gauss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nothing done.\n"
     ]
    }
   ],
   "source": [
    "%reset\n",
    "%load_ext dotenv\n",
    "%dotenv\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.mongo_utils import db_distance_info_sanitized\n",
    "import haversine as hs\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df = db_distance_info_sanitized()\n",
    "# X = df.loc[:, ~df.columns.isin(['_id', 'distance_time'])]\n",
    "X_geo = df[['origin.lat','origin.lon']]\n",
    "Y_geo = df['distance_time']\n",
    "# generate distance from target\n",
    "# sX['distance_km'] = list(map(hs.haversine, zip(X[\"origin.lat\"], X[\"origin.lon\"]), zip(X[\"target.lat\"], X[\"target.lon\"])))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Caskroom/miniconda/base/envs/DataScience/lib/python3.9/site-packages/sklearn/base.py:493: FutureWarning:\n",
      "\n",
      "The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
      "Feature names must be in the same order as they were in fit.\n",
      "\n",
      "\n"
     ]
    },
    {
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             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Public Transport From T-Centralen"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "\n",
    "kernel = 1 * RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e2))\n",
    "gaussian_process = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)\n",
    "gaussian_process.fit(X_geo, Y_geo)\n",
    "\n",
    "lon = np.linspace(17.8,18.2,20)\n",
    "lat = np.linspace(59.4,59.2,20)\n",
    "space = [[a,b,hs.haversine((a,b),(18.0591,59.330767))] for a in lon for b in lat]\n",
    "from scraper import plot\n",
    "\n",
    "import pandas as pd\n",
    "tlon = np.full(400,18.0591)\n",
    "tlat = np.full(400,59.330767)\n",
    "\n",
    "pred = gaussian_process.predict(pd.DataFrame({'origin.lon':[item[0] for item in space],'origin.lat':[item[1] for item in space]}))\n",
    "\n",
    "df = pd.DataFrame({'origin.lon':[item[0] for item in space],'origin.lat':[item[1] for item in space],'distance_time':pred})\n",
    "\n",
    "plot.distance_region(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[26.76405235, 25.40015721],\n",
       "       [25.97873798, 27.2408932 ],\n",
       "       [26.86755799, 24.02272212],\n",
       "       ...,\n",
       "       [-3.32126484, -0.81869834],\n",
       "       [-6.93197957, -1.90122961],\n",
       "       [-1.47464656, -1.07108841]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plot\n",
    "from matplotlib.colors import LogNorm\n",
    "from sklearn import mixture\n",
    "\n",
    "n_samples = 350\n",
    "\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "\n",
    "shiftedgaussian = np.random.randn(n_samples, 2) + np.array([25, 25])\n",
    "\n",
    "\n",
    "c = np.array([[0.0, -0.9], [3.5, 0.9]])\n",
    "stretchedgaussian = np.dot(np.random.randn(n_samples, 2), c)\n",
    "\n",
    "\n",
    "X_train = np.vstack([shiftedgaussian, stretchedgaussian])\n",
    "display(X_train)\n",
    "\n",
    "classifier = mixture.GaussianMixture(n_components=2, covariance_type=\"full\")\n",
    "classifier.fit(X_train)\n",
    "\n",
    "\n",
    "x = np.linspace(-25.0, 35.0)\n",
    "y = np.linspace(-25.0, 45.0)\n",
    "x = np.linspace(-10,50)\n",
    "y = np.linspace(0,50)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "XX = np.array([X.ravel(), Y.ravel()]).T\n",
    "Z = -classifier.score_samples(XX)\n",
    "Z = Z.reshape(X.shape)\n",
    "\n",
    "cs = plot.contour(\n",
    "    X, Y, Z, norm=LogNorm(vmin=2.0, vmax=1000.0), levels=np.logspace(0, 3, 10)\n",
    ")\n",
    "cb = plot.colorbar(cs, shrink=0.10, extend=\"both\")\n",
    "plot.scatter(X_train[:, 0], X_train[:, 1], 0.10)\n",
    "\n",
    "plot.title(\"Negative log-likelihood predicted by a Gaussian Mixture Method\")\n",
    "plot.axis(\"tight\")\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[59.33560755, 18.03537823],\n",
       "       [59.2736459 , 17.8959877 ],\n",
       "       [59.33995417, 18.08032747],\n",
       "       ...,\n",
       "       [59.3692127 , 17.957616  ],\n",
       "       [59.363752  , 17.9634453 ],\n",
       "       [59.3618883 , 17.9767488 ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_geo.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Caskroom/miniconda/base/envs/DataScience/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:430: ConvergenceWarning:\n",
      "\n",
      "The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.\n",
      "\n",
      "/usr/local/Caskroom/miniconda/base/envs/DataScience/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:420: ConvergenceWarning:\n",
      "\n",
      "The optimal value found for dimension 0 of parameter k2__length_scale is close to the specified lower bound 0.01. Decreasing the bound and calling fit again may find a better value.\n",
      "\n",
      "/usr/local/Caskroom/miniconda/base/envs/DataScience/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:420: ConvergenceWarning:\n",
      "\n",
      "The optimal value found for dimension 1 of parameter k2__length_scale is close to the specified lower bound 0.01. Decreasing the bound and calling fit again may find a better value.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "from itertools import product\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n",
    "\n",
    "X = np.array([[0,0],[2,0],[4,0],[6,0],[8,0],[10,0],[12,0],[14,0],[16,0],[0,2],\n",
    "                    [2,2],[4,2],[6,2],[8,2],[10,2],[12,2],[14,2],[16,2]])\n",
    "\n",
    "y = np.array([-54,-60,-62,-64,-66,-68,-70,-72,-74,-60,-62,-64,-66,\n",
    "                    -68,-70,-72,-74,-76])\n",
    "\n",
    "X = X_geo.values\n",
    "y = Y_geo.values\n",
    "\n",
    "# Input space\n",
    "x1 = np.linspace(X[:,0].min(), X[:,0].max()) #p\n",
    "x2 = np.linspace(X[:,1].min(), X[:,1].max()) #q\n",
    "x = (np.array([x1, x2])).T\n",
    "\n",
    "kernel = C(1.0, (1e-3, 1e3)) * RBF([5,5], (1e-2, 1e2))\n",
    "gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=15)\n",
    "\n",
    "gp.fit(X, y)\n",
    "\n",
    "x1x2 = np.array(list(product(x1, x2)))\n",
    "x2x1 = np.array(list(product(x2, x1)))\n",
    "y_pred, MSE = gp.predict(x1x2, return_std=True)\n",
    "\n",
    "X0p, X1p = x1x2[:,0].reshape(50,50), x1x2[:,1].reshape(50,50)\n",
    "Zp = np.reshape(y_pred,(50,50))\n",
    "\n",
    "# alternative way to generate equivalent X0p, X1p, Zp\n",
    "# X0p, X1p = np.meshgrid(x1, x2)\n",
    "# Zp = [gp.predict([(X0p[i, j], X1p[i, j]) for i in range(X0p.shape[0])]) for j in range(X0p.shape[1])]\n",
    "# Zp = np.array(Zp).T\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.pcolormesh(X0p, X1p, Zp)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Caskroom/miniconda/base/envs/DataScience/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:430: ConvergenceWarning:\n",
      "\n",
      "The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.\n",
      "\n",
      "/usr/local/Caskroom/miniconda/base/envs/DataScience/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:420: ConvergenceWarning:\n",
      "\n",
      "The optimal value found for dimension 0 of parameter k2__length_scale is close to the specified lower bound 0.01. Decreasing the bound and calling fit again may find a better value.\n",
      "\n",
      "/usr/local/Caskroom/miniconda/base/envs/DataScience/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:420: ConvergenceWarning:\n",
      "\n",
      "The optimal value found for dimension 1 of parameter k2__length_scale is close to the specified lower bound 0.01. Decreasing the bound and calling fit again may find a better value.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "config": {
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           -1786.7,
           949.2,
           514.9,
           -54.9,
           96.1,
           43.8,
           10.6,
           22.9,
           13.1,
           26.4,
           49.5,
           136.1,
           170.1,
           82,
           16.8,
           10,
           8.6,
           13.2,
           8.1,
           3.1,
           9.8,
           8.3,
           7,
           10.5,
           10.8,
           9.8,
           7.4,
           9.3,
           7.9,
           5.7,
           22.1,
           117.9,
           343.4,
           484.6,
           758.5,
           4071.1,
           12247.7,
           17187.8,
           10058.7,
           -756.3,
           -1695.9,
           3205.3,
           2563.6,
           -338.5,
           -14142.2,
           8873.1,
           20800.6,
           17315.7,
           5662.7,
           -1173.9,
           -244.7,
           1313.5,
           570.8,
           23.5,
           103.4,
           47.2,
           25,
           25.8,
           14.4,
           24.2,
           72.2,
           186.7,
           171,
           47.4,
           16.4,
           26.5,
           17.7,
           13.1,
           12,
           10.7,
           7.5,
           4.8,
           6.8,
           11.1,
           6.1,
           6.6,
           6.1,
           8,
           8,
           -4.3,
           55.8,
           338.7,
           852.1,
           1158.7,
           1947.9,
           8361.4,
           23512.1,
           35278.2,
           28341.9,
           10270.4,
           142.6,
           -1566,
           -5367.1,
           -7550,
           -8048.4,
           6600.7,
           11358.7,
           7273.9,
           1081.7,
           -995,
           521.2,
           1285.3,
           523.7,
           35.5,
           16.1,
           10,
           39.7,
           34.6,
           20.5,
           19.1,
           66.2,
           171.7,
           114.6,
           -4,
           21.5,
           56.4,
           37.4,
           16.4,
           12.1,
           5.9,
           -3.7,
           8.5,
           17.2,
           10,
           -4.5,
           1.3,
           3.9,
           8.2,
           15.1,
           -13.4,
           90.6,
           590.5,
           1436.7,
           2006.5,
           3589.8,
           13905.7,
           38058.5,
           60177.7,
           56626.4,
           30667.2,
           5888.2,
           -8904.3,
           -18909.6,
           -19872.6,
           -3402.3,
           1972,
           -940.3,
           -5091.1,
           -4926.1,
           -1693.7,
           699,
           987.2,
           440.1,
           33.5,
           -122.5,
           -67.4,
           38.1,
           38.3,
           24.3,
           17.3,
           46.7,
           117.4,
           49.5,
           -33.8,
           23,
           64.1,
           44.6,
           20.1,
           13,
           -1.8,
           -11.3,
           22.9,
           34.5,
           14.2,
           -4.8,
           3.7,
           7.4,
           13.3,
           24.2,
           -27.9,
           82.1,
           703.7,
           1762.5,
           2595.9,
           5105.1,
           19395.1,
           53065.4,
           87778.6,
           91037.5,
           58268.9,
           15445.3,
           -17313.9,
           -35721.1,
           -34973.7,
           -1017.5,
           -3448,
           -12235.8,
           -15623.4,
           -9761.5,
           -2226.6,
           684.4,
           589.2,
           349.9,
           54.5,
           -241.2,
           -153.4,
           18.9,
           33.9,
           23.9,
           20.8,
           32.9,
           66.1,
           17.1,
           -25.3,
           20.4,
           44.6,
           32.7,
           17.6,
           12.6,
           -3.6,
           -4.9,
           42.2,
           49.9,
           31.3,
           14.7,
           10.2,
           10.2,
           16.7,
           25.7,
           -45,
           25.3,
           594.2,
           1616.4,
           2573.3,
           5821.4,
           23045.1,
           64378.1,
           111203.4,
           123599.2,
           86763.7,
           26179,
           -25648.3,
           -52407.8,
           -49158.1,
           -553.1,
           -7764.6,
           -19161.1,
           -21092.7,
           -11617.7,
           -1960.4,
           734.4,
           216.5,
           243.7,
           95.7,
           -283.7,
           -202.8,
           -3.7,
           26.2,
           21.2,
           23.1,
           24.9,
           33.2,
           14.4,
           1.3,
           17.5,
           21.8,
           17.1,
           11,
           9.6,
           -0.2,
           8.5,
           51.7,
           53.2,
           54.8,
           42.6,
           7.9,
           1.6,
           10.9,
           18.2,
           -48,
           -30.3,
           349,
           1100.9,
           1960.5,
           5420.3,
           23457.7,
           68185.9,
           123333.8,
           144956.5,
           107633.1,
           33456.8,
           -33904.7,
           -66083.9,
           -59009.9,
           -1042.9,
           -9656.7,
           -20240.1,
           -20567.3,
           -10326.5,
           -1021.6,
           853.9,
           -50.5,
           127.2,
           136.9,
           -233.3,
           -184.6,
           -12.4,
           19.3,
           17.8,
           19.6,
           16.5,
           14.5,
           17,
           17.5,
           14.4,
           11.6,
           10.2,
           6.2,
           6,
           3,
           14.2,
           41.6,
           39.3,
           65.6,
           57.7,
           -3.7,
           -12.6,
           0.9,
           9.9,
           -28,
           -38.2,
           132.6,
           531.1,
           1121.9,
           4134.6,
           20396.2,
           62905.5,
           120045.3,
           148360.4,
           113626.3,
           32540.6,
           -43112.3,
           -75421.3,
           -62775.6,
           -1599.3,
           -8916.8,
           -16297.3,
           -15503.8,
           -7125.3,
           -14.8,
           918.6,
           -157.5,
           43.9,
           165.2,
           -120.6,
           -107.1,
           -0.1,
           14.7,
           14.5,
           15,
           11.5,
           8.7,
           16.1,
           17.7,
           10.3,
           9.1,
           9,
           5.7,
           5.3,
           4.1,
           9.4,
           20.8,
           16.7,
           52.7,
           50.8,
           -10.5,
           -15.5,
           -2.4,
           6.8,
           -0.4,
           -8.6,
           31,
           159.9,
           454,
           2551,
           14936,
           50109.2,
           102312.9,
           132787.3,
           102001.6,
           20914.3,
           -54342.5,
           -80664.5,
           -60796.6,
           -1812,
           -6380.6,
           -9803.4,
           -8711.4,
           -3694.1,
           546.5,
           850.7,
           -102.2,
           29.4,
           172.6,
           -12.6,
           -23.5,
           20.7,
           13.7,
           13.9,
           16.2,
           13.4,
           12.7,
           15,
           13.4,
           8.5,
           6.8,
           8.3,
           8.6,
           7.3,
           5.3,
           3.7,
           7.4,
           2.7,
           27.1,
           30.5,
           -4.6,
           -3.3,
           2.6,
           7.7,
           13.8,
           15.1,
           12.8,
           21.7,
           116.2,
           1219.6,
           8894.8,
           33857.5,
           75863.9,
           103385.8,
           75761.3,
           -227.6,
           -67475.8,
           -82754.9,
           -54889.7,
           -1656.9,
           -3361.4,
           -3633,
           -2939.1,
           -1222.5,
           598.5,
           682,
           31.9,
           61.9,
           151.8,
           26.7,
           -1.4,
           22.8,
           15.5,
           16.9,
           19.3,
           12.2,
           14.3,
           12.1,
           9.7,
           10.5,
           5.9,
           8.8,
           12.3,
           9,
           7.6,
           3.8,
           7,
           2.8,
           8.4,
           12.9,
           5.9,
           9.4,
           8.4,
           10.3,
           14,
           15.7,
           14,
           5.8,
           22.2,
           390.9,
           3935.7,
           18798.7,
           48495.4,
           68957.4,
           42202.5,
           -26187.5,
           -80551.3,
           -82246.2,
           -47167.2,
           -1143.9,
           -967.2,
           107.8,
           150.5,
           -62.3,
           437.8,
           490.3,
           106.5,
           78.3,
           108.9,
           -11.8,
           -62.5,
           -10.9,
           15.2,
           20.3,
           16,
           3.8,
           19.2,
           17.4,
           7.2,
           12.2,
           7.9,
           9.9,
           12.9,
           8.9,
           10.3,
           7.7,
           8.8,
           5.9,
           3.6,
           6.5,
           10.7,
           11.4,
           9.5,
           13.1,
           13.2,
           10.5,
           10.2,
           11.4,
           15,
           18.6,
           902.7,
           8147.6,
           26569.8,
           37902,
           9629,
           -50588.7,
           -90196.6,
           -78763.9,
           -39036.8,
           -124.7,
           313.4,
           707.6,
           287.9,
           38.3,
           364.2,
           311.3,
           26.4,
           39.4,
           70.5,
           -70.5,
           -152.4,
           -61.2,
           10.3,
           20.1,
           9.9,
           16.4,
           75,
           71.2,
           21,
           12,
           10.1,
           8.7,
           10.2,
           9,
           12.1,
           10.3,
           6.6,
           3.7,
           5.3,
           7.9,
           10.8,
           8.5,
           8.3,
           13,
           13.4,
           11.2,
           8,
           10.1,
           11.7,
           -72.8,
           -300.3,
           2636.2,
           12618.8,
           14886.1,
           -15950,
           -67854.8,
           -92992.9,
           -71366.2,
           -30921.1,
           1529.3,
           599.1,
           -1188.7,
           -1683.7,
           -479.5,
           421.3,
           123.9,
           -181.8,
           -32.8,
           58.8,
           -82.7,
           -185.3,
           -83.8,
           6.1,
           16.6,
           16.6,
           88.5,
           224.2,
           201.8,
           66.7,
           14.1,
           12.2,
           8.7,
           9.2,
           10,
           10.4,
           9.6,
           5.9,
           2.6,
           6.5,
           9.7,
           12.5,
           10.9,
           8.7,
           9.3,
           9.5,
           12.2,
           10.6,
           10.4,
           8.7,
           -43.6,
           -368.6,
           837.3,
           5204.1,
           -77.3,
           -32444.4,
           -75095.6,
           -87073.1,
           -59550.8,
           -22725.7,
           3588.4,
           313.3,
           -4111.9,
           -4361.9,
           -1215.3,
           446.3,
           -89.7,
           -387,
           -83.3,
           62.6,
           -41.5,
           -130.9,
           -57.3,
           8.5,
           13.5,
           40.8,
           208.7,
           420.6,
           355.2,
           121.8,
           16.6,
           13.6,
           13.2,
           11.3,
           9.8,
           6.4,
           7.3,
           9.6,
           8.2,
           8.5,
           10.6,
           12.1,
           11.8,
           8.3,
           7.2,
           7.9,
           14.6,
           16.8,
           14.5,
           13.6,
           2,
           -97.3,
           456.3,
           809.6,
           -9967.5,
           -41064.2,
           -72580.6,
           -73099.6,
           -44207.6,
           -14599.6,
           5378.5,
           -135.4,
           -6501.1,
           -6376.3,
           -1829.5,
           309.2,
           -288,
           -457.2,
           -78,
           52.5,
           3.5,
           -41.8,
           -9.8,
           12.5,
           12.4,
           64.8,
           302.1,
           532.5,
           410.8,
           132.3,
           10.3,
           10.4,
           15.9,
           12.8,
           10,
           8.6,
           10.1,
           12.5,
           12.9,
           13.8,
           10.2,
           0.9,
           17.1,
           28.2,
           15.3,
           8.6,
           19.7,
           24.9,
           15.4,
           13.3,
           11.2,
           15.5,
           -242.2,
           -3558.8,
           -17842,
           -44232,
           -62824.1,
           -54103.5,
           -27811.2,
           -7362,
           6137.9,
           -499.3,
           -7343.4,
           -6884.2,
           -2048.8,
           64.3,
           -390.1,
           -360.7,
           -30.8,
           24.1,
           15.4,
           10.7,
           14,
           8.9,
           12.4,
           67.1,
           298.1,
           465.5,
           307,
           83.2,
           3.8,
           10.7,
           17.2,
           16.1,
           14.4,
           14.4,
           17.5,
           15,
           14.6,
           18.3,
           8.7,
           52,
           231.6,
           297.6,
           132.4,
           -4.5,
           -6.3,
           19.3,
           14.4,
           9.4,
           6.9,
           -64.3,
           -1630.5,
           -8618.1,
           -24575.2,
           -43558.1,
           -49038.7,
           -34281.2,
           -13601.8,
           -2224.9,
           5552.8,
           -673.5,
           -6492.1,
           -5838.6,
           -1770.6,
           -123.1,
           -353.1,
           -181,
           14.5,
           6.7,
           10,
           12,
           8.4,
           0.1,
           14,
           48.1,
           205.9,
           272.2,
           128.7,
           28.9,
           17.1,
           21.8,
           7.1,
           12.7,
           26.6,
           19.3,
           20.3,
           21.3,
           21.2,
           8.8,
           66.5,
           578.4,
           1514.9,
           1730.7,
           869.3,
           74.7,
           -89.7,
           -13.9,
           24.7,
           41.8,
           91.4,
           -30.8,
           -2894.7,
           -12812.2,
           -28566.6,
           -39155.8,
           -33921.7,
           -17399,
           -4144.4,
           37.3,
           3991.4,
           -639.2,
           -4559.6,
           -3892.8,
           -1150.5,
           -156.3,
           -219.5,
           -37.4,
           23,
           19.5,
           13,
           -4.1,
           6.4,
           0.2,
           17.3,
           27.1,
           103.3,
           105.3,
           22.1,
           4.7,
           -7,
           -68,
           -178.2,
           -105.6,
           127.8,
           218.5,
           134.3,
           32.5,
           3.2,
           -24.4,
           472.2,
           2747.5,
           6005.2,
           6574.1,
           3628.3,
           727.3,
           -138.3,
           -43.2,
           46.9,
           116.6,
           309.3,
           339.5,
           -3031.9,
           -13895.8,
           -27408.3,
           -30563.4,
           -19358,
           -5630.7,
           -95.8,
           -114.8,
           2225.1,
           -447.5,
           -2470.1,
           -1955.1,
           -512.3,
           -82.3,
           -81.3,
           12.6,
           -4.1,
           40.8,
           11.1,
           -16.6,
           27.7,
           13.4,
           19.1,
           18.1,
           52.5,
           59.8,
           24.9,
           -73.3,
           -269.4,
           -623.9,
           -1045.2,
           -606.2,
           718.6,
           1460.8,
           960,
           160.1,
           -101.8,
           -2.3,
           1956.6,
           8755.5,
           17389.1,
           18642.8,
           10851.9,
           2827.9,
           -3.6,
           -39.9,
           39.2,
           117.9,
           430,
           680.3,
           -2018.4,
           -10971.2,
           -20255,
           -18424.9,
           -6933.1,
           642.9,
           52.6,
           -860.9,
           895.6,
           -210.2,
           -938.1,
           -657.5,
           -112.6,
           -6.8,
           -2.1,
           0.8,
           -32.8,
           38.8,
           -21.9,
           -34.6,
           50.7,
           25.6,
           16.2,
           17.9,
           47.5,
           92.6,
           35.4,
           -326.3,
           -966.6,
           -2140.9,
           -3337,
           -1747.7,
           2793.3,
           5591.5,
           3924.6,
           914.1,
           -145.5,
           460.7,
           5735.4,
           21437.3,
           40074.8,
           42386.5,
           25272.9,
           7090.5,
           109.8,
           -144,
           43.7,
           18.2,
           37,
           -46.5,
           -1259.2,
           -5550,
           -9214.6,
           -5573.4,
           1404.3,
           2132.7,
           -954.9,
           189.9,
           195.5,
           -42.4,
           -178.2,
           -96.2,
           19.2,
           20,
           18.2,
           -10.4,
           -31.6,
           18.8,
           -63,
           -55,
           47.7,
           24.5,
           12.3,
           18.3,
           46.8,
           105.6,
           -28.9,
           -675.2,
           -1949.3,
           -4739.1,
           -7427.4,
           -3381.9,
           7804.5,
           14949.2,
           10992.5,
           3413.1,
           674.1,
           2302.4,
           13123.1,
           42895.3,
           77005,
           80657.9,
           48337.4,
           13220.9,
           -742.7,
           -634,
           706.4,
           672.9,
           -671.6,
           -2729.6,
           -2732,
           -807.5,
           1275.6,
           3449.2,
           3270.9,
           -368.1,
           -740.4,
           4665.7,
           -41.7,
           18.8,
           25.3,
           19.8,
           21.1,
           19.1,
           13.2,
           1.8,
           -4,
           14.5,
           -55.1,
           -47.3,
           26.8,
           16.6,
           13.7,
           17.7,
           32.4,
           68.5,
           -100.7,
           -767.5,
           -2566.6,
           -7663.5,
           -12579.5,
           -4703.3,
           16744.5,
           30651,
           23345.5,
           9062.8,
           4006.8,
           6936.9,
           24637.9,
           72535.9,
           126954.1,
           132232.7,
           78830.9,
           19717.4,
           -3815.5,
           -1256.8,
           4176.7,
           4952.3,
           419.1,
           -6582.4,
           -7144.1,
           592.2,
           6798.1,
           4371.9,
           -3320.4,
           -6564.4,
           1379.1,
           12338.3,
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        },
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      }
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from scraper.plot import distance_region\n",
    "from itertools import product\n",
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel, WhiteKernel\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "X = X_geo.values\n",
    "y = Y_geo.values\n",
    "\n",
    "# Input space\n",
    "x1 = np.linspace(X[:,1].min(), X[:,1].max()) \n",
    "x2 = np.linspace(X[:,0].min(), X[:,0].max())\n",
    "\n",
    "x1 = np.linspace(17.95, 18.14)\n",
    "x2 = np.linspace(59.28, 59.38)\n",
    "\n",
    "# kernel =  ConstantKernel(1.0) + ConstantKernel(1.0) * RBF(10)  + WhiteKernel(5)\n",
    "\n",
    "kernel = ConstantKernel(1.0, (1e-3, 1e3)) * RBF([5,5], (1e-2, 1e2))\n",
    "gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=15)\n",
    "gp.fit(X, y)\n",
    "\n",
    "x2x1 = np.array(list(product(x2, x1)))\n",
    "y_pred, MSE = gp.predict(x2x1, return_std=True)\n",
    "\n",
    "new_data = np.column_stack((x2x1,y_pred))\n",
    "\n",
    "fig = distance_region(new_data) \n",
    "fig.update_layout(height=600, width=800, title_text=\"Prediction\",margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "input_data = df[['origin.lat', 'origin.lon', 'distance_time']]\n",
    "\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Contour(\n",
    "        z=y_pred,\n",
    "        x=x1, # horizontal axis\n",
    "        y=x2, # vertical axis\n",
    "        zmin = 0,\n",
    "        zmax = 4000,\n",
    "        opacity=0.6\n",
    "        # contours_coloring='lines',\n",
    "    ))\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "     x=input_data.iloc[:,1].values, y=input_data.iloc[:,0].values,\n",
    "     mode = 'markers',\n",
    "     text=input_data.iloc[:,2].values,\n",
    "     marker = dict(\n",
    "            color=input_data.iloc[:,2].values,\n",
    "            size = 3,\n",
    "            cmax = 4000,\n",
    "            cmin = 0,\n",
    "            colorscale=[\"Lime\",\"gold\",\"orangered\"],\n",
    "\n",
    "        )\n",
    "    ))\n",
    "fig.update_layout(height=600, width=800, title_text=\"Prediction\",margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()"
   ]
  }
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