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"import pandas as pd\n",
"import json \n",
"import numpy as np\n",
"import ast\n",
"from datetime import datetime\n",
"import plotly.graph_objs as go\n",
"from plotly.offline import plot\n",
"import plotly.offline as offline\n",
"from pandas.core.indexes import interval\n",
"import plotly.figure_factory as ff\n",
"pyolt=plot\n",
"import plotly.express as px\n",
"import math"
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"df = pd.read_excel('./data/data.xlsx') \n",
"df_junior = pd.read_excel('./data/junior.xlsx') \n",
"df_senior = pd.read_excel('./data/senior.xlsx') \n",
"data_entity = data_analysis(df)\n",
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"# s1=np.random.RandomState(12)\n",
"# #柯西分布\n",
"# x1=s1.standard_cauchy(200)-4\n",
"# #泊松分布\n",
"# x2=s1.uniform(1,10,200)\n",
"# #Gamma分布\n",
"# x3=s1.standard_gamma(3,200)+4\n",
"# #指数分布\n",
"# x4=s1.exponential(3,200)+8\n",
"x1 = list(data_entity.df.loc[:,'interval'])\n",
"x2 = list(data_entity_junior.df.loc[:,'interval'])\n",
"x3 = list(data_entity_senior.df.loc[:,'interval'])\n",
"layout={\"title\": \"学生用时分布\", \n",
" \"xaxis_title\": \"学生用时，单位秒\",\n",
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" }\n",
"\n",
"#数据组\n",
"hist_data=[x1,x2,x3]\n",
"\n",
"group_labels=['all','junior','senior']\n",
"\n",
"fig=ff.create_distplot(hist_data,group_labels,bin_size=100,histnorm = 'probability')\n",
"plot(fig,filename='./plot/时间分布直方图.html')\n",
"offline.iplot(fig) "
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},
"metadata": {},
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}
],
"source": [
"data1 = go.Bar(x = list(range(data_entity.problem_num)), y = [len(group) for group in data_entity.addition_list], name = 'all')\n",
"data2 = go.Bar(x = list(range(data_entity_junior.problem_num)), y = [len(group) for group in data_entity_junior.addition_list], name = 'junior') \n",
"data3 = go.Bar(x = list(range(data_entity_senior.problem_num)), y = [len(group) for group in data_entity_senior.addition_list], name = 'senior') \n",
"\n",
"\n",
"\n",
"layout={\"title\": \"不同题目的编码数量\", \n",
" \"xaxis_title\": \"题目编号\",\n",
" \"yaxis_title\": \"编码个数\",\n",
" # x轴坐标倾斜60度\n",
" \"xaxis\": {\"tickangle\": 60}\n",
" }\n",
"fig = go.Figure(data=[data1, data2, data3],layout=layout)\n",
"plot(fig,filename=\"./plot/plot_problem.html\",auto_open=False,image='png',image_height=800,image_width=1500)\n",
"offline.iplot(fig) "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "21722920",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>problem_num</th>\n",
" <th>accuracy</th>\n",
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" <th>8</th>\n",
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" <td>浇花（2）</td>\n",
" <td>0.699396</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>密码（1）</td>\n",
" <td>0.868580</td>\n",
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" <th>11</th>\n",
" <td>密码（2）</td>\n",
" <td>0.802115</td>\n",
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" <th>12</th>\n",
" <td>4进制编码（1）</td>\n",
" <td>0.944109</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>4进制编码（2）</td>\n",
" <td>0.861027</td>\n",
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" <th>14</th>\n",
" <td>滚筒（1）</td>\n",
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" <td>0.898792</td>\n",
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" <th>18</th>\n",
" <td>对应的形状（2）</td>\n",
" <td>0.537764</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" problem_num accuracy\n",
"0 小松鼠跳柱子（1） 0.824773\n",
"1 小松鼠跳柱子（2） 0.851964\n",
"2 填充小石子（1） 0.938066\n",
"3 填充小石子（2） 0.870091\n",
"4 嵌套的矩形（1） 0.930514\n",
"5 嵌套的矩形（2） 0.883686\n",
"6 模式的复制（1） 0.929003\n",
"7 模式的复制（2） 0.817221\n",
"8 浇花（1） 0.886707\n",
"9 浇花（2） 0.699396\n",
"10 密码（1） 0.868580\n",
"11 密码（2） 0.802115\n",
"12 4进制编码（1） 0.944109\n",
"13 4进制编码（2） 0.861027\n",
"14 滚筒（1） 0.867069\n",
"15 供水系统（1） 0.898792\n",
"16 供水系统（2） 0.879154\n",
"17 对应的形状（1） 0.823263\n",
"18 对应的形状（2） 0.537764"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_entity.success_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "07726293",
"metadata": {},
"outputs": [
{
"data": {
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" 0.5580524344569289,\n",
" 0.7153558052434457,\n",
" 0.5955056179775281,\n",
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" 0.7265917602996255,\n",
" 0.7265917602996255,\n",
" 0.8127340823970037,\n",
" 0.7528089887640449,\n",
" 0.6441947565543071,\n",
" 0.3146067415730337]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(data_entity_junior.success_df['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b1211516",
"metadata": {},
"outputs": [
{
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" <tr>\n",
" <th>0</th>\n",
" <td>小松鼠跳柱子（1）</td>\n",
" <td>0.865823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <th>3</th>\n",
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" <td>密码（1）</td>\n",
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" <tr>\n",
" <th>11</th>\n",
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" <th>12</th>\n",
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" <th>13</th>\n",
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" <th>14</th>\n",
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" <tr>\n",
" <th>15</th>\n",
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" <tr>\n",
" <th>16</th>\n",
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" <td>0.964557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>对应的形状（1）</td>\n",
" <td>0.944304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>对应的形状（2）</td>\n",
" <td>0.688608</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" problem_num accuracy\n",
"0 小松鼠跳柱子（1） 0.865823\n",
"1 小松鼠跳柱子（2） 0.878481\n",
"2 填充小石子（1） 0.984810\n",
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"4 嵌套的矩形（1） 0.987342\n",
"5 嵌套的矩形（2） 0.969620\n",
"6 模式的复制（1） 0.974684\n",
"7 模式的复制（2） 0.898734\n",
"8 浇花（1） 0.954430\n",
"9 浇花（2） 0.794937\n",
"10 密码（1） 0.972152\n",
"11 密码（2） 0.941772\n",
"12 4进制编码（1） 0.982278\n",
"13 4进制编码（2） 0.951899\n",
"14 滚筒（1） 0.962025\n",
"15 供水系统（1） 0.956962\n",
"16 供水系统（2） 0.964557\n",
"17 对应的形状（1） 0.944304\n",
"18 对应的形状（2） 0.688608"
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]
},
"metadata": {},
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}
],
"source": [
"data1 = go.Bar(x = data_entity.problem_num_list, y = list(data_entity.success_df['accuracy']), name = 'all')\n",
"data2 = go.Bar(x = data_entity_junior.problem_num_list, y = list(data_entity_junior.success_df['accuracy']), name = 'junior') \n",
"data3 = go.Bar(x = data_entity_senior.problem_num_list, y = list(data_entity_senior.success_df['accuracy']), name = 'senior') \n",
"\n",
"\n",
"\n",
"layout={\"title\": \"不同题目的正确率\", \n",
" \"xaxis_title\": \"题目编号\",\n",
" \"yaxis_title\": \"正确率\",\n",
" # x轴坐标倾斜60度\n",
" \"xaxis\": {\"tickangle\": 20}\n",
" }\n",
"fig = go.Figure(data=[data1, data2, data3],layout=layout)\n",
"plot(fig,filename=\"./plot/plot_problem_accuracy.html\",auto_open=False,image='png',image_height=800,image_width=1500)\n",
"offline.iplot(fig) "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3654a147",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Bar({\n",
" 'name': 'all',\n",
" 'x': [小松鼠跳柱子（1）, 小松鼠跳柱子（2）, 填充小石子（1）, 填充小石子（2）, 嵌套的矩形（1）, 嵌套的矩形（2）, 模式的复制（1）,\n",
" 模式的复制（2）, 浇花（1）, 浇花（2）, 密码（1）, 密码（2）, 4进制编码（1）, 4进制编码（2）, 滚筒（1）, 供水系统（1）,\n",
" 供水系统（2）, 对应的形状（1）, 对应的形状（2）],\n",
" 'y': [0.824773413897281, 0.851963746223565, 0.9380664652567976,\n",
" 0.8700906344410876, 0.9305135951661632, 0.8836858006042296,\n",
" 0.9290030211480362, 0.8172205438066465, 0.8867069486404834,\n",
" 0.6993957703927492, 0.8685800604229608, 0.8021148036253777,\n",
" 0.9441087613293051, 0.8610271903323263, 0.8670694864048338,\n",
" 0.8987915407854985, 0.879154078549849, 0.823262839879154,\n",
" 0.5377643504531722]\n",
"})"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f5164379",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"662"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_entity.row_num"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ae703f20",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"267"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_entity_junior.row_num"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a167e1a3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"395"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_entity_senior.row_num"
]
},
{
"cell_type": "markdown",
"id": "8f501f64",
"metadata": {},
"source": [
"#### 第10题（index=9）正确答案分布\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d4c8582f",
"metadata": {},
"outputs": [
{
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"config": {
"linkText": "Export to plot.ly",
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{
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"y": [
0.5917602996254682,
0.13857677902621723,
0.0149812734082397
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},
{
"name": "senior",
"type": "bar",
"x": [
"['02_09', '05_06']",
"['02_05', '06_09']",
"['02_06', '05_09']"
],
"y": [
0.8151898734177215,
0.07848101265822785,
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"var gd = document.getElementById('bddd02ef-fcb0-45a1-b64b-3278a627ed22');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x=[str(['2_16', '6_15', '12_14']),str(['2_15', '6_16', '12_14']),str(['2_12', '6_14', '15_16'])\\\n",
" ,str(['2_12', '6_15', '14_16']),str(['2_14', '6_12', '15_16']),str(['2_16', '6_15', '12_14'])\\\n",
" ,str(['2_16', '6_14', '12_15']),str(['2_15', '6_14', '12_16']),str(['2_14', '6_16', '12_15'])\\\n",
" ,str(['2_15', '6_12', '14_16']),str(['2_6', '12_14', '15_16']),str(['2_6', '12_16', '14_15'])\\\n",
" ,str(['2_6', '12_15', '14_16']),str(['2_16', '6_12', '14_15']),str(['2_12', '6_16', '14_15'])]\n",
"data1 = go.Bar(x = x, y = np.array([225,60,51\\\n",
" ,29,25,33\\\n",
" ,15,15,8\\\n",
" ,7,3,2\\\n",
" ,0,0,0])/data_entity.row_num, name = 'all')\n",
"data2 = go.Bar(x = x, y = np.array([63,19,26\\\n",
" ,4,15,7\\\n",
" ,4,6,2\\\n",
" ,2,0,0\\\n",
" ,0,0,0])/data_entity_junior.row_num, name = 'junior') \n",
"data3 = go.Bar(x = x, y = np.array([162,41,25\\\n",
" ,25,10,15\\\n",
" ,11,9,6\\\n",
" ,5,3,2\\\n",
" ,0,0,0])/data_entity_senior.row_num, name = 'senior') \n",
"\n",
"\n",
"\n",
"layout={\"title\": \"第11题（index=10）的正确答案分布，浇花（2）\", \n",
" \"xaxis_title\": \"答案编码\",\n",
" \"xaxis_range\": [0,6000],\n",
" \"yaxis_title\": \"分布率\",\n",
" # x轴坐标倾斜60度\n",
" \"xaxis\": {\"tickangle\": 60}\n",
" }\n",
"fig = go.Figure(data=[data1, data2, data3],layout=layout)\n",
"plot(fig,filename=\"./plot/plot_problem_浇花（2）_accuracy.html\",auto_open=False,image='png',image_height=800,image_width=1500)\n",
"offline.iplot(fig) "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "00eaaaab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.1, 0.2, 0.3]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(np.array([1,2,3])/10)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c9cd9826",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['220', '002', '020', '202', '022', '000', '222', '200']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### plot index =18 \n",
"df_18 = pd.read_excel('./output/default/18_count.xlsx')\n",
"verify_list = list(pd.read_excel('./output/default/18_count.xlsx').iloc[:,1])\n",
"not_in_list = []\n",
"for i in range(8):\n",
" if int(bin(i)[2:].replace('1','2')) not in verify_list:\n",
" not_in_list.append(int(bin(i)[2:].replace('1','2')))\n",
"# print(str(int(bin(i)[2:].replace('1','2'))).zfill(6))\n",
"defult_pd = list(pd.read_excel('./output/default/18_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"x = [str(int(float)).zfill(3) for float in list(defult_pd.loc[:,'list'])]\n",
"x += not_in_list\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ba94516f",
"metadata": {},
"outputs": [],
"source": [
"defult_pd = list(pd.read_excel('./output/default/18_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"defult_pd = pd.DataFrame(index = [str(int(float)).zfill(3) for float in list(defult_pd.loc[:,'list'])],data={'count':list(defult_pd.loc[:,'count']),'success':list(defult_pd.loc[:,'success'])})\n",
"\n",
"\n",
"junior_pd = list(pd.read_excel('./output/junior/18_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"junior_pd = pd.DataFrame(index = [str(int(float)).zfill(3) for float in list(junior_pd.loc[:,'list'])],data={'count':list(junior_pd.loc[:,'count']),'success':list(junior_pd.loc[:,'success'])})\n",
"\n",
"\n",
"senior_pd = list(pd.read_excel('./output/senior/18_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"senior_pd = pd.DataFrame(index = [str(int(float)).zfill(3) for float in list(senior_pd.loc[:,'list'])],data={'count':list(senior_pd.loc[:,'count']),'success':list(senior_pd.loc[:,'success'])})\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3aed74f5",
"metadata": {},
"outputs": [],
"source": [
"y1 =[]\n",
"y2 = []\n",
"y3 = []\n",
"for ans in x:\n",
" if ans in list(defult_pd.index):\n",
" y1.append(defult_pd.loc[ans,'count'])\n",
" else:\n",
" y1.append(0)\n",
"for ans in x:\n",
" if ans in list(junior_pd.index):\n",
" y2.append(junior_pd.loc[ans,'count'])\n",
" else:\n",
" y2.append(0)\n",
" \n",
"for ans in x:\n",
" if ans in list(senior_pd.index):\n",
" y3.append(senior_pd.loc[ans,'count'])\n",
" else:\n",
" y3.append(0)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "6fb7959a",
"metadata": {},
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],
"source": [
"#### plot index 18\n",
"\n",
"\n",
"data1 = go.Bar(x = x, y = np.array(y1)/data_entity.row_num, name = 'all')\n",
"data2 = go.Bar(x = x, y = np.array(y2)/data_entity_junior.row_num, name = 'junior') \n",
"data3 = go.Bar(x = x, y = np.array(y3)/data_entity_senior.row_num, name = 'senior') \n",
"\n",
"\n",
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" \"yaxis_title\": \"分布率\",\n",
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" \"xaxis\": {\"tickangle\": 60}\n",
" }\n",
"fig = go.Figure(data=[data1, data2, data3],layout=layout)\n",
"plot(fig,filename=\"./plot/plot_problem_供水系统（1）_accuracy.html\",auto_open=False,image='png',image_height=800,image_width=1500)\n",
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},
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"cell_type": "code",
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"id": "57a7440b",
"metadata": {},
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"source": [
"df_19 = pd.read_excel('./output/default/19_count.xlsx')\n",
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"defult_pd = list(pd.read_excel('./output/default/19_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
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"junior_pd = list(pd.read_excel('./output/junior/19_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"junior_pd = pd.DataFrame(index = [str(int(float)).zfill(6) for float in list(junior_pd.loc[:,'list'])],data={'count':list(junior_pd.loc[:,'count']),'success':list(junior_pd.loc[:,'success'])})\n",
"\n",
"\n",
"senior_pd = list(pd.read_excel('./output/senior/19_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"senior_pd = pd.DataFrame(index = [str(int(float)).zfill(6) for float in list(senior_pd.loc[:,'list'])],data={'count':list(senior_pd.loc[:,'count']),'success':list(senior_pd.loc[:,'success'])})\n"
]
},
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"cell_type": "code",
"execution_count": 23,
"id": "a7a3c223",
"metadata": {},
"outputs": [],
"source": [
"y1 =[]\n",
"y2 = []\n",
"y3 = []\n",
"for ans in x:\n",
" if ans in list(defult_pd.index):\n",
" y1.append(defult_pd.loc[ans,'count'])\n",
" else:\n",
" y1.append(0)\n",
"for ans in x:\n",
" if ans in list(junior_pd.index):\n",
" y2.append(junior_pd.loc[ans,'count'])\n",
" else:\n",
" y2.append(0)\n",
" \n",
"for ans in x:\n",
" if ans in list(senior_pd.index):\n",
" y3.append(senior_pd.loc[ans,'count'])\n",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#### plot index 19\n",
"\n",
"\n",
"data1 = go.Bar(x = x, y = np.array(y1)/data_entity.row_num, name = 'all')\n",
"data2 = go.Bar(x = x, y = np.array(y2)/data_entity_junior.row_num, name = 'junior') \n",
"data3 = go.Bar(x = x, y = np.array(y3)/data_entity_senior.row_num, name = 'senior') \n",
"\n",
"\n",
"\n",
"layout={\"title\": \"第17题（index=19）的正确答案分布，供水系统（2）\", \n",
" \"xaxis_title\": \"答案编码\",\n",
" \"yaxis_title\": \"分布率\",\n",
" # x轴坐标倾斜60度\n",
" \"xaxis\": {\"tickangle\": 60}\n",
" }\n",
"fig = go.Figure(data=[data1, data2, data3],layout=layout)\n",
"plot(fig,filename=\"./plot/plot_problem_供水系统（2）_accuracy.html\",auto_open=False,image='png',image_height=800,image_width=1500)\n",
"offline.iplot(fig) "
]
},
{
"cell_type": "markdown",
"id": "51c48047",
"metadata": {},
"source": [
"#### 寻找第19题所有的正确答案"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "5c2b66b1",
"metadata": {},
"outputs": [],
"source": [
"# 所有可能的五角星（0,a）和三角形（1,b）组合\n",
"seq_list = []\n",
"for i in range(8):\n",
" for j in range(int(math.pow(2,i+1))):\n",
" temp=str(bin(j))[2:].zfill(i+1).replace('0','a')\n",
" temp = temp.replace('1', 'b')\n",
" seq_list.append(temp)\n",
"# 所有可能的长方形（1）和圆形（0）组合\n",
"trans_list = []\n",
"for i in range(3):\n",
" for j in range(int(math.pow(2,i+1))):\n",
" trans_list.append(str(bin(j))[2:].zfill(i+1))\n",
"# 正确序列 \n",
"verify_str = '10100010010'"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "acb9cbdb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[['0011010', '10', '0'], ['1100101', '0', '10']]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"right_ans = []\n",
"cnt = 0\n",
"for seq in seq_list:\n",
" for star in trans_list:\n",
" for trian in trans_list:\n",
" cnt +=1\n",
" if seq.replace('a', star).replace('b', trian) == verify_str:\n",
" right_ans.append([seq.replace('a','0').replace('b','1'), star, trian])\n",
"right_ans"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "1a09f3d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['[[1, 1, 0, 0, 1, 0, 1], [[0], [1, 0]]]',\n",
" '[[0, 0, 1, 1, 0, 1, 0], [[1, 0], [0]]]']"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### plot index =22 \n",
"\n",
"defult_pd = list(pd.read_excel('./output/default/22_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"x = [str_ for str_ in list(defult_pd.loc[:,'list'])]\n",
"x "
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "71b530ec",
"metadata": {},
"outputs": [],
"source": [
"defult_pd = list(pd.read_excel('./output/default/22_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"defult_pd = pd.DataFrame(index = [str_ for str_ in list(defult_pd.loc[:,'list'])],data={'count':list(defult_pd.loc[:,'count']),'success':list(defult_pd.loc[:,'success'])})\n",
"\n",
"\n",
"junior_pd = list(pd.read_excel('./output/junior/22_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"junior_pd = pd.DataFrame(index = [str_ for str_ in list(junior_pd.loc[:,'list'])],data={'count':list(junior_pd.loc[:,'count']),'success':list(junior_pd.loc[:,'success'])})\n",
"\n",
"\n",
"senior_pd = list(pd.read_excel('./output/senior/22_count.xlsx').groupby('success'))[1][1].sort_values(by = ['count'], ascending = False)\n",
"senior_pd = pd.DataFrame(index = [str_ for str_ in list(senior_pd.loc[:,'list'])],data={'count':list(senior_pd.loc[:,'count']),'success':list(senior_pd.loc[:,'success'])})\n"
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" \n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data1 = go.Bar(x = x, y = np.array(y1)/data_entity.row_num, name = 'all')\n",
"data2 = go.Bar(x = x, y = np.array(y2)/data_entity_junior.row_num, name = 'junior') \n",
"data3 = go.Bar(x = x, y = np.array(y3)/data_entity_senior.row_num, name = 'senior') \n",
"\n",
"\n",
"\n",
"layout={\"title\": \"第19题（index=22）的正确答案分布，对应的形状（2）\", \n",
" \"xaxis_title\": \"答案编码\",\n",
" \"yaxis_title\": \"分布率\",\n",
" # x轴坐标倾斜60度\n",
" \"xaxis\": {\"tickangle\": 20}\n",
" }\n",
"fig = go.Figure(data=[data1, data2, data3],layout=layout)\n",
"plot(fig,filename=\"./plot/plot_problem_对应的形状（2）_accuracy.html\",auto_open=False,image='png',image_height=800,image_width=1500)\n",
"offline.iplot(fig) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60f5387c",
"metadata": {},
"outputs": [],
"source": []
}
],
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"name": "python3"
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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