'tj'

src/api/AI.js

@@ -60,7 +60,7 @@ export function clusterAnalysisPlot(data) {
 //关联规则挖掘
 export function associationRuleMining(data) {
   return request({
-    url: '/api/model/apriori',
+    url: '/api/python/associationRules',
     method: 'post',
     data:data,
   })

src/api/portraitModel.js

@@ -153,4 +153,4 @@ export function clusterAnalysis(data) {
     method: "post",
     data: data,
   });
-}
+}

src/views/digitalMarketingAlgorithms/components/associationRuleMining.vue

@@ -50,6 +50,9 @@
           <div class="left-top" style="margin-top: 50px">
             <span>三、模型参数设置</span>
           </div>
+          <div class="metrics-table">
+            <el-button @click="codeSetting">代码设置</el-button>
+          </div>
           <div class="metrics-table" style="margin-top: 10px">
             <span style="font-weight: 400; font-size: 12px; color: #ffffff">聚类方法：</span>
             <div class="metrics" style="margin-top: 10px">
@@ -322,9 +325,17 @@
       </div>
     </template>
   </pop-model>
+  <pop-model :showModel="runResultShow2" title="设置代码" @closePop="closeCode">
+    <template v-slot:content>
+        <div>
+          <codemirror :code="code"></codemirror>
+        </div>
+    </template>
+  </pop-model>
 </template>
 
 <script setup>
+import codemirror from "@/components/codemirror/index.vue"
 import useAlgorithmStore from "@/store/modules/algorithm.js";
 const algorithmStore = useAlgorithmStore();
 import * as portraitModel from "@/api/portraitModel";
@@ -344,6 +355,7 @@ const formData = reactive({
   value4: "",
   value5: "",
 });
+const code=ref('')
 const scrollbar=ref(null)
 const loading1 =ref(false)
 const task = () => {
@@ -377,78 +389,22 @@ const task = () => {
       proxy.$modal.msgSuccess("提交成功");
     });
 };
+const runResultShow2 = ref(false)
+const closeCode=()=>{
+  runResultShow2.value = false
+  const inputs = document.querySelectorAll('.codemirror pre code input')
+  const values = []
+  inputs.forEach(input => {
+    values.push(input.value)
+  })
+  input5.value=values[0]*100
+  input6.value=values[1]*100
+  input4.value='Apriori关联算法'
+}
 const dialogVisible = ref(false);
 const runResultShow = ref(false);
 const tableLabel = reactive([{ prop: "date", label: "" }]);
 const tableLabel2 = reactive([{ prop: "date", label: "" }]);
-const text = ref(`首先导入必要的库：
-\`\`\`python
-from itertools import chain, combinations
-\`\`\`
-接着定义几个辅助函数：
-\`\`\`python
-# 生成候选项集的所有非空子集
-def powerset(s):
-    return chain.from_iterable(combinations(s, r) for r in range(1, len(s)))
-# 计算支持度
-def calculate_support(itemset, transactions):
-    return sum(1 for transaction in transactions if itemset.issubset(transaction)) / len(transactions)
-\`\`\`
-现在我们来实现Apriori算法：
-\`\`\`python
-def apriori(transactions, min_support, min_confidence):
-    # 初始化频繁项集和关联规则列表
-    frequent_itemsets = []
-    association_rules = []
-    # 第一步：找出单项频繁项集
-    singletons = {frozenset([item]) for transaction in transactions for item in transaction}
-    singletons = {itemset for itemset in singletons if calculate_support(itemset, transactions) >= min_support}
-    frequent_itemsets.extend(singletons)
-    # 迭代找出所有其他频繁项集
-    prev_frequent_itemsets = singletons
-    while prev_frequent_itemsets:
-        # 生成新的候选项集
-        candidates = {itemset1 | itemset2 for itemset1 in prev_frequent_itemsets for itemset2 in prev_frequent_itemsets if len(itemset1 | itemset2) == len(itemset1) + 1}
-        # 计算支持度并筛选
-        new_frequent_itemsets = {itemset for itemset in candidates if calculate_support(itemset, transactions) >= min_support}
-        frequent_itemsets.extend(new_frequent_itemsets)
-        # 生成关联规则
-        for itemset in new_frequent_itemsets:
-            for subset in powerset(itemset):
-                subset = frozenset(subset)
-                diff = itemset - subset
-                if diff:
-                    confidence = calculate_support(itemset, transactions) / calculate_support(subset, transactions)
-                    if confidence >= min_confidence:
-                        association_rules.append((subset, diff, confidence))
-        prev_frequent_itemsets = new_frequent_itemsets
-    return frequent_itemsets, association_rules
-\`\`\`
-### 示例和输出
-假设我们有以下简单的购物数据集：
-\`\`\`python
-transactions = [
-    {'牛奶', '面包', '黄油'},
-    {'啤酒', '面包'},
-    {'牛奶', '啤酒', '黄油'},
-    {'牛奶', '鸡蛋'},
-    {'面包', '鸡蛋', '黄油'}
-]
-\`\`\`
-调用Apriori算法：
-\`\`\`python
-min_support = 0.4
-min_confidence = 0.5
-frequent_itemsets, association_rules = apriori(transactions, min_support, min_confidence)
-print("频繁项集：", frequent_itemsets)
-print("关联规则：", association_rules)
-\`\`\`
-输出可能如下：
-\`\`\`python
-频繁项集： [{'牛奶'}, {'面包'}, {'黄油'}, {'啤酒'}, {'鸡蛋'}, {'牛奶', '面包'}, {'牛奶', '黄油'}, {'面包', '黄油'}, {'啤酒', '黄油'}, {'面包', '啤酒'}]
-关联规则： [(('牛奶',), ('面包',), 0.6666666666666666), (('面包',), ('牛奶',), 0.6666666666666666), ...]
-\`\`\`
-通过这个实战应用，我们不仅学习了如何在Python中实现Apriori算法，还了解了它在购物篮分析中的具体应用。这为进一步的研究和实际应用提供了有用的指导。`);
 const getList = () => {
   API.selectionMetrics({ userId: JSON.parse(getUserInfo()).userId }).then((res) => {
     res.data.forEach((element) => {
@@ -548,6 +504,13 @@ const taskSubmit = () => {
   dialogVisible.value = true;
 };
 const tableData = ref([]);
+const codeSetting=()=>{
+  if(input.value==="购物车数据表"){
+    runResultShow2.value=true
+  }else{
+    proxy.$modal.msgWarning("请选择购物车数据表！")
+  }
+}
 const headerCellStyle = () => {
   return {
     backgroundColor: "#1882DE !important", // 设置表头背景颜色
@@ -572,7 +535,66 @@ const optionData2 = () => {
       tableData2.value = [];
       tableData2.value = res.data;
       proxy.$modal.msgSuccess("预处理成功！");
-    });
+    }).then(()=>{
+      code.value=`
+# -*- coding: utf-8 -*-
+import json
+from itertools import chain, combinations
+
+def powerset(s):
+    return chain.from_iterable(combinations(s, r) for r in range(1, len(s) + 1))
+
+def calculate_support(itemset, transactions):
+    return sum(1 for transaction in transactions if itemset.issubset(transaction)) / len(transactions)
+
+def apriori(transactions, min_support, min_confidence):
+    frequent_itemsets = []
+    association_rules = []
+
+    singletons = {frozenset([item]) for transaction in transactions for item in transaction}
+    singletons = {itemset for itemset in singletons if calculate_support(itemset, transactions) >= min_support}
+    frequent_itemsets.extend(singletons)
+
+    prev_frequent_itemsets = singletons
+    while prev_frequent_itemsets:
+        candidates = {itemset1 | itemset2 for itemset1 in prev_frequent_itemsets for itemset2 in prev_frequent_itemsets if len(itemset1 | itemset2) == len(itemset1) + 1}
+        new_frequent_itemsets = {itemset for itemset in candidates if calculate_support(itemset, transactions) >= min_support}
+        frequent_itemsets.extend(new_frequent_itemsets)
+
+        for itemset in new_frequent_itemsets:
+            for subset in powerset(itemset):
+                subset = frozenset(subset)
+                diff = itemset - subset
+                if diff:
+                    confidence = calculate_support(itemset, transactions) / calculate_support(subset, transactions)
+                    if confidence >= min_confidence:
+                        rule = {
+                            "correlation": list(subset),
+                            "associated": list(diff),
+                            "confidenceLevel": round(confidence, 2),
+                            "rule": f"[{', '.join(subset)}]==>[{', '.join(diff)}]==>{round(confidence, 2)}"
+                        }
+                        association_rules.append(rule)
+
+        prev_frequent_itemsets = new_frequent_itemsets
+
+    result = {
+        # "frequentItemsets": [list(itemset) for itemset in frequent_itemsets],
+        "associationRules": association_rules
+    }
+
+    print(json.dumps(result, ensure_ascii=False))
+
+
+transactions = [
+    ${resTable2.value.map(item => "{" + item.consumer_goods.split(',').map(good => "'" + good + "'").join(',') + "}").join(',')} 
+]
+
+min_support = ___
+min_confidence = ___
+apriori(transactions, min_support, min_confidence)
+      `
+    })
   }
 };
 
@@ -585,25 +607,31 @@ const computation = () => {
     return;
   }
   if (input5.value && input6.value && input4.value) {
-    const sendData = {
+    const sendData =ref({
-      confidence: parseFloat(input5.value) / 100,
+      minConfidence: parseFloat(input6.value) / 100,
-      support: parseFloat(input6.value) / 100,
+      minSupport: parseFloat(input5.value) / 100,
-      userId: JSON.parse(getUserInfo()).userId,
+      type: input.value,
-      deduplicatedDataList: resTable2.value,
+      data: Object.keys(resTable2.value[0]).reduce((acc, key) => {
-    };
+        acc[key] = resTable2.value.map(item => item[key]);
-    API.associationRuleMining(sendData)
+        return acc;
+      }, {}),
+    })
+    if(input.value==="购物车数据表"){
+      sendData.value.data = {consumer_goods:resTable2.value.map(item => item.consumer_goods.split(','))}
+    }
+    API.associationRuleMining(sendData.value)
       .then((res) => {
         loading1.value = true;
         uplodFlag.value = true;
         tableData3.value = [];
         tableLabel2.length = 0;
-        for (const key in res.data[0]) {
+        for (const key in res.data.associationRules[0]) {
           tableLabel2.push({
             label: key,
             prop: key,
           });
         }
-        tableData3.value = res.data;
+        tableData3.value = res.data.associationRules;
         //一秒后执行
         setTimeout(() => {
           loading1.value = false;

src/views/digitalMarketingAlgorithms/components/clusterAnalysis.vue

@@ -555,7 +555,6 @@ const headerCellStyle = () => {
 };
 const codeSetting=()=>{
   runResultShow2.value=true
-  console.log(runResultShow2.value);
 }
 const optionData2 = () => {
   if (tableData2.value.length === 0) {
@@ -581,9 +580,13 @@ const optionData2 = () => {
           loading2.value = false;
         }, 500);
       }).then(()=>{
-        age.value=resTable2.value.map(item=>item.age).join(',')
+        const fields = Object.keys(resTable2.value[0])
-        income.value=resTable2.value.map(item=>item.annual_income).join(',')
+        const data = {}
-        code.value=`
+        fields.forEach(field => {
+          const values = resTable2.value.map(item => item[field]).join(',')
+          data[field] = values
+        })
+code.value=`
 # -*- coding: utf-8 -*-
 import numpy as np
 import pandas as pd
@@ -593,8 +596,7 @@ import json
 
 # 模拟输入数据（可以根据实际输入动态更改）
 data = {
-    'age': [${age.value}],
+    ${Object.entries(data).map(([key, value]) => `'${key}': [${value}]`).join(',\n    ')}
-    'income': [${income.value}]
 }
 
 # 将字典转换为 DataFrame
@@ -744,10 +746,10 @@ const clusterAnalysisCalculation = () => {
             maxIterations: input6.value,
             clusteringFrequency: input5.value,
             // userId: JSON.parse(getUserInfo()).userId,
-            data: {
+            data:Object.keys(resTable2.value[0]).reduce((acc, key) => {
-              age: resTable2.value.map(item => item.age),
+                acc[key] = resTable2.value.map(item => item[key]);
-              income: resTable2.value.map(item => item.annual_income)
+                return acc;
-            },
+              }, {}),
           };
         API.clusterAnalysis2(sendData2).then((res) => {
           myChart.setOption({

src/views/digitalMarketingAlgorithms/index.vue

@@ -46,8 +46,8 @@ const activeIndex = (index) => {
 const map = {
   用户数据库: 1,
   描述性统计: 2,
-  关联规则挖掘: 3,
+  聚类分析: 3,
-  聚类分析: 4,
+  关联规则挖掘: 4,
   回归分析: 5,
   情感分析: 6,
 };