Stacking-hateval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"./Saved_Predict_and_Proba/HateVal/prob_train_hateval.csv\").dropna()\n",
    "val_df = pd.read_csv(\"./Saved_Predict_and_Proba/HateVal/prob_val_hateval.csv\").dropna()\n",
    "test_df = pd.read_csv(\"./Saved_Predict_and_Proba/HateVal/prob_test_hateval.csv\").dropna()\n",
    "\n",
    "probas_train, labels_train = train_df.drop(columns=[\"Unnamed: 0\", \"HS\"]), train_df[\"HS\"]\n",
    "probas_val, labels_val = val_df.drop(columns=[\"Unnamed: 0\", \"HS\"]), val_df[\"HS\"]\n",
    "probas_test, labels_test = test_df.drop(columns=[\"Unnamed: 0\", \"HS\"]), test_df[\"HS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_df_train_test(train_df, test_df, name, filter_first=True):\n",
    "    train = train_df.filter(regex=name, axis=1)\n",
    "    test = test_df.filter(regex=name, axis=1)\n",
    "    return train.to_numpy(), test.to_numpy(), train.columns\n",
    "\n",
    "def filter_collinearity(X_train, X_test):\n",
    "    X_train = X_train[:, ::2]\n",
    "    X_test = X_test[:, ::2]\n",
    "    return X_train, X_test\n",
    "stacking = LogisticRegression()\n",
    "\n",
    "def generate_confusion_matrix(stacking, X_test, labels_test, name):\n",
    "    disp = plot_confusion_matrix(stacking, X_test, \n",
    "                             labels_test, cmap=plt.cm.Blues, display_labels=['Non-hateful', 'hateful'])\n",
    "    disp.ax_.set_title(name)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('confusion_matrix_' + name+'.pdf', dpi=300)\n",
    "    \n",
    "def create_coefficient_plot(coefs, names, group, savename):\n",
    "    log_odds = np.exp(coefs.T)\n",
    "    log_odds = pd.DataFrame(log_odds,names,['Coef']).sort_values(by='Coef', ascending=False)\n",
    "    log_odds.round(3)\n",
    "    log_odds.plot.bar()\n",
    "    plt.ylabel('LR Coefficients')\n",
    "    plt.xlabel(group)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(savename+'.pdf', dpi=300)\n",
    "    \n",
    "def organize_names_df(names):\n",
    "    names_coeffs = names[::2]\n",
    "    names_coeffs = [name.replace(\"-0\", \"\") for name in names_coeffs]\n",
    "    names_coeffs = [name.replace(\"-CV\", \"-TF\") for name in names_coeffs]\n",
    "    return names_coeffs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking group A - Varying Classification methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "algorithms_list = ['SVM', 'MLP', 'KNN', 'RF', 'EXTRA', 'CNN', 'LR', 'NB']\n",
    "stacking = LogisticRegression(class_weight='balanced')\n",
    "\n",
    "results_A = np.zeros(len(algorithms_list))\n",
    "\n",
    "for idx_alg, algorithm in enumerate(algorithms_list):\n",
    "    X_val, X_test, cols = filter_df_train_test(probas_val, probas_test, algorithm)\n",
    "    X_val, X_test = filter_collinearity(X_val, X_test)\n",
    "    stacking.fit(X_val, labels_val)\n",
    "    y_pred = stacking.predict(X_test)\n",
    "    score = f1_score(labels_test, y_pred)\n",
    "    results_A[idx_alg] = score\n",
    "    name = \"Confusion Matrix \"+algorithm+\" (Group A)\"\n",
    "    \n",
    "    feature_names = organize_names_df(cols)\n",
    "    create_coefficient_plot(stacking.coef_, feature_names, algorithm+\" (Group A)\", \"Coefficients Group A - \"+algorithm)\n",
    "#    generate_confusion_matrix(stacking, X_test, labels_test, name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.61454545, 0.62395242, 0.56133942, 0.58724249, 0.592245  ,\n",
       "       0.59079284, 0.60772922, 0.60150376])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking group B - Varying Feature Representation methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fe_list = ['CV', 'TFIDF', 'W2V', 'GLOVE', 'FAST']\n",
    "stacking = LogisticRegression(class_weight='balanced')\n",
    "\n",
    "results_B = np.zeros(len(fe_list))\n",
    "for idx_alg, fe in enumerate(fe_list):\n",
    "    X_val, X_test, cols = filter_df_train_test(probas_val, probas_test, fe)\n",
    "    X_val, X_test = filter_collinearity(X_val, X_test)\n",
    "    stacking.fit(X_val, labels_val)\n",
    "    y_pred = stacking.predict(X_test)\n",
    "    results_B[idx_alg] = f1_score(labels_test, y_pred)\n",
    "    # hack for mismatched name.\n",
    "    if fe == 'CV':\n",
    "        name = \"Confusion Matrix TF (Group B)\"\n",
    "    else:\n",
    "        name = \"Confusion Matrix \"+fe+\" (Group B)\"\n",
    "    feature_names = organize_names_df(cols)\n",
    "    create_coefficient_plot(stacking.coef_, feature_names, fe+\" (Group B)\", \"Coefficients Group B - \"+fe)\n",
    "\n",
    "#     generate_confusion_matrix(stacking, X_test, labels_test, name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.59364133, 0.59231905, 0.62061463, 0.60233593, 0.61457233])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking group C -  ALL techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_C = 0\n",
    "stacking_C = LogisticRegression()\n",
    "X_val, X_test = filter_collinearity(probas_val.to_numpy(), probas_test.to_numpy())\n",
    "stacking_C.fit(X_val, labels_val)\n",
    "y_pred = stacking_C.predict(X_test)\n",
    "results_C = f1_score(labels_test, y_pred)\n",
    "disp = plot_confusion_matrix(stacking_C, X_test, \n",
    "                             labels_test, cmap=plt.cm.Blues, display_labels=['Non-hateful', 'hateful'])\n",
    "disp.ax_.set_title(\"Confusion Matrix - Group C\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('confusion_matrix_GROUP_C.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5941101152368758\n"
     ]
    }
   ],
   "source": [
    "print(results_C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5941101152368758"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.36354235,  0.97020815, -1.21310611, -1.13737858,  0.81299152,\n",
       "        -0.13231043, -0.60686795,  0.27319889,  0.89984741,  0.40810056,\n",
       "        -1.13374226, -0.96704131, -0.34377343,  0.47070996,  0.03418888,\n",
       "        -0.39636662, -0.95922167, -0.01415102, -0.17299496,  0.20397408,\n",
       "        -0.17782258,  0.37921519, -0.51503929,  0.06735434, -0.29305907,\n",
       "        -0.42190197, -0.3272335 , -0.76495109,  0.29792155,  0.80190736,\n",
       "        -0.33162791, -0.39362237,  0.19462249, -0.23291655, -0.17179063,\n",
       "        -0.17436087,  0.21911028, -0.43469222, -0.40046354, -0.7959246 ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacking_C.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking group D - Proposed Selection scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models_D = ['MLP-GLOVE','MLP-FAST','SVM-CV','MLP-W2V','SVM-W2V','LR-GLOVE','RF-W2V','CNN-W2V']\n",
    "names = 'MLP-GLOVE|MLP-FAST|SVM-CV|MLP-W2V|SVM-W2V|LR-GLOVE|RF-W2V|CNN-W2V'\n",
    "X_val, X_test, cols = filter_df_train_test(probas_val, probas_test, names)\n",
    "X_val, X_test = filter_collinearity(X_val, X_test)\n",
    "stacking_D = LogisticRegression()\n",
    "\n",
    "stacking_D.fit(X_val, labels_val)\n",
    "y_pred = stacking_D.predict(X_test)\n",
    "results_D = f1_score(labels_test, y_pred)\n",
    "disp=plot_confusion_matrix(stacking_D, X_test, labels_test, cmap=plt.cm.Blues,\n",
    "                           display_labels=['Non-hateful', 'hateful'])\n",
    "disp.ax_.set_title(\"Confusion Matrix - Group D\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('confusion_matrix_GroupD.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6213050752928053\n"
     ]
    }
   ],
   "source": [
    "print(results_D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.6937236 , -0.93342326,  0.31352523, -1.09842958, -0.79498981,\n",
       "        -0.19138758, -0.04566218, -1.67363917]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacking_D.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def render_mpl_table(data, col_width=3.0, row_height=0.625, font_size=14,\n",
    "                     header_color='#40466e', row_colors=['#f1f1f2', 'w'], edge_color='w',\n",
    "                     bbox=[0, 0, 1, 1], header_columns=0,\n",
    "                     ax=None, **kwargs):\n",
    "    if ax is None:\n",
    "        size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array([col_width, row_height])\n",
    "        fig, ax = plt.subplots(figsize=size)\n",
    "        ax.axis('off')\n",
    "    mpl_table = ax.table(cellText=data.values, bbox=bbox, colLabels=data.columns, **kwargs)\n",
    "    mpl_table.auto_set_font_size(False)\n",
    "    mpl_table.set_fontsize(font_size)\n",
    "\n",
    "    for k, cell in mpl_table._cells.items():\n",
    "        cell.set_edgecolor(edge_color)\n",
    "        if k[0] == 0 or k[1] < header_columns:\n",
    "            cell.set_text_props(weight='bold', color='w')\n",
    "            cell.set_facecolor(header_color)\n",
    "        else:\n",
    "            cell.set_facecolor(row_colors[k[0]%len(row_colors) ])\n",
    "    return ax.get_figure(), ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coefficients for group D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_D = ['MLP-GLOVE','MLP-FAST','SVM-TF','MLP-W2V','SVM-W2V','LR-GLOVE','RF-W2V','CNN-W2V']\n",
    "odds_D = np.exp(stacking_D.coef_.T)\n",
    "odds_D = pd.DataFrame(odds_D,models_D,['Coef']).sort_values(by='Coef', ascending=False)\n",
    "odds_D.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_coefficient_plot(stacking_D.coef_, models_D, 'Group D', 'testi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coefficients for group C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_coeffs = organize_names_df(probas_val.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SVM-TF',\n",
       " 'SVM-TFIDF',\n",
       " 'SVM-W2V',\n",
       " 'SVM-GLOVE',\n",
       " 'SVM-FAST',\n",
       " 'LR-TF',\n",
       " 'LR-TFIDF',\n",
       " 'LR-W2V',\n",
       " 'LR-GLOVE',\n",
       " 'LR-FAST',\n",
       " 'RF-TF',\n",
       " 'RF-TFIDF',\n",
       " 'RF-W2V',\n",
       " 'RF-GLOVE',\n",
       " 'RF-FAST',\n",
       " 'NB-TF',\n",
       " 'NB-TFIDF',\n",
       " 'NB-W2V',\n",
       " 'NB-GLOVE',\n",
       " 'NB-FAST',\n",
       " 'MLP-TF',\n",
       " 'MLP-TFIDF',\n",
       " 'MLP-W2V',\n",
       " 'MLP-GLOVE',\n",
       " 'MLP-FAST',\n",
       " 'EXTRA-TF',\n",
       " 'EXTRA-TFIDF',\n",
       " 'EXTRA-W2V',\n",
       " 'EXTRA-GLOVE',\n",
       " 'EXTRA-FAST',\n",
       " 'KNN-TF',\n",
       " 'KNN-TFIDF',\n",
       " 'KNN-W2V',\n",
       " 'KNN-GLOVE',\n",
       " 'KNN-FAST',\n",
       " 'CNN-TF',\n",
       " 'CNN-TFIDF',\n",
       " 'CNN-W2V',\n",
       " 'CNN-GLOVE',\n",
       " 'CNN-FAST']"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_coeffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_coefficient_plot(stacking_C.coef_, names_coeffs, 'All models - Group C', 'testi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“For every one-unit increase in [X variable], the odds that the observation is in (y class) are [coefficient] times as large as the odds that the observation is not in (y class) when all other variables are held constant.”\n",
    "\n",
    "\n",
    "So, as variable RM (again, average number of rooms) increases by one unit, the odds that the houses represented in the observation are in the target class (“1”) are over 6x as large as the odds that they won’t be in the target class. On the other hand, as concentration of nitric oxide increases by one unit, the odds that the houses are in the target class are only ~0.15. For odds less than 1 (our negative coefficients), we can take 1/odds to make even better sense of them. So as nitric oxide increases by 1, the odds that the house is NOT in the target class are 1/0.15 or 6.66x (ominous!) as likely as the odds that it IS in the target class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarizing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stacking_names = ['Logistic Regression']\n",
    "algorithms_list_A = [alg + ' (Group A)' for alg in algorithms_list]\n",
    "group_A_df = pd.DataFrame(results_A, columns=all_stacking_names, index=algorithms_list_A)\n",
    "\n",
    "fe_list_B = [fe + ' (Group B)' for fe in fe_list]\n",
    "group_B_df = pd.DataFrame(results_B, columns=all_stacking_names, index=fe_list_B)\n",
    "\n",
    "group_C_df = pd.DataFrame(results_C.reshape(1, 1), columns=all_stacking_names, index=['Group C'])\n",
    "group_D_df = pd.DataFrame(results_D.reshape(1, 1), columns=all_stacking_names, index=['Group D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('\\\\begin{tabular}{lr}\\n'\n",
      " '\\\\toprule\\n'\n",
      " '{} &  Logistic Regression \\\\\\\\\\n'\n",
      " '\\\\midrule\\n'\n",
      " 'SVM (Group A)   &               0.6145 \\\\\\\\\\n'\n",
      " 'MLP (Group A)   &               0.6240 \\\\\\\\\\n'\n",
      " 'KNN (Group A)   &               0.5613 \\\\\\\\\\n'\n",
      " 'RF (Group A)    &               0.5872 \\\\\\\\\\n'\n",
      " 'EXTRA (Group A) &               0.5922 \\\\\\\\\\n'\n",
      " 'CNN (Group A)   &               0.5908 \\\\\\\\\\n'\n",
      " 'LR (Group A)    &               0.6077 \\\\\\\\\\n'\n",
      " 'NB (Group A)    &               0.6015 \\\\\\\\\\n'\n",
      " 'CV (Group B)    &               0.5936 \\\\\\\\\\n'\n",
      " 'TFIDF (Group B) &               0.5923 \\\\\\\\\\n'\n",
      " 'W2V (Group B)   &               0.6206 \\\\\\\\\\n'\n",
      " 'GLOVE (Group B) &               0.6023 \\\\\\\\\\n'\n",
      " 'FAST (Group B)  &               0.6146 \\\\\\\\\\n'\n",
      " 'Group C         &               0.5941 \\\\\\\\\\n'\n",
      " 'Group D         &               0.6213 \\\\\\\\\\n'\n",
      " '\\\\bottomrule\\n'\n",
      " '\\\\end{tabular}\\n')\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "pprint(pd.concat([group_A_df, group_B_df, group_C_df, group_D_df]).round(4).to_latex())"
   ]
  }
 ],
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