appendix_basic_one_hot_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 (\n",
    "    LabelEncoder, OneHotEncoder, MinMaxScaler\n",
    "    )\n",
    "from sklearn.metrics import (\n",
    "    precision_score, recall_score, roc_auc_score, f1_score, confusion_matrix, accuracy_score\n",
    "    )\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "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": [
    {
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       "</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",
    "onehot = OneHotEncoder(sparse=False)\n",
    "\n",
    "cat_columns = ['magType', 'net', 'status', 'locationSource', 'magSource']\n",
    "\n",
    "# dfDummies = pd.get_dummies(df['categorical'], prefix = 'category')\n",
    "# df = pd.concat([df, dfDummies], axis=1)\n",
    "\n",
    "for cat in cat_columns:\n",
    "    quake_frame = pd.concat([quake_frame,\n",
    "                             pd.get_dummies(quake_frame[cat], prefix=cat)],\n",
    "                            axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale_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",
    "\n",
    "# scaler = MinMaxScaler()\n",
    "\n",
    "# quake_frame[scale_cols] = scaler.fit_transform(quake_frame[scale_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>...</th>\n",
       "      <th>magSource_ismp</th>\n",
       "      <th>magSource_ld</th>\n",
       "      <th>magSource_mb</th>\n",
       "      <th>magSource_nc</th>\n",
       "      <th>magSource_nm</th>\n",
       "      <th>magSource_nn</th>\n",
       "      <th>magSource_pr</th>\n",
       "      <th>magSource_se</th>\n",
       "      <th>magSource_uu</th>\n",
       "      <th>magSource_uw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.821833</td>\n",
       "      <td>-122.811833</td>\n",
       "      <td>2.640</td>\n",
       "      <td>1.22</td>\n",
       "      <td>md</td>\n",
       "      <td>19.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.018020</td>\n",
       "      <td>0.07</td>\n",
       "      <td>nc</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.816500</td>\n",
       "      <td>-122.791167</td>\n",
       "      <td>2.854</td>\n",
       "      <td>1.32</td>\n",
       "      <td>md</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.009009</td>\n",
       "      <td>0.07</td>\n",
       "      <td>nc</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.531000</td>\n",
       "      <td>-118.416167</td>\n",
       "      <td>5.134</td>\n",
       "      <td>1.29</td>\n",
       "      <td>md</td>\n",
       "      <td>15.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.027930</td>\n",
       "      <td>0.04</td>\n",
       "      <td>nc</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.484500</td>\n",
       "      <td>-124.276167</td>\n",
       "      <td>17.959</td>\n",
       "      <td>1.87</td>\n",
       "      <td>md</td>\n",
       "      <td>21.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.047750</td>\n",
       "      <td>0.07</td>\n",
       "      <td>nc</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.762833</td>\n",
       "      <td>-121.456333</td>\n",
       "      <td>6.002</td>\n",
       "      <td>2.05</td>\n",
       "      <td>md</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.029730</td>\n",
       "      <td>0.08</td>\n",
       "      <td>nc</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    latitude   longitude   depth   mag magType   nst    gap      dmin   rms  \\\n",
       "0  38.821833 -122.811833   2.640  1.22      md  19.0  107.0  0.018020  0.07   \n",
       "1  38.816500 -122.791167   2.854  1.32      md  37.0   36.0  0.009009  0.07   \n",
       "2  37.531000 -118.416167   5.134  1.29      md  15.0  105.0  0.027930  0.04   \n",
       "3  40.484500 -124.276167  17.959  1.87      md  21.0  100.0  0.047750  0.07   \n",
       "4  36.762833 -121.456333   6.002  2.05      md  43.0   39.0  0.029730  0.08   \n",
       "\n",
       "  net  ... magSource_ismp  magSource_ld  magSource_mb  magSource_nc  \\\n",
       "0  nc  ...              0             0             0             1   \n",
       "1  nc  ...              0             0             0             1   \n",
       "2  nc  ...              0             0             0             1   \n",
       "3  nc  ...              0             0             0             1   \n",
       "4  nc  ...              0             0             0             1   \n",
       "\n",
       "   magSource_nm magSource_nn magSource_pr magSource_se  magSource_uu  \\\n",
       "0             0            0            0            0             0   \n",
       "1             0            0            0            0             0   \n",
       "2             0            0            0            0             0   \n",
       "3             0            0            0            0             0   \n",
       "4             0            0            0            0             0   \n",
       "\n",
       "   magSource_uw  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quake_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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_Mb',\n",
    " 'magType_Md',\n",
    " 'magType_Ml',\n",
    " 'magType_Unknown',\n",
    " 'magType_ma',\n",
    " 'magType_mb',\n",
    " 'magType_mc',\n",
    " 'magType_md',\n",
    " 'magType_me',\n",
    " 'magType_mh',\n",
    " 'magType_ml',\n",
    " 'magType_mlg',\n",
    " 'magType_mlr',\n",
    " 'magType_mw',\n",
    " 'net_av',\n",
    " 'net_ci',\n",
    " 'net_hv',\n",
    " 'net_ismpkansas',\n",
    " 'net_ld',\n",
    " 'net_mb',\n",
    " 'net_nc',\n",
    " 'net_nm',\n",
    " 'net_nn',\n",
    " 'net_pr',\n",
    " 'net_se',\n",
    " 'net_uu',\n",
    " 'net_uw',\n",
    " 'status_automatic',\n",
    " 'status_manual',\n",
    " 'status_reviewed',\n",
    " 'locationSource_av',\n",
    " 'locationSource_ci',\n",
    " 'locationSource_hv',\n",
    " 'locationSource_ismp',\n",
    " 'locationSource_ld',\n",
    " 'locationSource_mb',\n",
    " 'locationSource_nc',\n",
    " 'locationSource_nm',\n",
    " 'locationSource_nn',\n",
    " 'locationSource_pr',\n",
    " 'locationSource_se',\n",
    " 'locationSource_uu',\n",
    " 'locationSource_uw',\n",
    " 'magSource_av',\n",
    " 'magSource_ci',\n",
    " 'magSource_hv',\n",
    " 'magSource_ismp',\n",
    " 'magSource_ld',\n",
    " 'magSource_mb',\n",
    " 'magSource_nc',\n",
    " 'magSource_nm',\n",
    " 'magSource_nn',\n",
    " 'magSource_pr',\n",
    " 'magSource_se',\n",
    " 'magSource_uu',\n",
    " 'magSource_uw']\n",
    "\n",
    "y_col = ['simple_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43248"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(quake_frame['simple_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_length = int(np.round(len(quake_frame.index) * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estim = 100\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=n_estim,\n",
    "                             random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(train_X, np.ravel(train_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pd.DataFrame(rfc.predict(valid_X), columns=['predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  0.9695851361669636\n",
      "Recall:  0.8747416762342135\n",
      "ROC score:  0.9368661331688865\n",
      "F1 score:  0.9197247706422018\n",
      "Accuracy score:  0.9945820874850294\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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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": 24,
   "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
}