Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "#import nltk\n",
    "import spacy\n",
    "import sklearn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('./data/train.csv')\n",
    "test = pd.read_csv('./data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size:  20000000\n",
      "Test size:  246955  -  1.234775 % of train size\n"
     ]
    }
   ],
   "source": [
    "train_size = len(train)\n",
    "test_size = len(test)\n",
    "print(\"Train size: \", train_size)\n",
    "print(\"Test size: \", test_size, \" - \", test_size*100/train_size, \"% of train size\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>title</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kit Maternidade Bolsa-mala Baby/bebe Vinho Men...</td>\n",
       "      <td>portuguese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Trocador De Fraldas Fisher Price Feminino Rosa...</td>\n",
       "      <td>portuguese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Motor Ventoinha - Fiat Idea / Palio 1.8 - A 04...</td>\n",
       "      <td>portuguese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Amortecedor Mola Batente D Dir New Civic 14 - ...</td>\n",
       "      <td>portuguese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Cadeirinha De Carro Bebê Princesa Princess 9 A...</td>\n",
       "      <td>portuguese</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                              title    language\n",
       "0   0  Kit Maternidade Bolsa-mala Baby/bebe Vinho Men...  portuguese\n",
       "1   1  Trocador De Fraldas Fisher Price Feminino Rosa...  portuguese\n",
       "2   2  Motor Ventoinha - Fiat Idea / Palio 1.8 - A 04...  portuguese\n",
       "3   3  Amortecedor Mola Batente D Dir New Civic 14 - ...  portuguese\n",
       "4   4  Cadeirinha De Carro Bebê Princesa Princess 9 A...  portuguese"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label_quality:  ['unreliable' 'reliable']\n",
      "languages:  ['spanish' 'portuguese']\n",
      "Number of categories:  1588\n"
     ]
    }
   ],
   "source": [
    "label_quality_types = train['label_quality'].unique()\n",
    "language_types = train['language'].unique()\n",
    "category_types = train['category'].unique()\n",
    "print(\"label_quality: \", label_quality_types)\n",
    "print(\"languages: \", language_types)\n",
    "print(\"Number of categories: \", len(category_types))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unreliable    18815755\n",
      "reliable       1184245\n",
      "Name: label_quality, dtype: int64\n",
      "Percentages:  [94.078775  5.921225]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_quality_freq = train['label_quality'].value_counts()\n",
    "print(label_quality_freq)\n",
    "print('Percentages: ', label_quality_freq.values*100/np.sum(label_quality_freq.values))\n",
    "plt = matplotlib.pyplot.pie(label_quality_freq.values, labels=label_quality_freq.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spanish       10000000\n",
      "portuguese    10000000\n",
      "Name: language, dtype: int64\n",
      "Percentages:  [0.5 0.5]\n"
     ]
    }
   ],
   "source": [
    "languages_freq = train['language'].value_counts()\n",
    "print(languages_freq)\n",
    "print('Percentages: ', languages_freq.values/np.sum(languages_freq.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Portuguese</th>\n",
       "      <th>Spanish:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>unreliable</td>\n",
       "      <td>46.53341</td>\n",
       "      <td>47.545365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>reliable</td>\n",
       "      <td>3.46659</td>\n",
       "      <td>2.454635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Portuguese   Spanish:\n",
       "unreliable    46.53341  47.545365\n",
       "reliable       3.46659   2.454635"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_quality_freq = train[train['language'] == 'portuguese']['label_quality'].value_counts()\n",
    "esp_quality_freq = train[train['language'] == 'spanish']['label_quality'].value_counts()\n",
    "quality_freq_per_lang = pd.DataFrame({'Portuguese': port_quality_freq*100/train_size, 'Spanish:': esp_quality_freq*100/train_size})\n",
    "quality_freq_per_lang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spanish       124987\n",
      "portuguese    121968\n",
      "Name: language, dtype: int64\n",
      "Percentages:  [0.50611245 0.49388755]\n"
     ]
    }
   ],
   "source": [
    "languages_freq_test = test['language'].value_counts()\n",
    "print(languages_freq_test)\n",
    "print('Percentages: ', languages_freq_test.values/np.sum(languages_freq_test.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Qntd  Percentage\n",
      "PANTS              35973  0.179865 %\n",
      "COFFEE_MAKERS      35104   0.17552 %\n",
      "BABY_CAR_SEATS     34163  0.170815 %\n",
      "MUSICAL_KEYBOARDS  33222   0.16611 %\n",
      "MATTRESSES         32967  0.164835 %\n",
      "PUREBRED_DOGS      32928   0.16464 %\n",
      "RANGES             32645  0.163225 %\n",
      "REFRIGERATORS      32635  0.163175 % \n",
      "...\n",
      "\n",
      "                                      Qntd  Percentage\n",
      "STADIOMETERS                           229  0.001145 %\n",
      "HONEY_EXTRACTORS                       216   0.00108 %\n",
      "FORCE_GAUGES                           213  0.001065 %\n",
      "CONSTRUCTION_LIME_BAGS                 206   0.00103 %\n",
      "COLD_FOOD_AND_DRINK_VENDING_MACHINES   162   0.00081 %\n",
      "PAINTBALL_SMOKE_GRENADES               154   0.00077 %\n",
      "COMMERCIAL_POPCORN_MACHINES            141  0.000705 %\n",
      "HAMBURGER_FORMERS                      109  0.000545 %\n",
      "\n",
      " count     1588.000000\n",
      "mean     12594.458438\n",
      "std       8218.029504\n",
      "min        109.000000\n",
      "25%       5042.750000\n",
      "50%      12399.500000\n",
      "75%      19104.750000\n",
      "max      35973.000000\n",
      "Name: Qntd, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "category_freq = train['category'].value_counts()\n",
    "category_freq = pd.DataFrame({'Qntd':  category_freq.values, 'Percentage': category_freq.apply(lambda x: '{0} %'.format(x*100/train_size))})\n",
    "print(category_freq.head(8), '\\n...\\n')\n",
    "print(category_freq.tail(8))\n",
    "print('\\n', category_freq['Qntd'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = matplotlib.pyplot.subplots(figsize=(20, 5), nrows=1, ncols=1)\n",
    "category_freq['Qntd'].plot(kind='bar', ax=axes).get_xaxis().set_ticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_freq_port = train[train['language'] == 'portuguese']['category'].value_counts()[category_freq.index].fillna(0)\n",
    "cat_freq_esp = train[train['language'] == 'spanish']['category'].value_counts()[category_freq.index].fillna(0)\n",
    "cat_freq_unrel = train[train['label_quality'] == 'unreliable']['category'].value_counts()[category_freq.index].fillna(0)\n",
    "cat_freq_rel = train[train['label_quality'] == 'reliable']['category'].value_counts()[category_freq.index].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = matplotlib.pyplot.subplots(figsize=(20, 20), nrows=4, ncols=1)\n",
    "cat_freq_port.plot(kind='bar', ax=axes[0]).get_xaxis().set_ticks([])\n",
    "cat_freq_esp.plot(kind='bar', ax=axes[1]).get_xaxis().set_ticks([])\n",
    "cat_freq_unrel.plot(kind='bar', ax=axes[2]).get_xaxis().set_ticks([])\n",
    "cat_freq_rel.plot(kind='bar', ax=axes[3]).get_xaxis().set_ticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PANTS                                   1405.0\n",
       "COFFEE_MAKERS                            110.0\n",
       "BABY_CAR_SEATS                          1583.0\n",
       "MUSICAL_KEYBOARDS                       1912.0\n",
       "MATTRESSES                              1653.0\n",
       "                                         ...  \n",
       "CONSTRUCTION_LIME_BAGS                   206.0\n",
       "COLD_FOOD_AND_DRINK_VENDING_MACHINES      50.0\n",
       "PAINTBALL_SMOKE_GRENADES                 154.0\n",
       "COMMERCIAL_POPCORN_MACHINES               69.0\n",
       "HAMBURGER_FORMERS                         63.0\n",
       "Name: category, Length: 1588, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(cat_freq_port - cat_freq_esp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N Categories only in portuguese: 14\n",
      "N Categories only in spanish: 12\n"
     ]
    }
   ],
   "source": [
    "cat_freq_port_norm = cat_freq_port/category_freq['Qntd']\n",
    "print('N Categories only in portuguese:' ,cat_freq_port_norm[cat_freq_port_norm == 1.00].count())\n",
    "print('N Categories only in spanish:' ,cat_freq_port_norm[cat_freq_port_norm == 0.00].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N Categories only in reliable: 0\n",
      "N Categories only in non-reliable: 193\n"
     ]
    }
   ],
   "source": [
    "cat_freq_rel_norm = cat_freq_rel/category_freq['Qntd']\n",
    "print('N Categories only in reliable:' ,cat_freq_rel_norm[cat_freq_rel_norm == 1.00].count())\n",
    "print('N Categories only in non-reliable:' ,cat_freq_rel_norm[cat_freq_rel_norm == 0.00].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "rel_cats_filters = cat_freq_rel[cat_freq_rel == 0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = matplotlib.pyplot.subplots(figsize=(20, 5), nrows=1, ncols=1)\n",
    "category_freq_qtnd = category_freq['Qntd'].copy()\n",
    "category_freq_negative = category_freq['Qntd'].copy()\n",
    "def conv(x):\n",
    "    if(x.name in rel_cats_filters):\n",
    "        x[0] = 0\n",
    "    return x\n",
    "def conv_neg(x):\n",
    "    if(x.name not in rel_cats_filters):\n",
    "        x[0] = 0\n",
    "    return x\n",
    "category_freq_qtnd = category_freq_qtnd.to_frame(0).apply(conv, axis=1)[0]\n",
    "category_freq_qtnd.plot(kind='bar', ax=axes).get_xaxis().set_ticks([])\n",
    "category_freq_negative = category_freq_negative.to_frame(0).apply(conv_neg, axis=1)[0]\n",
    "category_freq_negative.plot(kind='bar', ax=axes, color='r').get_xaxis().set_ticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_freq_rel_lang = train[train['label_quality'] == 'reliable'][['category', 'language']]\n",
    "cat_freq_rel_pt = cat_freq_rel_lang[cat_freq_rel_lang['language'] == 'portuguese']['category'].value_counts()[category_freq.index].fillna(0)\n",
    "cat_freq_rel_es = cat_freq_rel_lang[cat_freq_rel_lang['language'] == 'spanish']['category'].value_counts()[category_freq.index].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N Categories not in pt reliable: 408\n",
      "N Categories not in es reliable: 526\n"
     ]
    }
   ],
   "source": [
    "print('N Categories not in pt reliable:' ,cat_freq_rel_pt[cat_freq_rel_pt == 0.00].count())\n",
    "print('N Categories not in es reliable:' ,cat_freq_rel_es[cat_freq_rel_es == 0.00].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SWEATERS_AND_CARDIGANS', 'CHOCOLATES', 'CAR_CARBURETORS',\n",
       "       'WRENCH_SETS', 'SOUND_CONSOLES_AND_DJ_MIXERS', 'CLASSIC_CARS',\n",
       "       'CONCERT_TICKETS', 'DECORATIVE_PAINTINGS', 'PACKAGING_BOXES',\n",
       "       'FIDGET_SPINNERS',\n",
       "       ...\n",
       "       'BOXING_SPEED_BAGS', 'MANUAL_TROLLEYS', 'SCALE_RULERS',\n",
       "       'QUEUE_STANCHIONS', 'FORCE_GAUGES', 'CONSTRUCTION_LIME_BAGS',\n",
       "       'COLD_FOOD_AND_DRINK_VENDING_MACHINES', 'PAINTBALL_SMOKE_GRENADES',\n",
       "       'COMMERCIAL_POPCORN_MACHINES', 'HAMBURGER_FORMERS'],\n",
       "      dtype='object', length=408)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./da')\n",
    "cat_freq_rel_pt[cat_freq_rel_pt == 0.00].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f3262d50630>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = matplotlib.pyplot.subplots(figsize=(20, 5), nrows=1, ncols=1)\n",
    "(cat_freq_port/category_freq['Qntd']).plot(kind='bar', color='b', ax=axes).get_xaxis().set_ticks([])\n",
    "(cat_freq_esp*-1/category_freq['Qntd']).plot(kind='bar', color='r', ax=axes).get_xaxis().set_ticks([])\n",
    "axes.axhline(0.5)\n",
    "axes.axhline(0.75)\n",
    "axes.axhline(1)\n",
    "axes.axhline(-0.75)\n",
    "axes.axhline(-0.5)\n",
    "axes.axhline(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INDUSTRIAL_BLENDERS                0.527241\n",
       "MUSICAL_KEYBOARD_CASES_AND_BAGS    0.418224\n",
       "FOOTBALL_SHIRTS                    0.321192\n",
       "JACKETS_AND_COATS                  0.320763\n",
       "HANDBAGS                           0.319983\n",
       "                                     ...   \n",
       "GARDEN_CHAIRS                      0.000000\n",
       "INDUSTRIAL_COOKING_OVENS           0.000000\n",
       "ARTIFICIAL_FEATHERS                0.000000\n",
       "DOOR_AND_GATE_REMOTE_CONTROLS      0.000000\n",
       "HAMBURGER_FORMERS                  0.000000\n",
       "Length: 1588, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cat_freq_rel/category_freq['Qntd']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: \n",
      " count      2e+07\n",
      "mean     47.8666\n",
      "std      12.2868\n",
      "min            3\n",
      "25%           40\n",
      "50%           52\n",
      "75%           58\n",
      "max          120\n",
      "Name: length, dtype: object\n",
      "\n",
      "Test: \n",
      " count     246955\n",
      "mean     49.0328\n",
      "std      11.6908\n",
      "min            3\n",
      "25%           43\n",
      "50%           54\n",
      "75%           58\n",
      "max           60\n",
      "Name: length, dtype: object\n"
     ]
    }
   ],
   "source": [
    "train['length'] = train['title'].apply(len)\n",
    "test['length'] = test['title'].apply(len)\n",
    "print('Train: \\n', train['length'].describe().apply(lambda x: format(x, 'g')))\n",
    "print('\\nTest: \\n', test['length'].describe().apply(lambda x: format(x, 'g')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2129ae07710>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = matplotlib.pyplot.subplots(figsize=(20, 10), nrows=1, ncols=2)\n",
    "train['length'].hist(bins=100, ax=axes[0])\n",
    "test['length'].hist(bins=20, ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Len > 60:  81113  -  0.405565 % of the dataset\n"
     ]
    },
    {
     "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>title</th>\n",
       "      <th>label_quality</th>\n",
       "      <th>language</th>\n",
       "      <th>category</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>Tecla Levantacristal Derecha Original Fiat Str...</td>\n",
       "      <td>unreliable</td>\n",
       "      <td>spanish</td>\n",
       "      <td>CAR_WINDOW_SWITCHES</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Bomba Direccion Hidraulica Fiat Nuevo Palio Fa...</td>\n",
       "      <td>unreliable</td>\n",
       "      <td>spanish</td>\n",
       "      <td>CAR_POWER_STEERING_PUMPS</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>Árvore De Natal Dinamarca Verde 1,8 Metros 580...</td>\n",
       "      <td>unreliable</td>\n",
       "      <td>portuguese</td>\n",
       "      <td>CHRISTMAS_TREES</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>Leve Junto! 50 Protetores Auriculares De Silic...</td>\n",
       "      <td>unreliable</td>\n",
       "      <td>portuguese</td>\n",
       "      <td>SWIMMING_EARPLUGS</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>Suéter Feminino Linha Feito Em Tricô Decote V ...</td>\n",
       "      <td>unreliable</td>\n",
       "      <td>portuguese</td>\n",
       "      <td>SWEATERS_AND_CARDIGANS</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title label_quality  \\\n",
       "620   Tecla Levantacristal Derecha Original Fiat Str...    unreliable   \n",
       "1266  Bomba Direccion Hidraulica Fiat Nuevo Palio Fa...    unreliable   \n",
       "1284  Árvore De Natal Dinamarca Verde 1,8 Metros 580...    unreliable   \n",
       "1449  Leve Junto! 50 Protetores Auriculares De Silic...    unreliable   \n",
       "1738  Suéter Feminino Linha Feito Em Tricô Decote V ...    unreliable   \n",
       "\n",
       "        language                  category  length  \n",
       "620      spanish       CAR_WINDOW_SWITCHES      79  \n",
       "1266     spanish  CAR_POWER_STEERING_PUMPS      64  \n",
       "1284  portuguese           CHRISTMAS_TREES      73  \n",
       "1449  portuguese         SWIMMING_EARPLUGS      81  \n",
       "1738  portuguese    SWEATERS_AND_CARDIGANS      63  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len_gt_60 = train[train['length'] > 60]\n",
    "print('Len > 60: ', len(len_gt_60), ' - ', len(len_gt_60)*100/train_size, '% of the dataset' )\n",
    "len_gt_60.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unreliable    81113\n",
      "Name: label_quality, dtype: int64 \n",
      "\n",
      "spanish       45221\n",
      "portuguese    35892\n",
      "Name: language, dtype: int64 \n",
      "\n",
      "LAPTOP_BATTERIES                       1591\n",
      "HOUSE_FENCES                            828\n",
      "DESKTOP_COMPUTER_COOLERS_AND_FANS       788\n",
      "DECORATIVE_PAINTINGS                    673\n",
      "MATTRESSES                              563\n",
      "MICROMETERS                             557\n",
      "AUTOMOTIVE_SUSPENSION_CONTROL_ARMS      535\n",
      "LATEX_ENAMEL_AND_ACRYLIC_PAINTS         445\n",
      "SUITCASES                               441\n",
      "CAR_WINDOW_SWITCHES                     422\n",
      "PORTABLE_GENERATORS                     413\n",
      "BOX_SPRING_AND_MATTRESS_SETS            398\n",
      "OFFICE_CHAIRS                           366\n",
      "AUTOMOTIVE_BATTERIES                    345\n",
      "CELLPHONE_BATTERIES                     344\n",
      "SURVEILLANCE_MONITORING_KITS            343\n",
      "ENGINE_CRANKSHAFT_PULLEYS               321\n",
      "SCREWS                                  311\n",
      "ELECTRIC_DRILLS                         301\n",
      "CAR_SCANNERS                            301\n",
      "CV_JOINTS                               290\n",
      "BATHROOM_VANITIES                       280\n",
      "DRILLS_SCREWDRIVERS                     276\n",
      "RAM_MEMORY_MODULES                      273\n",
      "POWER_GRINDERS                          261\n",
      "CALIPERS                                259\n",
      "DRILL_BITS                              259\n",
      "ALARMS_AND_SENSORS                      258\n",
      "RACKS_AND_PINIONS                       257\n",
      "SURFACES_PROTECTIVE_PAINTS              254\n",
      "                                       ... \n",
      "HABERDASHERY_RIBBONS                      1\n",
      "FAX_MACHINES                              1\n",
      "SEWING_THREADS                            1\n",
      "NON_SLIP_BATHTUB_APPLIQUES_AND_MATS       1\n",
      "WOOD_BURNING_MACHINES                     1\n",
      "BOARD_GAMES                               1\n",
      "TEAPOTS                                   1\n",
      "ICE_MAKERS                                1\n",
      "SCALEXTRIC_CARS                           1\n",
      "VIDEO_CASSETTES                           1\n",
      "PENCIL_CASES                              1\n",
      "EDIBLE_SEEDS                              1\n",
      "CHARANGOS                                 1\n",
      "TONFAS                                    1\n",
      "CAR_EXHAUSTS                              1\n",
      "NEBULIZERS                                1\n",
      "CONCRETE_BLOCKS                           1\n",
      "DEBRIS_BAGS                               1\n",
      "ICE_CREAM_MACHINES                        1\n",
      "MEAT_HOOKS                                1\n",
      "CANOES                                    1\n",
      "DJEMBES                                   1\n",
      "PILATES_REFORMERS                         1\n",
      "URINALS                                   1\n",
      "CONFETTI_SHOOTERS                         1\n",
      "LAPTOP_SKINS                              1\n",
      "HEARING_AIDS                              1\n",
      "CUPCAKE_STANDS                            1\n",
      "MODELISM_SERVO_MOTORS                     1\n",
      "HEATING_PADS                              1\n",
      "Name: category, Length: 1347, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(len_gt_60['label_quality'].value_counts(), '\\n')\n",
    "print(len_gt_60['language'].value_counts(), '\\n')\n",
    "print(len_gt_60['category'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import util\n",
    "from joblib import load\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_preprocessed = pd.read_csv('data/train_preprocessed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_preprocessed['qntd_tokens'] = train_preprocessed['title'].apply(lambda x: str(x).count(' ')+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2e+07\n",
       "mean     5.63847\n",
       "std      1.87082\n",
       "min            1\n",
       "25%            4\n",
       "50%            6\n",
       "75%            7\n",
       "max           31\n",
       "Name: qntd_tokens, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_preprocessed['qntd_tokens'].describe().apply(lambda x: format(x, 'g'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc7fc85e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_preprocessed['qntd_tokens'].hist(bins=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       0.405690\n",
       "2       4.434205\n",
       "3      13.669580\n",
       "4      27.865630\n",
       "5      46.641755\n",
       "6      67.260590\n",
       "7      84.263520\n",
       "8      94.169695\n",
       "9      98.239660\n",
       "10     99.503170\n",
       "11     99.840135\n",
       "12     99.932215\n",
       "13     99.965240\n",
       "14     99.981630\n",
       "15     99.990465\n",
       "16     99.995220\n",
       "17     99.997545\n",
       "18     99.998550\n",
       "19     99.999190\n",
       "20     99.999600\n",
       "21     99.999840\n",
       "22     99.999920\n",
       "23     99.999965\n",
       "24     99.999970\n",
       "25     99.999975\n",
       "26     99.999985\n",
       "27     99.999990\n",
       "31    100.000000\n",
       "Name: qntd_tokens, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_preprocessed['qntd_tokens'].value_counts().sort_index().cumsum())*100/len(train_preprocessed['qntd_tokens'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}