random-forest-regressor.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",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data = pd.read_csv('data/combined_data/cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>H</th>\n",
       "      <th>PP</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM_2_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>30.3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>284.795833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>30.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>239.695455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.8</td>\n",
       "      <td>16.9</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>182.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>22.8</td>\n",
       "      <td>30.2</td>\n",
       "      <td>17.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>264.064286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>23.1</td>\n",
       "      <td>30.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>223.208333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      T    TM    Tm     H   PP   VV    V   VM      PM_2_5\n",
       "0  23.4  30.3  19.0  59.0  0.0  6.3  4.3  5.4  284.795833\n",
       "1  22.4  30.3  16.9  57.0  0.0  6.9  3.3  7.6  239.695455\n",
       "2  24.0  31.8  16.9  51.0  0.0  6.9  2.8  5.4  182.187500\n",
       "3  22.8  30.2  17.6  55.0  0.0  7.7  3.7  7.6  264.064286\n",
       "4  23.1  30.6  17.4  46.0  0.0  6.9  3.3  5.4  223.208333"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = combined_data.iloc[:, :-1]\n",
    "y = combined_data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=43)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RANDOM FOREST REGRESSOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      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=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor()\n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Coefficient of determination R^2 on train set 0.88638536440099'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'Coefficient of determination R^2 on train set {forest.score(X_train, y_train)}'\n",
    "# must be close to 1, 1 is perfect fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Coefficient of determination R^2 on test set 0.37597266315052047'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'Coefficient of determination R^2 on test set {forest.score(X_test, y_test)}'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### OVERFIT MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score = cross_val_score(forest, X, y, cv = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28783939872884884"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f53da36b650>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test - pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HYPERPARAMETER TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start=100, stop=1200, num=12)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num=6)]\n",
    "min_samples_split = [2, 5, 10, 15, 20]\n",
    "min_samples_leaf = [1, 2, 5, 10]\n",
    "params = {\n",
    "    'n_estimators': n_estimators,\n",
    "    'max_features': max_features,\n",
    "    'max_depth': max_depth,\n",
    "    'min_samples_leaf': min_samples_split,\n",
    "    'min_samples_leaf': min_samples_leaf\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=10, n_jobs=None,\n",
       "                                                   oob_score=False,\n",
       "                                                   random_state=N...\n",
       "                                                   warm_start=False),\n",
       "                   iid='warn', n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=43, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "search = RandomizedSearchCV(forest, params, scoring='neg_mean_squared_error', \n",
    "                            cv=5, n_iter=100, random_state=43, n_jobs=-1)\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 5}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4001.7051713401593"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f53da110710>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pred = search.predict(X_test)\n",
    "sns.distplot(y_test-pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Abs Error: 39.83346827723068\n",
      "Mean Sq Error: 2970.544809593515\n",
      "Root Mean Error: 54.50270460806064\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(f\"Mean Abs Error: {metrics.mean_absolute_error(y_test, pred)}\")\n",
    "print(f\"Mean Sq Error: {metrics.mean_squared_error(y_test, pred)}\")\n",
    "print(f\"Root Mean Error: {np.sqrt(metrics.mean_squared_error(y_test, pred))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='sqrt', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=2, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(search, open('random-forest.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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