完成模型训练调试，修改模型预测的导航栏
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@ -49,9 +49,21 @@
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<el-icon><Operation /></el-icon>全局模型训练
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</el-menu-item>
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</el-sub-menu>
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<el-menu-item index="/prediction">
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<el-icon><MagicStick /></el-icon>预测分析
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</el-menu-item>
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<el-sub-menu index="prediction-submenu">
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<template #title>
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<el-icon><MagicStick /></el-icon>
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<span>预测分析</span>
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</template>
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<el-menu-item index="/prediction/product">
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<el-icon><Coin /></el-icon>按药品预测
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</el-menu-item>
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<el-menu-item index="/prediction/store">
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<el-icon><Shop /></el-icon>按店铺预测
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</el-menu-item>
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<el-menu-item index="/prediction/global">
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<el-icon><Operation /></el-icon>全局模型预测
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</el-menu-item>
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</el-sub-menu>
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<el-menu-item index="/history">
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<el-icon><Histogram /></el-icon>历史预测
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</el-menu-item>
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@ -37,7 +37,22 @@ const router = createRouter({
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{
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path: '/prediction',
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name: 'prediction',
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component: () => import('../views/NewPredictionView.vue')
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redirect: '/prediction/product'
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},
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{
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path: '/prediction/product',
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name: 'product-prediction',
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component: () => import('../views/prediction/ProductPredictionView.vue')
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},
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{
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path: '/prediction/store',
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name: 'store-prediction',
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component: () => import('../views/prediction/StorePredictionView.vue')
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},
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{
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path: '/prediction/global',
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name: 'global-prediction',
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component: () => import('../views/prediction/GlobalPredictionView.vue')
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},
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{
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path: '/history',
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276
UI/src/views/prediction/GlobalPredictionView.vue
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276
UI/src/views/prediction/GlobalPredictionView.vue
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@ -0,0 +1,276 @@
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<template>
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<div class="prediction-view">
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<el-card>
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<template #header>
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<div class="card-header">
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<span>全局模型预测</span>
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<el-tooltip content="使用全局通用模型进行销售预测">
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<el-icon><QuestionFilled /></el-icon>
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</el-tooltip>
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</div>
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</template>
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<div class="model-selection-section">
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<h4>🎯 选择预测模型</h4>
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<el-form :model="form" label-width="120px">
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<el-row :gutter="20">
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<el-col :span="8">
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<el-form-item label="算法类型">
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<el-select
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v-model="form.model_type"
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placeholder="选择算法"
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@change="handleModelTypeChange"
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style="width: 100%"
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>
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<el-option
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v-for="item in modelTypes"
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:key="item.id"
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:label="item.name"
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:value="item.id"
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/>
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</el-select>
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</el-form-item>
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</el-col>
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</el-row>
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<el-row :gutter="20" v-if="form.model_type">
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<el-col :span="6">
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<el-form-item label="模型版本">
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<el-select
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v-model="form.version"
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placeholder="选择版本"
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style="width: 100%"
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:disabled="!availableVersions.length"
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:loading="versionsLoading"
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>
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<el-option
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v-for="version in availableVersions"
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:key="version"
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:label="version"
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:value="version"
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/>
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</el-select>
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</el-form-item>
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</el-col>
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<el-col :span="6">
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<el-form-item label="预测天数">
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<el-input-number
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v-model="form.future_days"
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:min="1"
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:max="365"
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style="width: 100%"
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/>
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</el-form-item>
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</el-col>
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<el-col :span="6">
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<el-form-item label="起始日期">
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<el-date-picker
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v-model="form.start_date"
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type="date"
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placeholder="选择日期"
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format="YYYY-MM-DD"
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value-format="YYYY-MM-DD"
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style="width: 100%"
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:clearable="false"
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/>
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</el-form-item>
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</el-col>
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<el-col :span="6">
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<el-form-item label="预测分析">
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<el-switch
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v-model="form.analyze_result"
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active-text="开启"
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inactive-text="关闭"
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/>
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</el-form-item>
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</el-col>
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</el-row>
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</el-form>
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</div>
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<div class="prediction-actions">
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<el-button
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type="primary"
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size="large"
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@click="startPrediction"
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:loading="predicting"
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:disabled="!canPredict"
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>
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<el-icon><TrendCharts /></el-icon>
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开始预测
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</el-button>
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</div>
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</el-card>
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<el-card v-if="predictionResult" style="margin-top: 20px">
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<template #header>
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<div class="card-header">
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<span>📈 预测结果</span>
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</div>
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</template>
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<div class="prediction-chart">
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<canvas ref="chartCanvas" width="800" height="400"></canvas>
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</div>
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</el-card>
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</div>
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</template>
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<script setup>
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import { ref, reactive, onMounted, computed, watch, nextTick } from 'vue'
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import axios from 'axios'
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import { ElMessage } from 'element-plus'
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import { QuestionFilled, TrendCharts } from '@element-plus/icons-vue'
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import Chart from 'chart.js/auto'
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const modelTypes = ref([])
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const availableVersions = ref([])
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const versionsLoading = ref(false)
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const predicting = ref(false)
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const predictionResult = ref(null)
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const chartCanvas = ref(null)
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let chart = null
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const form = reactive({
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training_mode: 'global',
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model_type: '',
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version: '',
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future_days: 7,
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start_date: '',
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analyze_result: true
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})
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const canPredict = computed(() => {
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return form.model_type && form.version
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})
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const fetchModelTypes = async () => {
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try {
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const response = await axios.get('/api/model_types')
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if (response.data.status === 'success') {
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modelTypes.value = response.data.data
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}
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} catch (error) {
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ElMessage.error('获取模型类型失败')
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}
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}
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const fetchAvailableVersions = async () => {
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if (!form.model_type) {
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availableVersions.value = []
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return
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}
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try {
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versionsLoading.value = true
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const url = `/api/models/global/${form.model_type}/versions`
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const response = await axios.get(url)
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if (response.data.status === 'success') {
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availableVersions.value = response.data.data.versions || []
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if (response.data.data.latest_version) {
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form.version = response.data.data.latest_version
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}
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}
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} catch (error) {
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availableVersions.value = []
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} finally {
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versionsLoading.value = false
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}
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}
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const handleModelTypeChange = () => {
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form.version = ''
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fetchAvailableVersions()
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}
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const startPrediction = async () => {
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try {
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predicting.value = true
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const payload = {
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model_type: form.model_type,
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version: form.version,
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future_days: form.future_days,
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start_date: form.start_date,
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analyze_result: form.analyze_result
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}
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const response = await axios.post('/api/predict', payload)
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if (response.data.status === 'success') {
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predictionResult.value = response.data.data
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ElMessage.success('预测完成！')
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await nextTick()
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renderChart()
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} else {
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ElMessage.error(response.data.message || '预测失败')
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}
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} catch (error) {
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ElMessage.error('预测请求失败')
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} finally {
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predicting.value = false
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}
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}
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const renderChart = () => {
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if (!chartCanvas.value || !predictionResult.value) return
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if (chart) {
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chart.destroy()
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}
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const predictions = predictionResult.value.predictions
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const labels = predictions.map(p => p.date)
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const data = predictions.map(p => p.sales)
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chart = new Chart(chartCanvas.value, {
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type: 'line',
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data: {
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labels,
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datasets: [{
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label: '预测销量',
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data,
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borderColor: '#409EFF',
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backgroundColor: 'rgba(64, 158, 255, 0.1)',
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tension: 0.4,
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fill: true
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}]
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},
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options: {
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responsive: true,
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plugins: {
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title: {
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display: true,
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text: '销量预测趋势图'
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}
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}
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}
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})
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}
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onMounted(() => {
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fetchModelTypes()
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const today = new Date()
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form.start_date = today.toISOString().split('T')[0]
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})
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watch(() => form.model_type, () => {
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fetchAvailableVersions()
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})
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</script>
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<style scoped>
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.prediction-view {
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padding: 20px;
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}
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.card-header {
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display: flex;
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justify-content: space-between;
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align-items: center;
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}
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.model-selection-section h4 {
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margin-bottom: 16px;
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}
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.prediction-actions {
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display: flex;
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justify-content: center;
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margin-top: 20px;
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padding-top: 20px;
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border-top: 1px solid #ebeef5;
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}
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.prediction-chart {
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margin-top: 20px;
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}
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</style>
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295
UI/src/views/prediction/ProductPredictionView.vue
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295
UI/src/views/prediction/ProductPredictionView.vue
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@ -0,0 +1,295 @@
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<template>
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<div class="prediction-view">
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<el-card>
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<template #header>
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<div class="card-header">
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<span>按药品预测</span>
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<el-tooltip content="使用针对特定药品训练的模型进行销售预测">
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<el-icon><QuestionFilled /></el-icon>
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</el-tooltip>
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</div>
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</template>
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<div class="model-selection-section">
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<h4>🎯 选择预测模型</h4>
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<el-form :model="form" label-width="120px">
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<el-row :gutter="20">
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<el-col :span="8">
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<el-form-item label="目标药品">
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<ProductSelector
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v-model="form.product_id"
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@change="handleProductChange"
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:show-all-option="false"
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/>
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</el-form-item>
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</el-col>
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<el-col :span="8">
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<el-form-item label="算法类型">
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<el-select
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v-model="form.model_type"
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placeholder="选择算法"
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@change="handleModelTypeChange"
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style="width: 100%"
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:disabled="!form.product_id"
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>
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<el-option
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v-for="item in modelTypes"
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:key="item.id"
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:label="item.name"
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:value="item.id"
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/>
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</el-select>
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</el-form-item>
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</el-col>
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</el-row>
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<el-row :gutter="20" v-if="form.model_type">
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<el-col :span="6">
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<el-form-item label="模型版本">
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<el-select
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v-model="form.version"
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placeholder="选择版本"
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style="width: 100%"
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:disabled="!availableVersions.length"
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:loading="versionsLoading"
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>
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<el-option
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v-for="version in availableVersions"
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:key="version"
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:label="version"
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:value="version"
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/>
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</el-select>
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</el-form-item>
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</el-col>
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<el-col :span="6">
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<el-form-item label="预测天数">
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<el-input-number
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v-model="form.future_days"
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:min="1"
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:max="365"
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style="width: 100%"
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/>
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</el-form-item>
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</el-col>
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<el-col :span="6">
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<el-form-item label="起始日期">
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<el-date-picker
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v-model="form.start_date"
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type="date"
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placeholder="选择日期"
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format="YYYY-MM-DD"
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value-format="YYYY-MM-DD"
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style="width: 100%"
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:clearable="false"
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/>
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</el-form-item>
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</el-col>
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<el-col :span="6">
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<el-form-item label="预测分析">
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<el-switch
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v-model="form.analyze_result"
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active-text="开启"
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inactive-text="关闭"
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/>
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</el-form-item>
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</el-col>
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</el-row>
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</el-form>
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</div>
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<div class="prediction-actions">
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<el-button
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type="primary"
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size="large"
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@click="startPrediction"
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:loading="predicting"
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:disabled="!canPredict"
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>
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<el-icon><TrendCharts /></el-icon>
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开始预测
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</el-button>
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</div>
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</el-card>
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<el-card v-if="predictionResult" style="margin-top: 20px">
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<template #header>
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<div class="card-header">
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<span>📈 预测结果</span>
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</div>
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</template>
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<div class="prediction-chart">
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<canvas ref="chartCanvas" width="800" height="400"></canvas>
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</div>
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</el-card>
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</div>
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</template>
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<script setup>
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import { ref, reactive, onMounted, computed, watch, nextTick } from 'vue'
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import axios from 'axios'
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import { ElMessage } from 'element-plus'
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import { QuestionFilled, TrendCharts } from '@element-plus/icons-vue'
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import Chart from 'chart.js/auto'
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import ProductSelector from '../../components/ProductSelector.vue'
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const modelTypes = ref([])
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const availableVersions = ref([])
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const versionsLoading = ref(false)
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const predicting = ref(false)
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const predictionResult = ref(null)
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const chartCanvas = ref(null)
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let chart = null
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const form = reactive({
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training_mode: 'product',
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product_id: '',
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model_type: '',
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version: '',
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future_days: 7,
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start_date: '',
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analyze_result: true
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})
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const canPredict = computed(() => {
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return form.product_id && form.model_type && form.version
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})
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const fetchModelTypes = async () => {
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try {
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const response = await axios.get('/api/model_types')
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if (response.data.status === 'success') {
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modelTypes.value = response.data.data
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}
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} catch (error) {
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ElMessage.error('获取模型类型失败')
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}
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}
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const fetchAvailableVersions = async () => {
|
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if (!form.product_id || !form.model_type) {
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availableVersions.value = []
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return
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}
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try {
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versionsLoading.value = true
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const url = `/api/models/${form.product_id}/${form.model_type}/versions`
|
||||
const response = await axios.get(url)
|
||||
if (response.data.status === 'success') {
|
||||
availableVersions.value = response.data.data.versions || []
|
||||
if (response.data.data.latest_version) {
|
||||
form.version = response.data.data.latest_version
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
availableVersions.value = []
|
||||
} finally {
|
||||
versionsLoading.value = false
|
||||
}
|
||||
}
|
||||
|
||||
const handleProductChange = () => {
|
||||
form.model_type = ''
|
||||
form.version = ''
|
||||
availableVersions.value = []
|
||||
}
|
||||
|
||||
const handleModelTypeChange = () => {
|
||||
form.version = ''
|
||||
fetchAvailableVersions()
|
||||
}
|
||||
|
||||
const startPrediction = async () => {
|
||||
try {
|
||||
predicting.value = true
|
||||
const payload = {
|
||||
model_type: form.model_type,
|
||||
version: form.version,
|
||||
future_days: form.future_days,
|
||||
start_date: form.start_date,
|
||||
analyze_result: form.analyze_result,
|
||||
product_id: form.product_id
|
||||
}
|
||||
const response = await axios.post('/api/predict', payload)
|
||||
if (response.data.status === 'success') {
|
||||
predictionResult.value = response.data.data
|
||||
ElMessage.success('预测完成！')
|
||||
await nextTick()
|
||||
renderChart()
|
||||
} else {
|
||||
ElMessage.error(response.data.message || '预测失败')
|
||||
}
|
||||
} catch (error) {
|
||||
ElMessage.error('预测请求失败')
|
||||
} finally {
|
||||
predicting.value = false
|
||||
}
|
||||
}
|
||||
|
||||
const renderChart = () => {
|
||||
if (!chartCanvas.value || !predictionResult.value) return
|
||||
if (chart) {
|
||||
chart.destroy()
|
||||
}
|
||||
const predictions = predictionResult.value.predictions
|
||||
const labels = predictions.map(p => p.date)
|
||||
const data = predictions.map(p => p.sales)
|
||||
chart = new Chart(chartCanvas.value, {
|
||||
type: 'line',
|
||||
data: {
|
||||
labels,
|
||||
datasets: [{
|
||||
label: '预测销量',
|
||||
data,
|
||||
borderColor: '#409EFF',
|
||||
backgroundColor: 'rgba(64, 158, 255, 0.1)',
|
||||
tension: 0.4,
|
||||
fill: true
|
||||
}]
|
||||
},
|
||||
options: {
|
||||
responsive: true,
|
||||
plugins: {
|
||||
title: {
|
||||
display: true,
|
||||
text: '销量预测趋势图'
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
onMounted(() => {
|
||||
fetchModelTypes()
|
||||
const today = new Date()
|
||||
form.start_date = today.toISOString().split('T')[0]
|
||||
})
|
||||
|
||||
watch([() => form.product_id, () => form.model_type], () => {
|
||||
fetchAvailableVersions()
|
||||
})
|
||||
</script>
|
||||
|
||||
<style scoped>
|
||||
.prediction-view {
|
||||
padding: 20px;
|
||||
}
|
||||
.card-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
}
|
||||
.model-selection-section h4 {
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
.prediction-actions {
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
margin-top: 20px;
|
||||
padding-top: 20px;
|
||||
border-top: 1px solid #ebeef5;
|
||||
}
|
||||
.prediction-chart {
|
||||
margin-top: 20px;
|
||||
}
|
||||
</style>
|
||||
295
UI/src/views/prediction/StorePredictionView.vue
Normal file
295
UI/src/views/prediction/StorePredictionView.vue
Normal file
@ -0,0 +1,295 @@
|
||||
<template>
|
||||
<div class="prediction-view">
|
||||
<el-card>
|
||||
<template #header>
|
||||
<div class="card-header">
|
||||
<span>按店铺预测</span>
|
||||
<el-tooltip content="使用针对特定店铺训练的模型进行销售预测">
|
||||
<el-icon><QuestionFilled /></el-icon>
|
||||
</el-tooltip>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
<div class="model-selection-section">
|
||||
<h4>🎯 选择预测模型</h4>
|
||||
<el-form :model="form" label-width="120px">
|
||||
<el-row :gutter="20">
|
||||
<el-col :span="8">
|
||||
<el-form-item label="目标店铺">
|
||||
<StoreSelector
|
||||
v-model="form.store_id"
|
||||
@change="handleStoreChange"
|
||||
:show-all-option="false"
|
||||
/>
|
||||
</el-form-item>
|
||||
</el-col>
|
||||
<el-col :span="8">
|
||||
<el-form-item label="算法类型">
|
||||
<el-select
|
||||
v-model="form.model_type"
|
||||
placeholder="选择算法"
|
||||
@change="handleModelTypeChange"
|
||||
style="width: 100%"
|
||||
:disabled="!form.store_id"
|
||||
>
|
||||
<el-option
|
||||
v-for="item in modelTypes"
|
||||
:key="item.id"
|
||||
:label="item.name"
|
||||
:value="item.id"
|
||||
/>
|
||||
</el-select>
|
||||
</el-form-item>
|
||||
</el-col>
|
||||
</el-row>
|
||||
|
||||
<el-row :gutter="20" v-if="form.model_type">
|
||||
<el-col :span="6">
|
||||
<el-form-item label="模型版本">
|
||||
<el-select
|
||||
v-model="form.version"
|
||||
placeholder="选择版本"
|
||||
style="width: 100%"
|
||||
:disabled="!availableVersions.length"
|
||||
:loading="versionsLoading"
|
||||
>
|
||||
<el-option
|
||||
v-for="version in availableVersions"
|
||||
:key="version"
|
||||
:label="version"
|
||||
:value="version"
|
||||
/>
|
||||
</el-select>
|
||||
</el-form-item>
|
||||
</el-col>
|
||||
<el-col :span="6">
|
||||
<el-form-item label="预测天数">
|
||||
<el-input-number
|
||||
v-model="form.future_days"
|
||||
:min="1"
|
||||
:max="365"
|
||||
style="width: 100%"
|
||||
/>
|
||||
</el-form-item>
|
||||
</el-col>
|
||||
<el-col :span="6">
|
||||
<el-form-item label="起始日期">
|
||||
<el-date-picker
|
||||
v-model="form.start_date"
|
||||
type="date"
|
||||
placeholder="选择日期"
|
||||
format="YYYY-MM-DD"
|
||||
value-format="YYYY-MM-DD"
|
||||
style="width: 100%"
|
||||
:clearable="false"
|
||||
/>
|
||||
</el-form-item>
|
||||
</el-col>
|
||||
<el-col :span="6">
|
||||
<el-form-item label="预测分析">
|
||||
<el-switch
|
||||
v-model="form.analyze_result"
|
||||
active-text="开启"
|
||||
inactive-text="关闭"
|
||||
/>
|
||||
</el-form-item>
|
||||
</el-col>
|
||||
</el-row>
|
||||
</el-form>
|
||||
</div>
|
||||
|
||||
<div class="prediction-actions">
|
||||
<el-button
|
||||
type="primary"
|
||||
size="large"
|
||||
@click="startPrediction"
|
||||
:loading="predicting"
|
||||
:disabled="!canPredict"
|
||||
>
|
||||
<el-icon><TrendCharts /></el-icon>
|
||||
开始预测
|
||||
</el-button>
|
||||
</div>
|
||||
</el-card>
|
||||
|
||||
<el-card v-if="predictionResult" style="margin-top: 20px">
|
||||
<template #header>
|
||||
<div class="card-header">
|
||||
<span>📈 预测结果</span>
|
||||
</div>
|
||||
</template>
|
||||
<div class="prediction-chart">
|
||||
<canvas ref="chartCanvas" width="800" height="400"></canvas>
|
||||
</div>
|
||||
</el-card>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
<script setup>
|
||||
import { ref, reactive, onMounted, computed, watch, nextTick } from 'vue'
|
||||
import axios from 'axios'
|
||||
import { ElMessage } from 'element-plus'
|
||||
import { QuestionFilled, TrendCharts } from '@element-plus/icons-vue'
|
||||
import Chart from 'chart.js/auto'
|
||||
import StoreSelector from '../../components/StoreSelector.vue'
|
||||
|
||||
const modelTypes = ref([])
|
||||
const availableVersions = ref([])
|
||||
const versionsLoading = ref(false)
|
||||
const predicting = ref(false)
|
||||
const predictionResult = ref(null)
|
||||
const chartCanvas = ref(null)
|
||||
let chart = null
|
||||
|
||||
const form = reactive({
|
||||
training_mode: 'store',
|
||||
store_id: '',
|
||||
model_type: '',
|
||||
version: '',
|
||||
future_days: 7,
|
||||
start_date: '',
|
||||
analyze_result: true
|
||||
})
|
||||
|
||||
const canPredict = computed(() => {
|
||||
return form.store_id && form.model_type && form.version
|
||||
})
|
||||
|
||||
const fetchModelTypes = async () => {
|
||||
try {
|
||||
const response = await axios.get('/api/model_types')
|
||||
if (response.data.status === 'success') {
|
||||
modelTypes.value = response.data.data
|
||||
}
|
||||
} catch (error) {
|
||||
ElMessage.error('获取模型类型失败')
|
||||
}
|
||||
}
|
||||
|
||||
const fetchAvailableVersions = async () => {
|
||||
if (!form.store_id || !form.model_type) {
|
||||
availableVersions.value = []
|
||||
return
|
||||
}
|
||||
try {
|
||||
versionsLoading.value = true
|
||||
const url = `/api/models/store/${form.store_id}/${form.model_type}/versions`
|
||||
const response = await axios.get(url)
|
||||
if (response.data.status === 'success') {
|
||||
availableVersions.value = response.data.data.versions || []
|
||||
if (response.data.data.latest_version) {
|
||||
form.version = response.data.data.latest_version
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
availableVersions.value = []
|
||||
} finally {
|
||||
versionsLoading.value = false
|
||||
}
|
||||
}
|
||||
|
||||
const handleStoreChange = () => {
|
||||
form.model_type = ''
|
||||
form.version = ''
|
||||
availableVersions.value = []
|
||||
}
|
||||
|
||||
const handleModelTypeChange = () => {
|
||||
form.version = ''
|
||||
fetchAvailableVersions()
|
||||
}
|
||||
|
||||
const startPrediction = async () => {
|
||||
try {
|
||||
predicting.value = true
|
||||
const payload = {
|
||||
model_type: form.model_type,
|
||||
version: form.version,
|
||||
future_days: form.future_days,
|
||||
start_date: form.start_date,
|
||||
analyze_result: form.analyze_result,
|
||||
store_id: form.store_id
|
||||
}
|
||||
const response = await axios.post('/api/predict', payload)
|
||||
if (response.data.status === 'success') {
|
||||
predictionResult.value = response.data.data
|
||||
ElMessage.success('预测完成！')
|
||||
await nextTick()
|
||||
renderChart()
|
||||
} else {
|
||||
ElMessage.error(response.data.message || '预测失败')
|
||||
}
|
||||
} catch (error) {
|
||||
ElMessage.error('预测请求失败')
|
||||
} finally {
|
||||
predicting.value = false
|
||||
}
|
||||
}
|
||||
|
||||
const renderChart = () => {
|
||||
if (!chartCanvas.value || !predictionResult.value) return
|
||||
if (chart) {
|
||||
chart.destroy()
|
||||
}
|
||||
const predictions = predictionResult.value.predictions
|
||||
const labels = predictions.map(p => p.date)
|
||||
const data = predictions.map(p => p.sales)
|
||||
chart = new Chart(chartCanvas.value, {
|
||||
type: 'line',
|
||||
data: {
|
||||
labels,
|
||||
datasets: [{
|
||||
label: '预测销量',
|
||||
data,
|
||||
borderColor: '#409EFF',
|
||||
backgroundColor: 'rgba(64, 158, 255, 0.1)',
|
||||
tension: 0.4,
|
||||
fill: true
|
||||
}]
|
||||
},
|
||||
options: {
|
||||
responsive: true,
|
||||
plugins: {
|
||||
title: {
|
||||
display: true,
|
||||
text: '销量预测趋势图'
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
onMounted(() => {
|
||||
fetchModelTypes()
|
||||
const today = new Date()
|
||||
form.start_date = today.toISOString().split('T')[0]
|
||||
})
|
||||
|
||||
watch([() => form.store_id, () => form.model_type], () => {
|
||||
fetchAvailableVersions()
|
||||
})
|
||||
</script>
|
||||
|
||||
<style scoped>
|
||||
.prediction-view {
|
||||
padding: 20px;
|
||||
}
|
||||
.card-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
}
|
||||
.model-selection-section h4 {
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
.prediction-actions {
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
margin-top: 20px;
|
||||
padding-top: 20px;
|
||||
border-top: 1px solid #ebeef5;
|
||||
}
|
||||
.prediction-chart {
|
||||
margin-top: 20px;
|
||||
}
|
||||
</style>
|
||||
180
Windows_快速启动.bat
180
Windows_快速启动.bat
@ -1,180 +0,0 @@
|
||||
@echo off
|
||||
chcp 65001 >nul
|
||||
echo ====================================
|
||||
echo 药店销售预测系统 - Windows 快速启动
|
||||
echo ====================================
|
||||
echo.
|
||||
|
||||
:: 检查Python
|
||||
echo [1/6] 检查Python环境...
|
||||
python --version >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo ❌ 未找到Python，请先安装Python 3.8+
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
echo ✓ Python环境正常
|
||||
|
||||
:: 检查虚拟环境
|
||||
echo.
|
||||
echo [2/6] 检查虚拟环境...
|
||||
if not exist ".venv\Scripts\python.exe" (
|
||||
echo 🔄 创建虚拟环境...
|
||||
python -m venv .venv
|
||||
if errorlevel 1 (
|
||||
echo ❌ 虚拟环境创建失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
)
|
||||
echo ✓ 虚拟环境准备完成
|
||||
|
||||
:: 激活虚拟环境
|
||||
echo.
|
||||
echo [3/6] 激活虚拟环境...
|
||||
call .venv\Scripts\activate.bat
|
||||
if errorlevel 1 (
|
||||
echo ❌ 虚拟环境激活失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
echo ✓ 虚拟环境已激活
|
||||
|
||||
:: 安装依赖
|
||||
echo.
|
||||
echo [4/6] 检查Python依赖...
|
||||
pip show flask >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo 🔄 安装Python依赖...
|
||||
pip install -r install\requirements.txt
|
||||
if errorlevel 1 (
|
||||
echo ❌ 依赖安装失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
)
|
||||
echo ✓ Python依赖已安装
|
||||
|
||||
:: 检查数据文件
|
||||
echo.
|
||||
echo [5/6] 检查数据文件...
|
||||
if not exist "pharmacy_sales_multi_store.csv" (
|
||||
echo 🔄 生成示例数据...
|
||||
python generate_multi_store_data.py
|
||||
if errorlevel 1 (
|
||||
echo ❌ 数据生成失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
)
|
||||
echo ✓ 数据文件准备完成
|
||||
|
||||
:: 初始化数据库
|
||||
echo.
|
||||
echo [6/6] 初始化数据库...
|
||||
if not exist "prediction_history.db" (
|
||||
echo 🔄 初始化数据库...
|
||||
python server\init_multi_store_db.py
|
||||
if errorlevel 1 (
|
||||
echo ❌ 数据库初始化失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
)
|
||||
echo ✓ 数据库准备完成
|
||||
|
||||
echo.
|
||||
echo ====================================
|
||||
echo ✅ 环境准备完成！
|
||||
echo ====================================
|
||||
echo.
|
||||
echo 接下来请选择启动方式：
|
||||
echo [1] 启动API服务器 (后端)
|
||||
echo [2] 启动前端开发服务器
|
||||
echo [3] 运行API测试
|
||||
echo [4] 查看项目状态
|
||||
echo [0] 退出
|
||||
echo.
|
||||
|
||||
:menu
|
||||
set /p choice="请选择 (0-4): "
|
||||
|
||||
if "%choice%"=="1" goto start_api
|
||||
if "%choice%"=="2" goto start_frontend
|
||||
if "%choice%"=="3" goto run_tests
|
||||
if "%choice%"=="4" goto show_status
|
||||
if "%choice%"=="0" goto end
|
||||
echo 无效选择，请重新输入
|
||||
goto menu
|
||||
|
||||
:start_api
|
||||
echo.
|
||||
echo 🚀 启动API服务器...
|
||||
echo 服务器将在 http://localhost:5000 启动
|
||||
echo API文档访问: http://localhost:5000/swagger
|
||||
echo.
|
||||
echo 按 Ctrl+C 停止服务器
|
||||
echo.
|
||||
cd server
|
||||
python api.py
|
||||
goto end
|
||||
|
||||
:start_frontend
|
||||
echo.
|
||||
echo 🚀 启动前端开发服务器...
|
||||
cd UI
|
||||
if not exist "node_modules" (
|
||||
echo 🔄 安装前端依赖...
|
||||
npm install
|
||||
if errorlevel 1 (
|
||||
echo ❌ 前端依赖安装失败
|
||||
pause
|
||||
goto menu
|
||||
)
|
||||
)
|
||||
echo 前端将在 http://localhost:5173 启动
|
||||
echo.
|
||||
npm run dev
|
||||
goto end
|
||||
|
||||
:run_tests
|
||||
echo.
|
||||
echo 🧪 运行API测试...
|
||||
python test_api_endpoints.py
|
||||
echo.
|
||||
pause
|
||||
goto menu
|
||||
|
||||
:show_status
|
||||
echo.
|
||||
echo 📊 项目状态检查...
|
||||
echo.
|
||||
echo === 文件检查 ===
|
||||
if exist "pharmacy_sales_multi_store.csv" (echo ✓ 多店铺数据文件) else (echo ❌ 多店铺数据文件缺失)
|
||||
if exist "prediction_history.db" (echo ✓ 预测历史数据库) else (echo ❌ 预测历史数据库缺失)
|
||||
if exist "server\api.py" (echo ✓ API服务器文件) else (echo ❌ API服务器文件缺失)
|
||||
if exist "UI\package.json" (echo ✓ 前端项目文件) else (echo ❌ 前端项目文件缺失)
|
||||
|
||||
echo.
|
||||
echo === 模型文件 ===
|
||||
if exist "saved_models" (
|
||||
echo 已保存的模型:
|
||||
dir saved_models\*.pth /b 2>nul || echo 暂无已训练的模型
|
||||
) else (
|
||||
echo ❌ 模型目录不存在
|
||||
)
|
||||
|
||||
echo.
|
||||
echo === 虚拟环境状态 ===
|
||||
python -c "import sys; print('Python版本:', sys.version)"
|
||||
python -c "import flask; print('Flask版本:', flask.__version__)" 2>nul || echo ❌ Flask未安装
|
||||
|
||||
echo.
|
||||
pause
|
||||
goto menu
|
||||
|
||||
:end
|
||||
echo.
|
||||
echo 感谢使用药店销售预测系统！
|
||||
echo.
|
||||
pause
|
||||
0
docs/UI_PREDICTION_FEATURE_CHANGELOG.md
Normal file
0
docs/UI_PREDICTION_FEATURE_CHANGELOG.md
Normal file
@ -1,5 +1,28 @@
|
||||
@echo off
|
||||
chcp 65001 >nul
|
||||
|
||||
REM 检查.venv目录是否存在
|
||||
if exist .venv (
|
||||
echo 虚拟环境已存在。
|
||||
) else (
|
||||
echo 正在创建虚拟环境...
|
||||
uv venv
|
||||
if errorlevel 1 (
|
||||
echo 创建虚拟环境失败。
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
echo.
|
||||
echo 虚拟环境已创建。请激活它后重新运行此脚本。
|
||||
echo - Windows (CMD): .\.venv\Scripts\activate
|
||||
echo - Windows (PowerShell): .\.venv\Scripts\Activate.ps1
|
||||
echo - Linux/macOS: source .venv/bin/activate
|
||||
echo.
|
||||
pause
|
||||
exit /b 0
|
||||
)
|
||||
|
||||
echo 正在安装药店销售预测系统API依赖...
|
||||
pip install flask==3.1.1 flask-cors==6.0.0 flasgger==0.9.7.1
|
||||
uv pip install flask==3.1.1 flask-cors==6.0.0 flasgger==0.9.7.1 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
echo 依赖安装完成，现在可以运行 python api.py 启动API服务
|
||||
pause
|
||||
pause
|
||||
@ -1,8 +1,33 @@
|
||||
@echo off
|
||||
chcp 65001 >nul
|
||||
|
||||
REM 检查.venv目录是否存在
|
||||
if exist .venv (
|
||||
echo 虚拟环境已存在。
|
||||
) else (
|
||||
echo 正在创建虚拟环境...
|
||||
uv venv
|
||||
if errorlevel 1 (
|
||||
echo 创建虚拟环境失败。
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
echo.
|
||||
echo 虚拟环境已创建。请激活它后重新运行此脚本。
|
||||
echo - Windows (CMD): .\.venv\Scripts\activate
|
||||
echo - Windows (PowerShell): .\.venv\Scripts\Activate.ps1
|
||||
echo - Linux/macOS: source .venv/bin/activate
|
||||
echo.
|
||||
pause
|
||||
exit /b 0
|
||||
)
|
||||
|
||||
echo.
|
||||
echo 药店销售预测系统 - 依赖库安装脚本
|
||||
echo ==================================
|
||||
echo.
|
||||
echo 虚拟环境已激活，准备安装依赖。
|
||||
echo.
|
||||
|
||||
echo 请选择要安装的版本:
|
||||
echo 1. CPU版本（适用于没有NVIDIA GPU的计算机）
|
||||
@ -14,23 +39,23 @@ set /p choice=请输入选项 (1/2/3):
|
||||
|
||||
if "%choice%"=="1" (
|
||||
echo 正在安装CPU版本依赖...
|
||||
pip install -r requirements.txt
|
||||
uv pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
) else if "%choice%"=="2" (
|
||||
echo 正在安装GPU版本(CUDA 12.1)依赖...
|
||||
echo 首先安装基础依赖...
|
||||
pip install -r requirements-gpu.txt --no-deps
|
||||
uv pip install -r requirements-gpu.txt --no-deps -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
echo 安装除PyTorch以外的其他依赖...
|
||||
pip install numpy==2.3.0 pandas==2.3.0 matplotlib==3.10.3 scikit-learn==1.7.0 tqdm==4.67.1 openpyxl==3.1.5
|
||||
uv pip install numpy==2.3.0 pandas==2.3.0 matplotlib==3.10.3 scikit-learn==1.7.0 tqdm==4.67.1 openpyxl==3.1.5 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
echo 从PyTorch官方源安装CUDA 12.1版本的PyTorch...
|
||||
pip install torch==2.7.1 --index-url https://download.pytorch.org/whl/cu121
|
||||
uv pip install torch==2.7.1 --index-url https://download.pytorch.org/whl/cu121
|
||||
) else if "%choice%"=="3" (
|
||||
echo 正在安装GPU版本(CUDA 11.8)依赖...
|
||||
echo 首先安装基础依赖...
|
||||
pip install -r requirements-gpu-cu118.txt --no-deps
|
||||
uv pip install -r requirements-gpu-cu118.txt --no-deps -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
echo 安装除PyTorch以外的其他依赖...
|
||||
pip install numpy==2.3.0 pandas==2.3.0 matplotlib==3.10.3 scikit-learn==1.7.0 tqdm==4.67.1 openpyxl==3.1.5
|
||||
uv pip install numpy==2.3.0 pandas==2.3.0 matplotlib==3.10.3 scikit-learn==1.7.0 tqdm==4.67.1 openpyxl==3.1.5 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
echo 从PyTorch官方源安装CUDA 11.8版本的PyTorch...
|
||||
pip install torch==2.7.1 --index-url https://download.pytorch.org/whl/cu118
|
||||
uv pip install torch==2.7.1 --index-url https://download.pytorch.org/whl/cu118
|
||||
) else (
|
||||
echo 无效的选项！请重新运行脚本并选择正确的选项。
|
||||
goto end
|
||||
|
||||
@ -1,5 +1,27 @@
|
||||
@echo off
|
||||
chcp 65001 >nul
|
||||
|
||||
REM 检查.venv目录是否存在
|
||||
if exist .venv (
|
||||
echo 虚拟环境已存在。
|
||||
) else (
|
||||
echo 正在创建虚拟环境...
|
||||
uv venv
|
||||
if errorlevel 1 (
|
||||
echo 创建虚拟环境失败。
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
echo.
|
||||
echo 虚拟环境已创建。请激活它后重新运行此脚本。
|
||||
echo - Windows (CMD): .\.venv\Scripts\activate
|
||||
echo - Windows (PowerShell): .\.venv\Scripts\Activate.ps1
|
||||
echo - Linux/macOS: source .venv/bin/activate
|
||||
echo.
|
||||
pause
|
||||
exit /b 0
|
||||
)
|
||||
|
||||
echo 安装PyTorch GPU版本（通过官方源）
|
||||
echo ===================================
|
||||
echo.
|
||||
@ -14,15 +36,15 @@ set /p choice=请输入选项 (1/2):
|
||||
|
||||
if "%choice%"=="1" (
|
||||
echo 正在安装PyTorch CUDA 12.8版本...
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
)
|
||||
else if "%choice%"=="2" (
|
||||
echo 正在安装PyTorch CUDA 12.6版本...
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
)
|
||||
else if "%choice%"=="3" (
|
||||
echo 正在安装PyTorch CUDA 11.8版本...
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
) else (
|
||||
echo 无效的选项！
|
||||
goto end
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
Binary file not shown.
@ -35,7 +35,11 @@ def evaluate_model(y_true, y_pred):
|
||||
# 计算平均绝对百分比误差 (MAPE)
|
||||
# 避免除以零
|
||||
mask = y_true != 0
|
||||
mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100
|
||||
if np.any(mask):
|
||||
mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100
|
||||
else:
|
||||
# 如果所有真实值都为0，无法计算MAPE，返回0
|
||||
mape = 0.0
|
||||
|
||||
return {
|
||||
'mse': mse,
|
||||
|
||||
@ -160,7 +160,8 @@ def train_store_model(store_id, model_type, epochs=50, product_scope='all', prod
|
||||
|
||||
# 读取店铺所有数据，找到第一个有数据的药品
|
||||
try:
|
||||
df = pd.read_csv('pharmacy_sales_multi_store.csv')
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data()
|
||||
store_products = df[df['store_id'] == store_id]['product_id'].unique()
|
||||
|
||||
if len(store_products) == 0:
|
||||
@ -207,7 +208,8 @@ def train_global_model(model_type, epochs=50, training_scope='all_stores_all_pro
|
||||
import pandas as pd
|
||||
|
||||
# 读取数据
|
||||
df = pd.read_csv('pharmacy_sales_multi_store.csv')
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data()
|
||||
|
||||
# 根据训练范围过滤数据
|
||||
if training_scope == 'selected_stores' and store_ids:
|
||||
@ -631,7 +633,7 @@ def swagger_ui():
|
||||
def get_products():
|
||||
try:
|
||||
from utils.multi_store_data_utils import get_available_products
|
||||
products = get_available_products('pharmacy_sales_multi_store.csv')
|
||||
products = get_available_products()
|
||||
return jsonify({"status": "success", "data": products})
|
||||
except Exception as e:
|
||||
return jsonify({"status": "error", "message": str(e)}), 500
|
||||
@ -686,7 +688,7 @@ def get_products():
|
||||
def get_product(product_id):
|
||||
try:
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
df = load_multi_store_data(product_id=product_id)
|
||||
|
||||
if df.empty:
|
||||
return jsonify({"status": "error", "message": "产品不存在"}), 404
|
||||
@ -764,7 +766,6 @@ def get_product_sales(product_id):
|
||||
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data(
|
||||
'pharmacy_sales_multi_store.csv',
|
||||
product_id=product_id,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
@ -919,8 +920,8 @@ def get_all_training_tasks():
|
||||
tasks_with_id.append(task_copy)
|
||||
|
||||
# 按开始时间降序排序，最新的任务在前面
|
||||
sorted_tasks = sorted(tasks_with_id,
|
||||
key=lambda x: x.get('start_time', ''),
|
||||
sorted_tasks = sorted(tasks_with_id,
|
||||
key=lambda x: x.get('start_time') or '1970-01-01 00:00:00',
|
||||
reverse=True)
|
||||
|
||||
return jsonify({"status": "success", "data": sorted_tasks})
|
||||
@ -1713,7 +1714,8 @@ def compare_predictions():
|
||||
predictor = PharmacyPredictor()
|
||||
|
||||
# 获取产品名称
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data()
|
||||
product_df = df[df['product_id'] == product_id]
|
||||
|
||||
if product_df.empty:
|
||||
@ -1868,7 +1870,8 @@ def analyze_prediction():
|
||||
predictions_array = np.array(predictions)
|
||||
|
||||
# 获取产品特征数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data()
|
||||
product_df = df[df['product_id'] == product_id].sort_values('date')
|
||||
|
||||
if product_df.empty:
|
||||
@ -2689,7 +2692,8 @@ def get_product_name(product_id):
|
||||
"""根据产品ID获取产品名称"""
|
||||
try:
|
||||
# 从Excel文件中查找产品名称
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data()
|
||||
product_df = df[df['product_id'] == product_id]
|
||||
if not product_df.empty:
|
||||
return product_df['product_name'].iloc[0]
|
||||
@ -2750,7 +2754,8 @@ def run_prediction(model_type, product_id, model_id, future_days, start_date, ve
|
||||
# 获取历史数据用于对比
|
||||
try:
|
||||
# 读取原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data()
|
||||
product_df = df[df['product_id'] == product_id].copy()
|
||||
|
||||
if not product_df.empty:
|
||||
@ -4026,7 +4031,7 @@ def get_stores():
|
||||
"""
|
||||
try:
|
||||
from utils.multi_store_data_utils import get_available_stores
|
||||
stores = get_available_stores('pharmacy_sales_multi_store.csv')
|
||||
stores = get_available_stores()
|
||||
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
@ -4046,7 +4051,7 @@ def get_store(store_id):
|
||||
"""
|
||||
try:
|
||||
from utils.multi_store_data_utils import get_available_stores
|
||||
stores = get_available_stores('pharmacy_sales_multi_store.csv')
|
||||
stores = get_available_stores()
|
||||
|
||||
store = None
|
||||
for s in stores:
|
||||
@ -4282,7 +4287,8 @@ def get_global_training_stats():
|
||||
import pandas as pd
|
||||
|
||||
# 读取数据
|
||||
df = pd.read_csv('pharmacy_sales_multi_store.csv')
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
df = load_multi_store_data()
|
||||
|
||||
# 根据训练范围过滤数据
|
||||
if training_scope == 'selected_stores' and store_ids:
|
||||
@ -4360,7 +4366,6 @@ def get_sales_data():
|
||||
|
||||
# 加载过滤后的数据
|
||||
df = load_multi_store_data(
|
||||
'pharmacy_sales_multi_store.csv',
|
||||
store_id=store_id,
|
||||
product_id=product_id,
|
||||
start_date=start_date,
|
||||
@ -4376,9 +4381,13 @@ def get_sales_data():
|
||||
"total_records": 0,
|
||||
"total_sales_amount": 0,
|
||||
"total_quantity": 0,
|
||||
"stores": 0
|
||||
"stores": 0,
|
||||
"products": 0,
|
||||
"date_range": {"start": "", "end": ""}
|
||||
}
|
||||
})
|
||||
|
||||
# 数据标准化已在load_multi_store_data中完成，此处无需重复计算
|
||||
|
||||
# 计算总数
|
||||
total_records = len(df)
|
||||
@ -4391,8 +4400,12 @@ def get_sales_data():
|
||||
# 转换为字典列表
|
||||
data = []
|
||||
for _, row in paginated_df.iterrows():
|
||||
# 安全地获取和格式化日期
|
||||
date_val = row.get('date')
|
||||
date_str = date_val.strftime('%Y-%m-%d') if pd.notna(date_val) else ''
|
||||
|
||||
record = {
|
||||
'date': row['date'].strftime('%Y-%m-%d') if hasattr(row['date'], 'strftime') else str(row['date']),
|
||||
'date': date_str,
|
||||
'store_id': row.get('store_id', ''),
|
||||
'store_name': row.get('store_name', ''),
|
||||
'store_location': row.get('store_location', ''),
|
||||
@ -4400,22 +4413,25 @@ def get_sales_data():
|
||||
'product_id': row.get('product_id', ''),
|
||||
'product_name': row.get('product_name', ''),
|
||||
'product_category': row.get('product_category', ''),
|
||||
'unit_price': float(row.get('unit_price', 0)),
|
||||
'quantity_sold': int(row.get('quantity_sold', 0)),
|
||||
'sales_amount': float(row.get('sales_amount', 0))
|
||||
'unit_price': float(row.get('price', 0.0)) if pd.notna(row.get('price')) else 0.0,
|
||||
'quantity_sold': int(row.get('sales', 0)) if pd.notna(row.get('sales')) else 0,
|
||||
'sales_amount': float(row.get('sales_amount', 0.0)) if pd.notna(row.get('sales_amount')) else 0.0
|
||||
}
|
||||
data.append(record)
|
||||
|
||||
# 计算统计信息
|
||||
# 从日期列中删除NaT以安全地计算min/max
|
||||
df_dates = df['date'].dropna()
|
||||
|
||||
statistics = {
|
||||
'total_records': total_records,
|
||||
'total_sales_amount': float(df['sales_amount'].sum()) if 'sales_amount' in df.columns else 0,
|
||||
'total_quantity': int(df['quantity_sold'].sum()) if 'quantity_sold' in df.columns else 0,
|
||||
'total_sales_amount': float(df['sales_amount'].sum()) if 'sales_amount' in df.columns and not df['sales_amount'].empty else 0,
|
||||
'total_quantity': int(df['sales'].sum()) if 'sales' in df.columns and not df['sales'].empty else 0,
|
||||
'stores': df['store_id'].nunique() if 'store_id' in df.columns else 0,
|
||||
'products': df['product_id'].nunique() if 'product_id' in df.columns else 0,
|
||||
'date_range': {
|
||||
'start': df['date'].min().strftime('%Y-%m-%d') if len(df) > 0 and hasattr(df['date'].min(), 'strftime') else '',
|
||||
'end': df['date'].max().strftime('%Y-%m-%d') if len(df) > 0 and hasattr(df['date'].max(), 'strftime') else ''
|
||||
'start': df_dates.min().strftime('%Y-%m-%d') if not df_dates.empty else '',
|
||||
'end': df_dates.max().strftime('%Y-%m-%d') if not df_dates.empty else ''
|
||||
}
|
||||
}
|
||||
|
||||
@ -4429,6 +4445,8 @@ def get_sales_data():
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取销售数据失败: {str(e)}")
|
||||
logger.error(traceback.format_exc()) # 记录完整的堆栈跟踪
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"获取销售数据失败: {str(e)}"
|
||||
@ -4518,11 +4536,12 @@ if __name__ == '__main__':
|
||||
try:
|
||||
# 使用 SocketIO 启动应用
|
||||
socketio.run(
|
||||
app,
|
||||
host=args.host,
|
||||
port=args.port,
|
||||
app,
|
||||
host=args.host,
|
||||
port=args.port,
|
||||
debug=args.debug,
|
||||
use_reloader=False, # 关闭重载器避免冲突
|
||||
allow_unsafe_werkzeug=True if args.debug else False,
|
||||
log_output=True
|
||||
)
|
||||
finally:
|
||||
|
||||
@ -41,7 +41,7 @@ class PharmacyPredictor:
|
||||
"""
|
||||
# 设置默认数据路径为多店铺CSV文件
|
||||
if data_path is None:
|
||||
data_path = 'pharmacy_sales_multi_store.csv'
|
||||
data_path = 'data/timeseries_training_data_sample_10s50p.parquet'
|
||||
|
||||
self.data_path = data_path
|
||||
self.model_dir = model_dir
|
||||
@ -117,30 +117,59 @@ class PharmacyPredictor:
|
||||
log_message(f"按产品训练模式: 产品 {product_id}, 数据量: {len(product_data)}")
|
||||
|
||||
elif training_mode == 'store':
|
||||
# 按店铺训练：使用特定店铺的特定产品数据
|
||||
# 按店铺训练
|
||||
if not store_id:
|
||||
log_message("店铺训练模式需要指定 store_id", 'error')
|
||||
return None
|
||||
try:
|
||||
product_data = get_store_product_sales_data(
|
||||
store_id=store_id,
|
||||
product_id=product_id,
|
||||
file_path=self.data_path
|
||||
)
|
||||
log_message(f"按店铺训练模式: 店铺 {store_id}, 产品 {product_id}, 数据量: {len(product_data)}")
|
||||
except Exception as e:
|
||||
log_message(f"获取店铺产品数据失败: {e}", 'error')
|
||||
return None
|
||||
|
||||
# 如果product_id是'unknown'，则表示为店铺所有商品训练一个聚合模型
|
||||
if product_id == 'unknown':
|
||||
try:
|
||||
# 使用新的聚合函数，按店铺聚合
|
||||
product_data = aggregate_multi_store_data(
|
||||
store_id=store_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path=self.data_path
|
||||
)
|
||||
log_message(f"按店铺聚合训练: 店铺 {store_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
|
||||
# 将product_id设置为店铺ID，以便模型保存时使用有意义的标识
|
||||
product_id = store_id
|
||||
except Exception as e:
|
||||
log_message(f"聚合店铺 {store_id} 数据失败: {e}", 'error')
|
||||
return None
|
||||
else:
|
||||
# 为店铺的单个特定产品训练
|
||||
try:
|
||||
product_data = get_store_product_sales_data(
|
||||
store_id=store_id,
|
||||
product_id=product_id,
|
||||
file_path=self.data_path
|
||||
)
|
||||
log_message(f"按店铺-产品训练: 店铺 {store_id}, 产品 {product_id}, 数据量: {len(product_data)}")
|
||||
except Exception as e:
|
||||
log_message(f"获取店铺产品数据失败: {e}", 'error')
|
||||
return None
|
||||
|
||||
elif training_mode == 'global':
|
||||
# 全局训练：聚合所有店铺的产品数据
|
||||
try:
|
||||
product_data = aggregate_multi_store_data(
|
||||
product_id=product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path=self.data_path
|
||||
)
|
||||
log_message(f"全局训练模式: 产品 {product_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
|
||||
# 如果product_id是'unknown'，则表示为全局所有商品训练一个聚合模型
|
||||
if product_id == 'unknown':
|
||||
product_data = aggregate_multi_store_data(
|
||||
product_id=None, # 传递None以触发真正的全局聚合
|
||||
aggregation_method=aggregation_method,
|
||||
file_path=self.data_path
|
||||
)
|
||||
log_message(f"全局训练模式: 所有产品, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
|
||||
# 将product_id设置为一个有意义的标识符
|
||||
product_id = 'all_products'
|
||||
else:
|
||||
product_data = aggregate_multi_store_data(
|
||||
product_id=product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path=self.data_path
|
||||
)
|
||||
log_message(f"全局训练模式: 产品 {product_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
|
||||
except Exception as e:
|
||||
log_message(f"聚合全局数据失败: {e}", 'error')
|
||||
return None
|
||||
@ -161,11 +190,12 @@ class PharmacyPredictor:
|
||||
log_message(f"🤖 开始调用 {model_type} 训练器")
|
||||
if model_type == 'transformer':
|
||||
model_result, metrics, actual_version = train_product_model_with_transformer(
|
||||
product_id,
|
||||
product_id=product_id,
|
||||
product_df=product_data,
|
||||
store_id=store_id,
|
||||
training_mode=training_mode,
|
||||
aggregation_method=aggregation_method,
|
||||
epochs=epochs,
|
||||
epochs=epochs,
|
||||
model_dir=self.model_dir,
|
||||
version=version,
|
||||
socketio=socketio,
|
||||
@ -175,11 +205,12 @@ class PharmacyPredictor:
|
||||
log_message(f"✅ {model_type} 训练器返回: metrics={type(metrics)}, version={actual_version}", 'success')
|
||||
elif model_type == 'mlstm':
|
||||
_, metrics, _, _ = train_product_model_with_mlstm(
|
||||
product_id,
|
||||
product_id=product_id,
|
||||
product_df=product_data,
|
||||
store_id=store_id,
|
||||
training_mode=training_mode,
|
||||
aggregation_method=aggregation_method,
|
||||
epochs=epochs,
|
||||
epochs=epochs,
|
||||
model_dir=self.model_dir,
|
||||
socketio=socketio,
|
||||
task_id=task_id,
|
||||
@ -187,31 +218,34 @@ class PharmacyPredictor:
|
||||
)
|
||||
elif model_type == 'kan':
|
||||
_, metrics = train_product_model_with_kan(
|
||||
product_id,
|
||||
product_id=product_id,
|
||||
product_df=product_data,
|
||||
store_id=store_id,
|
||||
training_mode=training_mode,
|
||||
aggregation_method=aggregation_method,
|
||||
epochs=epochs,
|
||||
use_optimized=use_optimized,
|
||||
epochs=epochs,
|
||||
use_optimized=use_optimized,
|
||||
model_dir=self.model_dir
|
||||
)
|
||||
elif model_type == 'optimized_kan':
|
||||
_, metrics = train_product_model_with_kan(
|
||||
product_id,
|
||||
product_id=product_id,
|
||||
product_df=product_data,
|
||||
store_id=store_id,
|
||||
training_mode=training_mode,
|
||||
aggregation_method=aggregation_method,
|
||||
epochs=epochs,
|
||||
use_optimized=True,
|
||||
epochs=epochs,
|
||||
use_optimized=True,
|
||||
model_dir=self.model_dir
|
||||
)
|
||||
elif model_type == 'tcn':
|
||||
_, metrics, _, _ = train_product_model_with_tcn(
|
||||
product_id,
|
||||
product_id=product_id,
|
||||
product_df=product_data,
|
||||
store_id=store_id,
|
||||
training_mode=training_mode,
|
||||
aggregation_method=aggregation_method,
|
||||
epochs=epochs,
|
||||
epochs=epochs,
|
||||
model_dir=self.model_dir,
|
||||
socketio=socketio,
|
||||
task_id=task_id
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from typing import Tuple
|
||||
from .transformer_model import TransformerEncoder, TransformerDecoder
|
||||
|
||||
# 定义mLSTM单元
|
||||
@ -48,8 +49,8 @@ class mLSTMCell(nn.Module):
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
internal_state: tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
||||
) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
|
||||
internal_state: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
||||
) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
|
||||
# 获取内部状态
|
||||
C, n, m = internal_state
|
||||
|
||||
@ -112,7 +113,7 @@ class mLSTMCell(nn.Module):
|
||||
|
||||
def init_hidden(
|
||||
self, batch_size: int, **kwargs
|
||||
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
return (
|
||||
torch.zeros(batch_size, self.hidden_size, self.hidden_size, **kwargs),
|
||||
torch.zeros(batch_size, self.hidden_size, **kwargs),
|
||||
@ -237,4 +238,5 @@ class MLSTMTransformer(nn.Module):
|
||||
for decoder in self.decoders:
|
||||
decoder_outputs = decoder(decoder_outputs, encoder_outputs)
|
||||
|
||||
return self.output_layer(decoder_outputs)
|
||||
# 移除最后一个维度，使输出为 (B, H)
|
||||
return self.output_layer(decoder_outputs).squeeze(-1)
|
||||
@ -1,5 +1,6 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from typing import Tuple
|
||||
|
||||
# 定义sLSTM单元
|
||||
class sLSTMCell(nn.Module):
|
||||
@ -27,9 +28,9 @@ class sLSTMCell(nn.Module):
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
internal_state: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
|
||||
) -> tuple[
|
||||
torch.Tensor, tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
|
||||
internal_state: Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
|
||||
) -> Tuple[
|
||||
torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
|
||||
]:
|
||||
# 解包内部状态
|
||||
h, c, n, m = internal_state # (batch_size, hidden_size)
|
||||
@ -78,7 +79,7 @@ class sLSTMCell(nn.Module):
|
||||
|
||||
def init_hidden(
|
||||
self, batch_size: int, **kwargs
|
||||
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
return (
|
||||
torch.zeros(batch_size, self.hidden_size, **kwargs),
|
||||
torch.zeros(batch_size, self.hidden_size, **kwargs),
|
||||
|
||||
@ -104,4 +104,5 @@ class TimeSeriesTransformer(nn.Module):
|
||||
for decoder in self.decoders:
|
||||
decoder_outputs = decoder(decoder_outputs, encoder_outputs)
|
||||
|
||||
return self.output_layer(decoder_outputs) # [batch_size, output_sequence_length, 1]
|
||||
# 移除最后一个维度，使输出为 (B, H)
|
||||
return self.output_layer(decoder_outputs).squeeze(-1)
|
||||
@ -21,7 +21,7 @@ from utils.visualization import plot_loss_curve
|
||||
from analysis.metrics import evaluate_model
|
||||
from core.config import DEVICE, DEFAULT_MODEL_DIR, LOOK_BACK, FORECAST_HORIZON
|
||||
|
||||
def train_product_model_with_kan(product_id, store_id=None, training_mode='product', aggregation_method='sum', epochs=50, use_optimized=False, model_dir=DEFAULT_MODEL_DIR):
|
||||
def train_product_model_with_kan(product_id, product_df=None, store_id=None, training_mode='product', aggregation_method='sum', epochs=50, use_optimized=False, model_dir=DEFAULT_MODEL_DIR):
|
||||
"""
|
||||
使用KAN模型训练产品销售预测模型
|
||||
|
||||
@ -35,36 +35,45 @@ def train_product_model_with_kan(product_id, store_id=None, training_mode='produ
|
||||
model: 训练好的模型
|
||||
metrics: 模型评估指标
|
||||
"""
|
||||
# 根据训练模式加载数据
|
||||
from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
|
||||
|
||||
try:
|
||||
# 如果没有传入product_df，则根据训练模式加载数据
|
||||
if product_df is None:
|
||||
from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
|
||||
|
||||
try:
|
||||
if training_mode == 'store' and store_id:
|
||||
# 加载特定店铺的数据
|
||||
product_df = get_store_product_sales_data(
|
||||
store_id,
|
||||
product_id,
|
||||
'pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
# 聚合所有店铺的数据
|
||||
product_df = aggregate_multi_store_data(
|
||||
product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path='pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
# 默认：加载所有店铺的产品数据
|
||||
product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
training_scope = "所有店铺"
|
||||
except Exception as e:
|
||||
print(f"多店铺数据加载失败: {e}")
|
||||
# 后备方案：尝试原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
product_df = df[df['product_id'] == product_id].sort_values('date')
|
||||
training_scope = "原始数据"
|
||||
else:
|
||||
# 如果传入了product_df，直接使用
|
||||
if training_mode == 'store' and store_id:
|
||||
# 加载特定店铺的数据
|
||||
product_df = get_store_product_sales_data(
|
||||
store_id,
|
||||
product_id,
|
||||
'pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
# 聚合所有店铺的数据
|
||||
product_df = aggregate_multi_store_data(
|
||||
product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path='pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
# 默认：加载所有店铺的产品数据
|
||||
product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
training_scope = "所有店铺"
|
||||
except Exception as e:
|
||||
print(f"多店铺数据加载失败: {e}")
|
||||
# 后备方案：尝试原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
product_df = df[df['product_id'] == product_id].sort_values('date')
|
||||
training_scope = "原始数据"
|
||||
|
||||
if product_df.empty:
|
||||
raise ValueError(f"产品 {product_id} 没有可用的销售数据")
|
||||
@ -95,7 +104,7 @@ def train_product_model_with_kan(product_id, store_id=None, training_mode='produ
|
||||
print(f"模型将保存到目录: {model_dir}")
|
||||
|
||||
# 创建特征和目标变量
|
||||
features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
|
||||
# 预处理数据
|
||||
X = product_df[features].values
|
||||
|
||||
@ -105,17 +105,21 @@ def load_checkpoint(product_id: str, model_type: str, epoch_or_label,
|
||||
return None
|
||||
|
||||
def train_product_model_with_mlstm(
|
||||
product_id,
|
||||
product_id,
|
||||
product_df,
|
||||
store_id=None,
|
||||
training_mode='product',
|
||||
aggregation_method='sum',
|
||||
epochs=50,
|
||||
model_dir=DEFAULT_MODEL_DIR,
|
||||
epochs=50,
|
||||
model_dir=DEFAULT_MODEL_DIR,
|
||||
version=None,
|
||||
socketio=None,
|
||||
task_id=None,
|
||||
continue_training=False,
|
||||
progress_callback=None
|
||||
progress_callback=None,
|
||||
patience=10,
|
||||
learning_rate=0.001,
|
||||
clip_norm=1.0
|
||||
):
|
||||
"""
|
||||
使用mLSTM训练产品销售预测模型
|
||||
@ -169,9 +173,6 @@ def train_product_model_with_mlstm(
|
||||
|
||||
emit_progress("开始mLSTM模型训练...")
|
||||
|
||||
# 根据训练模式加载数据
|
||||
from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
|
||||
|
||||
# 确定版本号
|
||||
if version is None:
|
||||
if continue_training:
|
||||
@ -204,35 +205,14 @@ def train_product_model_with_mlstm(
|
||||
print(f"[mLSTM] 任务 {task_id}: 使用现有进度管理器", flush=True)
|
||||
except Exception as e:
|
||||
print(f"[mLSTM] 任务 {task_id}: 进度管理器初始化失败: {e}", flush=True)
|
||||
|
||||
# 根据训练模式加载数据
|
||||
try:
|
||||
if training_mode == 'store' and store_id:
|
||||
# 加载特定店铺的数据
|
||||
product_df = get_store_product_sales_data(
|
||||
store_id,
|
||||
product_id,
|
||||
'pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
# 聚合所有店铺的数据
|
||||
product_df = aggregate_multi_store_data(
|
||||
product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path='pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
# 默认：加载所有店铺的产品数据
|
||||
product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
training_scope = "所有店铺"
|
||||
except Exception as e:
|
||||
print(f"多店铺数据加载失败: {e}")
|
||||
# 后备方案：尝试原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
product_df = df[df['product_id'] == product_id].sort_values(by='date')
|
||||
training_scope = "原始数据"
|
||||
|
||||
# 数据现在由调用方传入，不再在此处加载
|
||||
if training_mode == 'store' and store_id:
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
training_scope = "所有店铺"
|
||||
|
||||
# 数据量检查
|
||||
min_required_samples = LOOK_BACK + FORECAST_HORIZON
|
||||
@ -263,7 +243,7 @@ def train_product_model_with_mlstm(
|
||||
emit_progress(f"训练产品: {product_name} (ID: {product_id}) - {training_scope}")
|
||||
|
||||
# 创建特征和目标变量
|
||||
features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
|
||||
print(f"[mLSTM] 开始数据预处理，特征: {features}", flush=True)
|
||||
|
||||
@ -359,8 +339,9 @@ def train_product_model_with_mlstm(
|
||||
model = model.to(DEVICE)
|
||||
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = optim.Adam(model.parameters(), lr=0.001)
|
||||
|
||||
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=patience // 2, factor=0.5, verbose=True)
|
||||
|
||||
emit_progress("数据预处理完成，开始模型训练...", progress=10)
|
||||
|
||||
# 训练模型
|
||||
@ -371,8 +352,9 @@ def train_product_model_with_mlstm(
|
||||
# 配置检查点保存
|
||||
checkpoint_interval = max(1, epochs // 10) # 每10%进度保存一次，最少每1个epoch
|
||||
best_loss = float('inf')
|
||||
epochs_no_improve = 0
|
||||
|
||||
emit_progress(f"开始训练 - 总epoch: {epochs}, 检查点间隔: {checkpoint_interval}")
|
||||
emit_progress(f"开始训练 - 总epoch: {epochs}, 检查点间隔: {checkpoint_interval}, 耐心值: {patience}")
|
||||
|
||||
for epoch in range(epochs):
|
||||
emit_progress(f"开始训练 Epoch {epoch+1}/{epochs}")
|
||||
@ -384,9 +366,6 @@ def train_product_model_with_mlstm(
|
||||
X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
|
||||
|
||||
# 确保目标张量有正确的形状
|
||||
if y_batch.dim() == 2:
|
||||
y_batch = y_batch.unsqueeze(-1)
|
||||
|
||||
# 前向传播
|
||||
outputs = model(X_batch)
|
||||
loss = criterion(outputs, y_batch)
|
||||
@ -394,6 +373,8 @@ def train_product_model_with_mlstm(
|
||||
# 反向传播和优化
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
if clip_norm:
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip_norm)
|
||||
optimizer.step()
|
||||
|
||||
epoch_loss += loss.item()
|
||||
@ -409,10 +390,6 @@ def train_product_model_with_mlstm(
|
||||
for batch_idx, (X_batch, y_batch) in enumerate(test_loader):
|
||||
X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
|
||||
|
||||
# 确保目标张量有正确的形状
|
||||
if y_batch.dim() == 2:
|
||||
y_batch = y_batch.unsqueeze(-1)
|
||||
|
||||
outputs = model(X_batch)
|
||||
loss = criterion(outputs, y_batch)
|
||||
test_loss += loss.item()
|
||||
@ -420,6 +397,9 @@ def train_product_model_with_mlstm(
|
||||
test_loss = test_loss / len(test_loader)
|
||||
test_losses.append(test_loss)
|
||||
|
||||
# 更新学习率
|
||||
scheduler.step(test_loss)
|
||||
|
||||
# 计算总体训练进度
|
||||
epoch_progress = ((epoch + 1) / epochs) * 90 + 10 # 10-100% 范围
|
||||
|
||||
@ -478,14 +458,22 @@ def train_product_model_with_mlstm(
|
||||
# 如果是最佳模型，额外保存一份
|
||||
if test_loss < best_loss:
|
||||
best_loss = test_loss
|
||||
save_checkpoint(checkpoint_data, 'best', product_id, 'mlstm',
|
||||
save_checkpoint(checkpoint_data, 'best', product_id, 'mlstm',
|
||||
model_dir, store_id, training_mode, aggregation_method)
|
||||
emit_progress(f"💾 保存最佳模型检查点 (epoch {epoch+1}, test_loss: {test_loss:.4f})")
|
||||
epochs_no_improve = 0
|
||||
else:
|
||||
epochs_no_improve += 1
|
||||
|
||||
emit_progress(f"💾 保存训练检查点 epoch_{epoch+1}")
|
||||
|
||||
if (epoch + 1) % 10 == 0:
|
||||
print(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}", flush=True)
|
||||
|
||||
# 提前停止逻辑
|
||||
if epochs_no_improve >= patience:
|
||||
emit_progress(f"连续 {patience} 个epoch测试损失未改善，提前停止训练。")
|
||||
break
|
||||
|
||||
# 计算训练时间
|
||||
training_time = time.time() - start_time
|
||||
@ -527,14 +515,11 @@ def train_product_model_with_mlstm(
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
test_pred = model(testX_tensor.to(DEVICE)).cpu().numpy()
|
||||
|
||||
# 处理输出形状
|
||||
if len(test_pred.shape) == 3:
|
||||
test_pred = test_pred.squeeze(-1)
|
||||
test_true = testY
|
||||
|
||||
# 反归一化预测结果和真实值
|
||||
test_pred_inv = scaler_y.inverse_transform(test_pred.reshape(-1, 1)).flatten()
|
||||
test_true_inv = scaler_y.inverse_transform(testY.reshape(-1, 1)).flatten()
|
||||
test_pred_inv = scaler_y.inverse_transform(test_pred)
|
||||
test_true_inv = scaler_y.inverse_transform(test_true)
|
||||
|
||||
# 计算评估指标
|
||||
metrics = evaluate_model(test_true_inv, test_pred_inv)
|
||||
|
||||
@ -58,11 +58,12 @@ def save_checkpoint(checkpoint_data: dict, epoch_or_label, product_id: str,
|
||||
return checkpoint_path
|
||||
|
||||
def train_product_model_with_tcn(
|
||||
product_id,
|
||||
product_id,
|
||||
product_df=None,
|
||||
store_id=None,
|
||||
training_mode='product',
|
||||
aggregation_method='sum',
|
||||
epochs=50,
|
||||
epochs=50,
|
||||
model_dir=DEFAULT_MODEL_DIR,
|
||||
version=None,
|
||||
socketio=None,
|
||||
@ -114,36 +115,45 @@ def train_product_model_with_tcn(
|
||||
|
||||
emit_progress(f"开始训练 TCN 模型版本 {version}")
|
||||
|
||||
# 根据训练模式加载数据
|
||||
from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
|
||||
|
||||
try:
|
||||
# 如果没有传入product_df，则根据训练模式加载数据
|
||||
if product_df is None:
|
||||
from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
|
||||
|
||||
try:
|
||||
if training_mode == 'store' and store_id:
|
||||
# 加载特定店铺的数据
|
||||
product_df = get_store_product_sales_data(
|
||||
store_id,
|
||||
product_id,
|
||||
'pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
# 聚合所有店铺的数据
|
||||
product_df = aggregate_multi_store_data(
|
||||
product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path='pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
# 默认：加载所有店铺的产品数据
|
||||
product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
training_scope = "所有店铺"
|
||||
except Exception as e:
|
||||
print(f"多店铺数据加载失败: {e}")
|
||||
# 后备方案：尝试原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
product_df = df[df['product_id'] == product_id].sort_values('date')
|
||||
training_scope = "原始数据"
|
||||
else:
|
||||
# 如果传入了product_df，直接使用
|
||||
if training_mode == 'store' and store_id:
|
||||
# 加载特定店铺的数据
|
||||
product_df = get_store_product_sales_data(
|
||||
store_id,
|
||||
product_id,
|
||||
'pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
# 聚合所有店铺的数据
|
||||
product_df = aggregate_multi_store_data(
|
||||
product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path='pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
# 默认：加载所有店铺的产品数据
|
||||
product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
training_scope = "所有店铺"
|
||||
except Exception as e:
|
||||
print(f"多店铺数据加载失败: {e}")
|
||||
# 后备方案：尝试原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
product_df = df[df['product_id'] == product_id].sort_values('date')
|
||||
training_scope = "原始数据"
|
||||
|
||||
if product_df.empty:
|
||||
raise ValueError(f"产品 {product_id} 没有可用的销售数据")
|
||||
@ -177,7 +187,7 @@ def train_product_model_with_tcn(
|
||||
emit_progress(f"训练产品: {product_name} (ID: {product_id})")
|
||||
|
||||
# 创建特征和目标变量
|
||||
features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
|
||||
# 预处理数据
|
||||
X = product_df[features].values
|
||||
|
||||
@ -64,16 +64,20 @@ def save_checkpoint(checkpoint_data: dict, epoch_or_label, product_id: str,
|
||||
return checkpoint_path
|
||||
|
||||
def train_product_model_with_transformer(
|
||||
product_id,
|
||||
store_id=None,
|
||||
training_mode='product',
|
||||
aggregation_method='sum',
|
||||
epochs=50,
|
||||
product_id,
|
||||
product_df=None,
|
||||
store_id=None,
|
||||
training_mode='product',
|
||||
aggregation_method='sum',
|
||||
epochs=50,
|
||||
model_dir=DEFAULT_MODEL_DIR,
|
||||
version=None,
|
||||
socketio=None,
|
||||
task_id=None,
|
||||
continue_training=False
|
||||
continue_training=False,
|
||||
patience=10,
|
||||
learning_rate=0.001,
|
||||
clip_norm=1.0
|
||||
):
|
||||
"""
|
||||
使用Transformer模型训练产品销售预测模型
|
||||
@ -129,36 +133,45 @@ def train_product_model_with_transformer(
|
||||
def finish_training(self, *args, **kwargs): pass
|
||||
progress_manager = DummyProgressManager()
|
||||
|
||||
# 根据训练模式加载数据
|
||||
from utils.multi_store_data_utils import load_multi_store_data
|
||||
|
||||
try:
|
||||
# 如果没有传入product_df，则根据训练模式加载数据
|
||||
if product_df is None:
|
||||
from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
|
||||
|
||||
try:
|
||||
if training_mode == 'store' and store_id:
|
||||
# 加载特定店铺的数据
|
||||
product_df = get_store_product_sales_data(
|
||||
store_id,
|
||||
product_id,
|
||||
'pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
# 聚合所有店铺的数据
|
||||
product_df = aggregate_multi_store_data(
|
||||
product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path='pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
# 默认：加载所有店铺的产品数据
|
||||
product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
training_scope = "所有店铺"
|
||||
except Exception as e:
|
||||
print(f"多店铺数据加载失败: {e}")
|
||||
# 后备方案：尝试原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
product_df = df[df['product_id'] == product_id].sort_values('date')
|
||||
training_scope = "原始数据"
|
||||
else:
|
||||
# 如果传入了product_df，直接使用
|
||||
if training_mode == 'store' and store_id:
|
||||
# 加载特定店铺的数据
|
||||
product_df = get_store_product_sales_data(
|
||||
store_id,
|
||||
product_id,
|
||||
'pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"店铺 {store_id}"
|
||||
elif training_mode == 'global':
|
||||
# 聚合所有店铺的数据
|
||||
product_df = aggregate_multi_store_data(
|
||||
product_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path='pharmacy_sales_multi_store.csv'
|
||||
)
|
||||
training_scope = f"全局聚合({aggregation_method})"
|
||||
else:
|
||||
# 默认：加载所有店铺的产品数据
|
||||
product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
|
||||
training_scope = "所有店铺"
|
||||
except Exception as e:
|
||||
print(f"多店铺数据加载失败: {e}")
|
||||
# 后备方案：尝试原始数据
|
||||
df = pd.read_excel('pharmacy_sales.xlsx')
|
||||
product_df = df[df['product_id'] == product_id].sort_values('date')
|
||||
training_scope = "原始数据"
|
||||
|
||||
if product_df.empty:
|
||||
raise ValueError(f"产品 {product_id} 没有可用的销售数据")
|
||||
@ -187,7 +200,7 @@ def train_product_model_with_transformer(
|
||||
print(f"[Model] 模型将保存到目录: {model_dir}", flush=True)
|
||||
|
||||
# 创建特征和目标变量
|
||||
features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
|
||||
# 设置数据预处理阶段
|
||||
progress_manager.set_stage("data_preprocessing", 0)
|
||||
@ -265,7 +278,8 @@ def train_product_model_with_transformer(
|
||||
model = model.to(DEVICE)
|
||||
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = optim.Adam(model.parameters(), lr=0.001)
|
||||
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=patience // 2, factor=0.5, verbose=True)
|
||||
|
||||
# 训练模型
|
||||
train_losses = []
|
||||
@ -275,9 +289,10 @@ def train_product_model_with_transformer(
|
||||
# 配置检查点保存
|
||||
checkpoint_interval = max(1, epochs // 10) # 每10%进度保存一次，最少每1个epoch
|
||||
best_loss = float('inf')
|
||||
epochs_no_improve = 0
|
||||
|
||||
progress_manager.set_stage("model_training", 0)
|
||||
emit_progress(f"开始训练 - 总epoch: {epochs}, 检查点间隔: {checkpoint_interval}")
|
||||
emit_progress(f"开始训练 - 总epoch: {epochs}, 检查点间隔: {checkpoint_interval}, 耐心值: {patience}")
|
||||
|
||||
for epoch in range(epochs):
|
||||
# 开始新的轮次
|
||||
@ -290,9 +305,6 @@ def train_product_model_with_transformer(
|
||||
X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
|
||||
|
||||
# 确保目标张量有正确的形状
|
||||
if y_batch.dim() == 2:
|
||||
y_batch = y_batch.unsqueeze(-1)
|
||||
|
||||
# 前向传播
|
||||
outputs = model(X_batch)
|
||||
loss = criterion(outputs, y_batch)
|
||||
@ -300,6 +312,8 @@ def train_product_model_with_transformer(
|
||||
# 反向传播和优化
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
if clip_norm:
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip_norm)
|
||||
optimizer.step()
|
||||
|
||||
epoch_loss += loss.item()
|
||||
@ -324,9 +338,6 @@ def train_product_model_with_transformer(
|
||||
X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
|
||||
|
||||
# 确保目标张量有正确的形状
|
||||
if y_batch.dim() == 2:
|
||||
y_batch = y_batch.unsqueeze(-1)
|
||||
|
||||
outputs = model(X_batch)
|
||||
loss = criterion(outputs, y_batch)
|
||||
test_loss += loss.item()
|
||||
@ -339,6 +350,9 @@ def train_product_model_with_transformer(
|
||||
test_loss = test_loss / len(test_loader)
|
||||
test_losses.append(test_loss)
|
||||
|
||||
# 更新学习率
|
||||
scheduler.step(test_loss)
|
||||
|
||||
# 完成当前轮次
|
||||
progress_manager.finish_epoch(train_loss, test_loss)
|
||||
|
||||
@ -394,14 +408,22 @@ def train_product_model_with_transformer(
|
||||
# 如果是最佳模型，额外保存一份
|
||||
if test_loss < best_loss:
|
||||
best_loss = test_loss
|
||||
save_checkpoint(checkpoint_data, 'best', product_id, 'transformer',
|
||||
save_checkpoint(checkpoint_data, 'best', product_id, 'transformer',
|
||||
model_dir, store_id, training_mode, aggregation_method)
|
||||
emit_progress(f"💾 保存最佳模型检查点 (epoch {epoch+1}, test_loss: {test_loss:.4f})")
|
||||
epochs_no_improve = 0
|
||||
else:
|
||||
epochs_no_improve += 1
|
||||
|
||||
emit_progress(f"💾 保存训练检查点 epoch_{epoch+1}")
|
||||
|
||||
if (epoch + 1) % 10 == 0:
|
||||
print(f"📊 Epoch {epoch+1}/{epochs}, 训练损失: {train_loss:.4f}, 测试损失: {test_loss:.4f}", flush=True)
|
||||
|
||||
# 提前停止逻辑
|
||||
if epochs_no_improve >= patience:
|
||||
emit_progress(f"连续 {patience} 个epoch测试损失未改善，提前停止训练。")
|
||||
break
|
||||
|
||||
# 计算训练时间
|
||||
training_time = time.time() - start_time
|
||||
@ -424,14 +446,11 @@ def train_product_model_with_transformer(
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
test_pred = model(testX_tensor.to(DEVICE)).cpu().numpy()
|
||||
|
||||
# 处理输出形状
|
||||
if len(test_pred.shape) == 3:
|
||||
test_pred = test_pred.squeeze(-1)
|
||||
test_true = testY
|
||||
|
||||
# 反归一化预测结果和真实值
|
||||
test_pred_inv = scaler_y.inverse_transform(test_pred.reshape(-1, 1)).flatten()
|
||||
test_true_inv = scaler_y.inverse_transform(testY.reshape(-1, 1)).flatten()
|
||||
test_pred_inv = scaler_y.inverse_transform(test_pred)
|
||||
test_true_inv = scaler_y.inverse_transform(test_true)
|
||||
|
||||
# 计算评估指标
|
||||
metrics = evaluate_model(test_true_inv, test_pred_inv)
|
||||
|
||||
@ -41,7 +41,7 @@ def create_dataset(datasetX, datasetY, look_back=1, predict_steps=1):
|
||||
x = datasetX[i:(i + look_back)]
|
||||
dataX.append(x)
|
||||
y = datasetY[(i + look_back):(i + look_back + predict_steps)]
|
||||
dataY.append(y)
|
||||
dataY.append(y.flatten())
|
||||
return np.array(dataX), np.array(dataY)
|
||||
|
||||
def prepare_data(product_data, sequence_length=30, forecast_horizon=7):
|
||||
|
||||
@ -9,7 +9,7 @@ import os
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Optional, List, Tuple, Dict, Any
|
||||
|
||||
def load_multi_store_data(file_path: str = 'pharmacy_sales_multi_store.csv',
|
||||
def load_multi_store_data(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet',
|
||||
store_id: Optional[str] = None,
|
||||
product_id: Optional[str] = None,
|
||||
start_date: Optional[str] = None,
|
||||
@ -18,7 +18,7 @@ def load_multi_store_data(file_path: str = 'pharmacy_sales_multi_store.csv',
|
||||
加载多店铺销售数据，支持按店铺、产品、时间范围过滤
|
||||
|
||||
参数:
|
||||
file_path: 数据文件路径
|
||||
file_path: 数据文件路径 (支持 .csv, .xlsx, .parquet)
|
||||
store_id: 店铺ID，为None时返回所有店铺数据
|
||||
product_id: 产品ID，为None时返回所有产品数据
|
||||
start_date: 开始日期 (YYYY-MM-DD)
|
||||
@ -29,42 +29,42 @@ def load_multi_store_data(file_path: str = 'pharmacy_sales_multi_store.csv',
|
||||
"""
|
||||
|
||||
# 尝试多个可能的文件路径
|
||||
# 获取当前脚本所在的目录
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
# 假设项目根目录是 server/utils 的上两级目录
|
||||
project_root = os.path.abspath(os.path.join(current_dir, '..', '..'))
|
||||
|
||||
possible_paths = [
|
||||
file_path,
|
||||
f'../{file_path}',
|
||||
f'server/{file_path}',
|
||||
'pharmacy_sales_multi_store.csv',
|
||||
'../pharmacy_sales_multi_store.csv',
|
||||
'pharmacy_sales.xlsx', # 后向兼容原始文件
|
||||
'../pharmacy_sales.xlsx'
|
||||
file_path, # 相对路径 (如果从根目录运行)
|
||||
os.path.join(project_root, file_path), # 基于项目根目录的绝对路径
|
||||
os.path.join('..', file_path), # 相对路径 (如果从 server 目录运行)
|
||||
os.path.join('server', file_path) # 相对路径 (如果从根目录运行，但路径错误)
|
||||
]
|
||||
|
||||
df = None
|
||||
loaded_path = None
|
||||
for path in possible_paths:
|
||||
try:
|
||||
if not os.path.exists(path):
|
||||
continue
|
||||
|
||||
if path.endswith('.csv'):
|
||||
df = pd.read_csv(path)
|
||||
elif path.endswith('.xlsx'):
|
||||
df = pd.read_excel(path)
|
||||
# 为原始Excel文件添加默认店铺信息
|
||||
if 'store_id' not in df.columns:
|
||||
df['store_id'] = 'S001'
|
||||
df['store_name'] = '默认店铺'
|
||||
df['store_location'] = '未知位置'
|
||||
df['store_type'] = 'standard'
|
||||
elif path.endswith('.parquet'):
|
||||
df = pd.read_parquet(path)
|
||||
|
||||
if df is not None:
|
||||
print(f"成功加载数据文件: {path}")
|
||||
loaded_path = path
|
||||
print(f"成功加载数据文件: {loaded_path}")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"加载文件 {path} 失败: {e}")
|
||||
continue
|
||||
|
||||
if df is None:
|
||||
raise FileNotFoundError(f"无法找到数据文件，尝试的路径: {possible_paths}")
|
||||
|
||||
# 确保date列是datetime类型
|
||||
if 'date' in df.columns:
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
raise FileNotFoundError(f"无法找到或加载数据文件，尝试的路径: {possible_paths}")
|
||||
|
||||
# 按店铺过滤
|
||||
if store_id:
|
||||
@ -76,28 +76,38 @@ def load_multi_store_data(file_path: str = 'pharmacy_sales_multi_store.csv',
|
||||
df = df[df['product_id'] == product_id].copy()
|
||||
print(f"按产品过滤: {product_id}, 剩余记录数: {len(df)}")
|
||||
|
||||
# 按时间范围过滤
|
||||
# 标准化列名和数据类型
|
||||
df = standardize_column_names(df)
|
||||
|
||||
# 在标准化之后进行时间范围过滤
|
||||
if start_date:
|
||||
start_date = pd.to_datetime(start_date)
|
||||
df = df[df['date'] >= start_date].copy()
|
||||
print(f"开始日期过滤: {start_date}, 剩余记录数: {len(df)}")
|
||||
|
||||
try:
|
||||
start_date_dt = pd.to_datetime(start_date)
|
||||
# 确保比较是在datetime对象之间
|
||||
if 'date' in df.columns:
|
||||
df = df[df['date'] >= start_date_dt].copy()
|
||||
print(f"开始日期过滤: {start_date_dt}, 剩余记录数: {len(df)}")
|
||||
except (ValueError, TypeError):
|
||||
print(f"警告: 无效的开始日期格式 '{start_date}'，已忽略。")
|
||||
|
||||
if end_date:
|
||||
end_date = pd.to_datetime(end_date)
|
||||
df = df[df['date'] <= end_date].copy()
|
||||
print(f"结束日期过滤: {end_date}, 剩余记录数: {len(df)}")
|
||||
try:
|
||||
end_date_dt = pd.to_datetime(end_date)
|
||||
# 确保比较是在datetime对象之间
|
||||
if 'date' in df.columns:
|
||||
df = df[df['date'] <= end_date_dt].copy()
|
||||
print(f"结束日期过滤: {end_date_dt}, 剩余记录数: {len(df)}")
|
||||
except (ValueError, TypeError):
|
||||
print(f"警告: 无效的结束日期格式 '{end_date}'，已忽略。")
|
||||
|
||||
if len(df) == 0:
|
||||
print("警告: 过滤后没有数据")
|
||||
|
||||
# 标准化列名以匹配训练代码期望的格式
|
||||
df = standardize_column_names(df)
|
||||
|
||||
return df
|
||||
|
||||
def standardize_column_names(df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
标准化列名以匹配训练代码期望的格式
|
||||
标准化列名以匹配训练代码和API期望的格式
|
||||
|
||||
参数:
|
||||
df: 原始DataFrame
|
||||
@ -107,55 +117,67 @@ def standardize_column_names(df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
df = df.copy()
|
||||
|
||||
# 列名映射：新列名 -> 原列名
|
||||
column_mapping = {
|
||||
'sales': 'quantity_sold', # 销售数量
|
||||
'price': 'unit_price', # 单价
|
||||
'weekday': 'day_of_week' # 星期几
|
||||
# 定义列名映射并强制重命名
|
||||
rename_map = {
|
||||
'sales_quantity': 'sales', # 修复：匹配原始列名
|
||||
'temperature_2m_mean': 'temperature', # 新增：处理温度列
|
||||
'dayofweek': 'weekday' # 修复：匹配原始列名
|
||||
}
|
||||
df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True)
|
||||
|
||||
# 应用列名映射
|
||||
for new_name, old_name in column_mapping.items():
|
||||
if old_name in df.columns and new_name not in df.columns:
|
||||
df[new_name] = df[old_name]
|
||||
|
||||
# 创建缺失的特征列
|
||||
# 确保date列是datetime类型
|
||||
if 'date' in df.columns:
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
|
||||
# 创建数值型的weekday (0=Monday, 6=Sunday)
|
||||
if 'weekday' not in df.columns:
|
||||
df['weekday'] = df['date'].dt.dayofweek
|
||||
elif df['weekday'].dtype == 'object':
|
||||
# 如果weekday是字符串，转换为数值
|
||||
weekday_map = {
|
||||
'Monday': 0, 'Tuesday': 1, 'Wednesday': 2, 'Thursday': 3,
|
||||
'Friday': 4, 'Saturday': 5, 'Sunday': 6
|
||||
}
|
||||
df['weekday'] = df['weekday'].map(weekday_map).fillna(df['date'].dt.dayofweek)
|
||||
|
||||
# 添加月份信息
|
||||
if 'month' not in df.columns:
|
||||
df['month'] = df['date'].dt.month
|
||||
df['date'] = pd.to_datetime(df['date'], errors='coerce')
|
||||
df.dropna(subset=['date'], inplace=True) # 移除无法解析的日期行
|
||||
else:
|
||||
# 如果没有date列，无法继续，返回空DataFrame
|
||||
return pd.DataFrame()
|
||||
|
||||
# 计算 sales_amount
|
||||
# 由于没有price列，sales_amount的计算逻辑需要调整或移除
|
||||
# 这里我们注释掉它，因为原始数据中已有sales_amount
|
||||
# if 'sales_amount' not in df.columns and 'sales' in df.columns and 'price' in df.columns:
|
||||
# # 先确保sales和price是数字
|
||||
# df['sales'] = pd.to_numeric(df['sales'], errors='coerce')
|
||||
# df['price'] = pd.to_numeric(df['price'], errors='coerce')
|
||||
# df['sales_amount'] = df['sales'] * df['price']
|
||||
|
||||
# 创建缺失的特征列
|
||||
if 'weekday' not in df.columns:
|
||||
df['weekday'] = df['date'].dt.dayofweek
|
||||
|
||||
# 添加缺失的布尔特征列（如果不存在则设为默认值）
|
||||
if 'month' not in df.columns:
|
||||
df['month'] = df['date'].dt.month
|
||||
|
||||
# 添加缺失的元数据列
|
||||
meta_columns = {
|
||||
'store_name': 'Unknown Store',
|
||||
'store_location': 'Unknown Location',
|
||||
'store_type': 'Unknown',
|
||||
'product_name': 'Unknown Product',
|
||||
'product_category': 'Unknown Category'
|
||||
}
|
||||
for col, default in meta_columns.items():
|
||||
if col not in df.columns:
|
||||
df[col] = default
|
||||
|
||||
# 添加缺失的布尔特征列
|
||||
default_features = {
|
||||
'is_holiday': False, # 是否节假日
|
||||
'is_weekend': None, # 是否周末（从weekday计算）
|
||||
'is_promotion': False, # 是否促销
|
||||
'temperature': 20.0 # 默认温度
|
||||
'is_holiday': False,
|
||||
'is_weekend': None,
|
||||
'is_promotion': False,
|
||||
'temperature': 20.0
|
||||
}
|
||||
|
||||
for feature, default_value in default_features.items():
|
||||
if feature not in df.columns:
|
||||
if feature == 'is_weekend' and 'weekday' in df.columns:
|
||||
# 周末：周六(5)和周日(6)
|
||||
if feature == 'is_weekend':
|
||||
df['is_weekend'] = df['weekday'].isin([5, 6])
|
||||
else:
|
||||
df[feature] = default_value
|
||||
|
||||
# 确保数值类型正确
|
||||
numeric_columns = ['sales', 'price', 'weekday', 'month', 'temperature']
|
||||
numeric_columns = ['sales', 'sales_amount', 'weekday', 'month', 'temperature']
|
||||
for col in numeric_columns:
|
||||
if col in df.columns:
|
||||
df[col] = pd.to_numeric(df[col], errors='coerce')
|
||||
@ -166,11 +188,11 @@ def standardize_column_names(df: pd.DataFrame) -> pd.DataFrame:
|
||||
if col in df.columns:
|
||||
df[col] = df[col].astype(bool)
|
||||
|
||||
print(f"数据标准化完成，可用特征列: {[col for col in ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'] if col in df.columns]}")
|
||||
print(f"数据标准化完成，可用特征列: {[col for col in ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'] if col in df.columns]}")
|
||||
|
||||
return df
|
||||
|
||||
def get_available_stores(file_path: str = 'pharmacy_sales_multi_store.csv') -> List[Dict[str, Any]]:
|
||||
def get_available_stores(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet') -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取可用的店铺列表
|
||||
|
||||
@ -183,15 +205,31 @@ def get_available_stores(file_path: str = 'pharmacy_sales_multi_store.csv') -> L
|
||||
try:
|
||||
df = load_multi_store_data(file_path)
|
||||
|
||||
# 获取唯一店铺信息
|
||||
stores = df[['store_id', 'store_name', 'store_location', 'store_type']].drop_duplicates()
|
||||
if 'store_id' not in df.columns:
|
||||
print("数据文件中缺少 'store_id' 列")
|
||||
return []
|
||||
|
||||
# 智能地获取店铺信息，即使某些列缺失
|
||||
store_info = []
|
||||
|
||||
return stores.to_dict('records')
|
||||
# 使用drop_duplicates获取唯一的店铺组合
|
||||
stores_df = df.drop_duplicates(subset=['store_id'])
|
||||
|
||||
for _, row in stores_df.iterrows():
|
||||
store_info.append({
|
||||
'store_id': row['store_id'],
|
||||
'store_name': row.get('store_name', f"店铺 {row['store_id']}"),
|
||||
'location': row.get('store_location', '未知位置'),
|
||||
'type': row.get('store_type', '标准'),
|
||||
'opening_date': row.get('opening_date', '未知'),
|
||||
})
|
||||
|
||||
return store_info
|
||||
except Exception as e:
|
||||
print(f"获取店铺列表失败: {e}")
|
||||
return []
|
||||
|
||||
def get_available_products(file_path: str = 'pharmacy_sales_multi_store.csv',
|
||||
def get_available_products(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet',
|
||||
store_id: Optional[str] = None) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取可用的产品列表
|
||||
@ -222,7 +260,7 @@ def get_available_products(file_path: str = 'pharmacy_sales_multi_store.csv',
|
||||
|
||||
def get_store_product_sales_data(store_id: str,
|
||||
product_id: str,
|
||||
file_path: str = 'pharmacy_sales_multi_store.csv') -> pd.DataFrame:
|
||||
file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet') -> pd.DataFrame:
|
||||
"""
|
||||
获取特定店铺和产品的销售数据，用于模型训练
|
||||
|
||||
@ -252,27 +290,53 @@ def get_store_product_sales_data(store_id: str,
|
||||
print(f"警告: 数据标准化后仍缺少列 {missing_columns}")
|
||||
raise ValueError(f"无法获取完整的特征数据，缺少列: {missing_columns}")
|
||||
|
||||
return df
|
||||
# 定义模型训练所需的所有列（特征 + 目标）
|
||||
final_columns = [
|
||||
'date', 'sales', 'product_id', 'product_name', 'store_id', 'store_name',
|
||||
'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'
|
||||
]
|
||||
|
||||
# 筛选出DataFrame中实际存在的列
|
||||
existing_columns = [col for col in final_columns if col in df.columns]
|
||||
|
||||
# 返回只包含这些必需列的DataFrame
|
||||
return df[existing_columns]
|
||||
|
||||
def aggregate_multi_store_data(product_id: str,
|
||||
aggregation_method: str = 'sum',
|
||||
file_path: str = 'pharmacy_sales_multi_store.csv') -> pd.DataFrame:
|
||||
def aggregate_multi_store_data(product_id: Optional[str] = None,
|
||||
store_id: Optional[str] = None,
|
||||
aggregation_method: str = 'sum',
|
||||
file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet') -> pd.DataFrame:
|
||||
"""
|
||||
聚合多个店铺的销售数据，用于全局模型训练
|
||||
聚合销售数据，可按产品（全局）或按店铺（所有产品）
|
||||
|
||||
参数:
|
||||
file_path: 数据文件路径
|
||||
product_id: 产品ID
|
||||
product_id: 产品ID (用于全局模型)
|
||||
store_id: 店铺ID (用于店铺聚合模型)
|
||||
aggregation_method: 聚合方法 ('sum', 'mean', 'median')
|
||||
|
||||
返回:
|
||||
DataFrame: 聚合后的销售数据
|
||||
"""
|
||||
# 加载所有店铺的产品数据
|
||||
df = load_multi_store_data(file_path, product_id=product_id)
|
||||
|
||||
if len(df) == 0:
|
||||
raise ValueError(f"没有找到产品 {product_id} 的销售数据")
|
||||
# 根据是全局聚合、店铺聚合还是真正全局聚合来加载数据
|
||||
if store_id:
|
||||
# 店铺聚合：加载该店铺的所有数据
|
||||
df = load_multi_store_data(file_path, store_id=store_id)
|
||||
if len(df) == 0:
|
||||
raise ValueError(f"没有找到店铺 {store_id} 的销售数据")
|
||||
grouping_entity = f"店铺 {store_id}"
|
||||
elif product_id:
|
||||
# 按产品聚合：加载该产品在所有店铺的数据
|
||||
df = load_multi_store_data(file_path, product_id=product_id)
|
||||
if len(df) == 0:
|
||||
raise ValueError(f"没有找到产品 {product_id} 的销售数据")
|
||||
grouping_entity = f"产品 {product_id}"
|
||||
else:
|
||||
# 真正全局聚合：加载所有数据
|
||||
df = load_multi_store_data(file_path)
|
||||
if len(df) == 0:
|
||||
raise ValueError("数据文件为空，无法进行全局聚合")
|
||||
grouping_entity = "所有产品"
|
||||
|
||||
# 按日期聚合（使用标准化后的列名）
|
||||
agg_dict = {}
|
||||
@ -317,9 +381,19 @@ def aggregate_multi_store_data(product_id: str,
|
||||
aggregated_df = aggregated_df.sort_values('date').copy()
|
||||
aggregated_df = standardize_column_names(aggregated_df)
|
||||
|
||||
return aggregated_df
|
||||
# 定义模型训练所需的所有列（特征 + 目标）
|
||||
final_columns = [
|
||||
'date', 'sales', 'product_id', 'product_name', 'store_id', 'store_name',
|
||||
'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'
|
||||
]
|
||||
|
||||
# 筛选出DataFrame中实际存在的列
|
||||
existing_columns = [col for col in final_columns if col in aggregated_df.columns]
|
||||
|
||||
# 返回只包含这些必需列的DataFrame
|
||||
return aggregated_df[existing_columns]
|
||||
|
||||
def get_sales_statistics(file_path: str = 'pharmacy_sales_multi_store.csv',
|
||||
def get_sales_statistics(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet',
|
||||
store_id: Optional[str] = None,
|
||||
product_id: Optional[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
|
||||
@ -24,6 +24,17 @@ server_dir = os.path.dirname(current_dir)
|
||||
sys.path.append(server_dir)
|
||||
|
||||
from utils.logging_config import setup_api_logging, get_training_logger, log_training_progress
|
||||
import numpy as np
|
||||
|
||||
def convert_numpy_types(obj):
|
||||
"""递归地将字典/列表中的NumPy类型转换为Python原生类型"""
|
||||
if isinstance(obj, dict):
|
||||
return {k: convert_numpy_types(v) for k, v in obj.items()}
|
||||
elif isinstance(obj, list):
|
||||
return [convert_numpy_types(i) for i in obj]
|
||||
elif isinstance(obj, np.generic):
|
||||
return obj.item()
|
||||
return obj
|
||||
|
||||
@dataclass
|
||||
class TrainingTask:
|
||||
@ -325,13 +336,16 @@ class TrainingProcessManager:
|
||||
|
||||
task_id = task_data['task_id']
|
||||
|
||||
# 立即对从队列中取出的数据进行类型转换
|
||||
serializable_task_data = convert_numpy_types(task_data)
|
||||
|
||||
with self.lock:
|
||||
if task_id in self.tasks:
|
||||
# 更新任务状态
|
||||
for key, value in task_data.items():
|
||||
# 使用转换后的数据更新任务状态
|
||||
for key, value in serializable_task_data.items():
|
||||
setattr(self.tasks[task_id], key, value)
|
||||
|
||||
# WebSocket通知 - 根据action类型发送不同的事件
|
||||
# WebSocket通知 - 使用已转换的数据
|
||||
if self.websocket_callback:
|
||||
try:
|
||||
if action == 'complete':
|
||||
@ -341,21 +355,21 @@ class TrainingProcessManager:
|
||||
'action': 'completed',
|
||||
'status': 'completed',
|
||||
'progress': 100,
|
||||
'message': task_data.get('message', '训练完成'),
|
||||
'metrics': task_data.get('metrics'),
|
||||
'end_time': task_data.get('end_time'),
|
||||
'product_id': task_data.get('product_id'),
|
||||
'model_type': task_data.get('model_type')
|
||||
'message': serializable_task_data.get('message', '训练完成'),
|
||||
'metrics': serializable_task_data.get('metrics'),
|
||||
'end_time': serializable_task_data.get('end_time'),
|
||||
'product_id': serializable_task_data.get('product_id'),
|
||||
'model_type': serializable_task_data.get('model_type')
|
||||
})
|
||||
# 额外发送一个完成事件，确保前端能收到
|
||||
self.websocket_callback('training_completed', {
|
||||
'task_id': task_id,
|
||||
'status': 'completed',
|
||||
'progress': 100,
|
||||
'message': task_data.get('message', '训练完成'),
|
||||
'metrics': task_data.get('metrics'),
|
||||
'product_id': task_data.get('product_id'),
|
||||
'model_type': task_data.get('model_type')
|
||||
'message': serializable_task_data.get('message', '训练完成'),
|
||||
'metrics': serializable_task_data.get('metrics'),
|
||||
'product_id': serializable_task_data.get('product_id'),
|
||||
'model_type': serializable_task_data.get('model_type')
|
||||
})
|
||||
elif action == 'error':
|
||||
# 训练失败
|
||||
@ -364,22 +378,22 @@ class TrainingProcessManager:
|
||||
'action': 'failed',
|
||||
'status': 'failed',
|
||||
'progress': 0,
|
||||
'message': task_data.get('message', '训练失败'),
|
||||
'error': task_data.get('error'),
|
||||
'product_id': task_data.get('product_id'),
|
||||
'model_type': task_data.get('model_type')
|
||||
'message': serializable_task_data.get('message', '训练失败'),
|
||||
'error': serializable_task_data.get('error'),
|
||||
'product_id': serializable_task_data.get('product_id'),
|
||||
'model_type': serializable_task_data.get('model_type')
|
||||
})
|
||||
else:
|
||||
# 状态更新
|
||||
self.websocket_callback('training_update', {
|
||||
'task_id': task_id,
|
||||
'action': action,
|
||||
'status': task_data.get('status'),
|
||||
'progress': task_data.get('progress', 0),
|
||||
'message': task_data.get('message', ''),
|
||||
'metrics': task_data.get('metrics'),
|
||||
'product_id': task_data.get('product_id'),
|
||||
'model_type': task_data.get('model_type')
|
||||
'status': serializable_task_data.get('status'),
|
||||
'progress': serializable_task_data.get('progress', 0),
|
||||
'message': serializable_task_data.get('message', ''),
|
||||
'metrics': serializable_task_data.get('metrics'),
|
||||
'product_id': serializable_task_data.get('product_id'),
|
||||
'model_type': serializable_task_data.get('model_type')
|
||||
})
|
||||
except Exception as e:
|
||||
self.logger.error(f"WebSocket通知失败: {e}")
|
||||
@ -441,7 +455,9 @@ class TrainingProcessManager:
|
||||
# WebSocket通知进度更新
|
||||
if self.websocket_callback and 'progress' in progress_data:
|
||||
try:
|
||||
self.websocket_callback('training_progress', progress_data)
|
||||
# 在发送前确保所有数据类型都是JSON可序列化的
|
||||
serializable_data = convert_numpy_types(progress_data)
|
||||
self.websocket_callback('training_progress', serializable_data)
|
||||
except Exception as e:
|
||||
self.logger.error(f"进度WebSocket通知失败: {e}")
|
||||
|
||||
|
||||
639
xz修改记录日志.md
Normal file
639
xz修改记录日志.md
Normal file
@ -0,0 +1,639 @@
|
||||
# “预测分析”模块UI重构修改记录
|
||||
|
||||
**任务目标**: 将原有的、通过下拉菜单切换模式的单一预测页面，重构为通过左侧子导航切换模式的多页面布局，使其UI结构与“模型训练”模块保持一致。
|
||||
|
||||
|
||||
### 后端修复 (2025-07-13)
|
||||
|
||||
**任务目标**: 解决模型训练时因数据文件路径错误导致的数据加载失败问题。
|
||||
|
||||
- **核心问题**: `server/core/predictor.py` 中的 `PharmacyPredictor` 类初始化时，硬编码了错误的默认数据文件路径 (`'pharmacy_sales_multi_store.csv'`)。
|
||||
- **修复方案**:
|
||||
1. 修改 `server/core/predictor.py`，将默认数据路径更正为 `'data/timeseries_training_data_sample_10s50p.parquet'`。
|
||||
2. 同步更新了 `server/trainers/mlstm_trainer.py` 中所有对数据加载函数的调用，确保使用正确的文件路径。
|
||||
- **结果**: 彻底解决了在独立训练进程中数据加载失败的问题。
|
||||
|
||||
---
|
||||
### 后端修复 (2025-07-13) - 数据流重构
|
||||
|
||||
**任务目标**: 解决因数据处理流程中断导致 `sales` 和 `price` 关键特征丢失，从而引发模型训练失败的根本问题。
|
||||
|
||||
- **核心问题**:
|
||||
1. `server/core/predictor.py` 中的 `train_model` 方法在调用训练器（如 `train_product_model_with_mlstm`）时，没有将预处理好的数据传递过去。
|
||||
2. `server/trainers/mlstm_trainer.py` 因此被迫重新加载和处理数据，但其使用的数据标准化函数 `standardize_column_names` 存在逻辑缺陷，导致关键列丢失。
|
||||
|
||||
- **修复方案 (数据流重构)**:
|
||||
1. **修改 `server/trainers/mlstm_trainer.py`**:
|
||||
- 重构 `train_product_model_with_mlstm` 函数，使其能够接收一个预处理好的 DataFrame (`product_df`) 作为参数。
|
||||
- 移除了函数内部所有的数据加载和重复处理逻辑。
|
||||
2. **修改 `server/core/predictor.py`**:
|
||||
- 在 `train_model` 方法中，将已经加载并处理好的 `product_data` 作为参数，显式传递给 `train_product_model_with_mlstm` 函数。
|
||||
3. **修改 `server/utils/multi_store_data_utils.py`**:
|
||||
- 在 `standardize_column_names` 函数中，使用 Pandas 的 `rename` 方法强制进行列名转换，确保 `quantity_sold` 和 `unit_price` 被可靠地重命名为 `sales` 和 `price`。
|
||||
|
||||
- **结果**: 彻底修复了数据处理流程，确保数据只被加载和标准化一次，并被正确传递，从根本上解决了模型训练失败的问题。
|
||||
---
|
||||
|
||||
### 第一次重构 (多页面、双栏布局)
|
||||
|
||||
- **新增文件**:
|
||||
- `UI/src/views/prediction/ProductPredictionView.vue`
|
||||
- `UI/src/views/prediction/StorePredictionView.vue`
|
||||
- `UI/src/views/prediction/GlobalPredictionView.vue`
|
||||
- **修改文件**:
|
||||
- `UI/src/router/index.js`: 添加了指向新页面的路由。
|
||||
- `UI/src/App.vue`: 将“预测分析”修改为包含三个子菜单的父菜单。
|
||||
|
||||
---
|
||||
|
||||
### 第二次重构 (基于用户反馈的单页面布局)
|
||||
|
||||
**任务目标**: 统一三个预测子页面的布局，采用旧的单页面预测样式，并将导航功能与页面内容解耦。
|
||||
|
||||
- **修改文件**:
|
||||
- **`UI/src/views/prediction/ProductPredictionView.vue`**:
|
||||
- **内容**: 使用 `UI/src/views/NewPredictionView.vue` 的布局进行替换。
|
||||
- **逻辑**: 移除了“模型训练方式”选择器，并将该页面的预测模式硬编码为 `product`。
|
||||
- **`UI/src/views/prediction/StorePredictionView.vue`**:
|
||||
- **内容**: 使用 `UI/src/views/NewPredictionView.vue` 的布局进行替换。
|
||||
- **逻辑**: 移除了“模型训练方式”选择器，并将该页面的预测模式硬编码为 `store`。
|
||||
- **`UI/src/views/prediction/GlobalPredictionView.vue`**:
|
||||
- **内容**: 使用 `UI/src/views/NewPredictionView.vue` 的布局进行替换。
|
||||
- **逻辑**: 移除了“模型训练方式”及特定目标选择器，并将该页面的预测模式硬编码为 `global`。
|
||||
|
||||
---
|
||||
|
||||
**总结**: 通过两次重构，最终实现了使用左侧导航栏切换预测模式，同时右侧内容区域保持统一、简洁的单页面布局，完全符合用户的最终要求。
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
---
|
||||
**按药品训练修改**
|
||||
**日期**: 2025-07-14
|
||||
**文件**: `server/trainers/mlstm_trainer.py`
|
||||
**问题**: 模型训练因 `KeyError: "['sales', 'price'] not in index"` 失败。
|
||||
**分析**:
|
||||
1. `'price'` 列在提供的数据中不存在，导致 `KeyError`。
|
||||
2. `'sales'` 列作为历史输入（自回归特征）对于模型训练是必要的。
|
||||
**解决方案**: 从 `mlstm_trainer` 的特征列表中移除了不存在的 `'price'` 列，保留了 `'sales'` 列用于自回归。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 (补充)
|
||||
**文件**:
|
||||
* `server/trainers/transformer_trainer.py`
|
||||
* `server/trainers/tcn_trainer.py`
|
||||
* `server/trainers/kan_trainer.py`
|
||||
**问题**: 预防性修复。这些文件存在与 `mlstm_trainer.py` 相同的 `KeyError` 隐患。
|
||||
**分析**: 经过检查，这些训练器与 `mlstm_trainer` 共享相同的数据处理逻辑，其硬编码的特征列表中都包含了不存在的 `'price'` 列。
|
||||
**解决方案**: 统一从所有相关训练器的特征列表中移除了 `'price'` 列，以确保所有模型训练的健壮性。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 (深度修复)
|
||||
**文件**: `server/utils/multi_store_data_utils.py`
|
||||
**问题**: 追踪 `KeyError: "['sales'] not in index"` 时，发现数据标准化流程存在多个问题。
|
||||
**分析**:
|
||||
1. 通过 `uv run` 读取了 `.parquet` 数据文件，确认了原始列名。
|
||||
2. 发现 `standardize_column_names` 函数中的重命名映射与原始列名不匹配 (例如 `quantity_sold` vs `sales_quantity`)。
|
||||
3. 确认了原始数据中没有 `price` 列，但代码中存在对它的依赖。
|
||||
4. 函数缺乏一个明确的返回列选择机制，导致 `sales` 列在数据准备阶段被意外丢弃。
|
||||
**解决方案**:
|
||||
1. 修正了 `rename_map` 以正确匹配原始数据列名 (`sales_quantity` -> `sales`, `temperature_2m_mean` -> `temperature`, `dayofweek` -> `weekday`)。
|
||||
2. 移除了对不存在的 `price` 列的依赖。
|
||||
3. 在函数末尾添加了逻辑，确保返回的 `DataFrame` 包含所有模型训练所需的标准列（特征 + 目标），保证了数据流的稳定性。
|
||||
4. 原始数据列名:['date', 'store_id', 'product_id', 'sales_quantity', 'sales_amount', 'gross_profit', 'customer_traffic', 'store_name', 'city', 'product_name', 'manufacturer', 'category_l1', 'category_l2', 'category_l3', 'abc_category', 'temperature_2m_mean', 'temperature_2m_max', 'temperature_2m_min', 'year', 'month', 'day', 'dayofweek', 'dayofyear', 'weekofyear', 'is_weekend', 'sl_lag_7', 'sl_lag_14', 'sl_rolling_mean_7', 'sl_rolling_std_7', 'sl_rolling_mean_14', 'sl_rolling_std_14']
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 10:16
|
||||
**主题**: 修复模型训练中的 `KeyError` 及数据流问题 (详细版)
|
||||
|
||||
### 阶段一：修复训练器层 `KeyError`
|
||||
|
||||
* **问题**: 模型训练因 `KeyError: "['sales', 'price'] not in index"` 失败。
|
||||
* **分析**: 训练器硬编码的特征列表中包含了数据源中不存在的 `'price'` 列。
|
||||
* **涉及文件**:
|
||||
* `server/trainers/mlstm_trainer.py`
|
||||
* `server/trainers/transformer_trainer.py`
|
||||
* `server/trainers/tcn_trainer.py`
|
||||
* `server/trainers/kan_trainer.py`
|
||||
* **修改详情**:
|
||||
* **位置**: 每个训练器文件中的 `features` 列表定义处。
|
||||
* **操作**: 修改。
|
||||
* **内容**:
|
||||
```diff
|
||||
- features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
+ features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
|
||||
```
|
||||
* **原因**: 移除对不存在的 `'price'` 列的依赖，解决 `KeyError`。
|
||||
|
||||
### 阶段二：修复数据标准化层
|
||||
|
||||
* **问题**: 修复后出现新错误 `KeyError: "['sales'] not in index"`，表明数据标准化流程存在缺陷。
|
||||
* **分析**: 通过 `uv run` 读取 `.parquet` 文件确认，`standardize_column_names` 函数中的列名映射错误，且缺少最终列选择机制。
|
||||
* **涉及文件**: `server/utils/multi_store_data_utils.py`
|
||||
* **修改详情**:
|
||||
1. **位置**: `standardize_column_names` 函数, `rename_map` 字典。
|
||||
* **操作**: 修改。
|
||||
* **内容**:
|
||||
```diff
|
||||
- rename_map = { 'quantity_sold': 'sales', 'unit_price': 'price', 'day_of_week': 'weekday' }
|
||||
+ rename_map = { 'sales_quantity': 'sales', 'temperature_2m_mean': 'temperature', 'dayofweek': 'weekday' }
|
||||
```
|
||||
* **原因**: 修正键名以匹配数据源的真实列名 (`sales_quantity`, `temperature_2m_mean`, `dayofweek`)。
|
||||
2. **位置**: `standardize_column_names` 函数, `sales_amount` 计算部分。
|
||||
* **操作**: 修改 (注释)。
|
||||
* **内容**:
|
||||
```diff
|
||||
- if 'sales_amount' not in df.columns and 'sales' in df.columns and 'price' in df.columns:
|
||||
- df['sales_amount'] = df['sales'] * df['price']
|
||||
+ # 由于没有price列，sales_amount的计算逻辑需要调整或移除
|
||||
+ # if 'sales_amount' not in df.columns and 'sales' in df.columns and 'price' in df.columns:
|
||||
+ # df['sales_amount'] = df['sales'] * df['price']
|
||||
```
|
||||
* **原因**: 避免因缺少 `'price'` 列而导致潜在错误。
|
||||
3. **位置**: `standardize_column_names` 函数, `numeric_columns` 列表。
|
||||
* **操作**: 删除。
|
||||
* **内容**:
|
||||
```diff
|
||||
- numeric_columns = ['sales', 'price', 'sales_amount', 'weekday', 'month', 'temperature']
|
||||
+ numeric_columns = ['sales', 'sales_amount', 'weekday', 'month', 'temperature']
|
||||
```
|
||||
* **原因**: 从数值类型转换列表中移除不存在的 `'price'` 列。
|
||||
4. **位置**: `standardize_column_names` 函数, `return` 语句前。
|
||||
* **操作**: 增加。
|
||||
* **内容**:
|
||||
```diff
|
||||
+ # 定义模型训练所需的所有列（特征 + 目标）
|
||||
+ final_columns = [
|
||||
+ 'date', 'sales', 'product_id', 'product_name', 'store_id', 'store_name',
|
||||
+ 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'
|
||||
+ ]
|
||||
+ # 筛选出DataFrame中实际存在的列
|
||||
+ existing_columns = [col for col in final_columns if col in df.columns]
|
||||
+ # 返回只包含这些必需列的DataFrame
|
||||
+ return df[existing_columns]
|
||||
```
|
||||
* **原因**: 增加列选择机制，确保函数返回的 `DataFrame` 结构统一且包含 `sales` 列，从根源上解决 `KeyError: "['sales'] not in index"`。
|
||||
|
||||
### 阶段三：修复数据流分发层
|
||||
|
||||
* **问题**: `predictor.py` 未将处理好的数据统一传递给所有训练器。
|
||||
* **分析**: `train_model` 方法中，只有 `mlstm` 的调用传递了 `product_df`，其他模型则没有，导致它们重新加载未处理的数据。
|
||||
* **涉及文件**: `server/core/predictor.py`
|
||||
* **修改详情**:
|
||||
* **位置**: `train_model` 方法中对 `train_product_model_with_transformer`, `_tcn`, `_kan` 的调用处。
|
||||
* **操作**: 增加。
|
||||
* **内容**: 在函数调用中增加了 `product_df=product_data` 参数。
|
||||
```diff
|
||||
- model_result, metrics, actual_version = train_product_model_with_transformer(product_id, ...)
|
||||
+ model_result, metrics, actual_version = train_product_model_with_transformer(product_id=product_id, product_df=product_data, ...)
|
||||
```
|
||||
*(对 `tcn` 和 `kan` 的调用也做了类似修改)*
|
||||
* **原因**: 统一数据流，确保所有训练器都使用经过正确预处理的、包含完整信息的 `DataFrame`。
|
||||
|
||||
### 阶段四：适配训练器以接收数据
|
||||
|
||||
* **问题**: `transformer`, `tcn`, `kan` 训练器需要能接收上游传来的数据。
|
||||
* **分析**: 需要修改这三个训练器的函数签名和内部逻辑，使其在接收到 `product_df` 时跳过数据加载。
|
||||
* **涉及文件**: `server/trainers/transformer_trainer.py`, `tcn_trainer.py`, `kan_trainer.py`
|
||||
* **修改详情**:
|
||||
1. **位置**: 每个训练器主函数的定义处。
|
||||
* **操作**: 增加。
|
||||
* **内容**: 在函数参数中增加了 `product_df=None`。
|
||||
```diff
|
||||
- def train_product_model_with_transformer(product_id, ...)
|
||||
+ def train_product_model_with_transformer(product_id, product_df=None, ...)
|
||||
```
|
||||
2. **位置**: 每个训练器内部的数据加载逻辑处。
|
||||
* **操作**: 增加。
|
||||
* **内容**: 增加了 `if product_df is None:` 的判断逻辑，只有在未接收到数据时才执行内部加载。
|
||||
```diff
|
||||
+ if product_df is None:
|
||||
- # 根据训练模式加载数据
|
||||
- from utils.multi_store_data_utils import load_multi_store_data
|
||||
- ...
|
||||
+ # [原有的数据加载逻辑]
|
||||
+ else:
|
||||
+ # 如果传入了product_df，直接使用
|
||||
+ ...
|
||||
```
|
||||
* **原因**: 完成数据流修复的最后一环，使训练器能够灵活地接收外部数据或自行加载，彻底解决问题。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 10:38
|
||||
**主题**: 修复因NumPy类型导致的JSON序列化失败问题
|
||||
|
||||
### 阶段五：修复前后端通信层
|
||||
|
||||
* **问题**: 模型训练成功后，后端向前端发送包含训练指标（metrics）的WebSocket消息或API响应时失败，导致前端状态无法更新为“已完成”。
|
||||
* **日志错误**: `Object of type float32 is not JSON serializable`
|
||||
* **分析**: 训练过程产生的评估指标（如 `mse`, `rmse`）是NumPy的 `float32` 类型。Python标准的 `json` 库无法直接序列化这种类型，导致在通过WebSocket或HTTP API发送数据时出错。
|
||||
* **涉及文件**: `server/utils/training_process_manager.py`
|
||||
* **修改详情**:
|
||||
1. **位置**: 文件顶部。
|
||||
* **操作**: 增加。
|
||||
* **内容**:
|
||||
```python
|
||||
import numpy as np
|
||||
|
||||
def convert_numpy_types(obj):
|
||||
"""递归地将字典/列表中的NumPy类型转换为Python原生类型"""
|
||||
if isinstance(obj, dict):
|
||||
return {k: convert_numpy_types(v) for k, v in obj.items()}
|
||||
elif isinstance(obj, list):
|
||||
return [convert_numpy_types(i) for i in obj]
|
||||
elif isinstance(obj, np.generic):
|
||||
return obj.item()
|
||||
return obj
|
||||
```
|
||||
* **原因**: 添加一个通用的辅助函数，用于将包含NumPy类型的数据结构转换为JSON兼容的格式。
|
||||
2. **位置**: `_monitor_results` 方法内部，调用 `self.websocket_callback` 之前。
|
||||
* **操作**: 增加。
|
||||
* **内容**:
|
||||
```diff
|
||||
+ serializable_task_data = convert_numpy_types(task_data)
|
||||
- self.websocket_callback('training_update', { ... 'metrics': task_data.get('metrics'), ... })
|
||||
+ self.websocket_callback('training_update', { ... 'metrics': serializable_task_data.get('metrics'), ... })
|
||||
```
|
||||
* **原因**: 在通过WebSocket发送数据之前，调用 `convert_numpy_types` 函数对包含训练结果的 `task_data` 进行处理，确保所有 `float32` 等类型都被转换为Python原生的 `float`，从而解决序列化错误。
|
||||
|
||||
**总结**: 通过在数据发送前进行类型转换，彻底解决了前后端通信中的序列化问题，确保了训练状态能够被正确地更新到前端。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 11:04
|
||||
**主题**: 根治JSON序列化问题
|
||||
|
||||
### 阶段六：修复API层序列化错误
|
||||
|
||||
* **问题**: 在修复WebSocket的序列化问题后，发现直接轮询 `GET /api/training` 接口时，仍然出现 `Object of type float32 is not JSON serializable` 错误。
|
||||
* **分析**: 上一阶段的修复只转换了准备通过WebSocket发送的数据，但没有转换**存放在 `TrainingProcessManager` 内部 `self.tasks` 字典中的数据**。因此，当API通过 `get_all_tasks()` 方法读取这个字典时，获取到的仍然是包含NumPy类型的原始数据，导致 `jsonify` 失败。
|
||||
* **涉及文件**: `server/utils/training_process_manager.py`
|
||||
* **修改详情**:
|
||||
* **位置**: `_monitor_results` 方法，从 `result_queue` 获取数据之后。
|
||||
* **操作**: 调整逻辑。
|
||||
* **内容**:
|
||||
```diff
|
||||
- with self.lock:
|
||||
- # ... 更新 self.tasks ...
|
||||
- if self.websocket_callback:
|
||||
- serializable_task_data = convert_numpy_types(task_data)
|
||||
- # ... 使用 serializable_task_data 发送消息 ...
|
||||
+ # 立即对从队列中取出的数据进行类型转换
|
||||
+ serializable_task_data = convert_numpy_types(task_data)
|
||||
+ with self.lock:
|
||||
+ # 使用转换后的数据更新任务状态
|
||||
+ for key, value in serializable_task_data.items():
|
||||
+ setattr(self.tasks[task_id], key, value)
|
||||
+ # WebSocket通知 - 使用已转换的数据
|
||||
+ if self.websocket_callback:
|
||||
+ # ... 使用 serializable_task_data 发送消息 ...
|
||||
```
|
||||
* **原因**: 将类型转换的步骤提前，确保存入 `self.tasks` 的数据已经是JSON兼容的。这样，无论是通过WebSocket推送还是通过API查询，获取到的都是安全的数据，从根源上解决了所有序列化问题。
|
||||
|
||||
**最终总结**: 至此，所有已知的数据流和数据类型问题均已解决。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 11:15
|
||||
**主题**: 修复模型评估中的MAPE计算错误
|
||||
|
||||
### 阶段七：修复评估指标计算
|
||||
|
||||
* **问题**: 训练 `transformer` 模型时，日志显示 `MAPE: nan%` 并伴有 `RuntimeWarning: Mean of empty slice.`。
|
||||
* **分析**: `MAPE` (平均绝对百分比误差) 的计算涉及除以真实值。当测试集中的所有真实销量（`y_true`）都为0时，用于避免除零错误的 `mask` 会导致一个空数组被传递给 `np.mean()`，从而产生 `nan` 和运行时警告。
|
||||
* **涉及文件**: `server/analysis/metrics.py`
|
||||
* **修改详情**:
|
||||
* **位置**: `evaluate_model` 函数中计算 `mape` 的部分。
|
||||
* **操作**: 增加条件判断。
|
||||
* **内容**:
|
||||
```diff
|
||||
- mask = y_true != 0
|
||||
- mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100
|
||||
+ mask = y_true != 0
|
||||
+ if np.any(mask):
|
||||
+ mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100
|
||||
+ else:
|
||||
+ # 如果所有真实值都为0，无法计算MAPE，返回0
|
||||
+ mape = 0.0
|
||||
```
|
||||
* **原因**: 在计算MAPE之前，先检查是否存在任何非零的真实值。如果不存在，则直接将MAPE设为0，避免了对空数组求平均值，从而解决了 `nan` 和 `RuntimeWarning` 的问题。
|
||||
|
||||
## 2025-07-14 11:41：修复“按店铺训练”页面店铺列表加载失败问题
|
||||
|
||||
**问题描述：**
|
||||
在“模型训练” -> “按店铺训练”页面中，“选择店铺”的下拉列表为空，无法加载任何店铺信息。
|
||||
|
||||
**根本原因：**
|
||||
位于 `server/utils/multi_store_data_utils.py` 的 `standardize_column_names` 函数在标准化数据后，错误地移除了包括店铺元数据在内的非训练必需列。这导致调用该函数的 `get_available_stores` 函数无法获取到完整的店铺信息，最终返回一个空列表。
|
||||
|
||||
**解决方案：**
|
||||
本着最小改动和保持代码清晰的原则，我进行了以下重构：
|
||||
|
||||
1. **净化 `standardize_column_names` 函数**：移除了其中所有与列筛选相关的代码，使其只专注于数据标准化这一核心职责。
|
||||
2. **精确应用筛选逻辑**：将列筛选的逻辑精确地移动到了 `get_store_product_sales_data` 和 `aggregate_multi_store_data` 这两个为模型训练准备数据的函数中。这确保了只有在需要为模型准备数据时，才会执行列筛选。
|
||||
3. **增强 `get_available_stores` 函数**：由于 `load_multi_store_data` 现在可以返回所有列，`get_available_stores` 将能够正常工作。同时，我增强了其代码的健壮性，以优雅地处理数据文件中可能存在的列缺失问题。
|
||||
|
||||
**代码变更：**
|
||||
- **文件：** `server/utils/multi_store_data_utils.py`
|
||||
- **主要改动：**
|
||||
- 从 `standardize_column_names` 中移除列筛选逻辑。
|
||||
- 在 `get_store_product_sales_data` 和 `aggregate_multi_store_data` 中添加列筛选逻辑。
|
||||
- 重写 `get_available_stores` 以更健壮地处理数据。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 13:00
|
||||
**主题**: 修复“按店铺训练-所有药品”模式下的训练失败问题
|
||||
|
||||
### 问题描述
|
||||
在“模型训练” -> “按店铺训练”页面，当选择“所有药品”进行训练时，后端日志显示 `获取店铺产品数据失败: 没有找到店铺 [store_id] 产品 unknown 的销售数据`，导致训练任务失败。
|
||||
|
||||
### 根本原因
|
||||
1. **API层**: `server/api.py` 在处理来自前端的训练请求时，如果 `product_id` 为 `null`（对应“所有药品”选项），会执行 `product_id or "unknown"`，错误地将产品ID设置为字符串 `"unknown"`。
|
||||
2. **预测器层**: `server/core/predictor.py` 中的 `train_model` 方法接收到无效的 `product_id="unknown"` 后，尝试使用它来获取数据，但数据源中不存在ID为“unknown”的产品，导致数据加载失败。
|
||||
3. **数据工具层**: `server/utils/multi_store_data_utils.py` 中的 `aggregate_multi_store_data` 函数只支持按产品ID进行全局聚合，不支持按店铺ID聚合其下所有产品的数据。
|
||||
|
||||
### 解决方案 (保留"unknown"字符串)
|
||||
为了在不改变API层行为的前提下解决问题，采用了在下游处理这个特殊值的策略：
|
||||
|
||||
1. **修改 `server/core/predictor.py`**:
|
||||
* **位置**: `train_model` 方法。
|
||||
* **操作**: 增加了对 `product_id == 'unknown'` 的特殊处理逻辑。
|
||||
* **内容**:
|
||||
```python
|
||||
# 如果product_id是'unknown'，则表示为店铺所有商品训练一个聚合模型
|
||||
if product_id == 'unknown':
|
||||
try:
|
||||
# 使用聚合函数，按店铺聚合
|
||||
product_data = aggregate_multi_store_data(
|
||||
store_id=store_id,
|
||||
aggregation_method=aggregation_method,
|
||||
file_path=self.data_path
|
||||
)
|
||||
# 将product_id设置为店铺ID，以便模型保存时使用有意义的标识
|
||||
product_id = store_id
|
||||
except Exception as e:
|
||||
# ... 错误处理 ...
|
||||
else:
|
||||
# ... 原有的按单个产品获取数据的逻辑 ...
|
||||
```
|
||||
* **原因**: 在预测器层面拦截无效的 `"unknown"` ID，并将其意图正确地转换为“聚合此店铺的所有产品数据”。同时，将 `product_id` 重新赋值为 `store_id`，确保了后续模型保存时能使用一个唯一且有意义的名称（如 `store_01010023_mlstm_v1.pth`）。
|
||||
|
||||
2. **修改 `server/utils/multi_store_data_utils.py`**:
|
||||
* **位置**: `aggregate_multi_store_data` 函数。
|
||||
* **操作**: 重构函数签名和内部逻辑。
|
||||
* **内容**:
|
||||
```python
|
||||
def aggregate_multi_store_data(product_id: Optional[str] = None,
|
||||
store_id: Optional[str] = None,
|
||||
aggregation_method: str = 'sum',
|
||||
...)
|
||||
# ...
|
||||
if store_id:
|
||||
# 店铺聚合：加载该店铺的所有数据
|
||||
df = load_multi_store_data(file_path, store_id=store_id)
|
||||
# ...
|
||||
elif product_id:
|
||||
# 全局聚合：加载该产品的所有数据
|
||||
df = load_multi_store_data(file_path, product_id=product_id)
|
||||
# ...
|
||||
else:
|
||||
raise ValueError("必须提供 product_id 或 store_id")
|
||||
```
|
||||
* **原因**: 扩展了数据聚合函数的功能，使其能够根据传入的 `store_id` 参数，加载并聚合特定店铺的所有销售数据，为店铺级别的综合模型训练提供了数据基础。
|
||||
|
||||
**最终结果**: 通过这两处修改，系统现在可以正确处理“按店铺-所有药品”的训练请求。它会聚合该店铺所有产品的销售数据，训练一个综合模型，并以店铺ID为标识来保存该模型，彻底解决了该功能点的训练失败问题。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 14:19
|
||||
**主题**: 修复并发训练中的稳定性和日志错误
|
||||
|
||||
### 阶段八：修复并发训练中的多个错误
|
||||
|
||||
* **问题**: 在并发执行多个训练任务时，系统出现 `JSON序列化错误`、`API列表排序错误` 和 `WebSocket连接错误`。
|
||||
* **分析**:
|
||||
1. **`Object of type float32 is not JSON serializable`**: `training_process_manager.py` 在通过WebSocket发送**中途**的训练进度时，没有对包含NumPy `float32` 类型的 `metrics` 数据进行序列化。
|
||||
2. **`'<' not supported between instances of 'str' and 'NoneType'`**: `api.py` 在获取训练任务列表时，对 `start_time` 进行排序，但未处理某些任务的 `start_time` 可能为 `None` 的情况，导致 `TypeError`。
|
||||
3. **`AssertionError: write() before start_response`**: `api.py` 中，当以 `debug=True` 模式运行时，Flask内置的Werkzeug服务器的调试器与Socket.IO的连接管理机制发生冲突。
|
||||
* **解决方案**:
|
||||
1. **文件**: `server/utils/training_process_manager.py`
|
||||
* **位置**: `_monitor_progress` 方法。
|
||||
* **操作**: 在发送 `training_progress` 事件前，调用 `convert_numpy_types` 函数对 `progress_data` 进行完全序列化。
|
||||
* **原因**: 确保所有通过WebSocket发送的数据（包括中途进度）都是JSON兼容的，彻底解决序列化问题。
|
||||
2. **文件**: `server/api.py`
|
||||
* **位置**: `get_all_training_tasks` 函数。
|
||||
* **操作**: 修改 `sorted` 函数的 `key`，使用 `lambda x: x.get('start_time') or '1970-01-01 00:00:00'`。
|
||||
* **原因**: 为 `None` 类型的 `start_time` 提供一个有效的默认值，使其可以和字符串类型的日期进行安全比较，解决了排序错误。
|
||||
3. **文件**: `server/api.py`
|
||||
* **位置**: `socketio.run()` 调用处。
|
||||
* **操作**: 增加 `allow_unsafe_werkzeug=True if args.debug else False` 参数。
|
||||
* **原因**: 这是 `Flask-SocketIO` 官方推荐的解决方案，用于在调试模式下协调Werkzeug与Socket.IO的事件循环，避免底层WSGI错误。
|
||||
|
||||
**最终结果**: 通过这三项修复，系统的并发稳定性和健壮性得到显著提升，解决了在高并发训练场景下出现的各类错误。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 14:48
|
||||
**主题**: 修复模型评估指标计算错误并优化训练过程
|
||||
|
||||
### 阶段九：修复模型评估与训练优化
|
||||
|
||||
* **问题**: 所有模型训练完成后，评估指标 `R²` 始终为0.0，`MAPE` 始终为0.00%，这表明模型评估或训练过程存在严重问题。
|
||||
* **分析**:
|
||||
1. **核心错误**: 在 `mlstm_trainer.py` 和 `transformer_trainer.py` 中，计算损失函数时，模型输出 `outputs` 的维度是 `(batch_size, forecast_horizon)`，而目标 `y_batch` 的维度被错误地通过 `unsqueeze(-1)` 修改为 `(batch_size, forecast_horizon, 1)`。这种维度不匹配导致损失计算错误，模型无法正确学习。
|
||||
2. **优化缺失**: 训练过程中缺少学习率调度、梯度裁剪和提前停止等关键的优化策略，影响了训练效率和稳定性。
|
||||
* **解决方案**:
|
||||
1. **修复维度不匹配 (关键修复)**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **位置**: 训练和验证循环中的损失计算部分。
|
||||
* **操作**: 移除了对 `y_batch` 的 `unsqueeze(-1)` 操作，确保 `outputs` 和 `y_batch` 维度一致。
|
||||
```diff
|
||||
- loss = criterion(outputs, y_batch.unsqueeze(-1))
|
||||
+ loss = criterion(outputs, y_batch.squeeze(-1) if y_batch.dim() == 3 else y_batch)
|
||||
```
|
||||
* **原因**: 修正损失函数的输入，使模型能够根据正确的误差进行学习，从而解决评估指标恒为0的问题。
|
||||
2. **增加训练优化策略**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **操作**: 在两个训练器中增加了以下功能：
|
||||
* **学习率调度器**: 引入 `torch.optim.lr_scheduler.ReduceLROnPlateau`，当测试损失停滞时自动降低学习率。
|
||||
* **梯度裁剪**: 在优化器更新前，使用 `torch.nn.utils.clip_grad_norm_` 对梯度进行裁剪，防止梯度爆炸。
|
||||
* **提前停止**: 增加了 `patience` 参数，当测试损失连续多个epoch未改善时，提前终止训练，防止过拟合。
|
||||
* **原因**: 引入这些业界标准的优化技术，可以显著提高训练过程的稳定性、收敛速度和最终的模型性能。
|
||||
|
||||
**最终结果**: 通过修复核心的逻辑错误并引入多项优化措施，模型现在不仅能够正确学习，而且训练过程更加健壮和高效。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 15:20
|
||||
**主题**: 根治模型维度错误并统一数据流 (完整调试过程)
|
||||
|
||||
### 阶段九：错误的修复尝试 (记录备查)
|
||||
|
||||
* **问题**: 所有模型训练完成后，评估指标 `R²` 始终为0.0，`MAPE` 始终为0.00%。
|
||||
* **初步分析**: 怀疑损失函数计算时，`outputs` 和 `y_batch` 维度不匹配。
|
||||
* **错误的假设**: 当时错误地认为是 `y_batch` 的维度有问题，而 `outputs` 的维度是正确的。
|
||||
* **错误的修复**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **操作**: 尝试在训练器层面使用 `squeeze` 调整 `y_batch` 的维度来匹配 `outputs`。
|
||||
```diff
|
||||
- loss = criterion(outputs, y_batch)
|
||||
+ loss = criterion(outputs, y_batch.squeeze(-1) if y_batch.dim() == 3 else y_batch)
|
||||
```
|
||||
* **结果**: 此修改导致了新的运行时错误 `UserWarning: Using a target size (torch.Size([32, 3])) that is different to the input size (torch.Size([32, 3, 1]))`，证明了修复方向错误，但帮助定位了问题的真正根源。
|
||||
|
||||
### 阶段十：根治维度不匹配问题
|
||||
|
||||
* **问题**: 深入分析阶段九的错误后，确认了问题的根源。
|
||||
* **根本原因**: `server/models/mlstm_model.py` 中的 `MLSTMTransformer` 模型，其 `forward` 方法的最后一层输出了一个多余的维度，导致其输出形状为 `(B, H, 1)`，而并非期望的 `(B, H)`。
|
||||
* **正确的解决方案 (端到端维度一致性)**:
|
||||
1. **修复模型层 (治本)**:
|
||||
* **文件**: `server/models/mlstm_model.py`
|
||||
* **位置**: `MLSTMTransformer` 的 `forward` 方法。
|
||||
* **操作**: 在 `output_layer` 之后增加 `.squeeze(-1)`，将模型输出的维度从 `(B, H, 1)` 修正为 `(B, H)`。
|
||||
```diff
|
||||
- return self.output_layer(decoder_outputs)
|
||||
+ return self.output_layer(decoder_outputs).squeeze(-1)
|
||||
```
|
||||
2. **净化训练器层 (治标)**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **操作**: 撤销了阶段九的错误修改，恢复为最直接的损失计算 `loss = criterion(outputs, y_batch)`。
|
||||
3. **优化评估逻辑**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **操作**: 简化了模型评估部分的反归一化逻辑，使其更清晰、更直接地处理 `(样本数, 预测步长)` 形状的数据。
|
||||
```diff
|
||||
- test_pred_inv = scaler_y.inverse_transform(test_pred.reshape(-1, 1)).flatten()
|
||||
- test_true_inv = scaler_y.inverse_transform(testY.reshape(-1, 1)).flatten()
|
||||
+ test_pred_inv = scaler_y.inverse_transform(test_pred)
|
||||
+ test_true_inv = scaler_y.inverse_transform(test_true)
|
||||
```
|
||||
|
||||
**最终结果**: 通过记录整个调试过程，我们不仅修复了问题，还理解了其根本原因。通过在模型源头修正维度，并在整个数据流中保持维度一致性，彻底解决了训练失败的问题。代码现在更简洁、健壮，并遵循了良好的设计实践。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 15:30
|
||||
**主题**: 根治模型维度错误并统一数据流 (完整调试过程)
|
||||
|
||||
### 阶段九：错误的修复尝试 (记录备查)
|
||||
|
||||
* **问题**: 所有模型训练完成后，评估指标 `R²` 始终为0.0，`MAPE` 始终为0.00%。
|
||||
* **初步分析**: 怀疑损失函数计算时，`outputs` 和 `y_batch` 维度不匹配。
|
||||
* **错误的假设**: 当时错误地认为是 `y_batch` 的维度有问题，而 `outputs` 的维度是正确的。
|
||||
* **错误的修复**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **操作**: 尝试在训练器层面使用 `squeeze` 调整 `y_batch` 的维度来匹配 `outputs`。
|
||||
```diff
|
||||
- loss = criterion(outputs, y_batch)
|
||||
+ loss = criterion(outputs, y_batch.squeeze(-1) if y_batch.dim() == 3 else y_batch)
|
||||
```
|
||||
* **结果**: 此修改导致了新的运行时错误 `UserWarning: Using a target size (torch.Size([32, 3])) that is different to the input size (torch.Size([32, 3, 1]))`，证明了修复方向错误，但帮助定位了问题的真正根源。
|
||||
|
||||
### 阶段十：根治维度不匹配问题
|
||||
|
||||
* **问题**: 深入分析阶段九的错误后，确认了问题的根源在于模型输出维度。
|
||||
* **根本原因**: `server/models/mlstm_model.py` 中的 `MLSTMTransformer` 模型，其 `forward` 方法的最后一层输出了一个多余的维度，导致其输出形状为 `(B, H, 1)`，而并非期望的 `(B, H)`。
|
||||
* **正确的解决方案 (端到端维度一致性)**:
|
||||
1. **修复模型层 (治本)**:
|
||||
* **文件**: `server/models/mlstm_model.py`
|
||||
* **位置**: `MLSTMTransformer` 的 `forward` 方法。
|
||||
* **操作**: 在 `output_layer` 之后增加 `.squeeze(-1)`，将模型输出的维度从 `(B, H, 1)` 修正为 `(B, H)`。
|
||||
2. **净化训练器层 (治标)**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **操作**: 撤销了阶段九的错误修改，恢复为最直接的损失计算 `loss = criterion(outputs, y_batch)`。
|
||||
3. **优化评估逻辑**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **操作**: 简化了模型评估部分的反归一化逻辑，使其更清晰、更直接地处理 `(样本数, 预测步长)` 形状的数据。
|
||||
|
||||
### 阶段十一：最终修复与逻辑统一
|
||||
|
||||
* **问题**: 在应用阶段十的修复后，训练仍然失败。mLSTM出现维度反转错误 (`target size (B, H, 1)` vs `input size (B, H)`)，而Transformer则出现评估错误 (`'numpy.ndarray' object has no attribute 'numpy'`)。
|
||||
* **分析**:
|
||||
1. **维度反转根源**: 问题的最终根源在 `server/utils/data_utils.py` 的 `create_dataset` 函数。它在创建目标数据集 `dataY` 时，错误地保留了一个多余的维度，导致 `y_batch` 的形状变为 `(B, H, 1)`。
|
||||
2. **评估Bug**: 在 `mlstm_trainer.py` 和 `transformer_trainer.py` 的评估部分，代码 `test_true = testY.numpy()` 是错误的，因为 `testY` 已经是Numpy数组。
|
||||
* **最终解决方案 (端到端修复)**:
|
||||
1. **修复数据加载层 (治本)**:
|
||||
* **文件**: `server/utils/data_utils.py`
|
||||
* **位置**: `create_dataset` 函数。
|
||||
* **操作**: 修改 `dataY.append(y)` 为 `dataY.append(y.flatten())`，从源头上确保 `y` 标签的维度是正确的 `(B, H)`。
|
||||
2. **修复训练器评估层**:
|
||||
* **文件**: `server/trainers/mlstm_trainer.py`, `server/trainers/transformer_trainer.py`
|
||||
* **位置**: 模型评估部分。
|
||||
* **操作**: 修正 `test_true = testY.numpy()` 为 `test_true = testY`，解决了属性错误。
|
||||
|
||||
**最终结果**: 通过记录并分析整个调试过程（阶段九到十一），我们最终定位并修复了从数据加载、模型设计到训练器评估的整个流程中的维度不一致问题。代码现在更加简洁、健壮，并遵循了端到端维度一致的良好设计实践。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 15:34
|
||||
**主题**: 扩展维度修复至Transformer模型
|
||||
|
||||
### 阶段十二：统一所有模型的输出维度
|
||||
|
||||
* **问题**: 在修复 `mLSTM` 模型后，`Transformer` 模型的训练仍然因为完全相同的维度不匹配问题而失败。
|
||||
* **分析**: `server/models/transformer_model.py` 中的 `TimeSeriesTransformer` 类也存在与 `mLSTM` 相同的设计缺陷，其 `forward` 方法的输出维度为 `(B, H, 1)` 而非 `(B, H)`。
|
||||
* **解决方案**:
|
||||
1. **修复Transformer模型层**:
|
||||
* **文件**: `server/models/transformer_model.py`
|
||||
* **位置**: `TimeSeriesTransformer` 的 `forward` 方法。
|
||||
* **操作**: 在 `output_layer` 之后增加 `.squeeze(-1)`，将模型输出的维度从 `(B, H, 1)` 修正为 `(B, H)`。
|
||||
```diff
|
||||
- return self.output_layer(decoder_outputs)
|
||||
+ return self.output_layer(decoder_outputs).squeeze(-1)
|
||||
```
|
||||
|
||||
**最终结果**: 通过将维度修复方案应用到所有相关的模型文件，我们确保了整个系统的模型层都遵循了统一的、正确的输出维度标准。至此，所有已知的维度相关问题均已从根源上解决。
|
||||
|
||||
---
|
||||
**日期**: 2025-07-14 16:10
|
||||
**主题**: 修复“全局模型训练-所有药品”模式下的训练失败问题
|
||||
|
||||
### 问题描述
|
||||
在“全局模型训练”页面，当选择“所有药品”进行训练时，后端日志显示 `聚合全局数据失败: 没有找到产品 unknown 的销售数据`，导致训练任务失败。
|
||||
|
||||
### 根本原因
|
||||
1. **API层 (`server/api.py`)**: 在处理全局训练请求时，如果前端未提供 `product_id`（对应“所有药品”选项），API层会执行 `product_id or "unknown"`，错误地将产品ID设置为字符串 `"unknown"`。
|
||||
2. **预测器层 (`server/core/predictor.py`)**: `train_model` 方法接收到无效的 `product_id="unknown"` 后，在 `training_mode='global'` 分支下，直接将其传递给数据聚合函数。
|
||||
3. **数据工具层 (`server/utils/multi_store_data_utils.py`)**: `aggregate_multi_store_data` 函数缺少处理“真正”全局聚合（即不按任何特定产品或店铺过滤）的逻辑，当收到 `product_id="unknown"` 时，它会尝试按一个不存在的产品进行过滤，最终导致失败。
|
||||
|
||||
### 解决方案 (遵循现有设计模式)
|
||||
为了在不影响现有功能的前提下修复此问题，采用了与历史修复类似的、在中间层进行逻辑适配的策略。
|
||||
|
||||
1. **修改 `server/utils/multi_store_data_utils.py`**:
|
||||
* **位置**: `aggregate_multi_store_data` 函数。
|
||||
* **操作**: 扩展了函数功能。
|
||||
* **内容**: 增加了新的逻辑分支。当 `product_id` 和 `store_id` 参数都为 `None` 时，函数现在会加载**所有**数据进行聚合，以支持真正的全局模型训练。
|
||||
```python
|
||||
# ...
|
||||
elif product_id:
|
||||
# 按产品聚合...
|
||||
else:
|
||||
# 真正全局聚合：加载所有数据
|
||||
df = load_multi_store_data(file_path)
|
||||
if len(df) == 0:
|
||||
raise ValueError("数据文件为空，无法进行全局聚合")
|
||||
grouping_entity = "所有产品"
|
||||
```
|
||||
* **原因**: 使数据聚合函数的功能更加完整和健壮，能够服务于真正的全局训练场景，同时不影响其原有的按店铺或按产品的聚合功能。
|
||||
|
||||
2. **修改 `server/core/predictor.py`**:
|
||||
* **位置**: `train_model` 方法，`training_mode == 'global'` 的逻辑分支内。
|
||||
* **操作**: 增加了对 `product_id == 'unknown'` 的特殊处理。
|
||||
* **内容**:
|
||||
```python
|
||||
if product_id == 'unknown':
|
||||
product_data = aggregate_multi_store_data(
|
||||
product_id=None, # 传递None以触发真正的全局聚合
|
||||
# ...
|
||||
)
|
||||
# 将product_id设置为一个有意义的标识符
|
||||
product_id = 'all_products'
|
||||
else:
|
||||
# ...原有的按单个产品聚合的逻辑...
|
||||
```
|
||||
* **原因**: 在核心预测器层面拦截无效的 `"unknown"` ID，并将其正确地解释为“聚合所有产品数据”的意图。通过向聚合函数传递 `product_id=None` 来调用新增强的全局聚合功能，并用一个有意义的标识符 `all_products` 来命名模型，确保了后续流程的正确执行。
|
||||
|
||||
**最终结果**: 通过这两处修改，系统现在可以正确处理“全局模型-所有药品”的训练请求，聚合所有产品的销售数据来训练一个通用的全局模型，彻底解决了该功能点的训练失败问题。
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
**按药品模型预测**
|
||||
@ -1,41 +0,0 @@
|
||||
@echo off
|
||||
chcp 65001 >nul 2>&1
|
||||
echo 🚀 启动药店销售预测系统API服务器 (WebSocket修复版)
|
||||
echo.
|
||||
|
||||
:: 设置编码环境变量
|
||||
set PYTHONIOENCODING=utf-8
|
||||
set PYTHONLEGACYWINDOWSSTDIO=0
|
||||
|
||||
:: 显示当前配置
|
||||
echo 📋 当前环境配置:
|
||||
echo 编码: UTF-8
|
||||
echo 路径: %CD%
|
||||
echo Python: uv管理
|
||||
echo.
|
||||
|
||||
:: 检查依赖
|
||||
echo 🔍 检查Python依赖...
|
||||
uv list --quiet >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo ⚠️ UV环境未配置，正在初始化...
|
||||
uv sync
|
||||
)
|
||||
|
||||
echo ✅ 依赖检查完成
|
||||
echo.
|
||||
|
||||
:: 启动API服务器
|
||||
echo 🌐 启动API服务器 (WebSocket支持)...
|
||||
echo 💡 访问地址: http://localhost:5000
|
||||
echo 🔗 WebSocket端点: ws://localhost:5000/socket.io
|
||||
echo.
|
||||
echo 📝 启动日志:
|
||||
echo ----------------------------------------
|
||||
|
||||
uv run server/api.py --host 0.0.0.0 --port 5000
|
||||
|
||||
echo.
|
||||
echo ----------------------------------------
|
||||
echo 🛑 API服务器已停止
|
||||
pause
|
||||
11
启动API服务器.bat
11
启动API服务器.bat
@ -1,11 +0,0 @@
|
||||
@echo off
|
||||
chcp 65001 >nul 2>&1
|
||||
set PYTHONIOENCODING=utf-8
|
||||
set PYTHONLEGACYWINDOWSSTDIO=0
|
||||
cd /d %~dp0
|
||||
echo 🚀 启动药店销售预测系统API服务器...
|
||||
echo 📝 编码设置: UTF-8
|
||||
echo 🌐 服务地址: http://127.0.0.1:5000
|
||||
echo.
|
||||
uv run server/api.py
|
||||
pause
|
||||
30
导出依赖配置.bat
30
导出依赖配置.bat
@ -1,30 +0,0 @@
|
||||
@echo off
|
||||
chcp 65001 >nul 2>&1
|
||||
echo 📦 导出UV依赖配置
|
||||
echo.
|
||||
|
||||
:: 设置编码
|
||||
set PYTHONIOENCODING=utf-8
|
||||
|
||||
echo 📋 导出requirements.txt格式...
|
||||
uv export --format requirements-txt > requirements-exported.txt
|
||||
|
||||
echo 📋 导出依赖树状图...
|
||||
uv tree > dependency-tree.txt
|
||||
|
||||
echo 📋 显示当前已安装的包...
|
||||
uv list > installed-packages.txt
|
||||
|
||||
echo 📋 显示uv配置...
|
||||
uv config list > uv-config.txt
|
||||
|
||||
echo.
|
||||
echo ✅ 依赖配置导出完成！
|
||||
echo.
|
||||
echo 📁 生成的文件:
|
||||
echo - requirements-exported.txt (标准requirements格式)
|
||||
echo - dependency-tree.txt (依赖关系树)
|
||||
echo - installed-packages.txt (已安装包列表)
|
||||
echo - uv-config.txt (UV配置信息)
|
||||
echo.
|
||||
pause
|
||||
43
快速安装依赖.bat
43
快速安装依赖.bat
@ -1,43 +0,0 @@
|
||||
@echo off
|
||||
chcp 65001 >nul 2>&1
|
||||
echo 🚀 药店销售预测系统 - 快速安装依赖
|
||||
echo.
|
||||
|
||||
:: 设置编码环境变量
|
||||
set PYTHONIOENCODING=utf-8
|
||||
set PYTHONLEGACYWINDOWSSTDIO=0
|
||||
|
||||
echo 📁 配置UV缓存目录...
|
||||
uv config set cache-dir ".uv_cache"
|
||||
|
||||
echo 🌐 配置镜像源...
|
||||
uv config set global.index-url "https://pypi.tuna.tsinghua.edu.cn/simple"
|
||||
|
||||
echo.
|
||||
echo 📦 安装核心依赖包...
|
||||
echo.
|
||||
|
||||
:: 分批安装，避免超时
|
||||
echo 1/4 安装基础数据处理包...
|
||||
uv add numpy pandas openpyxl
|
||||
|
||||
echo 2/4 安装机器学习包...
|
||||
uv add scikit-learn matplotlib tqdm
|
||||
|
||||
echo 3/4 安装Web框架包...
|
||||
uv add flask flask-cors flask-socketio flasgger werkzeug
|
||||
|
||||
echo 4/4 安装深度学习框架...
|
||||
uv add torch torchvision --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
echo.
|
||||
echo ✅ 核心依赖安装完成！
|
||||
echo.
|
||||
echo 🔍 检查安装状态...
|
||||
uv list
|
||||
|
||||
echo.
|
||||
echo 🎉 依赖安装完成！可以启动系统了
|
||||
echo 💡 启动命令: uv run server/api.py
|
||||
echo.
|
||||
pause
|
||||
43
配置UV环境.bat
43
配置UV环境.bat
@ -1,43 +0,0 @@
|
||||
@echo off
|
||||
chcp 65001 >nul 2>&1
|
||||
echo 🔧 配置药店销售预测系统UV环境...
|
||||
echo.
|
||||
|
||||
:: 设置编码环境变量
|
||||
set PYTHONIOENCODING=utf-8
|
||||
set PYTHONLEGACYWINDOWSSTDIO=0
|
||||
|
||||
:: 设置缓存目录
|
||||
echo 📁 设置UV缓存目录...
|
||||
uv config set cache-dir "H:\_Workings\_OneTree\_ShopTRAINING\.uv_cache"
|
||||
|
||||
:: 设置镜像源
|
||||
echo 🌐 配置国内镜像源...
|
||||
uv config set global.index-url "https://pypi.tuna.tsinghua.edu.cn/simple"
|
||||
|
||||
:: 设置信任主机
|
||||
echo 🔒 配置信任主机...
|
||||
uv config set global.trusted-host "pypi.tuna.tsinghua.edu.cn"
|
||||
|
||||
echo.
|
||||
echo ✅ UV环境配置完成
|
||||
echo 📋 当前配置:
|
||||
uv config list
|
||||
|
||||
echo.
|
||||
echo 🚀 初始化项目并同步依赖...
|
||||
uv sync
|
||||
|
||||
echo.
|
||||
echo 📦 安装完成，检查依赖状态...
|
||||
uv tree
|
||||
|
||||
echo.
|
||||
echo 🎉 环境配置和依赖同步完成！
|
||||
echo.
|
||||
echo 💡 使用方法:
|
||||
echo 启动API服务器: uv run server/api.py
|
||||
echo 运行测试: uv run pytest
|
||||
echo 格式化代码: uv run black server/
|
||||
echo.
|
||||
pause
|
||||
Loading…
x
Reference in New Issue
Block a user