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AiLearning-Theory-Applying
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Add 道路同行时间预测

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benjas 4 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/.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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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 数据展示"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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 ultis import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"道路通行时间："
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>link_ID</th>\n",
" <th>date</th>\n",
" <th>time_interval</th>\n",
" <th>travel_time</th>\n",
" </tr>\n",
" </thead>\n",
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" <th>0</th>\n",
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" <td>2017-05-06</td>\n",
" <td>[2017-05-06 11:04:00,2017-05-06 11:06:00)</td>\n",
" <td>3.0</td>\n",
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" <th>1</th>\n",
" <td>3377906289434510514</td>\n",
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" <td>2017-05-06</td>\n",
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" <td>35.2</td>\n",
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" <td>2017-05-06</td>\n",
" <td>[2017-05-06 17:46:00,2017-05-06 17:48:00)</td>\n",
" <td>26.2</td>\n",
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" <td>3377906287934510514</td>\n",
" <td>2017-05-06</td>\n",
" <td>[2017-05-06 10:52:00,2017-05-06 10:54:00)</td>\n",
" <td>10.4</td>\n",
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" </tbody>\n",
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"text/plain": [
" link_ID date time_interval \\\n",
"0 4377906283422600514 2017-05-06 [2017-05-06 11:04:00,2017-05-06 11:06:00) \n",
"1 3377906289434510514 2017-05-06 [2017-05-06 10:42:00,2017-05-06 10:44:00) \n",
"2 3377906285934510514 2017-05-06 [2017-05-06 11:56:00,2017-05-06 11:58:00) \n",
"3 3377906285934510514 2017-05-06 [2017-05-06 17:46:00,2017-05-06 17:48:00) \n",
"4 3377906287934510514 2017-05-06 [2017-05-06 10:52:00,2017-05-06 10:54:00) \n",
"\n",
" travel_time \n",
"0 3.0 \n",
"1 1.0 \n",
"2 35.2 \n",
"3 26.2 \n",
"4 10.4 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('new_gy_contest_traveltime_training_data_second.txt',delimiter=';',dtype={'link_ID':object})\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"time_interval时间间隔，两分钟为单位\n",
"\n",
"travel_time平均通行时间"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"道理长宽情况："
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>link_ID</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>link_class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4377906289869500514</td>\n",
" <td>57</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4377906284594800514</td>\n",
" <td>247</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4377906289425800514</td>\n",
" <td>194</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4377906284525800514</td>\n",
" <td>839</td>\n",
" <td>3</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
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" <td>55</td>\n",
" <td>12</td>\n",
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"text/plain": [
" link_ID length width link_class\n",
"0 4377906289869500514 57 3 1\n",
"1 4377906284594800514 247 9 1\n",
"2 4377906289425800514 194 3 1\n",
"3 4377906284525800514 839 3 1\n",
"4 4377906284422600514 55 12 1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"link_df = pd.read_csv('gy_contest_link_info.txt',delimiter=';',dtype={'link_ID':object})\n",
"link_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"length长度 width宽度 link_class类别"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"道路之间连接情况："
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>link_ID</th>\n",
" <th>in_links</th>\n",
" <th>out_links</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
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" <th>4</th>\n",
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" <td>2</td>\n",
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"text/plain": [
" link_ID in_links out_links\n",
"0 4377906289869500514 1 1\n",
"1 4377906284594800514 1 1\n",
"2 4377906289425800514 1 1\n",
"3 4377906284525800514 1 1\n",
"4 4377906284422600514 2 1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"link_tops = pd.read_csv('gy_contest_link_top_update.txt',delimiter=',',dtype={'link_ID':object})\n",
"link_tops.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 任务：预测未来一个月平均通行结果，每两分钟一次\n",
"回归任务\n",
"\n",
"构建时间序列，基于前几天或者前几十天的数据预测"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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