evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.7) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n"
     ]
    }
   ],
   "source": [
    "import orchest\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import f1_score,accuracy_score,classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = orchest.get_inputs()\n",
    "lgbm, pred_lgbm = data[\"lgbm\"]\n",
    "rf, pred_rf, X_train, y_train, X_val, y_val, test = data[\"RF\"]\n",
    "nb, pred_nb = data[\"NB\"]\n",
    "lr, pred_lr = data[\"LR\"]\n",
    "total_score=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM F1 score: 0.7274275979557071\n",
      "LGBM Accuracy Score: 0.7898883782009193\n"
     ]
    }
   ],
   "source": [
    "lgbm_f1 = f1_score(y_val, pred_lgbm)\n",
    "lgbm_score = accuracy_score(y_val, pred_lgbm)\n",
    "print(\"LGBM F1 score:\", lgbm_f1)\n",
    "print(\"LGBM Accuracy Score:\",lgbm_score)\n",
    "total_score.append(['LGBM',lgbm_f1,lgbm_score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "cm = confusion_matrix(y_val, pred_lgbm)\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    cmap=\"Blues\",\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1,\n",
    "    annot=True,\n",
    "    fmt=\"\",\n",
    "    xticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    "    yticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    ")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF F1 score: 0.7343336275375111\n",
      "RF Accuracy Score: 0.8023637557452397\n"
     ]
    }
   ],
   "source": [
    "RF_f1 = f1_score(y_val, pred_rf)\n",
    "RF_score = accuracy_score(y_val, pred_rf)\n",
    "print(\"RF F1 score:\", RF_f1)\n",
    "print(\"RF Accuracy Score:\",RF_score)\n",
    "total_score.append(['RF',RF_f1,RF_score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "cm = confusion_matrix(y_val, pred_rf)\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    cmap=\"Blues\",\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1,\n",
    "    annot=True,\n",
    "    fmt=\"\",\n",
    "    xticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    "    yticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    ")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR F1 score: 0.7532244196044712\n",
      "LR Accuracy Score: 0.8115561391989494\n"
     ]
    }
   ],
   "source": [
    "LR_f1 = f1_score(y_val, pred_lr)\n",
    "LR_score = accuracy_score(y_val, pred_lr)\n",
    "print(\"LR F1 score:\", LR_f1)\n",
    "print(\"LR Accuracy Score:\",LR_score)\n",
    "total_score.append(['LR',LR_f1,LR_score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "cm = confusion_matrix(y_val, pred_lr)\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    cmap=\"Blues\",\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1,\n",
    "    annot=True,\n",
    "    fmt=\"\",\n",
    "    xticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    "    yticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    ")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NB F1 score: 0.7529610829103216\n",
      "NB Accuracy Score: 0.8082731451083388\n"
     ]
    }
   ],
   "source": [
    "NB_f1 = f1_score(y_val, pred_nb)\n",
    "NB_score = accuracy_score(y_val, pred_nb)\n",
    "print(\"NB F1 score:\", NB_f1)\n",
    "print(\"NB Accuracy Score:\",NB_score)\n",
    "total_score.append(['NB',NB_f1,NB_score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "cm = confusion_matrix(y_val, pred_nb)\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    cmap=\"Blues\",\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1,\n",
    "    annot=True,\n",
    "    fmt=\"\",\n",
    "    xticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    "    yticklabels=[\"Disaster\", \"Non_Disaster\"],\n",
    ")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "orchest.output(total_score,name='eval')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "orchest-kernel-29cddbd9-ee64-4453-9b86-1fbfff20b541"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}