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AiLearning-Theory-Applying
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benjas 5 years ago
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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/建模预测.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"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.utils import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_rows',150)\n",
"pd.set_option('display.max_columns',500)\n",
"pd.set_option('display.width',1000)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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],
"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",
"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",
"3 3377906280028510514 2017-03-01 2017-03-01 06:06:00 1.676886 True 1.671675 1.664941 1.659311 NaN NaN 48 144 0.0 6.0 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",
"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,
"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.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['lagging5', 'lagging4', 'lagging3', 'lagging2', 'lagging1']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 时间序列特征\n",
"lagging = 5\n",
"lagging_feature = ['lagging%01d' % e for e in range(lagging, 0, -1)]\n",
"lagging_feature"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"base_feature = [x for x in df.columns.values.tolist() if x not in ['time_interval_begin',\n",
" 'link_ID','link_ID_int',\n",
" 'date','travel_time',\n",
" 'imputationl','minute_series',\n",
" 'area','hour_en',\n",
" 'day_of_week']]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"base_feature = [x for x in base_feature if x not in lagging_feature]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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"
]
}
],
"source": [
"train_feature = list(base_feature)\n",
"train_feature.extend(lagging_feature)\n",
"valid_feature = list(base_feature)\n",
"valid_feature.extend(['minute_series', 'travel_time'])\n",
"print(train_feature)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"xgboost训练参数："
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"params_grid = {\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": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import ParameterGrid\n",
"grid = ParameterGrid(params_grid)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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",
" 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','predicted']].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','predicted']].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','predicted']].to_csv(file1,mode='a',\n",
" header=False,\n",
" index=False,\n",
" sep=';')\n",
" else:\n",
" test_df[['link_ID','date','time_interval','predicted']].to_csv(file4,mode='a',\n",
" header=False,\n",
" index=False,\n",
" sep=';')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"训练模块"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"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",
" y = 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",
" df_test = df_test[valid_feature].values\n",
" valid_data = bucket_data(df_test)\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=False,early_stopping_rounds=10,eval_set=eval_set)\n",
" return regressor, cross_valid(regressor, valid_data, lagging=lagging), regressor.best_iteration,regressor.best_score"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def train(df, params, best, vis=False):\n",
" train1 = df.loc[df['time_interval_begin'] <= pd.to_datetime('2017-03-24')]\n",
" train2 = df.loc[\n",
" (df['time_interval_begin']>pd.to_datetime('2017-03-24'))&(\n",
" df['time_interval_begin'] <= pd.to_datetime('2017-04-18'))]\n",
" train3 = df.loc[\n",
" (df['time_interval_begin']>pd.to_datetime('2017-04-18'))&(\n",
" df['time_interval_begin'] <= pd.to_datetime('2017-05-12'))]\n",
" train4 = df.loc[\n",
" (df['time_interval_begin']>pd.to_datetime('2017-05-12'))&(\n",
" df['time_interval_begin'] <= pd.to_datetime('2017-06-06'))]\n",
" train5 = df.loc[\n",
" (df['time_interval_begin']>pd.to_datetime('2017-06-06'))&(\n",
" df['time_interval_begin'] <= pd.to_datetime('2017-06-30'))]\n",
" \n",
" regressor, loss1, best_iteration1,best_score1 = fit_evaluate(pd.concat([train1,\n",
" train2,\n",
" train3,\n",
" train4]),train5,\n",
" params)\n",
" print(best_iteration1,best_score1,loss1)\n",
" \n",
" regressor, loss2, best_iteration2,best_score2 = fit_evaluate(pd.concat([train1,\n",
" train2,\n",
" train3,\n",
" train5]),train4,\n",
" params) \n",
" print(best_iteration2,best_score2,loss2)\n",
" \n",
" regressor, loss3, best_iteration3,best_score3 = fit_evaluate(pd.concat([train1,\n",
" train2,\n",
" train4,\n",
" train5]),train3,\n",
" params) \n",
" print(best_iteration3,best_score3,loss3) \n",
"\n",
" regressor, loss4, best_iteration4,best_score4 = fit_evaluate(pd.concat([train1,\n",
" train3,\n",
" train4,\n",
" train5]),train2,\n",
" params) \n",
" print(best_iteration4,best_score4,loss4) \n",
"\n",
" regressor, loss5, best_iteration5,best_score5 = fit_evaluate(pd.concat([train2,\n",
" train3,\n",
" train4,\n",
" train5]),train1,\n",
" params)\n",
" print(best_iteration5,best_score5,loss5) \n",
" \n",
" loss = [loss1,loss2, loss3, loss4, loss5]\n",
" params['loss_std'] = np.std(loss)\n",
" params['loss'] = str(loss)\n",
" params['mean_loss'] = np.mean(loss)\n",
" params['n_estimators'] = str([best_iteration1, best_iteration2, best_iteration3,\n",
" best_iteration4, best_iteration5])\n",
" params['best_score'] = str([best_score1, best_score2, best_score3,\n",
" best_score4, best_score5])\n",
" \n",
" print(str(params))\n",
" if np.mean(loss) <= best:\n",
" best = np.mean(loss)\n",
" print('best with:' + str(params))\n",
" return best"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[22:33:32] 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",
"99 0.234149 0.08976782525031814\n",
"[22:38:24] 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",
"99 0.216157 0.22570086184516117\n",
"[22:44:51] 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"
]
}
],
"source": [
"best = 1\n",
"for params in grid:\n",
" best = train(df, params, best)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"source": [
"xgboost_submit(df, submit_params)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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