2016-12-13(1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n",
      "ERROR (theano.sandbox.cuda): ERROR: Not using GPU. Initialisation of device 2 failed:\n",
      "initCnmem: cnmemInit call failed! Reason=CNMEM_STATUS_OUT_OF_MEMORY. numdev=1\n",
      "\n",
      "Using gpu device 0: GeForce GTX TITAN X (CNMeM is enabled with initial size: 95.0% of memory, cuDNN 4007)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO Training. Load the existed model: weights/201611232246_e10_1k1k.p\n",
      "Evaluate the model using fast estimation...\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n"
     ]
    }
   ],
   "source": [
    "run main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = pd.Series([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n",
      "ERROR (theano.sandbox.cuda): ERROR: Not using GPU. Initialisation of device 2 failed:\n",
      "initCnmem: cnmemInit call failed! Reason=CNMEM_STATUS_OUT_OF_MEMORY. numdev=1\n",
      "\n",
      "Using gpu device 0: GeForce GTX TITAN X (CNMeM is enabled with initial size: 95.0% of memory, cuDNN 4007)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to training the modle..., saving to weights/201612131946_e10_1k1k.p\n",
      "Training\n",
      "the length of the training data is  1998\n",
      "(4.3990784, 0.094624788, 0.058451464087564922)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6] : 6\n",
      "Epoch 0\n",
      "Epoch  0 Update  40 Cost  0.159506469965\n",
      "epoch took: 57.1951508522\n",
      "(1.961805, 0.7666328, 0.7578090021788968)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137] : 137\n",
      "Epoch 1\n",
      "Epoch  1 Update  80 Cost  0.123848386109\n",
      "Epoch  1 Update  120 Cost  0.100559875369\n",
      "epoch took: 64.6924409866\n",
      "(1.7282732, 0.80136764, 0.78317011749853882)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130] : 130\n",
      "Epoch 2\n",
      "Epoch  2 Update  160 Cost  0.102680817246\n",
      "epoch took: 63.5343949795\n",
      "(1.6057752, 0.80827612, 0.79076649761086237)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130, 219] : 219\n",
      "Epoch 3\n",
      "Epoch  3 Update  200 Cost  0.0866909921169\n",
      "Epoch  3 Update  240 Cost  0.0728017687798\n",
      "epoch took: 64.7918119431\n",
      "(1.5010504, 0.82934481, 0.80574402792069455)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130, 219, 247] : 247\n",
      "Epoch 4\n",
      "Epoch  4 Update  280 Cost  0.0577436499298\n",
      "epoch took: 61.1065990925\n",
      "(1.398512, 0.8464883, 0.81565037279532837)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130, 219, 247, 294] : 294\n",
      "Epoch 5\n",
      "Epoch  5 Update  320 Cost  0.0711076408625\n",
      "Epoch  5 Update  360 Cost  0.0572196319699\n",
      "epoch took: 63.5946121216\n",
      "(1.4260186, 0.84319091, 0.81686697173449641)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130, 219, 247, 294, 307] : 307\n",
      "Epoch 6\n",
      "Epoch  6 Update  400 Cost  0.0568135976791\n",
      "Epoch  6 Update  440 Cost  0.0915502756834\n",
      "epoch took: 61.811166048\n",
      "(1.2827817, 0.86647803, 0.82712216165434937)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130, 219, 247, 294, 307, 341] : 341\n",
      "Epoch 7\n",
      "Epoch  7 Update  480 Cost  0.0742112845182\n",
      "epoch took: 64.6665859222\n",
      "(1.600545, 0.81286985, 0.79403446394365085)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130, 219, 247, 294, 307, 341, 265] : 265\n",
      "Epoch 8\n",
      "Epoch  8 Update  520 Cost  0.0594262778759\n",
      "Epoch  8 Update  560 Cost  0.0591493323445\n",
      "epoch took: 64.6094470024\n",
      "(1.0595851, 0.8971628, 0.84758568335119677)\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "top_ [6, 137, 130, 219, 247, 294, 307, 341, 265, 452] : 452\n",
      "Epoch 9\n",
      "Epoch  9 Update  600 Cost  0.0532750226557\n",
      "epoch took: 65.1790440083\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n",
      "Finish preparing the data!\n",
      "Finished embedding,start projecting...\n"
     ]
    }
   ],
   "source": [
    "run main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 137, 130, 219, 247, 294, 307, 341, 265, 452]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sls.top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rank_results = sls.rank[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.982965931863728"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(rank_results)/len(rank_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rank_results = sls.rank[9][0:999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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t6S5JV0j6tKTXSJows9nMUxefJhafKbm1VUlPntTA9u2sSgoAAAB4lOt7DJ1zTtKlkr4t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\nGARJ5PQaMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd325e3d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 7.5)\n",
    "plt.plot(rank_results,'ro')\n",
    "plt.xlabel('Document Number')\n",
    "plt.ylabel('The ranking of the real pair')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    999.000000\n",
       "mean      25.964464\n",
       "std       49.284037\n",
       "min        1.500000\n",
       "25%        2.500000\n",
       "50%        5.500000\n",
       "75%       22.000000\n",
       "max      457.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(rank_results).describe()\n",
    "# 精确度很低，虽然每次运行结果会不同，但是这个精度还是使用演示的方法低"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.5,\n",
       " 25.5,\n",
       " 1.5,\n",
       " 2.5,\n",
       " 2.5,\n",
       " 9.5,\n",
       " 1.5,\n",
       " 5.5,\n",
       " 81.5,\n",
       " 3.5,\n",
       " 4.5,\n",
       " 1.5,\n",
       " 13.5,\n",
       " 11.5,\n",
       " 12.5,\n",
       " 11.5,\n",
       " 7.5,\n",
       " 1.5,\n",
       " 2.5,\n",
       " 5.5,\n",
       " 1.5,\n",
       " 258.5,\n",
       " 9.5,\n",
       " 7.5,\n",
       " 1.5,\n",
       " 145.5,\n",
       " 1.5,\n",
       " 27.5,\n",
       " 123.5,\n",
       " 1.5,\n",
       " 9.5,\n",
       " 1.5,\n",
       " 5.5,\n",
       " 1.5,\n",
       " 8.5,\n",
       " 1.5,\n",
       " 1.5,\n",
       " 1.5,\n",
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