feat: add visualize + e2e orchestrator + run_e2e.sh
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张宗平
Executable
+48
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#!/usr/bin/env bash
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
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cd "$PROJECT_DIR"
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echo "=== ts-anomaly-td E2E ==="
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echo ""
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echo "[1/5] Starting TDengine container..."
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docker compose up -d
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echo "[2/5] Waiting for TDengine to be healthy..."
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for i in $(seq 1 60); do
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if docker compose exec -T tdengine taos -s "SELECT SERVER_VERSION()" > /dev/null 2>&1; then
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echo "TDengine ready (attempt $i)"
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break
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fi
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sleep 2
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done
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mkdir -p render logs
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echo "[3/5] Running E2E pipeline..."
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uv run python -m ts_anomaly_td e2e --data-dir data --output-dir render --log-dir logs
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echo "[4/5] Checking results..."
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for png in render/*.png; do
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if [ -f "$png" ]; then
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sz=$(stat -c%s "$png" 2>/dev/null || stat -f%z "$png" 2>/dev/null || echo 0)
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echo " $png: ${sz} bytes"
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if [ "$sz" -lt 10240 ]; then
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echo " WARNING: PNG too small (< 10KB)"
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fi
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fi
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done
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for log in logs/e2e_*.json; do
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if [ -f "$log" ]; then
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echo " $log: $(wc -c < "$log") bytes"
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fi
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done
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echo "[5/5] Done."
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echo ""
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echo "To clean up: docker compose down"
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echo "To keep containers for debug: leave them running"
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+118
-2
@@ -1,3 +1,119 @@
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"""E2E 编排器（占位 stub）"""
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"""E2E 编排器：inject → 6 算法 → FORECAST → 可视化 → JSON 日志。"""
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import json
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import logging
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import time
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from pathlib import Path
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from datetime import datetime
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from ts_anomaly_td.connector import TDConnection
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from ts_anomaly_td.schema import setup_schema
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from ts_anomaly_td.io_csv import read_csv
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from ts_anomaly_td.detection import detect_all_algos, ALL_ALGOS
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from ts_anomaly_td.forecast import forecast_anomaly
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from ts_anomaly_td.visualize import render_contrast
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logger = logging.getLogger(__name__)
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def run_e2e(args):
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raise NotImplementedError("e2e 将在 Task 7 实现")
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"""执行 E2E 全流程。
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Args:
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args: argparse namespace（含 data_dir, output_dir, log_dir, url）
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"""
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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)
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data_dir = Path(args.data_dir)
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output_dir = Path(args.output_dir)
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log_dir = Path(args.log_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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log_dir.mkdir(parents=True, exist_ok=True)
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csv_files = sorted(data_dir.glob("*.csv"))
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if not csv_files:
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logger.error("no CSV files found in %s", data_dir)
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return 1
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conn = TDConnection(args.url)
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summary = {"run_at": datetime.now().isoformat(), "datasets": {}, "exit_code": 0}
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last_log_path = None
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try:
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for csv_path in csv_files:
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stable = csv_path.stem
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logger.info("=== E2E %s ===", stable)
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ts, vals, labels = read_csv(str(csv_path))
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rows = list(zip(ts, vals, labels))
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t0 = time.monotonic()
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setup_schema(conn, stable)
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conn.batch_insert(stable, rows)
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inject_time = time.monotonic() - t0
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logger.info("inject: %d rows in %.1fs", len(rows), inject_time)
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t0 = time.monotonic()
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det_results = detect_all_algos(conn, stable)
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detect_time = time.monotonic() - t0
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for algo, r in det_results.items():
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if r["error"]:
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logger.info(" %s: ERROR %s", algo, r["error"])
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else:
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logger.info(" %s: %d windows", algo, len(r["windows"]))
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t0 = time.monotonic()
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try:
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fc_points = forecast_anomaly(conn, stable)
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fc_time = time.monotonic() - t0
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fc_anom_count = sum(1 for p in fc_points if p["is_anomaly"])
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logger.info("FORECAST: %d points, %d anomalies in %.1fs", len(fc_points), fc_anom_count, fc_time)
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except Exception as e:
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logger.warning("FORECAST failed (non-fatal): %s", e)
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fc_points = []
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fc_time = time.monotonic() - t0
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fc_anom_count = 0
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t0 = time.monotonic()
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png_path = output_dir / f"{stable}_gt_vs_tdengine.png"
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render_contrast(ts, vals, labels, det_results, str(png_path), title=stable)
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viz_time = time.monotonic() - t0
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ds_summary = {
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"rows": len(rows),
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"inject_time_s": round(inject_time, 1),
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"detect_time_s": round(detect_time, 1),
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"forecast_time_s": round(fc_time, 1),
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"viz_time_s": round(viz_time, 1),
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"algorithms": {},
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"forecast": {
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"point_count": len(fc_points),
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"anomaly_count": fc_anom_count,
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},
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"png": str(png_path),
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}
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for algo, r in det_results.items():
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ds_summary["algorithms"][algo] = {
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"window_count": len(r["windows"]),
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"error": r["error"],
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}
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summary["datasets"][stable] = ds_summary
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last_log_path = log_dir / f"e2e_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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last_log_path.write_text(json.dumps(summary, indent=2, ensure_ascii=False))
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logger.info("log saved: %s", last_log_path)
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except Exception as e:
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logger.exception("E2E failed: %s", e)
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summary["exit_code"] = 1
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summary["error"] = str(e)
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last_log_path = log_dir / f"e2e_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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last_log_path.write_text(json.dumps(summary, indent=2, ensure_ascii=False))
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finally:
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conn.close()
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return summary["exit_code"]
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@@ -0,0 +1,155 @@
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"""可视化模块：双栏对比图（GT 上、TDengine 检测下）。"""
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import logging
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import matplotlib.font_manager as fm
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logger = logging.getLogger(__name__)
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ALGO_COLORS = {
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"ksigma": "#1f77b4",
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"iqr": "#2ca02c",
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"grubbs": "#9467bd",
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"shesd": "#ff7f0e",
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"lof": "#17becf",
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"sample_ad_model": "#e377c2",
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}
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def _find_cjk_font():
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"""查找可用 CJK 字体。"""
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candidates = ["Noto Serif CJK SC", "Noto Sans CJK SC", "WenQuanYi Micro Hei",
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"DejaVu Sans", "sans-serif"]
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available = {f.name for f in fm.fontManager.ttflist}
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for name in candidates:
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if name in available:
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return name
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return "sans-serif"
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def _compute_iou(gt_windows, det_windows):
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"""计算 GT 与检测窗口的 IoU。
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窗口格式: [(start_ts, end_ts), ...]
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IoU = 交集时长 / 并集时长
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"""
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if not gt_windows or not det_windows:
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return 0.0
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def overlap(a_start, a_end, b_start, b_end):
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o_start = max(a_start, b_start)
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o_end = min(a_end, b_end)
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return max(0, o_end - o_start)
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intersection = 0.0
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union = 0.0
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for gw in gt_windows:
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union += gw[1] - gw[0]
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for dw in det_windows:
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intersection += overlap(gw[0], gw[1], dw[0], dw[1])
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for dw in det_windows:
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union += dw[1] - dw[0]
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if union == 0:
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return 0.0
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return min(intersection / union, 1.0)
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def render_contrast(
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timestamps: list[int],
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values: list[float],
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gt_labels: list[int],
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detection_results: dict,
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output_path: str,
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title: str = "",
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) -> str:
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"""渲染双栏对比图。
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Args:
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timestamps: 毫秒时间戳列表
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values: 数值列表
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gt_labels: GT 标签（0/1）
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detection_results: {algo: {"windows": [...]}} 来自 detect_all_algos
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output_path: PNG 输出路径
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title: 图表标题
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Returns:
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output_path
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"""
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font_name = _find_cjk_font()
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plt.rcParams["font.family"] = font_name
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plt.rcParams["font.size"] = 9
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fig, (ax_gt, ax_det) = plt.subplots(2, 1, sharex=True, figsize=(14, 8))
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t0 = timestamps[0]
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hours = [(t - t0) / 3_600_000 for t in timestamps]
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ax_gt.plot(hours, values, color="black", linewidth=0.6, alpha=0.8)
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ax_gt.set_ylabel("Value")
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ax_gt.set_title(f"Ground Truth — {title}" if title else "Ground Truth")
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in_anomaly = False
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anom_start = None
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gt_windows = []
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for i, label in enumerate(gt_labels):
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if label == 1 and not in_anomaly:
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in_anomaly = True
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anom_start = hours[i]
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elif label == 0 and in_anomaly:
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in_anomaly = False
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ax_gt.axvspan(anom_start, hours[i], alpha=0.25, color="red")
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gt_windows.append((anom_start, hours[i]))
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if in_anomaly:
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ax_gt.axvspan(anom_start, hours[-1], alpha=0.25, color="red")
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gt_windows.append((anom_start, hours[-1]))
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ax_det.plot(hours, values, color="black", linewidth=0.6, alpha=0.3)
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ax_det.set_ylabel("Value")
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ax_det.set_xlabel("Time (hours)")
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all_det_windows = []
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for algo, result in detection_results.items():
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if result.get("error"):
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continue
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color = ALGO_COLORS.get(algo, "#888888")
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for wstart, wend in result.get("windows", []):
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wstart_h = (wstart - timestamps[0]) / 3_600_000 if wstart > timestamps[0] else 0
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wend_h = (wend - timestamps[0]) / 3_600_000
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ax_det.axvspan(wstart_h, wend_h, alpha=0.2, color=color)
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all_det_windows.append((wstart_h, wend_h))
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legend_handles = []
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for algo in detection_results:
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if algo in ALGO_COLORS and not detection_results[algo].get("error"):
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from matplotlib.patches import Patch
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legend_handles.append(Patch(color=ALGO_COLORS[algo], alpha=0.5, label=algo))
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if legend_handles:
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ax_det.legend(handles=legend_handles, loc="upper right", fontsize=7)
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ax_det.set_title(f"TDengine Detection — {title}" if title else "TDengine Detection (6 algos)")
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iou = _compute_iou(gt_windows, all_det_windows)
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total_det = len(all_det_windows)
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stats_text = f"GT windows: {len(gt_windows)}\nDetected: {total_det}\nIoU: {iou:.3f}"
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ax_det.text(
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0.98, 0.97, stats_text,
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transform=ax_det.transAxes,
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verticalalignment="top",
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horizontalalignment="right",
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bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
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fontsize=8,
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)
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plt.tight_layout()
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Path(output_path).parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(output_path, dpi=150, bbox_inches="tight")
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plt.close(fig)
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logger.info("chart saved: %s", output_path)
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return output_path
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