158 lines
7.0 KiB
Python
158 lines
7.0 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.forecast import fit_and_extrapolate, arima_forecast_with_grid_search
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from config.settings import MIN_PRE_DAYS, MAX_PRE_DAYS
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def evaluate_strategy_effectiveness(actual_series: pd.Series,
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counterfactual_series: pd.Series,
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severity_series: pd.Series,
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strategy_date: pd.Timestamp,
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window: int = 30):
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strategy_date = pd.to_datetime(strategy_date)
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window_end = strategy_date + pd.Timedelta(days=window - 1)
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pre_sev = severity_series.loc[strategy_date - pd.Timedelta(days=window):strategy_date - pd.Timedelta(days=1)].sum()
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post_sev = severity_series.loc[strategy_date:window_end].sum()
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actual_post = actual_series.loc[strategy_date:window_end]
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counter_post = counterfactual_series.loc[strategy_date:window_end].reindex(actual_post.index)
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window_len = len(actual_post)
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if window_len == 0:
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return False, False, (0.0, 0.0), '三级'
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effective_days = (actual_post < counter_post).sum()
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count_effective = effective_days >= (window_len / 2)
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severity_effective = post_sev < pre_sev
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cf_sum = counter_post.sum()
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F1 = (cf_sum - actual_post.sum()) / cf_sum if cf_sum > 0 else 0.0
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F2 = (pre_sev - post_sev) / pre_sev if pre_sev > 0 else 0.0
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if F1 > 0.5 and F2 > 0.5:
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safety_state = '一级'
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elif F1 > 0.3:
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safety_state = '二级'
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else:
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safety_state = '三级'
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return count_effective, severity_effective, (F1, F2), safety_state
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@st.cache_data(show_spinner=False)
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def generate_output_and_recommendations(combined_data: pd.DataFrame,
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strategy_types: list,
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region: str = '全市',
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horizon: int = 30):
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results = {}
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combined_data = combined_data.copy().asfreq('D')
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combined_data[['accident_count','severity']] = combined_data[['accident_count','severity']].fillna(0)
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combined_data['strategy_type'] = combined_data['strategy_type'].apply(lambda x: x if isinstance(x, list) else [])
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acc_full = combined_data['accident_count']
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sev_full = combined_data['severity']
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max_fit_days = max(horizon + 60, MAX_PRE_DAYS)
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for strategy in strategy_types:
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has_strategy = combined_data['strategy_type'].apply(lambda x: strategy in x)
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if not has_strategy.any():
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continue
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candidate_dates = has_strategy[has_strategy].index
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intervention_date = None
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fit_start_dt = None
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for dt in candidate_dates:
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fit_start_dt = max(acc_full.index.min(), dt - pd.Timedelta(days=max_fit_days))
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pre_hist = acc_full.loc[fit_start_dt:dt - pd.Timedelta(days=1)]
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if len(pre_hist) >= MIN_PRE_DAYS:
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intervention_date = dt
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break
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if intervention_date is None:
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intervention_date = candidate_dates[0]
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fit_start_dt = max(acc_full.index.min(), intervention_date - pd.Timedelta(days=max_fit_days))
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acc = acc_full.loc[fit_start_dt:]
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sev = sev_full.loc[fit_start_dt:]
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horizon_eff = max(7, min(horizon, len(acc.loc[intervention_date:]) ))
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glm_pred, svr_pred, residuals = fit_and_extrapolate(acc, intervention_date, days=horizon_eff)
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counter = None
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if svr_pred is not None:
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counter = svr_pred
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elif glm_pred is not None:
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counter = glm_pred
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else:
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try:
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arima_df = arima_forecast_with_grid_search(acc.loc[:intervention_date],
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start_date=intervention_date + pd.Timedelta(days=1),
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horizon=horizon_eff)
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counter = pd.Series(arima_df['forecast'].values, index=arima_df.index, name='cf_arima')
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residuals = (acc.reindex(counter.index) - counter)
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except Exception:
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counter = None
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if counter is None:
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continue
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count_eff, sev_eff, (F1, F2), state = evaluate_strategy_effectiveness(
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actual_series=acc,
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counterfactual_series=counter,
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severity_series=sev,
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strategy_date=intervention_date,
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window=horizon_eff
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)
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results[strategy] = {
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'effect_strength': float(residuals.dropna().mean()) if residuals is not None else 0.0,
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'adaptability': float(F1 + F2),
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'count_effective': bool(count_eff),
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'severity_effective': bool(sev_eff),
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'safety_state': state,
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'F1': float(F1),
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'F2': float(F2),
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'intervention_date': str(intervention_date.date())
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}
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# Secondary attempt with 14-day window if no results
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if not results:
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for strategy in strategy_types:
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has_strategy = combined_data['strategy_type'].apply(lambda x: strategy in x)
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if not has_strategy.any():
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continue
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intervention_date = has_strategy[has_strategy].index[0]
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glm_pred, svr_pred, residuals = fit_and_extrapolate(acc_full, intervention_date, days=14)
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counter = None
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if svr_pred is not None:
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counter = svr_pred
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elif glm_pred is not None:
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counter = glm_pred
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else:
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try:
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arima_df = arima_forecast_with_grid_search(acc_full.loc[:intervention_date],
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start_date=intervention_date + pd.Timedelta(days=1),
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horizon=14)
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counter = pd.Series(arima_df['forecast'].values, index=arima_df.index, name='cf_arima')
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residuals = (acc_full.reindex(counter.index) - counter)
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except Exception:
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counter = None
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if counter is None:
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continue
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count_eff, sev_eff, (F1, F2), state = evaluate_strategy_effectiveness(
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actual_series=acc_full,
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counterfactual_series=counter,
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severity_series=sev_full,
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strategy_date=intervention_date,
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window=14
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)
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results[strategy] = {
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'effect_strength': float(residuals.dropna().mean()) if residuals is not None else 0.0,
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'adaptability': float(F1 + F2),
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'count_effective': bool(count_eff),
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'severity_effective': bool(sev_eff),
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'safety_state': state,
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'F1': float(F1),
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'F2': float(F2),
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'intervention_date': str(intervention_date.date())
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}
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best_strategy = max(results, key=lambda x: results[x]['adaptability']) if results else None
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recommendation = f"建议在{region}区域长期实施策略类型 {best_strategy}" if best_strategy else "无足够数据推荐策略"
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return results, recommendation
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