From 68334206fb62bfb8a13e636bae4e8be02846bf4f Mon Sep 17 00:00:00 2001 From: qinzhenpen Date: Mon, 26 Aug 2024 18:15:06 +0800 Subject: [PATCH] =?UTF-8?q?=E6=8F=90=E4=BA=A4=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../components/associationRuleMining.vue | 3 ++- .../components/bigData.vue | 18 ++++++++----- .../components/descriptiveStatistics.vue | 19 ++++++------- .../components/emotion-analysis.vue | 27 +++++++++---------- 4 files changed, 35 insertions(+), 32 deletions(-) diff --git a/src/views/digitalMarketingAlgorithms/components/associationRuleMining.vue b/src/views/digitalMarketingAlgorithms/components/associationRuleMining.vue index 63a488f..c2aec1e 100644 --- a/src/views/digitalMarketingAlgorithms/components/associationRuleMining.vue +++ b/src/views/digitalMarketingAlgorithms/components/associationRuleMining.vue @@ -2,7 +2,8 @@
任务描述 -

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。

+

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。

diff --git a/src/views/digitalMarketingAlgorithms/components/bigData.vue b/src/views/digitalMarketingAlgorithms/components/bigData.vue index be77274..a478ed3 100644 --- a/src/views/digitalMarketingAlgorithms/components/bigData.vue +++ b/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) => { - 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 = ""; }; //选择自建表 const selectZJ = (item) => { diff --git a/src/views/digitalMarketingAlgorithms/components/descriptiveStatistics.vue b/src/views/digitalMarketingAlgorithms/components/descriptiveStatistics.vue index 597259e..4f9e6df 100644 --- a/src/views/digitalMarketingAlgorithms/components/descriptiveStatistics.vue +++ b/src/views/digitalMarketingAlgorithms/components/descriptiveStatistics.vue @@ -15,7 +15,7 @@
- +
一、选择指标 @@ -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: 205px; - height: 55px; + width: 150px; + height: 40px; background: #00f4ff; - //水平居中 + margin: auto; display: flex; align-items: center; justify-content: center; diff --git a/src/views/digitalMarketingAlgorithms/components/emotion-analysis.vue b/src/views/digitalMarketingAlgorithms/components/emotion-analysis.vue index 4a447ff..704371b 100644 --- a/src/views/digitalMarketingAlgorithms/components/emotion-analysis.vue +++ b/src/views/digitalMarketingAlgorithms/components/emotion-analysis.vue @@ -200,7 +200,7 @@
另一个挑战是: 大多数情况下文本是非结构化数据(not structured)。

- +

文本分析步骤

    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 @@

  • 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)
  • 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”。
  • 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)。
    • - +

    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。

  • 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)。

    • @@ -383,7 +383,7 @@
    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。

    - +

    分类器仅基于对其进行训练的数据集来确定情感：

  • 词义随着领域不同而改变。
  • 因此模型无法直接应用于其他领域。
    • @@ -401,15 +401,15 @@

    获得结果(gaining insights)

    词云(word cloud)

    五星好评的词云

    - +

    一星差评的词云：

    TFIDF能够用来凸显评论中有信息量的单词。

    - +

    LDA可以把评论归类为主题。圆盘的大小代表了词的权重。

    - +

    另一种可视化方式。

    - +
    @@ -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: 205px; - height: 55px; + width: 150px; + height: 40px; background: #00f4ff; - //水平居中 + margin: auto; display: flex; align-items: center; justify-content: center;