update pay ui

ml/run_training.sh

@@ -0,0 +1,99 @@
+#!/bin/bash
+# 概念异动检测模型训练脚本 (Linux)
+#
+# 使用方法:
+#   chmod +x run_training.sh
+#   ./run_training.sh
+#
+# 或指定参数:
+#   ./run_training.sh --start 2022-01-01 --epochs 100
+
+set -e
+
+echo "============================================================"
+echo "概念异动检测模型训练流程"
+echo "============================================================"
+echo ""
+
+# 获取脚本所在目录
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR/.."
+
+echo "[1/4] 检查环境..."
+python3 --version || { echo "Python3 未找到！"; exit 1; }
+
+# 检查 GPU
+if python3 -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')" 2>/dev/null; then
+    echo "PyTorch GPU 检测完成"
+else
+    echo "警告: PyTorch 未安装或无法检测 GPU"
+fi
+
+echo ""
+echo "[2/4] 检查依赖..."
+pip3 install -q torch pandas numpy pyarrow tqdm clickhouse-driver elasticsearch sqlalchemy pymysql
+
+echo ""
+echo "[3/4] 准备训练数据..."
+echo "从 ClickHouse 提取历史数据，这可能需要较长时间..."
+echo ""
+
+# 解析参数
+START_DATE="2022-01-01"
+END_DATE=""
+EPOCHS=100
+BATCH_SIZE=256
+TRAIN_END="2025-06-30"
+VAL_END="2025-09-30"
+
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --start)
+            START_DATE="$2"
+            shift 2
+            ;;
+        --end)
+            END_DATE="$2"
+            shift 2
+            ;;
+        --epochs)
+            EPOCHS="$2"
+            shift 2
+            ;;
+        --batch_size)
+            BATCH_SIZE="$2"
+            shift 2
+            ;;
+        --train_end)
+            TRAIN_END="$2"
+            shift 2
+            ;;
+        --val_end)
+            VAL_END="$2"
+            shift 2
+            ;;
+        *)
+            shift
+            ;;
+    esac
+done
+
+# 数据准备
+if [ -n "$END_DATE" ]; then
+    python3 ml/prepare_data.py --start "$START_DATE" --end "$END_DATE"
+else
+    python3 ml/prepare_data.py --start "$START_DATE"
+fi
+
+echo ""
+echo "[4/4] 训练模型..."
+echo "使用 GPU 加速训练..."
+echo ""
+
+python3 ml/train.py --epochs "$EPOCHS" --batch_size "$BATCH_SIZE" --train_end "$TRAIN_END" --val_end "$VAL_END"
+
+echo ""
+echo "============================================================"
+echo "训练完成！"
+echo "模型保存在: ml/checkpoints/"
+echo "============================================================"