appendix_basic_num_enc_no_scaling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import (\n",
    "    precision_score, recall_score, roc_auc_score, f1_score, confusion_matrix, accuracy_score\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "quake_frame = pd.read_csv('data/consolidated_data.csv')\n",
    "\n",
    "quake_frame.drop(['id', 'Unnamed: 0', 'place', 'time', 'updated'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sonic boom', 'earthquake', 'quarry blast', 'explosion',\n",
       "       'nuclear explosion', 'mine collapse', 'other event',\n",
       "       'chemical explosion', 'rock burst', 'ice quake', 'rockslide',\n",
       "       'Rock Slide', 'landslide', 'quarry', 'building collapse',\n",
       "       'mining explosion', 'sonicboom', 'acoustic noise', 'not reported',\n",
       "       'experimental explosion', 'collapse', 'meteorite',\n",
       "       'induced or triggered event', 'volcanic eruption', 'eq'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quake_frame.type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "natural_quakes = ['earthquake']\n",
    "quake_frame['simple_label'] = quake_frame['type'].apply(lambda x: x not in natural_quakes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.027042808333236575"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(quake_frame['simple_label'])/len(quake_frame.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple model, no imputation\n",
    "\n",
    "We'll start this off with a simple model, just a Random Forest for two classes that takes only rows that have no nans in them. Let's see how many we get.  \n",
    "Then we'll split the data 80/20 and run training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "latitude           0\n",
       "longitude          0\n",
       "depth              0\n",
       "mag                0\n",
       "magType            0\n",
       "nst                0\n",
       "gap                0\n",
       "dmin               0\n",
       "rms                0\n",
       "net                0\n",
       "type               0\n",
       "horizontalError    0\n",
       "depthError         0\n",
       "magError           0\n",
       "magNst             0\n",
       "status             0\n",
       "locationSource     0\n",
       "magSource          0\n",
       "simple_label       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quake_frame.dropna(inplace=True)\n",
    "quake_frame.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1227408"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(quake_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03523522740604591"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(quake_frame['simple_label'])/len(quake_frame.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>37.361674</td>\n",
       "      <td>4.841731</td>\n",
       "      <td>0.000</td>\n",
       "      <td>35.964167</td>\n",
       "      <td>37.573000</td>\n",
       "      <td>38.817000</td>\n",
       "      <td>62.030667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>-119.557707</td>\n",
       "      <td>10.027502</td>\n",
       "      <td>-179.098</td>\n",
       "      <td>-122.701333</td>\n",
       "      <td>-120.558833</td>\n",
       "      <td>-118.150167</td>\n",
       "      <td>179.661500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>6.016756</td>\n",
       "      <td>7.922880</td>\n",
       "      <td>-3.882</td>\n",
       "      <td>1.816000</td>\n",
       "      <td>4.413000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mag</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>1.258097</td>\n",
       "      <td>0.694405</td>\n",
       "      <td>-2.500</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>5.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nst</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>17.010182</td>\n",
       "      <td>13.671235</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>276.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gap</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>121.032150</td>\n",
       "      <td>65.767724</td>\n",
       "      <td>0.000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dmin</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>0.078264</td>\n",
       "      <td>0.342578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017120</td>\n",
       "      <td>0.037840</td>\n",
       "      <td>0.079990</td>\n",
       "      <td>141.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rms</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>0.097118</td>\n",
       "      <td>0.195847</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>64.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horizontalError</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>0.801039</td>\n",
       "      <td>2.296862</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>194.584100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depthError</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>2.773763</td>\n",
       "      <td>6.903563</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>725.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magError</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>0.158215</td>\n",
       "      <td>0.132854</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>6.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magNst</th>\n",
       "      <td>1227408.0</td>\n",
       "      <td>12.685281</td>\n",
       "      <td>17.846407</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>430.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count        mean        std      min         25%  \\\n",
       "latitude         1227408.0   37.361674   4.841731    0.000   35.964167   \n",
       "longitude        1227408.0 -119.557707  10.027502 -179.098 -122.701333   \n",
       "depth            1227408.0    6.016756   7.922880   -3.882    1.816000   \n",
       "mag              1227408.0    1.258097   0.694405   -2.500    0.800000   \n",
       "nst              1227408.0   17.010182  13.671235    0.000    8.000000   \n",
       "gap              1227408.0  121.032150  65.767724    0.000   72.000000   \n",
       "dmin             1227408.0    0.078264   0.342578    0.000    0.017120   \n",
       "rms              1227408.0    0.097118   0.195847    0.000    0.030000   \n",
       "horizontalError  1227408.0    0.801039   2.296862    0.000    0.270000   \n",
       "depthError       1227408.0    2.773763   6.903563    0.000    0.490000   \n",
       "magError         1227408.0    0.158215   0.132854    0.000    0.090000   \n",
       "magNst           1227408.0   12.685281  17.846407    0.000    4.000000   \n",
       "\n",
       "                        50%         75%         max  \n",
       "latitude          37.573000   38.817000   62.030667  \n",
       "longitude       -120.558833 -118.150167  179.661500  \n",
       "depth              4.413000    7.830000  211.000000  \n",
       "mag                1.180000    1.670000    5.840000  \n",
       "nst               13.000000   22.000000  276.000000  \n",
       "gap              105.000000  153.000000  360.000000  \n",
       "dmin               0.037840    0.079990  141.160000  \n",
       "rms                0.060000    0.130000   64.290000  \n",
       "horizontalError    0.410000    0.720000  194.584100  \n",
       "depthError         0.770000    1.460000  725.300000  \n",
       "magError           0.140000    0.200000    6.110000  \n",
       "magNst             8.000000   14.000000  430.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quake_frame.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, this changes the proportions slightly, but not too bad. If anything, one might suggest that at least the mild increase in proportion of non-earthquakes offsets the reduced dataset a little.  \n",
    "Okay, so the problematic values are no longer there, that's something.  \n",
    "Let's try this.  \n",
    "We'll start by mixing up the data frame, then encoding all the categories numerically and splitting it sklearn style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "quake_frame = quake_frame.sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "cat_columns = ['magType', 'net', 'status', 'locationSource', 'magSource']\n",
    "\n",
    "for cat in cat_columns:\n",
    "    quake_frame[cat + '_enc'] = le.fit_transform(quake_frame[cat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_cols = ['latitude',\n",
    " 'longitude',\n",
    " 'depth',\n",
    " 'mag',\n",
    " 'nst',\n",
    " 'gap',\n",
    " 'dmin',\n",
    " 'rms',\n",
    " 'horizontalError',\n",
    " 'depthError',\n",
    " 'magError',\n",
    " 'magNst',\n",
    " 'magType_enc',\n",
    " 'net_enc',\n",
    " 'status_enc',\n",
    " 'locationSource_enc',\n",
    " 'magSource_enc']\n",
    "\n",
    "y_col = ['simple_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_length = int(np.round(len(quake_frame.index) * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = quake_frame.loc[:train_length, x_cols]\n",
    "train_y = quake_frame.loc[:train_length, y_col]\n",
    "\n",
    "valid_X = quake_frame.loc[train_length:, x_cols]\n",
    "valid_y = quake_frame.loc[train_length:, y_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estim = 100\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=n_estim,\n",
    "                             random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(train_X, np.ravel(train_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pd.DataFrame(rfc.predict(valid_X), columns=['predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  0.9669431719697907\n",
      "Recall:  0.8966704936854191\n",
      "ROC score:  0.9477714132813087\n",
      "F1 score:  0.9304819205337463\n",
      "Accuracy score:  0.9952460872894958\n"
     ]
    }
   ],
   "source": [
    "prec = precision_score(valid_y, preds)\n",
    "reca = recall_score(valid_y, preds)\n",
    "roc = roc_auc_score(valid_y, preds)\n",
    "f1 = f1_score(valid_y, preds)\n",
    "acc = accuracy_score(valid_y, preds)\n",
    "conf_mat = confusion_matrix(valid_y, preds)\n",
    "\n",
    "print(\"Precision: \", prec)\n",
    "print(\"Recall: \", reca)\n",
    "print(\"ROC score: \", roc)\n",
    "print(\"F1 score: \", f1)\n",
    "print(\"Accuracy score: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(fper, tper):\n",
    "    plt.plot(fper, tper, color='orange', label='ROC')\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilities = rfc.predict_proba(valid_X)\n",
    "preds['pred_probs'] = probabilities[:, 1]\n",
    "fper, tper, thresholds = roc_curve(valid_y, preds['pred_probs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(fper, tper)"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}