modify: cleanup project structure and docs
This commit is contained in:
13
ui_sections/__init__.py
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13
ui_sections/__init__.py
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from .overview import render_overview
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from .forecast import render_forecast
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from .model_eval import render_model_eval
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from .strategy_eval import render_strategy_eval
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from .hotspot import render_hotspot
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__all__ = [
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'render_overview',
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'render_forecast',
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'render_model_eval',
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'render_strategy_eval',
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'render_hotspot',
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]
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185
ui_sections/forecast.py
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185
ui_sections/forecast.py
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from __future__ import annotations
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import pandas as pd
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import plotly.graph_objects as go
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import streamlit as st
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from services.forecast import (
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arima_forecast_with_grid_search,
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knn_forecast_counterfactual,
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fit_and_extrapolate,
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)
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def render_forecast(base: pd.DataFrame):
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st.subheader("多模型预测比较")
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if base is None or base.empty:
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st.info("暂无可用于预测的事故数据，请先在侧边栏上传数据并应用筛选。")
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st.session_state.setdefault(
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"forecast_state",
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{"results": None, "last_message": "暂无可用于预测的事故数据。"},
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)
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return
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forecast_state = st.session_state.setdefault(
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"forecast_state",
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{
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"selected_date": None,
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"horizon": 30,
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"results": None,
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"last_message": None,
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"data_signature": None,
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},
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)
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earliest_date = base.index.min().date()
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latest_date = base.index.max().date()
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fallback_date = max(
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(base.index.max() - pd.Timedelta(days=30)).date(),
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earliest_date,
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)
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current_signature = (
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earliest_date.isoformat(),
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latest_date.isoformat(),
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int(len(base)),
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float(base["accident_count"].sum()),
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)
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# Reset cached results if the underlying dataset has changed
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if forecast_state.get("data_signature") != current_signature:
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forecast_state.update(
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{
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"data_signature": current_signature,
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"results": None,
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"last_message": None,
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"selected_date": fallback_date,
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}
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)
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default_date = forecast_state.get("selected_date") or fallback_date
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if default_date < earliest_date:
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default_date = earliest_date
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if default_date > latest_date:
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default_date = latest_date
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with st.form(key="predict_form"):
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selected_date = st.date_input(
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"选择干预日期 / 预测起点",
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value=default_date,
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min_value=earliest_date,
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max_value=latest_date,
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)
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horizon = st.number_input(
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"预测天数",
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min_value=7,
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max_value=90,
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value=int(forecast_state.get("horizon", 30)),
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step=1,
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)
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submit_predict = st.form_submit_button("应用预测参数")
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forecast_state["selected_date"] = selected_date
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forecast_state["horizon"] = int(horizon)
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if submit_predict:
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history = base.loc[:pd.to_datetime(selected_date)]
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if len(history) < 10:
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forecast_state.update(
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{
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"results": None,
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"last_message": "干预前数据不足（至少需要 10 个观测点）。",
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}
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)
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else:
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with st.spinner("正在生成预测结果…"):
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warnings: list[str] = []
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try:
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train_series = history["accident_count"]
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arima_df = arima_forecast_with_grid_search(
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train_series,
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start_date=pd.to_datetime(selected_date) + pd.Timedelta(days=1),
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horizon=int(horizon),
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)
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except Exception as exc:
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arima_df = None
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warnings.append(f"ARIMA 运行失败：{exc}")
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knn_pred, _ = knn_forecast_counterfactual(
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base["accident_count"],
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pd.to_datetime(selected_date),
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horizon=int(horizon),
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)
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if knn_pred is None:
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warnings.append("KNN 预测未生成结果（历史数据不足或维度不满足要求）。")
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glm_pred, svr_pred, _ = fit_and_extrapolate(
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base["accident_count"],
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pd.to_datetime(selected_date),
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days=int(horizon),
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)
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if glm_pred is None and svr_pred is None:
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warnings.append("GLM/SVR 预测未生成结果，建议缩短预测窗口或检查源数据。")
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forecast_state.update(
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{
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"results": {
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"selected_date": selected_date,
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"horizon": int(horizon),
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"arima_df": arima_df,
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"knn_pred": knn_pred,
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"glm_pred": glm_pred,
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"svr_pred": svr_pred,
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"warnings": warnings,
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},
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"last_message": None,
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}
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)
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results = forecast_state.get("results")
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if not results:
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if forecast_state.get("last_message"):
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st.warning(forecast_state["last_message"])
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else:
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st.info("请设置预测参数并点击“应用预测参数”按钮。")
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return
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first_date = pd.to_datetime(results["selected_date"])
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horizon_days = int(results["horizon"])
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arima_df = results["arima_df"]
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knn_pred = results["knn_pred"]
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glm_pred = results["glm_pred"]
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svr_pred = results["svr_pred"]
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fig_pred = go.Figure()
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fig_pred.add_trace(
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go.Scatter(x=base.index, y=base["accident_count"], name="实际", mode="lines")
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)
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if arima_df is not None:
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fig_pred.add_trace(
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go.Scatter(x=arima_df.index, y=arima_df["forecast"], name="ARIMA", mode="lines")
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)
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if knn_pred is not None:
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fig_pred.add_trace(go.Scatter(x=knn_pred.index, y=knn_pred, name="KNN", mode="lines"))
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if glm_pred is not None:
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fig_pred.add_trace(go.Scatter(x=glm_pred.index, y=glm_pred, name="GLM", mode="lines"))
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if svr_pred is not None:
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fig_pred.add_trace(go.Scatter(x=svr_pred.index, y=svr_pred, name="SVR", mode="lines"))
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fig_pred.update_layout(
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title=f"多模型预测比较（起点：{first_date.date()}，预测 {horizon_days} 天）",
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xaxis_title="日期",
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yaxis_title="事故数",
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)
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st.plotly_chart(fig_pred, use_container_width=True)
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if arima_df is not None:
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st.download_button(
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"下载 ARIMA 预测 CSV",
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data=arima_df.to_csv().encode("utf-8-sig"),
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file_name="arima_forecast.csv",
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mime="text/csv",
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)
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for warning_text in results.get("warnings", []):
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st.warning(warning_text)
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189
ui_sections/hotspot.py
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189
ui_sections/hotspot.py
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from __future__ import annotations
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import json
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from datetime import datetime
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import plotly.express as px
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import streamlit as st
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from services.hotspot import (
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analyze_hotspot_frequency,
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calculate_hotspot_risk_score,
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generate_hotspot_strategies,
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prepare_hotspot_dataset,
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serialise_datetime_columns,
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)
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@st.cache_data(show_spinner=False)
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def _prepare_hotspot_data(df):
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return prepare_hotspot_dataset(df)
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def render_hotspot(accident_records, accident_source_name: str | None) -> None:
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st.header("📍 事故多发路口分析")
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st.markdown("独立分析事故数据，识别高风险路口并生成针对性策略。")
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if accident_records is None:
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st.info("请在左侧上传事故数据并点击“应用数据与筛选”后再执行热点分析。")
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st.markdown(
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"""
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### 📝 支持的数据格式要求：
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- **文件格式**：Excel (.xlsx)
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- **必要字段**：
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- `事故时间`
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- `事故类型`
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- `事故具体地点`
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- `所在街道`
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- `道路类型`
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- `路口路段类型`
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"""
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)
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return
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with st.spinner("正在准备事故热点数据…"):
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hotspot_data = _prepare_hotspot_data(accident_records)
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st.success(f"✅ 成功加载数据：{len(hotspot_data)} 条事故记录")
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metric_cols = st.columns(3)
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with metric_cols[0]:
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st.metric(
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"数据时间范围",
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f"{hotspot_data['事故时间'].min().strftime('%Y-%m-%d')} 至 {hotspot_data['事故时间'].max().strftime('%Y-%m-%d')}",
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)
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with metric_cols[1]:
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st.metric(
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"事故类型分布",
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f"财损: {len(hotspot_data[hotspot_data['事故类型'] == '财损'])}起",
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)
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with metric_cols[2]:
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st.metric("涉及区域", f"{hotspot_data['所在街道'].nunique()}个街道")
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st.subheader("🔧 分析参数设置")
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settings_cols = st.columns(3)
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with settings_cols[0]:
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time_window = st.selectbox(
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"统计时间窗口",
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options=["7D", "15D", "30D"],
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index=0,
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key="hotspot_window",
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)
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with settings_cols[1]:
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min_accidents = st.number_input(
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"最小事故数", min_value=1, max_value=50, value=3, key="hotspot_min_accidents"
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)
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with settings_cols[2]:
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top_n = st.slider("显示热点数量", min_value=3, max_value=20, value=8, key="hotspot_top_n")
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if not st.button("🚀 开始热点分析", type="primary"):
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return
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with st.spinner("正在分析事故热点分布…"):
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hotspots = analyze_hotspot_frequency(hotspot_data, time_window=time_window)
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hotspots = hotspots[hotspots["accident_count"] >= min_accidents]
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if hotspots.empty:
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st.warning("⚠️ 未找到符合条件的事故热点数据，请调整筛选参数。")
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return
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hotspots_with_risk = calculate_hotspot_risk_score(hotspots.head(top_n * 3))
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top_hotspots = hotspots_with_risk.head(top_n)
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st.subheader("📊 事故多发路口排名（前{0}个）".format(top_n))
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display_columns = {
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"accident_count": "累计事故数",
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"recent_count": "近期事故数",
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"trend_ratio": "趋势比例",
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"main_accident_type": "主要类型",
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"main_intersection_type": "路口类型",
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"risk_score": "风险评分",
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"risk_level": "风险等级",
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}
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display_df = top_hotspots[list(display_columns.keys())].rename(columns=display_columns)
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styled_df = display_df.style.format({"趋势比例": "{:.2f}", "风险评分": "{:.1f}"}).background_gradient(
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subset=["风险评分"], cmap="Reds"
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)
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st.dataframe(styled_df, use_container_width=True)
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st.subheader("🎯 针对性策略建议")
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strategies = generate_hotspot_strategies(top_hotspots, time_period="本周")
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for index, strategy_info in enumerate(strategies, start=1):
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message = f"**{index}. {strategy_info['strategy']}**"
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risk_level = strategy_info["risk_level"]
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if risk_level == "高风险":
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st.error(f"🚨 {message}")
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elif risk_level == "中风险":
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st.warning(f"⚠️ {message}")
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else:
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st.info(f"✅ {message}")
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st.subheader("📈 数据分析可视化")
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chart_cols = st.columns(2)
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with chart_cols[0]:
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fig_hotspots = px.bar(
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top_hotspots.head(10),
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x=top_hotspots.head(10).index,
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y=["accident_count", "recent_count"],
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title="事故频次TOP10分布",
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labels={"value": "事故数量", "variable": "类型", "index": "路口名称"},
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barmode="group",
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)
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fig_hotspots.update_layout(xaxis_tickangle=-45)
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st.plotly_chart(fig_hotspots, use_container_width=True)
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with chart_cols[1]:
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risk_distribution = top_hotspots["risk_level"].value_counts()
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fig_risk = px.pie(
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values=risk_distribution.values,
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names=risk_distribution.index,
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title="风险等级分布",
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color_discrete_map={"高风险": "red", "中风险": "orange", "低风险": "green"},
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)
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st.plotly_chart(fig_risk, use_container_width=True)
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st.subheader("💾 数据导出")
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download_cols = st.columns(2)
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with download_cols[0]:
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hotspot_csv = top_hotspots.to_csv().encode("utf-8-sig")
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st.download_button(
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"📥 下载热点数据CSV",
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data=hotspot_csv,
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file_name=f"accident_hotspots_{datetime.now().strftime('%Y%m%d')}.csv",
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mime="text/csv",
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)
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with download_cols[1]:
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serializable = serialise_datetime_columns(
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top_hotspots.reset_index(),
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columns=[col for col in top_hotspots.columns if "time" in col or "date" in col],
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)
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report_payload = {
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"analysis_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"time_window": time_window,
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"data_source": accident_source_name or "事故数据",
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"total_records": int(len(hotspot_data)),
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"analysis_parameters": {"min_accidents": int(min_accidents), "top_n": int(top_n)},
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"top_hotspots": serializable.to_dict("records"),
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"recommended_strategies": strategies,
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"summary": {
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"high_risk_count": int((top_hotspots["risk_level"] == "高风险").sum()),
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"medium_risk_count": int((top_hotspots["risk_level"] == "中风险").sum()),
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"total_analyzed_locations": int(len(hotspots)),
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"most_dangerous_location": top_hotspots.index[0]
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if len(top_hotspots)
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else "无",
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},
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}
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st.download_button(
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"📄 下载完整分析报告",
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data=json.dumps(report_payload, ensure_ascii=False, indent=2),
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file_name=f"hotspot_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M')}.json",
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mime="application/json",
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)
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with st.expander("📋 查看原始数据预览"):
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preview_cols = ["事故时间", "所在街道", "事故类型", "事故具体地点", "道路类型"]
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preview_df = hotspot_data[preview_cols].copy()
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st.dataframe(preview_df.head(10), use_container_width=True)
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32
ui_sections/model_eval.py
Normal file
32
ui_sections/model_eval.py
Normal file
@@ -0,0 +1,32 @@
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from __future__ import annotations
|
||||
|
||||
import pandas as pd
|
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import streamlit as st
|
||||
|
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from services.metrics import evaluate_models
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def render_model_eval(base: pd.DataFrame):
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st.subheader("模型预测效果对比")
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with st.form(key="model_eval_form"):
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horizon_sel = st.slider("评估窗口（天）", 7, 60, 30, step=1)
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submit_eval = st.form_submit_button("应用评估参数")
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|
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if not submit_eval:
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st.info("请设置评估窗口并点击“应用评估参数”按钮。")
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return
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try:
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df_metrics = evaluate_models(base['accident_count'], horizon=int(horizon_sel))
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st.dataframe(df_metrics, use_container_width=True)
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best_model = df_metrics['RMSE'].idxmin()
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st.success(f"过去 {int(horizon_sel)} 天中，RMSE 最低的模型是：**{best_model}**")
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st.download_button(
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"下载评估结果 CSV",
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||||
data=df_metrics.to_csv().encode('utf-8-sig'),
|
||||
file_name="model_evaluation.csv",
|
||||
mime="text/csv",
|
||||
)
|
||||
except ValueError as err:
|
||||
st.warning(str(err))
|
||||
|
||||
33
ui_sections/overview.py
Normal file
33
ui_sections/overview.py
Normal file
@@ -0,0 +1,33 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
import streamlit as st
|
||||
|
||||
def render_overview(base: pd.DataFrame, region_sel: str, start_dt: pd.Timestamp, end_dt: pd.Timestamp,
|
||||
strat_filter: list[str]):
|
||||
fig_line = go.Figure()
|
||||
fig_line.add_trace(go.Scatter(x=base.index, y=base['accident_count'], name='事故数', mode='lines'))
|
||||
fig_line.update_layout(title="事故数（过滤后）", xaxis_title="Date", yaxis_title="Count")
|
||||
st.plotly_chart(fig_line, use_container_width=True)
|
||||
|
||||
html = fig_line.to_html(full_html=True, include_plotlyjs='cdn')
|
||||
st.download_button("下载图表 HTML", data=html.encode('utf-8'),
|
||||
file_name="overview_series.html", mime="text/html")
|
||||
|
||||
st.dataframe(base, use_container_width=True)
|
||||
csv_bytes = base.to_csv(index=True).encode('utf-8-sig')
|
||||
st.download_button("下载当前视图 CSV", data=csv_bytes, file_name="filtered_view.csv", mime="text/csv")
|
||||
|
||||
meta = {
|
||||
"region": region_sel,
|
||||
"date_range": [str(start_dt.date()), str(end_dt.date())],
|
||||
"strategy_filter": strat_filter,
|
||||
"rows": int(len(base)),
|
||||
"min_date": str(base.index.min().date()) if len(base) else None,
|
||||
"max_date": str(base.index.max().date()) if len(base) else None,
|
||||
}
|
||||
st.download_button("下载运行参数 JSON", data=json.dumps(meta, ensure_ascii=False, indent=2).encode('utf-8'),
|
||||
file_name="run_metadata.json", mime="application/json")
|
||||
|
||||
50
ui_sections/strategy_eval.py
Normal file
50
ui_sections/strategy_eval.py
Normal file
@@ -0,0 +1,50 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import pandas as pd
|
||||
import streamlit as st
|
||||
|
||||
from services.strategy import generate_output_and_recommendations
|
||||
|
||||
|
||||
def render_strategy_eval(base: pd.DataFrame, all_strategy_types: list[str], region_sel: str):
|
||||
st.info(f"📌 检测到的策略类型：{', '.join(all_strategy_types) or '（数据中没有策略）'}")
|
||||
if not all_strategy_types:
|
||||
st.warning("数据中没有检测到策略。")
|
||||
return
|
||||
|
||||
with st.form(key="strategy_eval_form"):
|
||||
horizon_eval = st.slider("评估窗口（天）", 7, 60, 14, step=1)
|
||||
submit_strat_eval = st.form_submit_button("应用评估参数")
|
||||
|
||||
if not submit_strat_eval:
|
||||
st.info("请设置评估窗口并点击“应用评估参数”按钮。")
|
||||
return
|
||||
|
||||
results, recommendation = generate_output_and_recommendations(
|
||||
base,
|
||||
all_strategy_types,
|
||||
region=region_sel if region_sel != '全市' else '全市',
|
||||
horizon=horizon_eval,
|
||||
)
|
||||
|
||||
if not results:
|
||||
st.warning("⚠️ 未能完成策略评估。请尝试缩短评估窗口或扩大日期范围。")
|
||||
return
|
||||
|
||||
st.subheader("各策略指标")
|
||||
df_res = pd.DataFrame(results).T
|
||||
st.dataframe(df_res, use_container_width=True)
|
||||
st.success(f"⭐ 推荐：{recommendation}")
|
||||
|
||||
st.download_button(
|
||||
"下载策略评估 CSV",
|
||||
data=df_res.to_csv().encode('utf-8-sig'),
|
||||
file_name="strategy_evaluation_results.csv",
|
||||
mime="text/csv",
|
||||
)
|
||||
|
||||
if os.path.exists('recommendation.txt'):
|
||||
with open('recommendation.txt','r',encoding='utf-8') as f:
|
||||
st.download_button("下载推荐文本", data=f.read().encode('utf-8'), file_name="recommendation.txt")
|
||||
|
||||
Reference in New Issue
Block a user