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ZZDataAnalysis
Commit 748af152 authored by whlviolin's avatar whlviolin
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update

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cieccproject/app.py
View file @ 748af152
......@@ -105,9 +105,9 @@ def getRSRList():
def getDEAList():
filepath = ""
if(request.args["type"]=='CCR'):
filepath ="D:\EarthDataMiner\project\ZXDataAnalysis\demo\AHP\DEA_CCR.json"
filepath ="data/DEA_CCR.json"
else:
filepath ="D:\EarthDataMiner\project\ZXDataAnalysis\demo\AHP\DEA_BBC.json"
filepath ="data/DEA_BBC.json"
with open(filepath, 'r', encoding='UTF-8') as f:
res = f.read()
print(type(res))
......@@ -135,7 +135,33 @@ def getDEAList():
dea1.summary()
res = dea1.to_json()
'''
@app.route("/getLassoRegression", methods=["GET", "POST"])
def getLassoRegression():
filepath = "data/lasso.json"
with open(filepath, 'r', encoding='UTF-8') as f:
res = f.read()
print(type(res))
print(res)
result = {
"code": 200,
"msg": "success",
"data": res
}
return jsonify(result)
@app.route("/getOLS", methods=["GET", "POST"])
def getOLS():
filepath = "data/OLS.json"
with open(filepath, 'r', encoding='UTF-8') as f:
res = f.read()
print(type(res))
print(res)
result = {
"code": 200,
"msg": "success",
"data": res
}
return jsonify(result)
if __name__ == "__main__":
app.run(debug=True, port=8001)
......
cieccproject/data/Lasso回归.csv 0 → 100644
View file @ 748af152
This diff is collapsed.
cieccproject/data/OLS.json 0 → 100644
View file @ 748af152
[{"name": "线性回归分析结果表", "value": {"n": 100, "intercept and coef name": ["常数", "Q14-做功", "Q15-日常锻炼情况", "Q16-吃零食情况", "Q17-跑步情况", "Q18-玩电脑游戏情况", "Q19-逛街情况", "Q20-散步情况", "Q21-夜宵情况"], "非标准化系数": {"B": [24.40130009421884, 0.19262153327942105, -0.7305684152690275, 0.420598696162354, -1.6929872427089208, 0.627345706064012, 0.370522584477988, -0.13466999546083103, 2.5479398189917184], "标准差": [5.22992754140875, 0.018578844457831933, 1.1328979166523963, 0.9051201010269613, 1.0149530242046778, 1.1004777146781501, 1.245295268653324, 0.930574770490926, 1.483982450286351]}, "标准化系数": {"Beta": [0.7213624553988669, -0.05843961687248622, 0.03653916652566736, -0.14717695630974797, 0.05029679332372762, 0.026198972405919452, -0.01158697670584876, 0.14784902329513774]}, "t": [4.665705194004663, 10.367788681185779, -0.6448669421405474, 0.4646882725122745, -1.6680449265477622, 0.570066706209937, 0.29753793642745885, -0.14471700687714292, 1.7169608835334036], "P": [1.053059053389287e-05, 4.362276639130463e-17, 0.5206358467941066, 0.6432646441232268, 0.09874445573562211, 0.5700372353378148, 0.7667340382284769, 0.8852543465439333, 0.08938816463174061], "VIF": [1.047943666155264, 1.7777784289836556, 1.338434090167112, 1.6852614558054069, 1.6851260044506124, 1.6783671176969834, 1.3877277255568892, 1.6051634360888174], "R**2": 0.5796236726178019, "调整R**2": 0.5426675119688175, "F": {"F": 15.684087914953095, "P": 2.7363837020628006e-14}, "因变量": "Q6-体重kg"}, "text": {"图表说明": "上表格展示了本次模型的分析结果，包括模型的标准化系数、t值、VIF值、R²、调整R²等，用于模型的检验，并分析模型的公式。\n1. 线性回归模型要求总体回归系数不为0，即变量之间存在回归关系。根据F检验结果对模型进行检验。\n2. R²代表曲线回归的拟合程度，越接近1效果越好。\n3. VIF值代表多重共线性严重程度，用于检验模型是否呈现共线性，即解释变量间存在高度相关的关系（VIF应小于10或者5，严格为5）若VIF出现inf，则说明VIF值无穷大，建议检查共线性，或者使用岭回归。\n4. B是有常数情况下的的系数。\n5. 标准误=B/t值。\n6. 标准化系数是将数据标准化后得到的系数。\n7. VIF是共线性。\n8. F(df1,df2)是df1等于自变量数量；df2等于样本量-(自变量数量+1)。\n9. F检验是为了判断是否存在显著的线性关系，R²是为了判断回归直线与此线性模型拟合的优劣。在线性回归中主要关注F检验是否通过，而在某些情况下R²大小和模型解释度没有必然关系。", "智能分析": "F检验的结果分析可以得到，显著性P值为0.000，水平上呈现显著性，拒绝回归系数为0的原假设，因此模型基本满足要求。\n对于变量共线性表现，VIF全部小于10，因此模型没有多重共线性问题，模型构建良好。\n模型的公式如下：y=24.401 + 0.193*Q14-做功 - 0.731*Q15-日常锻炼情况 + 0.421*Q16-吃零食情况 - 1.693*Q17-跑步情况 + 0.627*Q18-玩电脑游戏情况 + 0.371*Q19-逛街情况 - 0.135*Q20-散步情况 + 2.548*Q21-夜宵情况 。"}}, {"name": "拟合效果图", "value": {"x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], "真实值": [54.5, 65.0, 58.5, 52.9, 60.2, 57.5, 67.1, 47.0, 60.5, 42.8, 53.7, 38.3, 63.0, 36.1, 57.0, 55.1, 48.0, 53.7, 58.6, 30.0, 44.4, 45.0, 50.0, 62.5, 43.6, 44.5, 59.5, 40.4, 57.0, 50.5, 37.5, 56.3, 62.1, 51.5, 52.0, 66.6, 63.5, 50.0, 60.5, 65.0, 57.3, 26.6, 32.5, 62.9, 55.0, 40.9, 50.2, 61.9, 51.0, 52.5, 48.3, 42.0, 47.0, 57.2, 50.0, 36.5, 30.2, 52.1, 56.2, 54.0, 78.3, 46.0, 61.0, 63.2, 34.9, 41.8, 65.0, 54.0, 63.0, 41.8, 36.8, 50.0, 32.5, 73.0, 44.0, 28.0, 34.0, 48.5, 47.5, 48.5, 41.0, 67.0, 62.5, 63.3, 51.2, 46.0, 45.0, 25.5, 51.7, 55.0, 55.0, 54.4, 47.0, 57.0, 37.7, 45.0, 53.0, 52.5, 34.1, 40.2], "预测值": [60.96758166246284, 65.4673089711356, 50.434065167565535, 53.58256795693698, 62.243832123520846, 59.34087808648165, 64.62709720555604, 48.273617218722634, 65.65367475067012, 46.925238845494306, 56.19757484430131, 48.00985403493017, 60.59047983133322, 45.11309990692115, 49.793221750669204, 58.665931379193836, 47.06891556427897, 57.879044615481305, 53.74765844807298, 39.1588988478713, 43.010678120680325, 47.19454177202918, 54.07358990856969, 73.51538472497953, 50.11570014397114, 43.8705363933502, 53.595666265590644, 48.80206847212683, 56.847051744265826, 44.909367534711464, 46.72882002405326, 48.206693809416734, 48.497251073109716, 45.23213642874133, 56.12757251272547, 49.79415991594871, 57.24596495808484, 48.48034606808392, 51.76612309204627, 49.82181068422105, 50.36186314357572, 34.902894888666495, 42.31157593833729, 57.07248768951406, 43.80307955226056, 44.869296737600116, 44.86334773242538, 48.52147405191554, 48.944825524492686, 49.034927344299604, 40.73053361843338, 45.35582831108174, 43.363323297609, 62.11265474950305, 46.14861479068929, 42.0504260418667, 38.11556752876169, 44.79257376147541, 63.816120615671515, 59.452598292203824, 64.46627615930407, 51.895329368777524, 59.466898257152906, 56.34532094271201, 43.90397593049274, 46.98498245074258, 71.98339157969592, 59.1593876381692, 64.22862340923433, 45.23191106731207, 41.15771304107032, 46.62352753135051, 41.13666475904604, 53.09857402802325, 49.160599672166605, 37.722503625896564, 42.713457110841205, 47.42797193017334, 49.109780155351636, 46.95820568840233, 46.09797555828173, 61.51866350858046, 56.03572815592926, 71.44923154905497, 47.798672805180054, 43.74085526742688, 53.44445340344848, 37.655010270773126, 53.18129845908712, 57.999156948187846, 52.16715419926452, 42.789170538247674, 43.88788574722746, 56.44832351765272, 42.05391994326932, 45.363720861649064, 42.38407520695003, 46.62357350820484, 40.584214873128204, 39.90180286372068]}, "text": "上图展示了本次模型的原始数据图、模型拟合值、模型预测值。"}, {"name": "模型路径图", "value": {"coef name": ["Q14-做功", "Q15-日常锻炼情况", "Q16-吃零食情况", "Q17-跑步情况", "Q18-玩电脑游戏情况", "Q19-逛街情况", "Q20-散步情况", "Q21-夜宵情况"], "coef": [0.7213624553988669, -0.05843961687248622, 0.03653916652566736, -0.14717695630974797, 0.05029679332372762, 0.026198972405919452, -0.01158697670584876, 0.14784902329513774], "y": "Q6-体重kg"}, "text": "上图以路径图形式展示了本次模型结果，主要包括模型的系数，用于分析X对于Y的影响关系情况。"}, {"name": "模型结果预测", "value": {"变量": ["常数", "Q14-做功", "Q15-日常锻炼情况", "Q16-吃零食情况", "Q17-跑步情况", "Q18-玩电脑游戏情况", "Q19-逛街情况", "Q20-散步情况", "Q21-夜宵情况"], "系数": [24.40130009421884, 0.19262153327942105, -0.7305684152690275, 0.420598696162354, -1.6929872427089208, 0.627345706064012, 0.370522584477988, -0.13466999546083103, 2.5479398189917184]}, "text": "上表格显示了线性回归模型的预测情况。"}]
\ No newline at end of file
cieccproject/data/lasso.json 0 → 100644
View file @ 748af152
[{"name": "模型系数表", "value": {"变量名": ["截距", "age", "sex", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6"], "标准化系数": [-1.42291888301942e-16, -0.04930485283754814, -0.10528742373527387, 0.3950480630066243, 0.1494990345857754, -0.04029857240418088, -0.13038008846830637, -0.00213936532567927, 0.07129263179793234, 0.2948622954448755, 0.035973813487822186], "非标准化系数": [152.145224976732, -97.03657107238281, -213.10441775769348, 597.1994529364002, 285.6014120876411, -36.947999079549746, -309.8807159996562, -0.0, 205.83054405466544, 466.9144818817507, 67.4125041702004], "R²": 0.5003669473423848}, "text": {"图表说明": "上表展示了模型系数情况，当模型中标准化变量系数为0时，代表这个变量被排除出模型。", "智能分析": "Lasso回归的结果显示：基于变量截距项、age、sex、bmi、bp、s1、s2、s3、s4、s5、s6的标准化系数，变量截距项、age、sex、bmi、bp、s1、s2、s3、s4、s5、s6被保留，没有变量被删除。\n模型的标准化公式：y=-0.0-0.049 × age-0.105 × sex+0.395 × bmi+0.149 × bp-0.04 × s1-0.13 × s2-0.002 × s3+0.071 × s4+0.295 × s5+0.036 × s6。\n模型的非标准化公式：y=152.145-97.037 × age-213.104 × sex+597.199 × bmi+285.601 × bp-36.948 × s1-309.881 × s2+0.0 × s3+205.831 × s4+466.914 × s5+67.413 × s6。"}}, {"name": "模型结果图", "value": {"x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], "真实值": [151, 135, 138, 110, 101, 171, 166, 144, 97, 168, 68, 49, 68, 245, 184, 202, 137, 85, 131, 283, 59, 341, 87, 102, 265, 276, 100, 61, 92, 259, 53, 75, 142, 225, 59, 182, 128, 52, 37, 170, 128, 163, 150, 178, 48, 202, 111, 85, 42, 170, 200, 113, 143, 52, 65, 141, 55, 134, 111, 98, 164, 96, 90, 162, 150, 279, 92, 83, 128, 302, 198, 95, 53, 134, 81, 104, 258, 229, 275, 200, 200, 84, 121, 161, 109, 115, 268, 274, 158, 107, 83, 103, 272, 85, 280, 336, 118, 235, 174, 259], "预测值": [200.73867554581406, 122.06071695336558, 82.09942294560125, 154.53215575880554, 107.74659322113223, 158.20156685490898, 226.21492367085517, 160.98433482860676, 145.84841678807229, 129.68314955301102, 122.48305928057587, 103.17444084771823, 125.02506046869433, 281.8052534377463, 173.3075323511796, 141.70126522733136, 115.52353826511843, 190.27267237658208, 123.58001215301985, 196.04524970973898, 83.71443725009021, 251.2862558507702, 133.46155412385787, 103.72383060499753, 213.42652649807692, 166.4793756230214, 136.634179995953, 142.1055565567197, 97.13974194928144, 207.75767687601103, 115.82644049459384, 73.89434903135816, 181.15649050234526, 161.01405079596472, 134.29097564169632, 150.55884377506288, 94.37616783310582, 208.55371302213865, 101.24475824694733, 127.41546215035282, 91.35269729889399, 178.80976452805368, 135.07191982202346, 121.97399198818746, 84.62839647870965, 147.98204333443238, 113.54195034965385, 149.87038292250128, 121.1743876064352, 195.85530214064627, 95.18591512211869, 101.34724656302866, 165.9339551032655, 81.82677946988389, 68.44983194456395, 168.7407136093325, 50.57045493397307, 158.80502410469404, 118.30336819861091, 100.16760974645949, 176.29517853500556, 74.73935443622756, 126.14087383154649, 120.96425655307901, 197.51861978005286, 209.70086601845765, 123.87338923917765, 122.89973665953357, 164.78495232629461, 163.26158382658463, 139.96085547789878, 167.77237000888655, 112.04317685167814, 83.94575895438615, 126.41614786179775, 59.92349573663044, 305.127775611591, 187.41722189542259, 203.85055870989564, 151.8404367274107, 158.98494881720148, 144.37970080901297, 175.64459982702974, 194.77426907407215, 115.1195718344502, 96.55519933013136, 201.57755150468182, 251.4065864092679, 78.64602152901436, 103.39536302253309, 83.44014627755666, 160.839342613944, 235.81944617168085, 80.60454954392486, 230.9222490137974, 255.6127217814594, 144.4869352463721, 160.59576220522231, 163.20496193395977, 252.83330168744084]}, "text": "上图展示了本次模型的原始数据图、模型拟合值、模型预测值。"}, {"name": "模型结果预测", "value": {"变量": ["截距", "age", "sex", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6"], "系数": [152.145224976732, -97.03657107238281, -213.10441775769348, 597.1994529364002, 285.6014120876411, -36.947999079549746, -309.8807159996562, -0.0, 205.83054405466544, 466.9144818817507, 67.4125041702004]}, "text": "上表格显示了经过Lasso回归后的模型预测情况。"}]
\ No newline at end of file
web/src/api/data.js
View file @ 748af152
......@@ -187,4 +187,23 @@ export function getDEAList(params) {
})
}
export function getLassoRegression(params) {
return request({
url: "http://localhost:8001/getLassoRegression",
method: 'get',
params
})
}
export function getOLSList(params) {
return request({
url: "http://localhost:8001/getOLS",
method: 'get',
params
})
}
web/src/utils/echartsUtil.js
View file @ 748af152
......@@ -209,3 +209,83 @@ export function getOptionByDEA(res) {
};
return option;
}
export function getOptionByLasso(res) {
let seriesList = []
let leg = []
let header = ['真实值','预测值']
let data = res['value']//["决策单元",'技术效益(BCC)','规模效益(CCR/BCC)','综合技术效益(CCR)']
let col = res['x']
// let realData = data['真实值']
// let analysisData = data['预测值']
for(let i in header){
let litem = header[i]
let datalist = data[litem]
let obj = {
name: litem,
type: 'line',
data: datalist,
symbol: 'none',
}
seriesList.push(obj)
}
console.log(col)
console.log(seriesList)
let option = {
title: {
text: ''
},
tooltip: {
trigger: 'axis',
position: function (pt) {
return [pt[0], '20%'];
},
valueFormatter: (value) => parseFloat(value).toFixed(Number(3))
},
legend: {
data:header
},
dataZoom: [
{
type: 'inside',
start: 0,
end: 100
},
{
start: 0,
end: 100
}
],
grid: {
left: '3%',
right: '4%',
// bottom: '3%',
containLabel: true
},
toolbox: {
feature: {
dataZoom: {
yAxisIndex: 'none'
},
restore: {},
saveAsImage: {}
}
},
xAxis: {
type: 'category',
boundaryGap: false,
data: col
},
yAxis: {
type: 'value',
min:"dataMin",
max:"dataMax",
boundaryGap: [0, '100%']
},
series: seriesList
};
return option;
}
web/src/views/algorithmModel/components/sysAllData.vue 0 → 100644
View file @ 748af152
<template>
<div style="height:100%;width:100%">
<div style="height: 100%;position:relative;" >
<div style="height:41px;line-height:41px; font-size:16px;font-weight:600;border-bottom:1px solid #e3e3e3">
<span style="margin-left:30px">{{algoName}}</span>
</div>
<div style="position: absolute; top: 41px; right:0px; left:0px; bottom:0px;overflow: scroll;">
<div style="padding-bottom: 100px;">
<div style="width: 100%; height:100%;margin:10px 30px;">
<span style="font-weight:600;font-size:14px">示例代码</span>
<div style="margin: 5px 0px ">
<div class="editor-box">
<Vue2CodemirrorMarkdown
ref="editor"
v-model="algoDes"
:indentUnit="2"
:fullscreen="false"
:hljsCss="hljsCss"
:imageUploader="imageObj"
tocPosition=""
@on-save="handleOnSave"
:toolbar="toolbar"
:config="{
lineNumbers: true,
}"
:showToolbar="true"
@on-change="handleOnChange"
/>
</div>
</div>
</div>
<div style="align-items: flex-end;margin-right:15px;margin-top:10px;margin-left:30px">
<el-button type="primary" style="width:200px;background-color: #0078D4" @click="handleClose">在分析工具中打开该算法</el-button>
</div>
</div>
</div>
</div>
</div>
</template>
<script>
import {getyear, getAddress, detDataTitle, getIndustry, getCondition, getTree, saveTempFile, createProject} from '@/api/data'
import { getOption } from "@/utils/echartsUtil"
import VabChart from '@/plugins/echarts'
import VueMarkdown from 'vue-markdown'
import Vue2CodemirrorMarkdown from 'vue2-codemirror-markdown'
// Markdown preview
import md from "vue2-codemirror-markdown/src/lib/core/markdown"
import MkPreview from "vue2-codemirror-markdown/src/components/preview/mk-preview"
export default {
name: 'Index',
components: {
VabChart,
Vue2CodemirrorMarkdown,
md,
MkPreview
},
data() {
return {
props: {
label: 'name',
children: 'zones',
isLeaf: 'leaf'
},
hljsCss: "darcula",
theme: 'base16-light',
content: "",
algdoc: {
display: "display"
},
imageObj: {
url: "http://127.0.0.1:82/upload",
data: {},
header: {},
accept: 'image/*',
onChange: (data) => {
console.log(data)
},
onreadystatechange: (xhr, e) => {
console.log(xhr, e)
}
},
toolbar: {
editable:false,
bold: true, // 粗体
italic: true, // 斜体
header: true, // 标题
underline: true, // 下划线
strikethrough: true, // 中划线
mark: true, // 标记
superscript: true, // 上角标
subscript: true, // 下角标
quote: true, // 引用
ol: true, // 有序列表
ul: true, // 无序列表
link: true, // 链接
imagelink: true, // 图片链接
code: true, // code
table: true, // 表格
fullscreen: true, // 全屏编辑
readmodel: true, // 沉浸式阅读
htmlcode: true, // 展示html源码
help: true, // 帮助
/* 1.3.5 */
undo: true, // 上一步
redo: true, // 下一步
trash: true, // 清空
save: true, // 保存（触发events中的save事件）
/* 1.4.2 */
navigation: true, // 导航目录
/* 2.1.8 */
alignleft: true, // 左对齐
aligncenter: true, // 居中
alignright: true, // 右对齐
/* 2.2.1 */
subfield: false, // 单双栏模式
preview: true, // 预览
},
algoName:'',
activeNames: "1",
idxData: [],
defaultProps: {
children: 'children',
label: 'name'
},
dataTitle: [],
address: [],
industry: [],
times: [ ],
condition: {
dataCondition: [],
address: [],
years: [],
industry: []
},
params: {
dataCondition: [],
address: [],
years: [],
industry: [],
algo: 'pearson'
},
resultData: false,
tableData: [],
tableHeader: [],
fwl: {},
options: [{
value: 'pearson',
label: '关联分析-Pearson'
}, {
value: 'spearman',
label: '关联分析—Spearman'
}, {
value: 'kendall',
label: '关联分析—Kendall'
}],
algo: '',
algoRes: {},
saveFileName: "",
saveFlag: false,
}
},
mounted() {
console.log("加载");
this.init();
// this.algoDes= md.render(this.algoDes)
},
methods: {
handleNodeClick(obj) {
this.checkDataTitle(obj)
},
handleOnSave(){
},
handleOnChange(){
},
handleClose(){
},
init() {
this.algoName = '秩和比综合评价法(RSR)'
this.algoDes =
'```py\n'+
'import numpy\n'+
'import pandas\n'+
'from zzdataanalysis.algorithm import analysis\n'+
'#生成案例数据\n'+
'data = pandas.DataFrame({\n'+
' "A": [1, 2, 3, 4, 5]\n'+
'})\n'+
'positive_columns = pandas.DataFrame({\n'+
' "A": [1, 2, 3, 4, 5]\n'+
'})\n'+
'negative_columns = pandas.DataFrame({\n'+
' "A": [1, 2, 3, 4, 5]\n'+
'})\n'+
'index_columns = 1 \n'+
"weight_type=\'entropy_method\'\n" +
"rank_make_method=\'integer_rank\'\n" +
"number_bins=3\n"+
'print(analysis.rsr(data, positive_columns, negative_columns,index_columns,weight_type,rank_make_method,number_bins))\n'+
'```\n'
},
}
}
</script>
<style scoped>
.center {
width: 100%;
display: flex;
background: #f5f5f5;
}
.left {
width: 18%;
height: 100%;
background: white;
padding-right: 10px;
border-right: 1px solid #e1e1e1;
border-bottom: 1px solid #e7e7e7;
padding: 0 0px 0 17px;
}
.right {
width: 18%;
height: 100%;
background: #dcfaff;
display: flex;
flex-direction: column;
border-right: 1px solid #e1e1e1;
}
.right .entry {
height:30%;
display: flex;
flex-direction: column;
background-color: #ffffff;
}
.right .entry .header {
height: 41px;
background: #004a6447;
/* box-shadow: 3px 0px 4px #8b8b8b; */
line-height: 41px;
padding-left: 20px;
position: inherit;
color: #545454;
font-weight: 700;
}
.right .entry .body {
flex: 1;
overflow-y: scroll;
padding-left: 20px;
}
.result {
width:100%;
background:#fbfbfb
}
.result .header {
width: 100%;
height: 40px;
line-height: 40px;
font-weight: 600;
background: #358FD4;
border-radius: 2px;
}
.right .btn:hover {
cursor: pointer;
}
</style>
<style>
.left .el-collapse-item__content {
padding-bottom: 7px;
font-size: 13px;
color: #303133;
line-height: 1.7692307692;
}
.left .el-collapse-item__header {
height: 41px;
line-height: 41px;
}
/*color: rgba(0, 0, 0, .87);*/
/* markdown preview css */
.algDoc-content .markdown-body {
color: inherit !important;
padding: 20px 0 !important;
}
.algDoc-content .markdown-body table {
color: #000 !important;
}
.algDoc .algDoc-content {
font-weight: 400;
line-height: 1.5rem;
font-family: "Nunito Sans", Helvetica, sans-serif;
margin-bottom: 1rem;
/*color: rgba(0, 0, 0, .87);*/
}
.editor-box {
height: 62vh;
}
</style>
web/src/views/dataHander/components/detail.vue 0 → 100644
View file @ 748af152
<template>
<div style="height: 100%;display: flex; flex-direction: column;">
<div style="background:#e8f1f2">
<vab-breadcrumb class="hidden-xs-only" :style="{'height':'30px', 'line-height':'30px', 'margin-left':'20px'}"/>
</div>
<div style="width:100%;height:100%;position: relative;background: white;">
<div style="flex: 1;display: flex;width: 100%;height: 100%;flex-direction: column;">
<div style="height:5%; font-size:16px; font-weight:600;margin-top:10px">
<span style="margin-left: 20px">通用方法:</span>
</div>
<div style="height:5%; font-size:14px;">
<div class="entry" style="margin-left: 100px;height:25%;width: 100%;border-right: 3px ">
<div class="body" style="border-right: 3px solid ;">
<template v-for="(item, index) in industry">
<el-button type="primary" style="width:100px;background-color: #0094d4" @click="handleClose">{{item.name}}</el-button>
</template>
</div>
</div>
</div>
<div style="height:5%; font-size:16px; font-weight:600;margin-top:10px;display: flex;flex-direction: row">
<div style="align-items: flex-start;margin-right : auto">
<span style="margin-left: 20px">数据视图:</span>
</div>
<div style="align-items: flex-end;margin-right:15px">
<el-button type="primary" style="width:100px;background-color: #0078D4" @click="handleClose">导出</el-button>
</div>
</div>
<div style="height:90%; margin-top:5px;margin-left:15px;margin-bottom:5px;margin-right:15px">
<el-table
border
height="100%"
:data="tableData"
:style="{'width': '100%'}">
<template v-for="item in tableHeader">
<el-table-column
:prop="item"
:label="item"
:sortable="true"
min-width="80">
</el-table-column>
</template>
</el-table>
</div>
</div>
</div>
</div>
</template>
<script>
import { readFile } from '@/api/data'
export default {
data() {
return {
tableData: [],
tableHeader: [],
industry:[
{key:'1',name:'标准化'},
{key:'2',name:'区间缩放'},
{key:'3',name:'非线性转换'},
{key:'4',name:'归一化'},
{key:'5',name:'二值化'},
{key:'6',name:'哑编码'},
{key:'7',name:'缺失值计算'},
{key:'8',name:'多项式转换'},
{key:'9',name:'描述性统计'},
{key:'10',name:'自定义转换'},
],
}
},
mounted() {
console.log("=====")
console.log(this.$route.query)
this.loadData()
},
methods: {
handleClose(){
},
loadData() {
this.tableData = [];
this.tableHeader = []
let name = this.$route.query.name
let params = {
"fId": this.$route.query.id,
"name": name
}
readFile(params).then(res => {
console.log(res)
this.tableHeader = res.data.header;
this.tableData = res.data.data;
}).catch(err => {
console.log(err)
})
},
}
}
</script>
web/src/views/dataHander/components/sysAllData.vue 0 → 100644
View file @ 748af152
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