Code_version_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#import matplotlib as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore data analysis\n",
    "\n",
    "We first import our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.read_csv('trainingData.csv')\n",
    "data_test = pd.read_csv('validationData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "       WAP001  WAP002  WAP003  WAP004  WAP005  WAP006  WAP007  WAP008  WAP009  \\\n",
       "0         100     100     100     100     100     100     100     100     100   \n",
       "1         100     100     100     100     100     100     100     100     100   \n",
       "2         100     100     100     100     100     100     100     -97     100   \n",
       "3         100     100     100     100     100     100     100     100     100   \n",
       "4         100     100     100     100     100     100     100     100     100   \n",
       "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
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       "19936     100     100     100     100     100     100     100     100     100   \n",
       "\n",
       "       WAP010  ...  WAP520  LONGITUDE      LATITUDE  FLOOR  BUILDINGID  \\\n",
       "0         100  ...     100 -7541.2643  4.864921e+06      2           1   \n",
       "1         100  ...     100 -7536.6212  4.864934e+06      2           1   \n",
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       "4         100  ...     100 -7632.1436  4.864982e+06      0           0   \n",
       "...       ...  ...     ...        ...           ...    ...         ...   \n",
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       "19936     100  ...     100 -7536.1658  4.864898e+06      3           1   \n",
       "\n",
       "       SPACEID  RELATIVEPOSITION  USERID  PHONEID   TIMESTAMP  \n",
       "0          106                 2       2       23  1371713733  \n",
       "1          106                 2       2       23  1371713691  \n",
       "2          103                 2       2       23  1371714095  \n",
       "3          102                 2       2       23  1371713807  \n",
       "4          122                 2      11       13  1369909710  \n",
       "...        ...               ...     ...      ...         ...  \n",
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       "19933      140                 2      18       10  1371710402  \n",
       "19934       13                 2      18       10  1371710921  \n",
       "19935      113                 2      18       10  1371711049  \n",
       "19936      112                 2      18       10  1371711025  \n",
       "\n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1111, 529)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the count is the same for all the WAP's and all of the features(longitude, latitude and floor). We drop the features that are not necessary and not interesting for our models(Building ID, PhoneID....)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling our dataset so that the predictions are more accurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data_train.drop(['LONGITUDE', 'LATITUDE', 'FLOOR', 'BUILDINGID','SPACEID','RELATIVEPOSITION','USERID','PHONEID','TIMESTAMP'], axis =1)\n",
    "X_test = data_test.drop(['LONGITUDE', 'LATITUDE', 'FLOOR', 'BUILDINGID','SPACEID','RELATIVEPOSITION','USERID','PHONEID','TIMESTAMP'], axis =1)\n",
    "\n",
    "y_train = data_train[['LONGITUDE','LATITUDE','FLOOR']]\n",
    "y_test = data_test[['LONGITUDE','LATITUDE','FLOOR']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 520 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WAP001  WAP002  WAP003  WAP004  WAP005  WAP006  WAP007  WAP008  WAP009  \\\n",
       "0     100     100     100     100     100     100     100     100     100   \n",
       "1     100     100     100     100     100     100     100     100     100   \n",
       "2     100     100     100     100     100     100     100     -97     100   \n",
       "3     100     100     100     100     100     100     100     100     100   \n",
       "4     100     100     100     100     100     100     100     100     100   \n",
       "\n",
       "   WAP010  ...  WAP511  WAP512  WAP513  WAP514  WAP515  WAP516  WAP517  \\\n",
       "0     100  ...     100     100     100     100     100     100     100   \n",
       "1     100  ...     100     100     100     100     100     100     100   \n",
       "2     100  ...     100     100     100     100     100     100     100   \n",
       "3     100  ...     100     100     100     100     100     100     100   \n",
       "4     100  ...     100     100     100     100     100     100     100   \n",
       "\n",
       "   WAP518  WAP519  WAP520  \n",
       "0     100     100     100  \n",
       "1     100     100     100  \n",
       "2     100     100     100  \n",
       "3     100     100     100  \n",
       "4     100     100     100  \n",
       "\n",
       "[5 rows x 520 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11e71cf40>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 437.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dataset has a huge part of undetected WAP and so we must clean our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dividing our dataset and separating the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "latitude_train = data_train['LATITUDE']\n",
    "longitude_train = data_train['LONGITUDE']\n",
    "floor_train = data_train['FLOOR']\n",
    "\n",
    "latitude_test = data_test['LATITUDE']\n",
    "longitude_test = data_test['LONGITUDE']\n",
    "floor_test = data_test['FLOOR']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling of our dataset (for better performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_std_train = sc.fit_transform(X_train)\n",
    "X_std_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19937, 520)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_std_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We try using different machine learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import neighbors \n",
    "from sklearn.svm import SVR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "floorClassifier = KNeighborsClassifier()\n",
    "regression_model= LinearRegression()\n",
    "model_svm = SVR()\n",
    "knnModel = neighbors.KNeighborsRegressor(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for latitude\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-4.907690225513307e+25"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"for latitude\")\n",
    "regression_model.fit(X_std_train,latitude_train)\n",
    "regression_model.score(X_std_test,latitude_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For longitude using knn\n",
      "time for KNN fitting is  2.6824939250946045\n",
      "time for KNN scoring is  5.842025995254517\n",
      "the accuracy is  0.9587314321911274\n"
     ]
    }
   ],
   "source": [
    "print(\"For longitude using knn\")\n",
    "start = time.time()\n",
    "knnModel.fit(X_std_train,longitude_train)\n",
    "end = time.time()\n",
    "print(\"time for KNN fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = knnModel.score(X_std_test,longitude_test)\n",
    "end2 = time.time()\n",
    "print(\"time for KNN scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for latitude\n",
      "the accuracy is\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9212451723594032"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"for latitude\")\n",
    "knnModel.fit(X_std_train,latitude_train)\n",
    "print(\"the accuracy is\")\n",
    "knnModel.score(X_std_test,latitude_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Floor classifier model\n",
      "time for KNN floor fitting is  2.305819272994995\n",
      "time for KNN floor scoring is  6.814572811126709\n",
      "the accuracy is  0.6552655265526552\n"
     ]
    }
   ],
   "source": [
    "print(\"Floor classifier model\")\n",
    "start = time.time()\n",
    "floorClassifier.fit(X_std_train,floor_train)\n",
    "end = time.time()\n",
    "print(\"time for KNN floor fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = floorClassifier.score(X_std_test,floor_test)\n",
    "end2 = time.time()\n",
    "print(\"time for KNN floor scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "regr_2 = DecisionTreeRegressor(max_depth=9)\n",
    "regr_classify = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For latitude using Decision Tree Regressor\n",
      "time for fitting is  0.5116629600524902\n",
      "time for scoring is  0.0028541088104248047\n",
      "the accuracy is  0.8004960706539345\n"
     ]
    }
   ],
   "source": [
    "print(\"For latitude using Decision Tree Regressor\")\n",
    "start = time.time()\n",
    "regr_2.fit(X_std_train,latitude_train)\n",
    "end = time.time()\n",
    "print(\"time for fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = regr_2.score(X_std_test,latitude_test)\n",
    "end2 = time.time()\n",
    "print(\"time for scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For longitude using Decision Tree Regressor\n",
      "the accuracy is \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7956178047303396"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For longitude using Decision Tree Regressor\")\n",
    "regr_2.fit(X_std_train,longitude_train)\n",
    "print(\"the accuracy is \")\n",
    "regr_2.score(X_std_test,longitude_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Floor classifier model\n",
      "time for DT fitting is  0.9755480289459229\n",
      "time for DT scoring is  0.0020580291748046875\n",
      "the accuracy is  0.7425742574257426\n"
     ]
    }
   ],
   "source": [
    "print(\"Floor classifier model\")\n",
    "start = time.time()\n",
    "regr_classify.fit(X_std_train,floor_train)\n",
    "end = time.time()\n",
    "print(\"time for DT fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = regr_classify.score(X_std_test,floor_test)\n",
    "end2 = time.time()\n",
    "print(\"time for DT scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "gnb2 =  MultinomialNB()\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "gnb3 = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For latitude using Gaussian Naive Bayes\n",
      "time for Gaussian NB fitting is  0.5145728588104248\n",
      "time for Gaussian NB scoring is  0.9603569507598877\n",
      "the accuracy is  0.0666066606660666\n"
     ]
    }
   ],
   "source": [
    "print(\"For latitude using Gaussian Naive Bayes\")\n",
    "start = time.time()\n",
    "gnb.fit(X_std_train,latitude_train.astype('int'))\n",
    "end = time.time()\n",
    "print(\"time for Gaussian NB fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = gnb.score(X_std_test,latitude_test.astype('int'))\n",
    "end2 = time.time()\n",
    "print(\"time for Gaussian NB scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For longitude using Gaussian Naive Bayes\n",
      "the accuracy is \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.07290729072907291"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For longitude using Gaussian Naive Bayes\")\n",
    "gnb.fit(X_std_train,longitude_train.astype('int'))\n",
    "print(\"the accuracy is \")\n",
    "gnb.score(X_std_test,longitude_test.astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For floor using Gaussian Naive Bayes\n",
      "time for Gaussian NB fitting is  0.4140129089355469\n",
      "time for Gaussian NB scoring is  0.03629302978515625\n",
      "the accuracy is  0.3582358235823582\n"
     ]
    }
   ],
   "source": [
    "print(\"For floor using Gaussian Naive Bayes\")\n",
    "start = time.time()\n",
    "gnb.fit(X_std_train,floor_train.astype('int'))\n",
    "end = time.time()\n",
    "print(\"time for Gaussian NB fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = gnb.score(X_std_test,floor_test.astype('int'))\n",
    "end2 = time.time()\n",
    "print(\"time for Gaussian NB scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as the result above, the performance of Gaussian Naive Bayes is much worse than KNN, DT and RF, even as the best performance, which is the prediction of floor has just 35% of the accuracy, but it uses the least amount of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For latitude using Mutinomial Naive Bayes\n",
      "the accuracy is \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.05310531053105311"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we do not use the Mutinomial Naive Bayes since the accuracy is too low\n",
    "print(\"For latitude using Mutinomial Naive Bayes\")\n",
    "gnb2.fit(abs(X_std_train),longitude_train.astype('int'))\n",
    "print(\"the accuracy is \")\n",
    "gnb2.score(abs(X_std_test),longitude_test.astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For longitude using Bernoulli Naive Bayes\n",
      "the accuracy is \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.10711071107110712"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we do not use the Bernoulli Naive Bayes since the accuracy is too low\n",
    "print(\"For longitude using Bernoulli Naive Bayes\")\n",
    "gnb3.fit(X_std_train,longitude_train.astype('int'))\n",
    "print(\"the accuracy is \")\n",
    "gnb3.score(X_std_test,longitude_test.astype('int'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rand_forest = RandomForestRegressor(max_depth=9)\n",
    "rand_forest_classify = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For latitude using Random Forest Regressor\n",
      "time for RF fitting is  28.354560136795044\n",
      "time for RF scoring is  0.018136024475097656\n",
      "the accuracy is  0.8433190755341298\n"
     ]
    }
   ],
   "source": [
    "print(\"For latitude using Random Forest Regressor\")\n",
    "start = time.time()\n",
    "rand_forest.fit(X_std_train,latitude_train)\n",
    "end = time.time()\n",
    "print(\"time for RF fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = rand_forest.score(X_std_test,latitude_test)\n",
    "end2 = time.time()\n",
    "print(\"time for RF scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For longitude using Random Forest Regressor\n",
      "the accuracy is \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8266651933605006"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For longitude using Random Forest Regressor\")\n",
    "rand_forest.fit(X_std_train,longitude_train)\n",
    "print(\"the accuracy is \")\n",
    "rand_forest.score(X_std_test,longitude_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for latitude\n",
      "the accuracy is\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9276728126299123"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"for latitude\")\n",
    "knnModel.fit(X_std_train,latitude_train)\n",
    "print(\"the accuracy is\")\n",
    "knnModel.score(X_std_test,latitude_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Floor classifier model\n",
      "time for RF floor fitting is  5.1197288036346436\n",
      "time for RF floor scoring is  0.049666643142700195\n",
      "the accuracy is  0.873087308730873\n"
     ]
    }
   ],
   "source": [
    "print(\"Floor classifier model\")\n",
    "start = time.time()\n",
    "rand_forest_classify.fit(X_std_train,floor_train)\n",
    "end = time.time()\n",
    "print(\"time for RF floor fitting is \", end - start)\n",
    "start2 = time.time()\n",
    "result = rand_forest_classify.score(X_std_test,floor_test)\n",
    "end2 = time.time()\n",
    "print(\"time for RF floor scoring is \", end2 - start2)\n",
    "print(\"the accuracy is \", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see that the performance of RF is similar to but better than DT in all aspects, but it is still worse than KNN for the longitude and latitude. However, the prediction of the floor is better than that in KNN, just like the performance of DT compared with KNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Another test for KNN (just for testing, not sure whether we will use it or not)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-62-22b6e65f0749>, line 16)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-62-22b6e65f0749>\"\u001b[0;36m, line \u001b[0;32m16\u001b[0m\n\u001b[0;31m    training_scores.append(regr.score(X_train,y_train)\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def test_KNeighborsRegressor_k_w(*data):\n",
    "    X_train,X_test,y_train,y_test =data\n",
    "    Ks=np.linspace(1,y_train.size,num=100,endpoint=False,dtype='int')\n",
    "    weights=['uniform','distance']\n",
    "    #绘图\n",
    "    fig=plt.figure()\n",
    "    ax=fig.add_subplot(1,1,1)\n",
    "    for weight in weights:\n",
    "        training_scores=[]\n",
    "        testing_scores=[]\n",
    "        for K in Ks:\n",
    "            regr=neighbors.KNeighborsRegressor(weights=weight,\n",
    "                n_neighbors=K)\n",
    "            regr.fit(X_train,y_train.astype('int'))\n",
    "            testing_scores.append(regr.score(X_test,y_test)\n",
    "            training_scores.append(regr.score(X_train,y_train)\n",
    "        ax.plot(Ks,testing_scores,label='testing score:weight=%s'%weight)\n",
    "        ax.plot(Ks,training_scores,label='training score:weight=%s'%weight)\n",
    "        ax.legend(loc='best')\n",
    "        ax.set_xlabel('K')\n",
    "        ax.set_ylabel('score')\n",
    "        ax.set_ylim(0,1.05)\n",
    "        ax.set_title('KNeighborsRegressor')\n",
    "        plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-53be7285af27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_KNeighborsRegressor_k_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_std_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_std_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatitude_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatitude_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-55-1f12d688f0b8>\u001b[0m in \u001b[0;36mtest_KNeighborsRegressor_k_w\u001b[0;34m(*data)\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0mtesting_scores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             training_scores.append(regr.score(X_train,\n\u001b[0m\u001b[1;32m     17\u001b[0m                 y_train.astype('int')))\n\u001b[1;32m     18\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtesting_scores\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'testing score:weight=%s'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/neighbors/_regression.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m         \u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/neighbors/_base.py\u001b[0m in \u001b[0;36mkneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    660\u001b[0m                 \u001b[0mdelayed_query\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_tree_query_parallel_helper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m                 \u001b[0mparallel_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"prefer\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"threads\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             chunked_results = Parallel(n_jobs, **parallel_kwargs)(\n\u001b[0m\u001b[1;32m    663\u001b[0m                 delayed_query(\n\u001b[1;32m    664\u001b[0m                     self._tree, X[s], n_neighbors, return_distance)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1046\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1047\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1048\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1049\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1050\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    864\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 866\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    867\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    782\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    785\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/neighbors/_base.py\u001b[0m in \u001b[0;36m_tree_query_parallel_helper\u001b[0;34m(tree, *args, **kwargs)\u001b[0m\n\u001b[1;32m    490\u001b[0m     \u001b[0munder\u001b[0m \u001b[0mPyPy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "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": [
    "test_KNeighborsRegressor_k_w(X_std_train, X_std_test, latitude_train, latitude_test)  "
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}