Day_025_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 範例 : 計程車費率預測\n",
    "https://www.kaggle.com/c/new-york-city-taxi-fare-prediction\n",
    "***\n",
    "- 使用程車費率預測競賽練習時間欄位處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-21 23:54:10 UTC</td>\n",
       "      <td>-73.990580</td>\n",
       "      <td>40.761071</td>\n",
       "      <td>-73.981128</td>\n",
       "      <td>40.758634</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-03 10:42:03 UTC</td>\n",
       "      <td>-73.988403</td>\n",
       "      <td>40.723431</td>\n",
       "      <td>-73.989647</td>\n",
       "      <td>40.741695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-16 18:58:58 UTC</td>\n",
       "      <td>-74.015785</td>\n",
       "      <td>40.715110</td>\n",
       "      <td>-74.012029</td>\n",
       "      <td>40.707888</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-13 16:10:54 UTC</td>\n",
       "      <td>-73.977322</td>\n",
       "      <td>40.787275</td>\n",
       "      <td>-73.958030</td>\n",
       "      <td>40.778838</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-12 03:25:56 UTC</td>\n",
       "      <td>-73.989683</td>\n",
       "      <td>40.729717</td>\n",
       "      <td>-73.982490</td>\n",
       "      <td>40.761887</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0  2011-10-21 23:54:10 UTC        -73.990580        40.761071   \n",
       "1  2015-02-03 10:42:03 UTC        -73.988403        40.723431   \n",
       "2  2014-03-16 18:58:58 UTC        -74.015785        40.715110   \n",
       "3  2009-06-13 16:10:54 UTC        -73.977322        40.787275   \n",
       "4  2014-06-12 03:25:56 UTC        -73.989683        40.729717   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  \n",
       "0         -73.981128         40.758634                2  \n",
       "1         -73.989647         40.741695                1  \n",
       "2         -74.012029         40.707888                2  \n",
       "3         -73.958030         40.778838                3  \n",
       "4         -73.982490         40.761887                3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "data_path = '../data2/'\n",
    "df = pd.read_csv(data_path + 'taxi_data1.csv')\n",
    "\n",
    "train_Y = df['fare_amount']\n",
    "df = df.drop(['fare_amount'] , axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_minute</th>\n",
       "      <th>pickup_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-21 23:54:10</td>\n",
       "      <td>-73.990580</td>\n",
       "      <td>40.761071</td>\n",
       "      <td>-73.981128</td>\n",
       "      <td>40.758634</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-03 10:42:03</td>\n",
       "      <td>-73.988403</td>\n",
       "      <td>40.723431</td>\n",
       "      <td>-73.989647</td>\n",
       "      <td>40.741695</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-16 18:58:58</td>\n",
       "      <td>-74.015785</td>\n",
       "      <td>40.715110</td>\n",
       "      <td>-74.012029</td>\n",
       "      <td>40.707888</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-13 16:10:54</td>\n",
       "      <td>-73.977322</td>\n",
       "      <td>40.787275</td>\n",
       "      <td>-73.958030</td>\n",
       "      <td>40.778838</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-12 03:25:56</td>\n",
       "      <td>-73.989683</td>\n",
       "      <td>40.729717</td>\n",
       "      <td>-73.982490</td>\n",
       "      <td>40.761887</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pickup_datetime  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0 2011-10-21 23:54:10        -73.990580        40.761071         -73.981128   \n",
       "1 2015-02-03 10:42:03        -73.988403        40.723431         -73.989647   \n",
       "2 2014-03-16 18:58:58        -74.015785        40.715110         -74.012029   \n",
       "3 2009-06-13 16:10:54        -73.977322        40.787275         -73.958030   \n",
       "4 2014-06-12 03:25:56        -73.989683        40.729717         -73.982490   \n",
       "\n",
       "   dropoff_latitude  passenger_count  pickup_year  pickup_month  pickup_day  \\\n",
       "0         40.758634                2         2011            10          21   \n",
       "1         40.741695                1         2015             2           3   \n",
       "2         40.707888                2         2014             3          16   \n",
       "3         40.778838                3         2009             6          13   \n",
       "4         40.761887                3         2014             6          12   \n",
       "\n",
       "   pickup_hour  pickup_minute  pickup_second  \n",
       "0           23             54             10  \n",
       "1           10             42              3  \n",
       "2           18             58             58  \n",
       "3           16             10             54  \n",
       "4            3             25             56  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 時間特徵分解方式:使用datetime\n",
    "df['pickup_datetime'] = df['pickup_datetime'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S UTC'))\n",
    "df['pickup_year'] = df['pickup_datetime'].apply(lambda x: datetime.datetime.strftime(x, '%Y')).astype('int64')\n",
    "df['pickup_month'] = df['pickup_datetime'].apply(lambda x: datetime.datetime.strftime(x, '%m')).astype('int64')\n",
    "df['pickup_day'] = df['pickup_datetime'].apply(lambda x: datetime.datetime.strftime(x, '%d')).astype('int64')\n",
    "df['pickup_hour'] = df['pickup_datetime'].apply(lambda x: datetime.datetime.strftime(x, '%H')).astype('int64')\n",
    "df['pickup_minute'] = df['pickup_datetime'].apply(lambda x: datetime.datetime.strftime(x, '%M')).astype('int64')\n",
    "df['pickup_second'] = df['pickup_datetime'].apply(lambda x: datetime.datetime.strftime(x, '%S')).astype('int64')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Reg Score : 0.026876871475636888\n",
      "Gradient Boosting Reg Score : 0.7118068756334723\n"
     ]
    }
   ],
   "source": [
    "# 將結果使用線性迴歸 / 梯度提升樹分別看結果\n",
    "df_temp = df.drop(['pickup_datetime'] , axis=1)\n",
    "scaler = MinMaxScaler()\n",
    "train_X = scaler.fit_transform(df_temp)\n",
    "Linear = LinearRegression()\n",
    "print(f'Linear Reg Score : {cross_val_score(Linear, train_X, train_Y, cv=5).mean()}')\n",
    "GDBT = GradientBoostingRegressor()\n",
    "print(f'Gradient Boosting Reg Score : {cross_val_score(GDBT, train_X, train_Y, cv=5).mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 對照範例，試著加入星期幾 (day of week) 與第幾周 (week of year) 這兩項特徵，  \n",
    "看看結果會比原本只有時間特徵分解的結果更好或更差?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_minute</th>\n",
       "      <th>pickup_second</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-21 23:54:10</td>\n",
       "      <td>-73.990580</td>\n",
       "      <td>40.761071</td>\n",
       "      <td>-73.981128</td>\n",
       "      <td>40.758634</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-03 10:42:03</td>\n",
       "      <td>-73.988403</td>\n",
       "      <td>40.723431</td>\n",
       "      <td>-73.989647</td>\n",
       "      <td>40.741695</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-16 18:58:58</td>\n",
       "      <td>-74.015785</td>\n",
       "      <td>40.715110</td>\n",
       "      <td>-74.012029</td>\n",
       "      <td>40.707888</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-13 16:10:54</td>\n",
       "      <td>-73.977322</td>\n",
       "      <td>40.787275</td>\n",
       "      <td>-73.958030</td>\n",
       "      <td>40.778838</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-12 03:25:56</td>\n",
       "      <td>-73.989683</td>\n",
       "      <td>40.729717</td>\n",
       "      <td>-73.982490</td>\n",
       "      <td>40.761887</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pickup_datetime  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0 2011-10-21 23:54:10        -73.990580        40.761071         -73.981128   \n",
       "1 2015-02-03 10:42:03        -73.988403        40.723431         -73.989647   \n",
       "2 2014-03-16 18:58:58        -74.015785        40.715110         -74.012029   \n",
       "3 2009-06-13 16:10:54        -73.977322        40.787275         -73.958030   \n",
       "4 2014-06-12 03:25:56        -73.989683        40.729717         -73.982490   \n",
       "\n",
       "   dropoff_latitude  passenger_count  pickup_year  pickup_month  pickup_day  \\\n",
       "0         40.758634                2         2011            10          21   \n",
       "1         40.741695                1         2015             2           3   \n",
       "2         40.707888                2         2014             3          16   \n",
       "3         40.778838                3         2009             6          13   \n",
       "4         40.761887                3         2014             6          12   \n",
       "\n",
       "   pickup_hour  pickup_minute  pickup_second  dayofweek  week  \n",
       "0           23             54             10          4    42  \n",
       "1           10             42              3          1     6  \n",
       "2           18             58             58          6    11  \n",
       "3           16             10             54          5    24  \n",
       "4            3             25             56          3    24  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加入星期幾與第幾周兩個特徵\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "# datetime.strptime(\"20171225\",\"%Y%m%d\").strftime(\"%W\")\n",
    "\n",
    "df['dayofweek']=df['pickup_datetime'].dt.dayofweek\n",
    "df['week']=df['pickup_datetime'].dt.week\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Reg Score : 0.026481023863708274\n",
      "Gradient Boosting Reg Score : 0.7103524008343043\n"
     ]
    }
   ],
   "source": [
    "# 將結果使用線性迴歸 / 梯度提升樹分別看結果\n",
    "df_temp = df.drop(['pickup_datetime'] , axis=1)\n",
    "train_X = scaler.fit_transform(df_temp)\n",
    "print(f'Linear Reg Score : {cross_val_score(Linear, train_X, train_Y, cv=5).mean()}')\n",
    "print(f'Gradient Boosting Reg Score : {cross_val_score(GDBT, train_X, train_Y, cv=5).mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_minute</th>\n",
       "      <th>pickup_second</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>week</th>\n",
       "      <th>day_cycle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-21 23:54:10</td>\n",
       "      <td>-73.990580</td>\n",
       "      <td>40.761071</td>\n",
       "      <td>-73.981128</td>\n",
       "      <td>40.758634</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>-0.025450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-03 10:42:03</td>\n",
       "      <td>-73.988403</td>\n",
       "      <td>40.723431</td>\n",
       "      <td>-73.989647</td>\n",
       "      <td>40.741695</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-16 18:58:58</td>\n",
       "      <td>-74.015785</td>\n",
       "      <td>40.715110</td>\n",
       "      <td>-74.012029</td>\n",
       "      <td>40.707888</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.967083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-13 16:10:54</td>\n",
       "      <td>-73.977322</td>\n",
       "      <td>40.787275</td>\n",
       "      <td>-73.958030</td>\n",
       "      <td>40.778838</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.888817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-12 03:25:56</td>\n",
       "      <td>-73.989683</td>\n",
       "      <td>40.729717</td>\n",
       "      <td>-73.982490</td>\n",
       "      <td>40.761887</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>0.782427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pickup_datetime  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0 2011-10-21 23:54:10        -73.990580        40.761071         -73.981128   \n",
       "1 2015-02-03 10:42:03        -73.988403        40.723431         -73.989647   \n",
       "2 2014-03-16 18:58:58        -74.015785        40.715110         -74.012029   \n",
       "3 2009-06-13 16:10:54        -73.977322        40.787275         -73.958030   \n",
       "4 2014-06-12 03:25:56        -73.989683        40.729717         -73.982490   \n",
       "\n",
       "   dropoff_latitude  passenger_count  pickup_year  pickup_month  pickup_day  \\\n",
       "0         40.758634                2         2011            10          21   \n",
       "1         40.741695                1         2015             2           3   \n",
       "2         40.707888                2         2014             3          16   \n",
       "3         40.778838                3         2009             6          13   \n",
       "4         40.761887                3         2014             6          12   \n",
       "\n",
       "   pickup_hour  pickup_minute  pickup_second  dayofweek  week  day_cycle  \n",
       "0           23             54             10          4    42  -0.025450  \n",
       "1           10             42              3          1     6   0.333601  \n",
       "2           18             58             58          6    11  -0.967083  \n",
       "3           16             10             54          5    24  -0.888817  \n",
       "4            3             25             56          3    24   0.782427  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加上\"日週期\"特徵 (參考講義\"週期循環特徵\")\n",
    "import math\n",
    "df['day_cycle'] = df['pickup_hour']/12 + df['pickup_minute']/720 + df['pickup_second']/43200\n",
    "df['day_cycle'] = df['day_cycle'].map(lambda x:math.sin(x*math.pi))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Reg Score : 0.02608035229379433\n",
      "Gradient Boosting Reg Score : 0.7155043152309042\n"
     ]
    }
   ],
   "source": [
    "# 將結果使用線性迴歸 / 梯度提升樹分別看結果\n",
    "df_temp = df.drop(['pickup_datetime'] , axis=1)\n",
    "train_X = scaler.fit_transform(df_temp)\n",
    "print(f'Linear Reg Score : {cross_val_score(Linear, train_X, train_Y, cv=5).mean()}')\n",
    "print(f'Gradient Boosting Reg Score : {cross_val_score(GDBT, train_X, train_Y, cv=5).mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業2\n",
    "* 對照範例的日週期效果，試著參考投影片完成年週期與周週期的特徵 (也可以用你自己想到的方式)，  \n",
    "看看結果會比範例中的結果更好或更差?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_minute</th>\n",
       "      <th>pickup_second</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>week</th>\n",
       "      <th>day_cycle</th>\n",
       "      <th>year_cycle</th>\n",
       "      <th>week_cycle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-21 23:54:10</td>\n",
       "      <td>-73.990580</td>\n",
       "      <td>40.761071</td>\n",
       "      <td>-73.981128</td>\n",
       "      <td>40.758634</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>-0.025450</td>\n",
       "      <td>0.777146</td>\n",
       "      <td>-0.965926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-03 10:42:03</td>\n",
       "      <td>-73.988403</td>\n",
       "      <td>40.723431</td>\n",
       "      <td>-73.989647</td>\n",
       "      <td>40.741695</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333601</td>\n",
       "      <td>0.453990</td>\n",
       "      <td>0.955573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-16 18:58:58</td>\n",
       "      <td>-74.015785</td>\n",
       "      <td>40.715110</td>\n",
       "      <td>-74.012029</td>\n",
       "      <td>40.707888</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.967083</td>\n",
       "      <td>-0.275637</td>\n",
       "      <td>-0.222521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-13 16:10:54</td>\n",
       "      <td>-73.977322</td>\n",
       "      <td>40.787275</td>\n",
       "      <td>-73.958030</td>\n",
       "      <td>40.778838</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.888817</td>\n",
       "      <td>-0.974370</td>\n",
       "      <td>-0.930874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-12 03:25:56</td>\n",
       "      <td>-73.989683</td>\n",
       "      <td>40.729717</td>\n",
       "      <td>-73.982490</td>\n",
       "      <td>40.761887</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>0.782427</td>\n",
       "      <td>-0.978148</td>\n",
       "      <td>0.330279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pickup_datetime  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0 2011-10-21 23:54:10        -73.990580        40.761071         -73.981128   \n",
       "1 2015-02-03 10:42:03        -73.988403        40.723431         -73.989647   \n",
       "2 2014-03-16 18:58:58        -74.015785        40.715110         -74.012029   \n",
       "3 2009-06-13 16:10:54        -73.977322        40.787275         -73.958030   \n",
       "4 2014-06-12 03:25:56        -73.989683        40.729717         -73.982490   \n",
       "\n",
       "   dropoff_latitude  passenger_count  pickup_year  pickup_month  pickup_day  \\\n",
       "0         40.758634                2         2011            10          21   \n",
       "1         40.741695                1         2015             2           3   \n",
       "2         40.707888                2         2014             3          16   \n",
       "3         40.778838                3         2009             6          13   \n",
       "4         40.761887                3         2014             6          12   \n",
       "\n",
       "   pickup_hour  pickup_minute  pickup_second  dayofweek  week  day_cycle  \\\n",
       "0           23             54             10          4    42  -0.025450   \n",
       "1           10             42              3          1     6   0.333601   \n",
       "2           18             58             58          6    11  -0.967083   \n",
       "3           16             10             54          5    24  -0.888817   \n",
       "4            3             25             56          3    24   0.782427   \n",
       "\n",
       "   year_cycle  week_cycle  \n",
       "0    0.777146   -0.965926  \n",
       "1    0.453990    0.955573  \n",
       "2   -0.275637   -0.222521  \n",
       "3   -0.974370   -0.930874  \n",
       "4   -0.978148    0.330279  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加上\"年週期\"與\"周週期\"特徵\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "#year cos((⽉/6 + ⽇/180 )π)\n",
    "df['year_cycle'] = df['pickup_month']/6 + df['pickup_day']/180\n",
    "df['year_cycle'] = df['year_cycle'].map(lambda x:math.cos(x*math.pi))\n",
    "#week sin((星期幾/3.5 + ⼩時/84 )π)\n",
    "df['week_cycle'] = df['dayofweek']/3.5 + df['pickup_hour']/84\n",
    "df['week_cycle'] = df['week_cycle'].map(lambda x:math.sin(x*math.pi))\n",
    "\n",
    "#day sin((⼩時/12 + 分/720 + 秒/43200 )π)\n",
    "#df['day_cycle'] = df['pickup_hour']/12 + df['pickup_minute']/720 + df['pickup_second']/43200\n",
    "#df['day_cycle'] = df['day_cycle'].map(lambda x:math.sin(x*math.pi))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Reg Score : 0.025923304097199208\n",
      "Gradient Boosting Reg Score : 0.7148357770975061\n"
     ]
    }
   ],
   "source": [
    "# 將結果使用線性迴歸 / 梯度提升樹分別看結果\n",
    "df_temp = df.drop(['pickup_datetime'] , axis=1)\n",
    "train_X = scaler.fit_transform(df_temp)\n",
    "print(f'Linear Reg Score : {cross_val_score(Linear, train_X, train_Y, cv=5).mean()}')\n",
    "print(f'Gradient Boosting Reg Score : {cross_val_score(GDBT, train_X, train_Y, cv=5).mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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