3_Model_building_first attempt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "# %qtconsole\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from IPython.display import display\n",
    "def display_all(df):\n",
    "    with pd.option_context(\"display.max_rows\", 1000): \n",
    "        with pd.option_context(\"display.max_columns\", 1000): \n",
    "            display(df)\n",
    "\t\t\t\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\t\t\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# warnings.filterwarnings(action='once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_date</th>\n",
       "      <th>road_name</th>\n",
       "      <th>adt</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sampling_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1974-07-12</td>\n",
       "      <td>COLWILL RD</td>\n",
       "      <td>125.0</td>\n",
       "      <td>-36.832191</td>\n",
       "      <td>174.636777</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1974-08-11</td>\n",
       "      <td>HOBSONVILLE RD</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>-36.803623</td>\n",
       "      <td>174.638971</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1974-08-11</td>\n",
       "      <td>HOBSONVILLE RD</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>-36.797016</td>\n",
       "      <td>174.650038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1974-08-11</td>\n",
       "      <td>TAIKATA RD</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>-36.841192</td>\n",
       "      <td>174.651345</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974-08-11</td>\n",
       "      <td>GLORIA AVE</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>-36.848881</td>\n",
       "      <td>174.650064</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  count_date       road_name     adt   latitude   longitude  sampling_count\n",
       "0 1974-07-12      COLWILL RD   125.0 -36.832191  174.636777               3\n",
       "1 1974-08-11  HOBSONVILLE RD  1900.0 -36.803623  174.638971              22\n",
       "2 1974-08-11  HOBSONVILLE RD  2853.0 -36.797016  174.650038               1\n",
       "3 1974-08-11      TAIKATA RD  2490.0 -36.841192  174.651345               1\n",
       "4 1974-08-11      GLORIA AVE  1823.0 -36.848881  174.650064               5"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'data/merged_date_v2.csv', parse_dates = ['count_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def add_datepart(df, fldname, drop=True, time=False, errors=\"raise\"):\t\n",
    "\n",
    "    fld = df[fldname]\n",
    "    fld_dtype = fld.dtype\n",
    "    if isinstance(fld_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype):\n",
    "        fld_dtype = np.datetime64\n",
    "\n",
    "    if not np.issubdtype(fld_dtype, np.datetime64):\n",
    "        df[fldname] = fld = pd.to_datetime(fld, infer_datetime_format=True, errors=errors)\n",
    "    targ_pre = re.sub('[Dd]ate$', '', fldname)\n",
    "    attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear',\n",
    "            'Is_month_end', 'Is_month_start', 'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']\n",
    "    if time: attr = attr + ['Hour', 'Minute', 'Second']\n",
    "    for n in attr: df[targ_pre + n] = getattr(fld.dt, n.lower())\n",
    "    df[targ_pre + 'Elapsed'] = fld.astype(np.int64) // 10 ** 9\n",
    "    if drop: df.drop(fldname, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add date parts\n",
    "# df.reset_index(inplace = True)\n",
    "add_datepart(df, 'count_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['road_name_code'] = df['road_name'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['road_name', 'adt', 'latitude', 'longitude', 'sampling_count',\n",
       "       'count_Year', 'count_Month', 'count_Week', 'count_Day',\n",
       "       'count_Dayofweek', 'count_Dayofyear', 'count_Is_month_end',\n",
       "       'count_Is_month_start', 'count_Is_quarter_end',\n",
       "       'count_Is_quarter_start', 'count_Is_year_end', 'count_Is_year_start',\n",
       "       'count_Elapsed', 'road_name_code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>road_name</th>\n",
       "      <th>adt</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sampling_count</th>\n",
       "      <th>count_Year</th>\n",
       "      <th>count_Month</th>\n",
       "      <th>count_Week</th>\n",
       "      <th>count_Day</th>\n",
       "      <th>count_Dayofweek</th>\n",
       "      <th>count_Dayofyear</th>\n",
       "      <th>count_Is_month_end</th>\n",
       "      <th>count_Is_month_start</th>\n",
       "      <th>count_Is_quarter_end</th>\n",
       "      <th>count_Is_quarter_start</th>\n",
       "      <th>count_Is_year_end</th>\n",
       "      <th>count_Is_year_start</th>\n",
       "      <th>count_Elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>774</td>\n",
       "      <td>125.0</td>\n",
       "      <td>-36.832191</td>\n",
       "      <td>174.636777</td>\n",
       "      <td>3</td>\n",
       "      <td>1974</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>193</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>142819200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1695</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>-36.803623</td>\n",
       "      <td>174.638971</td>\n",
       "      <td>22</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1695</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>-36.797016</td>\n",
       "      <td>174.650038</td>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3628</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>-36.841192</td>\n",
       "      <td>174.651345</td>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1365</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>-36.848881</td>\n",
       "      <td>174.650064</td>\n",
       "      <td>5</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   road_name     adt   latitude   longitude  sampling_count  count_Year  \\\n",
       "0        774   125.0 -36.832191  174.636777               3        1974   \n",
       "1       1695  1900.0 -36.803623  174.638971              22        1974   \n",
       "2       1695  2853.0 -36.797016  174.650038               1        1974   \n",
       "3       3628  2490.0 -36.841192  174.651345               1        1974   \n",
       "4       1365  1823.0 -36.848881  174.650064               5        1974   \n",
       "\n",
       "   count_Month  count_Week  count_Day  count_Dayofweek  count_Dayofyear  \\\n",
       "0            7          28         12                4              193   \n",
       "1            8          32         11                6              223   \n",
       "2            8          32         11                6              223   \n",
       "3            8          32         11                6              223   \n",
       "4            8          32         11                6              223   \n",
       "\n",
       "   count_Is_month_end  count_Is_month_start  count_Is_quarter_end  \\\n",
       "0               False                 False                 False   \n",
       "1               False                 False                 False   \n",
       "2               False                 False                 False   \n",
       "3               False                 False                 False   \n",
       "4               False                 False                 False   \n",
       "\n",
       "   count_Is_quarter_start  count_Is_year_end  count_Is_year_start  \\\n",
       "0                   False              False                False   \n",
       "1                   False              False                False   \n",
       "2                   False              False                False   \n",
       "3                   False              False                False   \n",
       "4                   False              False                False   \n",
       "\n",
       "   count_Elapsed  \n",
       "0      142819200  \n",
       "1      145411200  \n",
       "2      145411200  \n",
       "3      145411200  \n",
       "4      145411200  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcols = ['road_name_code', 'latitude', 'longitude', \n",
    "       'count_Year', 'count_Month', 'count_Week', 'count_Day',\n",
    "       'count_Dayofweek', 'count_Dayofyear', 'count_Is_month_end',\n",
    "       'count_Is_month_start', 'count_Is_quarter_end',\n",
    "       'count_Is_quarter_start', 'count_Is_year_end', 'count_Is_year_start',\n",
    "       'count_Elapsed']\n",
    "ycol = 'adt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(x,y): \n",
    "    # calculates r^2\n",
    "    return math.sqrt(np.square(np.subtract(x,y)).mean())\n",
    "def get_scores(m, X_train, X_valid, y_train, y_valid):\n",
    "    # returns rmsq training, r^2 validation, avg accuracy training, avg accuracy validation, oob\n",
    "    res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid),\n",
    "                m.score(X_train, y_train), m.score(X_valid, y_valid)]\n",
    "    if hasattr(m, 'oob_score_'): \n",
    "        res.append(m.oob_score_)\n",
    "    return res\n",
    "\n",
    "def KFold_fit_regression_fit(model, X, y, splits = 5, repeats = 10, random_state = None):\n",
    "    folds = RepeatedKFold(n_splits = splits, n_repeats = repeats, random_state = random_state)\n",
    "#     predicted_solution = []\n",
    "#     correct_solution = []\n",
    "    results = []\n",
    "    for train_ind, test_ind in folds.split(X, y):\n",
    "        X_train, X_test = X.iloc[train_ind,:], X.iloc[test_ind,:]\n",
    "        y_train, y_test = y[train_ind], y[test_ind]\n",
    "        model.fit(X_train, y_train)\n",
    "        r = get_scores(model,X_train, X_test, y_train, y_test)\n",
    "#         predicted_solution += list(model.predict(X_test))\n",
    "#         correct_solution += list(y_test)\n",
    "        results.append(r)\n",
    "    return np.vstack(results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kfold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
       "           oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=500, oob_score= True)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1468.967855146438,\n",
       "  4102.878027104442,\n",
       "  0.9671035172933449,\n",
       "  0.743212704719542,\n",
       "  0.757088344510431],\n",
       " [1436.6949050331023,\n",
       "  3872.3652364781965,\n",
       "  0.9684402018118787,\n",
       "  0.7739189153874679,\n",
       "  0.7670426104085492],\n",
       " [1430.0484511688526,\n",
       "  3857.0724308684844,\n",
       "  0.9690531319618426,\n",
       "  0.766113702773024,\n",
       "  0.7711441437404165],\n",
       " [1440.6966708913965,\n",
       "  3981.2006201591375,\n",
       "  0.9684309986751617,\n",
       "  0.7559464541471671,\n",
       "  0.7672302276024793],\n",
       " [1463.0467730107875,\n",
       "  3909.934537201361,\n",
       "  0.967124052087147,\n",
       "  0.77350651951373,\n",
       "  0.7577262270038259]]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[xcols]\n",
    "y = df[ycol]\n",
    "results = KFold_fit_regression_fit(m, X, y, splits = 5, repeats=1, random_state=100 )\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,4,5])\n",
    "y = np.array([2,3,4,5,6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.44789093e+03, 3.94469017e+03, 9.68030380e-01, 7.62539659e-01,\n",
       "       7.64046311e-01])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = np.vstack(results)\n",
    "# r = r.mean(axis = 0)\n",
    "r.mean(axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use oob for model selection and parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>road_name</th>\n",
       "      <th>adt</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sampling_count</th>\n",
       "      <th>count_Year</th>\n",
       "      <th>count_Month</th>\n",
       "      <th>count_Week</th>\n",
       "      <th>count_Day</th>\n",
       "      <th>count_Dayofweek</th>\n",
       "      <th>count_Dayofyear</th>\n",
       "      <th>count_Is_month_end</th>\n",
       "      <th>count_Is_month_start</th>\n",
       "      <th>count_Is_quarter_end</th>\n",
       "      <th>count_Is_quarter_start</th>\n",
       "      <th>count_Is_year_end</th>\n",
       "      <th>count_Is_year_start</th>\n",
       "      <th>count_Elapsed</th>\n",
       "      <th>road_name_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COLWILL RD</td>\n",
       "      <td>125.0</td>\n",
       "      <td>-36.832191</td>\n",
       "      <td>174.636777</td>\n",
       "      <td>3</td>\n",
       "      <td>1974</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>193</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>142819200</td>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOBSONVILLE RD</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>-36.803623</td>\n",
       "      <td>174.638971</td>\n",
       "      <td>22</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>1695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOBSONVILLE RD</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>-36.797016</td>\n",
       "      <td>174.650038</td>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>1695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TAIKATA RD</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>-36.841192</td>\n",
       "      <td>174.651345</td>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>3628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GLORIA AVE</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>-36.848881</td>\n",
       "      <td>174.650064</td>\n",
       "      <td>5</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>1365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HOBSONVILLE RD</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>-36.814339</td>\n",
       "      <td>174.629872</td>\n",
       "      <td>2</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>1695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TE ATATU RD</td>\n",
       "      <td>3733.0</td>\n",
       "      <td>-36.832667</td>\n",
       "      <td>174.648011</td>\n",
       "      <td>11</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>3695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KOTUKU ST</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>-36.850907</td>\n",
       "      <td>174.648874</td>\n",
       "      <td>5</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>2029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HOBSONVILLE RD</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>-36.818111</td>\n",
       "      <td>174.615714</td>\n",
       "      <td>20</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>1695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TE ATATU RD</td>\n",
       "      <td>14450.0</td>\n",
       "      <td>-36.856203</td>\n",
       "      <td>174.651780</td>\n",
       "      <td>10</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>145411200</td>\n",
       "      <td>3695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        road_name      adt   latitude   longitude  sampling_count  count_Year  \\\n",
       "0      COLWILL RD    125.0 -36.832191  174.636777               3        1974   \n",
       "1  HOBSONVILLE RD   1900.0 -36.803623  174.638971              22        1974   \n",
       "2  HOBSONVILLE RD   2853.0 -36.797016  174.650038               1        1974   \n",
       "3      TAIKATA RD   2490.0 -36.841192  174.651345               1        1974   \n",
       "4      GLORIA AVE   1823.0 -36.848881  174.650064               5        1974   \n",
       "5  HOBSONVILLE RD   2163.0 -36.814339  174.629872               2        1974   \n",
       "6     TE ATATU RD   3733.0 -36.832667  174.648011              11        1974   \n",
       "7       KOTUKU ST   1700.0 -36.850907  174.648874               5        1974   \n",
       "8  HOBSONVILLE RD   3900.0 -36.818111  174.615714              20        1974   \n",
       "9     TE ATATU RD  14450.0 -36.856203  174.651780              10        1974   \n",
       "\n",
       "   count_Month  count_Week  count_Day  count_Dayofweek  count_Dayofyear  \\\n",
       "0            7          28         12                4              193   \n",
       "1            8          32         11                6              223   \n",
       "2            8          32         11                6              223   \n",
       "3            8          32         11                6              223   \n",
       "4            8          32         11                6              223   \n",
       "5            8          32         11                6              223   \n",
       "6            8          32         11                6              223   \n",
       "7            8          32         11                6              223   \n",
       "8            8          32         11                6              223   \n",
       "9            8          32         11                6              223   \n",
       "\n",
       "   count_Is_month_end  count_Is_month_start  count_Is_quarter_end  \\\n",
       "0               False                 False                 False   \n",
       "1               False                 False                 False   \n",
       "2               False                 False                 False   \n",
       "3               False                 False                 False   \n",
       "4               False                 False                 False   \n",
       "5               False                 False                 False   \n",
       "6               False                 False                 False   \n",
       "7               False                 False                 False   \n",
       "8               False                 False                 False   \n",
       "9               False                 False                 False   \n",
       "\n",
       "   count_Is_quarter_start  count_Is_year_end  count_Is_year_start  \\\n",
       "0                   False              False                False   \n",
       "1                   False              False                False   \n",
       "2                   False              False                False   \n",
       "3                   False              False                False   \n",
       "4                   False              False                False   \n",
       "5                   False              False                False   \n",
       "6                   False              False                False   \n",
       "7                   False              False                False   \n",
       "8                   False              False                False   \n",
       "9                   False              False                False   \n",
       "\n",
       "   count_Elapsed  road_name_code  \n",
       "0      142819200             774  \n",
       "1      145411200            1695  \n",
       "2      145411200            1695  \n",
       "3      145411200            3628  \n",
       "4      145411200            1365  \n",
       "5      145411200            1695  \n",
       "6      145411200            3695  \n",
       "7      145411200            2029  \n",
       "8      145411200            1695  \n",
       "9      145411200            3695  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>road_name_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>count_Year</th>\n",
       "      <th>count_Month</th>\n",
       "      <th>count_Week</th>\n",
       "      <th>count_Day</th>\n",
       "      <th>count_Dayofweek</th>\n",
       "      <th>count_Dayofyear</th>\n",
       "      <th>count_Is_month_end</th>\n",
       "      <th>count_Is_month_start</th>\n",
       "      <th>count_Is_quarter_end</th>\n",
       "      <th>count_Is_quarter_start</th>\n",
       "      <th>count_Is_year_end</th>\n",
       "      <th>count_Is_year_start</th>\n",
       "      <th>count_Elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11304</th>\n",
       "      <td>2097</td>\n",
       "      <td>-36.824130</td>\n",
       "      <td>174.611905</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1427155200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9479</th>\n",
       "      <td>2666</td>\n",
       "      <td>-36.711192</td>\n",
       "      <td>174.778044</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1372809600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1591</th>\n",
       "      <td>523</td>\n",
       "      <td>-36.903297</td>\n",
       "      <td>174.621134</td>\n",
       "      <td>1985</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>166</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>487641600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17963</th>\n",
       "      <td>2447</td>\n",
       "      <td>-36.888823</td>\n",
       "      <td>174.633541</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1519862400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10037</th>\n",
       "      <td>3931</td>\n",
       "      <td>-36.614636</td>\n",
       "      <td>174.714830</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>338</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1386115200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       road_name_code   latitude   longitude  count_Year  count_Month  \\\n",
       "11304            2097 -36.824130  174.611905        2015            3   \n",
       "9479             2666 -36.711192  174.778044        2013            7   \n",
       "1591              523 -36.903297  174.621134        1985            6   \n",
       "17963            2447 -36.888823  174.633541        2018            3   \n",
       "10037            3931 -36.614636  174.714830        2013           12   \n",
       "\n",
       "       count_Week  count_Day  count_Dayofweek  count_Dayofyear  \\\n",
       "11304          13         24                1               83   \n",
       "9479           27          3                2              184   \n",
       "1591           24         15                5              166   \n",
       "17963           9          1                3               60   \n",
       "10037          49          4                2              338   \n",
       "\n",
       "       count_Is_month_end  count_Is_month_start  count_Is_quarter_end  \\\n",
       "11304               False                 False                 False   \n",
       "9479                False                 False                 False   \n",
       "1591                False                 False                 False   \n",
       "17963               False                  True                 False   \n",
       "10037               False                 False                 False   \n",
       "\n",
       "       count_Is_quarter_start  count_Is_year_end  count_Is_year_start  \\\n",
       "11304                   False              False                False   \n",
       "9479                    False              False                False   \n",
       "1591                    False              False                False   \n",
       "17963                   False              False                False   \n",
       "10037                   False              False                False   \n",
       "\n",
       "       count_Elapsed  \n",
       "11304     1427155200  \n",
       "9479      1372809600  \n",
       "1591       487641600  \n",
       "17963     1519862400  \n",
       "10037     1386115200  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "def split_vals(df, n):\n",
    "    return df[:n].copy(), df[n:].copy()\n",
    "train_size = int(df.shape[0]*0.75)\n",
    "df_model = shuffle(df)\n",
    "X, y = df_model[xcols], df_model[ycol]\n",
    "X_train, X_test = split_vals(X, train_size)\n",
    "y_train, y_test = split_vals(y, train_size)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=38,\n",
       "           max_features=0.9, max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=3, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=150, n_jobs=3,\n",
       "           oob_score=True, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.7304259274406519"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.9099333969704781"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = RandomForestRegressor (n_estimators=150, max_depth=38, min_samples_leaf = 3, max_features=0.9, oob_score= True, random_state=1, n_jobs  = 3)\n",
    "m.fit(X_train, y_train)\n",
    "m.oob_score_\n",
    "m.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7345326081051224"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.9639209597647438"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.oob_score_\n",
    "m.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cols</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>longitude</td>\n",
       "      <td>0.281669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>latitude</td>\n",
       "      <td>0.245266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>road_name_code</td>\n",
       "      <td>0.179227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>count_Elapsed</td>\n",
       "      <td>0.140939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>count_Dayofyear</td>\n",
       "      <td>0.040654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>count_Dayofweek</td>\n",
       "      <td>0.037727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>count_Day</td>\n",
       "      <td>0.033203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>count_Week</td>\n",
       "      <td>0.018571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>count_Year</td>\n",
       "      <td>0.015339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>count_Month</td>\n",
       "      <td>0.006086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>count_Is_month_start</td>\n",
       "      <td>0.000575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>count_Is_month_end</td>\n",
       "      <td>0.000424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>count_Is_quarter_start</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>count_Is_year_start</td>\n",
       "      <td>0.000116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>count_Is_quarter_end</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>count_Is_year_end</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      cols       imp\n",
       "2                longitude  0.281669\n",
       "1                 latitude  0.245266\n",
       "0           road_name_code  0.179227\n",
       "15           count_Elapsed  0.140939\n",
       "8          count_Dayofyear  0.040654\n",
       "7          count_Dayofweek  0.037727\n",
       "6                count_Day  0.033203\n",
       "5               count_Week  0.018571\n",
       "3               count_Year  0.015339\n",
       "4              count_Month  0.006086\n",
       "10    count_Is_month_start  0.000575\n",
       "9       count_Is_month_end  0.000424\n",
       "12  count_Is_quarter_start  0.000161\n",
       "14     count_Is_year_start  0.000116\n",
       "11    count_Is_quarter_end  0.000042\n",
       "13       count_Is_year_end  0.000000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rf_feat_importance(m, df):\n",
    "    return pd.DataFrame({'cols':df.columns, 'imp':m.feature_importances_}\n",
    "                       ).sort_values('imp', ascending=False)\n",
    "imp = rf_feat_importance(m, X_train)\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_drop = imp['cols'][-6:]\n",
    "X.drop(columns=cols_to_drop, inplace = True)\n",
    "X_train, X_test = split_vals(X, train_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=3, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=3,\n",
       "           oob_score=True, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.7377931623237937"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.9138686665807716"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = RandomForestRegressor (n_estimators=1000, max_depth=None, min_samples_leaf = 3, max_features='auto', oob_score= True, random_state=1, n_jobs  = 3)\n",
    "m.fit(X_train, y_train)\n",
    "m.oob_score_\n",
    "m.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.47601271444342336"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "m2 = KNeighborsRegressor(n_neighbors=5, n_jobs  = 1)\n",
    "m2.fit(X_train, y_train)\n",
    "m2.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2372.982170831708,\n",
       " 4275.619459080817,\n",
       " 0.9138686665807716,\n",
       " 0.7238955204055199,\n",
       " 0.7377931623237937]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_scores(m, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15576 entries, 11304 to 608\n",
      "Data columns (total 10 columns):\n",
      "road_name_code     15576 non-null int16\n",
      "latitude           15576 non-null float64\n",
      "longitude          15576 non-null float64\n",
      "count_Year         15576 non-null int64\n",
      "count_Month        15576 non-null int64\n",
      "count_Week         15576 non-null int64\n",
      "count_Day          15576 non-null int64\n",
      "count_Dayofweek    15576 non-null int64\n",
      "count_Dayofyear    15576 non-null int64\n",
      "count_Elapsed      15576 non-null int64\n",
      "dtypes: float64(2), int16(1), int64(7)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "pd.dt X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build model and plot partial dependency plot to estimate the effects over time"
   ]
  }
 ],
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