2023-07-08 16:23:44 +08:00
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"cells": [
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{
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"cell_type": "code",
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2023-08-19 10:35:48 +08:00
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"execution_count": 14,
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2023-07-08 16:23:44 +08:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>自变量</th>\n",
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" <th>level</th>\n",
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2023-08-19 10:35:48 +08:00
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" <th>系统恢复用时R1</th>\n",
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" <th>产业-企业边累计扰乱次数R2</th>\n",
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" <th>产业-企业边最大传导深度R3</th>\n",
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" <th>产业-企业边断裂总数R4</th>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>15</th>\n",
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" <td>新供应关系构成概率P7</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>低</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.240</td>\n",
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" <td>2.672</td>\n",
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" <td>1.143</td>\n",
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" <td>0.7640</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>16</th>\n",
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" <td>新供应关系构成概率P7</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>中</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.132</td>\n",
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" <td>2.674</td>\n",
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" <td>1.143</td>\n",
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" <td>0.7859</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>17</th>\n",
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" <td>新供应关系构成概率P7</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>高</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.179</td>\n",
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" <td>2.649</td>\n",
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" <td>1.124</td>\n",
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" <td>0.7575</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>9</th>\n",
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" <td>是否已有连接偏好P4</td>\n",
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" <td>不倾向</td>\n",
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" <td>2.177</td>\n",
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" <td>2.668</td>\n",
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" <td>1.141</td>\n",
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" <td>0.7804</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>是否已有连接偏好P4</td>\n",
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" <td>倾向</td>\n",
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" <td>2.191</td>\n",
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" <td>2.663</td>\n",
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" <td>1.133</td>\n",
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" <td>0.7579</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>是否规模偏好P2</td>\n",
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" <td>不倾向</td>\n",
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" <td>2.171</td>\n",
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" <td>2.669</td>\n",
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" <td>1.137</td>\n",
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" <td>0.7726</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>是否规模偏好P2</td>\n",
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" <td>倾向</td>\n",
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" <td>2.196</td>\n",
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" <td>2.661</td>\n",
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" <td>1.137</td>\n",
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" <td>0.7657</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>18</th>\n",
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" <td>最大尝试时间步P8</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>低</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>1.726</td>\n",
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" <td>2.646</td>\n",
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" <td>1.123</td>\n",
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" <td>0.7782</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>19</th>\n",
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" <td>最大尝试时间步P8</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>中</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.186</td>\n",
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" <td>2.682</td>\n",
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" <td>1.144</td>\n",
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" <td>0.7599</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>20</th>\n",
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" <td>最大尝试时间步P8</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>高</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.640</td>\n",
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" <td>2.667</td>\n",
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" <td>1.143</td>\n",
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" <td>0.7694</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>7</th>\n",
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" <td>最大尝试次数P3</td>\n",
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" <td>低</td>\n",
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" <td>2.286</td>\n",
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" <td>2.691</td>\n",
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" <td>1.154</td>\n",
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" <td>0.8254</td>\n",
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2023-07-10 14:47:23 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>6</th>\n",
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" <td>最大尝试次数P3</td>\n",
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" <td>中</td>\n",
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" <td>2.124</td>\n",
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" <td>2.652</td>\n",
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" <td>1.127</td>\n",
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" <td>0.7431</td>\n",
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2023-07-10 14:47:23 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>5</th>\n",
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" <td>最大尝试次数P3</td>\n",
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" <td>高</td>\n",
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" <td>2.141</td>\n",
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" <td>2.652</td>\n",
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" <td>1.130</td>\n",
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" <td>0.7390</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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2023-07-10 14:47:23 +08:00
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" <td>采购策略P1</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>单供应商</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.261</td>\n",
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" <td>2.519</td>\n",
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" <td>1.121</td>\n",
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" <td>0.7919</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>1</th>\n",
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2023-07-10 14:47:23 +08:00
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" <td>采购策略P1</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>双供应商</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.146</td>\n",
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" <td>2.650</td>\n",
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" <td>1.133</td>\n",
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" <td>0.7615</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>0</th>\n",
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2023-07-10 14:47:23 +08:00
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" <td>采购策略P1</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>三供应商</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.144</td>\n",
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" <td>2.826</td>\n",
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" <td>1.156</td>\n",
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" <td>0.7541</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>10</th>\n",
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" <td>额外产能分布P5</td>\n",
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" <td>均匀分布</td>\n",
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" <td>2.316</td>\n",
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" <td>2.681</td>\n",
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" <td>1.158</td>\n",
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" <td>0.8403</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>额外产能分布P5</td>\n",
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" <td>正态分布</td>\n",
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" <td>2.052</td>\n",
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" <td>2.650</td>\n",
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" <td>1.115</td>\n",
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" <td>0.6980</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>额外产能分布参数P6</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>低</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.436</td>\n",
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" <td>2.705</td>\n",
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" <td>1.171</td>\n",
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" <td>0.9121</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>13</th>\n",
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" <td>额外产能分布参数P6</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>中</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>2.202</td>\n",
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" <td>2.666</td>\n",
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" <td>1.142</td>\n",
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" <td>0.7655</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" <tr>\n",
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2023-08-19 10:35:48 +08:00
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" <th>12</th>\n",
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" <td>额外产能分布参数P6</td>\n",
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2023-07-08 16:23:44 +08:00
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" <td>高</td>\n",
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2023-08-19 10:35:48 +08:00
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" <td>1.914</td>\n",
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" <td>2.624</td>\n",
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" <td>1.098</td>\n",
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" <td>0.6299</td>\n",
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2023-07-08 16:23:44 +08:00
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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2023-08-19 10:35:48 +08:00
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" 自变量 level 系统恢复用时R1 产业-企业边累计扰乱次数R2 产业-企业边最大传导深度R3 产业-企业边断裂总数R4\n",
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"15 新供应关系构成概率P7 低 2.240 2.672 1.143 0.7640\n",
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"16 新供应关系构成概率P7 中 2.132 2.674 1.143 0.7859\n",
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"17 新供应关系构成概率P7 高 2.179 2.649 1.124 0.7575\n",
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"9 是否已有连接偏好P4 不倾向 2.177 2.668 1.141 0.7804\n",
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"8 是否已有连接偏好P4 倾向 2.191 2.663 1.133 0.7579\n",
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"4 是否规模偏好P2 不倾向 2.171 2.669 1.137 0.7726\n",
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"3 是否规模偏好P2 倾向 2.196 2.661 1.137 0.7657\n",
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"18 最大尝试时间步P8 低 1.726 2.646 1.123 0.7782\n",
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"19 最大尝试时间步P8 中 2.186 2.682 1.144 0.7599\n",
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"20 最大尝试时间步P8 高 2.640 2.667 1.143 0.7694\n",
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"7 最大尝试次数P3 低 2.286 2.691 1.154 0.8254\n",
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"6 最大尝试次数P3 中 2.124 2.652 1.127 0.7431\n",
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"5 最大尝试次数P3 高 2.141 2.652 1.130 0.7390\n",
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"2 采购策略P1 单供应商 2.261 2.519 1.121 0.7919\n",
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"1 采购策略P1 双供应商 2.146 2.650 1.133 0.7615\n",
|
|
|
|
|
"0 采购策略P1 三供应商 2.144 2.826 1.156 0.7541\n",
|
|
|
|
|
"10 额外产能分布P5 均匀分布 2.316 2.681 1.158 0.8403\n",
|
|
|
|
|
"11 额外产能分布P5 正态分布 2.052 2.650 1.115 0.6980\n",
|
|
|
|
|
"14 额外产能分布参数P6 低 2.436 2.705 1.171 0.9121\n",
|
|
|
|
|
"13 额外产能分布参数P6 中 2.202 2.666 1.142 0.7655\n",
|
|
|
|
|
"12 额外产能分布参数P6 高 1.914 2.624 1.098 0.6299"
|
2023-07-08 16:23:44 +08:00
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|
]
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},
|
2023-08-19 10:35:48 +08:00
|
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"execution_count": 14,
|
2023-07-08 16:23:44 +08:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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|
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"source": [
|
|
|
|
|
"import pandas as pd\n",
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
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"import seaborn as sns\n",
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|
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"\n",
|
|
|
|
|
"config = {\"figure.dpi\": 300,\n",
|
|
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" \"font.family\": 'serif',\n",
|
|
|
|
|
" \"font.serif\": ['SimSun']}\n",
|
|
|
|
|
"df = pd.read_csv('analysis/anova_visualization.csv', encoding='utf-8-sig')\n",
|
|
|
|
|
"df['sort_index'] = df['level'].map({'不倾向':0,\n",
|
|
|
|
|
" '倾向':1,\n",
|
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|
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|
" '低':0,\n",
|
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|
|
|
" '中':1,\n",
|
|
|
|
|
" '高':2,\n",
|
|
|
|
|
" '单供应商':0,\n",
|
|
|
|
|
" '双供应商':1,\n",
|
|
|
|
|
" '三供应商':2})\n",
|
|
|
|
|
"df.sort_values(['自变量', 'sort_index'], inplace=True)\n",
|
|
|
|
|
"df.drop(columns='sort_index', inplace=True)\n",
|
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"df"
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|
]
|
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|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 22,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
2023-08-19 10:35:48 +08:00
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
" 水平 响应变量 均值\n",
|
|
|
|
|
"0 单供应商 系统恢复用时R1 2.261\n",
|
|
|
|
|
"1 单供应商 产业-企业边累计扰乱次数R2 2.519\n",
|
|
|
|
|
"2 单供应商 产业-企业边最大传导深度R3 1.121\n",
|
|
|
|
|
"4 双供应商 系统恢复用时R1 2.146\n",
|
|
|
|
|
"5 双供应商 产业-企业边累计扰乱次数R2 2.650\n",
|
|
|
|
|
"6 双供应商 产业-企业边最大传导深度R3 1.133\n",
|
|
|
|
|
"8 三供应商 系统恢复用时R1 2.144\n",
|
|
|
|
|
"9 三供应商 产业-企业边累计扰乱次数R2 2.826\n",
|
|
|
|
|
"10 三供应商 产业-企业边最大传导深度R3 1.156\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
2023-07-08 16:23:44 +08:00
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"Text(0.5, 1.0, '采购策略P1')"
|
2023-07-08 16:23:44 +08:00
|
|
|
]
|
|
|
|
|
},
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 22,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2023-08-19 10:35:48 +08:00
|
|
|
"image/png": "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
|
2023-07-08 16:23:44 +08:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1920x1440 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"x_name = '采购策略P1'\n",
|
2023-07-08 16:23:44 +08:00
|
|
|
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
|
2023-08-19 10:35:48 +08:00
|
|
|
"df_x = df_x.loc[df_x['响应变量'].isin(['系统恢复用时R1','产业-企业边累计扰乱次数R2','产业-企业边最大传导深度R3'])]\n",
|
|
|
|
|
"print(df_x)\n",
|
2023-07-08 16:23:44 +08:00
|
|
|
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
|
|
|
|
|
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
|
|
|
|
|
"ax.set_title(x_name)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 16,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"Text(0.5, 1.0, '最大尝试次数P3')"
|
2023-07-08 16:23:44 +08:00
|
|
|
]
|
|
|
|
|
},
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 16,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
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|
|
|
{
|
|
|
|
|
"data": {
|
2023-08-19 10:35:48 +08:00
|
|
|
"image/png": "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
|
2023-07-08 16:23:44 +08:00
|
|
|
"text/plain": [
|
|
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|
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"<Figure size 1920x1440 with 1 Axes>"
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|
]
|
|
|
|
|
},
|
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|
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|
"metadata": {},
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|
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"output_type": "display_data"
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}
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|
|
],
|
|
|
|
|
"source": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"x_name = '最大尝试次数P3'\n",
|
2023-07-08 16:23:44 +08:00
|
|
|
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
|
|
|
|
|
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
|
|
|
|
|
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
|
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|
"ax.set_title(x_name)"
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]
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},
|
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{
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|
"cell_type": "code",
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 17,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"Text(0.5, 1.0, '额外产能分布P5')"
|
2023-07-08 16:23:44 +08:00
|
|
|
]
|
|
|
|
|
},
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 17,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
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|
},
|
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|
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|
{
|
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|
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|
"data": {
|
2023-08-19 10:35:48 +08:00
|
|
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"image/png": "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
|
2023-07-08 16:23:44 +08:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1920x1440 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"x_name = '额外产能分布P5'\n",
|
2023-07-08 16:23:44 +08:00
|
|
|
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
|
|
|
|
|
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
|
|
|
|
|
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
|
|
|
|
|
"ax.set_title(x_name)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 18,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"Text(0.5, 1.0, '额外产能分布参数P6')"
|
2023-07-08 16:23:44 +08:00
|
|
|
]
|
|
|
|
|
},
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 18,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2023-08-19 10:35:48 +08:00
|
|
|
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|
2023-07-08 16:23:44 +08:00
|
|
|
"text/plain": [
|
|
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|
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"<Figure size 1920x1440 with 1 Axes>"
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]
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|
},
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"metadata": {},
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"output_type": "display_data"
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}
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|
|
],
|
|
|
|
|
"source": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"x_name = '额外产能分布参数P6'\n",
|
2023-07-08 16:23:44 +08:00
|
|
|
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
|
|
|
|
|
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
|
|
|
|
|
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
|
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|
|
"ax.set_title(x_name)"
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|
]
|
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|
},
|
|
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|
{
|
|
|
|
|
"cell_type": "code",
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 38,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"Text(0.5, 1.0, '新供应关系构成概率P7')"
|
2023-07-08 16:23:44 +08:00
|
|
|
]
|
|
|
|
|
},
|
2023-08-19 10:35:48 +08:00
|
|
|
"execution_count": 38,
|
2023-07-08 16:23:44 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
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|
{
|
|
|
|
|
"data": {
|
2023-08-19 10:35:48 +08:00
|
|
|
"image/png": "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
|
2023-07-08 16:23:44 +08:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1920x1440 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2023-08-19 10:35:48 +08:00
|
|
|
"x_name = '新供应关系构成概率P7'\n",
|
2023-07-08 16:23:44 +08:00
|
|
|
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
|
2023-08-19 10:35:48 +08:00
|
|
|
"df_x = df_x.loc[df_x['响应变量'].isin(['产业-企业边累计扰乱次数R2'])]\n",
|
|
|
|
|
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
|
|
|
|
|
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", palette=sns.color_palette([sns.color_palette(\"deep\")[1]]))\n",
|
|
|
|
|
"ax.set_title(x_name)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 36,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"Text(0.5, 1.0, '最大尝试时间步P8')"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 36,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
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"text/plain": [
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"<Figure size 1920x1440 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"x_name = '最大尝试时间步P8'\n",
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"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
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|
"df_x = df_x.loc[df_x['响应变量'].isin(['系统恢复用时R1','产业-企业边累计扰乱次数R2','产业-企业边最大传导深度R3'])]\n",
|
2023-07-08 16:23:44 +08:00
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"sns.set_theme(style=\"whitegrid\", rc=config)\n",
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"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
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"ax.set_title(x_name)"
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]
|
2023-08-19 10:35:48 +08:00
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
|
2023-07-08 16:23:44 +08:00
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}
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],
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"metadata": {
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"kernelspec": {
|
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"display_name": "iiabm_py3.8.8",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
|
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|
"name": "python",
|
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
|
