Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "naWvqMEMHglO"
   },
   "source": [
    "<p style=\"font-size:32px;text-align:center\"> <b>Social network Graph Link Prediction - Facebook Challenge</b> </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "a3y_Ww4ngbAA",
    "outputId": "5ce8532a-e946-4e9d-dd09-36577e6d5a77"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9wb9N5RzHglP"
   },
   "outputs": [],
   "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\n",
    "from pandas import HDFStore,DataFrame\n",
    "from pandas import read_hdf\n",
    "from scipy.sparse.linalg import svds, eigs\n",
    "import gc\n",
    "from tqdm import tqdm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XC4OJFKkHglU"
   },
   "outputs": [],
   "source": [
    "#reading\n",
    "from pandas import read_hdf\n",
    "df_final_train = read_hdf('drive/My Drive/data/fea_sample/storage_sample_stage4.h5', 'train_df',mode='r')\n",
    "df_final_test = read_hdf('drive/My Drive/data/fea_sample/storage_sample_stage4.h5', 'test_df',mode='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "5Gm-BHRkHglW",
    "outputId": "2b578101-1aef-4fd8-c9b3-f89f3bf12fc8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['source_node', 'destination_node', 'indicator_link',\n",
       "       'jaccard_followers', 'jaccard_followees', 'cosine_followers',\n",
       "       'cosine_followees', 'num_followers_s', 'num_followees_s',\n",
       "       'num_followees_d', 'inter_followers', 'inter_followees', 'adar_index',\n",
       "       'follows_back', 'same_comp', 'shortest_path', 'weight_in', 'weight_out',\n",
       "       'weight_f1', 'weight_f2', 'weight_f3', 'weight_f4', 'page_rank_s',\n",
       "       'page_rank_d', 'katz_s', 'katz_d', 'hubs_s', 'hubs_d', 'authorities_s',\n",
       "       'authorities_d', 'svd_u_s_1', 'svd_u_s_2', 'svd_u_s_3', 'svd_u_s_4',\n",
       "       'svd_u_s_5', 'svd_u_s_6', 'svd_u_d_1', 'svd_u_d_2', 'svd_u_d_3',\n",
       "       'svd_u_d_4', 'svd_u_d_5', 'svd_u_d_6', 'svd_v_s_1', 'svd_v_s_2',\n",
       "       'svd_v_s_3', 'svd_v_s_4', 'svd_v_s_5', 'svd_v_s_6', 'svd_v_d_1',\n",
       "       'svd_v_d_2', 'svd_v_d_3', 'svd_v_d_4', 'svd_v_d_5', 'svd_v_d_6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XRW7VZ4AHglc"
   },
   "outputs": [],
   "source": [
    "y_train = df_final_train.indicator_link\n",
    "y_test = df_final_test.indicator_link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4lj9M_qtHglf"
   },
   "outputs": [],
   "source": [
    "df_final_train.drop(['source_node', 'destination_node','indicator_link'],axis=1,inplace=True)\n",
    "df_final_test.drop(['source_node', 'destination_node','indicator_link'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "colab_type": "code",
    "id": "lIEc91uVHgli",
    "outputId": "e3960ab9-b0dd-4afd-f202-f4ffb33dbf2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimators =  10 Train Score 0.9063252121775113 test Score 0.8745605278006858\n",
      "Estimators =  50 Train Score 0.9205725512208812 test Score 0.9125653355634538\n",
      "Estimators =  100 Train Score 0.9238690848446947 test Score 0.9141199714153599\n",
      "Estimators =  250 Train Score 0.9239789348046863 test Score 0.9188007232664732\n",
      "Estimators =  450 Train Score 0.9237190618658074 test Score 0.9161507685828595\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Estimators vs score at depth of 5')"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Ag0RcQ+wEPi8pAB+CXwkXf0PgQuAiZLem857b0Q8Xq54y+nrD6xk4sghvPPc2ZUOxfqz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\n0M4tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimators = [10,50,100,250,450]\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in estimators:\n",
    "    clf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=52, min_samples_split=120,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=i, n_jobs=-1,random_state=25,verbose=0,warm_start=False)\n",
    "    clf.fit(df_final_train,y_train)\n",
    "    train_sc = f1_score(y_train,clf.predict(df_final_train))\n",
    "    test_sc = f1_score(y_test,clf.predict(df_final_test))\n",
    "    test_scores.append(test_sc)\n",
    "    train_scores.append(train_sc)\n",
    "    print('Estimators = ',i,'Train Score',train_sc,'test Score',test_sc)\n",
    "plt.plot(estimators,train_scores,label='Train Score')\n",
    "plt.plot(estimators,test_scores,label='Test Score')\n",
    "plt.xlabel('Estimators')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Estimators vs score at depth of 5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nZxhrTdQHglm",
    "outputId": "53382b8e-148d-4c22-8066-d9807fb27012"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth =  3 Train Score 0.8916120853581238 test Score 0.8687934859875491\n",
      "depth =  9 Train Score 0.9572226298198419 test Score 0.9222953031452904\n",
      "depth =  11 Train Score 0.9623451340902863 test Score 0.9252318758281279\n",
      "depth =  15 Train Score 0.9634267621927706 test Score 0.9231288356496615\n",
      "depth =  20 Train Score 0.9631629153051491 test Score 0.9235051024711141\n",
      "depth =  35 Train Score 0.9634333127085721 test Score 0.9235601652753184\n",
      "depth =  50 Train Score 0.9634333127085721 test Score 0.9235601652753184\n",
      "depth =  70 Train Score 0.9634333127085721 test Score 0.9235601652753184\n",
      "depth =  130 Train Score 0.9634333127085721 test Score 0.9235601652753184\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "depths = [3,9,11,15,20,35,50,70,130]\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in depths:\n",
    "    clf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=i, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=52, min_samples_split=120,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=115, n_jobs=-1,random_state=25,verbose=0,warm_start=False)\n",
    "    clf.fit(df_final_train,y_train)\n",
    "    train_sc = f1_score(y_train,clf.predict(df_final_train))\n",
    "    test_sc = f1_score(y_test,clf.predict(df_final_test))\n",
    "    test_scores.append(test_sc)\n",
    "    train_scores.append(train_sc)\n",
    "    print('depth = ',i,'Train Score',train_sc,'test Score',test_sc)\n",
    "plt.plot(depths,train_scores,label='Train Score')\n",
    "plt.plot(depths,test_scores,label='Test Score')\n",
    "plt.xlabel('Depth')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Depth vs score at depth of 5 at estimators = 115')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MrG1Lfl3Hglq",
    "outputId": "7b6e3c97-8d66-455d-cd79-213f824719cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean test scores [0.96225043 0.96215493 0.96057081 0.96194015 0.96330005]\n",
      "mean train scores [0.96294922 0.96266735 0.96115674 0.96263457 0.96430539]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "from scipy.stats import uniform\n",
    "\n",
    "param_dist = {\"n_estimators\":sp_randint(105,125),\n",
    "              \"max_depth\": sp_randint(10,15),\n",
    "              \"min_samples_split\": sp_randint(110,190),\n",
    "              \"min_samples_leaf\": sp_randint(25,65)}\n",
    "\n",
    "clf = RandomForestClassifier(random_state=25,n_jobs=-1)\n",
    "\n",
    "rf_random = RandomizedSearchCV(clf, param_distributions=param_dist,\n",
    "                                   n_iter=5,cv=10,scoring='f1',random_state=25)\n",
    "\n",
    "rf_random.fit(df_final_train,y_train)\n",
    "print('mean test scores',rf_random.cv_results_['mean_test_score'])\n",
    "print('mean train scores',rf_random.cv_results_['mean_train_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hme3j_diHglu",
    "outputId": "581ccc02-7770-4a94-d003-257af4e81570"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=14, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=28, min_samples_split=111,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=121, n_jobs=-1,\n",
      "            oob_score=False, random_state=25, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(rf_random.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qu4KIEweHglx"
   },
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=14, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=28, min_samples_split=111,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=121, n_jobs=-1,\n",
    "            oob_score=False, random_state=25, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dax83GfLHgl1"
   },
   "outputs": [],
   "source": [
    "clf.fit(df_final_train,y_train)\n",
    "y_train_pred = clf.predict(df_final_train)\n",
    "y_test_pred = clf.predict(df_final_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PTtAp1iTHgl4",
    "outputId": "e2e3d725-3c4d-4374-d303-8f294b570977"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train f1 score 0.9652533106548414\n",
      "Test f1 score 0.9241678239279553\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print('Train f1 score',f1_score(y_train,y_train_pred))\n",
    "print('Test f1 score',f1_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5XfUkZYQHgl7"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "def plot_confusion_matrix(test_y, predict_y):\n",
    "    C = confusion_matrix(test_y, predict_y)\n",
    "    \n",
    "    A =(((C.T)/(C.sum(axis=1))).T)\n",
    "    \n",
    "    B =(C/C.sum(axis=0))\n",
    "    plt.figure(figsize=(20,4))\n",
    "    \n",
    "    labels = [0,1]\n",
    "    # representing A in heatmap format\n",
    "    cmap=sns.light_palette(\"blue\")\n",
    "    plt.subplot(1, 3, 1)\n",
    "    sns.heatmap(C, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Confusion matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 2)\n",
    "    sns.heatmap(B, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Precision matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 3)\n",
    "    # representing B in heatmap format\n",
    "    sns.heatmap(A, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Recall matrix\")\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s6t11dhTHgl-",
    "outputId": "189de3be-1c99-4653-f56f-12b18b0f4db7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train confusion_matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test confusion_matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Train confusion_matrix')\n",
    "plot_confusion_matrix(y_train,y_train_pred)\n",
    "print('Test confusion_matrix')\n",
    "plot_confusion_matrix(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "61TF-VLeHgmD",
    "outputId": "795f1b39-61c7-470f-e2d0-1fe6dc7ac5fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr,tpr,ths = roc_curve(y_test,y_test_pred)\n",
    "auc_sc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='navy',label='ROC curve (area = %0.2f)' % auc_sc)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic with test data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HEZ7uvN6HgmK",
    "outputId": "6f762256-54fc-4e2f-865b-22342033eb2c",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = df_final_train.columns\n",
    "importances = clf.feature_importances_\n",
    "indices = (np.argsort(importances))[-25:]\n",
    "plt.figure(figsize=(10,12))\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='r', align='center')\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preferential Attachment :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yYUGXgCT3UC3"
   },
   "outputs": [],
   "source": [
    "from pandas import read_hdf\n",
    "train = read_hdf('drive/My Drive/data/fea_sample/storage_sample_stage4.h5', 'train_df',mode='r')\n",
    "test = read_hdf('drive/My Drive/data/fea_sample/storage_sample_stage4.h5', 'test_df',mode='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "10nAZAb75hyV"
   },
   "outputs": [],
   "source": [
    "Y_train = train.indicator_link\n",
    "Y_test = test.indicator_link "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "XBGlOh0fmG4i",
    "outputId": "11829d85-f738-4d42-f527-85a5cb0367f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "if os.path.isfile('drive/My Drive/data/after_eda/train_pos_after_eda.csv'):\n",
    "    graph_train=nx.read_edgelist('drive/My Drive/data/after_eda/train_pos_after_eda.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
    "    print(nx.info(graph_train))\n",
    "else:\n",
    "    print(\"Run the EDA cells or download the files from drive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EA-xWw98nPua"
   },
   "outputs": [],
   "source": [
    "def PA_follower(a,b):\n",
    "    try:\n",
    "        return(len(set(graph_train.predecessors(a)))*len(set(graph_train.predecessors(b))))\n",
    "    except:\n",
    "        pass\n",
    "  \n",
    "def PA_followee(a,b):\n",
    "    try:\n",
    "        return (len(set(graph_train.successors(a)))*len(set(graph_train.successors(b))))\n",
    "    except:\n",
    "        pass \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vvsAkP-XpZ-G"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "train['follower_preferential'] = train.apply(lambda row: PA_follower(row['source_node'],row['destination_node']),axis=1)\n",
    "test['follower_preferential'] = test.apply(lambda row: PA_follower(row['source_node'],row['destination_node']),axis=1)\n",
    "\n",
    "\n",
    "train['followee_preferential'] = train.apply(lambda row: PA_followee(row['source_node'],row['destination_node']),axis=1)\n",
    "test['followee_preferential'] = test.apply(lambda row: PA_followee(row['source_node'],row['destination_node']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "Ze9Xb71op8iz",
    "outputId": "31d6fc8e-24ef-41e8-e31f-1350bbdd2715"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_node</th>\n",
       "      <th>destination_node</th>\n",
       "      <th>indicator_link</th>\n",
       "      <th>jaccard_followers</th>\n",
       "      <th>jaccard_followees</th>\n",
       "      <th>cosine_followers</th>\n",
       "      <th>cosine_followees</th>\n",
       "      <th>num_followers_s</th>\n",
       "      <th>num_followees_s</th>\n",
       "      <th>num_followees_d</th>\n",
       "      <th>inter_followers</th>\n",
       "      <th>inter_followees</th>\n",
       "      <th>adar_index</th>\n",
       "      <th>follows_back</th>\n",
       "      <th>same_comp</th>\n",
       "      <th>shortest_path</th>\n",
       "      <th>weight_in</th>\n",
       "      <th>weight_out</th>\n",
       "      <th>weight_f1</th>\n",
       "      <th>weight_f2</th>\n",
       "      <th>weight_f3</th>\n",
       "      <th>weight_f4</th>\n",
       "      <th>page_rank_s</th>\n",
       "      <th>page_rank_d</th>\n",
       "      <th>katz_s</th>\n",
       "      <th>katz_d</th>\n",
       "      <th>hubs_s</th>\n",
       "      <th>hubs_d</th>\n",
       "      <th>authorities_s</th>\n",
       "      <th>authorities_d</th>\n",
       "      <th>svd_u_s_1</th>\n",
       "      <th>svd_u_s_2</th>\n",
       "      <th>svd_u_s_3</th>\n",
       "      <th>svd_u_s_4</th>\n",
       "      <th>svd_u_s_5</th>\n",
       "      <th>svd_u_s_6</th>\n",
       "      <th>svd_u_d_1</th>\n",
       "      <th>svd_u_d_2</th>\n",
       "      <th>svd_u_d_3</th>\n",
       "      <th>svd_u_d_4</th>\n",
       "      <th>svd_u_d_5</th>\n",
       "      <th>svd_u_d_6</th>\n",
       "      <th>svd_v_s_1</th>\n",
       "      <th>svd_v_s_2</th>\n",
       "      <th>svd_v_s_3</th>\n",
       "      <th>svd_v_s_4</th>\n",
       "      <th>svd_v_s_5</th>\n",
       "      <th>svd_v_s_6</th>\n",
       "      <th>svd_v_d_1</th>\n",
       "      <th>svd_v_d_2</th>\n",
       "      <th>svd_v_d_3</th>\n",
       "      <th>svd_v_d_4</th>\n",
       "      <th>svd_v_d_5</th>\n",
       "      <th>svd_v_d_6</th>\n",
       "      <th>follower_preferential</th>\n",
       "      <th>followee_preferential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>273084</td>\n",
       "      <td>1505602</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.627964</td>\n",
       "      <td>0.094491</td>\n",
       "      <td>1.005929</td>\n",
       "      <td>0.877964</td>\n",
       "      <td>2.045290e-06</td>\n",
       "      <td>3.459963e-07</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>1.943132e-13</td>\n",
       "      <td>1.941103e-13</td>\n",
       "      <td>9.226339e-16</td>\n",
       "      <td>2.231877e-15</td>\n",
       "      <td>-1.666226e-13</td>\n",
       "      <td>4.613397e-13</td>\n",
       "      <td>1.043044e-05</td>\n",
       "      <td>6.676960e-13</td>\n",
       "      <td>2.451081e-13</td>\n",
       "      <td>3.584580e-12</td>\n",
       "      <td>-2.038017e-11</td>\n",
       "      <td>5.397495e-13</td>\n",
       "      <td>1.068946e-06</td>\n",
       "      <td>1.192357e-12</td>\n",
       "      <td>2.279485e-12</td>\n",
       "      <td>3.580837e-12</td>\n",
       "      <td>-7.216133e-13</td>\n",
       "      <td>3.925975e-13</td>\n",
       "      <td>1.983691e-06</td>\n",
       "      <td>1.545075e-13</td>\n",
       "      <td>8.108434e-13</td>\n",
       "      <td>1.719702e-14</td>\n",
       "      <td>-1.355368e-12</td>\n",
       "      <td>4.675307e-13</td>\n",
       "      <td>1.128591e-06</td>\n",
       "      <td>6.616550e-14</td>\n",
       "      <td>9.771077e-13</td>\n",
       "      <td>4.159752e-14</td>\n",
       "      <td>66.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>832016</td>\n",
       "      <td>1543415</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.187135</td>\n",
       "      <td>0.028382</td>\n",
       "      <td>0.343828</td>\n",
       "      <td>94</td>\n",
       "      <td>61</td>\n",
       "      <td>142</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>16.362912</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.102598</td>\n",
       "      <td>0.127000</td>\n",
       "      <td>0.229598</td>\n",
       "      <td>0.013030</td>\n",
       "      <td>0.332196</td>\n",
       "      <td>0.356598</td>\n",
       "      <td>2.353458e-07</td>\n",
       "      <td>6.427660e-07</td>\n",
       "      <td>0.000845</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>3.906648e-11</td>\n",
       "      <td>9.424102e-11</td>\n",
       "      <td>1.208074e-11</td>\n",
       "      <td>1.273080e-10</td>\n",
       "      <td>7.051088e-13</td>\n",
       "      <td>-8.250564e-11</td>\n",
       "      <td>-1.717702e-10</td>\n",
       "      <td>3.705016e-02</td>\n",
       "      <td>1.032392e-11</td>\n",
       "      <td>7.207497e-10</td>\n",
       "      <td>1.644399e-12</td>\n",
       "      <td>-2.047752e-10</td>\n",
       "      <td>-3.943936e-10</td>\n",
       "      <td>8.617541e-02</td>\n",
       "      <td>2.399540e-11</td>\n",
       "      <td>1.738682e-09</td>\n",
       "      <td>2.685878e-13</td>\n",
       "      <td>-3.316849e-11</td>\n",
       "      <td>-6.236048e-11</td>\n",
       "      <td>1.345726e-02</td>\n",
       "      <td>3.703479e-12</td>\n",
       "      <td>2.251737e-10</td>\n",
       "      <td>1.245101e-12</td>\n",
       "      <td>-1.636948e-10</td>\n",
       "      <td>-3.112650e-10</td>\n",
       "      <td>6.738902e-02</td>\n",
       "      <td>2.607801e-11</td>\n",
       "      <td>2.372904e-09</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>8662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1325247</td>\n",
       "      <td>760242</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.156957</td>\n",
       "      <td>0.566038</td>\n",
       "      <td>28</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>10.991826</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.185695</td>\n",
       "      <td>0.154303</td>\n",
       "      <td>0.339999</td>\n",
       "      <td>0.028653</td>\n",
       "      <td>0.525694</td>\n",
       "      <td>0.494302</td>\n",
       "      <td>6.211019e-07</td>\n",
       "      <td>5.179801e-07</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>0.000855</td>\n",
       "      <td>7.730764e-114</td>\n",
       "      <td>4.067322e-114</td>\n",
       "      <td>2.681298e-113</td>\n",
       "      <td>2.199205e-113</td>\n",
       "      <td>-4.900734e-18</td>\n",
       "      <td>1.096831e-18</td>\n",
       "      <td>-6.816555e-19</td>\n",
       "      <td>-2.226453e-18</td>\n",
       "      <td>6.710556e-19</td>\n",
       "      <td>-8.161336e-19</td>\n",
       "      <td>-2.606312e-18</td>\n",
       "      <td>6.392474e-19</td>\n",
       "      <td>-4.245830e-19</td>\n",
       "      <td>-1.315747e-18</td>\n",
       "      <td>3.849627e-19</td>\n",
       "      <td>-1.131971e-18</td>\n",
       "      <td>-1.510265e-18</td>\n",
       "      <td>1.454979e-19</td>\n",
       "      <td>-2.380564e-19</td>\n",
       "      <td>-7.021227e-19</td>\n",
       "      <td>1.940403e-19</td>\n",
       "      <td>-3.365389e-19</td>\n",
       "      <td>-1.238370e-18</td>\n",
       "      <td>1.438175e-19</td>\n",
       "      <td>-1.852863e-19</td>\n",
       "      <td>-5.901864e-19</td>\n",
       "      <td>1.629341e-19</td>\n",
       "      <td>-2.572452e-19</td>\n",
       "      <td>980.0</td>\n",
       "      <td>902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1368400</td>\n",
       "      <td>1006992</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.696923</td>\n",
       "      <td>0.117851</td>\n",
       "      <td>0.985599</td>\n",
       "      <td>1.105172</td>\n",
       "      <td>2.998153e-07</td>\n",
       "      <td>1.704245e-06</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>5.443738e-17</td>\n",
       "      <td>4.139999e-16</td>\n",
       "      <td>2.413250e-14</td>\n",
       "      <td>6.688064e-15</td>\n",
       "      <td>-9.965436e-12</td>\n",
       "      <td>4.077137e-13</td>\n",
       "      <td>5.083778e-10</td>\n",
       "      <td>1.985267e-14</td>\n",
       "      <td>2.471968e-12</td>\n",
       "      <td>1.004354e-15</td>\n",
       "      <td>-2.629029e-13</td>\n",
       "      <td>1.428940e-11</td>\n",
       "      <td>9.146784e-11</td>\n",
       "      <td>8.590132e-12</td>\n",
       "      <td>2.406658e-10</td>\n",
       "      <td>7.638106e-15</td>\n",
       "      <td>-2.170899e-13</td>\n",
       "      <td>5.206150e-13</td>\n",
       "      <td>6.058498e-11</td>\n",
       "      <td>1.514614e-11</td>\n",
       "      <td>1.513483e-12</td>\n",
       "      <td>4.498061e-13</td>\n",
       "      <td>-9.818087e-10</td>\n",
       "      <td>3.454672e-11</td>\n",
       "      <td>5.213635e-08</td>\n",
       "      <td>9.595823e-13</td>\n",
       "      <td>3.047045e-10</td>\n",
       "      <td>1.246592e-13</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140165</td>\n",
       "      <td>1708748</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>1.301511</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>2.301511</td>\n",
       "      <td>1.603023</td>\n",
       "      <td>4.349180e-07</td>\n",
       "      <td>2.089590e-07</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>3.887821e-16</td>\n",
       "      <td>4.721269e-16</td>\n",
       "      <td>7.552255e-16</td>\n",
       "      <td>2.734009e-18</td>\n",
       "      <td>-3.408943e-13</td>\n",
       "      <td>1.125207e-13</td>\n",
       "      <td>3.190883e-07</td>\n",
       "      <td>8.550846e-14</td>\n",
       "      <td>1.405351e-13</td>\n",
       "      <td>7.172313e-15</td>\n",
       "      <td>-2.550169e-13</td>\n",
       "      <td>2.424636e-13</td>\n",
       "      <td>2.436301e-07</td>\n",
       "      <td>3.679960e-14</td>\n",
       "      <td>1.348520e-13</td>\n",
       "      <td>8.710393e-15</td>\n",
       "      <td>-4.795514e-13</td>\n",
       "      <td>1.521297e-13</td>\n",
       "      <td>1.197283e-07</td>\n",
       "      <td>1.999809e-14</td>\n",
       "      <td>3.360247e-13</td>\n",
       "      <td>1.407670e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_node  destination_node  ...  follower_preferential  followee_preferential\n",
       "0       273084           1505602  ...                   66.0                  120.0\n",
       "1       832016           1543415  ...                 1598.0                 8662.0\n",
       "2      1325247            760242  ...                  980.0                  902.0\n",
       "3      1368400           1006992  ...                   22.0                   35.0\n",
       "4       140165           1708748  ...                    5.0                   33.0\n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DOT SVD :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "tVlkwKSdrP9B",
    "outputId": "2ab7e142-e257-4c7f-dc7d-039b832701fa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100002it [00:48, 2063.69it/s]\n"
     ]
    }
   ],
   "source": [
    "svd_dot_u = []\n",
    "svd_dot_v = []\n",
    "for i, j in tqdm(train.iterrows()):\n",
    "    train_u_source = []\n",
    "    train_u_dest = []\n",
    "    train_u_source.append(train['svd_u_s_1'][i])\n",
    "    train_u_source.append(train['svd_u_s_2'][i])\n",
    "    train_u_source.append(train['svd_u_s_3'][i])\n",
    "    train_u_source.append(train['svd_u_s_4'][i])\n",
    "    train_u_source.append(train['svd_u_s_5'][i])\n",
    "    train_u_source.append(train['svd_u_s_6'][i])\n",
    "    \n",
    "    train_u_dest.append(train['svd_u_d_1'][i])\n",
    "    train_u_dest.append(train['svd_u_d_2'][i])\n",
    "    train_u_dest.append(train['svd_u_d_3'][i])\n",
    "    train_u_dest.append(train['svd_u_d_4'][i])\n",
    "    train_u_dest.append(train['svd_u_d_5'][i])\n",
    "    train_u_dest.append(train['svd_u_d_6'][i])\n",
    "    \n",
    "    svd_dot_u.append(np.dot(train_u_source, train_u_dest))\n",
    "    \n",
    "    train_v_source = []\n",
    "    train_v_dest = []\n",
    "    train_v_source.append(train['svd_v_s_1'][i])\n",
    "    train_v_source.append(train['svd_v_s_2'][i])\n",
    "    train_v_source.append(train['svd_v_s_3'][i])\n",
    "    train_v_source.append(train['svd_v_s_4'][i])\n",
    "    train_v_source.append(train['svd_v_s_5'][i])\n",
    "    train_v_source.append(train['svd_v_s_6'][i])\n",
    "    \n",
    "    train_v_dest.append(train['svd_v_d_1'][i])\n",
    "    train_v_dest.append(train['svd_v_d_2'][i])\n",
    "    train_v_dest.append(train['svd_v_d_3'][i])\n",
    "    train_v_dest.append(train['svd_v_d_4'][i])\n",
    "    train_v_dest.append(train['svd_v_d_5'][i])\n",
    "    train_v_dest.append(train['svd_v_d_6'][i])\n",
    "    \n",
    "    svd_dot_v.append(np.dot(train_v_source, train_v_dest))\n",
    "\n",
    "train['svd_dot_u'] = svd_dot_u\n",
    "train['svd_dot_v'] = svd_dot_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9R4-LfmqviyH",
    "outputId": "a4e2773d-05c5-4466-ddfc-2277fd8299e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "50002it [00:23, 2121.31it/s]\n"
     ]
    }
   ],
   "source": [
    "svd_dot_u = []\n",
    "svd_dot_v = []\n",
    "for i, j in tqdm(test.iterrows()):\n",
    "    test_u_source = []\n",
    "    test_u_dest = []\n",
    "    test_u_source.append(test['svd_u_s_1'][i])\n",
    "    test_u_source.append(test['svd_u_s_2'][i])\n",
    "    test_u_source.append(test['svd_u_s_3'][i])\n",
    "    test_u_source.append(test['svd_u_s_4'][i])\n",
    "    test_u_source.append(test['svd_u_s_5'][i])\n",
    "    test_u_source.append(test['svd_u_s_6'][i])\n",
    "    \n",
    "    test_u_dest.append(test['svd_u_d_1'][i])\n",
    "    test_u_dest.append(test['svd_u_d_2'][i])\n",
    "    test_u_dest.append(test['svd_u_d_3'][i])\n",
    "    test_u_dest.append(test['svd_u_d_4'][i])\n",
    "    test_u_dest.append(test['svd_u_d_5'][i])\n",
    "    test_u_dest.append(test['svd_u_d_6'][i])\n",
    "    \n",
    "    svd_dot_u.append(np.dot(test_u_source, test_u_dest))\n",
    "    \n",
    "    test_v_source = []\n",
    "    test_v_dest = []\n",
    "    test_v_source.append(test['svd_v_s_1'][i])\n",
    "    test_v_source.append(test['svd_v_s_2'][i])\n",
    "    test_v_source.append(test['svd_v_s_3'][i])\n",
    "    test_v_source.append(test['svd_v_s_4'][i])\n",
    "    test_v_source.append(test['svd_v_s_5'][i])\n",
    "    test_v_source.append(test['svd_v_s_6'][i])\n",
    "    \n",
    "    test_v_dest.append(test['svd_v_d_1'][i])\n",
    "    test_v_dest.append(test['svd_v_d_2'][i])\n",
    "    test_v_dest.append(test['svd_v_d_3'][i])\n",
    "    test_v_dest.append(test['svd_v_d_4'][i])\n",
    "    test_v_dest.append(test['svd_v_d_5'][i])\n",
    "    test_v_dest.append(test['svd_v_d_6'][i])\n",
    "    \n",
    "    svd_dot_v.append(np.dot(test_v_source, test_v_dest))\n",
    "\n",
    "test['svd_dot_u'] = svd_dot_u\n",
    "test['svd_dot_v'] = svd_dot_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "vDojsg1OyopN",
    "outputId": "e7513284-0f30-45c8-9b3c-4f3e073541d9"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<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",
       "      <th>indicator_link</th>\n",
       "      <th>jaccard_followers</th>\n",
       "      <th>jaccard_followees</th>\n",
       "      <th>cosine_followers</th>\n",
       "      <th>cosine_followees</th>\n",
       "      <th>num_followers_s</th>\n",
       "      <th>num_followees_s</th>\n",
       "      <th>num_followees_d</th>\n",
       "      <th>inter_followers</th>\n",
       "      <th>inter_followees</th>\n",
       "      <th>adar_index</th>\n",
       "      <th>follows_back</th>\n",
       "      <th>same_comp</th>\n",
       "      <th>shortest_path</th>\n",
       "      <th>weight_in</th>\n",
       "      <th>weight_out</th>\n",
       "      <th>weight_f1</th>\n",
       "      <th>weight_f2</th>\n",
       "      <th>weight_f3</th>\n",
       "      <th>weight_f4</th>\n",
       "      <th>page_rank_s</th>\n",
       "      <th>page_rank_d</th>\n",
       "      <th>katz_s</th>\n",
       "      <th>katz_d</th>\n",
       "      <th>hubs_s</th>\n",
       "      <th>hubs_d</th>\n",
       "      <th>authorities_s</th>\n",
       "      <th>authorities_d</th>\n",
       "      <th>svd_u_s_1</th>\n",
       "      <th>svd_u_s_2</th>\n",
       "      <th>svd_u_s_3</th>\n",
       "      <th>svd_u_s_4</th>\n",
       "      <th>svd_u_s_5</th>\n",
       "      <th>svd_u_s_6</th>\n",
       "      <th>svd_u_d_1</th>\n",
       "      <th>svd_u_d_2</th>\n",
       "      <th>svd_u_d_3</th>\n",
       "      <th>svd_u_d_4</th>\n",
       "      <th>svd_u_d_5</th>\n",
       "      <th>svd_u_d_6</th>\n",
       "      <th>svd_v_s_1</th>\n",
       "      <th>svd_v_s_2</th>\n",
       "      <th>svd_v_s_3</th>\n",
       "      <th>svd_v_s_4</th>\n",
       "      <th>svd_v_s_5</th>\n",
       "      <th>svd_v_s_6</th>\n",
       "      <th>svd_v_d_1</th>\n",
       "      <th>svd_v_d_2</th>\n",
       "      <th>svd_v_d_3</th>\n",
       "      <th>svd_v_d_4</th>\n",
       "      <th>svd_v_d_5</th>\n",
       "      <th>svd_v_d_6</th>\n",
       "      <th>follower_preferential</th>\n",
       "      <th>followee_preferential</th>\n",
       "      <th>svd_dot_u</th>\n",
       "      <th>svd_dot_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>848424</td>\n",
       "      <td>784690</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.258199</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.636163</td>\n",
       "      <td>0.097590</td>\n",
       "      <td>0.894362</td>\n",
       "      <td>1.014128</td>\n",
       "      <td>6.557971e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>0.000786</td>\n",
       "      <td>3.243237e-16</td>\n",
       "      <td>1.745627e-16</td>\n",
       "      <td>2.969838e-15</td>\n",
       "      <td>9.269213e-14</td>\n",
       "      <td>-9.987979e-12</td>\n",
       "      <td>2.283676e-13</td>\n",
       "      <td>1.439968e-10</td>\n",
       "      <td>6.136162e-13</td>\n",
       "      <td>4.188171e-13</td>\n",
       "      <td>5.983361e-15</td>\n",
       "      <td>-1.026186e-11</td>\n",
       "      <td>5.316372e-13</td>\n",
       "      <td>5.843225e-10</td>\n",
       "      <td>1.166047e-13</td>\n",
       "      <td>2.253356e-11</td>\n",
       "      <td>3.220367e-15</td>\n",
       "      <td>-2.148852e-13</td>\n",
       "      <td>1.883148e-13</td>\n",
       "      <td>5.904807e-11</td>\n",
       "      <td>2.701538e-12</td>\n",
       "      <td>4.341620e-13</td>\n",
       "      <td>5.535503e-14</td>\n",
       "      <td>-9.994076e-10</td>\n",
       "      <td>5.791910e-10</td>\n",
       "      <td>3.512364e-07</td>\n",
       "      <td>2.486658e-09</td>\n",
       "      <td>2.771146e-09</td>\n",
       "      <td>1.727694e-12</td>\n",
       "      <td>84.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>8.425267e-20</td>\n",
       "      <td>2.074808e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>483294</td>\n",
       "      <td>1255532</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.235702</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.942809</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.178511</td>\n",
       "      <td>1.649916</td>\n",
       "      <td>2.172064e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>1.702625e-19</td>\n",
       "      <td>2.706300e-15</td>\n",
       "      <td>2.522357e-16</td>\n",
       "      <td>5.277458e-15</td>\n",
       "      <td>-2.079151e-15</td>\n",
       "      <td>7.009864e-16</td>\n",
       "      <td>6.970710e-10</td>\n",
       "      <td>9.561769e-15</td>\n",
       "      <td>1.728794e-15</td>\n",
       "      <td>3.122935e-18</td>\n",
       "      <td>-1.960277e-11</td>\n",
       "      <td>2.861530e-11</td>\n",
       "      <td>1.939773e-08</td>\n",
       "      <td>1.907382e-12</td>\n",
       "      <td>3.797410e-11</td>\n",
       "      <td>4.992848e-14</td>\n",
       "      <td>-4.054309e-13</td>\n",
       "      <td>2.895682e-13</td>\n",
       "      <td>2.545382e-10</td>\n",
       "      <td>2.248568e-14</td>\n",
       "      <td>3.600957e-13</td>\n",
       "      <td>4.701436e-15</td>\n",
       "      <td>-9.360516e-12</td>\n",
       "      <td>3.206809e-10</td>\n",
       "      <td>4.668696e-08</td>\n",
       "      <td>6.665777e-12</td>\n",
       "      <td>1.495979e-10</td>\n",
       "      <td>9.836670e-14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.352160e-17</td>\n",
       "      <td>1.188376e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>626190</td>\n",
       "      <td>1729265</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.242536</td>\n",
       "      <td>0.544047</td>\n",
       "      <td>0.073127</td>\n",
       "      <td>0.845558</td>\n",
       "      <td>0.786583</td>\n",
       "      <td>1.853369e-06</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000789</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>9.426796e-14</td>\n",
       "      <td>4.116616e-16</td>\n",
       "      <td>2.253244e-15</td>\n",
       "      <td>2.079387e-15</td>\n",
       "      <td>-1.782290e-11</td>\n",
       "      <td>2.389627e-11</td>\n",
       "      <td>1.443050e-06</td>\n",
       "      <td>2.358030e-12</td>\n",
       "      <td>2.689638e-12</td>\n",
       "      <td>1.739178e-12</td>\n",
       "      <td>-1.863934e-13</td>\n",
       "      <td>1.806783e-13</td>\n",
       "      <td>2.544597e-07</td>\n",
       "      <td>1.693295e-13</td>\n",
       "      <td>2.712748e-13</td>\n",
       "      <td>7.594641e-15</td>\n",
       "      <td>-4.148103e-13</td>\n",
       "      <td>4.618813e-12</td>\n",
       "      <td>1.122150e-05</td>\n",
       "      <td>1.778927e-12</td>\n",
       "      <td>2.740535e-13</td>\n",
       "      <td>4.199834e-14</td>\n",
       "      <td>-4.253075e-13</td>\n",
       "      <td>4.789463e-13</td>\n",
       "      <td>3.479824e-07</td>\n",
       "      <td>1.630549e-13</td>\n",
       "      <td>3.954708e-13</td>\n",
       "      <td>3.875785e-14</td>\n",
       "      <td>150.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>3.671980e-13</td>\n",
       "      <td>3.904885e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>947219</td>\n",
       "      <td>425228</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.162221</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.463733</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.625954</td>\n",
       "      <td>0.765244</td>\n",
       "      <td>7.000791e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.000884</td>\n",
       "      <td>9.876114e-14</td>\n",
       "      <td>1.039593e-13</td>\n",
       "      <td>1.511694e-14</td>\n",
       "      <td>3.478438e-14</td>\n",
       "      <td>-4.491246e-11</td>\n",
       "      <td>9.917404e-13</td>\n",
       "      <td>7.891250e-06</td>\n",
       "      <td>9.458624e-12</td>\n",
       "      <td>2.715848e-11</td>\n",
       "      <td>1.822074e-12</td>\n",
       "      <td>-1.070733e-10</td>\n",
       "      <td>2.888035e-12</td>\n",
       "      <td>2.070703e-05</td>\n",
       "      <td>7.229355e-12</td>\n",
       "      <td>3.563659e-11</td>\n",
       "      <td>1.917977e-12</td>\n",
       "      <td>-8.942488e-12</td>\n",
       "      <td>5.535630e-12</td>\n",
       "      <td>5.223676e-06</td>\n",
       "      <td>7.917166e-13</td>\n",
       "      <td>4.020707e-12</td>\n",
       "      <td>2.817657e-13</td>\n",
       "      <td>-2.162590e-11</td>\n",
       "      <td>6.939194e-12</td>\n",
       "      <td>1.879861e-05</td>\n",
       "      <td>4.384816e-12</td>\n",
       "      <td>1.239414e-11</td>\n",
       "      <td>6.483485e-13</td>\n",
       "      <td>407.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1.634044e-10</td>\n",
       "      <td>9.819784e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>991374</td>\n",
       "      <td>975044</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.042767</td>\n",
       "      <td>0.347833</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6.136433</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.188982</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.438982</td>\n",
       "      <td>0.047246</td>\n",
       "      <td>0.627964</td>\n",
       "      <td>0.688982</td>\n",
       "      <td>7.103008e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>1.557332e-15</td>\n",
       "      <td>1.096037e-14</td>\n",
       "      <td>5.180869e-15</td>\n",
       "      <td>1.296135e-14</td>\n",
       "      <td>-9.778448e-13</td>\n",
       "      <td>5.724610e-13</td>\n",
       "      <td>4.951883e-06</td>\n",
       "      <td>1.396834e-12</td>\n",
       "      <td>2.153249e-11</td>\n",
       "      <td>2.873183e-14</td>\n",
       "      <td>-2.275780e-11</td>\n",
       "      <td>1.007615e-12</td>\n",
       "      <td>2.517628e-06</td>\n",
       "      <td>1.394103e-10</td>\n",
       "      <td>2.961969e-11</td>\n",
       "      <td>2.022115e-13</td>\n",
       "      <td>-3.804979e-12</td>\n",
       "      <td>1.593011e-13</td>\n",
       "      <td>1.035016e-06</td>\n",
       "      <td>1.361574e-13</td>\n",
       "      <td>1.154623e-12</td>\n",
       "      <td>9.656662e-14</td>\n",
       "      <td>-8.742904e-12</td>\n",
       "      <td>7.467370e-12</td>\n",
       "      <td>1.256880e-05</td>\n",
       "      <td>3.636983e-12</td>\n",
       "      <td>3.948463e-12</td>\n",
       "      <td>2.415863e-13</td>\n",
       "      <td>324.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>1.246700e-11</td>\n",
       "      <td>1.300892e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_node  destination_node  ...     svd_dot_u     svd_dot_v\n",
       "0       848424            784690  ...  8.425267e-20  2.074808e-17\n",
       "1       483294           1255532  ...  1.352160e-17  1.188376e-17\n",
       "2       626190           1729265  ...  3.671980e-13  3.904885e-12\n",
       "3       947219            425228  ...  1.634044e-10  9.819784e-11\n",
       "4       991374            975044  ...  1.246700e-11  1.300892e-11\n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EHE3USK9ywWR"
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier as xgb\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4_i8tQhy57wU"
   },
   "outputs": [],
   "source": [
    "train.drop(['source_node', 'destination_node','indicator_link'],axis=1,inplace=True)\n",
    "test.drop(['source_node', 'destination_node','indicator_link'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "JkxCXrLd0olD",
    "outputId": "cd7d4889-e663-4ca9-bad0-6b3a355746c2"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jaccard_followers</th>\n",
       "      <th>jaccard_followees</th>\n",
       "      <th>cosine_followers</th>\n",
       "      <th>cosine_followees</th>\n",
       "      <th>num_followers_s</th>\n",
       "      <th>num_followees_s</th>\n",
       "      <th>num_followees_d</th>\n",
       "      <th>inter_followers</th>\n",
       "      <th>inter_followees</th>\n",
       "      <th>adar_index</th>\n",
       "      <th>follows_back</th>\n",
       "      <th>same_comp</th>\n",
       "      <th>shortest_path</th>\n",
       "      <th>weight_in</th>\n",
       "      <th>weight_out</th>\n",
       "      <th>weight_f1</th>\n",
       "      <th>weight_f2</th>\n",
       "      <th>weight_f3</th>\n",
       "      <th>weight_f4</th>\n",
       "      <th>page_rank_s</th>\n",
       "      <th>page_rank_d</th>\n",
       "      <th>katz_s</th>\n",
       "      <th>katz_d</th>\n",
       "      <th>hubs_s</th>\n",
       "      <th>hubs_d</th>\n",
       "      <th>authorities_s</th>\n",
       "      <th>authorities_d</th>\n",
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       "      <th>svd_u_s_4</th>\n",
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       "      <th>svd_u_d_5</th>\n",
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       "      <th>svd_v_s_2</th>\n",
       "      <th>svd_v_s_3</th>\n",
       "      <th>svd_v_s_4</th>\n",
       "      <th>svd_v_s_5</th>\n",
       "      <th>svd_v_s_6</th>\n",
       "      <th>svd_v_d_1</th>\n",
       "      <th>svd_v_d_2</th>\n",
       "      <th>svd_v_d_3</th>\n",
       "      <th>svd_v_d_4</th>\n",
       "      <th>svd_v_d_5</th>\n",
       "      <th>svd_v_d_6</th>\n",
       "      <th>follower_preferential</th>\n",
       "      <th>followee_preferential</th>\n",
       "      <th>svd_dot_u</th>\n",
       "      <th>svd_dot_v</th>\n",
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       "      <td>6</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.627964</td>\n",
       "      <td>0.094491</td>\n",
       "      <td>1.005929</td>\n",
       "      <td>0.877964</td>\n",
       "      <td>2.045290e-06</td>\n",
       "      <td>3.459963e-07</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>1.943132e-13</td>\n",
       "      <td>1.941103e-13</td>\n",
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       "      <td>2.231877e-15</td>\n",
       "      <td>-1.666226e-13</td>\n",
       "      <td>4.613397e-13</td>\n",
       "      <td>1.043044e-05</td>\n",
       "      <td>6.676960e-13</td>\n",
       "      <td>2.451081e-13</td>\n",
       "      <td>3.584580e-12</td>\n",
       "      <td>-2.038017e-11</td>\n",
       "      <td>5.397495e-13</td>\n",
       "      <td>1.068946e-06</td>\n",
       "      <td>1.192357e-12</td>\n",
       "      <td>2.279485e-12</td>\n",
       "      <td>3.580837e-12</td>\n",
       "      <td>-7.216133e-13</td>\n",
       "      <td>3.925975e-13</td>\n",
       "      <td>1.983691e-06</td>\n",
       "      <td>1.545075e-13</td>\n",
       "      <td>8.108434e-13</td>\n",
       "      <td>1.719702e-14</td>\n",
       "      <td>-1.355368e-12</td>\n",
       "      <td>4.675307e-13</td>\n",
       "      <td>1.128591e-06</td>\n",
       "      <td>6.616550e-14</td>\n",
       "      <td>9.771077e-13</td>\n",
       "      <td>4.159752e-14</td>\n",
       "      <td>66.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.114958e-11</td>\n",
       "      <td>2.238775e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.187135</td>\n",
       "      <td>0.028382</td>\n",
       "      <td>0.343828</td>\n",
       "      <td>94</td>\n",
       "      <td>61</td>\n",
       "      <td>142</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>16.362912</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.102598</td>\n",
       "      <td>0.127000</td>\n",
       "      <td>0.229598</td>\n",
       "      <td>0.013030</td>\n",
       "      <td>0.332196</td>\n",
       "      <td>0.356598</td>\n",
       "      <td>2.353458e-07</td>\n",
       "      <td>6.427660e-07</td>\n",
       "      <td>0.000845</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>3.906648e-11</td>\n",
       "      <td>9.424102e-11</td>\n",
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       "      <td>7.051088e-13</td>\n",
       "      <td>-8.250564e-11</td>\n",
       "      <td>-1.717702e-10</td>\n",
       "      <td>3.705016e-02</td>\n",
       "      <td>1.032392e-11</td>\n",
       "      <td>7.207497e-10</td>\n",
       "      <td>1.644399e-12</td>\n",
       "      <td>-2.047752e-10</td>\n",
       "      <td>-3.943936e-10</td>\n",
       "      <td>8.617541e-02</td>\n",
       "      <td>2.399540e-11</td>\n",
       "      <td>1.738682e-09</td>\n",
       "      <td>2.685878e-13</td>\n",
       "      <td>-3.316849e-11</td>\n",
       "      <td>-6.236048e-11</td>\n",
       "      <td>1.345726e-02</td>\n",
       "      <td>3.703479e-12</td>\n",
       "      <td>2.251737e-10</td>\n",
       "      <td>1.245101e-12</td>\n",
       "      <td>-1.636948e-10</td>\n",
       "      <td>-3.112650e-10</td>\n",
       "      <td>6.738902e-02</td>\n",
       "      <td>2.607801e-11</td>\n",
       "      <td>2.372904e-09</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>8662.0</td>\n",
       "      <td>3.192812e-03</td>\n",
       "      <td>9.068719e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.156957</td>\n",
       "      <td>0.566038</td>\n",
       "      <td>28</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>10.991826</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.185695</td>\n",
       "      <td>0.154303</td>\n",
       "      <td>0.339999</td>\n",
       "      <td>0.028653</td>\n",
       "      <td>0.525694</td>\n",
       "      <td>0.494302</td>\n",
       "      <td>6.211019e-07</td>\n",
       "      <td>5.179801e-07</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>0.000855</td>\n",
       "      <td>7.730764e-114</td>\n",
       "      <td>4.067322e-114</td>\n",
       "      <td>2.681298e-113</td>\n",
       "      <td>2.199205e-113</td>\n",
       "      <td>-4.900734e-18</td>\n",
       "      <td>1.096831e-18</td>\n",
       "      <td>-6.816555e-19</td>\n",
       "      <td>-2.226453e-18</td>\n",
       "      <td>6.710556e-19</td>\n",
       "      <td>-8.161336e-19</td>\n",
       "      <td>-2.606312e-18</td>\n",
       "      <td>6.392474e-19</td>\n",
       "      <td>-4.245830e-19</td>\n",
       "      <td>-1.315747e-18</td>\n",
       "      <td>3.849627e-19</td>\n",
       "      <td>-1.131971e-18</td>\n",
       "      <td>-1.510265e-18</td>\n",
       "      <td>1.454979e-19</td>\n",
       "      <td>-2.380564e-19</td>\n",
       "      <td>-7.021227e-19</td>\n",
       "      <td>1.940403e-19</td>\n",
       "      <td>-3.365389e-19</td>\n",
       "      <td>-1.238370e-18</td>\n",
       "      <td>1.438175e-19</td>\n",
       "      <td>-1.852863e-19</td>\n",
       "      <td>-5.901864e-19</td>\n",
       "      <td>1.629341e-19</td>\n",
       "      <td>-2.572452e-19</td>\n",
       "      <td>980.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>1.787503e-35</td>\n",
       "      <td>2.467873e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.696923</td>\n",
       "      <td>0.117851</td>\n",
       "      <td>0.985599</td>\n",
       "      <td>1.105172</td>\n",
       "      <td>2.998153e-07</td>\n",
       "      <td>1.704245e-06</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>5.443738e-17</td>\n",
       "      <td>4.139999e-16</td>\n",
       "      <td>2.413250e-14</td>\n",
       "      <td>6.688064e-15</td>\n",
       "      <td>-9.965436e-12</td>\n",
       "      <td>4.077137e-13</td>\n",
       "      <td>5.083778e-10</td>\n",
       "      <td>1.985267e-14</td>\n",
       "      <td>2.471968e-12</td>\n",
       "      <td>1.004354e-15</td>\n",
       "      <td>-2.629029e-13</td>\n",
       "      <td>1.428940e-11</td>\n",
       "      <td>9.146784e-11</td>\n",
       "      <td>8.590132e-12</td>\n",
       "      <td>2.406658e-10</td>\n",
       "      <td>7.638106e-15</td>\n",
       "      <td>-2.170899e-13</td>\n",
       "      <td>5.206150e-13</td>\n",
       "      <td>6.058498e-11</td>\n",
       "      <td>1.514614e-11</td>\n",
       "      <td>1.513483e-12</td>\n",
       "      <td>4.498061e-13</td>\n",
       "      <td>-9.818087e-10</td>\n",
       "      <td>3.454672e-11</td>\n",
       "      <td>5.213635e-08</td>\n",
       "      <td>9.595823e-13</td>\n",
       "      <td>3.047045e-10</td>\n",
       "      <td>1.246592e-13</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.710376e-20</td>\n",
       "      <td>3.159386e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>1.301511</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>2.301511</td>\n",
       "      <td>1.603023</td>\n",
       "      <td>4.349180e-07</td>\n",
       "      <td>2.089590e-07</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>3.887821e-16</td>\n",
       "      <td>4.721269e-16</td>\n",
       "      <td>7.552255e-16</td>\n",
       "      <td>2.734009e-18</td>\n",
       "      <td>-3.408943e-13</td>\n",
       "      <td>1.125207e-13</td>\n",
       "      <td>3.190883e-07</td>\n",
       "      <td>8.550846e-14</td>\n",
       "      <td>1.405351e-13</td>\n",
       "      <td>7.172313e-15</td>\n",
       "      <td>-2.550169e-13</td>\n",
       "      <td>2.424636e-13</td>\n",
       "      <td>2.436301e-07</td>\n",
       "      <td>3.679960e-14</td>\n",
       "      <td>1.348520e-13</td>\n",
       "      <td>8.710393e-15</td>\n",
       "      <td>-4.795514e-13</td>\n",
       "      <td>1.521297e-13</td>\n",
       "      <td>1.197283e-07</td>\n",
       "      <td>1.999809e-14</td>\n",
       "      <td>3.360247e-13</td>\n",
       "      <td>1.407670e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.773952e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   jaccard_followers  jaccard_followees  ...     svd_dot_u     svd_dot_v\n",
       "0                  0           0.000000  ...  1.114958e-11  2.238775e-12\n",
       "1                  0           0.187135  ...  3.192812e-03  9.068719e-04\n",
       "2                  0           0.369565  ...  1.787503e-35  2.467873e-36\n",
       "3                  0           0.000000  ...  4.710376e-20  3.159386e-18\n",
       "4                  0           0.000000  ...  7.773952e-14  0.000000e+00\n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "id": "KQ2Fw9sc908L",
    "outputId": "e6806a72-870f-45c1-d6d8-f2f615f84cb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18 µs, sys: 0 ns, total: 18 µs\n",
      "Wall time: 20 µs\n",
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=1,\n",
      "              max_delta_step=0, max_depth=5, min_child_weight=1, missing=None,\n",
      "              n_estimators=50, n_jobs=-1, nthread=None,\n",
      "              objective='reg:squarederror', random_state=0, reg_alpha=0,\n",
      "              reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
      "              subsample=1, verbosity=1)\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "tuned_parameters={'n_estimators':[1,5,10,50,100,200,500,800,1000],'max_depth':[1,4,5,7,8,10,50],'learning_rate': [0.001,0.01,1]}\n",
    "model=RandomizedSearchCV(xgb.XGBClassifier(objective='reg:squarederror',n_jobs=-1),tuned_parameters,scoring='f1',cv=5)\n",
    "model.fit(train,Y_train)\n",
    "print(model.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "IVTt_Vzu_1LE",
    "outputId": "8a018636-5a0e-47fa-c0b5-fd8e215073f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train Score: 0.993826728165928\n",
      "Test Score: 0.9113191781115421\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "clf = xgb.XGBClassifier(max_depth=5,n_estimators=50,learning_rate = 1,n_jobs=-1)\n",
    "clf.fit(train,Y_train)\n",
    "predict_ytr = clf.predict(train)\n",
    "predict_yte = clf.predict(test)\n",
    "print(f'\\nTrain Score: {f1_score(Y_train,predict_ytr)}')\n",
    "print(f'Test Score: {f1_score(Y_test,predict_yte)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "colab_type": "code",
    "id": "RdixtOuBsK9H",
    "outputId": "6f3fb5c7-436b-46b1-de55-678feb3dee48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train confusion_matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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qR8wJSVI95oQkNUm3ijwR8Xlgd+C60jagcV2SJPUz5oQkqR5zQpKapDtFnu8C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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Train confusion_matrix')\n",
    "plot_confusion_matrix(Y_train,predict_ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "colab_type": "code",
    "id": "KPuzew3D0ISL",
    "outputId": "3a7bac67-e04d-4d0d-b241-62b8e83f0cd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test confusion_matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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Nd8g6APhLRfn+xV22NgfeKKZ13QxsHxErFAsubw/cXGx7MyI2L95r/4pjSZK6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\n4JDi9X7kOF/5tzAQOAcYAtwF7Dl06NC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      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Test confusion_matrix')\n",
    "plot_confusion_matrix(Y_test,predict_yte)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1] After applying features like Preferential Attachment and SVD DOT we got our test F1 score to be  0.911.<br>\n",
    "2] Before applying these features we got f1 score = 0.924 which was better. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "FB_Models.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}