bow_logistic_xgb_with_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn import linear_model\n",
    "import numpy as np\n",
    "import time\n",
    "import sys\n",
    "from sklearn.model_selection import train_test_split\n",
    "import scipy\n",
    "from sklearn.metrics import log_loss\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "font = {'family' : 'Arial',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In this project I present a model which takes in two different questions and classifies them based on if they are duplicate or not. The data is from Quora dataset, and contains supervised pairs of questions and labels based on whether the questions are duplicate or not.\n",
    "I present two plots, of feature selection and one of model selection.\n",
    "\n",
    "Plot 1,2 shows that transformed duplicate questions have high cosine similarity and eucldean distances as compared to non duplicate questions\n",
    "\n",
    "Plot 3 shows model selection ln Logistic regression on Bag of words model on the dataset without any additional features. A baseline acuracy of 80 % is achieved. Application of XGBoost shows 82.5% accuracy (2.5 % acuracy improvement) as compared to Logistic regression. \n",
    "\n",
    "A word2vec baseline model is at a below link on my github page ... requires 32 GB Memory machine to run ..\n",
    "score accuracy is 76% on the validation set with 80/20 split\n",
    "https://github.com/aspk/Quora_question_pairs_NLP_Kaggle/blob/master/quora_word_to_vec_addition/quora_question_pairs_xgboost_sklearn.ipynb\n",
    "\n",
    "Addtion of features with and the two models was used to form an ensemble model with 83% + accuracy \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0  \n",
       "4            Which fish would survive in salt water?             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "train = train.dropna(how=\"any\").reset_index(drop=True)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot of cosine distances of histograms is shown for duplicate and non diuplicate questions. I observed a smaller median of 0.36 for duplicates as compared to 0.59 for non duplicates for the questions for cosine distances. Other distance metrics like eucledian distances are considered as well.These features could be use in logistic regession along with TfIDF ZVectorizer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voabulary size is 4412\n"
     ]
    }
   ],
   "source": [
    "def getBOW(min_df = 100,ngrams = 1,analyzer = 'word',train = train):\n",
    "    # bag of letter sequences (chars)\n",
    "    \"\"\"\n",
    "    BOW = TfidfVectorizer(min_df = min_df, analyzer=analyzer, ngram_range=(1,ngrams), lowercase=True)\n",
    "    \"\"\"\n",
    "    \n",
    "    BOW = TfidfVectorizer(min_df = min_df, analyzer=analyzer, ngram_range=(1,ngrams), lowercase=True)\n",
    "\n",
    "    BOW.fit(pd.concat((train['question1'],train['question2'])).unique())\n",
    "    print('Voabulary size is {}'.format(len(BOW.vocabulary_)))\n",
    "    return BOW\n",
    "BOW = getBOW(min_df=100,ngrams = 1,analyzer = 'word')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainq1_trans = BOW.transform(train['question1'][train['is_duplicate']==1].values)\n",
    "trainq2_trans = BOW.transform(train['question2'][train['is_duplicate']==1].values)\n",
    "trainq1_trans_non = BOW.transform(train['question1'][train['is_duplicate']==0].values)\n",
    "trainq2_trans_non = BOW.transform(train['question2'][train['is_duplicate']==0].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akhileshsk/anaconda3/lib/python3.6/site-packages/scipy/spatial/distance.py:698: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist = 1.0 - uv / np.sqrt(uu * vv)\n"
     ]
    }
   ],
   "source": [
    "from scipy import spatial\n",
    "trainq1_trans = trainq1_trans.toarray()\n",
    "trainq2_trans = trainq2_trans.toarray()\n",
    "\n",
    "trainq1_trans_non = trainq1_trans_non.toarray()\n",
    "trainq2_trans_non = trainq2_trans_non.toarray()\n",
    "\n",
    "sim_cos_dist = np.zeros(trainq1_trans.shape[0])\n",
    "sim_cos_dist_non = np.zeros(trainq1_trans_non.shape[0])\n",
    "\n",
    "for i in range(trainq1_trans.shape[0]):\n",
    "    sim_cos_dist[i] = spatial.distance.cosine(trainq1_trans[i],trainq2_trans[i])\n",
    "    \n",
    "for i in range(trainq1_trans_non.shape[0]):\n",
    "    sim_cos_dist_non[i] = spatial.distance.cosine(trainq1_trans_non[i],trainq2_trans_non[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "\n",
    "sim_cos_dist = sim_cos_dist[~np.isnan(sim_cos_dist)]\n",
    "sim_cos_dist_non = sim_cos_dist_non[~np.isnan(sim_cos_dist_non)]\n",
    "\n",
    "n, bins, patches = plt.hist(sim_cos_dist_non, bins = 'auto', density=0, facecolor='b', alpha=0.4,label = 'Non duplicate')\n",
    "n, bins, patches = plt.hist(sim_cos_dist, bins = 'auto', density=0, facecolor='g', alpha=0.8,label = 'Duplicate')\n",
    "plt.legend(frameon = False)\n",
    "plt.axvline(np.median(sim_cos_dist_non),color = 'b')\n",
    "plt.axvline(np.median(sim_cos_dist),color = 'g')\n",
    "ax = plt.gca()\n",
    "ax.text(0.2,1, 'Median is {}'.format(np.round(np.median(sim_cos_dist),2)),size=20)\n",
    "ax.text(0.6,1, 'Median is {}'.format(np.round(np.median(sim_cos_dist_non),2)),size=20)\n",
    "\n",
    "\n",
    "plt.title('Cosine distance for duplicate and non duplicate questions')\n",
    "plt.xlabel('Cosine Distance')\n",
    "plt.ylabel('Count')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_euc_dist = np.zeros(trainq1_trans.shape[0])\n",
    "sim_euc_dist_non = np.zeros(trainq1_trans_non.shape[0])\n",
    "\n",
    "for i in range(trainq1_trans.shape[0]):\n",
    "    sim_euc_dist[i] = spatial.distance.euclidean(trainq1_trans[i],trainq2_trans[i])\n",
    "    \n",
    "for i in range(trainq1_trans_non.shape[0]):\n",
    "    sim_euc_dist_non[i] = spatial.distance.euclidean(trainq1_trans_non[i],trainq2_trans_non[i])\n",
    "    \n",
    "plt.figure(figsize=(20,6))\n",
    "\n",
    "sim_euc_dist = sim_euc_dist[~np.isnan(sim_euc_dist)][sim_euc_dist != float('-inf')]\n",
    "sim_euc_dist_non = sim_euc_dist_non[~np.isnan(sim_euc_dist_non)][sim_euc_dist_non != float('-inf')]\n",
    "\n",
    "n, bins, patches = plt.hist(sim_euc_dist_non, bins = 'auto', density=0, facecolor='b', alpha=0.4,label = 'Non duplicate')\n",
    "n, bins, patches = plt.hist(sim_euc_dist, bins = 'auto', density=0, facecolor='g', alpha=0.8,label = 'Duplicate')\n",
    "plt.legend(frameon = False)\n",
    "plt.axvline(np.median(sim_euc_dist_non),color = 'b')\n",
    "plt.axvline(np.median(sim_euc_dist),color = 'g')\n",
    "ax = plt.gca()\n",
    "ax.text(0.5,0.8, 'Median is {}'.format(np.round(np.median(sim_euc_dist),2)),size=20)\n",
    "ax.text(0.9,0.8, 'Median is {}'.format(np.round(np.median(sim_euc_dist_non),2)),size=20)\n",
    "\n",
    "\n",
    "plt.title('Eucledean distance for duplicate and non duplicate questions')\n",
    "plt.xlabel('Eucledean Distance')\n",
    "plt.ylabel('Count')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Logistic Regression with sag solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getBOW(min_df = 100,ngrams = 1,analyzer = 'word',train = train):\n",
    "    # bag of letter sequences (chars)\n",
    "    \"\"\"\n",
    "    BOW = TfidfVectorizer(min_df = min_df, analyzer=analyzer, ngram_range=(1,ngrams), lowercase=True)\n",
    "    \"\"\"\n",
    "    \n",
    "    BOW = TfidfVectorizer(min_df = min_df, analyzer=analyzer, ngram_range=(1,ngrams), lowercase=True)\n",
    "\n",
    "    BOW.fit(pd.concat((train['question1'],train['question2'])).unique())\n",
    "    print('Voabulary size is {}'.format(len(BOW.vocabulary_)))\n",
    "    return BOW\n",
    "# word level analyzer\n",
    "def train_params(min_df = 100,ngrams = 1,analyzer = 'word',train = train):\n",
    "    start_time = time.time()\n",
    "    print('BOW and logistic regression')\n",
    "    BOW = getBOW(min_df=min_df,ngrams = ngrams,analyzer = analyzer)\n",
    "    trainq1_trans = BOW.transform(train['question1'].values)\n",
    "    trainq2_trans = BOW.transform(train['question2'].values)\n",
    "    labels = train['is_duplicate'].values\n",
    "    X = scipy.sparse.hstack((trainq1_trans,trainq2_trans))\n",
    "    y = labels\n",
    "    X_train,X_valid,y_train,y_valid = train_test_split(X,y, test_size = 0.33, random_state = 42)\n",
    "    model = linear_model.LogisticRegression(n_jobs=4,solver = 'sag',max_iter = 1000)        \n",
    "    print('fitting ...')\n",
    "    model.fit(X_train,y_train)\n",
    "    print('predicting ...')\n",
    "    y_pred = model.predict(X_valid)\n",
    "    loss = log_loss(y_valid,y_pred)\n",
    "    print('log_loss ... {}'.format(loss))\n",
    "    accuracy = accuracy_score(y_valid,y_pred)\n",
    "    print('accuracy is ... {}'.format(accuracy))\n",
    "    auc = roc_auc_score(y_valid,y_pred)\n",
    "    print('auc is  ... {}'.format(auc))\n",
    "    #print(\"log loss is {}\".format(loss))\n",
    "    end_time =time.time()\n",
    "    print(\"total time elapsed is {}\".format(end_time-start_time))\n",
    "    return [loss,accuracy,auc]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BOW and logistic regression\n",
      "Voabulary size is 8850\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.505646646538578\n",
      "accuracy is ... 0.7537383352696473\n",
      "auc is  ... 0.7153265847533274\n",
      "total time elapsed is 34.959336042404175\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[8.505646646538578, 0.7537383352696473, 0.7153265847533274]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_params(min_df = 100,ngrams = 2,analyzer = 'word',train = train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# parameter search for min_df and ngrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df 0: ng 1\n",
      "BOW and logistic regression\n",
      "Voabulary size is 86152\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.295431298682637\n",
      "accuracy is ... 0.7598246074279503\n",
      "auc is  ... 0.7214971104342696\n",
      "total time elapsed is 27.526909828186035\n",
      "df 0: ng 2\n",
      "BOW and logistic regression\n",
      "Voabulary size is 1250860\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 7.1897278618903755\n",
      "accuracy is ... 0.7918374995315369\n",
      "auc is  ... 0.7526986272803659\n",
      "total time elapsed is 48.95925498008728\n",
      "df 0: ng 3\n",
      "BOW and logistic regression\n",
      "Voabulary size is 3858251\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 6.9474073682759885\n",
      "accuracy is ... 0.7988532024135218\n",
      "auc is  ... 0.7574807531520612\n",
      "total time elapsed is 86.17084693908691\n",
      "df 0: ng 4\n",
      "BOW and logistic regression\n",
      "Voabulary size is 7039102\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 6.842038447729713\n",
      "accuracy is ... 0.8019038339017351\n",
      "auc is  ... 0.7589035751811409\n",
      "total time elapsed is 125.26557898521423\n",
      "df 10: ng 1\n",
      "BOW and logistic regression\n",
      "Voabulary size is 18596\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.481828232232859\n",
      "accuracy is ... 0.7544279129033467\n",
      "auc is  ... 0.7153806480281523\n",
      "total time elapsed is 18.86371922492981\n",
      "df 10: ng 2\n",
      "BOW and logistic regression\n",
      "Voabulary size is 76183\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 7.629578700485103\n",
      "accuracy is ... 0.7791027995352846\n",
      "auc is  ... 0.7424055837298557\n",
      "total time elapsed is 39.69491219520569\n",
      "df 10: ng 3\n",
      "BOW and logistic regression\n",
      "Voabulary size is 109050\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 7.522399713780833\n",
      "accuracy is ... 0.7822058988869317\n",
      "auc is  ... 0.7453920404943909\n",
      "total time elapsed is 66.03091406822205\n",
      "df 10: ng 4\n",
      "BOW and logistic regression\n",
      "Voabulary size is 123846\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 7.516445319970246\n",
      "accuracy is ... 0.7823782932953566\n",
      "auc is  ... 0.7455539504722531\n",
      "total time elapsed is 93.65973806381226\n",
      "df 100: ng 1\n",
      "BOW and logistic regression\n",
      "Voabulary size is 4412\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.99467675939943\n",
      "accuracy is ... 0.7395795075516246\n",
      "auc is  ... 0.6971902765845422\n",
      "total time elapsed is 19.80805778503418\n",
      "df 100: ng 2\n",
      "BOW and logistic regression\n",
      "Voabulary size is 8850\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.505905534789466\n",
      "accuracy is ... 0.7537308398605854\n",
      "auc is  ... 0.7153206512324949\n",
      "total time elapsed is 37.022358894348145\n",
      "df 100: ng 3\n",
      "BOW and logistic regression\n",
      "Voabulary size is 10579\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.472250883257358\n",
      "accuracy is ... 0.7547052430386388\n",
      "auc is  ... 0.7166728660779991\n",
      "total time elapsed is 60.292145013809204\n",
      "df 100: ng 4\n",
      "BOW and logistic regression\n",
      "Voabulary size is 11347\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.45723541265228\n",
      "accuracy is ... 0.755139976764232\n",
      "auc is  ... 0.7170509289512392\n",
      "total time elapsed is 86.59971213340759\n",
      "df 200: ng 1\n",
      "BOW and logistic regression\n",
      "Voabulary size is 2670\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.236473429262428\n",
      "accuracy is ... 0.7325787954877637\n",
      "auc is  ... 0.6881420261377045\n",
      "total time elapsed is 17.508670806884766\n",
      "df 200: ng 2\n",
      "BOW and logistic regression\n",
      "Voabulary size is 4678\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.85772832682567\n",
      "accuracy is ... 0.7435445789453959\n",
      "auc is  ... 0.7027712529813768\n",
      "total time elapsed is 34.9983971118927\n",
      "df 200: ng 3\n",
      "BOW and logistic regression\n",
      "Voabulary size is 5421\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.831322366519156\n",
      "accuracy is ... 0.7443091106697148\n",
      "auc is  ... 0.7038301342500063\n",
      "total time elapsed is 60.10129117965698\n",
      "df 200: ng 4\n",
      "BOW and logistic regression\n",
      "Voabulary size is 5735\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 8.808022501852182\n",
      "accuracy is ... 0.7449836974852903\n",
      "auc is  ... 0.7044192689554832\n",
      "total time elapsed is 84.20424008369446\n",
      "df 400: ng 1\n",
      "BOW and logistic regression\n",
      "Voabulary size is 1458\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.668806493760664\n",
      "accuracy is ... 0.720061462354308\n",
      "auc is  ... 0.6709362935499266\n",
      "total time elapsed is 16.90588903427124\n",
      "df 400: ng 2\n",
      "BOW and logistic regression\n",
      "Voabulary size is 2368\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.296276009893468\n",
      "accuracy is ... 0.7308473559944534\n",
      "auc is  ... 0.6863431596803833\n",
      "total time elapsed is 34.793834924697876\n",
      "df 400: ng 3\n",
      "BOW and logistic regression\n",
      "Voabulary size is 2690\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.23776862567391\n",
      "accuracy is ... 0.732541318442454\n",
      "auc is  ... 0.6886465775065026\n",
      "total time elapsed is 57.13713622093201\n",
      "df 400: ng 4\n",
      "BOW and logistic regression\n",
      "Voabulary size is 2833\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.225083395052536\n",
      "accuracy is ... 0.7329085934864895\n",
      "auc is  ... 0.6891450718501178\n",
      "total time elapsed is 82.3406150341034\n",
      "df 600: ng 1\n",
      "BOW and logistic regression\n",
      "Voabulary size is 1018\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.81999259945128\n",
      "accuracy is ... 0.7156841434621295\n",
      "auc is  ... 0.6641937263829079\n",
      "total time elapsed is 17.86868190765381\n",
      "df 600: ng 2\n",
      "BOW and logistic regression\n",
      "Voabulary size is 1557\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.524091971038047\n",
      "accuracy is ... 0.7242513960199378\n",
      "auc is  ... 0.6770895800517818\n",
      "total time elapsed is 33.69250297546387\n",
      "df 600: ng 3\n",
      "BOW and logistic regression\n",
      "Voabulary size is 1733\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.478011126968198\n",
      "accuracy is ... 0.7255855788329648\n",
      "auc is  ... 0.6790403515480369\n",
      "total time elapsed is 56.37773108482361\n",
      "df 600: ng 4\n",
      "BOW and logistic regression\n",
      "Voabulary size is 1811\n",
      "fitting ...\n",
      "predicting ...\n",
      "log_loss ... 9.473610092629496\n",
      "accuracy is ... 0.725713000787018\n",
      "auc is  ... 0.6791878595664971\n",
      "total time elapsed is 81.94585180282593\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-a840e895e462>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'df {}: ng {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mng\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mmet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mngrams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mng\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0manalyzer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'word'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'logistic.npy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(file, arr, allow_pickle, fix_imports)\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m         format.write_array(fid, arr, allow_pickle=allow_pickle,\n\u001b[1;32m    511\u001b[0m                            pickle_kwargs=pickle_kwargs)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m     \"\"\"\n\u001b[0;32m--> 544\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "df_arr = [0,10,100,200,400,600]\n",
    "ngrams = [1,2,3,4]\n",
    "met = np.zeros(6,4,3)\n",
    "for i,df in enumerate(df_arr):\n",
    "    for j,ng in enumerate(ngrams):\n",
    "        print('df {}: ng {}'.format(df,ng))\n",
    "        met[i,j,:] = train_params(min_df = df,ngrams = ng,analyzer = 'word',train = train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_met = np.zeros([6,4,3])\n",
    "for m in met:\n",
    "    new_met[m[0],m[1],:] = m[2]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save('logistic.npy',new_met)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot 3 : Model parameter selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "logistic = np.load('logistic.npy')\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.suptitle('BOW Logistic Regression', fontsize=16)\n",
    "\n",
    "plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n",
    "plt.subplot(1,2,1)\n",
    "ax = plt.gca()\n",
    "for i in range(6):\n",
    "    plt.plot(np.arange(1,5),100*logistic[i,:,1],label = df_arr[i],marker = '.',markersize=12)\n",
    "\n",
    "plt.xticks([1,2,3])\n",
    "plt.xlabel('Ngrams', fontsize=20)\n",
    "plt.ylabel('Accuracy (%)', fontsize=20)\n",
    "plt.legend(frameon = False,title = 'min_df')\n",
    "#ax.text(0.01,1, 'Axes 3',ha='center',va='center',size=24,alpha=.5)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "for i in range(4):\n",
    "    plt.plot(df_arr,100*logistic[:,i,1],label = ngrams[i],marker = '.',markersize=12)\n",
    "ax = plt.gca()\n",
    "plt.xlabel('Min data frequency (min_df)', fontsize=20)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(frameon = False,title = 'Ngrams')\n",
    "plt.savefig('bow_logistic_reg.png',bbox_inches='tight',dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Training for additional 2% accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df 0: ng 4\n",
      "BOW and xgb\n",
      "Voabulary size is 7039133\n",
      "training ...\n",
      "[00:02:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 21106 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[0]\teval-auc:0.759269\ttrain-auc:0.8815\n",
      "[00:03:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18950 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[1]\teval-auc:0.788551\ttrain-auc:0.920605\n",
      "[00:05:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 14922 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[2]\teval-auc:0.803625\ttrain-auc:0.937281\n",
      "[00:06:24] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 15752 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[3]\teval-auc:0.812103\ttrain-auc:0.948337\n",
      "[00:07:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 13386 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[4]\teval-auc:0.81859\ttrain-auc:0.956268\n",
      "[00:08:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 14944 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[5]\teval-auc:0.826703\ttrain-auc:0.964918\n",
      "[00:09:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 7570 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[6]\teval-auc:0.830825\ttrain-auc:0.968118\n",
      "[00:10:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 8364 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[7]\teval-auc:0.835081\ttrain-auc:0.971546\n",
      "[00:10:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 6348 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[8]\teval-auc:0.83829\ttrain-auc:0.973874\n",
      "[00:11:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 3550 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[9]\teval-auc:0.840422\ttrain-auc:0.975147\n",
      "[00:11:47] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 3354 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[10]\teval-auc:0.842104\ttrain-auc:0.976254\n",
      "[00:12:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 2988 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[11]\teval-auc:0.843578\ttrain-auc:0.977268\n",
      "[00:12:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 2912 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[12]\teval-auc:0.845037\ttrain-auc:0.978392\n",
      "[00:12:57] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1978 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[13]\teval-auc:0.845946\ttrain-auc:0.978945\n",
      "[00:13:21] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 2796 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[14]\teval-auc:0.847271\ttrain-auc:0.979899\n",
      "[00:13:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 3984 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[15]\teval-auc:0.848579\ttrain-auc:0.981233\n",
      "[00:14:20] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 4522 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[16]\teval-auc:0.849966\ttrain-auc:0.982569\n",
      "[00:14:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 3034 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[17]\teval-auc:0.85124\ttrain-auc:0.983222\n",
      "[00:15:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 3166 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[18]\teval-auc:0.852432\ttrain-auc:0.984226\n",
      "[00:15:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1150 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[19]\teval-auc:0.852999\ttrain-auc:0.984498\n",
      "[00:15:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1854 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[20]\teval-auc:0.853627\ttrain-auc:0.984967\n",
      "[00:16:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1836 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[21]\teval-auc:0.854207\ttrain-auc:0.985398\n",
      "[00:16:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1180 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[22]\teval-auc:0.854699\ttrain-auc:0.985684\n",
      "[00:16:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 2200 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[23]\teval-auc:0.855448\ttrain-auc:0.986213\n",
      "[00:17:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1452 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[24]\teval-auc:0.856019\ttrain-auc:0.986555\n",
      "[00:17:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 3308 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[25]\teval-auc:0.856515\ttrain-auc:0.987273\n",
      "[00:17:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1078 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[26]\teval-auc:0.856984\ttrain-auc:0.987559\n",
      "[00:18:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1724 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[27]\teval-auc:0.857507\ttrain-auc:0.98791\n",
      "[00:18:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 2768 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[28]\teval-auc:0.858117\ttrain-auc:0.988541\n",
      "[00:18:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 2672 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[29]\teval-auc:0.858409\ttrain-auc:0.989124\n",
      "[00:19:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1450 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[30]\teval-auc:0.858793\ttrain-auc:0.989402\n",
      "[00:19:37] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1390 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[31]\teval-auc:0.859154\ttrain-auc:0.989677\n",
      "[00:19:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1456 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[32]\teval-auc:0.859828\ttrain-auc:0.989934\n",
      "[00:20:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 1544 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[33]\teval-auc:0.860205\ttrain-auc:0.990199\n",
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      "[34]\teval-auc:0.860835\ttrain-auc:0.990408\n",
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      "[35]\teval-auc:0.861095\ttrain-auc:0.990535\n",
      "[00:21:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 722 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[36]\teval-auc:0.861418\ttrain-auc:0.990708\n",
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      "[37]\teval-auc:0.861714\ttrain-auc:0.990838\n",
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      "[38]\teval-auc:0.862331\ttrain-auc:0.99103\n",
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      "[39]\teval-auc:0.862768\ttrain-auc:0.991239\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[41]\teval-auc:0.863336\ttrain-auc:0.991608\n",
      "[00:22:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 736 extra nodes, 0 pruned nodes, max_depth=50\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[965]\teval-auc:0.898824\ttrain-auc:0.999998\n",
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      "[04:38:15] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 432 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[996]\teval-auc:0.899004\ttrain-auc:0.999999\n",
      "[04:38:30] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 260 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[997]\teval-auc:0.899007\ttrain-auc:0.999999\n",
      "[04:38:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 240 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[998]\teval-auc:0.899005\ttrain-auc:0.999999\n",
      "[04:38:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 236 extra nodes, 0 pruned nodes, max_depth=50\n",
      "[999]\teval-auc:0.899006\ttrain-auc:0.999999\n",
      "log_loss ... 0.42901860353926985\n",
      "accuracy is ... 0.8239341608202915\n",
      "auc is  ... 0.8990064812533745\n",
      "total time elapsed is 16812.796167612076\n"
     ]
    }
   ],
   "source": [
    "def train_params_xgb(min_df = 100,ngrams = 1,analyzer = 'word',train = train):\n",
    "    start_time = time.time()\n",
    "    print('BOW and xgb')\n",
    "    BOW = getBOW(min_df=min_df,ngrams = ngrams,analyzer = analyzer)\n",
    "    trainq1_trans = BOW.transform(train['question1'].values)\n",
    "    trainq2_trans = BOW.transform(train['question2'].values)\n",
    "    labels = train['is_duplicate'].values\n",
    "    X = scipy.sparse.hstack((trainq1_trans,trainq2_trans))\n",
    "    y = labels\n",
    "    X_train,X_valid,y_train,y_valid = train_test_split(X,y, test_size = 0.33, random_state = 42)\n",
    "    dtrain = xgb.DMatrix(X_train,label = y_train)\n",
    "    dvalid = xgb.DMatrix(X_valid,label = y_valid)\n",
    "    print('training ...')\n",
    "    param = {'max_depth':50, 'eta':0.3, 'silent':0, 'objective':'binary:logistic','subsample':0.9,'gamma':0 }\n",
    "    param['nthread'] = 7   \n",
    "    param['eval_metric'] = 'auc'\n",
    "    #param['tree_method'] = 'hist'\n",
    "    num_round = 1000\n",
    "    evallist  = [(dvalid,'eval'),(dtrain,'train')]\n",
    "    bst = xgb.train(param, dtrain, num_round, evallist)\n",
    "    y_pred = bst.predict(dvalid)\n",
    "    loss = log_loss(y_valid,y_pred)\n",
    "    print('log_loss ... {}'.format(loss))\n",
    "    accuracy = accuracy_score(y_valid,np.round(y_pred).astype(int))\n",
    "    print('accuracy is ... {}'.format(accuracy))\n",
    "    auc = roc_auc_score(y_valid,y_pred)\n",
    "    print('auc is  ... {}'.format(auc))\n",
    "    end_time =time.time()\n",
    "    print(\"total time elapsed is {}\".format(end_time-start_time))\n",
    "    return [loss,accuracy,auc]\n",
    "\n",
    "df_arr = [0]\n",
    "ngrams = [4]\n",
    "for i,df in enumerate(df_arr):\n",
    "    for j,ng in enumerate(ngrams):\n",
    "        print('df {}: ng {}'.format(df,ng))\n",
    "        met_xgb = train_params_xgb(min_df = df,ngrams = ng,analyzer = 'word',train = train)\n",
    "np.save('xgb_met.npy',[met_xgb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_met = np.load('xgb_met.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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