examples/enron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Copyright 2016-present, Facebook, Inc.\n",
    "# All rights reserved.\n",
    "\n",
    "# This source code is licensed under the license found in the\n",
    "# LICENSE-examples file in the root directory of this source tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate pysparnn on Enron data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import inspect\n",
    "\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.neighbors import LSHForest\n",
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make sure you run 'python setup.py install' first!\n",
    "import pysparnn.cluster_index as ci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# fetch data\n",
    "\n",
    "\n",
    "!wget https://www.cs.cmu.edu/~./enron/enron_mail_20150507.tgz\n",
    "\n",
    "\n",
    "_ = !tar -xzvf enron_mail_20150507.tgz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load enron data \n",
    "import os\n",
    "import sys\n",
    "\n",
    "docs = []\n",
    "max_docs = 100000\n",
    "for folder, subs, files in os.walk('maildir'):\n",
    "    for filename in files:\n",
    "        with open(os.path.join(folder, filename), 'r') as src:\n",
    "            try:\n",
    "                txt = ' '.join(src.readlines())\n",
    "                if len(txt) > 0:\n",
    "                    docs.append(txt)\n",
    "            except:\n",
    "                pass\n",
    "        if len(docs) > max_docs:\n",
    "            break                \n",
    "    if len(docs) > max_docs:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num docs: 100001\n",
      "Avg doc length: 413.757442426\n",
      "Num unique words: 942676\n"
     ]
    }
   ],
   "source": [
    "print('Num docs: {}'.format(len(docs)))\n",
    "print('Avg doc length: {}'.format(np.mean([len(x.split()) for x in docs])))\n",
    "words = set()\n",
    "for doc in docs:\n",
    "    words.update(doc.split())\n",
    "print('Num unique words: {}'.format(len(words)))\n",
    "del words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turn documents into vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import LSHForest, NearestNeighbors \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tv = TfidfVectorizer(decode_error='ignore')\n",
    "\n",
    "features = csr_matrix(tv.fit_transform(docs))\n",
    "\n",
    "doc_index = np.array(range(len(docs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_features = features[:2000]\n",
    "train_features = features[2000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an answer key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', leaf_size=30, metric='cosine',\n",
       "         metric_params=None, n_jobs=1, n_neighbors=5, p=2, radius=1.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = NearestNeighbors(algorithm='brute', metric='cosine')\n",
    "        \n",
    "knn.fit(train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.97951602935791"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "# get 1 NN for each document\n",
    "answers = knn.kneighbors(test_features, n_neighbors=1, return_distance=False)\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/spencebeecher/anaconda2/lib/python2.7/site-packages/sklearn/neighbors/base.py:211: UserWarning: cannot use tree with sparse input: using brute force\n",
      "  warnings.warn(\"cannot use tree with sparse input: \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "52.95571780204773"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bknn = NearestNeighbors(algorithm='ball_tree')\n",
    "        \n",
    "bknn.fit(train_features)\n",
    "\n",
    "t0 = time.time()\n",
    "# get 1 NN for each document\n",
    "_ = bknn.kneighbors(test_features, n_neighbors=1, return_distance=False)\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build models to compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114.92523193359375"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "snn = ci.MultiClusterIndex(train_features, doc_index, num_indexes=2)\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.300570964813232"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "lshf = LSHForest(n_neighbors=1)\n",
    "        \n",
    "lshf.fit(train_features)\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pysparnn_utils\n",
    "import time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.965\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "23.034273862838745"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "\n",
    "results = snn.search(test_features, return_distance=False)\n",
    "\n",
    "print('Recall: {}'.format(pysparnn_utils.recall(answers, results).mean()))\n",
    "\n",
    "snn_time = time.time() - t0\n",
    "snn_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9245\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11.743115901947021"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only search one index instead of 2\n",
    "t0 = time.time()\n",
    "\n",
    "results = snn.search(test_features, return_distance=False, num_indexes=1)\n",
    "\n",
    "print('Recall: {}'.format(pysparnn_utils.recall(answers, results).mean()))\n",
    "\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.7185\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "77.58664608001709"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "\n",
    "results = lshf.kneighbors(test_features, return_distance=False)\n",
    "\n",
    "print('Recall: {}'.format(pysparnn_utils.recall(answers, results).mean()))\n",
    "lsh_time = time.time() - t0\n",
    "lsh_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3683130860568533"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LSH is x times slower than snn\n",
    "lsh_time / snn_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Track Pysparnn vs Bruteforce as a function of index size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/spencebeecher/anaconda2/lib/python2.7/site-packages/pysparnn/matrix_distance.py:117: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 100001 but corresponding boolean dimension is 1000\n",
      "  records = self.records_data[index]\n"
     ]
    }
   ],
   "source": [
    "snn_results = []\n",
    "\n",
    "for n in np.linspace(1000, 80000, 5):\n",
    "    feats = train_features[:n]\n",
    "    \n",
    "    ########## brute force ############\n",
    "    bf = NearestNeighbors(algorithm='brute', metric='cosine')\n",
    "\n",
    "    bf.fit(feats)\n",
    "\n",
    "    # get 1 NN for each document\n",
    "    t0 = time.time()\n",
    "    \n",
    "    answers = bf.kneighbors(test_features, n_neighbors=1, return_distance=False)\n",
    "    \n",
    "    bf_time = time.time() - t0\n",
    "    \n",
    "    \n",
    "    ########## snn ############\n",
    "    snn = ci.MultiClusterIndex(feats, doc_index, num_indexes=2)\n",
    "    # only search one index instead of 2\n",
    "    t0 = time.time()\n",
    "\n",
    "    results = snn.search(test_features, return_distance=False, num_indexes=1)\n",
    "    \n",
    "    snn_time = time.time() - t0\n",
    "    snn_recall = pysparnn_utils.recall(answers, results).mean()\n",
    "    \n",
    "    # results\n",
    "    snn_results.append({\n",
    "            'n': n,\n",
    "            'snn_recall': snn_recall , \n",
    "            'snn_time': snn_time,\n",
    "            'bf_recall': 1.0,\n",
    "            'bf_time': bf_time\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x206f66050>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dLBuBI8Cb7r42HesEfgX8D8Bj5qVjnYQ1vmlma83sb9O01jzguJk9E3a1LDCz\nDmlYZ6zbgRfD6bSr090PAY8D+8LtnnT3t1JdaxRCoCqvu0ncGnXKq5ldArwMPODun3NxbSmv1d3L\n3P0qgk/ao8xsaDV1pbROM7sZOOrBlxbWtnzK38/Q1919OHATcK+Z/TVp9p4SfFIdDjwR1nqG4JNp\nutUZLGjWBrgNWBLOSrs6zawzwVfs5BIcFXzJzL5XTW1NWmsUQuCohd9jZGa9gGPh/IMEfYfl+obz\nappfaRkzawV0cveihhRlZq0JAuB5d381nWsFcPdTQAEwLg3r/Dpwm5l9BPwOuM7MngeOpFmdALj7\n4fD+U4KuwFGk33t6ANjv7uvCx68QhEK61VnuRmC9ux8PH6djndcDH7l7Ufgp/Q/ANamutSWGgFE5\n/ZYCd4fTdwGvxsyfGI6m5wEDgTXh4dhJMxtlZgZMqbLMXeH0d4CVjajzXwn69f45XWs1s27lZyqY\nWXvgb4Ad6Vanu89y9xx3/zJB/+hKd78TWJZOdQKYWYfwCBAz+xJBP/ZW0u89PQrsN7NB4awxwLZ0\nqzPGJIIPAOXSsc59wGgzaxduYwywPeW1NmYgJt1uBP2Bh4Di8A2fSjAI8xbBGTgrgM4x7R8iGHHf\nAYyNmX81wX/MPcA/x8zPBH4fzl8N9G9gnV8HSoFNBKP/Gwg+YXdJp1qBYWFtm4AtwOxwflrVWaXm\nb3JhYDjt6iToay//u28FZqZxrVcQfGPAJuDfCc4OSsc6OwCfAh1j5qVdneG65oTb3QI8B7RJda26\nWExEJMJaYneQiIjESSEgIhJhCgERkQhTCIiIRJhCQEQkwhQCIiIRphAQEYkwhYCISIQpBCTSzCzX\nzLaH35L5gZn9p5llproukaaiEBAJvpPlX9z9cuAkMCHF9Yg0GYWACHzs7lvD6fUEv+IkEgkKAZHg\nCwfLlRJ8l75IJCgERBr540AizZlCQCSxv+Qk0qzoq6RFRCJMRwIiIhGmEBARiTCFgIhIhCkEREQi\nTCEgIhJhCgERkQhTCIiIRJhCQEQkwv4/WW1egacM/bEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206f2f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(snn_results)[['n', 'snn_recall', 'bf_recall']].plot(x='n', ylim=(0, 1), linewidth=4,\n",
    "                                                                 title='Recall is roughly flat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x206f95e90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1gmGJwxixZASnzCmvNgUjChIfF88TTZ4IidZ9MPCny+Zqp0tmfWNMPWPM887y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206fa1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(snn_results)[['n', 'snn_time', 'bf_time']]\n",
    "df.plot(x='n', linewidth=5,\n",
    "        title='Time per query for bruteforce increases linearly\\nfor pysparnn it grows somewhat like a square root')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}