dummy_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#FF8800\"> Load training data </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"datasets/train_data.tsv\", \n",
    "                       sep = \"\\t\",\n",
    "                       usecols = [\"label\",\"label_binary\",\"text\"]\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15928, 3)\n"
     ]
    },
    {
     "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>label</th>\n",
       "      <th>label_binary</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>Bandwagon,Reductio_ad_hitlerum</td>\n",
       "      <td>Propaganda</td>\n",
       "      <td>Adolf Hitler as “a very great man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8801</th>\n",
       "      <td>Whataboutism,Straw_Men,Red_Herring</td>\n",
       "      <td>Propaganda</td>\n",
       "      <td>a bigger problem is the fact they actually cha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14714</th>\n",
       "      <td>No_Propaganda</td>\n",
       "      <td>No_Propaganda</td>\n",
       "      <td>– even for people who are mostly OK with Franc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    label   label_binary  \\\n",
       "6432       Bandwagon,Reductio_ad_hitlerum     Propaganda   \n",
       "8801   Whataboutism,Straw_Men,Red_Herring     Propaganda   \n",
       "14714                       No_Propaganda  No_Propaganda   \n",
       "\n",
       "                                                    text  \n",
       "6432                   Adolf Hitler as “a very great man  \n",
       "8801   a bigger problem is the fact they actually cha...  \n",
       "14714  – even for people who are mostly OK with Franc...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_df.shape)\n",
    "train_df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### The training data, which was created in generate_data.ipynb, contains +15,900 samples with binary and multi-class labels. Here we'll focus on multi-class classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#FF8800\"> Create, fit, & predict with Dummy Classifier\n",
    "> ### Creating a simple baseline model is a good first step for a classification or even regression problem (e.g. predict the mean value). It offers some perspective on the difficulty of the problem as well as a way to gauge how much \"value\" a more sophisticated model offers. Here we'll use a stratified approach so the classifier will offer some predictions across various labels instead of only predicting the most common label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = DummyClassifier(strategy = \"stratified\", random_state = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DummyClassifier(constant=None, random_state=8, strategy='stratified')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy.fit(train_df[\"text\"],train_df[\"label\"]) # fit the model to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_df = pd.read_csv(\"datasets/dev_data.tsv\", \n",
    "                       sep = \"\\t\",\n",
    "                       usecols = [\"label\",\"label_binary\",\"text\"]\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2850, 3)\n"
     ]
    },
    {
     "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>label</th>\n",
       "      <th>label_binary</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2582</th>\n",
       "      <td>Loaded_Language</td>\n",
       "      <td>Propaganda</td>\n",
       "      <td>severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2725</th>\n",
       "      <td>Repetition</td>\n",
       "      <td>Propaganda</td>\n",
       "      <td>the affidavit says</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>No_Propaganda</td>\n",
       "      <td>No_Propaganda</td>\n",
       "      <td>facts far outweighed the dangers which are cit...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                label   label_binary  \\\n",
       "2582  Loaded_Language     Propaganda   \n",
       "2725       Repetition     Propaganda   \n",
       "1327    No_Propaganda  No_Propaganda   \n",
       "\n",
       "                                                   text  \n",
       "2582                                             severe  \n",
       "2725                                 the affidavit says  \n",
       "1327  facts far outweighed the dangers which are cit...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dev_df.shape)\n",
    "dev_df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### We'll use the dev data to evaluate the performance of our model. Normally, we would split our training data so we could adjust hyperparameters and evaluate our initial models before using the limited and precious dev data. However, that's 1) not as useful for creating a dummy classifier and 2) an approach I'll outline in logistic_regression.ipynb. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_y_pred = dummy.predict(dev_df[\"text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### This deceptively simple step is where our dummy classifier generates predictions based on the fit above and the sample text from the dev data. Actually, the dummy classifier doesn't even rely on the actual text here. It's simply using the proportions of the labels found in the training data during fitting. We can highlight this fact by calling .predict on meaningless data while still getting predictions. The .predict call here is mainly used to determine how many predictions (i.e. the length of the input) to generate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "any_text = [\"anything\" for i in range(len(dev_df[\"text\"]))]\n",
    "dummy_y_pred = dummy.predict(any_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#FF8800\"> Visualizing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = list(dummy.classes_)\n",
    "cm = confusion_matrix(dev_df[\"label\"], dummy_y_pred, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Below is a function from user Calvin Duy Canh Tran on Stack Overflow to plot a confusion matrix with any number of labels. The confusion_matrix function above outputs an unwieldly np.ndarray which I would rather not wrestle with especially given the 15 classes involved. You can check out the original post on SO here: https://stackoverflow.com/a/50386871/1576558."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    accuracy = np.trace(cm) / np.sum(cm).astype('float')\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Purples')\n",
    "\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=90)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm = cm, target_names = labels, normalize = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### A confusion matrix visualizes the accuracy of the predictions. If a classifer had 100% accuracy then only the diagonal would contain values greater than zero. In this plot the darker purple the cell the higher the count.\n",
    "> ### <span style=\"color:#8800ff\"> We can see the dummy classifier performed best with the \"No_Propaganda\", \"Loaded_Language\", and \"Name_Calling,Labeling\" labels. Overall, it performed quite poorly. While our baseline is low there's a lot of room for improvement with our other models!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    precision    recall  f1-score   support\n",
      "\n",
      "               Appeal_to_Authority       0.00      0.00      0.00        14\n",
      "          Appeal_to_fear-prejudice       0.02      0.02      0.02        44\n",
      "    Bandwagon,Reductio_ad_hitlerum       0.00      0.00      0.00         5\n",
      "           Black-and-White_Fallacy       0.00      0.00      0.00        22\n",
      "         Causal_Oversimplification       0.02      0.06      0.03        18\n",
      "                             Doubt       0.07      0.12      0.09        66\n",
      "         Exaggeration,Minimisation       0.04      0.04      0.04        68\n",
      "                       Flag-Waving       0.06      0.02      0.03        87\n",
      "                   Loaded_Language       0.12      0.14      0.13       324\n",
      "             Name_Calling,Labeling       0.04      0.04      0.04       183\n",
      "                     No_Propaganda       0.64      0.62      0.63      1789\n",
      "                        Repetition       0.07      0.06      0.06       144\n",
      "                           Slogans       0.00      0.00      0.00        40\n",
      "       Thought-terminating_Cliches       0.00      0.00      0.00        17\n",
      "Whataboutism,Straw_Men,Red_Herring       0.07      0.03      0.05        29\n",
      "\n",
      "                          accuracy                           0.42      2850\n",
      "                         macro avg       0.08      0.08      0.08      2850\n",
      "                      weighted avg       0.42      0.42      0.42      2850\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(dev_df[\"label\"], dummy_y_pred, target_names=labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### We can also evaluate label-specific precision, recall, and f1 score results for the dummy classifier. This is a more precise way of viewing the information contained in the plot above.\n",
    "> ### <span style=\"color:#8800ff\"> The labels where the model performed best correspond to those with the highest support (i.e. the number of actual occurrences of a label). This aligns with the stratified strategy used. However, labels with support below 29 (e.g. \"Black-and-White_Fallacy\") are not predicted at all because they account for less than 1% of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(f1_score(dev_df[\"label\"], dummy_y_pred, average = 'micro'),2)"
   ]
  },
  {
   "cell_type": "markdown",
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    "> ### <span style=\"color:#8800ff\"> Finally, we can quantify the overall performance of the dummy classifier using one number. Here we'll use the micro f1 score which assesses the overall performance while taking class imbalance into account (e.g. performing extremely well on a couple small labels is not the same as performing decently across all labels)."
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    "# <span style=\"color:#FF8800\"> END"
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