main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Review</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>good and interesting</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>This class is very helpful to me. Currently, I...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>like!Prof and TAs are helpful and the discussi...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Easy to follow and includes a lot basic and im...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Really nice teacher!I could got the point eazl...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                             Review  Label\n",
       "0   0                               good and interesting      5\n",
       "1   1  This class is very helpful to me. Currently, I...      5\n",
       "2   2  like!Prof and TAs are helpful and the discussi...      5\n",
       "3   3  Easy to follow and includes a lot basic and im...      5\n",
       "4   4  Really nice teacher!I could got the point eazl...      4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['Id'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Review</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>good and interesting</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This class is very helpful to me. Currently, I...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>like!Prof and TAs are helpful and the discussi...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Easy to follow and includes a lot basic and im...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Really nice teacher!I could got the point eazl...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  Label\n",
       "0                               good and interesting      5\n",
       "1  This class is very helpful to me. Currently, I...      5\n",
       "2  like!Prof and TAs are helpful and the discussi...      5\n",
       "3  Easy to follow and includes a lot basic and im...      5\n",
       "4  Really nice teacher!I could got the point eazl...      4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Review    0\n",
       "Label     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    79173\n",
       "4    18054\n",
       "3     5071\n",
       "1     2469\n",
       "2     2251\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "to_delete = np.random.choice(data[data['Label'] == 5].index,size=61000,replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(to_delete,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    18173\n",
       "4    18054\n",
       "3     5071\n",
       "1     2469\n",
       "2     2251\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Review    object\n",
       "Label      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data['Review']\n",
    "y = data['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "cv = CountVectorizer()\n",
    "tv = TfidfVectorizer()\n",
    "cv.fit(X)\n",
    "X = cv.transform(X)\n",
    "# X.reshape(-1,1)\n",
    "# X = pd.DataFrame(X)\n",
    "y = pd.DataFrame(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import *\n",
    "from sklearn.model_selection import *\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4877879182963929"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review : Worst course in the world the worst of the worst I hate the teacher of the course and the teacher is like a nerd and he sucks I hate him and the course the instructions are the worst in the world\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = input('Review : ')\n",
    "text = cv.transform([text])\n",
    "model.predict(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV] splitter=random, min_samples_leaf=1, max_features=auto, max_depth=None, criterion=entropy \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  splitter=random, min_samples_leaf=1, max_features=auto, max_depth=None, criterion=entropy, score=0.444, total=   1.6s\n",
      "[CV] splitter=random, min_samples_leaf=1, max_features=auto, max_depth=None, criterion=entropy \n"
     ]
    },
    {
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     ]
    },
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      "[CV]  splitter=random, min_samples_leaf=1, max_features=auto, max_depth=None, criterion=entropy, score=0.455, total=   1.7s\n",
      "[CV] splitter=random, min_samples_leaf=1, max_features=auto, max_depth=None, criterion=entropy \n"
     ]
    },
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      "[CV] splitter=random, min_samples_leaf=1, max_features=log2, max_depth=None, criterion=gini \n"
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     ]
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      "[CV]  splitter=best, min_samples_leaf=2, max_features=sqrt, max_depth=25, criterion=gini, score=0.454, total=   0.2s\n",
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      "[CV] splitter=best, min_samples_leaf=8, max_features=sqrt, max_depth=20, criterion=entropy \n",
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      "[CV] splitter=best, min_samples_leaf=8, max_features=sqrt, max_depth=20, criterion=entropy \n",
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      "[CV] splitter=best, min_samples_leaf=8, max_features=sqrt, max_depth=20, criterion=entropy \n",
      "[CV]  splitter=best, min_samples_leaf=8, max_features=sqrt, max_depth=20, criterion=entropy, score=0.449, total=   0.1s\n",
      "[CV] splitter=best, min_samples_leaf=8, max_features=sqrt, max_depth=20, criterion=entropy \n",
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      "[CV] splitter=best, min_samples_leaf=8, max_features=sqrt, max_depth=20, criterion=entropy \n",
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      "[CV] splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy \n",
      "[CV]  splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy, score=0.452, total=   0.1s\n",
      "[CV] splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy \n",
      "[CV]  splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy, score=0.463, total=   0.1s\n",
      "[CV] splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy \n",
      "[CV]  splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy, score=0.468, total=   0.1s\n",
      "[CV] splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy \n",
      "[CV]  splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy, score=0.462, total=   0.1s\n",
      "[CV] splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy \n",
      "[CV]  splitter=best, min_samples_leaf=8, max_features=auto, max_depth=25, criterion=entropy, score=0.460, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:   49.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(criterion='entropy', max_features='auto',\n",
      "                       min_samples_leaf=4, splitter='random')\n",
      "0.49621903520208605\n",
      "0.4940459997633305\n"
     ]
    }
   ],
   "source": [
    "grid = {\n",
    "#     \"n_estimators\":[10,100,200,400,800,1600,125,250,500,1250,2500],\n",
    "    \"max_depth\":[None,5,10,15,20,25],\n",
    "    \"criterion\":[\"gini\",\"entropy\",],\n",
    "    'splitter':['best','random'],\n",
    "    \"max_features\":[\"auto\",\"sqrt\",'log2'],\n",
    "    \"min_samples_leaf\":[1,2,4,6,8,10],\n",
    "}\n",
    "model_rs = DecisionTreeClassifier()\n",
    "rs = RandomizedSearchCV(estimator=model_rs,param_distributions=grid,cv=5,n_iter=25,verbose=25)\n",
    "rs.fit(X_train,y_train)\n",
    "print(rs.best_estimator_)\n",
    "print(rs.score(X_test,y_test))\n",
    "print(rs.best_score_)\n",
    "import pickle\n",
    "pickle.dump(model_rs,open('./model_random.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = 'Worst course in the world the worst of the worst I hate the teacher of the course and the teacher is like a nerd and he sucks I hate him and the course the instructions are the worst in the world'\n",
    "text = cv.transform([text])\n",
    "rs.predict(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 660 candidates, totalling 3300 fits\n",
      "[CV] criterion=gini, max_depth=None, max_features=auto, min_samples_leaf=2, splitter=best \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
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    {
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      "[CV]  criterion=gini, max_depth=None, max_features=auto, min_samples_leaf=2, splitter=best, score=0.476, total=   0.3s\n",
      "[CV] criterion=gini, max_depth=None, max_features=auto, min_samples_leaf=2, splitter=best \n"
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      "[CV] criterion=gini, max_depth=None, max_features=auto, min_samples_leaf=2, splitter=best \n"
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     ]
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    {
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     "text": [
      "[CV]  criterion=gini, max_depth=None, max_features=sqrt, min_samples_leaf=4, splitter=best, score=0.496, total=   0.2s\n",
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      "[CV]  criterion=gini, max_depth=None, max_features=log2, min_samples_leaf=2, splitter=best, score=0.406, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=None, max_features=log2, min_samples_leaf=10, splitter=best, score=0.403, total=   0.0s\n",
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      "[CV]  criterion=gini, max_depth=5, max_features=auto, min_samples_leaf=8, splitter=random, score=0.397, total=   0.0s\n",
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    {
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      "[CV]  criterion=gini, max_depth=5, max_features=log2, min_samples_leaf=6, splitter=best, score=0.396, total=   0.0s\n",
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     ]
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    {
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      "[CV]  criterion=gini, max_depth=10, max_features=sqrt, min_samples_leaf=2, splitter=random, score=0.404, total=   0.1s\n",
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     ]
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    {
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     "text": [
      "[CV]  criterion=gini, max_depth=15, max_features=auto, min_samples_leaf=8, splitter=random, score=0.426, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=15, max_features=sqrt, min_samples_leaf=6, splitter=random, score=0.412, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=15, max_features=log2, min_samples_leaf=6, splitter=best, score=0.407, total=   0.0s\n",
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      "[CV]  criterion=gini, max_depth=20, max_features=auto, min_samples_leaf=4, splitter=best, score=0.453, total=   0.1s\n",
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    {
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      "[CV]  criterion=gini, max_depth=20, max_features=sqrt, min_samples_leaf=2, splitter=random, score=0.448, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=20, max_features=sqrt, min_samples_leaf=10, splitter=random, score=0.423, total=   0.1s\n",
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     ]
    },
    {
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     "text": [
      "[CV]  criterion=gini, max_depth=20, max_features=log2, min_samples_leaf=10, splitter=best, score=0.406, total=   0.0s\n",
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     "text": [
      "[CV]  criterion=gini, max_depth=25, max_features=auto, min_samples_leaf=8, splitter=random, score=0.438, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=25, max_features=sqrt, min_samples_leaf=6, splitter=random, score=0.449, total=   0.1s\n",
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    {
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      "[CV]  criterion=gini, max_depth=30, max_features=auto, min_samples_leaf=4, splitter=best, score=0.469, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=30, max_features=sqrt, min_samples_leaf=2, splitter=best, score=0.464, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=30, max_features=log2, min_samples_leaf=8, splitter=random, score=0.396, total=   0.0s\n",
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      "[CV]  criterion=gini, max_depth=35, max_features=log2, min_samples_leaf=2, splitter=random, score=0.398, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=40, max_features=auto, min_samples_leaf=10, splitter=best, score=0.484, total=   0.1s\n",
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      "[CV]  criterion=gini, max_depth=40, max_features=log2, min_samples_leaf=6, splitter=best, score=0.402, total=   0.0s\n",
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    {
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     "text": [
      "[CV]  criterion=gini, max_depth=45, max_features=auto, min_samples_leaf=4, splitter=best, score=0.462, total=   0.2s\n",
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      "[CV]  criterion=gini, max_depth=45, max_features=sqrt, min_samples_leaf=2, splitter=random, score=0.425, total=   0.2s\n",
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      "[CV]  criterion=gini, max_depth=45, max_features=sqrt, min_samples_leaf=10, splitter=random, score=0.447, total=   0.2s\n",
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      "[CV]  criterion=gini, max_depth=45, max_features=log2, min_samples_leaf=10, splitter=best, score=0.396, total=   0.0s\n",
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    {
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     "text": [
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      "[CV]  criterion=gini, max_depth=50, max_features=sqrt, min_samples_leaf=6, splitter=best, score=0.489, total=   0.2s\n",
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    {
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     "text": [
      "[CV]  criterion=gini, max_depth=50, max_features=log2, min_samples_leaf=4, splitter=random, score=0.405, total=   0.1s\n",
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      "[CV]  criterion=entropy, max_depth=None, max_features=auto, min_samples_leaf=2, splitter=random, score=0.493, total=   0.5s\n",
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     ]
    },
    {
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     "text": [
      "[CV]  criterion=entropy, max_depth=None, max_features=auto, min_samples_leaf=10, splitter=random, score=0.473, total=   0.3s\n",
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     ]
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    {
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     "text": [
      "[CV]  criterion=entropy, max_depth=None, max_features=sqrt, min_samples_leaf=8, splitter=best, score=0.487, total=   0.1s\n",
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    {
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     "text": [
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     ]
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    {
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     "text": [
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    {
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      "[CV]  criterion=entropy, max_depth=35, max_features=auto, min_samples_leaf=2, splitter=random, score=0.427, total=   0.1s\n",
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    {
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    {
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      "[CV]  criterion=entropy, max_depth=40, max_features=sqrt, min_samples_leaf=10, splitter=best, score=0.482, total=   0.1s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=40, max_features=log2, min_samples_leaf=8, splitter=random, score=0.398, total=   0.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=45, max_features=auto, min_samples_leaf=6, splitter=best, score=0.461, total=   0.1s\n",
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      "[CV] criterion=entropy, max_depth=45, max_features=auto, min_samples_leaf=10, splitter=best \n",
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      "[CV] criterion=entropy, max_depth=45, max_features=auto, min_samples_leaf=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=45, max_features=auto, min_samples_leaf=10, splitter=best, score=0.485, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=45, max_features=auto, min_samples_leaf=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=45, max_features=auto, min_samples_leaf=10, splitter=random, score=0.464, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=45, max_features=auto, min_samples_leaf=10, splitter=random \n"
     ]
    }
   ],
   "source": [
    "grid = {\n",
    "    \"max_depth\":[None,5,10,15,20,25,30,35,40,45,50],\n",
    "    \"criterion\":[\"gini\",\"entropy\",],\n",
    "    'splitter':['best','random'],\n",
    "    \"max_features\":[\"auto\",\"sqrt\",'log2'],\n",
    "    \"min_samples_leaf\":[2,4,6,8,10],\n",
    "}\n",
    "model_gs = DecisionTreeClassifier()\n",
    "gs = GridSearchCV(estimator=model_gs,param_grid=grid,cv=5,verbose=25)\n",
    "gs.fit(X_train,y_train)\n",
    "print(gs.best_estimator_)\n",
    "print(gs.score(X_test,y_test))\n",
    "print(gs.best_score_)\n",
    "pickle.dump(model_gs,open('./model_grid.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = 'Worst course in the world the worst of the worst I hate the teacher of the course and the teacher is like a nerd and he sucks I hate him and the course the instructions are the worst in the world'\n",
    "text = cv.transform([text])\n",
    "model_rs.predict(text)"
   ]
  }
 ],
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