msb-public-java
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AiLearning-Theory-Applying
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Update 建模预测-checkpoint.ipynb

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benjas 5 years ago
parent 82d3b12d47
commit f8a8126ae1
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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
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@ -2,15 +2,25 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 17,
"metadata": {}, "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": [ "source": [
"import pandas as pd\n", "import pandas as pd\n",
"import numpy as np\n", "import numpy as np\n",
"from scipy.interpolate import UnivariateSpline\n", "from scipy.interpolate import UnivariateSpline\n",
"from sklearn import linear_model\n", "from sklearn import linear_model\n",
"import xgboost as xgb\n", "import xgboost as xgb\n",
"from sklearn.externals import joblib\n",
"from sklearn.utils import *" "from sklearn.utils import *"
] ]
}, },
@ -27,7 +37,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -55,7 +65,7 @@
" <th>date</th>\n", " <th>date</th>\n",
" <th>time_interval_begin</th>\n", " <th>time_interval_begin</th>\n",
" <th>travel_time</th>\n", " <th>travel_time</th>\n",
" <th>imputationl</th>\n", " <th>imputation1</th>\n",
" <th>lagging1</th>\n", " <th>lagging1</th>\n",
" <th>lagging2</th>\n", " <th>lagging2</th>\n",
" <th>lagging3</th>\n", " <th>lagging3</th>\n",
@ -290,7 +300,7 @@
"</div>" "</div>"
], ],
"text/plain": [ "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", "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", "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", "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" "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": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# 读取处理好的特征数据\n", "# 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()" "df.head()"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -320,7 +330,7 @@
"['lagging5', 'lagging4', 'lagging3', 'lagging2', 'lagging1']" "['lagging5', 'lagging4', 'lagging3', 'lagging2', 'lagging1']"
] ]
}, },
"execution_count": 5, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -334,7 +344,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -348,7 +358,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -357,14 +367,14 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "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", "cell_type": "code",
"execution_count": 9, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -403,7 +413,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -411,6 +421,106 @@
"grid = ParameterGrid(params_grid)" "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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -420,11 +530,11 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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", "def fit_evaluate(df, df_test, params):\n",
" df = df.dropna()\n", " df = df.dropna()\n",
" X = df[train_feature].values\n", " X = df[train_feature].values\n",
@ -446,12 +556,12 @@
" min_child_weight=params['min_child_weight'],\n", " min_child_weight=params['min_child_weight'],\n",
" reg_alpha=params['reg_alpha'])\n", " reg_alpha=params['reg_alpha'])\n",
" regressor.fit(X_train,y_train,verbose=False,early_stopping_rounds=10,eval_set=eval_set)\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", "cell_type": "code",
"execution_count": 15, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -523,17 +633,593 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====================================\n",
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"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",
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"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",
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"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": {}, "metadata": {},
"outputs": [], "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", "cell_type": "code",
"execution_count": null, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "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": { "metadata": {

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