737 lines
35 KiB
Python
737 lines
35 KiB
Python
from __future__ import annotations
|
||
|
||
import os
|
||
from datetime import datetime, timedelta
|
||
import json
|
||
|
||
import numpy as np
|
||
import pandas as pd
|
||
from typing import Optional
|
||
|
||
from sklearn.ensemble import IsolationForest
|
||
|
||
import streamlit as st
|
||
import plotly.graph_objects as go
|
||
|
||
# --- Optional deps (graceful fallback)
|
||
try:
|
||
from scipy.stats import ttest_ind, mannwhitneyu
|
||
HAS_SCIPY = True
|
||
except Exception:
|
||
HAS_SCIPY = False
|
||
|
||
try:
|
||
from streamlit_autorefresh import st_autorefresh
|
||
HAS_AUTOREFRESH = True
|
||
except Exception:
|
||
HAS_AUTOREFRESH = False
|
||
|
||
# Add import for OpenAI API
|
||
try:
|
||
from openai import OpenAI
|
||
HAS_OPENAI = True
|
||
except Exception:
|
||
HAS_OPENAI = False
|
||
|
||
|
||
from services.io import (
|
||
load_and_clean_data,
|
||
aggregate_daily_data,
|
||
aggregate_daily_data_by_region,
|
||
load_accident_records,
|
||
)
|
||
from services.forecast import (
|
||
arima_forecast_with_grid_search,
|
||
knn_forecast_counterfactual,
|
||
fit_and_extrapolate,
|
||
)
|
||
from services.strategy import (
|
||
evaluate_strategy_effectiveness,
|
||
generate_output_and_recommendations,
|
||
)
|
||
from services.metrics import evaluate_models
|
||
|
||
try:
|
||
from ui_sections import (
|
||
render_overview,
|
||
render_forecast,
|
||
render_model_eval,
|
||
render_strategy_eval,
|
||
render_hotspot,
|
||
)
|
||
except Exception: # pragma: no cover - fallback to inline logic
|
||
render_overview = None
|
||
render_forecast = None
|
||
render_model_eval = None
|
||
render_strategy_eval = None
|
||
render_hotspot = None
|
||
|
||
def detect_anomalies(series: pd.Series, contamination: float = 0.1):
|
||
series = series.asfreq('D').fillna(0)
|
||
iso = IsolationForest(n_estimators=50, contamination=contamination, random_state=42, n_jobs=-1)
|
||
yhat = iso.fit_predict(series.values.reshape(-1, 1))
|
||
anomaly_mask = (yhat == -1)
|
||
anomaly_indices = series.index[anomaly_mask]
|
||
|
||
fig = go.Figure()
|
||
fig.add_trace(go.Scatter(x=series.index, y=series.values, mode='lines', name='Accident Count'))
|
||
fig.add_trace(go.Scatter(x=anomaly_indices, y=series.loc[anomaly_indices], mode='markers',
|
||
marker=dict(color='red', size=10), name='Anomalies'))
|
||
fig.update_layout(title="Anomaly Detection in Accident Count",
|
||
xaxis_title="Date", yaxis_title="Count")
|
||
return anomaly_indices, fig
|
||
|
||
|
||
|
||
|
||
def intervention_model(series: pd.Series,
|
||
intervention_date: pd.Timestamp,
|
||
intervention_type: str = 'persistent',
|
||
effect_type: str = 'sudden',
|
||
omega: float = 0.5,
|
||
decay: float = 10.0,
|
||
lag: int = 0):
|
||
series = series.asfreq('D').fillna(0)
|
||
intervention_date = pd.to_datetime(intervention_date)
|
||
Z_t = pd.Series(0.0, index=series.index)
|
||
if intervention_type == 'persistent':
|
||
Z_t.loc[intervention_date:] = 1.0
|
||
else:
|
||
post_len = len(Z_t.loc[intervention_date:])
|
||
Z_t.loc[intervention_date:] = np.exp(-np.arange(post_len) / decay)
|
||
if effect_type == 'gradual':
|
||
Z_t = Z_t * np.linspace(0, 1, len(Z_t))
|
||
Z_t = Z_t.shift(lag).fillna(0)
|
||
Y_t = series + omega * Z_t
|
||
return Y_t, Z_t
|
||
|
||
|
||
# =======================
|
||
# 3. UI Helpers
|
||
# =======================
|
||
|
||
def compute_kpis(df_city: pd.DataFrame, arima_df: Optional[pd.DataFrame],
|
||
today: pd.Timestamp, window:int=30):
|
||
# 今日/昨日
|
||
today_date = pd.to_datetime(today.date())
|
||
yesterday = today_date - pd.Timedelta(days=1)
|
||
this_week_start = today_date - pd.Timedelta(days=today_date.weekday()) # 周一
|
||
last_week_start = this_week_start - pd.Timedelta(days=7)
|
||
this_week_end = today_date
|
||
|
||
today_cnt = int(df_city['accident_count'].get(today_date, 0))
|
||
yest_cnt = int(df_city['accident_count'].get(yesterday, 0))
|
||
wow = (today_cnt - yest_cnt) / yest_cnt if yest_cnt > 0 else 0.0
|
||
|
||
this_week = df_city.loc[this_week_start:this_week_end]['accident_count'].sum()
|
||
last_week = df_city.loc[last_week_start:last_week_start + pd.Timedelta(days=(this_week_end - this_week_start).days)]['accident_count'].sum()
|
||
yoy = (this_week - last_week) / last_week if last_week > 0 else 0.0
|
||
|
||
# 预测偏差（近7天）
|
||
forecast_bias = None
|
||
if arima_df is not None:
|
||
recent = df_city.index.max() - pd.Timedelta(days=6)
|
||
actual = df_city.loc[recent:df_city.index.max(), 'accident_count']
|
||
fcst = arima_df['forecast'].reindex(actual.index).fillna(method='ffill')
|
||
denom = fcst.replace(0, np.nan)
|
||
bias = (np.abs(actual - fcst) / denom).dropna()
|
||
forecast_bias = float(bias.mean()) if len(bias) else None
|
||
|
||
# 策略覆盖（近30天）
|
||
last_window = df_city.index.max() - pd.Timedelta(days=window-1)
|
||
strat_days = df_city.loc[last_window:, 'strategy_type'].apply(lambda x: len(x) > 0).sum()
|
||
coverage = strat_days / window
|
||
|
||
# 上线策略数（去重）
|
||
active_strats = set(s for lst in df_city.loc[last_window:, 'strategy_type'] for s in lst)
|
||
active_count = len(active_strats)
|
||
|
||
# 近30天安全等级（用 generate_output_and_recommendations 里 best 的等级）
|
||
# 这里只取最近出现过的策略做评估
|
||
strategies = sorted(active_strats)
|
||
safety_state = '—'
|
||
if strategies:
|
||
res, _ = generate_output_and_recommendations(df_city.loc[last_window:], strategies, region='全市', horizon=min(30, len(df_city.loc[last_window:])))
|
||
if res:
|
||
# 取适配度最高的策略的安全等级
|
||
best = max(res, key=lambda k: res[k]['adaptability'])
|
||
safety_state = res[best]['safety_state']
|
||
|
||
return {
|
||
'today_cnt': today_cnt,
|
||
'wow': wow,
|
||
'this_week': int(this_week),
|
||
'yoy': yoy,
|
||
'forecast_bias': forecast_bias,
|
||
'active_count': active_count,
|
||
'coverage': coverage,
|
||
'safety_state': safety_state
|
||
}
|
||
|
||
|
||
def significance_test(pre: pd.Series, post: pd.Series):
|
||
pre = pre.dropna(); post = post.dropna()
|
||
if len(pre) < 3 or len(post) < 3:
|
||
return None, None
|
||
if HAS_SCIPY:
|
||
try:
|
||
stat, p = ttest_ind(pre, post, equal_var=False)
|
||
except Exception:
|
||
stat, p = mannwhitneyu(pre, post, alternative='two-sided')
|
||
return float(stat), float(p)
|
||
return None, None
|
||
|
||
|
||
def save_fig_as_html(fig, filename):
|
||
html = fig.to_html(full_html=True, include_plotlyjs='cdn')
|
||
with open(filename, 'w', encoding='utf-8') as f:
|
||
f.write(html)
|
||
return filename
|
||
|
||
|
||
# =======================
|
||
# 4. App
|
||
# =======================
|
||
|
||
|
||
# =======================
|
||
# 4. App
|
||
# =======================
|
||
def run_streamlit_app():
|
||
# Must be the first Streamlit command
|
||
st.set_page_config(page_title="Traffic Safety Analysis", layout="wide")
|
||
st.title("🚦 Traffic Safety Intervention Analysis System")
|
||
|
||
# Sidebar — Upload & Global Filters & Auto Refresh
|
||
st.sidebar.header("数据与筛选")
|
||
|
||
default_min_date = pd.to_datetime('2022-01-01').date()
|
||
default_max_date = pd.to_datetime('2022-12-31').date()
|
||
|
||
def clamp_date_range(requested, minimum, maximum):
|
||
"""Ensure the requested tuple stays within [minimum, maximum]."""
|
||
if not isinstance(requested, (list, tuple)):
|
||
requested = (requested, requested)
|
||
start, end = requested
|
||
if start > end:
|
||
start, end = end, start
|
||
if end < minimum or start > maximum:
|
||
return minimum, maximum
|
||
start = max(minimum, start)
|
||
end = min(maximum, end)
|
||
return start, end
|
||
|
||
# Initialize session state to store processed data (before rendering controls)
|
||
if 'processed_data' not in st.session_state:
|
||
st.session_state['processed_data'] = {
|
||
'combined_city': None,
|
||
'combined_by_region': None,
|
||
'accident_data': None,
|
||
'accident_records': None,
|
||
'strategy_data': None,
|
||
'all_regions': ["全市"],
|
||
'all_strategy_types': [],
|
||
'min_date': default_min_date,
|
||
'max_date': default_max_date,
|
||
'region_sel': "全市",
|
||
'date_range': (default_min_date, default_max_date),
|
||
'strat_filter': [],
|
||
'accident_source_name': None,
|
||
}
|
||
|
||
sidebar_state = st.session_state['processed_data']
|
||
|
||
available_regions = sidebar_state['all_regions'] if sidebar_state['all_regions'] else ["全市"]
|
||
current_region = sidebar_state['region_sel'] if sidebar_state['region_sel'] in available_regions else available_regions[0]
|
||
available_strategies = sidebar_state['all_strategy_types']
|
||
current_strategies = [s for s in sidebar_state['strat_filter'] if s in available_strategies]
|
||
|
||
min_date = sidebar_state['min_date']
|
||
max_date = sidebar_state['max_date']
|
||
raw_start, raw_end = sidebar_state['date_range']
|
||
start_default = max(min_date, min(raw_start, max_date))
|
||
end_default = max(start_default, min(raw_end, max_date))
|
||
|
||
# Create a form for data inputs to batch updates
|
||
with st.sidebar.form(key="data_input_form"):
|
||
accident_file = st.file_uploader("上传事故数据 (Excel)", type=['xlsx'])
|
||
strategy_file = st.file_uploader("上传交通策略数据 (Excel)", type=['xlsx'])
|
||
|
||
# Global filters
|
||
st.markdown("---")
|
||
st.subheader("全局筛选器")
|
||
region_sel = st.selectbox(
|
||
"区域",
|
||
options=available_regions,
|
||
index=available_regions.index(current_region),
|
||
key="region_select",
|
||
)
|
||
date_range = st.date_input(
|
||
"时间范围",
|
||
value=(start_default, end_default),
|
||
min_value=min_date,
|
||
max_value=max_date,
|
||
)
|
||
strat_filter = st.multiselect(
|
||
"策略类型（过滤）",
|
||
options=available_strategies,
|
||
default=current_strategies,
|
||
help="为空表示不过滤策略；选择后仅保留当天包含所选策略的日期",
|
||
)
|
||
|
||
# Apply button for data loading and filtering
|
||
apply_button = st.form_submit_button("应用数据与筛选")
|
||
|
||
# Auto-refresh controls (outside the form, as it’s independent)
|
||
st.sidebar.markdown("---")
|
||
st.sidebar.subheader("实时刷新")
|
||
auto = st.sidebar.checkbox("自动刷新", value=False, help="启用后将按间隔自动刷新页面")
|
||
interval = st.sidebar.number_input("刷新间隔（秒）", min_value=5, max_value=600, value=30, step=5)
|
||
if auto and HAS_AUTOREFRESH:
|
||
st_autorefresh(interval=int(interval*1000), key="autorefresh")
|
||
elif auto and not HAS_AUTOREFRESH:
|
||
st.sidebar.info("未安装 `streamlit-autorefresh`，请使用上方“重新运行”按钮或关闭再开启此开关。")
|
||
|
||
# Add OpenAI API key input in sidebar
|
||
st.sidebar.markdown("---")
|
||
st.sidebar.subheader("AI API 配置")
|
||
openai_api_key = st.sidebar.text_input("AI API Key", value='sk-959e0b065c774b1db6e30bf7589680f9', type="password", help="用于 AI 分析结果的 API 密钥")
|
||
open_ai_base_url = st.sidebar.text_input("AI Base Url", value='https://api.deepseek.com', type='default')
|
||
|
||
# Process data only when Apply button is clicked
|
||
if apply_button and accident_file and strategy_file:
|
||
with st.spinner("数据载入中…"):
|
||
# Load and clean data
|
||
accident_records = load_accident_records(accident_file, require_location=True)
|
||
accident_data, strategy_data = load_and_clean_data(accident_file, strategy_file)
|
||
combined_city = aggregate_daily_data(accident_data, strategy_data)
|
||
combined_by_region = aggregate_daily_data_by_region(accident_data, strategy_data)
|
||
|
||
# Update available options for filters
|
||
all_regions = ["全市"] + sorted(accident_data['region'].unique().tolist())
|
||
all_strategy_types = sorted({s for lst in combined_city['strategy_type'] for s in lst})
|
||
min_date = combined_city.index.min().date()
|
||
max_date = combined_city.index.max().date()
|
||
|
||
# Store processed data in session state
|
||
sanitized_start, sanitized_end = clamp_date_range(date_range, min_date, max_date)
|
||
st.session_state['processed_data'].update({
|
||
'combined_city': combined_city,
|
||
'combined_by_region': combined_by_region,
|
||
'accident_data': accident_data,
|
||
'accident_records': accident_records,
|
||
'strategy_data': strategy_data,
|
||
'all_regions': all_regions,
|
||
'all_strategy_types': all_strategy_types,
|
||
'min_date': min_date,
|
||
'max_date': max_date,
|
||
'region_sel': region_sel,
|
||
'date_range': (sanitized_start, sanitized_end),
|
||
'strat_filter': strat_filter,
|
||
'accident_source_name': getattr(accident_file, "name", "事故数据.xlsx"),
|
||
})
|
||
|
||
sanitized_start, sanitized_end = clamp_date_range(date_range, min_date, max_date)
|
||
|
||
# Persist the latest sidebar selections for display and downstream filtering
|
||
st.session_state['processed_data']['region_sel'] = region_sel
|
||
st.session_state['processed_data']['date_range'] = (sanitized_start, sanitized_end)
|
||
st.session_state['processed_data']['strat_filter'] = strat_filter
|
||
|
||
# Retrieve data from session state
|
||
combined_city = st.session_state['processed_data']['combined_city']
|
||
combined_by_region = st.session_state['processed_data']['combined_by_region']
|
||
accident_data = st.session_state['processed_data']['accident_data']
|
||
accident_records = st.session_state['processed_data']['accident_records']
|
||
strategy_data = st.session_state['processed_data']['strategy_data']
|
||
all_regions = st.session_state['processed_data']['all_regions']
|
||
all_strategy_types = st.session_state['processed_data']['all_strategy_types']
|
||
min_date = st.session_state['processed_data']['min_date']
|
||
max_date = st.session_state['processed_data']['max_date']
|
||
region_sel = st.session_state['processed_data']['region_sel']
|
||
date_range = st.session_state['processed_data']['date_range']
|
||
strat_filter = st.session_state['processed_data']['strat_filter']
|
||
accident_source_name = st.session_state['processed_data']['accident_source_name']
|
||
|
||
# Update selectbox and multiselect options dynamically (outside the form for display)
|
||
st.sidebar.markdown("---")
|
||
st.sidebar.subheader("当前筛选状态")
|
||
st.sidebar.write(f"区域: {region_sel}")
|
||
st.sidebar.write(f"时间范围: {date_range[0]} 至 {date_range[1]}")
|
||
st.sidebar.write(f"策略类型: {', '.join(strat_filter) or '无'}")
|
||
|
||
# Proceed only if data is available
|
||
if combined_city is not None and combined_by_region is not None:
|
||
start_dt = pd.to_datetime(date_range[0])
|
||
end_dt = pd.to_datetime(date_range[1])
|
||
if region_sel == "全市":
|
||
base = combined_city.loc[start_dt:end_dt].copy()
|
||
else:
|
||
block = combined_by_region.xs(region_sel, level='region').copy()
|
||
base = block.loc[start_dt:end_dt]
|
||
if strat_filter:
|
||
mask = base['strategy_type'].apply(lambda x: any(s in x for s in strat_filter))
|
||
base = base[mask]
|
||
|
||
# Last refresh info
|
||
if 'last_refresh' not in st.session_state:
|
||
st.session_state['last_refresh'] = datetime.now()
|
||
last_refresh = st.session_state['last_refresh']
|
||
|
||
# Compute ARIMA for KPI bias
|
||
arima_df = None
|
||
try:
|
||
arima_df = arima_forecast_with_grid_search(
|
||
base['accident_count'], base.index.max() + pd.Timedelta(days=1), horizon=7
|
||
)
|
||
except Exception:
|
||
pass
|
||
|
||
# KPI Overview
|
||
kpi = compute_kpis(base, arima_df, today=pd.Timestamp('2022-12-01'))
|
||
c1, c2, c3, c4, c5, c6 = st.columns(6)
|
||
c1.metric("今日事故数", f"{kpi['today_cnt']}", f"{kpi['wow']*100:.1f}% 环比")
|
||
c2.metric("本周事故数", f"{kpi['this_week']}", f"{kpi['yoy']*100:.1f}% 同比")
|
||
c3.metric("近7天预测偏差", ("{:.1f}%".format(kpi['forecast_bias']*100) if kpi['forecast_bias'] is not None else "—"))
|
||
c4.metric("近30天策略数", f"{kpi['active_count']}")
|
||
c5.metric("近30天策略覆盖率", f"{kpi['coverage']*100:.1f}%")
|
||
c6.metric("近30天安全等级", kpi['safety_state'])
|
||
|
||
# Top-right meta
|
||
meta_col1, meta_col2 = st.columns([4, 1])
|
||
with meta_col2:
|
||
st.caption(f"🕒 最近刷新：{last_refresh.strftime('%Y-%m-%d %H:%M:%S')}")
|
||
|
||
tab_labels = [
|
||
"🏠 总览",
|
||
"📍 事故热点",
|
||
"🔍 AI 分析",
|
||
"📈 预测模型",
|
||
"📊 模型评估",
|
||
"⚠️ 异常检测",
|
||
"📝 策略评估",
|
||
"⚖️ 策略对比",
|
||
"🧪 情景模拟",
|
||
]
|
||
default_tab = st.session_state.get("active_tab", tab_labels[0])
|
||
if default_tab not in tab_labels:
|
||
default_tab = tab_labels[0]
|
||
selected_tab = st.radio(
|
||
"功能分区",
|
||
tab_labels,
|
||
index=tab_labels.index(default_tab),
|
||
horizontal=True,
|
||
label_visibility="collapsed",
|
||
)
|
||
st.session_state["active_tab"] = selected_tab
|
||
|
||
|
||
if selected_tab == "🏠 总览":
|
||
if render_overview is not None:
|
||
render_overview(base, region_sel, start_dt, end_dt, strat_filter)
|
||
else:
|
||
st.warning("概览模块未能加载，请检查 `ui_sections/overview.py`。")
|
||
|
||
elif selected_tab == "📍 事故热点":
|
||
if render_hotspot is not None:
|
||
render_hotspot(accident_records, accident_source_name)
|
||
else:
|
||
st.warning("事故热点模块未能加载，请检查 `ui_sections/hotspot.py`。")
|
||
|
||
elif selected_tab == "🔍 AI 分析":
|
||
from openai import OpenAI
|
||
st.subheader("AI 数据分析与改进建议")
|
||
if not HAS_OPENAI:
|
||
st.warning("未安装 `openai` 库。请安装后重试。")
|
||
elif not openai_api_key:
|
||
st.info("请在左侧边栏输入 OpenAI API Key 以启用 AI 分析。")
|
||
else:
|
||
if all_strategy_types:
|
||
# Generate results if not already
|
||
results, recommendation = generate_output_and_recommendations(base, all_strategy_types,
|
||
region=region_sel if region_sel != '全市' else '全市')
|
||
df_res = pd.DataFrame(results).T
|
||
kpi_json = json.dumps(kpi, ensure_ascii=False, indent=2)
|
||
results_json = df_res.to_json(orient="records", force_ascii=False)
|
||
recommendation_text = recommendation
|
||
|
||
# Prepare data to send
|
||
data_to_analyze = {
|
||
"kpis": kpi_json,
|
||
"strategy_results": results_json,
|
||
"recommendation": recommendation_text
|
||
}
|
||
data_str = json.dumps(data_to_analyze, ensure_ascii=False)
|
||
|
||
prompt = (
|
||
"你是一名资深交通安全数据分析顾问。请基于以下结构化数据输出一份专业报告，需包含：\n"
|
||
"1. 核心指标洞察：按要点总结事故趋势、显著波动及可能原因。\n"
|
||
"2. 策略绩效评估：对比主要策略的优势、短板与适用场景。\n"
|
||
"3. 优化建议：为短期（0-3个月）、中期（3-12个月）与长期（12个月以上）分别给出2-3条可操作措施。\n"
|
||
"请保持正式语气，引用关键数值支撑结论，并用清晰的小节或列表呈现。\n"
|
||
f"数据摘要：{data_str}\n"
|
||
)
|
||
if st.button("上传数据至 AI 并获取分析"):
|
||
if not openai_api_key.strip():
|
||
st.info("请提供有效的 AI API Key。")
|
||
elif not open_ai_base_url.strip():
|
||
st.info("请提供可访问的 AI Base Url。")
|
||
else:
|
||
try:
|
||
client = OpenAI(
|
||
base_url=open_ai_base_url,
|
||
# sk-xxx替换为自己的key
|
||
api_key=openai_api_key
|
||
)
|
||
st.markdown("### AI 分析结果与改进思路")
|
||
placeholder = st.empty()
|
||
accumulated_response: list[str] = []
|
||
with st.spinner("AI 正在生成专业报告，请稍候…"):
|
||
stream = client.chat.completions.create(
|
||
model="deepseek-chat",
|
||
messages=[
|
||
{
|
||
"role": "system",
|
||
"content": "You are a professional traffic safety analyst who writes concise, well-structured Chinese reports."
|
||
},
|
||
{"role": "user", "content": prompt},
|
||
],
|
||
stream=True,
|
||
)
|
||
for chunk in stream:
|
||
delta = chunk.choices[0].delta if chunk.choices else None
|
||
piece = getattr(delta, "content", None) if delta else None
|
||
if piece:
|
||
accumulated_response.append(piece)
|
||
placeholder.markdown("".join(accumulated_response), unsafe_allow_html=True)
|
||
final_text = "".join(accumulated_response)
|
||
if not final_text:
|
||
placeholder.info("AI 未返回可用内容，请稍后重试或检查凭据配置。")
|
||
except Exception as e:
|
||
st.error(f"调用 OpenAI API 失败：{str(e)}")
|
||
else:
|
||
st.warning("没有策略数据可供分析。")
|
||
|
||
# Update refresh time
|
||
st.session_state['last_refresh'] = datetime.now()
|
||
|
||
elif selected_tab == "📈 预测模型":
|
||
if render_forecast is not None:
|
||
render_forecast(base)
|
||
else:
|
||
st.subheader("多模型预测比较")
|
||
# 使用表单封装交互组件
|
||
with st.form(key="predict_form"):
|
||
# 缩短默认回溯窗口，提升首次渲染速度
|
||
default_date = base.index.max() - pd.Timedelta(days=30) if len(base) else pd.Timestamp('2022-01-01')
|
||
selected_date = st.date_input("选择干预日期 / 预测起点", value=default_date)
|
||
horizon = st.number_input("预测天数", min_value=7, max_value=90, value=30, step=1)
|
||
submit_predict = st.form_submit_button("应用预测参数")
|
||
|
||
if submit_predict and len(base.loc[:pd.to_datetime(selected_date)]) >= 10:
|
||
first_date = pd.to_datetime(selected_date)
|
||
try:
|
||
train_series = base['accident_count'].loc[:first_date]
|
||
arima30 = arima_forecast_with_grid_search(
|
||
train_series,
|
||
start_date=first_date + pd.Timedelta(days=1),
|
||
horizon=horizon
|
||
)
|
||
except Exception as e:
|
||
st.warning(f"ARIMA 运行失败：{e}")
|
||
arima30 = None
|
||
|
||
knn_pred, _ = knn_forecast_counterfactual(base['accident_count'],
|
||
first_date,
|
||
horizon=horizon)
|
||
glm_pred, svr_pred, residuals = fit_and_extrapolate(base['accident_count'],
|
||
first_date,
|
||
days=horizon)
|
||
|
||
fig_pred = go.Figure()
|
||
fig_pred.add_trace(go.Scatter(x=base.index, y=base['accident_count'],
|
||
name="实际", mode="lines"))
|
||
if arima30 is not None:
|
||
fig_pred.add_trace(go.Scatter(x=arima30.index, y=arima30['forecast'],
|
||
name="ARIMA", mode="lines"))
|
||
if knn_pred is not None:
|
||
fig_pred.add_trace(go.Scatter(x=knn_pred.index, y=knn_pred,
|
||
name="KNN", mode="lines"))
|
||
if glm_pred is not None:
|
||
fig_pred.add_trace(go.Scatter(x=glm_pred.index, y=glm_pred,
|
||
name="GLM", mode="lines"))
|
||
if svr_pred is not None:
|
||
fig_pred.add_trace(go.Scatter(x=svr_pred.index, y=svr_pred,
|
||
name="SVR", mode="lines"))
|
||
|
||
fig_pred.update_layout(
|
||
title=f"多模型预测比较（起点：{first_date.date()}，预测 {horizon} 天）",
|
||
xaxis_title="日期", yaxis_title="事故数"
|
||
)
|
||
st.plotly_chart(fig_pred, use_container_width=True)
|
||
|
||
col_dl1, col_dl2 = st.columns(2)
|
||
if arima30 is not None:
|
||
col_dl1.download_button("下载 ARIMA 预测 CSV",
|
||
data=arima30.to_csv().encode("utf-8-sig"),
|
||
file_name="arima_forecast.csv",
|
||
mime="text/csv")
|
||
elif submit_predict:
|
||
st.info("⚠️ 干预前数据较少，可能影响拟合质量。")
|
||
else:
|
||
st.info("请设置预测参数并点击“应用预测参数”按钮。")
|
||
|
||
# --- Tab 3: 模型评估
|
||
elif selected_tab == "📊 模型评估":
|
||
if render_model_eval is not None:
|
||
render_model_eval(base)
|
||
else:
|
||
st.subheader("模型预测效果对比")
|
||
with st.form(key="model_eval_form"):
|
||
horizon_sel = st.slider("评估窗口（天）", 7, 60, 30, step=1)
|
||
submit_eval = st.form_submit_button("应用评估参数")
|
||
|
||
if submit_eval:
|
||
try:
|
||
df_metrics = evaluate_models(base['accident_count'], horizon=horizon_sel)
|
||
st.dataframe(df_metrics, use_container_width=True)
|
||
best_model = df_metrics['RMSE'].idxmin()
|
||
st.success(f"过去 {horizon_sel} 天中，RMSE 最低的模型是：**{best_model}**")
|
||
st.download_button(
|
||
"下载评估结果 CSV",
|
||
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))
|
||
else:
|
||
st.info("请设置评估窗口并点击“应用评估参数”按钮。")
|
||
|
||
# --- Tab 4: 异常检测
|
||
elif selected_tab == "⚠️ 异常检测":
|
||
anomalies, anomaly_fig = detect_anomalies(base['accident_count'])
|
||
st.plotly_chart(anomaly_fig, use_container_width=True)
|
||
st.write(f"检测到异常点：{len(anomalies)} 个")
|
||
st.download_button("下载异常日期 CSV",
|
||
data=anomalies.to_series().to_csv(index=False).encode('utf-8-sig'),
|
||
file_name="anomalies.csv", mime="text/csv")
|
||
|
||
# --- Tab 5: 策略评估
|
||
elif selected_tab == "📝 策略评估":
|
||
if render_strategy_eval is not None:
|
||
render_strategy_eval(base, all_strategy_types, region_sel)
|
||
else:
|
||
st.warning("策略评估模块不可用，请检查 `ui_sections/strategy_eval.py`。")
|
||
|
||
# --- Tab 6: 策略对比
|
||
elif selected_tab == "⚖️ 策略对比":
|
||
def strategy_metrics(strategy):
|
||
mask = base['strategy_type'].apply(lambda x: strategy in x)
|
||
if not mask.any():
|
||
return None
|
||
dt = mask[mask].index[0]
|
||
glm_pred, svr_pred, residuals = fit_and_extrapolate(base['accident_count'], dt, days=30)
|
||
if svr_pred is None:
|
||
return None
|
||
actual_post = base['accident_count'].loc[dt:dt+pd.Timedelta(days=29)]
|
||
pre = base['accident_count'].loc[dt-pd.Timedelta(days=30):dt-pd.Timedelta(days=1)]
|
||
stat, p = significance_test(pre, actual_post)
|
||
count_eff, sev_eff, (F1, F2), state = evaluate_strategy_effectiveness(
|
||
actual_series=base['accident_count'],
|
||
counterfactual_series=svr_pred,
|
||
severity_series=base['severity'],
|
||
strategy_date=dt, window=30
|
||
)
|
||
return {
|
||
"干预日": str(dt.date()),
|
||
"前30天事故": int(pre.sum()),
|
||
"后30天事故": int(actual_post.sum()),
|
||
"每日均值(前/后)": (float(pre.mean()), float(actual_post.mean())),
|
||
"t统计/p值": (stat, p),
|
||
"F1/F2": (float(F1), float(F2)),
|
||
"有效天数过半?": bool(count_eff),
|
||
"严重度下降?": bool(sev_eff),
|
||
"安全等级": state
|
||
}
|
||
if all_strategy_types:
|
||
st.subheader("策略对比")
|
||
with st.form(key="strategy_compare_form"):
|
||
colA, colB = st.columns(2)
|
||
with colA:
|
||
sA = st.selectbox("策略 A", options=all_strategy_types, key="stratA")
|
||
with colB:
|
||
sB = st.selectbox("策略 B", options=[s for s in all_strategy_types if s != st.session_state.get("stratA")], key="stratB")
|
||
submit_compare = st.form_submit_button("应用策略对比")
|
||
|
||
if submit_compare:
|
||
mA = strategy_metrics(sA)
|
||
mB = strategy_metrics(sB)
|
||
if mA and mB:
|
||
show = pd.DataFrame({
|
||
"指标": ["干预日", "前30天事故", "后30天事故", "每日均值(前)", "每日均值(后)", "t统计", "p值", "F1", "F2", "有效天数过半?", "严重度下降?", "安全等级"],
|
||
f"{sA}": [mA["干预日"], mA["前30天事故"], mA["后30天事故"],
|
||
mA["每日均值(前/后)"][0], mA["每日均值(前/后)"][1],
|
||
mA["t统计/p值"][0], mA["t统计/p值"][1],
|
||
mA["F1/F2"][0], mA["F1/F2"][1],
|
||
mA["有效天数过半?"], mA["严重度下降?"], mA["安全等级"]],
|
||
f"{sB}": [mB["干预日"], mB["前30天事故"], mB["后30天事故"],
|
||
mB["每日均值(前/后)"][0], mB["每日均值(前/后)"][1],
|
||
mB["t统计/p值"][0], mB["t统计/p值"][1],
|
||
mB["F1/F2"][0], mB["F1/F2"][1],
|
||
mB["有效天数过半?"], mB["严重度下降?"], mB["安全等级"]],
|
||
})
|
||
st.dataframe(show, use_container_width=True)
|
||
st.download_button("下载对比表 CSV",
|
||
data=show.to_csv(index=False).encode('utf-8-sig'),
|
||
file_name="strategy_compare.csv", mime="text/csv")
|
||
else:
|
||
st.info("所选策略可能缺少足够的干预前数据或未在当前过滤范围内出现。")
|
||
else:
|
||
st.info("请选择策略并点击“应用策略对比”按钮。")
|
||
else:
|
||
st.warning("没有策略可供对比。")
|
||
|
||
# --- Tab 7: 情景模拟
|
||
elif selected_tab == "🧪 情景模拟":
|
||
st.subheader("情景模拟")
|
||
st.write("选择一个日期与策略，模拟“在该日期上线该策略”的影响：")
|
||
with st.form(key="simulation_form"):
|
||
sim_date = st.date_input("模拟策略上线日期", value=(base.index.max() - pd.Timedelta(days=14)))
|
||
sim_strategy = st.selectbox("模拟策略类型", options=all_strategy_types or ["示例策略"])
|
||
sim_days = st.slider("模拟天数", 7, 60, 30)
|
||
submit_simulation = st.form_submit_button("应用模拟参数")
|
||
|
||
if submit_simulation:
|
||
glm_pred, svr_pred, residuals = fit_and_extrapolate(base['accident_count'], pd.to_datetime(sim_date), days=sim_days)
|
||
if svr_pred is None:
|
||
st.warning("干预前数据不足，无法进行模拟。")
|
||
else:
|
||
count_eff, sev_eff, (F1, F2), state = evaluate_strategy_effectiveness(
|
||
actual_series=base['accident_count'],
|
||
counterfactual_series=svr_pred,
|
||
severity_series=base['severity'],
|
||
strategy_date=pd.to_datetime(sim_date),
|
||
window=sim_days
|
||
)
|
||
fig_sim = go.Figure()
|
||
fig_sim.add_trace(go.Scatter(x=base.index, y=base['accident_count'], name='实际', mode='lines'))
|
||
fig_sim.add_trace(go.Scatter(x=svr_pred.index, y=svr_pred, name='Counterfactual(SVR)', mode='lines'))
|
||
fig_sim.update_layout(title=f"情景模拟：{sim_strategy} 自 {sim_date} 起", xaxis_title="日期", yaxis_title="事故数")
|
||
st.plotly_chart(fig_sim, use_container_width=True)
|
||
|
||
st.success(f"模拟结果：F1={F1:.2f}, F2={F2:.2f}, 等级={state}；"
|
||
f"{'事故数在多数天小于counterfactual' if count_eff else '效果不明显'}；"
|
||
f"{'严重度下降' if sev_eff else '严重度无下降'}。")
|
||
st.download_button("下载模拟图 HTML",
|
||
data=open(save_fig_as_html(fig_sim, "simulation.html"), "rb").read(),
|
||
file_name="simulation.html", mime="text/html")
|
||
else:
|
||
st.info("请设置模拟参数并点击“应用模拟参数”按钮。")
|
||
|
||
else:
|
||
st.info("请先在左侧上传事故数据与策略数据，并点击“应用数据与筛选”按钮。")
|
||
|
||
if __name__ == "__main__":
|
||
run_streamlit_app()
|