modify: cleanup project structure and docs
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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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