comparison.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 pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = orchest.get_inputs()\n",
    "model,test,f1_score,accuracy_score = data[\"ensemble\"]\n",
    "total_score = data[\"eval\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['LGBM', 0.7274275979557071, 0.7898883782009193],\n",
       " ['RF', 0.7343336275375111, 0.8023637557452397],\n",
       " ['LR', 0.7532244196044712, 0.8115561391989494],\n",
       " ['NB', 0.7529610829103216, 0.8082731451083388],\n",
       " ['Ensemble', 0.7347670250896057, 0.8056467498358503]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_score.append(['Ensemble',f1_score,accuracy_score])\n",
    "total_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Model</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGBM</td>\n",
       "      <td>0.727428</td>\n",
       "      <td>0.789888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.734334</td>\n",
       "      <td>0.802364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.753224</td>\n",
       "      <td>0.811556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.752961</td>\n",
       "      <td>0.808273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ensemble</td>\n",
       "      <td>0.734767</td>\n",
       "      <td>0.805647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model  F1 Score  Accuracy Score\n",
       "0      LGBM  0.727428        0.789888\n",
       "1        RF  0.734334        0.802364\n",
       "2        LR  0.753224        0.811556\n",
       "3        NB  0.752961        0.808273\n",
       "4  Ensemble  0.734767        0.805647"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scoring = pd.DataFrame(total_score,columns=['Model','F1 Score','Accuracy Score'])\n",
    "Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2,1, figsize = (8, 15))\n",
    "\n",
    "sns.barplot(x=\"Model\", y=\"F1 Score\", data=Scoring,ax=ax1)\n",
    "ax1.set_title(\"F1 Score\", fontsize = 12);\n",
    "sns.barplot(x=\"Model\", y=\"Accuracy Score\", data=Scoring,ax=ax2)\n",
    "ax2.set_title(\"Accuracy Score\", fontsize = 12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "orchest.output((model,test),name=\"sub\")"
   ]
  }
 ],
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