33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
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from __future__ import annotations
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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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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'),
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file_name="model_evaluation.csv",
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mime="text/csv",
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)
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except ValueError as err:
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st.warning(str(err))
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