The Science of Where Seagrass Grows.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from arcgis import GIS\n",
    "from arcgis.features import FeatureLayer\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gis = GIS()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>CID</th>\n",
       "      <th>Join_Count</th>\n",
       "      <th>NameEMU</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Present</th>\n",
       "      <th>TARGET_FID</th>\n",
       "      <th>appO2</th>\n",
       "      <th>depth</th>\n",
       "      <th>dissO2</th>\n",
       "      <th>nitrate</th>\n",
       "      <th>percO2</th>\n",
       "      <th>phosphate</th>\n",
       "      <th>salinity</th>\n",
       "      <th>silicate</th>\n",
       "      <th>temp</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Epipelagic, Warm to Very Warm, Euhaline, Oxic,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.097869</td>\n",
       "      <td>0</td>\n",
       "      <td>5.038187</td>\n",
       "      <td>0.503541</td>\n",
       "      <td>102.16810</td>\n",
       "      <td>0.070516</td>\n",
       "      <td>35.69495</td>\n",
       "      <td>3.878356</td>\n",
       "      <td>24.75329</td>\n",
       "      <td>{'y': 3070014.173990999, 'spatialReference': {...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Epipelagic, Warm to Very Warm, Euhaline, Oxic,...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.051061</td>\n",
       "      <td>0</td>\n",
       "      <td>4.710738</td>\n",
       "      <td>0.836354</td>\n",
       "      <td>99.08915</td>\n",
       "      <td>0.124987</td>\n",
       "      <td>35.98500</td>\n",
       "      <td>1.521608</td>\n",
       "      <td>24.13740</td>\n",
       "      <td>{'y': 3463385.7571735503, 'spatialReference': ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CID  Join_Count                                            NameEMU  \\\n",
       "0   10           1  Epipelagic, Warm to Very Warm, Euhaline, Oxic,...   \n",
       "1   10           1  Epipelagic, Warm to Very Warm, Euhaline, Oxic,...   \n",
       "\n",
       "   OBJECTID  Present  TARGET_FID     appO2  depth    dissO2   nitrate  \\\n",
       "0         1        0           1 -0.097869      0  5.038187  0.503541   \n",
       "1         2        0           2  0.051061      0  4.710738  0.836354   \n",
       "\n",
       "      percO2  phosphate  salinity  silicate      temp  \\\n",
       "0  102.16810   0.070516  35.69495  3.878356  24.75329   \n",
       "1   99.08915   0.124987  35.98500  1.521608  24.13740   \n",
       "\n",
       "                                               SHAPE  \n",
       "0  {'y': 3070014.173990999, 'spatialReference': {...  \n",
       "1  {'y': 3463385.7571735503, 'spatialReference': ...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://services3.arcgis.com/oZfKvdlWHN1MwS48/arcgis/rest/services/MachineLearningSeagrass/FeatureServer/1'\n",
    "df = FeatureLayer(url).query().df\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>salinity</th>\n",
       "      <th>temp</th>\n",
       "      <th>phosphate</th>\n",
       "      <th>nitrate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>dissO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.69495</td>\n",
       "      <td>24.75329</td>\n",
       "      <td>0.070516</td>\n",
       "      <td>0.503541</td>\n",
       "      <td>3.878356</td>\n",
       "      <td>5.038187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.98500</td>\n",
       "      <td>24.13740</td>\n",
       "      <td>0.124987</td>\n",
       "      <td>0.836354</td>\n",
       "      <td>1.521608</td>\n",
       "      <td>4.710738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.58104</td>\n",
       "      <td>18.00565</td>\n",
       "      <td>1.032444</td>\n",
       "      <td>15.172270</td>\n",
       "      <td>10.742860</td>\n",
       "      <td>3.839985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.46936</td>\n",
       "      <td>14.55408</td>\n",
       "      <td>0.936634</td>\n",
       "      <td>14.109170</td>\n",
       "      <td>9.925514</td>\n",
       "      <td>3.654587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.41380</td>\n",
       "      <td>13.48950</td>\n",
       "      <td>0.980522</td>\n",
       "      <td>14.746720</td>\n",
       "      <td>10.737210</td>\n",
       "      <td>3.721700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   salinity      temp  phosphate    nitrate   silicate    dissO2\n",
       "0  35.69495  24.75329   0.070516   0.503541   3.878356  5.038187\n",
       "1  35.98500  24.13740   0.124987   0.836354   1.521608  4.710738\n",
       "2  35.58104  18.00565   1.032444  15.172270  10.742860  3.839985\n",
       "3  35.46936  14.55408   0.936634  14.109170   9.925514  3.654587\n",
       "4  35.41380  13.48950   0.980522  14.746720  10.737210  3.721700"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['salinity', 'temp', 'phosphate','nitrate', 'silicate', 'dissO2']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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HIzFb8doLAJx/sv+dDcSi+pL5JXIcfVeDpmmaremLa5qmaTamW7yapmm2VZZnDy4qnXg1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2917ab49c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "            square=True, annot = True, linecolor = 'k', linewidths = 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "categories = pd.get_dummies(df['NameEMU'])\n",
    "categories.columns = ['c1', 'c2', 'c3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data.join(categories)\n",
    "y = df['Present']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfco = RandomForestClassifier(n_estimators=500, oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=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=500, n_jobs=1,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfco.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x291744eb048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "predict_vars = list(X_train.columns)\n",
    "pos = np.arange(len(predict_vars)) + .5\n",
    "# Sort Variables wrt. to Their Importance\n",
    "imp_sorted = np.argsort(rfco.feature_importances_)\n",
    "# New List of Sorted Variable Names\n",
    "sorted_vars = [predict_vars[i] for i in imp_sorted]\n",
    "\n",
    "# Plot Variable Importances\n",
    "plt.barh(pos, rfco.feature_importances_[imp_sorted], align='center')\n",
    "plt.yticks(pos, sorted_vars)\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.9469666666666666\n",
      "Locations with Seagrass = 9345\n",
      "Predicted Locations with Seagrass = 9216\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = rfco.predict(X_test)\n",
    "print('Accuracy = ' + str(metrics.accuracy_score(y_test, y_test_pred)))\n",
    "\n",
    "# Print Accuracy Metrics\n",
    "print('Locations with Seagrass = ' + str(len(np.where(y_test==1)[0])) )\n",
    "print('Predicted Locations with Seagrass = ' + str(len(np.where(y_test_pred==1)[0])))"
   ]
  }
 ],
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