Final_Expalinability_Feature_Importance.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#for text pre-processing\n",
    "import re, string\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "#nltk.download('punkt')\n",
    "#nltk.download('averaged_perceptron_tagger')\n",
    "#nltk.download('wordnet')\n",
    "#nltk.download('omw-1.4')\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "from sklearn.calibration import calibration_curve, CalibrationDisplay\n",
    "from sklearn import metrics"
   ]
  },
  {
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   "id": "mdzRhEZxjdVa",
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   "outputs": [
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     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
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       "True"
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     "execution_count": 3,
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   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72defce8",
   "metadata": {
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   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords_list = set(stopwords.words('english'))\n",
    "#stopwords_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a25d2018",
   "metadata": {
    "id": "a25d2018"
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   "source": [
    "train_data = pd.read_csv(\"train_data.csv\")\n",
    "train_results = pd.read_csv(\"train_results.csv\")\n",
    "test_data = pd.read_csv(\"test_data.csv\")"
   ]
  },
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     "output_type": "stream",
     "text": [
      "1040323\n",
      "560175\n"
     ]
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   ],
   "source": [
    "print(len(train_data))\n",
    "print(len(test_data))"
   ]
  },
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       "      <td>Anyway Im getting of for a while</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>My red, Apache isn't feelin too well this morn...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>its 11:30pm and i dont wanna sleep; so i debat...</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>Why does twitter eat my DM's?  Not happy</td>\n",
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       "      <td>getting ready 2 watch mental</td>\n",
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       "      <th>1040319</th>\n",
       "      <td>1040319</td>\n",
       "      <td>Wristcutters and Half Nelson are on Sundance F...</td>\n",
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       "      <th>1040320</th>\n",
       "      <td>1040320</td>\n",
       "      <td>@t_isfortammy Going out in Northbridge makes m...</td>\n",
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       "    <tr>\n",
       "      <th>1040321</th>\n",
       "      <td>1040321</td>\n",
       "      <td>@iModel_ lol.... Gorgeous...lol! U look sooo m...</td>\n",
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       "      <td>1040322</td>\n",
       "      <td>@iamjonathancook why?</td>\n",
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       "              id                                               text\n",
       "0              0                  Anyway Im getting of for a while \n",
       "1              1  My red, Apache isn't feelin too well this morn...\n",
       "2              2  @danyelljoy you should be  its great. friday w...\n",
       "3              3  its 11:30pm and i dont wanna sleep; so i debat...\n",
       "4              4          Why does twitter eat my DM's?  Not happy \n",
       "...          ...                                                ...\n",
       "1040318  1040318                      getting ready 2 watch mental \n",
       "1040319  1040319  Wristcutters and Half Nelson are on Sundance F...\n",
       "1040320  1040320  @t_isfortammy Going out in Northbridge makes m...\n",
       "1040321  1040321  @iModel_ lol.... Gorgeous...lol! U look sooo m...\n",
       "1040322  1040322                             @iamjonathancook why? \n",
       "\n",
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    "train_data.isnull().sum()"
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       "      <td>so cut... missed out on all the TCE EXTREME ME...</td>\n",
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       "      <td>1</td>\n",
       "      <td>Thank you very much Wasin  this is awesome lol</td>\n",
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       "      <td>2</td>\n",
       "      <td>Squeezing in some internet time before Dustin ...</td>\n",
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       "      <td>@fanoou; Oh i do have moral but i'm gonna be b...</td>\n",
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       "      <td>@NSuissa   I just bought Sammy some joint/hip ...</td>\n",
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       "      <td>@meganintime So is mine and I've only got abou...</td>\n",
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       "    <tr>\n",
       "      <th>560171</th>\n",
       "      <td>560171</td>\n",
       "      <td>@FairyForest Thanks cutie ) Wishing u a wonder...</td>\n",
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       "      <td>560172</td>\n",
       "      <td>ok know i just got home my family just drop me...</td>\n",
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       "      <th>560173</th>\n",
       "      <td>560173</td>\n",
       "      <td>long run status: complete. Now time for church...</td>\n",
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       "    <tr>\n",
       "      <th>560174</th>\n",
       "      <td>560174</td>\n",
       "      <td>@textualoffender Doestovesky yes,remaining 3 w...</td>\n",
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       "            id                                               text\n",
       "0            0  so cut... missed out on all the TCE EXTREME ME...\n",
       "1            1     Thank you very much Wasin  this is awesome lol\n",
       "2            2  Squeezing in some internet time before Dustin ...\n",
       "3            3  @fanoou; Oh i do have moral but i'm gonna be b...\n",
       "4            4  @NSuissa   I just bought Sammy some joint/hip ...\n",
       "...        ...                                                ...\n",
       "560170  560170  @meganintime So is mine and I've only got abou...\n",
       "560171  560171  @FairyForest Thanks cutie ) Wishing u a wonder...\n",
       "560172  560172  ok know i just got home my family just drop me...\n",
       "560173  560173  long run status: complete. Now time for church...\n",
       "560174  560174  @textualoffender Doestovesky yes,remaining 3 w...\n",
       "\n",
       "[560175 rows x 2 columns]"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "test_data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a28d148",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1a28d148",
    "outputId": "fc367628-322a-4c16-d697-038f8164908c"
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    {
     "data": {
      "text/plain": [
       "0         so cut... missed out on all the TCE EXTREME ME...\n",
       "1            Thank you very much Wasin  this is awesome lol\n",
       "2         Squeezing in some internet time before Dustin ...\n",
       "3         @fanoou; Oh i do have moral but i'm gonna be b...\n",
       "4         @NSuissa   I just bought Sammy some joint/hip ...\n",
       "                                ...                        \n",
       "560170    @meganintime So is mine and I've only got abou...\n",
       "560171    @FairyForest Thanks cutie ) Wishing u a wonder...\n",
       "560172    ok know i just got home my family just drop me...\n",
       "560173    long run status: complete. Now time for church...\n",
       "560174    @textualoffender Doestovesky yes,remaining 3 w...\n",
       "Name: text, Length: 560175, dtype: object"
      ]
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     "execution_count": 24,
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       "         target\n",
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       "...         ...\n",
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       "1040322       0\n",
       "\n",
       "[1040323 rows x 1 columns]"
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     "metadata": {},
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    "full_train_label = train_results['target'].replace(['negative', 'neutral', 'positive'] , [0,1,2])\n",
    "train_target = pd.DataFrame(full_train_label)\n",
    "train_target"
   ]
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   "id": "eaca144f",
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       "2    520436\n",
       "0    519803\n",
       "1        84\n",
       "Name: target, dtype: int64"
      ]
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     "execution_count": 26,
     "metadata": {},
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  {
   "cell_type": "code",
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   "id": "f41823c8",
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    "id": "f41823c8",
    "outputId": "574d014b-2837-46bb-952c-3b79f0726969"
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    {
     "data": {
      "text/plain": [
       "positive    520436\n",
       "negative    519803\n",
       "neutral         84\n",
       "Name: target, dtype: int64"
      ]
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     "metadata": {},
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   "execution_count": 28,
   "id": "42b39dd8",
   "metadata": {
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   "source": [
    "#convert to lowercase, strip and remove punctuations\n",
    "def preprocess(text):\n",
    "    text = text.lower() \n",
    "    text=  text.strip()  \n",
    "    text=  re.compile('<.*?>').sub('', text) \n",
    "    text = re.compile('[%s]' % re.escape(string.punctuation)).sub(' ', text)  \n",
    "    text = re.sub('\\s+', ' ', text)  \n",
    "    text = re.sub(r'\\[[0-9]*\\]',' ',text) \n",
    "    text= re.sub(r'[^\\w\\s]', '', str(text).lower().strip())\n",
    "    text = re.sub(r'\\d',' ',text) \n",
    "    text = re.sub(r'\\s+',' ',text) \n",
    "    text = re.sub(r'https?://\\S+|www\\.\\S+', '', text) # Removes url\n",
    "    text = re.compile(r'<[^>]+>').sub('', text) #Removes HTML tags:\n",
    "    text = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', text) # Single character removal\n",
    "    text = re.sub(r'\\s+', ' ', text) # Remove multiple spaces\n",
    "    return text\n",
    "\n",
    "# STOPWORD REMOVAL\n",
    "def stopword(string):\n",
    "    a= [i for i in string.split() if i not in stopwords.words('english')]\n",
    "    return ' '.join(a)\n",
    "\n",
    "#LEMMATIZATION\n",
    "# Initialize the lemmatizer\n",
    "wl = WordNetLemmatizer()\n",
    " \n",
    "# This is a helper function to map NTLK position tags\n",
    "def get_wordnet_pos(tag):\n",
    "    if tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:\n",
    "        return wordnet.NOUN\n",
    "    \n",
    "# Tokenize the sentence\n",
    "def lemmatizer(string):\n",
    "    word_pos_tags = nltk.pos_tag(word_tokenize(string)) # Get position tags\n",
    "    a=[wl.lemmatize(tag[0], get_wordnet_pos(tag[1])) for idx, tag in enumerate(word_pos_tags)] # Map the position tag and lemmatize the word/token\n",
    "    return \" \".join(a)\n",
    "\n",
    "def finalpreprocess(string):\n",
    "    return lemmatizer(stopword(preprocess(string)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "83820909",
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    "id": "83820909",
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       "      <td>@iModel_ lol.... Gorgeous...lol! U look sooo m...</td>\n",
       "      <td>imodel lol gorgeous lol look sooo much like th...</td>\n",
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       "      <td>1040322</td>\n",
       "      <td>@iamjonathancook why?</td>\n",
       "      <td>iamjonathancook why</td>\n",
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "              id                                               text  \\\n",
       "0              0                  Anyway Im getting of for a while    \n",
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       "2              2  @danyelljoy you should be  its great. friday w...   \n",
       "3              3  its 11:30pm and i dont wanna sleep; so i debat...   \n",
       "4              4          Why does twitter eat my DM's?  Not happy    \n",
       "...          ...                                                ...   \n",
       "1040318  1040318                      getting ready 2 watch mental    \n",
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       "1040320  1040320  @t_isfortammy Going out in Northbridge makes m...   \n",
       "1040321  1040321  @iModel_ lol.... Gorgeous...lol! U look sooo m...   \n",
       "1040322  1040322                             @iamjonathancook why?    \n",
       "\n",
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       "0                           anyway im getting of for while  \n",
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       "4                     why does twitter eat my dm not happy  \n",
       "...                                                    ...  \n",
       "1040318                         getting ready watch mental  \n",
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       "1040320  t isfortammy going out in northbridge makes me...  \n",
       "1040321  imodel lol gorgeous lol look sooo much like th...  \n",
       "1040322                                iamjonathancook why  \n",
       "\n",
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       "                                                clean_text\n",
       "0                           anyway im getting of for while\n",
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       "2        danyelljoy you should be its great friday will...\n",
       "3        its pm and dont wanna sleep so debated with my...\n",
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       "...                                                    ...\n",
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       "1040322                                iamjonathancook why\n",
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       "                                                clean_text  target\n",
       "0                           anyway im getting of for while       2\n",
       "1        my red apache isn feelin too well this morning...       0\n",
       "2        danyelljoy you should be its great friday will...       2\n",
       "3        its pm and dont wanna sleep so debated with my...       2\n",
       "4                     why does twitter eat my dm not happy       0\n",
       "...                                                    ...     ...\n",
       "1040318                         getting ready watch mental       2\n",
       "1040319  wristcutters and half nelson are on sundance f...       0\n",
       "1040320  t isfortammy going out in northbridge makes me...       0\n",
       "1040321  imodel lol gorgeous lol look sooo much like th...       2\n",
       "1040322                                iamjonathancook why       0\n",
       "\n",
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       "      <td>so cut... missed out on all the TCE EXTREME ME...</td>\n",
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       "      <td>Thank you very much Wasin  this is awesome lol</td>\n",
       "      <td>thank you very much wasin this is awesome lol</td>\n",
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       "      <th>3</th>\n",
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       "      <td>@fanoou; Oh i do have moral but i'm gonna be b...</td>\n",
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       "    <tr>\n",
       "      <th>560171</th>\n",
       "      <td>560171</td>\n",
       "      <td>@FairyForest Thanks cutie ) Wishing u a wonder...</td>\n",
       "      <td>fairyforest thanks cutie wishing a wonderful m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560172</th>\n",
       "      <td>560172</td>\n",
       "      <td>ok know i just got home my family just drop me...</td>\n",
       "      <td>ok know just got home my family just drop me o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560173</th>\n",
       "      <td>560173</td>\n",
       "      <td>long run status: complete. Now time for church...</td>\n",
       "      <td>long run status complete now time for church m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560174</th>\n",
       "      <td>560174</td>\n",
       "      <td>@textualoffender Doestovesky yes,remaining 3 w...</td>\n",
       "      <td>textualoffender doestovesky yes remaining will...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560175 rows × 3 columns</p>\n",
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       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-e94afb78-dee6-4484-9d2b-6f15304e49dc');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  "
      ],
      "text/plain": [
       "            id                                               text  \\\n",
       "0            0  so cut... missed out on all the TCE EXTREME ME...   \n",
       "1            1     Thank you very much Wasin  this is awesome lol   \n",
       "2            2  Squeezing in some internet time before Dustin ...   \n",
       "3            3  @fanoou; Oh i do have moral but i'm gonna be b...   \n",
       "4            4  @NSuissa   I just bought Sammy some joint/hip ...   \n",
       "...        ...                                                ...   \n",
       "560170  560170  @meganintime So is mine and I've only got abou...   \n",
       "560171  560171  @FairyForest Thanks cutie ) Wishing u a wonder...   \n",
       "560172  560172  ok know i just got home my family just drop me...   \n",
       "560173  560173  long run status: complete. Now time for church...   \n",
       "560174  560174  @textualoffender Doestovesky yes,remaining 3 w...   \n",
       "\n",
       "                                               clean_text  \n",
       "0       so cut missed out on all the tce extreme merch...  \n",
       "1           thank you very much wasin this is awesome lol  \n",
       "2       squeezing in some internet time before dustin ...  \n",
       "3       fanoou oh do have moral but m gonna be bigger ...  \n",
       "4       nsuissa just bought sammy some joint hip suppl...  \n",
       "...                                                   ...  \n",
       "560170  meganintime so is mine and ve only got about w...  \n",
       "560171  fairyforest thanks cutie wishing a wonderful m...  \n",
       "560172  ok know just got home my family just drop me o...  \n",
       "560173  long run status complete now time for church m...  \n",
       "560174  textualoffender doestovesky yes remaining will...  \n",
       "\n",
       "[560175 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['clean_text']=test_data['text'].map(lambda s:preprocess(s)) \n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e947083b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "e947083b",
    "outputId": "159b8e16-93d2-48ca-caeb-0e09af364d5d"
   },
   "outputs": [
    {
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       "                                                     [key], {});\n",
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       "\n",
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      "text/plain": [
       "                                               clean_text\n",
       "0       so cut missed out on all the tce extreme merch...\n",
       "1           thank you very much wasin this is awesome lol\n",
       "2       squeezing in some internet time before dustin ...\n",
       "3       fanoou oh do have moral but m gonna be bigger ...\n",
       "4       nsuissa just bought sammy some joint hip suppl...\n",
       "...                                                   ...\n",
       "560170  meganintime so is mine and ve only got about w...\n",
       "560171  fairyforest thanks cutie wishing a wonderful m...\n",
       "560172  ok know just got home my family just drop me o...\n",
       "560173  long run status complete now time for church m...\n",
       "560174  textualoffender doestovesky yes remaining will...\n",
       "\n",
       "[560175 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_clean = pd.DataFrame(test_data['clean_text'])\n",
    "df_test_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04f7ccac",
   "metadata": {
    "id": "04f7ccac"
   },
   "outputs": [],
   "source": [
    "X_train = df_train_clean['clean_text']\n",
    "X_test = df_test_clean['clean_text']\n",
    "y_train = df_train_clean['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2869aba4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2869aba4",
    "outputId": "8c937ac2-d2eb-4bf7-f0f2-1645d78ef2d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1040323 1040323 560175\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train), len(y_train), len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b4dc6647",
   "metadata": {
    "id": "b4dc6647"
   },
   "outputs": [],
   "source": [
    "# vectorization using Bag-of-Words (with Tf-Idf ) and Word2Vec\n",
    "#Tf-Idf\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1,2))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04vA2706UpkX",
   "metadata": {
    "id": "04vA2706UpkX"
   },
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "92553bf1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "92553bf1",
    "outputId": "d0ac25ec-da45-43c8-8ad7-781e72b17420"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Test Predictions\n",
      "[0 2 0 ... 0 2 2]\n",
      "560175\n"
     ]
    },
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-328f44b5-f5bf-4dc9-901a-a77aff5e6487 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-328f44b5-f5bf-4dc9-901a-a77aff5e6487');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        target\n",
       "id            \n",
       "0            0\n",
       "1            2\n",
       "2            0\n",
       "3            0\n",
       "4            0\n",
       "...        ...\n",
       "560170       0\n",
       "560171       0\n",
       "560172       0\n",
       "560173       2\n",
       "560174       2\n",
       "\n",
       "[560175 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "lr  = LogisticRegression(C=1.0, max_iter=500)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "\n",
    "#Predict y value for test dataset\n",
    "testy_predict = lr.predict(X_test_vectors_tfidf)\n",
    "testy_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "\n",
    "print('\\n')\n",
    "print(\"Test Predictions\")\n",
    "print(testy_predict)\n",
    "print(len(testy_predict))\n",
    "df = pd.DataFrame(testy_predict)\n",
    "df.index.name= 'id'\n",
    "df.columns = ['target']\n",
    "file_name = 'test_predictions_lr_200k.csv'\n",
    "df.to_csv(file_name, index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c-P8vMRUteJ",
   "metadata": {
    "id": "6c-P8vMRUteJ"
   },
   "source": [
    "Explainability for logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "vw0D2elyP7Dv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vw0D2elyP7Dv",
    "outputId": "8fad5ed7-02ce-4fdf-f907-97a0c61a2952"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: shap in /usr/local/lib/python3.8/dist-packages (0.41.0)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from shap) (1.0.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from shap) (1.21.6)\n",
      "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.8/dist-packages (from shap) (1.5.0)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from shap) (1.7.3)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from shap) (1.3.5)\n",
      "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.8/dist-packages (from shap) (4.64.1)\n",
      "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.8/dist-packages (from shap) (0.0.7)\n",
      "Requirement already satisfied: numba in /usr/local/lib/python3.8/dist-packages (from shap) (0.56.4)\n",
      "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.8/dist-packages (from shap) (21.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>20.9->shap) (3.0.9)\n",
      "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from numba->shap) (5.1.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from numba->shap) (57.4.0)\n",
      "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.8/dist-packages (from numba->shap) (0.39.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->numba->shap) (3.11.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->shap) (2022.6)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->shap) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->shap) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->shap) (3.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "VeiZ-XPqP94W",
   "metadata": {
    "id": "VeiZ-XPqP94W"
   },
   "outputs": [],
   "source": [
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "NSBMK8ivN_QG",
   "metadata": {
    "id": "NSBMK8ivN_QG"
   },
   "outputs": [],
   "source": [
    "# We choose the first 1000 datapoints to limit computation memory and time\n",
    "explainer = shap.LinearExplainer(\n",
    "    lr,\n",
    "    X_train_vectors_tfidf[:1000]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "gADzxG1cQu5i",
   "metadata": {
    "id": "gADzxG1cQu5i"
   },
   "outputs": [],
   "source": [
    "shap_values = explainer.shap_values(X_test_vectors_tfidf[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "VZhixjJqT6Kb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VZhixjJqT6Kb",
    "outputId": "98435b06-f3e9-4fb2-a088-47a87779c5e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 10000)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_values[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "psLXAN8tU0QJ",
   "metadata": {
    "id": "psLXAN8tU0QJ"
   },
   "source": [
    "## Feature Importance -Explainabilty on Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "kod7VtDPUyd8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 693
    },
    "id": "kod7VtDPUyd8",
    "outputId": "1df065a5-ed90-49e1-d2c9-816a0578969f"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x684 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_array = X_test_vectors_tfidf[:50].toarray()\n",
    "# display importance of words according to their shap values\n",
    "shap.summary_plot(shap_values,\n",
    "                  X_test_array,\n",
    "                  feature_names=tfidf_vectorizer.get_feature_names_out(),\n",
    "                  max_display=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1Lha5Jl8KZQE",
   "metadata": {
    "id": "1Lha5Jl8KZQE"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "machine_shape": "hm",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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