metrics_eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76e8c9d9-f27a-4ed5-aebf-88bee2595e40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported\n"
     ]
    }
   ],
   "source": [
    "# Regression of examination scores of students\n",
    "# Evaluation of metrics\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from configparser import ConfigParser\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=UserWarning)\n",
    "\n",
    "print(\"Libraries imported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "18a4542f-9f23-46b1-a02c-8d15fcb88e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['polynomial', 'sgd', 'svr', 'forest', 'knn']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the evaluation scores from output dir\n",
    "# filename\n",
    "filename = \"output_all/prediction_score.conf\"\n",
    "config = ConfigParser(allow_no_value=True)\n",
    "config.read(filename)\n",
    "config.sections()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60a369f8-6de4-4efb-bf0b-9505483efa7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "['polynomial', 'sgd', 'svr', 'forest', 'knn']\n"
     ]
    }
   ],
   "source": [
    "# A list to hold the values before converting them to \n",
    "# DataFrame. Using DataFrame made it easier to plot\n",
    "# charts.\n",
    "mean_abs_err = []\n",
    "median_abs_err = []\n",
    "root_mse = []\n",
    "\n",
    "# Model names are in the sections\n",
    "model_name_list = config.sections()\n",
    "print(type(model_name_list))\n",
    "print(model_name_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b3aff21-359d-4050-862b-e0a24550fbd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_abs_err \n",
      "[6.679894793695229, 7.576304145786787, 6.214876119363975, 6.020123336579033, 6.2412482032735195]\n",
      "median_abs_err \n",
      "[5.28125, 6.248588418494222, 4.582313589462245, 4.450000000000003, 4.571428571428569]\n",
      "root_mse \n",
      "[8.674631076103877, 9.558710839887567, 8.411625846686492, 8.227283968176966, 8.429147461598332]\n"
     ]
    }
   ],
   "source": [
    "# Get the score values under each section\n",
    "# Sample of the score file\n",
    "# [polynomial]\n",
    "# mean_abs_err = 6.760388773937033\n",
    "# median_abs_err = 5.34375\n",
    "# mean_sq_err = 77.09386487921815\n",
    "# root_mse = 8.780311206285239\n",
    "\n",
    "for model_name in model_name_list:\n",
    "    mean_abs_err.append(float(config[model_name]['mean_abs_err']))\n",
    "    median_abs_err.append(float(config[model_name]['median_abs_err']))\n",
    "    root_mse.append(float(config[model_name]['root_mse']))\n",
    "\n",
    "print(f\"mean_abs_err \\n{mean_abs_err}\")\n",
    "print(f\"median_abs_err \\n{median_abs_err}\")\n",
    "print(f\"root_mse \\n{root_mse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e694f1f-828a-4a63-a0f8-ae1cfc157c65",
   "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_name</th>\n",
       "      <th>mean_abs_err</th>\n",
       "      <th>median_abs_err</th>\n",
       "      <th>root_mse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>polynomial</td>\n",
       "      <td>6.679895</td>\n",
       "      <td>5.281250</td>\n",
       "      <td>8.674631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sgd</td>\n",
       "      <td>7.576304</td>\n",
       "      <td>6.248588</td>\n",
       "      <td>9.558711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>svr</td>\n",
       "      <td>6.214876</td>\n",
       "      <td>4.582314</td>\n",
       "      <td>8.411626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>forest</td>\n",
       "      <td>6.020123</td>\n",
       "      <td>4.450000</td>\n",
       "      <td>8.227284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>knn</td>\n",
       "      <td>6.241248</td>\n",
       "      <td>4.571429</td>\n",
       "      <td>8.429147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_name  mean_abs_err  median_abs_err  root_mse\n",
       "0  polynomial      6.679895        5.281250  8.674631\n",
       "1         sgd      7.576304        6.248588  9.558711\n",
       "2         svr      6.214876        4.582314  8.411626\n",
       "3      forest      6.020123        4.450000  8.227284\n",
       "4         knn      6.241248        4.571429  8.429147"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put the scores into a dataframe\n",
    "\n",
    "score_df = pd.DataFrame()\n",
    "score_df['model_name'] = model_name_list\n",
    "score_df['mean_abs_err'] = mean_abs_err\n",
    "score_df['median_abs_err'] = median_abs_err\n",
    "score_df['root_mse'] = root_mse\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "86c562d7-c026-4de0-bc47-0dd91eff5f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar chart to see the score of each\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))\n",
    "\n",
    "sns.pointplot(data=score_df, x=\"model_name\", y=\"mean_abs_err\", ax=axes[0]);\n",
    "axes[0].set_title(\"Mean absolute error\");\n",
    "\n",
    "sns.pointplot(data=score_df, x=\"model_name\", y=\"median_abs_err\", ax=axes[1]);\n",
    "axes[1].set_title(\"Median absolute error\");\n",
    "\n",
    "sns.pointplot(data=score_df, x=\"model_name\", y=\"root_mse\", ax=axes[2]);\n",
    "axes[2].set_title(\"Root mean squared error\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb810ad-82ef-4219-a8f7-e222f2bc437c",
   "metadata": {},
   "source": [
    "Observations\n",
    "\n",
    "Linear models like polynomial linear regression and stochastic gradient descent produces the errors more than the other three algorithms.\n",
    "\n",
    "Notice that random forest has the least errors for all three metrics. It would be chosen as the algorithms for the problem.\n",
    "\n",
    "The table below ranks the algorithms by each of the error metrics.\n",
    "\n",
    "With random forest as the regressor, the predicted scores will be \n",
    "- +/- 6.0 marks if mean absolute error is used. This is affected by extreme values.\n",
    "- +/- 4.5 marks if median absolute error is used. This is the best choice of error margins because it is not affected by extreme values.\n",
    "- +/- 8.2 marks if root mean squared error is used. This is affected by extreme values (because of the mean) and amplify errors (because of the square). \n",
    "\n",
    "Therefore with random forest, the predicted score will be likely to be within +/- 4.5 points of the real score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3816d71f-3d7c-406a-a28c-e384e27d8dbd",
   "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_name</th>\n",
       "      <th>mean_abs_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>forest</td>\n",
       "      <td>6.020123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>svr</td>\n",
       "      <td>6.214876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>knn</td>\n",
       "      <td>6.241248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polynomial</td>\n",
       "      <td>6.679895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sgd</td>\n",
       "      <td>7.576304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_name  mean_abs_err\n",
       "0      forest      6.020123\n",
       "1         svr      6.214876\n",
       "2         knn      6.241248\n",
       "3  polynomial      6.679895\n",
       "4         sgd      7.576304"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by mean absolute error\n",
    "score_df[['model_name', 'mean_abs_err']].sort_values(by='mean_abs_err', ascending=True, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31fe2b6a-2957-4bd3-b979-5a5051a900df",
   "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_name</th>\n",
       "      <th>median_abs_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>forest</td>\n",
       "      <td>4.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>knn</td>\n",
       "      <td>4.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>svr</td>\n",
       "      <td>4.582314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polynomial</td>\n",
       "      <td>5.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sgd</td>\n",
       "      <td>6.248588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_name  median_abs_err\n",
       "0      forest        4.450000\n",
       "1         knn        4.571429\n",
       "2         svr        4.582314\n",
       "3  polynomial        5.281250\n",
       "4         sgd        6.248588"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by median absolute error\n",
    "score_df[['model_name', 'median_abs_err']].sort_values(by='median_abs_err', ascending=True, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "782e8bb7-35e3-4a95-820b-9b016b6f7d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>root_mse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>forest</td>\n",
       "      <td>8.227284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>svr</td>\n",
       "      <td>8.411626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>knn</td>\n",
       "      <td>8.429147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polynomial</td>\n",
       "      <td>8.674631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sgd</td>\n",
       "      <td>9.558711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_name  root_mse\n",
       "0      forest  8.227284\n",
       "1         svr  8.411626\n",
       "2         knn  8.429147\n",
       "3  polynomial  8.674631\n",
       "4         sgd  9.558711"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by root mean squared error\n",
    "score_df[['model_name', 'root_mse']].sort_values(by='root_mse', ascending=True, ignore_index=True)"
   ]
  }
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