Yelp_Authorship_Verification_SVM_3000_0.92_RESULTS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000000\n",
      "2000000\n",
      "3000000\n",
      "4000000\n",
      "CPU times: user 43 s, sys: 4.22 s, total: 47.3 s\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "reviews = []\n",
    "with open(\"yelp_academic_dataset_review.json\") as f:\n",
    "    i = 0\n",
    "    for line in f:\n",
    "        if i % 1000000 == 0:\n",
    "            print(i)\n",
    "        i += 1\n",
    "        reviews.append(json.loads(line))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'user_id': 'KpkOkG6RIf4Ra25Lhhxf1A', 'stars': 5, 'review_id': 'NxL8SIC5yqOdnlXCg18IBg', 'text': \"If you enjoy service by someone who is as competent as he is personable, I would recommend Corey Kaplan highly. The time he has spent here has been very productive and working with him educational and enjoyable. I hope not to need him again (though this is highly unlikely) but knowing he is there if I do is very nice. By the way, I'm not from El Centro, CA. but Scottsdale, AZ.\", 'useful': 0, 'date': '2011-10-10', 'cool': 0, 'funny': 0, 'type': 'review', 'business_id': '2aFiy99vNLklCx3T_tGS9A'}\n"
     ]
    }
   ],
   "source": [
    "print(reviews[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "by_author = {} # author : \"review 1\\n review 2\\n review 3\"\n",
    "for review in reviews:\n",
    "    uid = review['user_id']\n",
    "    if uid in by_author:\n",
    "        by_author[uid] += \"\\n{}\".format(review['text'])\n",
    "    else:\n",
    "        by_author[uid] = \"{}\".format(review['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1029432"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(by_author)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Get authors who have written at least 10k characters in total\n",
    "by_author = {a: by_author[a] for a in by_author if len(by_author[a]) > 6010}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71346"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(by_author)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "known_texts = []\n",
    "unknown_texts = []\n",
    "\n",
    "# skip some words in the middle so we can't infer anything by 'matching' broken texts\n",
    "for a in by_author:\n",
    "    known_texts += [by_author[a][:3000]]\n",
    "    unknown_texts += [by_author[a][3010:6010]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71346"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unknown_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "total = 71300\n",
    "half = int(total/2)\n",
    "\n",
    "known_same = known_texts[:half]\n",
    "unknown_same = unknown_texts[:half]\n",
    "\n",
    "known_diff = known_texts[half:total]\n",
    "unknown_diff = unknown_texts[half:total]\n",
    "\n",
    "# move unknown diffs up by one\n",
    "unknown_diff = unknown_diff[1:] + [unknown_diff[0]]\n",
    "\n",
    "knowns = known_same + known_diff\n",
    "unknowns = unknown_same + unknown_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71300"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(knowns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "n = int(len(knowns)/2)\n",
    "labels = ([1] * n) + ([0] * n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11min 16s, sys: 9.25 s, total: 11min 26s\n",
      "Wall time: 11min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "char_tf = TfidfVectorizer(analyzer='char', ngram_range=(2,3), min_df=0.01, lowercase=False)\n",
    "word_tf = TfidfVectorizer(ngram_range=(1,2), lowercase=False, min_df=0.01)\n",
    "vectorizer = FeatureUnion([\n",
    "    ('char', char_tf),\n",
    "    ('word', word_tf)\n",
    "])\n",
    "\n",
    "vectorizer.fit(knowns + unknowns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\n",
      "CPU times: user 11min 54s, sys: 11.4 s, total: 12min 5s\n",
      "Wall time: 12min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "known_vecs = vectorizer.transform(knowns)\n",
    "print(\".\")\n",
    "unknown_vecs = vectorizer.transform(unknowns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71300"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(knowns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(71300, 17662)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "known_vecs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71300"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16058, 58018, 4324, 11929, 7843, 46526, 26537, 42836, 35330, 17246]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import shuffle\n",
    "indices = list(range(len(labels)))\n",
    "shuffle(indices)\n",
    "indices[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71300"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "train_indices = indices[:60000]\n",
    "test_indices = indices[60000:]\n",
    "\n",
    "known_train = known_vecs[train_indices, :]\n",
    "unknown_train = unknown_vecs[train_indices, :]\n",
    "train_labels = labels[train_indices]\n",
    "\n",
    "known_test = known_vecs[test_indices, :]\n",
    "unknown_test = unknown_vecs[test_indices, :]\n",
    "test_labels = labels[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_indices' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-4d35395c70aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'test_indices' is not defined"
     ]
    }
   ],
   "source": [
    "len(test_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.92      0.92      5702\n",
      "          1       0.92      0.92      0.92      5598\n",
      "\n",
      "avg / total       0.92      0.92      0.92     11300\n",
      "\n",
      "CPU times: user 29.6 s, sys: 1.88 s, total: 31.4 s\n",
      "Wall time: 31.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_pairs = np.abs(known_train - unknown_train)\n",
    "test_pairs = np.abs(known_test - unknown_test)\n",
    "svm = LinearSVC()\n",
    "svm.fit(train_pairs, train_labels)\n",
    "preds = svm.predict(test_pairs)\n",
    "print(classification_report(test_labels, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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