toxic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Word removal-experiment with word removals both in high frequency and low frequency space\n",
    "\n",
    "Taxonomy- Ordered heirarchial taxonomy can help you reduce entropy in content. Similar to what decision trees accomplish. This is SUPER unappreciated in most text analytics literature\n",
    "\n",
    "Lemmatization. _ it sometimes helps improve structure of text\n",
    "\n",
    "Data Quality check- Good data is like good food. Have you checked if you have any useless data? pruning data set helps improve structure a lot!\n",
    "\n",
    "Hyper Parameter tuning- Did you exhaustively search the subspaces to fine tune the model. Most models show upto 10% improvement by just hyperparameter tuning.\n",
    "\n",
    "Word Gram conversion- you can improve feature set by converting important bigrams and trigrams into unigrams \"food processing\" can be changed to \"food_processing\"\n",
    "\n",
    "Data preparation- Count vectors, tf-idf, word2vec .. et al.. Naive bayes seems to work better Count vectors,\n",
    "\n",
    "**Shuffle with stratification for model fitting***\n",
    "\n",
    "Colab google drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>question_text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00002165364db923c7e6</td>\n",
       "      <td>How did Quebec nationalists see their province...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000032939017120e6e44</td>\n",
       "      <td>Do you have an adopted dog, how would you enco...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000412ca6e4628ce2cf</td>\n",
       "      <td>Why does velocity affect time? Does velocity a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000042bf85aa498cd78e</td>\n",
       "      <td>How did Otto von Guericke used the Magdeburg h...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000455dfa3e01eae3af</td>\n",
       "      <td>Can I convert montra helicon D to a mountain b...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qid                                      question_text  \\\n",
       "0  00002165364db923c7e6  How did Quebec nationalists see their province...   \n",
       "1  000032939017120e6e44  Do you have an adopted dog, how would you enco...   \n",
       "2  0000412ca6e4628ce2cf  Why does velocity affect time? Does velocity a...   \n",
       "3  000042bf85aa498cd78e  How did Otto von Guericke used the Magdeburg h...   \n",
       "4  0000455dfa3e01eae3af  Can I convert montra helicon D to a mountain b...   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>question_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00014894849d00ba98a9</td>\n",
       "      <td>My voice range is A2-C5. My chest voice goes u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000156468431f09b3cae</td>\n",
       "      <td>How much does a tutor earn in Bangalore?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000227734433360e1aae</td>\n",
       "      <td>What are the best made pocket knives under $20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005e06fbe3045bd2a92</td>\n",
       "      <td>Why would they add a hypothetical scenario tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00068a0f7f41f50fc399</td>\n",
       "      <td>What is the dresscode for Techmahindra freshers?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qid                                      question_text\n",
       "0  00014894849d00ba98a9  My voice range is A2-C5. My chest voice goes u...\n",
       "1  000156468431f09b3cae           How much does a tutor earn in Bangalore?\n",
       "2  000227734433360e1aae  What are the best made pocket knives under $20...\n",
       "3  0005e06fbe3045bd2a92  Why would they add a hypothetical scenario tha...\n",
       "4  00068a0f7f41f50fc399   What is the dresscode for Techmahindra freshers?"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Lets Start Removing Redundancies***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mispell_dict = {\"aren't\" : \"are not\",\n",
    "\"can't\" : \"cannot\",\n",
    "\"couldn't\" : \"could not\",\n",
    "\"didn't\" : \"did not\",\n",
    "\"doesn't\" : \"does not\",\n",
    "\"don't\" : \"do not\",\n",
    "\"hadn't\" : \"had not\",\n",
    "\"hasn't\" : \"has not\",\n",
    "\"haven't\" : \"have not\",\n",
    "\"he'd\" : \"he would\",\n",
    "\"he'll\" : \"he will\",\n",
    "\"he's\" : \"he is\",\n",
    "\"i'd\" : \"I would\",\n",
    "\"i'd\" : \"I had\",\n",
    "\"i'll\" : \"I will\",\n",
    "\"i'm\" : \"I am\",\n",
    "\"isn't\" : \"is not\",\n",
    "\"it's\" : \"it is\",\n",
    "\"it'll\":\"it will\",\n",
    "\"i've\" : \"I have\",\n",
    "\"let's\" : \"let us\",\n",
    "\"mightn't\" : \"might not\",\n",
    "\"mustn't\" : \"must not\",\n",
    "\"shan't\" : \"shall not\",\n",
    "\"she'd\" : \"she would\",\n",
    "\"she'll\" : \"she will\",\n",
    "\"she's\" : \"she is\",\n",
    "\"shouldn't\" : \"should not\",\n",
    "\"that's\" : \"that is\",\n",
    "\"there's\" : \"there is\",\n",
    "\"they'd\" : \"they would\",\n",
    "\"they'll\" : \"they will\",\n",
    "\"they're\" : \"they are\",\n",
    "\"they've\" : \"they have\",\n",
    "\"we'd\" : \"we would\",\n",
    "\"we're\" : \"we are\",\n",
    "\"weren't\" : \"were not\",\n",
    "\"we've\" : \"we have\",\n",
    "\"what'll\" : \"what will\",\n",
    "\"what're\" : \"what are\",\n",
    "\"what's\" : \"what is\",\n",
    "\"what've\" : \"what have\",\n",
    "\"where's\" : \"where is\",\n",
    "\"who'd\" : \"who would\",\n",
    "\"who'll\" : \"who will\",\n",
    "\"who're\" : \"who are\",\n",
    "\"who's\" : \"who is\",\n",
    "\"who've\" : \"who have\",\n",
    "\"won't\" : \"will not\",\n",
    "\"wouldn't\" : \"would not\",\n",
    "\"you'd\" : \"you would\",\n",
    "\"you'll\" : \"you will\",\n",
    "\"you're\" : \"you are\",\n",
    "\"you've\" : \"you have\",\n",
    "\"'re\": \" are\",\n",
    "\"wasn't\": \"was not\",\n",
    "\"we'll\":\" will\",\n",
    "\"didn't\": \"did not\",\n",
    "\"tryin'\":\"trying\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_str(df):\n",
    "    df['question_text'] = df['question_text'].str.lower()\n",
    "    df['question_text'] = df['question_text'].map(lambda x: (' ').join([mispell_dict[w] if w in mispell_dict else w for w in x.split(\" \")]))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = clean_str(train)\n",
    "test = clean_str(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "qid              0\n",
       "question_text    0\n",
       "target           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_nan = len(train) - train.count()\n",
    "count_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punctuation(text):\n",
    "    '''a function for removing punctuation'''\n",
    "    import string\n",
    "    translator = str.maketrans('', '', string.punctuation)\n",
    "    return text.translate(translator)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>question_text</th>\n",
       "      <th>target</th>\n",
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       "      <td>how did quebec nationalists see their province...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>do you have an adopted dog how would you encou...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000412ca6e4628ce2cf</td>\n",
       "      <td>why does velocity affect time does velocity af...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000042bf85aa498cd78e</td>\n",
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       "      <th>4</th>\n",
       "      <td>0000455dfa3e01eae3af</td>\n",
       "      <td>can i convert montra helicon d to a mountain b...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qid                                      question_text  \\\n",
       "0  00002165364db923c7e6  how did quebec nationalists see their province...   \n",
       "1  000032939017120e6e44  do you have an adopted dog how would you encou...   \n",
       "2  0000412ca6e4628ce2cf  why does velocity affect time does velocity af...   \n",
       "3  000042bf85aa498cd78e  how did otto von guericke used the magdeburg h...   \n",
       "4  0000455dfa3e01eae3af  can i convert montra helicon d to a mountain b...   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['question_text'] =train['question_text'].apply(remove_punctuation)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sw = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stopwords(text):\n",
    "    '''a function for removing the stopword'''\n",
    "    text = [word.lower() for word in text.split() if word.lower() not in sw]\n",
    "    return \" \".join(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>question_text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00002165364db923c7e6</td>\n",
       "      <td>quebec nationalists see province nation 1960s</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000032939017120e6e44</td>\n",
       "      <td>adopted dog would encourage people adopt shop</td>\n",
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       "      <th>2</th>\n",
       "      <td>0000412ca6e4628ce2cf</td>\n",
       "      <td>velocity affect time velocity affect space geo...</td>\n",
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       "      <th>3</th>\n",
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       "      <td>otto von guericke used magdeburg hemispheres</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000455dfa3e01eae3af</td>\n",
       "      <td>convert montra helicon mountain bike changing ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00004f9a462a357c33be</td>\n",
       "      <td>gaza slowly becoming auschwitz dachau treblink...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00005059a06ee19e11ad</td>\n",
       "      <td>quora automatically ban conservative opinions ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0000559f875832745e2e</td>\n",
       "      <td>crazy wash wipe groceries germs everywhere</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00005bd3426b2d0c8305</td>\n",
       "      <td>thing dressing moderately different dressing m...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00006e6928c5df60eacb</td>\n",
       "      <td>ever phase wherein became ignorant people love...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qid                                      question_text  \\\n",
       "0  00002165364db923c7e6      quebec nationalists see province nation 1960s   \n",
       "1  000032939017120e6e44      adopted dog would encourage people adopt shop   \n",
       "2  0000412ca6e4628ce2cf  velocity affect time velocity affect space geo...   \n",
       "3  000042bf85aa498cd78e       otto von guericke used magdeburg hemispheres   \n",
       "4  0000455dfa3e01eae3af  convert montra helicon mountain bike changing ...   \n",
       "5  00004f9a462a357c33be  gaza slowly becoming auschwitz dachau treblink...   \n",
       "6  00005059a06ee19e11ad  quora automatically ban conservative opinions ...   \n",
       "7  0000559f875832745e2e         crazy wash wipe groceries germs everywhere   \n",
       "8  00005bd3426b2d0c8305  thing dressing moderately different dressing m...   \n",
       "9  00006e6928c5df60eacb  ever phase wherein became ignorant people love...   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "5       0  \n",
       "6       0  \n",
       "7       0  \n",
       "8       0  \n",
       "9       0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['question_text'] = train['question_text'].apply(stopwords)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***EXPLORATION TIME***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from subprocess import check_output\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg = train[train['target']==1]\n",
    "pos = train[train['target']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x7fc9da4513c8>\n"
     ]
    },
    {
     "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": [
    "mpl.rcParams['font.size']=12                #10 \n",
    "mpl.rcParams['savefig.dpi']=100             #72 \n",
    "mpl.rcParams['figure.subplot.bottom']=.1 \n",
    "\n",
    "\n",
    "stopwords = set(STOPWORDS)\n",
    "\n",
    "\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='white',\n",
    "                          stopwords=stopwords,\n",
    "                          max_words=200,\n",
    "                          max_font_size=40, \n",
    "                          random_state=42\n",
    "                         ).generate(str(neg['question_text']))\n",
    "\n",
    "print(wordcloud)\n",
    "fig = plt.figure(0)\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x7fc9da35cdd8>\n"
     ]
    },
    {
     "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": [
    "mpl.rcParams['font.size']=12                #10 \n",
    "mpl.rcParams['savefig.dpi']=100             #72 \n",
    "mpl.rcParams['figure.subplot.bottom']=.1 \n",
    "\n",
    "\n",
    "stopwords = set(STOPWORDS)\n",
    "\n",
    "\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='white',\n",
    "                          stopwords=stopwords,\n",
    "                          max_words=200,\n",
    "                          max_font_size=40, \n",
    "                          random_state=42\n",
    "                         ).generate(str(pos['question_text']))\n",
    "\n",
    "print(wordcloud)\n",
    "fig = plt.figure(0)\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_ngrams(text, n_gram=1):\n",
    "    token = [token for token in text.lower().split(\" \") if token != \"\" if token not in STOPWORDS]\n",
    "    ngrams = zip(*[token[i:] for i in range(n_gram)])\n",
    "    return [\" \".join(ngram) for ngram in ngrams]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_dict = defaultdict(int)\n",
    "for sent in pos[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "posFreqOG = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "posFreqOG.columns = [\"word\", \"wordcount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>word</th>\n",
       "      <th>wordcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>best way</td>\n",
       "      <td>7007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>year old</td>\n",
       "      <td>3364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>united states</td>\n",
       "      <td>3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high school</td>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>computer science</td>\n",
       "      <td>2631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               word  wordcount\n",
       "0          best way       7007\n",
       "1          year old       3364\n",
       "2     united states       3135\n",
       "3       high school       2685\n",
       "4  computer science       2631"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posFreqOG.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_dict = defaultdict(int)\n",
    "for sent in neg[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "negFreqOG = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "negFreqOG.columns = [\"word\", \"wordcount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(443059, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negFreqOG.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>word</th>\n",
       "      <th>wordcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>donald trump</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white people</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>black people</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>united states</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>president trump</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              word  wordcount\n",
       "0     donald trump       1321\n",
       "1     white people        864\n",
       "2     black people        805\n",
       "3    united states        573\n",
       "4  president trump        405"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negFreqOG.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "topNegOG = negFreqOG.head(100)\n",
    "topPosOG = posFreqOG.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cPosOG = set(topPosOG['word'])\n",
    "cNegOG = set(topNegOG['word'])\n",
    "commonOG = cPosOG.intersection(cNegOG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(commonOG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'around world',\n",
       " 'best way',\n",
       " 'donald trump',\n",
       " 'even though',\n",
       " 'many people',\n",
       " 'new york',\n",
       " 'new zealand',\n",
       " 'north korea',\n",
       " 'people say',\n",
       " 'people think',\n",
       " 'president trump',\n",
       " 'questions quora',\n",
       " 'saudi arabia',\n",
       " 'social media',\n",
       " 'united states',\n",
       " 'year old',\n",
       " 'years ago',\n",
       " 'years old'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commonOG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['question_text'] = train['question_text'].map(lambda x: (' ').join([commonOG[w] if w in commonOG else w for w in x.split(\" \")]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Let's see the change***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg = train[train['target']==1]\n",
    "pos = train[train['target']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x7fc9d855edd8>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x7fc9d872da20>\n"
     ]
    },
    {
     "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": [
    "mpl.rcParams['font.size']=12                #10 \n",
    "mpl.rcParams['savefig.dpi']=100             #72 \n",
    "mpl.rcParams['figure.subplot.bottom']=.1 \n",
    "\n",
    "\n",
    "stopwords = set(STOPWORDS)\n",
    "\n",
    "\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='white',\n",
    "                          stopwords=stopwords,\n",
    "                          max_words=200,\n",
    "                          max_font_size=40, \n",
    "                          random_state=42\n",
    "                         ).generate(str(pos['question_text']))\n",
    "\n",
    "print(wordcloud)\n",
    "fig = plt.figure(0)\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.title('Positive Values after removal')\n",
    "plt.show()\n",
    "\n",
    "mpl.rcParams['font.size']=12                #10 \n",
    "mpl.rcParams['savefig.dpi']=100             #72 \n",
    "mpl.rcParams['figure.subplot.bottom']=.1 \n",
    "\n",
    "\n",
    "stopwords = set(STOPWORDS)\n",
    "\n",
    "\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='white',\n",
    "                          stopwords=stopwords,\n",
    "                          max_words=200,\n",
    "                          max_font_size=40, \n",
    "                          random_state=42\n",
    "                         ).generate(str(neg['question_text']))\n",
    "\n",
    "print(wordcloud)\n",
    "fig = plt.figure(0)\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.title('Negative Values after removal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>word</th>\n",
       "      <th>wordcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>donald trump</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white people</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>black people</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>united states</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>president trump</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              word  wordcount\n",
       "0     donald trump       1321\n",
       "1     white people        864\n",
       "2     black people        805\n",
       "3    united states        573\n",
       "4  president trump        405"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_dict = defaultdict(int)\n",
    "for sent in neg[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "negFreq = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "negFreq.columns = [\"word\", \"wordcount\"]\n",
    "\n",
    "negFreq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>word</th>\n",
       "      <th>wordcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>best way</td>\n",
       "      <td>7007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>year old</td>\n",
       "      <td>3364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>united states</td>\n",
       "      <td>3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high school</td>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>computer science</td>\n",
       "      <td>2631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               word  wordcount\n",
       "0          best way       7007\n",
       "1          year old       3364\n",
       "2     united states       3135\n",
       "3       high school       2685\n",
       "4  computer science       2631"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_dict = defaultdict(int)\n",
    "for sent in pos[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "posFreq = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "posFreq.columns = [\"word\", \"wordcount\"]\n",
    "\n",
    "posFreq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_dict = defaultdict(int)\n",
    "for sent in pos[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "posFreqClean = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "posFreqClean.columns = [\"word\", \"wordcount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_dict = defaultdict(int)\n",
    "for sent in neg[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "negFreqClean = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "negFreqClean.columns = [\"word\", \"wordcount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "topNegClean = negFreqOG.head(100)\n",
    "topPosClean = posFreqOG.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cPosClean= set(topPosClean['word'])\n",
    "cNegClean = set(topNegClean['word'])\n",
    "commonClean = cPosClean.intersection(cNegClean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(commonClean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'around world',\n",
       " 'best way',\n",
       " 'donald trump',\n",
       " 'even though',\n",
       " 'many people',\n",
       " 'new york',\n",
       " 'new zealand',\n",
       " 'north korea',\n",
       " 'people say',\n",
       " 'people think',\n",
       " 'president trump',\n",
       " 'questions quora',\n",
       " 'saudi arabia',\n",
       " 'social media',\n",
       " 'united states',\n",
       " 'year old',\n",
       " 'years ago',\n",
       " 'years old'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commonClean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Start Plag***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly import tools\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def horizontal_bar_chart(df, color):\n",
    "    trace = go.Bar(\n",
    "        y=df[\"word\"].values[::-1],\n",
    "        x=df[\"wordcount\"].values[::-1],\n",
    "        showlegend=False,\n",
    "        orientation = 'h',\n",
    "        marker=dict(\n",
    "            color=color,\n",
    "        ),\n",
    "    )\n",
    "    return trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]\n",
      "\n"
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    },
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         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Frequent bigrams of sincere questions",
          "x": 0.2125,
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          "xref": "paper",
          "y": 1,
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        "paper_bgcolor": "rgb(233,233,233)",
        "title": {
         "text": "Bigram Count Plots"
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        "width": 900,
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      "text/vnd.plotly.v1+html": [
       "<div id=\"4b5811df-691f-45b2-9457-a2ee7a10760a\" style=\"height: 1200px; width: 900px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4b5811df-691f-45b2-9457-a2ee7a10760a\", [{\"marker\": {\"color\": \"orange\"}, \"orientation\": \"h\", \"showlegend\": false, \"x\": [773, 790, 801, 810, 832, 846, 849, 864, 867, 868, 873, 879, 884, 923, 947, 958, 981, 1017, 1018, 1036, 1109, 1113, 1117, 1136, 1141, 1155, 1167, 1174, 1183, 1202, 1209, 1238, 1290, 1316, 1386, 1442, 1448, 1473, 1496, 1664, 1823, 1881, 2002, 2053, 2234, 2631, 2685, 3135, 3364, 7007], \"y\": [\"credit card\", \"win fight\", \"harry potter\", \"best books\", \"earn money\", \"many times\", \"years ago\", \"civil engineering\", \"long term\", \"2 years\", \"useful tips\", \"star wars\", \"tv series\", \"world war\", \"class 12\", \"best book\", \"good idea\", \"real estate\", \"different types\", \"tv show\", \"good bad\", \"mechanical engineering\", \"first time\", \"real life\", \"best friend\", \"advice give\", \"best place\", \"much cost\", \"people think\", \"new york\", \"pros cons\", \"much money\", \"machine learning\", \"much time\", \"make money\", \"major accomplishments\", \"best ways\", \"north korea\", \"jee mains\", \"years old\", \"donald trump\", \"even though\", \"many people\", \"social media\", \"long take\", \"computer science\", \"high school\", \"united states\", \"year old\", \"best way\"], \"type\": \"bar\", \"uid\": \"72966ac9-34d8-4027-83c4-d06cd67fc7cd\", \"xaxis\": \"x\", \"yaxis\": \"y\"}, {\"marker\": {\"color\": \"orange\"}, \"orientation\": \"h\", \"showlegend\": false, \"x\": [117, 117, 121, 121, 123, 126, 128, 129, 129, 130, 131, 132, 144, 150, 151, 152, 154, 158, 162, 166, 171, 172, 172, 174, 176, 184, 202, 206, 208, 211, 219, 220, 229, 236, 246, 250, 258, 258, 306, 318, 342, 368, 374, 396, 400, 405, 573, 805, 864, 1321], \"y\": [\"best way\", \"people india\", \"barack obama\", \"north indian\", \"human rights\", \"white house\", \"british people\", \"fake news\", \"want sex\", \"stupid questions\", \"people still\", \"democratic party\", \"american people\", \"gay people\", \"men women\", \"illegal immigrants\", \"indian people\", \"people believe\", \"black women\", \"african americans\", \"years old\", \"questions quora\", \"people say\", \"narendra modi\", \"middle east\", \"indian men\", \"indian women\", \"south indians\", \"gun control\", \"people quora\", \"black men\", \"people hate\", \"white men\", \"white women\", \"north indians\", \"north korea\", \"indian muslims\", \"indian girls\", \"chinese people\", \"people think\", \"even though\", \"year old\", \"hillary clinton\", \"many people\", \"trump supporters\", \"president trump\", \"united states\", \"black people\", \"white people\", \"donald trump\"], \"type\": \"bar\", \"uid\": \"86a00444-b660-42d7-8f13-a020937171b5\", \"xaxis\": \"x2\", \"yaxis\": \"y2\"}], {\"annotations\": [{\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Frequent bigrams of sincere questions\", \"x\": 0.2125, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Frequent bigrams of insincere questions\", \"x\": 0.7875, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}], \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.425]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.575, 1.0]}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 1.0]}, \"height\": 1200, \"width\": 900, \"paper_bgcolor\": \"rgb(233,233,233)\", \"title\": {\"text\": \"Bigram Count Plots\"}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_dict = defaultdict(int)\n",
    "for sent in pos[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "fd_sorted = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "fd_sorted.columns = [\"word\", \"wordcount\"]\n",
    "trace0 = horizontal_bar_chart(fd_sorted.head(50), 'orange')\n",
    "\n",
    "\n",
    "freq_dict = defaultdict(int)\n",
    "for sent in neg[\"question_text\"]:\n",
    "    for word in generate_ngrams(sent,2):\n",
    "        freq_dict[word] += 1\n",
    "fd_sorted = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])\n",
    "fd_sorted.columns = [\"word\", \"wordcount\"]\n",
    "trace1 = horizontal_bar_chart(fd_sorted.head(50), 'orange')\n",
    "\n",
    "# Creating two subplots\n",
    "fig = tools.make_subplots(rows=1, cols=2, vertical_spacing=0.04,horizontal_spacing=0.15,\n",
    "                          subplot_titles=[\"Frequent bigrams of sincere questions\", \n",
    "                                          \"Frequent bigrams of insincere questions\"])\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 2)\n",
    "fig['layout'].update(height=1200, width=900, paper_bgcolor='rgb(233,233,233)', title=\"Bigram Count Plots\")\n",
    "py.iplot(fig, filename='word-plots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***end Plag***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chetan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "pos.quesion_text = pos.question_text.str.replace('\\d+', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>qid</th>\n",
       "      <th>question_text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00002165364db923c7e6</td>\n",
       "      <td>quebec nationalists see province nation 1960s</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000032939017120e6e44</td>\n",
       "      <td>adopted dog would encourage people adopt shop</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000412ca6e4628ce2cf</td>\n",
       "      <td>velocity affect time velocity affect space geo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000042bf85aa498cd78e</td>\n",
       "      <td>otto von guericke used magdeburg hemispheres</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000455dfa3e01eae3af</td>\n",
       "      <td>convert montra helicon mountain bike changing ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qid                                      question_text  \\\n",
       "0  00002165364db923c7e6      quebec nationalists see province nation 1960s   \n",
       "1  000032939017120e6e44      adopted dog would encourage people adopt shop   \n",
       "2  0000412ca6e4628ce2cf  velocity affect time velocity affect space geo...   \n",
       "3  000042bf85aa498cd78e       otto von guericke used magdeburg hemispheres   \n",
       "4  0000455dfa3e01eae3af  convert montra helicon mountain bike changing ...   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***OG ML Stuff***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer # It takes the words of each sentence and creates a vocabulary of all the unique words in the sentences. This vocabulary can then be used to create a feature vector of the count of the words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = train['question_text'].values\n",
    "y = train['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    1225312\n",
       "1      80810\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('target').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Soooo it's unbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "vals_train, vals_test, y_train, y_test = train_test_split(vals, y,\n",
    "                                                    stratify=y, \n",
    "                                                    test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(vals_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<979591x209120 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 6112265 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = vectorizer.transform(vals_train)\n",
    "X_test  = vectorizer.transform(vals_test)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!Classify Time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chetan/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9517319948182561\n",
      "Accuracy(VAL): 0.9517136198400764\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train, y_train)\n",
    "score = classifier.score(X_test, y_test)\n",
    "\n",
    "print(\"Accuracy:\", score)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train, y_train)\n",
    "score = classifier.score(X_test, y_test)\n",
    "\n",
    "print(\"Accuracy(VAL):\", score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9434540671482953\n",
      "Accuracy(val): 0.944513690889992\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "classifier2 = MultinomialNB()\n",
    "classifier2.fit(X_train, y_train)\n",
    "score = classifier2.score(X_test, y_test)\n",
    "\n",
    "print(\"Accuracy:\", score)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)\n",
    "classifier2 = MultinomialNB()\n",
    "classifier2.fit(X_train, y_train)\n",
    "score = classifier2.score(X_test, y_test)\n",
    "\n",
    "print(\"Accuracy(val):\", score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.944513690889992\n",
      "Accuracy(val): 0.9450404402644772\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "classifier3 = MultinomialNB()\n",
    "classifier3.fit(X_train, y_train)\n",
    "score = classifier3.score(X_test, y_test)\n",
    "\n",
    "print(\"Accuracy:\", score)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)\n",
    "classifier3 = MultinomialNB()\n",
    "classifier3.fit(X_train, y_train)\n",
    "score = classifier3.score(X_test, y_test)\n",
    "\n",
    "print(\"Accuracy(val):\", score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9381283859725417\n",
      "Accuracy(val): 0.9381283859725417\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf4 = RandomForestClassifier(n_estimators=500, max_depth=2)\n",
    "clf4.fit(X_train, y_train)\n",
    "score = clf4.score(X_test, y_test)\n",
    "print(\"Accuracy:\", score)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)\n",
    "clf4 = RandomForestClassifier(n_estimators=500, max_depth=2)\n",
    "clf4.fit(X_train, y_train)\n",
    "score = clf4.score(X_test, y_test)\n",
    "\n",
    "print(\"Accuracy(val):\", score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***HYPE TIME!?***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Term Frequency — Inverse Document Frequency (TF — IDF)\n",
    "\n",
    "TF-IDF is a weighted model commonly used for information retrieval problems. It aims to convert the text documents into vector models on the basis of occurrence of words in the documents without taking considering the exact ordering. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer()\n",
    "tfidf.fit(train['question_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train['question_text']\n",
    "y = train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    stratify=y, \n",
    "                                                    test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chi-Squared for Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "tfidf = TfidfVectorizer(max_features=30000,ngram_range=(1, 3))\n",
    "X_tfidf = tfidf.fit_transform(train.question_text)\n",
    "y = train['target']\n",
    "chi2score = chi2(X_tfidf, y)[1]\n",
    "plt.figure(figsize=(16,8))\n",
    "scores = list(zip(tfidf.get_feature_names(), chi2score))\n",
    "chi2 = sorted(scores, key=lambda x:x[1])\n",
    "topchi2 = list(zip(*chi2[-20:]))\n",
    "x = range(len(topchi2[1]))\n",
    "labels = topchi2[0]\n",
    "plt.barh(x,topchi2[1], align='center', alpha=0.5)\n",
    "plt.plot(topchi2[1], x, '-o', markersize=5, alpha=0.8)\n",
    "plt.yticks(x, labels)\n",
    "plt.xlabel('$\\chi^2$')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "tfidf = TfidfVectorizer(max_features=30000,ngram_range=(1, 3))\n",
    "X_tfidf = tfidf.fit_transform(train.question_text)\n",
    "y = train['target']\n",
    "chi2score = chi2(X_tfidf, y)[0]\n",
    "plt.figure(figsize=(16,8))\n",
    "scores = list(zip(tfidf.get_feature_names(), chi2score))\n",
    "chi2 = sorted(scores, key=lambda x:x[1])\n",
    "topchi2 = list(zip(*chi2[-20:]))\n",
    "x = range(len(topchi2[1]))\n",
    "labels = topchi2[0]\n",
    "plt.barh(x,topchi2[1], align='center', alpha=0.5)\n",
    "plt.plot(topchi2[1], x, '-o', markersize=5, alpha=0.8)\n",
    "plt.yticks(x, labels)\n",
    "plt.xlabel('$\\chi^2$')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTM TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, LSTM\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pad sequences\n",
    "\n",
    "In order to feed this data into our RNN, all input documents must have the same length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(979591, 62) (979591, 2)\n",
      "(326531, 62) (326531, 2)\n"
     ]
    }
   ],
   "source": [
    "max_fatures = 30000\n",
    "tokenizer = Tokenizer(nb_words=max_fatures, split=' ')\n",
    "tokenizer.fit_on_texts(train['question_text'].values)\n",
    "X1 = tokenizer.texts_to_sequences(train['question_text'].values)\n",
    "X1 = pad_sequences(X1)\n",
    "Y1 = pd.get_dummies(train['target']).values\n",
    "X1_train, X1_test, Y1_train, Y1_test = train_test_split(X1,Y1, random_state = 42)\n",
    "print(X1_train.shape,Y1_train.shape)\n",
    "print(X1_test.shape,Y1_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chetan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: UserWarning: The `dropout` argument is no longer support in `Embedding`. You can apply a `keras.layers.SpatialDropout1D` layer right after the `Embedding` layer to get the same behavior.\n",
      "  \"\"\"\n",
      "/home/chetan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: UserWarning: Update your `LSTM` call to the Keras 2 API: `LSTM(200, dropout=0.2, recurrent_dropout=0.2)`\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 62, 150)           4500000   \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 200)               280800    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 402       \n",
      "=================================================================\n",
      "Total params: 4,781,202\n",
      "Trainable params: 4,781,202\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "embed_dim = 150\n",
    "lstm_out = 200\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(max_fatures, embed_dim,input_length = X1.shape[1], dropout=0.2))\n",
    "model.add(LSTM(lstm_out, dropout_U=0.2,dropout_W=0.2))\n",
    "model.add(Dense(2,activation='softmax'))\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chetan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      " - 8373s - loss: 0.1198 - acc: 0.9526\n",
      "Epoch 2/3\n",
      " - 8102s - loss: 0.1077 - acc: 0.9577\n",
      "Epoch 3/3\n",
      " - 7757s - loss: 0.1016 - acc: 0.9606\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd4c7e18d68>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 32\n",
    "model.fit(X1_train, Y1_train, nb_epoch = 3, batch_size=batch_size, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.11\n",
      "acc: 0.96\n"
     ]
    }
   ],
   "source": [
    "score,acc = model.evaluate(X1_test, Y1_test, verbose = 2, batch_size = batch_size)\n",
    "print(\"score: %.2f\" % (score))\n",
    "print(\"acc: %.2f\" % (acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos_acc 58.18245649230151 %\n",
      "neg_acc 97.9304743763699 %\n"
     ]
    }
   ],
   "source": [
    "pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0\n",
    "for x in range(len(X1_test)):\n",
    "    \n",
    "    result = model.predict(X1_test[x].reshape(1,X1_test.shape[1]),batch_size=1,verbose = 2)[0]\n",
    "   \n",
    "    if np.argmax(result) == np.argmax(Y1_test[x]):\n",
    "        if np.argmax(Y1_test[x]) == 0:\n",
    "            neg_correct += 1\n",
    "        else:\n",
    "            pos_correct += 1\n",
    "       \n",
    "    if np.argmax(Y1_test[x]) == 0:\n",
    "        neg_cnt += 1\n",
    "    else:\n",
    "        pos_cnt += 1\n",
    "\n",
    "\n",
    "\n",
    "print(\"pos_acc\", pos_correct/pos_cnt*100, \"%\")\n",
    "print(\"neg_acc\", neg_correct/neg_cnt*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import model_from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "json_file = open('model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"model.h5\")\n",
    "print(\"Loaded model from disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import text, sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxlen = 62\n",
    "max_features = 50000\n",
    "X_test = test[\"question_text\"].fillna(\"dieter\").values\n",
    "X_test = tokenizer.texts_to_sequences(X_test)\n",
    "x_test = sequence.pad_sequences(X_test, maxlen=maxlen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = loaded_model.predict(x_test, batch_size=1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56370, 2)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_te = (y_pred[:,0] > 0.5).astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_df = pd.DataFrame({\"qid\": test[\"qid\"], \"prediction\": y_te})\n",
    "submit_df.to_csv(\"sample_submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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