Fifth.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('clean_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "df['sentiment'] = df['sentiment'].map({0:0,4:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1600000 entries, 0 to 1599999\n",
      "Data columns (total 2 columns):\n",
      "sentiment    1600000 non-null int64\n",
      "text         1596714 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 24.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1596714 entries, 0 to 1599999\n",
      "Data columns (total 2 columns):\n",
      "sentiment    1596714 non-null int64\n",
      "text         1596714 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 36.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['text'] #define all other columns except the target variable\n",
    "y = df['sentiment'] #define the target variable\n",
    "\n",
    "x_train, x_validation_and_test, y_train, y_validation_and_test = train_test_split(x, y, test_size = 0.02, random_state = 42)\n",
    "\n",
    "x_validation, x_test, y_validation, y_test = train_test_split(x_validation_and_test, y_validation_and_test, \n",
    "                                                              test_size = 0.5, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction Using TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Term Frequency** measures how frequently a term occurs in a document. Since every document is different in length, it is possible that a term would appear much more times in long documents than shorter ones. Thus, the term frequency is often divided by the document length or the total number of terms in the document as a way of normalization: \n",
    "\n",
    "$$TF(t) = \\frac{\\text{Number of times term t appears in a document}}{\\text{Total number of terms in the document}}$$\n",
    "\n",
    "**Inverse Document Frequency** measures how important a term is. While computing TF, all terms are considered equally important. However it is known that certain terms, such as \"is\", \"of\", and \"that\", may appear a lot of times but have little importance. Thus we need to weigh down the frequent terms while scale up the rare ones, by computing the following: \n",
    "\n",
    "$$IDF(t) = \\log_e{\\frac{\\text{Total number of documents}} {\\text{Number of documents with term t in it}}}$$\n",
    "\n",
    "Combining these two, we get TF-IDF.\n",
    "\n",
    "$$TF-IDF(t) = {TF(t)}\\times{IDF(t)}$$\n",
    "\n",
    "The higher the TFIDF score, the rarer the term and vice versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from time import time\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_accuracy = 0\n",
    "def accuracy_summary(pipeline, x_train, y_train, x_test, y_test):\n",
    "    if len(x_test[y_test==0])/len(x_test)>0.5:\n",
    "        null_accuracy = len(x_test[y_test==0])/len(x_test)\n",
    "    else:\n",
    "        null_accuracy = 1 - len(x_test[y_test==0])/len(x_test)\n",
    "    t0 = time()\n",
    "    sentiment_fit = pipeline.fit(x_train, y_train)\n",
    "    y_pred = sentiment_fit.predict(x_test)\n",
    "    train_test_time = time() - t0\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(\"Null accuracy: {0:.2f}%\".format(null_accuracy*100))\n",
    "    print(\"Accuracy: {0:.2f}%\".format(accuracy*100))\n",
    "    if accuracy>null_accuracy:\n",
    "        print(\"Model is {0:.2f}% more accurate than null accuracy\".format((accuracy-null_accuracy)*100))\n",
    "    elif accuracy==null_accuracy:\n",
    "        print(\"Model has the same accuracy as null accuracy\")\n",
    "    else:\n",
    "        print(\"Model is {0:.2f}% less accurate than null accuracy\".format((null_accuracy-accuracy)*100))\n",
    "    print(\"Train and test time: {0:.2f}s\".format(train_test_time))\n",
    "    print(\"-\"*50)\n",
    "    return accuracy, train_test_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer()\n",
    "lr = LogisticRegression()\n",
    "n_features = np.arange(10000, 100001, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nfeature_accuracy_checker(vectorizer = tfidf, n_features = n_features, stop_words = None, \n",
    "                              ngram_range = (1,1), classifier = lr):\n",
    "    result = []\n",
    "    print(classifier, \"\\n\")\n",
    "    for n in n_features:\n",
    "        vectorizer.set_params(stop_words = stop_words, max_features = n, ngram_range=ngram_range)\n",
    "        checker_pipeline = Pipeline([('vectorizer', vectorizer), ('classifier', classifier)])\n",
    "        print(\"Validation result for {0} features\".format(n))\n",
    "        nfeature_accuracy, ttime = accuracy_summary(checker_pipeline, x_train, y_train, x_validation, y_validation)\n",
    "        result.append((n, nfeature_accuracy, ttime))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULT FOR UNIGRAM\n",
      "\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False) \n",
      "\n",
      "Validation result for 10000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 79.60%\n",
      "Model is 29.41% more accurate than null accuracy\n",
      "Train and test time: 59.01s\n",
      "--------------------------------------------------\n",
      "Validation result for 20000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 79.87%\n",
      "Model is 29.69% more accurate than null accuracy\n",
      "Train and test time: 54.30s\n",
      "--------------------------------------------------\n",
      "Validation result for 30000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 79.90%\n",
      "Model is 29.72% more accurate than null accuracy\n",
      "Train and test time: 68.17s\n",
      "--------------------------------------------------\n",
      "Validation result for 40000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.00%\n",
      "Model is 29.82% more accurate than null accuracy\n",
      "Train and test time: 69.55s\n",
      "--------------------------------------------------\n",
      "Validation result for 50000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.00%\n",
      "Model is 29.81% more accurate than null accuracy\n",
      "Train and test time: 71.66s\n",
      "--------------------------------------------------\n",
      "Validation result for 60000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.04%\n",
      "Model is 29.86% more accurate than null accuracy\n",
      "Train and test time: 61.56s\n",
      "--------------------------------------------------\n",
      "Validation result for 70000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.04%\n",
      "Model is 29.86% more accurate than null accuracy\n",
      "Train and test time: 72.51s\n",
      "--------------------------------------------------\n",
      "Validation result for 80000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.08%\n",
      "Model is 29.90% more accurate than null accuracy\n",
      "Train and test time: 74.03s\n",
      "--------------------------------------------------\n",
      "Validation result for 90000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.09%\n",
      "Model is 29.91% more accurate than null accuracy\n",
      "Train and test time: 63.56s\n",
      "--------------------------------------------------\n",
      "Validation result for 100000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.09%\n",
      "Model is 29.91% more accurate than null accuracy\n",
      "Train and test time: 74.55s\n",
      "--------------------------------------------------\n",
      "Wall time: 11min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print(\"RESULT FOR UNIGRAM\\n\")\n",
    "feature_result_ug_t = nfeature_accuracy_checker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULT FOR BIGRAM\n",
      "\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False) \n",
      "\n",
      "Validation result for 10000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.54%\n",
      "Model is 30.36% more accurate than null accuracy\n",
      "Train and test time: 167.01s\n",
      "--------------------------------------------------\n",
      "Validation result for 20000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 81.59%\n",
      "Model is 31.41% more accurate than null accuracy\n",
      "Train and test time: 175.60s\n",
      "--------------------------------------------------\n",
      "Validation result for 30000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 81.74%\n",
      "Model is 31.55% more accurate than null accuracy\n",
      "Train and test time: 185.52s\n",
      "--------------------------------------------------\n",
      "Validation result for 40000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.06%\n",
      "Model is 31.88% more accurate than null accuracy\n",
      "Train and test time: 179.94s\n",
      "--------------------------------------------------\n",
      "Validation result for 50000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.07%\n",
      "Model is 31.88% more accurate than null accuracy\n",
      "Train and test time: 200.66s\n",
      "--------------------------------------------------\n",
      "Validation result for 60000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.28%\n",
      "Model is 32.09% more accurate than null accuracy\n",
      "Train and test time: 193.28s\n",
      "--------------------------------------------------\n",
      "Validation result for 70000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.34%\n",
      "Model is 32.15% more accurate than null accuracy\n",
      "Train and test time: 190.85s\n",
      "--------------------------------------------------\n",
      "Validation result for 80000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.29%\n",
      "Model is 32.10% more accurate than null accuracy\n",
      "Train and test time: 192.06s\n",
      "--------------------------------------------------\n",
      "Validation result for 90000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.45%\n",
      "Model is 32.26% more accurate than null accuracy\n",
      "Train and test time: 198.65s\n",
      "--------------------------------------------------\n",
      "Validation result for 100000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.36%\n",
      "Model is 32.18% more accurate than null accuracy\n",
      "Train and test time: 197.53s\n",
      "--------------------------------------------------\n",
      "Wall time: 31min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print(\"RESULT FOR BIGRAM\\n\")\n",
    "feature_result_bg_t = nfeature_accuracy_checker(ngram_range=(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULT FOR TRIGRAM\n",
      "\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False) \n",
      "\n",
      "Validation result for 10000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.42%\n",
      "Model is 30.23% more accurate than null accuracy\n",
      "Train and test time: 362.89s\n",
      "--------------------------------------------------\n",
      "Validation result for 20000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 81.40%\n",
      "Model is 31.21% more accurate than null accuracy\n",
      "Train and test time: 337.88s\n",
      "--------------------------------------------------\n",
      "Validation result for 30000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 81.67%\n",
      "Model is 31.48% more accurate than null accuracy\n",
      "Train and test time: 387.06s\n",
      "--------------------------------------------------\n",
      "Validation result for 40000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 81.89%\n",
      "Model is 31.71% more accurate than null accuracy\n",
      "Train and test time: 392.50s\n",
      "--------------------------------------------------\n",
      "Validation result for 50000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.30%\n",
      "Model is 32.12% more accurate than null accuracy\n",
      "Train and test time: 479.93s\n",
      "--------------------------------------------------\n",
      "Validation result for 60000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.21%\n",
      "Model is 32.03% more accurate than null accuracy\n",
      "Train and test time: 423.31s\n",
      "--------------------------------------------------\n",
      "Validation result for 70000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.16%\n",
      "Model is 31.98% more accurate than null accuracy\n",
      "Train and test time: 317.22s\n",
      "--------------------------------------------------\n",
      "Validation result for 80000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.36%\n",
      "Model is 32.18% more accurate than null accuracy\n",
      "Train and test time: 255.64s\n",
      "--------------------------------------------------\n",
      "Validation result for 90000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.33%\n",
      "Model is 32.14% more accurate than null accuracy\n",
      "Train and test time: 262.42s\n",
      "--------------------------------------------------\n",
      "Validation result for 100000 features\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.35%\n",
      "Model is 32.17% more accurate than null accuracy\n",
      "Train and test time: 226.53s\n",
      "--------------------------------------------------\n",
      "Wall time: 57min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print(\"RESULT FOR TRIGRAM\\n\")\n",
    "feature_result_tg_t = nfeature_accuracy_checker(ngram_range=(1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_plot_ug_t = pd.DataFrame(feature_result_ug_t, columns = ['nfeatures', 'validation_accuracy', 'train_test_time'])\n",
    "feature_plot_bg_t = pd.DataFrame(feature_result_bg_t, columns=['nfeatures', 'validation_accuracy', 'train_test_time'])\n",
    "feature_plot_tg_t = pd.DataFrame(feature_result_tg_t, columns = ['nfeatures', 'validation_accuracy', 'train_test_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_plot_ug_c = pd.read_csv('ug-with-stopwords.csv', index_col=[0])\n",
    "feature_plot_bg_c = pd.read_csv('bg.csv', index_col=[0])\n",
    "feature_plot_tg_c = pd.read_csv('tg.csv', index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c5ec82a320>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 704
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(feature_plot_ug_c.nfeatures, feature_plot_ug_c.validation_accuracy, label='Unigram CountVectorizer', color='orange')\n",
    "plt.plot(feature_plot_ug_t.nfeatures, feature_plot_ug_t.validation_accuracy, label='Unigram TFIDF', linestyle=':', color='orange')\n",
    "plt.plot(feature_plot_bg_c.nfeatures, feature_plot_bg_c.validation_accuracy, label='Bigram CountVectorizer', color='gold')\n",
    "plt.plot(feature_plot_bg_t.nfeatures, feature_plot_bg_t.validation_accuracy, label='Bigram TFIDF', linestyle=':', color='gold')\n",
    "plt.plot(feature_plot_tg_c.nfeatures, feature_plot_tg_c.validation_accuracy, label='Trigram CountVectorizer', color='blue')\n",
    "plt.plot(feature_plot_tg_t.nfeatures, feature_plot_tg_t.validation_accuracy, label='Trigram TFIDF', linestyle=':', color='blue')\n",
    "plt.title('N-gram (1-3) accuracy ')\n",
    "plt.xlabel('Number of features')\n",
    "plt.ylabel('Validation accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, we can clearly see that using bigram and trigrams boosts the performance of the model in CountVectorizer and TfidfVectorizer both. Also, for bigram and trigram, TfidfVectorizer gives better performance than CountVectorizer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bigram TfidfVectorizer at 90000 features gives the highest validation accuracy at 82.45%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithms Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neighbors import NearestCentroid\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['Ridge Classifier', 'Logistic Regression', 'Perceptron', 'Passive-Agressive Classifier', 'Stochastic Gradient Descent',\n",
    "         'LinearSVC', 'L1 based LinearSVC', 'KNN', 'Nearest Centroid', 'Multinomial NB', 'Bernoulli NB', 'Adaboost']\n",
    "classifiers = [\n",
    "    RidgeClassifier(),\n",
    "    LogisticRegression(),\n",
    "    Perceptron(),\n",
    "    PassiveAggressiveClassifier(),\n",
    "    SGDClassifier(),\n",
    "    LinearSVC(),\n",
    "    Pipeline([\n",
    "        ('feature_selection', SelectFromModel(LinearSVC(penalty='l1', dual=False))),\n",
    "        ('classification', LinearSVC(penalty='l2'))\n",
    "    ]),\n",
    "    KNeighborsClassifier(),\n",
    "    NearestCentroid(),\n",
    "    MultinomialNB(),\n",
    "    BernoulliNB(),\n",
    "    AdaBoostClassifier(),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "zipped_clf = zip(names, classifiers)\n",
    "tfidf = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifier_comparator(vectorizer = tfidf, n_features=10000, stop_words=None, ngram_range=(1,1), classifier=zipped_clf):\n",
    "    result = []\n",
    "    vectorizer.set_params(stop_words=stop_words, ngram_range=ngram_range, max_features=n_features)\n",
    "    for n, c in classifier:\n",
    "        pipeline = Pipeline([('vectorizer', vectorizer), ('classifier', c)])\n",
    "        print('Validation result for {}'.format(n), c)\n",
    "        clf_accuracy, ttime = accuracy_summary(pipeline, x_train, y_train, x_validation, y_validation)\n",
    "        result.append((n, clf_accuracy, ttime))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation result for Ridge Classifier RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
      "        max_iter=None, normalize=False, random_state=None, solver='auto',\n",
      "        tol=0.001)\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.30%\n",
      "Model is 32.12% more accurate than null accuracy\n",
      "Train and test time: 180.80s\n",
      "--------------------------------------------------\n",
      "Validation result for Logistic Regression LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.45%\n",
      "Model is 32.26% more accurate than null accuracy\n",
      "Train and test time: 184.68s\n",
      "--------------------------------------------------\n",
      "Validation result for Perceptron Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n",
      "      fit_intercept=True, max_iter=None, n_iter=None, n_iter_no_change=5,\n",
      "      n_jobs=None, penalty=None, random_state=0, shuffle=True, tol=None,\n",
      "      validation_fraction=0.1, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in Perceptron in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null accuracy: 50.18%\n",
      "Accuracy: 76.26%\n",
      "Model is 26.08% more accurate than null accuracy\n",
      "Train and test time: 114.74s\n",
      "--------------------------------------------------\n",
      "Validation result for Passive-Agressive Classifier PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,\n",
      "              early_stopping=False, fit_intercept=True, loss='hinge',\n",
      "              max_iter=None, n_iter=None, n_iter_no_change=5, n_jobs=None,\n",
      "              random_state=None, shuffle=True, tol=None,\n",
      "              validation_fraction=0.1, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in PassiveAggressiveClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null accuracy: 50.18%\n",
      "Accuracy: 79.57%\n",
      "Model is 29.39% more accurate than null accuracy\n",
      "Train and test time: 113.99s\n",
      "--------------------------------------------------\n",
      "Validation result for Stochastic Gradient Descent SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
      "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
      "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
      "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
      "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
      "       validation_fraction=0.1, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null accuracy: 50.18%\n",
      "Accuracy: 78.66%\n",
      "Model is 28.48% more accurate than null accuracy\n",
      "Train and test time: 114.96s\n",
      "--------------------------------------------------\n",
      "Validation result for LinearSVC LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.41%\n",
      "Model is 32.23% more accurate than null accuracy\n",
      "Train and test time: 784.64s\n",
      "--------------------------------------------------\n",
      "Validation result for L1 based LinearSVC Pipeline(memory=None,\n",
      "     steps=[('feature_selection', SelectFromModel(estimator=LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l1', random_state=None, tol=0.0001,\n",
      "     verbose=0),\n",
      "        max_features=None, n...ax_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0))])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null accuracy: 50.18%\n",
      "Accuracy: 82.30%\n",
      "Model is 32.12% more accurate than null accuracy\n",
      "Train and test time: 977.12s\n",
      "--------------------------------------------------\n",
      "Validation result for KNN KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "           weights='uniform')\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 61.86%\n",
      "Model is 11.67% more accurate than null accuracy\n",
      "Train and test time: 2190.19s\n",
      "--------------------------------------------------\n",
      "Validation result for Nearest Centroid NearestCentroid(metric='euclidean', shrink_threshold=None)\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 72.55%\n",
      "Model is 22.36% more accurate than null accuracy\n",
      "Train and test time: 66.09s\n",
      "--------------------------------------------------\n",
      "Validation result for Multinomial NB MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 80.15%\n",
      "Model is 29.97% more accurate than null accuracy\n",
      "Train and test time: 63.32s\n",
      "--------------------------------------------------\n",
      "Validation result for Bernoulli NB BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 79.92%\n",
      "Model is 29.74% more accurate than null accuracy\n",
      "Train and test time: 64.56s\n",
      "--------------------------------------------------\n",
      "Validation result for Adaboost AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=1.0, n_estimators=50, random_state=None)\n",
      "Null accuracy: 50.18%\n",
      "Accuracy: 70.19%\n",
      "Model is 20.01% more accurate than null accuracy\n",
      "Train and test time: 736.74s\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "bigram_comparison = classifier_comparator(n_features=90000, ngram_range=(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Ridge Classifier', 0.8230099580384543, 180.7979292869568),\n",
       " ('Logistic Regression', 0.8244504290098328, 184.67657470703125),\n",
       " ('Perceptron', 0.7626354355858959, 114.7407968044281),\n",
       " ('Passive-Agressive Classifier', 0.795703638754932, 113.99101781845093),\n",
       " ('Stochastic Gradient Descent', 0.7866224087179808, 114.96089768409729),\n",
       " ('LinearSVC', 0.8241372831464896, 784.6449770927429),\n",
       " ('L1 based LinearSVC', 0.8230099580384543, 977.1225030422211),\n",
       " ('KNN', 0.6185883384480491, 2190.193979740143),\n",
       " ('Nearest Centroid', 0.7254963361933989, 66.0920844078064),\n",
       " ('Multinomial NB', 0.8015281518131145, 63.32321882247925),\n",
       " ('Bernoulli NB', 0.7992108724243753, 64.5629370212555),\n",
       " ('Adaboost', 0.7019477672699944, 736.7351906299591)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Classifier', 'Accuracy', 'Time']\n",
    "df = pd.DataFrame(bigram_comparison, columns = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Classifier</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ridge Classifier</td>\n",
       "      <td>0.823010</td>\n",
       "      <td>180.797929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.824450</td>\n",
       "      <td>184.676575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>0.762635</td>\n",
       "      <td>114.740797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Passive-Agressive Classifier</td>\n",
       "      <td>0.795704</td>\n",
       "      <td>113.991018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>0.786622</td>\n",
       "      <td>114.960898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.824137</td>\n",
       "      <td>784.644977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L1 based LinearSVC</td>\n",
       "      <td>0.823010</td>\n",
       "      <td>977.122503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.618588</td>\n",
       "      <td>2190.193980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nearest Centroid</td>\n",
       "      <td>0.725496</td>\n",
       "      <td>66.092084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Multinomial NB</td>\n",
       "      <td>0.801528</td>\n",
       "      <td>63.323219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bernoulli NB</td>\n",
       "      <td>0.799211</td>\n",
       "      <td>64.562937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Adaboost</td>\n",
       "      <td>0.701948</td>\n",
       "      <td>736.735191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Classifier  Accuracy         Time\n",
       "0               Ridge Classifier  0.823010   180.797929\n",
       "1            Logistic Regression  0.824450   184.676575\n",
       "2                     Perceptron  0.762635   114.740797\n",
       "3   Passive-Agressive Classifier  0.795704   113.991018\n",
       "4    Stochastic Gradient Descent  0.786622   114.960898\n",
       "5                      LinearSVC  0.824137   784.644977\n",
       "6             L1 based LinearSVC  0.823010   977.122503\n",
       "7                            KNN  0.618588  2190.193980\n",
       "8               Nearest Centroid  0.725496    66.092084\n",
       "9                 Multinomial NB  0.801528    63.323219\n",
       "10                  Bernoulli NB  0.799211    64.562937\n",
       "11                      Adaboost  0.701948   736.735191"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Classifier</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.824450</td>\n",
       "      <td>184.676575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.824137</td>\n",
       "      <td>784.644977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ridge Classifier</td>\n",
       "      <td>0.823010</td>\n",
       "      <td>180.797929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L1 based LinearSVC</td>\n",
       "      <td>0.823010</td>\n",
       "      <td>977.122503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Multinomial NB</td>\n",
       "      <td>0.801528</td>\n",
       "      <td>63.323219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bernoulli NB</td>\n",
       "      <td>0.799211</td>\n",
       "      <td>64.562937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Passive-Agressive Classifier</td>\n",
       "      <td>0.795704</td>\n",
       "      <td>113.991018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>0.786622</td>\n",
       "      <td>114.960898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>0.762635</td>\n",
       "      <td>114.740797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nearest Centroid</td>\n",
       "      <td>0.725496</td>\n",
       "      <td>66.092084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Adaboost</td>\n",
       "      <td>0.701948</td>\n",
       "      <td>736.735191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.618588</td>\n",
       "      <td>2190.193980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Classifier  Accuracy         Time\n",
       "1            Logistic Regression  0.824450   184.676575\n",
       "5                      LinearSVC  0.824137   784.644977\n",
       "0               Ridge Classifier  0.823010   180.797929\n",
       "6             L1 based LinearSVC  0.823010   977.122503\n",
       "9                 Multinomial NB  0.801528    63.323219\n",
       "10                  Bernoulli NB  0.799211    64.562937\n",
       "3   Passive-Agressive Classifier  0.795704   113.991018\n",
       "4    Stochastic Gradient Descent  0.786622   114.960898\n",
       "2                     Perceptron  0.762635   114.740797\n",
       "8               Nearest Centroid  0.725496    66.092084\n",
       "11                      Adaboost  0.701948   736.735191\n",
       "7                            KNN  0.618588  2190.193980"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='Accuracy', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}