diff --git a/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/.ipynb_checkpoints/建模预测-checkpoint.ipynb b/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/.ipynb_checkpoints/建模预测-checkpoint.ipynb
index 9f83756..dc38cc5 100644
--- a/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/.ipynb_checkpoints/建模预测-checkpoint.ipynb
+++ b/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/.ipynb_checkpoints/建模预测-checkpoint.ipynb
@@ -2,15 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+      "  warnings.warn(msg, category=DeprecationWarning)\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "from scipy.interpolate import UnivariateSpline\n",
     "from sklearn import linear_model\n",
     "import xgboost as xgb\n",
+    "from sklearn.externals import joblib\n",
     "from sklearn.utils import *"
    ]
   },
@@ -27,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -55,7 +65,7 @@
        "      <th>date</th>\n",
        "      <th>time_interval_begin</th>\n",
        "      <th>travel_time</th>\n",
-       "      <th>imputationl</th>\n",
+       "      <th>imputation1</th>\n",
        "      <th>lagging1</th>\n",
        "      <th>lagging2</th>\n",
        "      <th>lagging3</th>\n",
@@ -290,7 +300,7 @@
        "</div>"
       ],
       "text/plain": [
-       "               link_ID        date time_interval_begin  travel_time  imputationl  lagging1  lagging2  lagging3  lagging4  lagging5  length  area  vacation  minute_series  day_of_week  day_of_week_en  hour_en  week_hour_1.0,1.0  week_hour_1.0,2.0  week_hour_1.0,3.0  week_hour_2.0,1.0  week_hour_2.0,2.0  week_hour_2.0,3.0  week_hour_3.0,1.0  week_hour_3.0,2.0  week_hour_3.0,3.0  links_num_2  links_num_3  links_num_4  links_num_5  width_3  width_6  width_9  width_12  width_15  link_ID_en\n",
+       "               link_ID        date time_interval_begin  travel_time  imputation1  lagging1  lagging2  lagging3  lagging4  lagging5  length  area  vacation  minute_series  day_of_week  day_of_week_en  hour_en  week_hour_1.0,1.0  week_hour_1.0,2.0  week_hour_1.0,3.0  week_hour_2.0,1.0  week_hour_2.0,2.0  week_hour_2.0,3.0  week_hour_3.0,1.0  week_hour_3.0,2.0  week_hour_3.0,3.0  links_num_2  links_num_3  links_num_4  links_num_5  width_3  width_6  width_9  width_12  width_15  link_ID_en\n",
        "0  3377906280028510514  2017-03-01 2017-03-01 06:00:00     1.659311         True       NaN       NaN       NaN       NaN       NaN      48   144       0.0            0.0            3             1.0      1.0                  1                  0                  0                  0                  0                  0                  0                  0                  0            1            0            0            0        1        0        0         0         0          47\n",
        "1  3377906280028510514  2017-03-01 2017-03-01 06:02:00     1.664941         True  1.659311       NaN       NaN       NaN       NaN      48   144       0.0            2.0            3             1.0      1.0                  1                  0                  0                  0                  0                  0                  0                  0                  0            1            0            0            0        1        0        0         0         0          47\n",
        "2  3377906280028510514  2017-03-01 2017-03-01 06:04:00     1.671675         True  1.664941  1.659311       NaN       NaN       NaN      48   144       0.0            4.0            3             1.0      1.0                  1                  0                  0                  0                  0                  0                  0                  0                  0            1            0            0            0        1        0        0         0         0          47\n",
@@ -298,20 +308,20 @@
        "4  3377906280028510514  2017-03-01 2017-03-01 06:08:00     1.682314         True  1.676886  1.671675  1.664941  1.659311       NaN      48   144       0.0            8.0            3             1.0      1.0                  1                  0                  0                  0                  0                  0                  0                  0                  0            1            0            0            0        1        0        0         0         0          47"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# 读取处理好的特征数据\n",
-    "df = pd.read_csv('com_trainning.txt', delimiter=';',parse_dates=['time_interval_begin'],dtype={'link_ID':object})\n",
+    "df = pd.read_csv('trainning.txt', delimiter=';',parse_dates=['time_interval_begin'],dtype={'link_ID':object})\n",
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -320,7 +330,7 @@
        "['lagging5', 'lagging4', 'lagging3', 'lagging2', 'lagging1']"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -357,14 +367,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['length', 'vacation', 'day_of_week_en', 'week_hour_1.0,1.0', 'week_hour_1.0,2.0', 'week_hour_1.0,3.0', 'week_hour_2.0,1.0', 'week_hour_2.0,2.0', 'week_hour_2.0,3.0', 'week_hour_3.0,1.0', 'week_hour_3.0,2.0', 'week_hour_3.0,3.0', 'links_num_2', 'links_num_3', 'links_num_4', 'links_num_5', 'width_3', 'width_6', 'width_9', 'width_12', 'width_15', 'link_ID_en', 'lagging5', 'lagging4', 'lagging3', 'lagging2', 'lagging1']\n"
+      "['imputation1', 'length', 'vacation', 'day_of_week_en', 'week_hour_1.0,1.0', 'week_hour_1.0,2.0', 'week_hour_1.0,3.0', 'week_hour_2.0,1.0', 'week_hour_2.0,2.0', 'week_hour_2.0,3.0', 'week_hour_3.0,1.0', 'week_hour_3.0,2.0', 'week_hour_3.0,3.0', 'links_num_2', 'links_num_3', 'links_num_4', 'links_num_5', 'width_3', 'width_6', 'width_9', 'width_12', 'width_15', 'link_ID_en', 'lagging5', 'lagging4', 'lagging3', 'lagging2', 'lagging1']\n"
      ]
     }
    ],
@@ -385,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,6 +421,106 @@
     "grid = ParameterGrid(params_grid)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bucket_data(lines):\n",
+    "    bucket = {}\n",
+    "    for line in lines:\n",
+    "        time_series = line[-2]\n",
+    "        bucket[time_series] = []\n",
+    "    for line in lines:\n",
+    "        time_series, y1 = line[-2:]\n",
+    "        line = np.delete(line, -2, axis=0)\n",
+    "        bucket[time_series].append(line)\n",
+    "    return bucket\n",
+    "\n",
+    "\n",
+    "def cross_valid(regressor, bucket, lagging):\n",
+    "    valid_loss = []\n",
+    "    last = [[] for i in range(len(bucket[list(bucket.keys())[0]]))]\n",
+    "    for time_series in sorted(bucket.keys(), key=float):\n",
+    "        if time_series >= 120:\n",
+    "            if int(time_series) in range(120,120+lagging*2,2):\n",
+    "                last = np.concatenate((last, np.array(bucket[time_series], dtype=float)[:, -1].reshape(-1,1)),axis=1)\n",
+    "            else:\n",
+    "                batch = np.array(bucket[time_series], dtype=float)\n",
+    "                y = batch[:,-1]\n",
+    "                batch = np.delete(batch, -1, axis=1)\n",
+    "                print(\"====================================\")\n",
+    "                print(batch.shape, last.shape, type(time_series))\n",
+    "                print(\"====================================\")\n",
+    "                batch = np.concatenate((batch, last), axis=1)\n",
+    "                y_pre = regressor.predict(batch)\n",
+    "                last = np.delete(last, 0, axis=1)\n",
+    "                last = np.concatenate((last, y_pre.reshape(-1,1)),axis=1)\n",
+    "                loss = np.mean(abs(np.expm1(y) - np.expm1(y_pre))/np.expm1(y))\n",
+    "                valid_loss.append(loss)\n",
+    "    return np.mean(valid_loss)\n",
+    "\n",
+    "\n",
+    "def mape_ln(y, d):\n",
+    "    c = d.get_label()\n",
+    "    result = np.sum(np.abs((np.expm1(y)-np.expm1(c))/np.expm1(c)))/len(c)\n",
+    "    return 'mape', result\n",
+    "\n",
+    "\n",
+    "def submission(train_feature, regressor,df, file1,file2,file3,file4):\n",
+    "    test_df = df.loc[((df['time_interval_begin'].dt.year==2017)&(df['time_interval_begin'].dt.month==7)\n",
+    "                     &(df['time_interval_begin'].dt.hour.isin([7,14,17]))\n",
+    "                      &(df['time_interval_begin'].dt.minute==58))].copy()\n",
+    "    test_df['lagging5'] = test_df['lagging4']\n",
+    "    test_df['lagging4'] = test_df['lagging3']\n",
+    "    test_df['lagging3'] = test_df['lagging2']\n",
+    "    test_df['lagging2'] = test_df['lagging1']\n",
+    "    test_df['lagging1'] = test_df['travel_time']\n",
+    "    with open(file1, 'w'):\n",
+    "        pass\n",
+    "    with open(file2, 'w'):\n",
+    "        pass\n",
+    "    with open(file3, 'w'):\n",
+    "        pass\n",
+    "    with open(file4, 'w'):\n",
+    "        pass\n",
+    "    for i in range(30):\n",
+    "        test_X = test_df[train_feature]\n",
+    "        y_prediction = regressor.predict(test_X.values)\n",
+    "        test_df['lagging5'] = test_df['lagging4']\n",
+    "        test_df['lagging4'] = test_df['lagging3']\n",
+    "        test_df['lagging3'] = test_df['lagging2']\n",
+    "        test_df['lagging2'] = test_df['lagging1']\n",
+    "        test_df['lagging1'] = y_prediction\n",
+    "        \n",
+    "        test_df['prediction'] = np.expm1(y_prediction)\n",
+    "        test_df['time_interval_begin'] = test_df['time_interval_begin']+pd.DateOffset(minutes=2)\n",
+    "        test_df['time_interval'] = test_df['time_interval_begin'].map(\n",
+    "            lambda x: '[' + str(x)+','+str(x+pd.DateOffset(minutes=2))+')')\n",
+    "        test_df.time_interval = test_df.time_interval.astype(object)\n",
+    "        if i < 7:\n",
+    "            test_df[['link_ID','date','time_interval','prediction']].to_csv(file1,mode='a',\n",
+    "                                                                          header=False,\n",
+    "                                                                          index=False,\n",
+    "                                                                          sep=';')\n",
+    "        elif (7 <= i) and (i < 14):\n",
+    "            test_df[['link_ID','date','time_interval','prediction']].to_csv(file2,mode='a',\n",
+    "                                                                          header=False,\n",
+    "                                                                          index=False,\n",
+    "                                                                          sep=';')\n",
+    "        elif (14 <= i) and (i < 22):\n",
+    "            test_df[['link_ID','date','time_interval','prediction']].to_csv(file1,mode='a',\n",
+    "                                                                          header=False,\n",
+    "                                                                          index=False,\n",
+    "                                                                          sep=';')\n",
+    "        else:\n",
+    "            test_df[['link_ID','date','time_interval','prediction']].to_csv(file4,mode='a',\n",
+    "                                                                          header=False,\n",
+    "                                                                          index=False,\n",
+    "                                                                          sep=';')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -420,11 +530,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import train_test_split\n",
     "def fit_evaluate(df, df_test, params):\n",
     "    df = df.dropna()\n",
     "    X = df[train_feature].values\n",
@@ -446,12 +556,12 @@
     "                             min_child_weight=params['min_child_weight'],\n",
     "                            reg_alpha=params['reg_alpha'])\n",
     "    regressor.fit(X_train,y_train,verbose=False,early_stopping_rounds=10,eval_set=eval_set)\n",
-    "    return regressor, cross_validate(regressor, valid_data, lagging=lagging), regressor.best_iteration,regressor.best_score"
+    "    return regressor, cross_valid(regressor, valid_data, lagging=lagging), regressor.best_iteration,regressor.best_score"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,17 +633,593 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[16:02:58] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "99 0.231729 0.09787323564628972\n",
+      "[16:08:54] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9900, 23) (9900, 5) <class 'float'>\n",
+      "====================================\n",
+      "99 0.211948 0.22588986922596394\n",
+      "[16:14:56] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "99 0.207832 0.269828138777363\n",
+      "[16:21:05] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9504, 23) (9504, 5) <class 'float'>\n",
+      "====================================\n",
+      "99 0.205743 0.27878690843594917\n",
+      "[16:27:05] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "====================================\n",
+      "(9108, 23) (9108, 5) <class 'float'>\n",
+      "====================================\n",
+      "99 0.206546 0.2825731100341743\n",
+      "{'colsample_bytree': 0.6, 'gamma': 0, 'learning_rate': 0.05, 'max_depth': 7, 'min_child_weight': 1, 'n_estimators': '[99, 99, 99, 99, 99]', 'reg_alpha': 2, 'subsample': 0.6, 'loss_std': 0.06956988861011186, 'loss': '[0.09787323564628972, 0.22588986922596394, 0.269828138777363, 0.27878690843594917, 0.2825731100341743]', 'mean_loss': 0.23099025242394805, 'best_score': '[0.231729, 0.211948, 0.207832, 0.205743, 0.206546]'}\n",
+      "best with:{'colsample_bytree': 0.6, 'gamma': 0, 'learning_rate': 0.05, 'max_depth': 7, 'min_child_weight': 1, 'n_estimators': '[99, 99, 99, 99, 99]', 'reg_alpha': 2, 'subsample': 0.6, 'loss_std': 0.06956988861011186, 'loss': '[0.09787323564628972, 0.22588986922596394, 0.269828138777363, 0.27878690843594917, 0.2825731100341743]', 'mean_loss': 0.23099025242394805, 'best_score': '[0.231729, 0.211948, 0.207832, 0.205743, 0.206546]'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "best = 1\n",
+    "for params in grid:\n",
+    "    best = train(df, params, best)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "submit_params = {\n",
+    "    'learning_rate':0.05,\n",
+    "    'n_estimators':100,\n",
+    "    'subsample':0.6,\n",
+    "    'colsample_bytree':0.6,\n",
+    "    'max_depth':7,\n",
+    "    'min_child_weight':1,\n",
+    "    'reg_alpha':2,\n",
+    "    'gamma':0\n",
+    "}"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def xgboost_submit(df, params):\n",
+    "    train_df = df.loc[df['time_interval_begin']<pd.to_datetime('2017-07-01')]\n",
+    "    \n",
+    "    train_df = train_df.dropna()\n",
+    "    X = train_df[train_feature].values\n",
+    "    y = train_df['travel_time'].values\n",
+    "    \n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
+    "    \n",
+    "    eval_set = [(X_test, y_test)]\n",
+    "    regressor = xgb.XGBRegressor(learning_rate=params['learning_rate'],\n",
+    "                             n_estimators=params['n_estimators'],\n",
+    "                            booster='gbtree', objective='reg:linear',\n",
+    "                            n_jobs=-1,subsample=params['subsample'],\n",
+    "                            colsample_bytree=params['colsample_bytree'],\n",
+    "                            random_state=0,max_depth=params['max_depth'],\n",
+    "                            gamma=params['gamma'],\n",
+    "                             min_child_weight=params['min_child_weight'],\n",
+    "                            reg_alpha=params['reg_alpha'])\n",
+    "    regressor.fit(X_train,y_train,verbose=True,early_stopping_rounds=10,\n",
+    "                  eval_metric=mape_ln,eval_set=eval_set)\n",
+    "    joblib.dump(regressor, 'model/xgbr.pkl')\n",
+    "    print(regressor)\n",
+    "    submission(train_feature, regressor,df, \n",
+    "               'submission/xgbrl.txt','submission/xgbr2.txt',\n",
+    "              'submission/xgbr3.txt','submission/xgbr4.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[17:47:53] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[0]\tvalidation_0-rmse:2.02747\tvalidation_0-mape:0.867894\n",
+      "Multiple eval metrics have been passed: 'validation_0-mape' will be used for early stopping.\n",
+      "\n",
+      "Will train until validation_0-mape hasn't improved in 10 rounds.\n",
+      "[1]\tvalidation_0-rmse:1.92734\tvalidation_0-mape:0.850712\n",
+      "[2]\tvalidation_0-rmse:1.83231\tvalidation_0-mape:0.83309\n",
+      "[3]\tvalidation_0-rmse:1.74204\tvalidation_0-mape:0.815116\n",
+      "[4]\tvalidation_0-rmse:1.65635\tvalidation_0-mape:0.796798\n",
+      "[5]\tvalidation_0-rmse:1.57575\tvalidation_0-mape:0.777818\n",
+      "[6]\tvalidation_0-rmse:1.49911\tvalidation_0-mape:0.758668\n",
+      "[7]\tvalidation_0-rmse:1.4258\tvalidation_0-mape:0.739614\n",
+      "[8]\tvalidation_0-rmse:1.35624\tvalidation_0-mape:0.720407\n",
+      "[9]\tvalidation_0-rmse:1.29025\tvalidation_0-mape:0.701088\n",
+      "[10]\tvalidation_0-rmse:1.22764\tvalidation_0-mape:0.681732\n",
+      "[11]\tvalidation_0-rmse:1.16884\tvalidation_0-mape:0.662053\n",
+      "[12]\tvalidation_0-rmse:1.11249\tvalidation_0-mape:0.642755\n",
+      "[13]\tvalidation_0-rmse:1.05955\tvalidation_0-mape:0.62329\n",
+      "[14]\tvalidation_0-rmse:1.00937\tvalidation_0-mape:0.603984\n",
+      "[15]\tvalidation_0-rmse:0.961824\tvalidation_0-mape:0.584883\n",
+      "[16]\tvalidation_0-rmse:0.916274\tvalidation_0-mape:0.56632\n",
+      "[17]\tvalidation_0-rmse:0.873956\tvalidation_0-mape:0.547599\n",
+      "[18]\tvalidation_0-rmse:0.833681\tvalidation_0-mape:0.529301\n",
+      "[19]\tvalidation_0-rmse:0.7949\tvalidation_0-mape:0.511751\n",
+      "[20]\tvalidation_0-rmse:0.758183\tvalidation_0-mape:0.494576\n",
+      "[21]\tvalidation_0-rmse:0.724009\tvalidation_0-mape:0.477586\n",
+      "[22]\tvalidation_0-rmse:0.69164\tvalidation_0-mape:0.46109\n",
+      "[23]\tvalidation_0-rmse:0.660537\tvalidation_0-mape:0.445265\n",
+      "[24]\tvalidation_0-rmse:0.631783\tvalidation_0-mape:0.429777\n",
+      "[25]\tvalidation_0-rmse:0.604043\tvalidation_0-mape:0.414922\n",
+      "[26]\tvalidation_0-rmse:0.5778\tvalidation_0-mape:0.400545\n",
+      "[27]\tvalidation_0-rmse:0.553042\tvalidation_0-mape:0.386658\n",
+      "[28]\tvalidation_0-rmse:0.530196\tvalidation_0-mape:0.373128\n",
+      "[29]\tvalidation_0-rmse:0.508169\tvalidation_0-mape:0.36026\n",
+      "[30]\tvalidation_0-rmse:0.487437\tvalidation_0-mape:0.3479\n",
+      "[31]\tvalidation_0-rmse:0.468318\tvalidation_0-mape:0.336023\n",
+      "[32]\tvalidation_0-rmse:0.450428\tvalidation_0-mape:0.324693\n",
+      "[33]\tvalidation_0-rmse:0.433156\tvalidation_0-mape:0.31389\n",
+      "[34]\tvalidation_0-rmse:0.416931\tvalidation_0-mape:0.303565\n",
+      "[35]\tvalidation_0-rmse:0.401729\tvalidation_0-mape:0.293732\n",
+      "[36]\tvalidation_0-rmse:0.387835\tvalidation_0-mape:0.284394\n",
+      "[37]\tvalidation_0-rmse:0.37451\tvalidation_0-mape:0.275516\n",
+      "[38]\tvalidation_0-rmse:0.362507\tvalidation_0-mape:0.267262\n",
+      "[39]\tvalidation_0-rmse:0.35083\tvalidation_0-mape:0.259271\n",
+      "[40]\tvalidation_0-rmse:0.339946\tvalidation_0-mape:0.251728\n",
+      "[41]\tvalidation_0-rmse:0.330117\tvalidation_0-mape:0.244688\n",
+      "[42]\tvalidation_0-rmse:0.321113\tvalidation_0-mape:0.238073\n",
+      "[43]\tvalidation_0-rmse:0.312602\tvalidation_0-mape:0.231857\n",
+      "[44]\tvalidation_0-rmse:0.304387\tvalidation_0-mape:0.225904\n",
+      "[45]\tvalidation_0-rmse:0.296781\tvalidation_0-mape:0.220307\n",
+      "[46]\tvalidation_0-rmse:0.290079\tvalidation_0-mape:0.21522\n",
+      "[47]\tvalidation_0-rmse:0.283964\tvalidation_0-mape:0.210527\n",
+      "[48]\tvalidation_0-rmse:0.278207\tvalidation_0-mape:0.206073\n",
+      "[49]\tvalidation_0-rmse:0.272557\tvalidation_0-mape:0.201743\n",
+      "[50]\tvalidation_0-rmse:0.267379\tvalidation_0-mape:0.197712\n",
+      "[51]\tvalidation_0-rmse:0.262584\tvalidation_0-mape:0.193932\n",
+      "[52]\tvalidation_0-rmse:0.258517\tvalidation_0-mape:0.190628\n",
+      "[53]\tvalidation_0-rmse:0.254437\tvalidation_0-mape:0.187326\n",
+      "[54]\tvalidation_0-rmse:0.250997\tvalidation_0-mape:0.184496\n",
+      "[55]\tvalidation_0-rmse:0.247837\tvalidation_0-mape:0.181864\n",
+      "[56]\tvalidation_0-rmse:0.244739\tvalidation_0-mape:0.179262\n",
+      "[57]\tvalidation_0-rmse:0.242088\tvalidation_0-mape:0.176975\n",
+      "[58]\tvalidation_0-rmse:0.239432\tvalidation_0-mape:0.174694\n",
+      "[59]\tvalidation_0-rmse:0.236956\tvalidation_0-mape:0.172544\n",
+      "[60]\tvalidation_0-rmse:0.23472\tvalidation_0-mape:0.170567\n",
+      "[61]\tvalidation_0-rmse:0.232673\tvalidation_0-mape:0.168749\n",
+      "[62]\tvalidation_0-rmse:0.230954\tvalidation_0-mape:0.167218\n",
+      "[63]\tvalidation_0-rmse:0.229382\tvalidation_0-mape:0.165806\n",
+      "[64]\tvalidation_0-rmse:0.227969\tvalidation_0-mape:0.164547\n",
+      "[65]\tvalidation_0-rmse:0.226601\tvalidation_0-mape:0.163268\n",
+      "[66]\tvalidation_0-rmse:0.22546\tvalidation_0-mape:0.162225\n",
+      "[67]\tvalidation_0-rmse:0.224374\tvalidation_0-mape:0.161249\n",
+      "[68]\tvalidation_0-rmse:0.223225\tvalidation_0-mape:0.160179\n",
+      "[69]\tvalidation_0-rmse:0.222167\tvalidation_0-mape:0.159196\n",
+      "[70]\tvalidation_0-rmse:0.221212\tvalidation_0-mape:0.158295\n",
+      "[71]\tvalidation_0-rmse:0.220377\tvalidation_0-mape:0.157483\n",
+      "[72]\tvalidation_0-rmse:0.219618\tvalidation_0-mape:0.156731\n",
+      "[73]\tvalidation_0-rmse:0.219029\tvalidation_0-mape:0.156166\n",
+      "[74]\tvalidation_0-rmse:0.218453\tvalidation_0-mape:0.155643\n",
+      "[75]\tvalidation_0-rmse:0.217805\tvalidation_0-mape:0.154995\n",
+      "[76]\tvalidation_0-rmse:0.217225\tvalidation_0-mape:0.154441\n",
+      "[77]\tvalidation_0-rmse:0.216778\tvalidation_0-mape:0.154038\n",
+      "[78]\tvalidation_0-rmse:0.21637\tvalidation_0-mape:0.153691\n",
+      "[79]\tvalidation_0-rmse:0.21592\tvalidation_0-mape:0.153251\n",
+      "[80]\tvalidation_0-rmse:0.215582\tvalidation_0-mape:0.152955\n",
+      "[81]\tvalidation_0-rmse:0.2153\tvalidation_0-mape:0.152717\n",
+      "[82]\tvalidation_0-rmse:0.215012\tvalidation_0-mape:0.152489\n",
+      "[83]\tvalidation_0-rmse:0.214672\tvalidation_0-mape:0.15214\n",
+      "[84]\tvalidation_0-rmse:0.214429\tvalidation_0-mape:0.151959\n",
+      "[85]\tvalidation_0-rmse:0.214134\tvalidation_0-mape:0.151676\n",
+      "[86]\tvalidation_0-rmse:0.213853\tvalidation_0-mape:0.151404\n",
+      "[87]\tvalidation_0-rmse:0.213573\tvalidation_0-mape:0.151144\n",
+      "[88]\tvalidation_0-rmse:0.213402\tvalidation_0-mape:0.151023\n",
+      "[89]\tvalidation_0-rmse:0.213262\tvalidation_0-mape:0.15091\n",
+      "[90]\tvalidation_0-rmse:0.21304\tvalidation_0-mape:0.150693\n",
+      "[91]\tvalidation_0-rmse:0.212849\tvalidation_0-mape:0.150517\n",
+      "[92]\tvalidation_0-rmse:0.212735\tvalidation_0-mape:0.150436\n",
+      "[93]\tvalidation_0-rmse:0.21258\tvalidation_0-mape:0.150284\n",
+      "[94]\tvalidation_0-rmse:0.212416\tvalidation_0-mape:0.150137\n",
+      "[95]\tvalidation_0-rmse:0.212256\tvalidation_0-mape:0.150004\n",
+      "[96]\tvalidation_0-rmse:0.212172\tvalidation_0-mape:0.149958\n",
+      "[97]\tvalidation_0-rmse:0.212046\tvalidation_0-mape:0.149827\n",
+      "[98]\tvalidation_0-rmse:0.211958\tvalidation_0-mape:0.149779\n",
+      "[99]\tvalidation_0-rmse:0.211834\tvalidation_0-mape:0.149654\n",
+      "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
+      "             colsample_bynode=1, colsample_bytree=0.6, gamma=0,\n",
+      "             importance_type='gain', learning_rate=0.05, max_delta_step=0,\n",
+      "             max_depth=7, min_child_weight=1, missing=None, n_estimators=100,\n",
+      "             n_jobs=-1, nthread=None, objective='reg:linear', random_state=0,\n",
+      "             reg_alpha=2, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
+      "             silent=None, subsample=0.6, verbosity=1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "xgboost_submit(df, submit_params)"
+   ]
   }
  ],
  "metadata": {