提交修改

src/views/digitalMarketingAlgorithms/components/associationRuleMining.vue

@@ -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">

src/views/digitalMarketingAlgorithms/components/bigData.vue

@@ -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) => {
+  marketingAlgorithmApi
-    loading.value = false;
+    .getMarketingAlgorithmImport(formdata)
+    .then((res) => {
       getZJData();
+      selectZJ();
+      loading.value = false;
       proxy.$modal.msgSuccess("导入成功");
+    })
+    .catch((err) => {
+      loading.value = false;
     });
+  e.target.value = "";
 };
 //选择自建表
 const selectZJ = (item) => {

src/views/digitalMarketingAlgorithms/components/descriptiveStatistics.vue

@@ -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>一、选择指标</span>
@@ -363,7 +363,7 @@ const g_taskAnswer = ref({
   summation: 4993.0,
   observations: 100,
 });
-
+const flat = ref(false);
 // 预处理
 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 = () => {
 };
 // 实训任务提交
 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 {
-      //  判断value的值等不等于g_taskAnswer.value每一项都要相等才行
       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: 150px;
-      width: 205px;
+      height: 40px;
-      height: 55px;
       background: #00f4ff;
-      //水平居中
+      margin: auto;
       display: flex;
       align-items: center;
       justify-content: center;

src/views/digitalMarketingAlgorithms/components/emotion-analysis.vue

@@ -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(如: Goose, geese, goose, gander, ganders)。词干法看单词的组成(如：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” 和 "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([]);
 // 下载状态
 const downloadStatus = ref(false);
 //提交校验
+const flat = ref(false);
 const g_submitVerification = ref({
   max: "13",
   frequency: "3",
@@ -590,7 +591,6 @@ const knowledgeImport = () => {
 };
 // 实训任务提交
 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: 150px;
-      width: 205px;
+      height: 40px;
-      height: 55px;
       background: #00f4ff;
-      //水平居中
+      margin: auto;
       display: flex;
       align-items: center;
       justify-content: center;