提交修改

dev-QQq
qinzhenpen 2 years ago
parent 279c5091cc
commit 68334206fb

@ -2,7 +2,8 @@
<!-- <el-scrollbar ref="scrollbar" height="800px"> -->
<div class="main-top">
<span style="font-weight: bold; font-size: 18px; color: #3596eb">任务描述</span>
<p style="font-weight: 400; font-size: 12px; color: #e2e7ee !important">使用购物车数据对所购商品进行购物篮分析关联规则挖掘并提交实训任务</p>
<p style="font-weight: 400; font-size: 12px; color: #e2e7ee !important">使用购物车数据对所购商品进行购物篮分析关联规则挖掘其中min_support = 0.4
min_confidence = 0.5最后将运行结果提交实训任务</p>
</div>
<div class="main-but">
<el-button @click="runResultShow = true">

@ -67,7 +67,6 @@ const tableLabel = ref([
{ prop: "age", label: "年龄" },
{ prop: "annualIncome", label: "年收入" },
{ prop: "spendingScore", label: "消费水平" },
{ prop: "consumerGoods", label: "消费商品" },
]);
const tableLabel3 = ref([
{ prop: "id", label: "ID" },
@ -156,11 +155,18 @@ const importData = (e) => {
const formdata = new FormData();
formdata.append("file", files);
formdata.append("userId", userInfo.userId);
marketingAlgorithmApi.getMarketingAlgorithmImport(formdata).then((res) => {
loading.value = false;
getZJData();
proxy.$modal.msgSuccess("导入成功");
});
marketingAlgorithmApi
.getMarketingAlgorithmImport(formdata)
.then((res) => {
getZJData();
selectZJ();
loading.value = false;
proxy.$modal.msgSuccess("导入成功");
})
.catch((err) => {
loading.value = false;
});
e.target.value = "";
};
//
const selectZJ = (item) => {

@ -15,7 +15,7 @@
</div>
<div class="app-main">
<div class="main-left">
<el-scrollbar height="592px" style="padding-right: 10px" min-size="5">
<el-scrollbar height="640px" style="padding-right: 10px" min-size="5">
<div>
<div class="left-top">
<span>选择指标</span>
@ -363,7 +363,7 @@ const g_taskAnswer = ref({
summation: 4993.0,
observations: 100,
});
const flat = ref(false);
//
const preProcess = (tetx) => {
if (analysisData.value.length == 0) {
@ -442,7 +442,7 @@ const modelCalculation = () => {
}, 1000);
})
.catch((err) => {
loading.value = true;
loading.value = false;
});
};
function processArrayData(keys, data) {
@ -472,17 +472,15 @@ const knowledgeImport = () => {
};
//
const submitTask = () => {
const flat = false;
for (let key in formInline.value) {
if (formInline.value[key] == "") {
const label = tableLabel.find((item) => item.prop == key);
proxy.$modal.msgWarning(`${label.label}不能为空`);
return;
} else {
// valueg_taskAnswer.value
if (Number(formInline.value[key]) != g_taskAnswer.value[key]) {
const label = tableLabel.find((item) => item.prop == key);
flat = true
flat.value = true;
proxy.$modal.msgWarning(`${label.label}错误`);
return;
}
@ -492,7 +490,7 @@ const submitTask = () => {
.submit({
userId: n_dataTableQuery.value.userId,
taskName: "描述性统计",
numberOfErrors: flat ? 1 : 0,
numberOfErrors: flat.value ? 1 : 0,
})
.then((res) => {
dialogVisible.value = false;
@ -604,11 +602,10 @@ const download = () => {
}
}
.startOver {
margin-left: -10px;
width: 205px;
height: 55px;
width: 150px;
height: 40px;
background: #00f4ff;
//
margin: auto;
display: flex;
align-items: center;
justify-content: center;

@ -200,7 +200,7 @@
<br />
另一个挑战是: 大多数情况下文本是非结构化数据(not structured)
</p>
<img src="@/assets/images/f4001.png" alt="" style="width: 600px;"/>
<img src="@/assets/images/f4001.png" alt="" style="width: 600px" />
<p style="font-weight: 700; font-size: 22px">文本分析步骤</p>
<p>
&nbsp;&nbsp;&nbsp;&nbsp;1.句法分析(Parsing): 是指处理非结构化文本使其具有一定的结构供将来分析的过程句法分析将文本进行解构然后以一种更为结构化的方式来呈现(unsturctured -> sturctured)
@ -251,7 +251,7 @@
<li>停止词(stop word): 在给定语言中并非所有的单词都需要被考虑(比如the, a, of, and, to等这些不太可能有助于语义的理解)</li>
<li>词根法(Lemmatization)和词干法(Stemming): 词根法看单词的意义(: Goose, geese, goose, gander, ganders)词干法看单词的组成(walk, walking, Walk, walks, walked)最受欢迎的是"Porter stemmer", WordNet</li>
<li>词袋法(Bag-of-words representation): 将文档转化成高维向量(high-dimensional vector)向量指示了文档中各个单词的 存在/不存在/出现频率(presence/absence/frequency)</li>
<img src="@/assets/images/f4002.png" alt="" style="width: 600px;height: 500px;"/>
<img src="@/assets/images/f4002.png" alt="" style="width: 600px; height: 500px" />
<p>词袋法足够简单(朴素且过分简化问题 naive and over-simplified) 并被广泛应用于文本分析问题中 (是入门的好方法)其将文档表示为一组词语(单词)而忽略了其他信息(如顺序 order上下文 context推论 inferences和语义 semantics)比如a dog bites a man "a man bites a dog"意思完全不同但是他们在词袋法里是同一种表示</p>
<li>语料库的表示(Representation of a corpus): 语料库可以大到包括一种或者多种语言的所有文档也可以小到仅限于特定领域(focused on a specific domains)</li>
<p>
@ -383,7 +383,7 @@
<br />
分类方法(朴素贝叶斯最大熵或者支持向量机SVM) 经常被用来提取语料库统计以用于情感分析
</p>
<img src="@/assets/images/f4013.png" alt="" style="width: 600px;height: 300px;"/>
<img src="@/assets/images/f4013.png" alt="" style="width: 600px; height: 300px" />
<p>分类器仅基于对其进行训练的数据集来确定情感</p>
<li>词义随着领域不同而改变</li>
<li>因此模型无法直接应用于其他领域</li>
@ -401,15 +401,15 @@
<p style="font-weight: 700; font-size: 22px">获得结果(gaining insights)</p>
<p style="font-weight: 600; font-size: 18px">词云(word cloud)</p>
<p>五星好评的词云</p>
<img src="@/assets/images/f4015.png" alt="" style="width: 600px;height: 400px;"/>
<img src="@/assets/images/f4015.png" alt="" style="width: 600px; height: 400px" />
<p>一星差评的词云</p>
<img src="@/assets/images/f4016.png" alt="" />
<p>TFIDF能够用来凸显评论中有信息量的单词</p>
<img src="@/assets/images/f4017.png" alt="" style="width: 600px;height: 400px;"/>
<img src="@/assets/images/f4017.png" alt="" style="width: 600px; height: 400px" />
<p>LDA可以把评论归类为主题圆盘的大小代表了词的权重</p>
<img src="@/assets/images/f4018.png" alt="" style="width: 600px;height: 400px;"/>
<img src="@/assets/images/f4018.png" alt="" style="width: 600px; height: 400px" />
<p>另一种可视化方式</p>
<img src="@/assets/images/f4019.png" alt="" style="width: 600px;height: 400px;"/>
<img src="@/assets/images/f4019.png" alt="" style="width: 600px; height: 400px" />
</div>
</template>
</popModel>
@ -501,6 +501,7 @@ const aCommentOpinion = ref([]);
//
const downloadStatus = ref(false);
//
const flat = ref(false);
const g_submitVerification = ref({
max: "13",
frequency: "3",
@ -590,7 +591,6 @@ const knowledgeImport = () => {
};
//
const submitTask = () => {
let flat = false;
for (let key in formInline.value) {
if (formInline.value[key] == "") {
const label = tableLabel.find((item) => item.prop == key);
@ -600,7 +600,7 @@ const submitTask = () => {
if (formInline.value[key] != g_submitVerification.value[key]) {
const label = tableLabel.find((item) => item.prop == key);
proxy.$modal.msgWarning(`${label.label}错误`);
flat = true;
flat.value = true;
return;
}
}
@ -609,7 +609,7 @@ const submitTask = () => {
.submit({
userId: n_dataTableQuery.value.userId,
taskName: "情感分析",
numberOfErrors: flat ? 1 : 0,
numberOfErrors: flat.value ? 1 : 0,
})
.then((res) => {
dialogVisible.value = false;
@ -937,11 +937,10 @@ const switchingModels = () => {
}
}
.startOver {
margin-left: -10px;
width: 205px;
height: 55px;
width: 150px;
height: 40px;
background: #00f4ff;
//
margin: auto;
display: flex;
align-items: center;
justify-content: center;

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