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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\kurti\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "import string\n",
    "\n",
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "from sklearn.model_selection import learning_curve, StratifiedKFold\n",
    "\n",
    "nltk.download(\"stopwords\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,\n",
    "                        n_jobs=None, save_file=None, train_sizes=np.linspace(.1, 1.0, 5), scoring=\"f1\"):\n",
    "    # https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py\n",
    "    \"\"\"\n",
    "    Generate 3 plots: the test and training learning curve, the training\n",
    "    samples vs fit times curve, the fit times vs score curve.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
    "        An object of that type which is cloned for each validation.\n",
    "\n",
    "    title : string\n",
    "        Title for the chart.\n",
    "\n",
    "    X : array-like, shape (n_samples, n_features)\n",
    "        Training vector, where n_samples is the number of samples and\n",
    "        n_features is the number of features.\n",
    "\n",
    "    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
    "        Target relative to X for classification or regression;\n",
    "        None for unsupervised learning.\n",
    "\n",
    "    axes : array of 3 axes, optional (default=None)\n",
    "        Axes to use for plotting the curves.\n",
    "\n",
    "    ylim : tuple, shape (ymin, ymax), optional\n",
    "        Defines minimum and maximum yvalues plotted.\n",
    "\n",
    "    cv : int, cross-validation generator or an iterable, optional\n",
    "        Determines the cross-validation splitting strategy.\n",
    "        Possible inputs for cv are:\n",
    "\n",
    "          - None, to use the default 5-fold cross-validation,\n",
    "          - integer, to specify the number of folds.\n",
    "          - :term:`CV splitter`,\n",
    "          - An iterable yielding (train, test) splits as arrays of indices.\n",
    "\n",
    "        For integer/None inputs, if ``y`` is binary or multiclass,\n",
    "        :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
    "        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
    "\n",
    "        Refer :ref:`User Guide <cross_validation>` for the various\n",
    "        cross-validators that can be used here.\n",
    "\n",
    "    n_jobs : int or None, optional (default=None)\n",
    "        Number of jobs to run in parallel.\n",
    "        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
    "        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
    "        for more details.\n",
    "\n",
    "    train_sizes : array-like, shape (n_ticks,), dtype float or int\n",
    "        Relative or absolute numbers of training examples that will be used to\n",
    "        generate the learning curve. If the dtype is float, it is regarded as a\n",
    "        fraction of the maximum size of the training set (that is determined\n",
    "        by the selected validation method), i.e. it has to be within (0, 1].\n",
    "        Otherwise it is interpreted as absolute sizes of the training sets.\n",
    "        Note that for classification the number of samples usually have to\n",
    "        be big enough to contain at least one sample from each class.\n",
    "        (default: np.linspace(0.1, 1.0, 5))\n",
    "    \"\"\"\n",
    "    if axes is None:\n",
    "        _, axes = plt.subplots(1, 3, figsize=(20, 6))\n",
    "\n",
    "    axes[0].set_title(title)\n",
    "    if ylim is not None:\n",
    "        axes[0].set_ylim(*ylim)\n",
    "    axes[0].set_xlabel(\"Training examples\")\n",
    "    axes[0].set_ylabel(\"Score\")\n",
    "\n",
    "    # get learning curve results\n",
    "    train_sizes, train_scores, test_scores, fit_times, _ = \\\n",
    "        learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,\n",
    "                       train_sizes=train_sizes,\n",
    "                       return_times=True,\n",
    "                       scoring=scoring)\n",
    "    # create means and standard deviations from train, test and fit times\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    fit_times_mean = np.mean(fit_times, axis=1)\n",
    "    fit_times_std = np.std(fit_times, axis=1)\n",
    "\n",
    "    # Plot learning curve\n",
    "    axes[0].grid()\n",
    "    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                         train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                         color=\"r\")\n",
    "    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                         test_scores_mean + test_scores_std, alpha=0.1,\n",
    "                         color=\"g\")\n",
    "    axes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "                 label=\"Training score\")\n",
    "    axes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "                 label=\"Cross-validation score\")\n",
    "    axes[0].legend(loc=\"best\")\n",
    "\n",
    "    # Plot n_samples vs fit_times\n",
    "    axes[1].grid()\n",
    "    axes[1].plot(train_sizes, fit_times_mean, 'o-')\n",
    "    axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,\n",
    "                         fit_times_mean + fit_times_std, alpha=0.1)\n",
    "    axes[1].set_xlabel(\"Training examples\")\n",
    "    axes[1].set_ylabel(\"fit_times\")\n",
    "    axes[1].set_title(\"Scalability of the model\")\n",
    "\n",
    "    # Plot fit_time vs score\n",
    "    axes[2].grid()\n",
    "    axes[2].plot(fit_times_mean, test_scores_mean, 'o-')\n",
    "    axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,\n",
    "                         test_scores_mean + test_scores_std, alpha=0.1)\n",
    "    axes[2].set_xlabel(\"fit_times\")\n",
    "    axes[2].set_ylabel(\"Score\")\n",
    "    axes[2].set_title(\"Performance of the model\")\n",
    "    \n",
    "    if save_file:\n",
    "        plt.savefig(f\"images/{save_file}.png\")\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_tweet(tweet):\n",
    "    \"\"\"\n",
    "    Process tweet function.\n",
    "    Input:\n",
    "        tweet: a string containing a tweet\n",
    "    Returns:\n",
    "        tweets_clean: a list of words containing the processed tweet\n",
    "\n",
    "    *Taken from Coursera NLP Specialization Course 1, week 1 programming\n",
    "    assignment*\n",
    "    \"\"\"\n",
    "    stemmer = PorterStemmer()\n",
    "    stopwords_english = stopwords.words('english')\n",
    "    # remove stock market tickers like $GE\n",
    "    tweet = re.sub(r'\\$\\w*', '', str(tweet))\n",
    "    # remove old style retweet text \"RT\"\n",
    "    tweet = re.sub(r'^RT[\\s]+', '', str(tweet))\n",
    "    # remove hyperlinks\n",
    "    tweet = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', str(tweet))\n",
    "    # remove hashtags\n",
    "    # only removing the hash # sign from the word\n",
    "    tweet = re.sub(r'#', '', str(tweet))\n",
    "    # tokenize tweets\n",
    "    tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,\n",
    "                               reduce_len=True)\n",
    "    tweet_tokens = tokenizer.tokenize(tweet)\n",
    "\n",
    "    tweets_clean = []\n",
    "    for word in tweet_tokens:\n",
    "        if (word not in stopwords_english and  # remove stopwords\n",
    "                word not in string.punctuation):  # remove punctuation\n",
    "            # tweets_clean.append(word)\n",
    "            stem_word = stemmer.stem(word)  # stemming word\n",
    "            tweets_clean.append(stem_word)\n",
    "\n",
    "    return \" \".join(tweets_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7613, 2), (7613,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read train data\n",
    "df = pd.read_csv(\"../inputs/train.csv\")\n",
    "# shuffle data\n",
    "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "# create new column \"all_text\"\n",
    "df[\"all_text\"] = df[\"text\"] + df[\"keyword\"] + df[\"location\"]\n",
    "# split into features and labels\n",
    "X = df.drop([\"text\", \"keyword\", \"location\", \"target\"], axis=1)\n",
    "y = df[\"target\"]\n",
    "\n",
    "# process tweets\n",
    "X[\"all_text\"] = X[\"all_text\"].apply(process_tweet)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>all_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3796</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3185</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7769</td>\n",
       "      <td>dt rt ‰ ûïthe col polic catch pickpocket liver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9810</td>\n",
       "      <td>respons trauma children addict develop defens ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                           all_text\n",
       "0  3796                                                nan\n",
       "1  3185                                                nan\n",
       "2  7769  dt rt ‰ ûïthe col polic catch pickpocket liver...\n",
       "3   191                                                nan\n",
       "4  9810  respons trauma children addict develop defens ..."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vectorize text \n",
    "vec = CountVectorizer()\n",
    "X_vec = vec.fit_transform(X[\"all_text\"].values)\n",
    "\n",
    "# initialize classifier \n",
    "clf = MultinomialNB()\n",
    "\n",
    "# initialize cross-validation \n",
    "skf = StratifiedKFold(n_splits=5, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(estimator=clf, title=\"Naive Bayes Learning Curve\", X=X_vec, y=y, axes=None, ylim=None, cv=skf,\n",
    "                    n_jobs=None, save_file=\"incorrect_naive_bayes\", train_sizes=np.linspace(.1, 1.0, 5), scoring=\"f1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>all_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new weapon caus un-imagin destruct destructionnon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f @ing thing gishwh got soak delug go pad tamp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dt rt ‰ ûïthe col polic catch pickpocket liver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aftershock back school kick great want thank e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>respons trauma children addict develop defens ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            all_text\n",
       "0  new weapon caus un-imagin destruct destructionnon\n",
       "1  f @ing thing gishwh got soak delug go pad tamp...\n",
       "2  dt rt ‰ ûïthe col polic catch pickpocket liver...\n",
       "3  aftershock back school kick great want thank e...\n",
       "4  respons trauma children addict develop defens ..."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new empty df \n",
    "X_new = pd.DataFrame()\n",
    "# new features with correctly joined columns\n",
    "X_new[\"all_text\"] = df[\"text\"] + df[\"keyword\"].fillna(\"none\") + df[\"location\"].fillna(\"none\")\n",
    "# process tweets\n",
    "X_new[\"all_text\"] = X_new[\"all_text\"].apply(process_tweet)\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vectorize text\n",
    "X_new_vec = vec.fit_transform(X_new[\"all_text\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(estimator=clf, title=\"Naive Bayes Learning Curve\", X=X_new_vec, y=y, axes=None, ylim=None, cv=skf,\n",
    "                    n_jobs=None, save_file=\"fixed_df_naive_bayes\", train_sizes=np.linspace(.1, 1.0, 5), scoring=\"f1\")"
   ]
  },
  {
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}