02a_earthquake_basic_weights.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, MinMaxScaler\n",
    "    )\n",
    "from sklearn.metrics import (\n",
    "    precision_score, recall_score, roc_auc_score, f1_score, confusion_matrix, accuracy_score, roc_curve\n",
    "    )\n",
    "import seaborn as sns"
   ]
  },
  {
   "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 = pd.concat([quake_frame,\n",
    "                             pd.get_dummies(quake_frame[cat], prefix=cat)],\n",
    "                            axis=1)\n",
    "\n",
    "scale_cols = ['latitude', 'longitude', 'depth', 'mag', 'nst', 'gap', 'dmin', 'rms', 'horizontalError',\n",
    " 'depthError', 'magError', 'magNst']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "quake_frame[scale_cols] = scaler.fit_transform(quake_frame[scale_cols])\n",
    "\n",
    "x_cols = ['latitude', 'longitude', 'depth', 'mag', 'nst', 'gap', 'dmin', 'rms', 'horizontalError', 'depthError',\n",
    " 'magError', 'magNst', 'magType_Mb', 'magType_Md', 'magType_Ml', 'magType_Unknown', 'magType_ma', 'magType_mb',\n",
    " 'magType_mc', 'magType_md', 'magType_me', 'magType_mh', 'magType_ml', 'magType_mlg', 'magType_mlr', 'magType_mw',\n",
    " 'net_av', 'net_ci', 'net_hv', 'net_ismpkansas', 'net_ld', 'net_mb', 'net_nc', 'net_nm', 'net_nn', 'net_pr',\n",
    " 'net_se', 'net_uu', 'net_uw', 'status_automatic', 'status_manual', 'status_reviewed', 'locationSource_av',\n",
    " 'locationSource_ci', 'locationSource_hv', 'locationSource_ismp', 'locationSource_ld', 'locationSource_mb',\n",
    " 'locationSource_nc', 'locationSource_nm', 'locationSource_nn', 'locationSource_pr', 'locationSource_se',\n",
    " 'locationSource_uu', 'locationSource_uw', 'magSource_av', 'magSource_ci', 'magSource_hv', 'magSource_ismp',\n",
    " 'magSource_ld', 'magSource_mb', 'magSource_nc', 'magSource_nm', 'magSource_nn', 'magSource_pr', 'magSource_se',\n",
    " 'magSource_uu', 'magSource_uw']\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,\n",
    "                             class_weight='balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\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:  0.969984607491021\n",
      "Recall:  0.8681974741676234\n",
      "ROC score:  0.9336045908165166\n",
      "F1 score:  0.9162728704713436\n",
      "Accuracy score:  0.9943702593265494\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": [
    "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": 19,
   "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": 20,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}