week5_p67_feature_important.ipynb

{
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   "outputs": [],
   "source": [
    "# https://www.kaggle.com/code/pasqualebalsebre/a-ml-analysis-of-california-housing-data/notebook\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n6385     -118.06     34.16                46.0       1467.0           298.0   \n7596     -118.25     33.89                35.0       1582.0           391.0   \n16035    -122.46     37.72                49.0       1207.0           255.0   \n8659     -118.41     33.85                16.0       6123.0          1989.0   \n16702    -120.58     35.00                37.0        523.0           119.0   \n...          ...       ...                 ...          ...             ...   \n1732     -122.35     37.96                32.0       1991.0           504.0   \n1603     -122.14     37.88                34.0       6986.0          1096.0   \n7659     -118.22     33.84                35.0       1131.0           273.0   \n20108    -120.35     37.86                25.0        287.0            57.0   \n3065     -119.24     35.67                32.0       3216.0           750.0   \n\n       population  households  median_income  ocean_proximity_<1H OCEAN  \\\n6385        816.0       267.0         3.6705                          0   \n7596       1957.0       404.0         2.4537                          1   \n16035       658.0       220.0         4.0859                          0   \n8659       2853.0      1789.0         4.4250                          0   \n16702       374.0        95.0         1.4726                          1   \n...           ...         ...            ...                        ...   \n1732       1139.0       423.0         2.0353                          0   \n1603       2865.0      1124.0         6.2275                          0   \n7659       1007.0       269.0         4.0219                          1   \n20108       118.0        50.0         2.3056                          0   \n3065       2639.0       709.0         2.0025                          0   \n\n       ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n6385                        1                       0   \n7596                        0                       0   \n16035                       0                       0   \n8659                        0                       0   \n16702                       0                       0   \n...                       ...                     ...   \n1732                        0                       0   \n1603                        0                       0   \n7659                        0                       0   \n20108                       1                       0   \n3065                        1                       0   \n\n       ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \n6385                          0                           0  \n7596                          0                           0  \n16035                         0                           1  \n8659                          0                           1  \n16702                         0                           0  \n...                         ...                         ...  \n1732                          1                           0  \n1603                          1                           0  \n7659                          0                           0  \n20108                         0                           0  \n3065                          0                           0  \n\n[4128 rows x 13 columns]",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>ocean_proximity_&lt;1H OCEAN</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>6385</th>\n      <td>-118.06</td>\n      <td>34.16</td>\n      <td>46.0</td>\n      <td>1467.0</td>\n      <td>298.0</td>\n      <td>816.0</td>\n      <td>267.0</td>\n      <td>3.6705</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7596</th>\n      <td>-118.25</td>\n      <td>33.89</td>\n      <td>35.0</td>\n      <td>1582.0</td>\n      <td>391.0</td>\n      <td>1957.0</td>\n      <td>404.0</td>\n      <td>2.4537</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16035</th>\n      <td>-122.46</td>\n      <td>37.72</td>\n      <td>49.0</td>\n      <td>1207.0</td>\n      <td>255.0</td>\n      <td>658.0</td>\n      <td>220.0</td>\n      <td>4.0859</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8659</th>\n      <td>-118.41</td>\n      <td>33.85</td>\n      <td>16.0</td>\n      <td>6123.0</td>\n      <td>1989.0</td>\n      <td>2853.0</td>\n      <td>1789.0</td>\n      <td>4.4250</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>16702</th>\n      <td>-120.58</td>\n      <td>35.00</td>\n      <td>37.0</td>\n      <td>523.0</td>\n      <td>119.0</td>\n      <td>374.0</td>\n      <td>95.0</td>\n      <td>1.4726</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1732</th>\n      <td>-122.35</td>\n      <td>37.96</td>\n      <td>32.0</td>\n      <td>1991.0</td>\n      <td>504.0</td>\n      <td>1139.0</td>\n      <td>423.0</td>\n      <td>2.0353</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1603</th>\n      <td>-122.14</td>\n      <td>37.88</td>\n      <td>34.0</td>\n      <td>6986.0</td>\n      <td>1096.0</td>\n      <td>2865.0</td>\n      <td>1124.0</td>\n      <td>6.2275</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7659</th>\n      <td>-118.22</td>\n      <td>33.84</td>\n      <td>35.0</td>\n      <td>1131.0</td>\n      <td>273.0</td>\n      <td>1007.0</td>\n      <td>269.0</td>\n      <td>4.0219</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20108</th>\n      <td>-120.35</td>\n      <td>37.86</td>\n      <td>25.0</td>\n      <td>287.0</td>\n      <td>57.0</td>\n      <td>118.0</td>\n      <td>50.0</td>\n      <td>2.3056</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3065</th>\n      <td>-119.24</td>\n      <td>35.67</td>\n      <td>32.0</td>\n      <td>3216.0</td>\n      <td>750.0</td>\n      <td>2639.0</td>\n      <td>709.0</td>\n      <td>2.0025</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4128 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataFrame\n",
    "data = pd.read_csv(\"data/housing.csv\")\n",
    "# Convert Categorical Data Values to Features\n",
    "data = pd.get_dummies(data=data, columns=[\"ocean_proximity\"], prefix=\"ocean_proximity\")\n",
    "# Fill the NAN value with the intermediate value\n",
    "data.fillna(data.mean(), inplace=True)\n",
    "\n",
    "# Independent columns\n",
    "X = data.iloc[:, data.columns != \"median_house_value\"]\n",
    "# target column (median_house_value)\n",
    "Y = data.iloc[:, 8]\n",
    "\n",
    "# Since it is impossible to calculate with the original amount of data, only 20% is used to calculate.\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.2, test_size=0.8)\n",
    "\n",
    "X_train"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Specs         Score\n",
      "3                  total_rooms  9.772920e+06\n",
      "5                   population  3.988363e+06\n",
      "4               total_bedrooms  1.402919e+06\n",
      "6                   households  1.303777e+06\n",
      "2           housing_median_age  2.319302e+04\n",
      "7                median_income  1.171746e+04\n",
      "9       ocean_proximity_INLAND  6.565265e+03\n",
      "11    ocean_proximity_NEAR BAY  4.079131e+03\n",
      "12  ocean_proximity_NEAR OCEAN  3.993245e+03\n",
      "8    ocean_proximity_<1H OCEAN  3.746993e+03\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "# Selection with SelectKBest class to extract top 10 best features\n",
    "bestFeatures = SelectKBest(score_func=chi2, k=10)\n",
    "# Negative longitude data is converted to positive data using absolute values.\n",
    "fit = bestFeatures.fit(X.abs(), Y)\n",
    "\n",
    "dfColumns = pd.DataFrame(X.columns)\n",
    "dfScores = pd.DataFrame(fit.scores_)\n",
    "\n",
    "featureScores = pd.concat([dfColumns, dfScores], axis=1)\n",
    "featureScores.columns = [\"Specs\", \"Score\"]\n",
    "\n",
    "print(featureScores.nlargest(10,\"Score\"))"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.22430262e-01 1.21982255e-01 1.13524690e-01 1.24743369e-01\n",
      " 1.23041898e-01 1.24880947e-01 1.23055388e-01 1.36580383e-01\n",
      " 2.80659369e-03 1.91672193e-03 1.23991581e-04 2.10121292e-03\n",
      " 2.81228764e-03]\n"
     ]
    }
   ],
   "source": [
    "model = ExtraTreesClassifier()\n",
    "model.fit(X_train, Y_train)\n",
    "print(model.feature_importances_)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(10).plot(kind=\"barh\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}