7.2-4 Custom classifier (combined).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Custom Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame([(\"i love spending time with my friends and family\", \"positive\"),\n",
    "                     (\"that was the best meal i've ever had in my life\", \"positive\"),\n",
    "                     (\"i feel so grateful for everything i have in my life\", \"positive\"),\n",
    "                     (\"i received a promotion at work and i couldn't be happier\", \"positive\"),\n",
    "                     (\"watching a beautiful sunset always fills me with joy\", \"positive\"),\n",
    "                     (\"my partner surprised me with a thoughtful gift and it made my day\", \"positive\"),\n",
    "                     (\"i am so proud of my daughter for graduating with honors\", \"positive\"),\n",
    "                     (\"listening to my favorite music always puts me in a good mood\", \"positive\"),\n",
    "                     (\"i love the feeling of accomplishment after completing a challenging task\", \"positive\"),\n",
    "                     (\"i am excited to go on vacation next week\", \"positive\"),\n",
    "                     (\"i feel so overwhelmed with work and responsibilities\", \"negative\"),\n",
    "                     (\"the traffic during my commute is always so frustrating\", \"negative\"),\n",
    "                     (\"i received a parking ticket and it ruined my day\", \"negative\"),\n",
    "                     (\"i got into an argument with my partner and we're not speaking\", \"negative\"),\n",
    "                     (\"i have a headache and i feel terrible\", \"negative\"),\n",
    "                     (\"i received a rejection letter for the job i really wanted\", \"negative\"),\n",
    "                     (\"my car broke down and it's going to be expensive to fix\", \"negative\"),\n",
    "                     (\"i'm feeling sad because i miss my friends who live far away\", \"negative\"),\n",
    "                     (\"i'm frustrated because i can't seem to make progress on my project\", \"negative\"),\n",
    "                     (\"i'm disappointed because my team lost the game\", \"negative\")\n",
    "                    ],\n",
    "                    columns=['text', 'sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data['text']\n",
    "y = data['sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# text vectorization to bow - CountVectorizer\n",
    "countvec = CountVectorizer()\n",
    "countvec_fit = countvec.fit_transform(X)\n",
    "bag_of_words = pd.DataFrame(countvec_fit.toarray(), columns = countvec.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    accomplishment  after  always  am  an  and  argument  at  away  be  ...  \\\n",
       "0                0      0       0   0   0    0         0   0     0   0  ...   \n",
       "1                0      0       0   0   1    1         1   0     0   0  ...   \n",
       "2                0      0       0   1   0    0         0   0     0   0  ...   \n",
       "3                0      0       0   0   0    0         0   0     0   0  ...   \n",
       "4                0      0       0   0   0    1         0   0     0   0  ...   \n",
       "5                0      0       0   0   0    1         0   0     0   0  ...   \n",
       "6                0      0       0   0   0    0         0   0     0   0  ...   \n",
       "7                0      0       0   0   0    1         0   0     0   0  ...   \n",
       "8                1      1       0   0   0    0         0   0     0   0  ...   \n",
       "9                0      0       0   0   0    1         0   1     0   1  ...   \n",
       "10               0      0       0   1   0    0         0   0     0   0  ...   \n",
       "11               0      0       0   0   0    0         0   0     0   0  ...   \n",
       "12               0      0       0   0   0    1         0   0     0   0  ...   \n",
       "13               0      0       1   0   0    0         0   0     0   0  ...   \n",
       "14               0      0       1   0   0    0         0   0     0   0  ...   \n",
       "15               0      0       0   0   0    0         0   0     1   0  ...   \n",
       "16               0      0       1   0   0    0         0   0     0   0  ...   \n",
       "17               0      0       0   0   0    0         0   0     0   0  ...   \n",
       "18               0      0       0   0   0    1         0   0     0   1  ...   \n",
       "19               0      0       0   0   0    1         0   0     0   0  ...   \n",
       "\n",
       "    vacation  ve  wanted  was  watching  we  week  who  with  work  \n",
       "0          0   0       0    0         0   0     0    0     0     0  \n",
       "1          0   0       0    0         0   1     0    0     1     0  \n",
       "2          0   0       0    0         0   0     0    0     1     0  \n",
       "3          0   0       1    0         0   0     0    0     0     0  \n",
       "4          0   0       0    0         0   0     0    0     1     0  \n",
       "5          0   0       0    0         0   0     0    0     1     1  \n",
       "6          0   0       0    0         0   0     0    0     0     0  \n",
       "7          0   0       0    0         0   0     0    0     1     0  \n",
       "8          0   0       0    0         0   0     0    0     0     0  \n",
       "9          0   0       0    0         0   0     0    0     0     1  \n",
       "10         1   0       0    0         0   0     1    0     0     0  \n",
       "11         0   1       0    1         0   0     0    0     0     0  \n",
       "12         0   0       0    0         0   0     0    0     0     0  \n",
       "13         0   0       0    0         0   0     0    0     0     0  \n",
       "14         0   0       0    0         0   0     0    0     0     0  \n",
       "15         0   0       0    0         0   0     0    1     0     0  \n",
       "16         0   0       0    0         1   0     0    0     1     0  \n",
       "17         0   0       0    0         0   0     0    0     0     0  \n",
       "18         0   0       0    0         0   0     0    0     0     0  \n",
       "19         0   0       0    0         0   0     0    0     0     0  \n",
       "\n",
       "[20 rows x 118 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_of_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(bag_of_words, y, test_size=0.3, random_state = 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(random_state=0).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3333333333333333"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred_lr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.33      0.33      0.33         3\n",
      "    positive       0.33      0.33      0.33         3\n",
      "\n",
      "    accuracy                           0.33         6\n",
      "   macro avg       0.33      0.33      0.33         6\n",
      "weighted avg       0.33      0.33      0.33         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = MultinomialNB().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_nb = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3333333333333333"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred_nb, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.33      0.33      0.33         3\n",
      "    positive       0.33      0.33      0.33         3\n",
      "\n",
      "    accuracy                           0.33         6\n",
      "   macro avg       0.33      0.33      0.33         6\n",
      "weighted avg       0.33      0.33      0.33         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression, SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm = SGDClassifier().fit(X_train, y_train)\n",
    "# possible hyper params, loss function, regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_svm = svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3333333333333333"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred_svm, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.33      0.33      0.33         3\n",
      "    positive       0.33      0.33      0.33         3\n",
      "\n",
      "    accuracy                           0.33         6\n",
      "   macro avg       0.33      0.33      0.33         6\n",
      "weighted avg       0.33      0.33      0.33         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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