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AiLearning-Theory-Applying
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Update 基于回归与插值完成序列特征

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pull/2/head
benjas 4 years ago
parent f6ccd1407c
commit 9165da8698
2 changed files with 1389 additions and 44 deletions
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1250
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/道路通行时间预测-checkpoint.ipynb
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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/道路通行时间预测.ipynb
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@ -2708,9 +2708,188 @@
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"text/plain": [
" link_ID time_interval_begin date travel_time \\\n",
"0 3377906280028510514 2017-03-01 06:00:00 2017-03-01 NaN \n",
"1 3377906280028510514 2017-03-01 06:02:00 2017-03-01 NaN \n",
"2 3377906280028510514 2017-03-01 06:04:00 2017-03-01 NaN \n",
"3 3377906280028510514 2017-03-01 06:06:00 2017-03-01 NaN \n",
"4 3377906280028510514 2017-03-01 06:08:00 2017-03-01 NaN \n",
"\n",
" travel_time2 date_trend minute_trend length width link_num area \\\n",
"0 NaN 1.960745 -0.252121 48 3 2 144 \n",
"1 NaN 1.960745 -0.246743 48 3 2 144 \n",
"2 NaN 1.960745 -0.241428 48 3 2 144 \n",
"3 NaN 1.960745 -0.236176 48 3 2 144 \n",
"4 NaN 1.960745 -0.230986 48 3 2 144 \n",
"\n",
" vacation minute hour day week_day mouth link_ID_en \n",
"0 0.0 0 6 1 3 3 75 \n",
"1 0.0 2 6 1 3 3 75 \n",
"2 0.0 4 6 1 3 3 75 \n",
"3 0.0 6 6 1 3 3 75 \n",
"4 0.0 8 6 1 3 3 75 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 通行时间长的编号大\n",
"sorted_link = np.sort(df['link_ID_en'].unique())\n",

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