SVC_NLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ddf01f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ea80e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"Resources/train.csv\")\n",
    "test = pd.read_csv(\"Resources/test.csv\")\n",
    "sample_submission = pd.read_csv(\"Resources/sample_submission.csv\", dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "841dcb91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tweet-preprocessor in /Users/michaelkori/opt/anaconda3/lib/python3.8/site-packages (0.6.0)\r\n"
     ]
    }
   ],
   "source": [
    "# install tweet-preprocessor to clean tweets \n",
    "!pip install tweet-preprocessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7e307a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove special character using the regular expression library \n",
    "import re \n",
    "\n",
    "# Set up punctuations we want to be replaced \n",
    "REPLACE_NO_SPACE = re.compile(\"(\\.)|(\\;)|(\\:)|(\\!)|(\\')|(\\?)|(\\,)|(\\\")|(\\|)|(\\()|(\\))|(\\[)|(\\])|(\\%)|(\\$)|(\\>)|(\\<)|(\\{)|(\\})\")\n",
    "REPLACE_WITH_SPACE = re.compile(\"(<br\\s/><br\\s/?)|(-)|(/)|(:).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e819845",
   "metadata": {},
   "outputs": [],
   "source": [
    "import preprocessor as p\n",
    "\n",
    "def clean_tweets(df):\n",
    "    tempArr = []\n",
    "    for line in df:\n",
    "        # Send to tweet_processor\n",
    "        tmpL = p.clean(line)\n",
    "        # Remove Punctuation\n",
    "        tmpL = REPLACE_NO_SPACE.sub(\"\", tmpL.lower()) # Convert All Tweets to Lower Case\n",
    "        tmpL = REPLACE_WITH_SPACE.sub(\" \", tmpL)\n",
    "        tempArr.append(tmpL)\n",
    "    return tempArr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84470f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean Training Data \n",
    "train_tweet = clean_tweets(train['text'])\n",
    "train_tweet = pd.DataFrame(train_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ebc490df",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "      <th>clean_tweet</th>\n",
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       "  </thead>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "      <td>our deeds are the reason of this may allah for...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "      <td>forest fire near la ronge sask canada</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "      <td>all residents asked to shelter in place are be...</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "      <td>people receive evacuation orders in california</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "      <td>just got sent this photo from ruby as smoke fr...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   id keyword location                                               text  \\\n",
       "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
       "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
       "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
       "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
       "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
       "\n",
       "   target                                        clean_tweet  \n",
       "0       1  our deeds are the reason of this may allah for...  \n",
       "1       1              forest fire near la ronge sask canada  \n",
       "2       1  all residents asked to shelter in place are be...  \n",
       "3       1     people receive evacuation orders in california  \n",
       "4       1  just got sent this photo from ruby as smoke fr...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append cleaned tweets to the trained data \n",
    "train['clean_tweet'] = train_tweet\n",
    "\n",
    "# Compare the cleaned and uncleaned tweets \n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5c1fc61",
   "metadata": {},
   "outputs": [
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       "      <td>our deeds are the reason of this may allah for...</td>\n",
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       "      <td>forest fire near la ronge sask canada</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>all residents asked to shelter in place are be...</td>\n",
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       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>people receive evacuation orders in california</td>\n",
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       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   id keyword location  target  \\\n",
       "0   1     NaN      NaN       1   \n",
       "1   4     NaN      NaN       1   \n",
       "2   5     NaN      NaN       1   \n",
       "3   6     NaN      NaN       1   \n",
       "4   7     NaN      NaN       1   \n",
       "\n",
       "                                         clean_tweet  \n",
       "0  our deeds are the reason of this may allah for...  \n",
       "1              forest fire near la ronge sask canada  \n",
       "2  all residents asked to shelter in place are be...  \n",
       "3     people receive evacuation orders in california  \n",
       "4  just got sent this photo from ruby as smoke fr...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop unnecessary columns \n",
    "\n",
    "train = train.drop(['text'], axis=1)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ed7d2f8",
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       "      <th>clean_tweet</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>10861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...</td>\n",
       "      <td>earthquake safety los angeles safety fasteners...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>10865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Storm in RI worse than last hurricane. My city...</td>\n",
       "      <td>storm in ri worse than last hurricane my city&amp;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>10868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Green Line derailment in Chicago http://t.co/U...</td>\n",
       "      <td>green line derailment in chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3261</th>\n",
       "      <td>10874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MEG issues Hazardous Weather Outlook (HWO) htt...</td>\n",
       "      <td>meg issues hazardous weather outlook hwo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>10875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#CityofCalgary has activated its Municipal Eme...</td>\n",
       "      <td>has activated its municipal emergency plan</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         id keyword location  \\\n",
       "3258  10861     NaN      NaN   \n",
       "3259  10865     NaN      NaN   \n",
       "3260  10868     NaN      NaN   \n",
       "3261  10874     NaN      NaN   \n",
       "3262  10875     NaN      NaN   \n",
       "\n",
       "                                                   text  \\\n",
       "3258  EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...   \n",
       "3259  Storm in RI worse than last hurricane. My city...   \n",
       "3260  Green Line derailment in Chicago http://t.co/U...   \n",
       "3261  MEG issues Hazardous Weather Outlook (HWO) htt...   \n",
       "3262  #CityofCalgary has activated its Municipal Eme...   \n",
       "\n",
       "                                            clean_tweet  \n",
       "3258  earthquake safety los angeles safety fasteners...  \n",
       "3259  storm in ri worse than last hurricane my city&...  \n",
       "3260                   green line derailment in chicago  \n",
       "3261           meg issues hazardous weather outlook hwo  \n",
       "3262         has activated its municipal emergency plan  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean the test data and append the cleaned tweets to the test data \n",
    "\n",
    "test_tweet = clean_tweets(test['text'])\n",
    "test_tweet = pd.DataFrame(test_tweet)\n",
    "\n",
    "# Append cleaned tweets to the training data \n",
    "\n",
    "test['clean_tweet'] = test_tweet\n",
    "\n",
    "# Compare the cleaned and uncleaned tweets \n",
    "\n",
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db4502f6",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>clean_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>just happened a terrible car crash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>heard about is different cities stay safe ever...</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>there is a forest fire at spot pond geese are ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>apocalypse lighting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>typhoon soudelor kills in china and taiwan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   id                                        clean_tweet\n",
       "0   0                 just happened a terrible car crash\n",
       "1   2  heard about is different cities stay safe ever...\n",
       "2   3  there is a forest fire at spot pond geese are ...\n",
       "3   9                                apocalypse lighting\n",
       "4  11         typhoon soudelor kills in china and taiwan"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "# Drop unnecessary columns \n",
    "\n",
    "test = test.drop(['text', 'location', 'keyword'], axis=1)\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dcd3a17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test and Train split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Extract the labels from the train data \n",
    "\n",
    "y = train.target.values\n",
    "\n",
    "# Use 70% for the training and 30% for the test \n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(train.clean_tweet, y, stratify=y, random_state=1, test_size=0.3, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "527181de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorize tweets using CountVectorizer \n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "870859e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorize tweets for model building\n",
    "\n",
    "vectorizer = CountVectorizer(binary=True)\n",
    "\n",
    "# Learn a vocabulary dictionary of all tokens in the raw documennts \n",
    "\n",
    "vectorizer.fit(list(x_train) + list(x_test))\n",
    "\n",
    "# Transform documents to document-term matrix \n",
    "\n",
    "x_train_vec = vectorizer.transform(x_train) \n",
    "x_test_vec = vectorizer.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0dd35798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply Support Vector Classifier (SVC) \n",
    "\n",
    "from sklearn import svm\n",
    "\n",
    "# Classify using support vector classifier \n",
    "\n",
    "svm = svm.SVC(kernel = 'linear', probability=True)\n",
    "\n",
    "# Fit the SVC model based on the given training data \n",
    "\n",
    "prob = svm.fit(x_train_vec, y_train).predict_proba(x_test_vec) \n",
    "\n",
    "# Perform classification and prediction on samples in x_test\n",
    "\n",
    "y_pred_svm = svm.predict(x_test_vec) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0202141b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score for SVC is  75.56917688266199 %\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score for SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(\"Accuracy score for SVC is \", accuracy_score(y_test, y_pred_svm) * 100, '%') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "417686ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate f1 Score\n",
    "\n",
    "from sklearn import feature_extraction, linear_model, model_selection, preprocessing\n",
    "\n",
    "scores2 = sklearn.metrics.f1_score(y_test, y_pred_svm, average='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "028ed4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.704135737009544"
      ]
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "scores2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "19096819",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_pred_svm = svm.predict(x_train_vec) \n",
    "mylist = list(sample_pred_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e6bcf770",
   "metadata": {},
   "outputs": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>10861</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>10865</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>10868</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3261</th>\n",
       "      <td>10874</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>10875</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3263 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id target\n",
       "0         0      0\n",
       "1         2      0\n",
       "2         3      0\n",
       "3         9      0\n",
       "4        11      0\n",
       "...     ...    ...\n",
       "3258  10861      0\n",
       "3259  10865      0\n",
       "3260  10868      0\n",
       "3261  10874      0\n",
       "3262  10875      0\n",
       "\n",
       "[3263 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fc13ed77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3101"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist.count(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d72fd5a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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