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anova
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{
"cells": [
{
"cell_type": "code",
without + with experiment 20230821
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"execution_count": 6,
anova
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>自变量</th>\n",
" <th>level</th>\n",
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" <th>系统恢复用时R1</th>\n",
" <th>产业-企业边累计扰乱次数R2</th>\n",
" <th>产业-企业边最大传导深度R3</th>\n",
" <th>产业-企业边断裂总数R4</th>\n",
anova
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
drop proactive + analysis
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" <th>15</th>\n",
" <td>新供应关系构成概率P7</td>\n",
anova
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" <td>低</td>\n",
drop proactive + analysis
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" <td>2.240</td>\n",
" <td>2.672</td>\n",
" <td>1.143</td>\n",
" <td>0.7640</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>16</th>\n",
" <td>新供应关系构成概率P7</td>\n",
anova
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" <td>中</td>\n",
drop proactive + analysis
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" <td>2.132</td>\n",
" <td>2.674</td>\n",
" <td>1.143</td>\n",
" <td>0.7859</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>17</th>\n",
" <td>新供应关系构成概率P7</td>\n",
anova
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" <td>高</td>\n",
drop proactive + analysis
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" <td>2.179</td>\n",
" <td>2.649</td>\n",
" <td>1.124</td>\n",
" <td>0.7575</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>9</th>\n",
" <td>是否已有连接偏好P4</td>\n",
" <td>不倾向</td>\n",
" <td>2.177</td>\n",
" <td>2.668</td>\n",
" <td>1.141</td>\n",
" <td>0.7804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>是否已有连接偏好P4</td>\n",
" <td>倾向</td>\n",
" <td>2.191</td>\n",
" <td>2.663</td>\n",
" <td>1.133</td>\n",
" <td>0.7579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>是否规模偏好P2</td>\n",
" <td>不倾向</td>\n",
" <td>2.171</td>\n",
" <td>2.669</td>\n",
" <td>1.137</td>\n",
" <td>0.7726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>是否规模偏好P2</td>\n",
" <td>倾向</td>\n",
" <td>2.196</td>\n",
" <td>2.661</td>\n",
" <td>1.137</td>\n",
" <td>0.7657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>最大尝试时间步P8</td>\n",
anova
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" <td>低</td>\n",
drop proactive + analysis
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" <td>1.726</td>\n",
" <td>2.646</td>\n",
" <td>1.123</td>\n",
" <td>0.7782</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>19</th>\n",
" <td>最大尝试时间步P8</td>\n",
anova
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" <td>中</td>\n",
drop proactive + analysis
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" <td>2.186</td>\n",
" <td>2.682</td>\n",
" <td>1.144</td>\n",
" <td>0.7599</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>20</th>\n",
" <td>最大尝试时间步P8</td>\n",
anova
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" <td>高</td>\n",
drop proactive + analysis
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" <td>2.640</td>\n",
" <td>2.667</td>\n",
" <td>1.143</td>\n",
" <td>0.7694</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>7</th>\n",
" <td>最大尝试次数P3</td>\n",
" <td>低</td>\n",
" <td>2.286</td>\n",
" <td>2.691</td>\n",
" <td>1.154</td>\n",
" <td>0.8254</td>\n",
doc paper
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>6</th>\n",
" <td>最大尝试次数P3</td>\n",
" <td>中</td>\n",
" <td>2.124</td>\n",
" <td>2.652</td>\n",
" <td>1.127</td>\n",
" <td>0.7431</td>\n",
doc paper
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>5</th>\n",
" <td>最大尝试次数P3</td>\n",
" <td>高</td>\n",
" <td>2.141</td>\n",
" <td>2.652</td>\n",
" <td>1.130</td>\n",
" <td>0.7390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
doc paper
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" <td>采购策略P1</td>\n",
anova
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" <td>单供应商</td>\n",
drop proactive + analysis
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" <td>2.261</td>\n",
" <td>2.519</td>\n",
" <td>1.121</td>\n",
" <td>0.7919</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>1</th>\n",
doc paper
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" <td>采购策略P1</td>\n",
anova
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" <td>双供应商</td>\n",
drop proactive + analysis
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" <td>2.146</td>\n",
" <td>2.650</td>\n",
" <td>1.133</td>\n",
" <td>0.7615</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>0</th>\n",
doc paper
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" <td>采购策略P1</td>\n",
anova
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" <td>三供应商</td>\n",
drop proactive + analysis
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" <td>2.144</td>\n",
" <td>2.826</td>\n",
" <td>1.156</td>\n",
" <td>0.7541</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>10</th>\n",
" <td>额外产能分布P5</td>\n",
" <td>均匀分布</td>\n",
" <td>2.316</td>\n",
" <td>2.681</td>\n",
" <td>1.158</td>\n",
" <td>0.8403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>额外产能分布P5</td>\n",
" <td>正态分布</td>\n",
" <td>2.052</td>\n",
" <td>2.650</td>\n",
" <td>1.115</td>\n",
" <td>0.6980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>额外产能分布参数P6</td>\n",
anova
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" <td>低</td>\n",
drop proactive + analysis
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" <td>2.436</td>\n",
" <td>2.705</td>\n",
" <td>1.171</td>\n",
" <td>0.9121</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>13</th>\n",
" <td>额外产能分布参数P6</td>\n",
anova
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" <td>中</td>\n",
drop proactive + analysis
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" <td>2.202</td>\n",
" <td>2.666</td>\n",
" <td>1.142</td>\n",
" <td>0.7655</td>\n",
anova
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" </tr>\n",
" <tr>\n",
drop proactive + analysis
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" <th>12</th>\n",
" <td>额外产能分布参数P6</td>\n",
anova
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" <td>高</td>\n",
drop proactive + analysis
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" <td>1.914</td>\n",
" <td>2.624</td>\n",
" <td>1.098</td>\n",
" <td>0.6299</td>\n",
anova
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
drop proactive + analysis
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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",
"15 新供应关系构成概率P7 低 2.240 2.672 1.143 0.7640\n",
"16 新供应关系构成概率P7 中 2.132 2.674 1.143 0.7859\n",
"17 新供应关系构成概率P7 高 2.179 2.649 1.124 0.7575\n",
"9 是否已有连接偏好P4 不倾向 2.177 2.668 1.141 0.7804\n",
"8 是否已有连接偏好P4 倾向 2.191 2.663 1.133 0.7579\n",
"4 是否规模偏好P2 不倾向 2.171 2.669 1.137 0.7726\n",
"3 是否规模偏好P2 倾向 2.196 2.661 1.137 0.7657\n",
"18 最大尝试时间步P8 低 1.726 2.646 1.123 0.7782\n",
"19 最大尝试时间步P8 中 2.186 2.682 1.144 0.7599\n",
"20 最大尝试时间步P8 高 2.640 2.667 1.143 0.7694\n",
"7 最大尝试次数P3 低 2.286 2.691 1.154 0.8254\n",
"6 最大尝试次数P3 中 2.124 2.652 1.127 0.7431\n",
"5 最大尝试次数P3 高 2.141 2.652 1.130 0.7390\n",
"2 采购策略P1 单供应商 2.261 2.519 1.121 0.7919\n",
"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"
anova
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]
},
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"execution_count": 6,
anova
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"config = {\"figure.dpi\": 300,\n",
" \"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",
" '低':0,\n",
" '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",
"df"
]
},
{
"cell_type": "code",
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"execution_count": 11,
anova
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"metadata": {},
"outputs": [
drop proactive + analysis
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{
without + with experiment 20230821
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"name": "stderr",
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"output_type": "stream",
"text": [
without + with experiment 20230821
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"C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\1808291987.py:10: UserWarning: \n",
"The markers list has fewer values (1) than needed (3) and will cycle, which may produce an uninterpretable plot.\n",
" ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
drop proactive + analysis
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]
},
anova
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{
"data": {
without + with experiment 20230821
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"image/png": "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
anova
2023-07-08 16:23:44 +08:00
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"x_name = '采购策略P1'\n",
without + with experiment 20230821
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"y_choose = [0, 1, 2]\n",
"y_prop = pd.DataFrame({'y_name': ['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",
" 'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
" 'palette': sns.color_palette(\"deep\")[0:4]})\n",
"df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
"df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
anova
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"sns.set_theme(style=\"whitegrid\", rc=config)\n",
without + with experiment 20230821
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"ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
" markers=['o'],\n",
" dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
" palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
" legend='brief')\n",
"ax.set_title(x_name)\n",
"for item in df_x.groupby('响应变量'):\n",
" for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
" ax.text(x, y+0.05, f'{m:.2f}')"
anova
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]
},
{
"cell_type": "code",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 12,
anova
2023-07-08 16:23:44 +08:00
"metadata": {},
"outputs": [
{
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\1224603408.py:10: UserWarning: \n",
"The markers list has fewer values (1) than needed (4) and will cycle, which may produce an uninterpretable plot.\n",
" ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
]
anova
2023-07-08 16:23:44 +08:00
},
{
"data": {
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"image/png": "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
anova
2023-07-08 16:23:44 +08:00
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"x_name = '最大尝试次数P3'\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"y_choose = [0, 1, 2, 3]\n",
"y_prop = pd.DataFrame({'y_name': ['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",
" 'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
" 'palette': sns.color_palette(\"deep\")[0:4]})\n",
"df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
"df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
anova
2023-07-08 16:23:44 +08:00
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
" markers=['o'],\n",
" dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
" palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
" legend='brief')\n",
"ax.set_title(x_name)\n",
"for item in df_x.groupby('响应变量'):\n",
" for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
" ax.text(x, y+0.05, f'{m:.2f}')"
anova
2023-07-08 16:23:44 +08:00
]
},
{
"cell_type": "code",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 13,
anova
2023-07-08 16:23:44 +08:00
"metadata": {},
"outputs": [
{
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\2665207915.py:10: UserWarning: \n",
"The markers list has fewer values (1) than needed (4) and will cycle, which may produce an uninterpretable plot.\n",
" ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
]
anova
2023-07-08 16:23:44 +08:00
},
{
"data": {
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"image/png": "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
anova
2023-07-08 16:23:44 +08:00
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"x_name = '额外产能分布P5'\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"y_choose = [0, 1, 2, 3]\n",
"y_prop = pd.DataFrame({'y_name': ['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",
" 'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
" 'palette': sns.color_palette(\"deep\")[0:4]})\n",
"df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
"df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
anova
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"sns.set_theme(style=\"whitegrid\", rc=config)\n",
without + with experiment 20230821
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"ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
" markers=['o'],\n",
" dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
" palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
" legend='brief')\n",
"ax.set_title(x_name)\n",
"for item in df_x.groupby('响应变量'):\n",
" for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
" ax.text(x, y+0.05, f'{m:.2f}')"
anova
2023-07-08 16:23:44 +08:00
]
},
{
"cell_type": "code",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 14,
anova
2023-07-08 16:23:44 +08:00
"metadata": {},
"outputs": [
{
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\759130329.py:10: UserWarning: \n",
"The markers list has fewer values (1) than needed (4) and will cycle, which may produce an uninterpretable plot.\n",
" ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
]
anova
2023-07-08 16:23:44 +08:00
},
{
"data": {
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"image/png": "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
anova
2023-07-08 16:23:44 +08:00
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"x_name = '额外产能分布参数P6'\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"y_choose = [0, 1, 2, 3]\n",
"y_prop = pd.DataFrame({'y_name': ['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",
" 'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
" 'palette': sns.color_palette(\"deep\")[0:4]})\n",
"df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
"df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
anova
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"sns.set_theme(style=\"whitegrid\", rc=config)\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
" markers=['o'],\n",
" dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
" palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
" legend='brief')\n",
"ax.set_title(x_name)\n",
"for item in df_x.groupby('响应变量'):\n",
" for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
" ax.text(x, y+0.05, f'{m:.2f}')"
anova
2023-07-08 16:23:44 +08:00
]
},
{
"cell_type": "code",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 17,
anova
2023-07-08 16:23:44 +08:00
"metadata": {},
"outputs": [
{
"data": {
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"image/png": "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
anova
2023-07-08 16:23:44 +08:00
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"x_name = '新供应关系构成概率P7'\n",
without + with experiment 20230821
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"y_choose =[1]\n",
"y_prop = pd.DataFrame({'y_name': ['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",
" 'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
" 'palette': sns.color_palette(\"deep\")[0:4]})\n",
"df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
"df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
" markers=['o'],\n",
" dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
" palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
" legend='brief')\n",
"ax.set_title(x_name)\n",
"for item in df_x.groupby('响应变量'):\n",
" for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
" ax.text(x, y+0.0005, f'{m:.2f}')"
drop proactive + analysis
2023-08-19 10:35:48 +08:00
]
},
{
"cell_type": "code",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 18,
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"metadata": {},
"outputs": [
{
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\1838672856.py:10: UserWarning: \n",
"The markers list has fewer values (1) than needed (3) and will cycle, which may produce an uninterpretable plot.\n",
" ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
]
drop proactive + analysis
2023-08-19 10:35:48 +08:00
},
{
"data": {
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"image/png": "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
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_name = '最大尝试时间步P8'\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"y_choose=[0,1,2]\n",
"y_prop = pd.DataFrame({'y_name': ['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",
" 'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
" 'palette': sns.color_palette(\"deep\")[0:4]})\n",
"df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
"df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
anova
2023-07-08 16:23:44 +08:00
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
" markers=['o'],\n",
" dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
" palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
" legend='brief')\n",
"ax.set_title(x_name)\n",
"for item in df_x.groupby('响应变量'):\n",
" for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
" ax.text(x, y+0.05, f'{m:.2f}')"
anova
2023-07-08 16:23:44 +08:00
]
}
],
"metadata": {
"kernelspec": {
"display_name": "iiabm_py3.8.8",
"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": "3.8.8"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}