solutions/ch_10/exercise_1.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Predicting Star Temperature with Elastic Net Linear Regression\n",
    "Using the Open Exoplanet Catalogue database: https://github.com/OpenExoplanetCatalogue/open_exoplanet_catalogue/\n",
    "\n",
    "## Data License\n",
    "Copyright (C) 2012 Hanno Rein\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this database and associated scripts (the \"Database\"), to deal in the Database without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Database, and to permit persons to whom the Database is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Database. A reference to the Database shall be included in all scientific publications that make use of the Database.\n",
    "\n",
    "THE DATABASE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATABASE OR THE USE OR OTHER DEALINGS IN THE DATABASE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>magV</th>\n",
       "      <th>mass</th>\n",
       "      <th>spectraltype</th>\n",
       "      <th>magJ</th>\n",
       "      <th>radius</th>\n",
       "      <th>magB</th>\n",
       "      <th>magH</th>\n",
       "      <th>magK</th>\n",
       "      <th>metallicity</th>\n",
       "      <th>name</th>\n",
       "      <th>planets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4742.0</td>\n",
       "      <td>4.740</td>\n",
       "      <td>2.70</td>\n",
       "      <td>G8 III</td>\n",
       "      <td>2.943</td>\n",
       "      <td>19.000</td>\n",
       "      <td>5.740</td>\n",
       "      <td>2.484</td>\n",
       "      <td>2.282</td>\n",
       "      <td>-0.350</td>\n",
       "      <td>11 Com</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4340.0</td>\n",
       "      <td>5.024</td>\n",
       "      <td>1.80</td>\n",
       "      <td>K4III</td>\n",
       "      <td>2.876</td>\n",
       "      <td>24.080</td>\n",
       "      <td>6.415</td>\n",
       "      <td>2.091</td>\n",
       "      <td>1.939</td>\n",
       "      <td>0.040</td>\n",
       "      <td>11 UMi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4813.0</td>\n",
       "      <td>5.220</td>\n",
       "      <td>2.20</td>\n",
       "      <td>K0III</td>\n",
       "      <td>3.019</td>\n",
       "      <td>11.000</td>\n",
       "      <td>6.240</td>\n",
       "      <td>2.608</td>\n",
       "      <td>2.331</td>\n",
       "      <td>-0.240</td>\n",
       "      <td>14 And</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5311.0</td>\n",
       "      <td>6.670</td>\n",
       "      <td>1.00</td>\n",
       "      <td>K0 V</td>\n",
       "      <td>5.158</td>\n",
       "      <td>0.708</td>\n",
       "      <td>7.570</td>\n",
       "      <td>4.803</td>\n",
       "      <td>4.714</td>\n",
       "      <td>0.430</td>\n",
       "      <td>14 Her</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5825.0</td>\n",
       "      <td>5.950</td>\n",
       "      <td>1.11</td>\n",
       "      <td>G2V</td>\n",
       "      <td>5.090</td>\n",
       "      <td>1.243</td>\n",
       "      <td>6.590</td>\n",
       "      <td>4.720</td>\n",
       "      <td>4.430</td>\n",
       "      <td>0.096</td>\n",
       "      <td>16 Cygni A</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature   magV  mass spectraltype   magJ  radius   magB   magH   magK  \\\n",
       "0       4742.0  4.740  2.70       G8 III  2.943  19.000  5.740  2.484  2.282   \n",
       "1       4340.0  5.024  1.80        K4III  2.876  24.080  6.415  2.091  1.939   \n",
       "2       4813.0  5.220  2.20        K0III  3.019  11.000  6.240  2.608  2.331   \n",
       "3       5311.0  6.670  1.00         K0 V  5.158   0.708  7.570  4.803  4.714   \n",
       "4       5825.0  5.950  1.11          G2V  5.090   1.243  6.590  4.720  4.430   \n",
       "\n",
       "   metallicity        name  planets  \n",
       "0       -0.350      11 Com      1.0  \n",
       "1        0.040      11 UMi      1.0  \n",
       "2       -0.240      14 And      1.0  \n",
       "3        0.430      14 Her      2.0  \n",
       "4        0.096  16 Cygni A      0.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "stars = pd.read_csv('../../ch_10/data/stars.csv')\n",
    "stars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3183 entries, 0 to 3182\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   temperature   2793 non-null   float64\n",
      " 1   magV          1274 non-null   float64\n",
      " 2   mass          3090 non-null   float64\n",
      " 3   spectraltype  1206 non-null   object \n",
      " 4   magJ          1563 non-null   float64\n",
      " 5   radius        2721 non-null   float64\n",
      " 6   magB          1003 non-null   float64\n",
      " 7   magH          1556 non-null   float64\n",
      " 8   magK          1585 non-null   float64\n",
      " 9   metallicity   2411 non-null   float64\n",
      " 10  name          3183 non-null   object \n",
      " 11  planets       3183 non-null   float64\n",
      "dtypes: float64(10), object(2)\n",
      "memory usage: 298.5+ KB\n"
     ]
    }
   ],
   "source": [
    "stars.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>magV</th>\n",
       "      <th>mass</th>\n",
       "      <th>magJ</th>\n",
       "      <th>radius</th>\n",
       "      <th>magB</th>\n",
       "      <th>magH</th>\n",
       "      <th>magK</th>\n",
       "      <th>metallicity</th>\n",
       "      <th>planets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2793.000000</td>\n",
       "      <td>1274.000000</td>\n",
       "      <td>3090.000000</td>\n",
       "      <td>1563.000000</td>\n",
       "      <td>2721.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>1556.000000</td>\n",
       "      <td>1585.000000</td>\n",
       "      <td>2411.000000</td>\n",
       "      <td>3183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5532.146084</td>\n",
       "      <td>10.304334</td>\n",
       "      <td>0.960175</td>\n",
       "      <td>9.899493</td>\n",
       "      <td>1.605091</td>\n",
       "      <td>10.730635</td>\n",
       "      <td>9.525366</td>\n",
       "      <td>9.408291</td>\n",
       "      <td>0.017893</td>\n",
       "      <td>1.273327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1715.076581</td>\n",
       "      <td>3.148937</td>\n",
       "      <td>0.362107</td>\n",
       "      <td>3.226399</td>\n",
       "      <td>3.689890</td>\n",
       "      <td>3.032962</td>\n",
       "      <td>3.253978</td>\n",
       "      <td>3.214560</td>\n",
       "      <td>0.183447</td>\n",
       "      <td>0.853176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>485.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-1.150000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>-1.380000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>-2.090000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5078.000000</td>\n",
       "      <td>7.950000</td>\n",
       "      <td>0.807250</td>\n",
       "      <td>7.013500</td>\n",
       "      <td>0.812000</td>\n",
       "      <td>8.512500</td>\n",
       "      <td>6.664750</td>\n",
       "      <td>6.589000</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5627.000000</td>\n",
       "      <td>10.471500</td>\n",
       "      <td>0.964000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>10.248500</td>\n",
       "      <td>10.161000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5949.000000</td>\n",
       "      <td>12.543500</td>\n",
       "      <td>1.110000</td>\n",
       "      <td>12.607500</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>12.230250</td>\n",
       "      <td>12.119000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>57000.000000</td>\n",
       "      <td>24.300000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>83.800000</td>\n",
       "      <td>19.860000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>19.160000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        temperature         magV         mass         magJ       radius  \\\n",
       "count   2793.000000  1274.000000  3090.000000  1563.000000  2721.000000   \n",
       "mean    5532.146084    10.304334     0.960175     9.899493     1.605091   \n",
       "std     1715.076581     3.148937     0.362107     3.226399     3.689890   \n",
       "min      485.000000     0.010000     0.010000    -1.150000     0.000014   \n",
       "25%     5078.000000     7.950000     0.807250     7.013500     0.812000   \n",
       "50%     5627.000000    10.471500     0.964000    10.600000     0.999000   \n",
       "75%     5949.000000    12.543500     1.110000    12.607500     1.270000   \n",
       "max    57000.000000    24.300000     4.500000    21.600000    83.800000   \n",
       "\n",
       "              magB         magH         magK  metallicity      planets  \n",
       "count  1003.000000  1556.000000  1585.000000  2411.000000  3183.000000  \n",
       "mean     10.730635     9.525366     9.408291     0.017893     1.273327  \n",
       "std       3.032962     3.253978     3.214560     0.183447     0.853176  \n",
       "min       0.720000    -1.380000    -1.490000    -2.090000     0.000000  \n",
       "25%       8.512500     6.664750     6.589000    -0.060000     1.000000  \n",
       "50%      10.810000    10.248500    10.161000     0.020000     1.000000  \n",
       "75%      13.010000    12.230250    12.119000     0.110000     1.000000  \n",
       "max      19.860000    20.800000    19.160000     0.560000     9.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(stars.corr(), vmin=-1, vmax=1, center=0, annot=True, fmt='.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = stars[[\n",
    "    'metallicity', 'temperature', 'magJ', 'radius', \n",
    "    'magB', 'magH', 'magK', 'mass', 'planets'\n",
    "]].dropna()\n",
    "\n",
    "y = data.pop('temperature')\n",
    "X = data\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid search for best hyperparameters in elastic net pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture \n",
    "# don't show warning messages or output for this cell\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('scale', MinMaxScaler()), \n",
    "    ('net', ElasticNet(random_state=0))\n",
    "])\n",
    "\n",
    "search_space = {\n",
    "    'net__alpha': [0.1, 0.5, 1, 1.5, 2, 5],\n",
    "    'net__l1_ratio': np.linspace(0, 1, num=10),\n",
    "    'net__fit_intercept': [True, False]\n",
    "}\n",
    "\n",
    "elastic_net = GridSearchCV(pipeline, search_space, cv=5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the best hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'net__alpha': 2, 'net__fit_intercept': True, 'net__l1_ratio': 1.0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R<sup>2</sup>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8565828773951133"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net.score(X_test, y_test) # R-squared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.0, 'metallicity'),\n",
       " (0.0, 'magJ'),\n",
       " (-230.95202113409036, 'radius'),\n",
       " (-9749.434488243596, 'magB'),\n",
       " (0.0, 'magH'),\n",
       " (9343.22858456506, 'magK'),\n",
       " (1053.0380032666212, 'mass'),\n",
       " (-141.41014698708304, 'planets')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    "    (coef, feature) for coef, feature in \n",
    "    zip(elastic_net.best_estimator_.named_steps['net'].coef_, X_train.columns)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5047.900329702881"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net.best_estimator_.named_steps['net'].intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='Observation', ylabel='Residual'>,\n",
       "       <AxesSubplot:xlabel='Residual', ylabel='Density'>], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.regression import plot_residuals\n",
    "plot_residuals(y_test, elastic_net.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<div>\n",
    "    <a href=\"../../ch_10/red_wine.ipynb\">\n",
    "        <button>&#8592; Chapter 10</button>\n",
    "    </a>\n",
    "    <a href=\"./exercise_2.ipynb\">\n",
    "        <button style=\"float: right;\">Next Solution &#8594;</button>\n",
    "    </a>\n",
    "</div>\n",
    "<hr>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}