - https://www3.nd.edu/~dial/publications/lichtenwalter2010new.pdf\n",
        "    - https://www.youtube.com/watch?v=2M77Hgy17cg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJhG3ZczORlW",
        "colab_type": "text"
      },
      "source": [
        "### Business objectives and constraints:  \n",
        "- No low-latency requirement.\n",
        "- Probability of prediction is useful to recommend ighest probability links"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUZLwsmKORlX",
        "colab_type": "text"
      },
      "source": [
        "### Performance metric for supervised learning:  \n",
        "- Both precision and recall is important so F1 score is good choice\n",
        "- Confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDE330RZORlY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importing Libraries\n",
        "# please do go through this python notebook: \n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import csv\n",
        "import pandas as pd#pandas to create small dataframes \n",
        "import datetime #Convert to unix time\n",
        "import time #Convert to unix time\n",
        "# if numpy is not installed already : pip3 install numpy\n",
        "import numpy as np#Do aritmetic operations on arrays\n",
        "# matplotlib: used to plot graphs\n",
        "import matplotlib\n",
        "import matplotlib.pylab as plt\n",
        "import seaborn as sns#Plots\n",
        "from matplotlib import rcParams#Size of plots  \n",
        "from sklearn.cluster import MiniBatchKMeans, KMeans#Clustering\n",
        "import math\n",
        "import pickle\n",
        "import os\n",
        "# to install xgboost: pip3 install xgboost\n",
        "import xgboost as xgb\n",
        "\n",
        "import warnings\n",
        "import networkx as nx\n",
        "import pdb\n",
        "import pickle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZyClAckGSRoH",
        "colab_type": "text"
      },
      "source": [
        "<h1> Reading Data </h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INekEKc3RSwG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qj_OeLNURS97",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = '1eXqWmEX_QtxzGkq1rjpUn5QIzfkPeYW4'\n",
        "downloaded = drive.CreateFile({'id':train_data})\n",
        "downloaded.GetContentFile('train.csv')\n",
        "\n",
        "train_y = '1CGpGDJ379IrPpH604v3TU1YlRyxLXwXc'\n",
        "downloaded = drive.CreateFile({'id':train_y})\n",
        "downloaded.GetContentFile('train_y.csv')\n",
        "\n",
        "missing_edge_final = '11q3GmUJGwCWmNjkyn46zpdxa7Jh7LuAD'\n",
        "downloaded = drive.CreateFile({'id':missing_edge_final})\n",
        "downloaded.GetContentFile('missing_edges_final.p')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TS1Uy_1fORld",
        "colab_type": "code",
        "outputId": "e38ea5ef-fc6e-497c-b3eb-f4b7e4504a98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "#reading graph\n",
        "if not os.path.isfile('train_woheader.csv'):\n",
        "    traincsv = pd.read_csv('train.csv')\n",
        "    print(traincsv[traincsv.isna().any(1)])\n",
        "    print(traincsv.info())\n",
        "    print(\"Number of diplicate entries: \",sum(traincsv.duplicated()))\n",
        "    traincsv.to_csv('train_woheader.csv',header=False,index=False)\n",
        "    !cp train_woheader.csv drive/My\\ Drive/\n",
        "    print(\"saved the graph into file\")\n",
        "else:\n",
        "    g=nx.read_edgelist('train_woheader.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
        "    print(\"File Present\")\n",
        "    print(nx.info(g))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File Present\n",
            "Name: \n",
            "Type: DiGraph\n",
            "Number of nodes: 1862220\n",
            "Number of edges: 9437519\n",
            "Average in degree:   5.0679\n",
            "Average out degree:   5.0679\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2Q04T3bVJy6",
        "colab_type": "code",
        "outputId": "888d08e0-3b73-4854-9e44-93a9134c47a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "traincsv = pd.read_csv('train.csv')\n",
        "traincsv.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>source_node</th>\n",
              "      <th>destination_node</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>690569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>315892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>189226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>834328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1615927</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   source_node  destination_node\n",
              "0            1            690569\n",
              "1            1            315892\n",
              "2            1            189226\n",
              "3            2            834328\n",
              "4            2           1615927"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Md2fkMcVDvW",
        "colab_type": "code",
        "outputId": "d5f84e68-abbb-4cf6-ff7f-21b362be2dfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('drive')\n",
        "#!cp train_woheader.csv drive/My\\ Drive/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xC4VMFglORlj",
        "colab_type": "text"
      },
      "source": [
        "> Displaying a sub graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQkDP0lO7XB2",
        "colab_type": "code",
        "outputId": "e4e01ab5-c9a1-47d2-ed11-434cf8139b6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "if not os.path.isfile('train_woheader_sample.csv'):\n",
        "    pd.read_csv('train.csv', nrows=50).to_csv('train_woheader_sample.csv',header=False,index=False)\n",
        "    !cp train_woheader_sample.csv drive/My\\ Drive/\n",
        "subgraph=nx.read_edgelist('train_woheader_sample.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
        "# https://stackoverflow.com/questions/9402255/drawing-a-huge-graph-with-networkx-and-matplotlib\n",
        "\n",
        "pos=nx.spring_layout(subgraph)\n",
        "nx.draw(subgraph,pos,node_color='#A0CBE2',edge_color='#00bb5e',width=1,edge_cmap=plt.cm.Blues,with_labels=True)\n",
        "plt.savefig(\"graph_sample.pdf\")\n",
        "!cp graph_sample.pdf drive/My\\ Drive/\n",
        "print(nx.info(subgraph))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name: \n",
            "Type: DiGraph\n",
            "Number of nodes: 66\n",
            "Number of edges: 50\n",
            "Average in degree:   0.7576\n",
            "Average out degree:   0.7576\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dtdD9AQEBTJ48udAwh5O/b+LikT1M/mVXoWs4hdH88+4EJ9zmyNd7MRb0UXau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Dh0u3Z2dn069fP2rWrIlAIOD69esFjk9KSmLYsGEYGRlRvYoZf6w+VGTQe+pzl+H21TmxZbX0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\nglYRgU8QBEHQKiLwCYIgCFrl/wBJ//Skkp2QlQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lPpKICcORlp",
        "colab_type": "text"
      },
      "source": [
        "# 1. Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjWUUhhcORlq",
        "colab_type": "code",
        "outputId": "e4cb0351-4a2d-4a7d-d02b-191cc96113f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# No of Unique persons \n",
        "g=nx.read_edgelist('train_woheader.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
        "print(\"The number of unique persons\",len(g.nodes()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The number of unique persons 1862220\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCLMjnJjORlt",
        "colab_type": "text"
      },
      "source": [
        "## 1.1 No of followers for each person"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ts6dBpzEZiil",
        "colab_type": "code",
        "outputId": "046809a0-8840-42aa-ca7e-6af8809f8858",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Example :\n",
        "print(\"In degree of node 19  :\",g.in_degree(19))\n",
        "print(\"Out degree of node 19 :\",g.out_degree(19))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In degree of node 19  : 0\n",
            "Out degree of node 19 : 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnCPaVKDORlu",
        "colab_type": "code",
        "outputId": "13d1d180-f13e-491c-e43d-40dde1116b3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "indegree_dist = list(dict(g.in_degree()).values())\n",
        "indegree_dist.sort()\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(indegree_dist)\n",
        "plt.xlabel('Index No')\n",
        "plt.ylabel('No Of Followers')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zcg2wf0TMA34FuCwzf5qZjwCXASeVZftm5jXZ+mvxaYY/Ju3b6BXHA3dmZqeO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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7u12QubSORlx",
        "colab_type": "code",
        "outputId": "5ef4e814-1b50-4e97-974b-c87decd985b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "#Zooming in to clearly undertand the scenario for the 0 to 1500000 nodes\n",
        "indegree_dist.sort()\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(indegree_dist[0:1500000])\n",
        "plt.xlabel('Index No')\n",
        "plt.ylabel('No Of Followers')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HZ+ajwFERsSdwUUQ8IzM3dL8mItYB6wCmp6cHLF+SVKfLbt7MrqtWcuSaPesupVJHT0/2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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjE6NNmxORl2",
        "colab_type": "code",
        "outputId": "ebc9db11-2253-4524-98c9-05c7ba122813",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.boxplot(indegree_dist)\n",
        "plt.ylabel('No Of Followers')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD4CAYAAAAD6PrjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FhifjG6RORl6",
        "colab_type": "code",
        "outputId": "a3205289-1a06-4693-892e-4016872f8ecf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "### 90-100 percentile\n",
        "for i in range(0,11):\n",
        "    print(90+i,'percentile value is',np.percentile(indegree_dist,90+i))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90 percentile value is 12.0\n",
            "91 percentile value is 13.0\n",
            "92 percentile value is 14.0\n",
            "93 percentile value is 15.0\n",
            "94 percentile value is 17.0\n",
            "95 percentile value is 19.0\n",
            "96 percentile value is 21.0\n",
            "97 percentile value is 24.0\n",
            "98 percentile value is 29.0\n",
            "99 percentile value is 40.0\n",
            "100 percentile value is 552.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gUzb6i7ORl9",
        "colab_type": "text"
      },
      "source": [
        "99% of data having followers of 40 only."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Jl6nJ-2ORl-",
        "colab_type": "code",
        "outputId": "8dd491db-bc7d-477d-e640-e423aee77b4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "### 99-100 percentile\n",
        "for i in range(10,110,10):\n",
        "    print(99+(i/100),'percentile value is',np.percentile(indegree_dist,99+(i/100)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99.1 percentile value is 42.0\n",
            "99.2 percentile value is 44.0\n",
            "99.3 percentile value is 47.0\n",
            "99.4 percentile value is 50.0\n",
            "99.5 percentile value is 55.0\n",
            "99.6 percentile value is 61.0\n",
            "99.7 percentile value is 70.0\n",
            "99.8 percentile value is 84.0\n",
            "99.9 percentile value is 112.0\n",
            "100.0 percentile value is 552.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWJW9_RrORmB",
        "colab_type": "code",
        "outputId": "a3094bdc-115c-4bcc-a913-b313fd545937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        }
      },
      "source": [
        "%matplotlib inline\n",
        "sns.set_style('ticks')\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(11.7, 8.27)\n",
        "sns.distplot(indegree_dist, color='#16A085')\n",
        "plt.xlabel('PDF of Indegree')\n",
        "sns.despine()\n",
        "#plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAEYhcAEAAGAUAhcAAABGIXABAABgFAIXAAAARiFwAQAAYBQCFwAAAEYhcAEAAGAUAhcA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\nA3e0SxGPdjlhxF57e7umT58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            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjGhgPp2ORmH",
        "colab_type": "text"
      },
      "source": [
        "## 1.2 No of people each person is following"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTqMlVSQORmI",
        "colab_type": "code",
        "outputId": "04cdd84c-c1f5-46e4-dea6-836e2f46423a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "outdegree_dist = list(dict(g.out_degree()).values())\n",
        "outdegree_dist.sort()\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(outdegree_dist)\n",
        "plt.xlabel('Index No')\n",
        "plt.ylabel('No Of people each person is following')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bbfbs2Tp+/Liqqqo0cuRI9ezZU6tXr3a44aSkJC1atKhW28KFCxUZGans7GxFRkbaHgGS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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7Nx6PJJORmL",
        "colab_type": "code",
        "outputId": "f7a8ccb4-46a6-4da3-b873-1cb98cc451b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "#Zooming in\n",
        "outdegree_dist.sort()\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(outdegree_dist[0:1500000])\n",
        "plt.xlabel('Index No')\n",
        "plt.ylabel('No Of people each person is following')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U5syZc3HVAgAA+DnLmStjTK02YOvWrX/2Fm5r8+fP17XXXnth1QEAAPdYLeiXLGeu4uPj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\nFQAAgAcRrgAAADzo/wPc3oQGNKl9EQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlEw-IrTORmO",
        "colab_type": "code",
        "outputId": "e3dcb77d-f27e-4581-d60b-5577e8e4413c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.boxplot(outdegree_dist)\n",
        "plt.ylabel('No Of people each person is following')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DhwMwbtw4cnJygMot0seNGwfA8OHDOXToEM1w0bncB7Q3lLgzw7BITEwkPDyc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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCiWqsBeORmS",
        "colab_type": "code",
        "outputId": "8d55a88d-1aa6-4736-d9d2-17ae0f14a541",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "### 90-100 percentile\n",
        "for i in range(0,11):\n",
        "    print(90+i,'percentile value is',np.percentile(outdegree_dist,90+i))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90 percentile value is 12.0\n",
            "91 percentile value is 13.0\n",
            "92 percentile value is 14.0\n",
            "93 percentile value is 15.0\n",
            "94 percentile value is 17.0\n",
            "95 percentile value is 19.0\n",
            "96 percentile value is 21.0\n",
            "97 percentile value is 24.0\n",
            "98 percentile value is 29.0\n",
            "99 percentile value is 40.0\n",
            "100 percentile value is 1566.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHVdrxfAORmW",
        "colab_type": "code",
        "outputId": "a4b9b1de-7a53-476d-e429-63bcfdb9705c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "### 99-100 percentile\n",
        "for i in range(10,110,10):\n",
        "    print(99+(i/100),'percentile value is',np.percentile(outdegree_dist,99+(i/100)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99.1 percentile value is 42.0\n",
            "99.2 percentile value is 45.0\n",
            "99.3 percentile value is 48.0\n",
            "99.4 percentile value is 52.0\n",
            "99.5 percentile value is 56.0\n",
            "99.6 percentile value is 63.0\n",
            "99.7 percentile value is 73.0\n",
            "99.8 percentile value is 90.0\n",
            "99.9 percentile value is 123.0\n",
            "100.0 percentile value is 1566.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_-KMWvUORma",
        "colab_type": "code",
        "outputId": "5833ab4b-7127-4936-a4a1-d1bdebea78be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        }
      },
      "source": [
        "sns.set_style('ticks')\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(11.7, 8.27)\n",
        "sns.distplot(outdegree_dist, color='#16A085')\n",
        "plt.xlabel('PDF of Outdegree')\n",
        "sns.despine()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IhC8AAACMQPgCAADACIQvAAAAjED4AgAAwAiELwAAAIxA+AIAAMAIhC8AAACMQPgCAADA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\ngg==\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gq06cAPsORmd",
        "colab_type": "code",
        "outputId": "1baf8b81-dd58-48cd-fd2d-0eae092b1277",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('No of persons those are not following anyone are' ,sum(np.array(outdegree_dist)==0),'and % is',\n",
        "                                sum(np.array(outdegree_dist)==0)*100/len(outdegree_dist) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of persons those are not following anyone are 274512 and % is 14.741115442858524\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28WmUtuwORml",
        "colab_type": "code",
        "outputId": "b156e604-3573-4c23-e962-56215060aaae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('No of persons having zero followers are' ,sum(np.array(indegree_dist)==0),'and % is',\n",
        "                                sum(np.array(indegree_dist)==0)*100/len(indegree_dist) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of persons having zero followers are 188043 and % is 10.097786512871734\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afwOk-EAORmr",
        "colab_type": "code",
        "outputId": "e054a76c-ef27-4100-af30-227215cfe6e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "count=0\n",
        "for i in g.nodes():\n",
        "    if len(list(g.predecessors(i)))==0 :\n",
        "        if len(list(g.successors(i)))==0:\n",
        "            count+=1\n",
        "print('No of persons those are not not following anyone and also not having any followers are',count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of persons those are not not following anyone and also not having any followers are 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCRpdjITORmu",
        "colab_type": "text"
      },
      "source": [
        "## 1.3 both followers + following "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7v68L-mORmv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from collections import Counter\n",
        "dict_in = dict(g.in_degree())\n",
        "dict_out = dict(g.out_degree())\n",
        "d = Counter(dict_in) + Counter(dict_out)\n",
        "in_out_degree = np.array(list(d.values()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6msC6dluORmy",
        "colab_type": "code",
        "outputId": "5ebd02b8-ec81-42ea-fcc7-c1940f5ab87c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "in_out_degree_sort = sorted(in_out_degree)\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(in_out_degree_sort)\n",
        "plt.xlabel('Index No')\n",
        "plt.ylabel('No Of people each person is following + followers')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FZ9sAAABQNxxeo2az2bR//3776/3798tms0mSzGaz8yoDAADwcg5n1KZPn66nn35aJSUl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\nAEnSmjVrFBwcfNkddunSRd9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dEZmZmamysjLl5OQoOztbnTp1qnZsTCaTunXrpvXr10s6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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QralmNoAORm2",
        "colab_type": "code",
        "outputId": "055a35c7-580d-4aac-814f-b52c10dd1be5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "#Zooming in\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(in_out_degree_sort[0:1500000])\n",
        "plt.xlabel('Index No')\n",
        "plt.ylabel('No Of people each person is following + followers')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6qU2yg4OD1aBBA4WFhekf//iHdu3a5em6AAAByFmSu+RgsBF+yrLHa/ny5Vqy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\nAACb/P85OU12cUyeDgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tw981sJQORm6",
        "colab_type": "code",
        "outputId": "3d5694b4-d075-41dc-cd78-c0571eb76429",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "### 90-100 percentile\n",
        "for i in range(0,11):\n",
        "    print(90+i,'percentile value is',np.percentile(in_out_degree_sort,90+i))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90 percentile value is 24.0\n",
            "91 percentile value is 26.0\n",
            "92 percentile value is 28.0\n",
            "93 percentile value is 31.0\n",
            "94 percentile value is 33.0\n",
            "95 percentile value is 37.0\n",
            "96 percentile value is 41.0\n",
            "97 percentile value is 48.0\n",
            "98 percentile value is 58.0\n",
            "99 percentile value is 79.0\n",
            "100 percentile value is 1579.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h67hARicORm-",
        "colab_type": "code",
        "outputId": "d39640e4-6b73-4b71-b34d-1c32447de90b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "### 99-100 percentile\n",
        "for i in range(10,110,10):\n",
        "    print(99+(i/100),'percentile value is',np.percentile(in_out_degree_sort,99+(i/100)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99.1 percentile value is 83.0\n",
            "99.2 percentile value is 87.0\n",
            "99.3 percentile value is 93.0\n",
            "99.4 percentile value is 99.0\n",
            "99.5 percentile value is 108.0\n",
            "99.6 percentile value is 120.0\n",
            "99.7 percentile value is 138.0\n",
            "99.8 percentile value is 168.0\n",
            "99.9 percentile value is 221.0\n",
            "100.0 percentile value is 1579.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHT0CMUzORnB",
        "colab_type": "code",
        "outputId": "e7aa03ed-941c-4757-e1e1-9616b546263d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Min of no of followers + following is',in_out_degree.min())\n",
        "print(np.sum(in_out_degree==in_out_degree.min()),' persons having minimum no of followers + following')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min of no of followers + following is 1\n",
            "334291  persons having minimum no of followers + following\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_ulBWM3ORnG",
        "colab_type": "code",
        "outputId": "42a577b2-7dfd-44fd-c80d-8eaf7f03fc93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Max of no of followers + following is',in_out_degree.max())\n",
        "print(np.sum(in_out_degree==in_out_degree.max()),' persons having maximum no of followers + following')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Max of no of followers + following is 1579\n",
            "1  persons having maximum no of followers + following\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6a61nDvYORnK",
        "colab_type": "code",
        "outputId": "be948500-f524-4149-a747-e87079512283",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('No of persons having followers + following less than 10 are',np.sum(in_out_degree<10))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of persons having followers + following less than 10 are 1320326\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGkfI4aRORnO",
        "colab_type": "code",
        "outputId": "30f68da8-1337-47cd-96d8-f6212fb3a0a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('No of weakly connected components',len(list(nx.weakly_connected_components(g))))\n",
        "count=0\n",
        "for i in list(nx.weakly_connected_components(g)):\n",
        "    if len(i)==2:\n",
        "        count+=1\n",
        "print('weakly connected components wit 2 nodes',count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of weakly connected components 45558\n",
            "weakly connected components wit 2 nodes 32195\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6uqmjurORnT",
        "colab_type": "text"
      },
      "source": [
        "# 2. Posing a problem as classification problem "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZYvDAwDORnU",
        "colab_type": "text"
      },
      "source": [
        "## 2.1 Generating some edges which are not present in graph for supervised learning  \n",
        "Generated Bad links from graph which are not in graph and whose shortest path is greater than 2. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWYsq0HU9VHr",
        "colab_type": "code",
        "outputId": "94b9cef8-fc07-44fb-ecdd-e84469d34eef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "%%time\n",
        "###generating bad edges from given graph\n",
        "import random\n",
        "if not os.path.isfile('missing_edges_final.p'):\n",
        "    #getting all set of edges\n",
        "    r = csv.reader(open('train_woheader.csv','r'))\n",
        "    edges = dict()\n",
        "    for edge in r:\n",
        "        edges[(edge[0], edge[1])] = 1\n",
        "        \n",
        "        \n",
        "    missing_edges = set([])\n",
        "    while (len(missing_edges)<9437519):\n",
        "        a=random.randint(1, 1862220)\n",
        "        b=random.randint(1, 1862220)\n",
        "        tmp = edges.get((a,b),-1)\n",
        "        if tmp == -1 and a!=b:\n",
        "            try:\n",
        "                if nx.shortest_path_length(g,source=a,target=b) > 2: \n",
        "\n",
        "                    missing_edges.add((a,b))\n",
        "                else:\n",
        "                    continue  \n",
        "            except:  \n",
        "                    missing_edges.add((a,b))              \n",
        "        else:\n",
        "            continue\n",
        "    pickle.dump(missing_edges,open('missing_edges_final.p','wb'))\n",
        "else:\n",
        "    print('File Present')\n",
        "    missing_edges = pickle.load(open('missing_edges_final.p','rb'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File Present\n",
            "CPU times: user 2.18 s, sys: 579 ms, total: 2.76 s\n",
            "Wall time: 2.76 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gVf5nUwORnb",
        "colab_type": "code",
        "outputId": "fd76e00a-2edb-43cb-fb4e-f799c866298d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(missing_edges)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9437519"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOFQEKhjORne",
        "colab_type": "text"
      },
      "source": [
        "## 2.2 Training and Test data split:  \n",
        "Removed edges from Graph and used as test data and after removing used that graph for creating features for Train and test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJHTrPXEORng",
        "colab_type": "code",
        "outputId": "90568030-b7c7-481f-852d-6085483ce1be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from numpy import savetxt\n",
        "\n",
        "if (not os.path.isfile('train_pos_after_eda.csv')) and (not os.path.isfile('test_pos_after_eda.csv')):\n",
        "    #reading total data df\n",
        "    df_pos = pd.read_csv('train.csv')\n",
        "    df_neg = pd.DataFrame(list(missing_edges), columns=['source_node', 'destination_node'])\n",
        "    \n",
        "    print(\"Number of nodes in the graph with edges\", df_pos.shape[0])\n",
        "    print(\"Number of nodes in the graph without edges\", df_neg.shape[0])\n",
        "    \n",
        "    #Train test split \n",
        "    #Spiltted data into 80-20 \n",
        "    #positive links and negative links seperatly because we need only positive training data for creating graph \n",
        "    #and for feature generation\n",
        "    X_train_pos, X_test_pos, y_train_pos, y_test_pos  = train_test_split(df_pos,np.ones(len(df_pos)),test_size=0.2, random_state=9)\n",
        "    X_train_neg, X_test_neg, y_train_neg, y_test_neg  = train_test_split(df_neg,np.zeros(len(df_neg)),test_size=0.2, random_state=9)\n",
        "    \n",
        "    print('='*60)\n",
        "    print(\"Number of nodes in the train data graph with edges\", X_train_pos.shape[0],\"=\",y_train_pos.shape[0])\n",
        "    print(\"Number of nodes in the train data graph without edges\", X_train_neg.shape[0],\"=\", y_train_neg.shape[0])\n",
        "    print('='*60)\n",
        "    print(\"Number of nodes in the test data graph with edges\", X_test_pos.shape[0],\"=\",y_test_pos.shape[0])\n",
        "    print(\"Number of nodes in the test data graph without edges\", X_test_neg.shape[0],\"=\",y_test_neg.shape[0])\n",
        "\n",
        "    #removing header and saving\n",
        "    X_train_pos.to_csv('train_pos_after_eda.csv',header=False, index=False)\n",
        "    X_test_pos.to_csv('test_pos_after_eda.csv',header=False, index=False)\n",
        "    X_train_neg.to_csv('train_neg_after_eda.csv',header=False, index=False)\n",
        "    X_test_neg.to_csv('test_neg_after_eda.csv',header=False, index=False)\n",
        "\n",
        "    savetxt('y_train_pos.csv',y_train_pos,delimiter=',')\n",
        "    savetxt('y_test_pos.csv',y_test_pos,delimiter=',')\n",
        "    savetxt('y_train_neg.csv',y_train_neg,delimiter=',')\n",
        "    savetxt('y_test_neg.csv',y_test_neg,delimiter=',')\n",
        "else:\n",
        "    #Graph from Traing data only \n",
        "    print('File Present')\n",
        "    del missing_edges"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of nodes in the graph with edges 9437519\n",
            "Number of nodes in the graph without edges 9437519\n",
            "============================================================\n",
            "Number of nodes in the train data graph with edges 7550015 = 7550015\n",
            "Number of nodes in the train data graph without edges 7550015 = 7550015\n",
            "============================================================\n",
            "Number of nodes in the test data graph with edges 1887504 = 1887504\n",
            "Number of nodes in the test data graph without edges 1887504 = 1887504\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WV0XEthu4lq_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp train_pos_after_eda.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp test_pos_after_eda.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp train_neg_after_eda.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp test_neg_after_eda.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "\n",
        "!cp y_train_pos.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp y_test_pos.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp y_train_neg.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp y_test_neg.csv /content/drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qW7fX9OuORnk",
        "colab_type": "code",
        "outputId": "45091856-9f0d-4345-8e18-7e8cdaf7462a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "if (os.path.isfile('train_pos_after_eda.csv')) and (os.path.isfile('test_pos_after_eda.csv')):        \n",
        "    train_graph=nx.read_edgelist('train_pos_after_eda.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
        "    test_graph=nx.read_edgelist('test_pos_after_eda.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
        "    print(nx.info(train_graph))\n",
        "    print(nx.info(test_graph))\n",
        "\n",
        "    # finding the unique nodes in the both train and test graphs\n",
        "    train_nodes_pos = set(train_graph.nodes())\n",
        "    test_nodes_pos = set(test_graph.nodes())\n",
        "\n",
        "    trY_teY = len(train_nodes_pos.intersection(test_nodes_pos))\n",
        "    trY_teN = len(train_nodes_pos - test_nodes_pos)\n",
        "    teY_trN = len(test_nodes_pos - train_nodes_pos)\n",
        "\n",
        "    print('no of people common in train and test -- ',trY_teY)\n",
        "    print('no of people present in train but not present in test -- ',trY_teN)\n",
        "\n",
        "    print('no of people present in test but not present in train -- ',teY_trN)\n",
        "    print(' % of people not there in Train but exist in Test in total Test data are {} %'.format(teY_trN/len(test_nodes_pos)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name: \n",
            "Type: DiGraph\n",
            "Number of nodes: 1780722\n",
            "Number of edges: 7550015\n",
            "Average in degree:   4.2399\n",
            "Average out degree:   4.2399\n",
            "Name: \n",
            "Type: DiGraph\n",
            "Number of nodes: 1144623\n",
            "Number of edges: 1887504\n",
            "Average in degree:   1.6490\n",
            "Average out degree:   1.6490\n",
            "no of people common in train and test --  1063125\n",
            "no of people present in train but not present in test --  717597\n",
            "no of people present in test but not present in train --  81498\n",
            " % of people not there in Train but exist in Test in total Test data are 7.1200735962845405 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHjj-khHORno",
        "colab_type": "text"
      },
      "source": [
        "> We have a cold start problem here. This means there are 7.12% people(node) present in the test dataset but not in the train dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDT9PGNWORno",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#final train and test data sets\n",
        "if (not os.path.isfile('train_after_eda.csv')) and \\\n",
        "(not os.path.isfile('test_after_eda.csv')) and \\\n",
        "(not os.path.isfile('train_y.csv')) and \\\n",
        "(not os.path.isfile('test_y.csv')) and \\\n",
        "(os.path.isfile('train_pos_after_eda.csv')) and \\\n",
        "(os.path.isfile('test_pos_after_eda.csv')) and \\\n",
        "(os.path.isfile('train_neg_after_eda.csv')) and \\\n",
        "(os.path.isfile('test_neg_after_eda.csv')):\n",
        "    \n",
        "    X_train_pos = pd.read_csv('train_pos_after_eda.csv', names=['source_node', 'destination_node'])\n",
        "    X_test_pos = pd.read_csv('test_pos_after_eda.csv', names=['source_node', 'destination_node'])\n",
        "    X_train_neg = pd.read_csv('train_neg_after_eda.csv', names=['source_node', 'destination_node'])\n",
        "    X_test_neg = pd.read_csv('test_neg_after_eda.csv', names=['source_node', 'destination_node'])\n",
        "\n",
        "    y_train_pos = pd.read_csv('y_train_pos.csv')\n",
        "    y_train_neg = pd.read_csv('y_train_neg.csv')\n",
        "    y_test_pos = pd.read_csv('y_test_pos.csv')\n",
        "    y_test_neg = pd.read_csv('y_test_neg.csv')\n",
        "\n",
        "    print('='*60)\n",
        "    print(\"Number of nodes in the train data graph with edges\", X_train_pos.shape[0])\n",
        "    print(\"Number of nodes in the train data graph without edges\", X_train_neg.shape[0])\n",
        "    print('='*60)\n",
        "    print(\"Number of nodes in the test data graph with edges\", X_test_pos.shape[0])\n",
        "    print(\"Number of nodes in the test data graph without edges\", X_test_neg.shape[0])\n",
        "\n",
        "    \n",
        "    X_train = X_train_pos.append(X_train_neg,ignore_index=True)\n",
        "    y_train = np.concatenate((y_train_pos,y_train_neg))\n",
        "    X_test = X_test_pos.append(X_test_neg,ignore_index=True)\n",
        "    y_test = np.concatenate((y_test_pos,y_test_neg)) \n",
        "    \n",
        "    X_train.to_csv('train_after_eda.csv',header=False,index=False)\n",
        "    X_test.to_csv('test_after_eda.csv',header=False,index=False)\n",
        "    pd.DataFrame(y_train.astype(int)).to_csv('train_y.csv',header=False,index=False)\n",
        "    pd.DataFrame(y_test.astype(int)).to_csv('test_y.csv',header=False,index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAyNnx6JLEo-",
        "colab_type": "code",
        "outputId": "31993a24-d9c4-4648-eb80-5adcae664035",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!cp train_after_eda.csv drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp test_after_eda.csv drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp train_y.csv drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/\n",
        "!cp test_y.csv drive/My\\ Drive/Facebook\\ Friend\\ Recommend\\ Graph\\ /EDA/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cp: cannot stat 'train_after_eda.csv': No such file or directory\n",
            "cp: cannot stat 'test_after_eda.csv': No such file or directory\n",
            "cp: cannot stat 'test_y.csv': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8QpQoOTORnr",
        "colab_type": "code",
        "outputId": "a844dca8-89a5-4710-a7c4-ee162801cd5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(\"Data points in train data\",X_train.shape)\n",
        "print(\"Data points in test data\",X_test.shape)\n",
        "print(\"Shape of traget variable in train\",y_train.shape)\n",
        "print(\"Shape of traget variable in test\", y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data points in train data (15100030, 2)\n",
            "Data points in test data (3775008, 2)\n",
            "Shape of traget variable in train (15100030,)\n",
            "Shape of traget variable in test (3775008,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-UNBofWlgbO",
        "colab_type": "text"
      },
      "source": [
        "Computed and stored the data for featurization.\n",
        "\n",
        "Please check out FB_featurization.ipynb"
      ]
    }
  ]
}