FB_Models.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FB_Models.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "naWvqMEMHglO"
      },
      "source": [
        "<p style=\"font-size:32px;text-align:center\"> <b>Social network Graph Link Prediction - Facebook Challenge</b> </p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wb9N5RzHglP"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XC4OJFKkHglU"
      },
      "source": [
        "#reading\n",
        "from pandas import read_hdf\n",
        "df_final_train = read_hdf('data/fea_sample/storage_sample_stage4.h5', 'train_df',mode='r')\n",
        "df_final_test = read_hdf('data/fea_sample/storage_sample_stage4.h5', 'test_df',mode='r')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Gm-BHRkHglW",
        "outputId": "d2c2294c-113e-4fdb-d3e5-dc167dcf5fbc"
      },
      "source": [
        "df_final_train.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRW7VZ4AHglc"
      },
      "source": [
        "y_train = df_final_train.indicator_link\n",
        "y_test = df_final_test.indicator_link"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lj9M_qtHglf"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIEc91uVHgli",
        "outputId": "31f04b72-ebe5-4b13-ccca-a5ae3bc4f09c"
      },
      "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')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,1,'Estimators vs score at depth of 5')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZxhrTdQHglm",
        "outputId": "53382b8e-148d-4c22-8066-d9807fb27012"
      },
      "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()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrG1Lfl3Hglq",
        "outputId": "7b6e3c97-8d66-455d-cd79-213f824719cc"
      },
      "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'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hme3j_diHglu",
        "outputId": "581ccc02-7770-4a94-d003-257af4e81570"
      },
      "source": [
        "print(rf_random.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu4KIEweHglx"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dax83GfLHgl1"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTtAp1iTHgl4",
        "outputId": "e2e3d725-3c4d-4374-d303-8f294b570977"
      },
      "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))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train f1 score 0.9652533106548414\n",
            "Test f1 score 0.9241678239279553\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XfUkZYQHgl7"
      },
      "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()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6t11dhTHgl-",
        "outputId": "189de3be-1c99-4653-f56f-12b18b0f4db7"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train confusion_matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test confusion_matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61TF-VLeHgmD",
        "outputId": "795f1b39-61c7-470f-e2d0-1fe6dc7ac5fd"
      },
      "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()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "HEZ7uvN6HgmK",
        "outputId": "6f762256-54fc-4e2f-865b-22342033eb2c"
      },
      "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()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}