Quora_Questions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#importar datos a una lista\n",
    "import pandas as pd\n",
    "import nltk\n",
    "from tqdm import tqdm, tqdm_notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisis (hay que expandir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/quora/train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero total de preguntas: 404290\n",
      "Numero de preguntas repetidas: 149263, (36.92%)\n",
      "Valores nulos: \n",
      "id              0\n",
      "qid1            0\n",
      "qid2            0\n",
      "question1       0\n",
      "question2       2\n",
      "is_duplicate    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Numero total de preguntas: {total}\".format(total = len(df)))\n",
    "print(\"Numero de preguntas repetidas: {b}, ({a:.2f}%)\"\n",
    "      .format(b=df[\"is_duplicate\"].sum(),a=df[\"is_duplicate\"].mean()*100) )\n",
    "print(\"Valores nulos: \\n{}\".format(df.isnull().sum()))\n",
    "\n",
    "#Si hubiera campos vacios (habia 2) rellenamos con empty\n",
    "df = df.fillna(\"empty\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Registramos pandas en tqdm para usar progress_apply y progress_map en vez de los propios de pandas\n",
    "tqdm.pandas(tqdm_notebook)\n",
    "new_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [00:00, 1940.56it/s]           \n",
      "100it [00:00, 1979.84it/s]           \n"
     ]
    }
   ],
   "source": [
    "new_df['question1'] = df.progress_apply(lambda row: nltk.word_tokenize(row['question1']), axis=1)\n",
    "new_df['question2'] = df.progress_apply(lambda row: nltk.word_tokenize(row['question2']), axis=1)\n",
    "new_df[\"avgQ1Q2\"] = -1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
    {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "      <th>avgQ1Q2</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, inv...</td>\n",
       "      <td>[What, is, the, step, by, step, guide, to, inv...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</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-...</td>\n",
       "      <td>[What, would, happen, if, the, Indian, governm...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</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, in...</td>\n",
       "      <td>[How, can, Internet, speed, be, increased, by,...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</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, c...</td>\n",
       "      <td>[Find, the, remainder, when, [, math, ], 23^, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</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, suga...</td>\n",
       "      <td>[Which, fish, would, survive, in, salt, water, ?]</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</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, inv...   \n",
       "1   1     3     4  [What, is, the, story, of, Kohinoor, (, Koh-i-...   \n",
       "2   2     5     6  [How, can, I, increase, the, speed, of, my, in...   \n",
       "3   3     7     8  [Why, am, I, mentally, very, lonely, ?, How, c...   \n",
       "4   4     9    10  [Which, one, dissolve, in, water, quikly, suga...   \n",
       "\n",
       "                                           question2  is_duplicate  avgQ1Q2  \n",
       "0  [What, is, the, step, by, step, guide, to, inv...             0     -1.0  \n",
       "1  [What, would, happen, if, the, Indian, governm...             0     -1.0  \n",
       "2  [How, can, Internet, speed, be, increased, by,...             0     -1.0  \n",
       "3  [Find, the, remainder, when, [, math, ], 23^, ...             0     -1.0  \n",
       "4  [Which, fish, would, survive, in, salt, water, ?]             0     -1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "## Codigo no util, guardado por si lo uso en el informe final\n",
    "#guardamos las preguntas por separados\n",
    "df = pd.read_csv(\"./data/quora/train.csv\", nrows=100)\n",
    "df = df.where((pd.notnull(df)), \"Vacio\")\n",
    "#df[\"tokensQ1\"] = df[\"question1\"].apply(nltk.word_tokenize)\n",
    "#df[\"tokensQ2\"] = df[\"question2\"].apply(nltk.word_tokenize)\n",
    "\n",
    "#df[\"question1\"].to_csv(\"./data/quora/trainQ1.csv\")\n",
    "#df[\"question2\"].to_csv(\"./data/quora/trainQ2.csv\")\n",
    "new_df = pd.DataFrame(columns=[\"unique_id\",\"id1\",\"id2\",\"q1\",\"q2\"])\n",
    "for row in tqdm(df.itertuples(), total=df.shape[0]):\n",
    "    new_df[\"unique_id\"] = df[\"id\"]\n",
    "    new_df[\"id1\"] = df[\"qid1\"]\n",
    "    new_df[\"id2\"] = df[\"qid2\"]\n",
    "    new_df[\"q1\"] = df[\"question1\"].apply(nltk.word_tokenize)\n",
    "    new_df[\"q2\"] = df[\"question2\"].apply(nltk.word_tokenize)\n",
    "    new_df[\"avgQ1Q2\"] = float(-1)\n",
    "\n",
    "pd.to_numeric(new_df[\"avgQ1Q2\"])\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "\n",
    "model = gensim.models.KeyedVectors.load_word2vec_format('./data/GoogleNews-vectors-negative300.bin.gz', \n",
    "                                                        binary=True, limit=50000)\n",
    "#model.similarity('woman', 'man')\n",
    "#model['man']\n",
    "#model.most_similar(positive=['woman', 'king'], negative=['man'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.spatial.distance as distance\n",
    "\n",
    "def avg_feature_vector(words, num_features, model):\n",
    "    \"\"\" Metodo que saca el vector medio de un cantidad de palabras dadas \n",
    "        word: palabras separadas de las que queremos obtener el vector medio\n",
    "        num_features: El tam del vector que representa a una palabra del modelo usado\n",
    "    \"\"\"\n",
    "    \"\"\"https://stackoverflow.com/questions/22129943/how-to-calculate-the-sentence-similarity-using-word2vec-model-of-gensim-with-pyt\"\"\"\n",
    "    featureVec = np.zeros((num_features,), dtype=\"float32\")\n",
    "    nwords = 0\n",
    "\n",
    "    for word in words:\n",
    "        if word in model:\n",
    "            nwords = nwords + 1\n",
    "            featureVec = np.add(featureVec, model[word])\n",
    "\n",
    "    if(nwords>0):\n",
    "        featureVec = np.divide(featureVec, nwords)\n",
    "    return featureVec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 477.18it/s]\n"
     ]
    }
   ],
   "source": [
    "for line in tqdm(new_df.itertuples(), total=new_df.shape[0]):\n",
    "    sentence_1_avg_vector = avg_feature_vector(new_df[\"question1\"][line[0]], num_features=300, model=model)\n",
    "    sentence_2_avg_vector = avg_feature_vector(new_df[\"question2\"][line[0]], num_features=300, model=model)\n",
    "    #new_df[\"avgQ1Q2\"][line[0]] =  1 - distance.cosine(sentence_1_avg_vector,sentence_2_avg_vector)\n",
    "    new_df.loc[\"avgQ1Q2\",line[0]] =  1 - distance.cosine(sentence_1_avg_vector,sentence_2_avg_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_df.to_csv(\"./quora/new_df.csv\", index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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