Random Forest Regressor-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Test3_Full_Original.csv',\n",
    "            delimiter=',', names=['x', 'y', 'Magicv2', 'localization4', 'localizationlab', 'Magicv2_5g', 'UVIC_Eduroam1', 'UVIC_Eduroam2', 'localization3', 'localization_lab5g', 'localization1', 'localization4_5g', 'UVIC_Eduroam3', 'localization2', 'localization3_5g', 'UVIC_Eduroam4', 'localization1_5g'])\n",
    "df = df.loc[:,['x','y','Magicv2', 'localization4', 'localizationlab', 'Magicv2_5g', \n",
    "       'localization3', 'localization_lab5g', 'localization1', 'localization4_5g', 'localization2',\n",
    "       'localization3_5g', 'localization1_5g']]\n",
    "\n",
    "df['Average'] = (df.Magicv2 + df.localization4 + df.localizationlab + df.Magicv2_5g + df.localization3 + df.localization_lab5g + df.localization1 + df.localization4_5g + df.localization2 + df.localization3_5g + df.localization1_5g)/11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Average', axis = 1)\n",
    "y = df['Average']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': [True, False],\n",
      " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],\n",
      " 'max_features': ['auto', 'sqrt'],\n",
      " 'min_samples_leaf': [1, 2, 4],\n",
      " 'min_samples_split': [2, 5, 10],\n",
      " 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  29 tasks      | elapsed:   12.0s\n",
      "[Parallel(n_jobs=-1)]: Done 150 tasks      | elapsed:   46.3s\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  1.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=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=100,\n",
       "                                                   n_jobs=None, oob_score=Fals...\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [200, 400, 600, 800,\n",
       "                                                         1000, 1200, 1400, 1600,\n",
       "                                                         1800, 2000]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "RandomForestRegModel = RandomForestRegressor()\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator = RandomForestRegModel, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "# Fit the random search model\n",
    "rf_random.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 2000,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 100,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=10, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=4,\n",
       "                      min_samples_split=5, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=600, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestRegModel = RandomForestRegressor(n_estimators = 600, min_samples_split = 5, min_samples_leaf = 4, max_features = \"auto\", max_depth = 10, bootstrap = True)\n",
    "RandomForestRegModel.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       -74.24500409, -76.13255547, -74.07293937, -74.07293937,\n",
       "       -74.07293937, -74.07293937, -74.10088212, -74.13714314,\n",
       "       -74.13166573, -74.09592664, -74.09592664, -74.11590125,\n",
       "       -74.04454084, -74.11590125, -74.07215054, -74.04454084,\n",
       "       -74.05930683, -74.05930683, -74.05930683, -74.02155357])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = RandomForestRegModel.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
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     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "</table>\n",
       "<p>124 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Actual  Predicted\n",
       "496 -74.090909 -74.430164\n",
       "497 -74.181818 -74.435882\n",
       "498 -74.090909 -74.377960\n",
       "499 -74.181818 -74.114632\n",
       "500 -75.000000 -74.731802\n",
       "..         ...        ...\n",
       "615 -70.363636 -74.044541\n",
       "616 -70.181818 -74.059307\n",
       "617 -70.454545 -74.059307\n",
       "618 -70.363636 -74.059307\n",
       "619 -70.272727 -74.021554\n",
       "\n",
       "[124 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 1.5749787865863143\n",
      "Mean Squared Error: 3.7689914922035057\n",
      "Root Mean Squared Error: 1.9413890625537957\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "plt.plot(df.Actual.index, df.Actual)\n",
    "plt.plot(df.Actual.index, df.Predicted)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 13 candidates, totalling 65 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  29 tasks      | elapsed:   13.5s\n",
      "[Parallel(n_jobs=-1)]: Done  65 out of  65 | elapsed:   29.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=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=100,\n",
       "                                                   n_jobs=None, oob_score=F...\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [200, 400, 600, 800,\n",
       "                                                         1000, 1200, 1400, 1600,\n",
       "                                                         1800, 2000]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "Fullmodel = RandomForestRegressor()\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator = Fullmodel, param_distributions = random_grid, n_iter = 13, verbose=2,n_jobs = -1)\n",
    "# Fit the random search model\n",
    "rf_random.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1200,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 100,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=100, max_features='sqrt', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=5,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=1200, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestRegModel = RandomForestRegressor(n_estimators = 1200, min_samples_split = 2, min_samples_leaf = 5, max_features = \"sqrt\", max_depth = 100, bootstrap = False)\n",
    "RandomForestRegModel.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       -72.22375077, -72.72930341, -70.46457907, -70.39201012,\n",
       "       -70.46652349, -70.34986044, -70.60862442, -70.80752823,\n",
       "       -70.58408556, -70.43903307, -70.4843629 , -71.02445867,\n",
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       "       -70.53142981, -70.56952533, -70.53094818, -70.43405855])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = RandomForestRegModel.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3677927080123295"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "<p>620 rows × 2 columns</p>\n",
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      "text/plain": [
       "        Actual  Predicted\n",
       "0   -66.181818 -65.811002\n",
       "1   -65.636364 -65.268543\n",
       "2   -64.909091 -65.191670\n",
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       "\n",
       "[620 rows x 2 columns]"
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     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':y, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.23611414455005392\n",
      "Mean Squared Error: 0.13527147606704265\n",
      "Root Mean Squared Error: 0.3677927080123295\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y, y_pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": [
    "plt.plot(df.Actual.index, df.Actual)\n",
    "plt.plot(df.Actual.index, df.Predicted)\n",
    "plt.title('Random Forst Regressor')\n",
    "plt.xlabel('Time Step')\n",
    "plt.ylabel('RSSI')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}