HaoYizhi
/
IIabm
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
IIabm/anova_visualization.ipynb

574 lines
1.3 MiB
Plaintext
Raw Permalink Normal View History

anova
2023-07-08 16:23:44 +08:00
{
"cells": [
{
"cell_type": "code",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 6,
anova
2023-07-08 16:23:44 +08:00
"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",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <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
2023-07-08 16:23:44 +08:00
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>15</th>\n",
" <td>新供应关系构成概率P7</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>低</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.240</td>\n",
" <td>2.672</td>\n",
" <td>1.143</td>\n",
" <td>0.7640</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>16</th>\n",
" <td>新供应关系构成概率P7</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>中</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.132</td>\n",
" <td>2.674</td>\n",
" <td>1.143</td>\n",
" <td>0.7859</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>17</th>\n",
" <td>新供应关系构成概率P7</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>高</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.179</td>\n",
" <td>2.649</td>\n",
" <td>1.124</td>\n",
" <td>0.7575</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <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
2023-07-08 16:23:44 +08:00
" <td>低</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>1.726</td>\n",
" <td>2.646</td>\n",
" <td>1.123</td>\n",
" <td>0.7782</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>19</th>\n",
" <td>最大尝试时间步P8</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>中</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.186</td>\n",
" <td>2.682</td>\n",
" <td>1.144</td>\n",
" <td>0.7599</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>20</th>\n",
" <td>最大尝试时间步P8</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>高</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.640</td>\n",
" <td>2.667</td>\n",
" <td>1.143</td>\n",
" <td>0.7694</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <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
2023-07-10 14:47:23 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <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
2023-07-10 14:47:23 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <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
2023-07-10 14:47:23 +08:00
" <td>采购策略P1</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>单供应商</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.261</td>\n",
" <td>2.519</td>\n",
" <td>1.121</td>\n",
" <td>0.7919</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>1</th>\n",
doc paper
2023-07-10 14:47:23 +08:00
" <td>采购策略P1</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>双供应商</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.146</td>\n",
" <td>2.650</td>\n",
" <td>1.133</td>\n",
" <td>0.7615</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>0</th>\n",
doc paper
2023-07-10 14:47:23 +08:00
" <td>采购策略P1</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>三供应商</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.144</td>\n",
" <td>2.826</td>\n",
" <td>1.156</td>\n",
" <td>0.7541</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <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
2023-07-08 16:23:44 +08:00
" <td>低</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.436</td>\n",
" <td>2.705</td>\n",
" <td>1.171</td>\n",
" <td>0.9121</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>13</th>\n",
" <td>额外产能分布参数P6</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>中</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>2.202</td>\n",
" <td>2.666</td>\n",
" <td>1.142</td>\n",
" <td>0.7655</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" <tr>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <th>12</th>\n",
" <td>额外产能分布参数P6</td>\n",
anova
2023-07-08 16:23:44 +08:00
" <td>高</td>\n",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" <td>1.914</td>\n",
" <td>2.624</td>\n",
" <td>1.098</td>\n",
" <td>0.6299</td>\n",
anova
2023-07-08 16:23:44 +08:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
drop proactive + analysis
2023-08-19 10:35:48 +08:00
" 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
2023-07-08 16:23:44 +08:00
]
},
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 6,
anova
2023-07-08 16:23:44 +08:00
"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",
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"execution_count": 11,
anova
2023-07-08 16:23:44 +08:00
"metadata": {},
"outputs": [
drop proactive + analysis
2023-08-19 10:35:48 +08:00
{
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"name": "stderr",
drop proactive + analysis
2023-08-19 10:35:48 +08:00
"output_type": "stream",
"text": [
without + with experiment 20230821
2023-08-22 21:04:34 +08:00
"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
2023-08-19 10:35:48 +08:00
]
},
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 = '采购策略P1'\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
]
},
{
"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
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": 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
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": 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
2023-08-22 21:04:34 +08:00
"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
}