87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
|
from __future__ import annotations
|
|||
|
|
|
|||
|
|
import numpy as np
|
|||
|
|
import pandas as pd
|
|||
|
|
from sklearn.metrics import mean_absolute_error, mean_squared_error
|
|||
|
|
from statsmodels.tsa.arima.model import ARIMA
|
|||
|
|
import streamlit as st
|
|||
|
|
|
|||
|
|
|
|||
|
|
@st.cache_data(show_spinner=False)
|
|||
|
|
def evaluate_models(series: pd.Series,
|
|||
|
|
horizon: int = 30,
|
|||
|
|
lookback: int = 14,
|
|||
|
|
p_values: range = range(0, 4),
|
|||
|
|
d_values: range = range(0, 2),
|
|||
|
|
q_values: range = range(0, 4)) -> pd.DataFrame:
|
|||
|
|
"""
|
|||
|
|
留出法（最后 horizon 天作为验证集）比较 ARIMA / KNN / GLM / SVR，
|
|||
|
|
输出 MAE・RMSE・MAPE，并按 RMSE 升序排序。
|
|||
|
|
"""
|
|||
|
|
series = series.asfreq('D').fillna(0)
|
|||
|
|
if len(series) <= horizon + 10:
|
|||
|
|
raise ValueError("序列太短，无法留出 %d 天进行评估。" % horizon)
|
|||
|
|
|
|||
|
|
train, test = series.iloc[:-horizon], series.iloc[-horizon:]
|
|||
|
|
|
|||
|
|
def _to_series_like(pred, a_index):
|
|||
|
|
if isinstance(pred, pd.Series):
|
|||
|
|
return pred.reindex(a_index)
|
|||
|
|
return pd.Series(pred, index=a_index)
|
|||
|
|
|
|||
|
|
def _metrics(a: pd.Series, p) -> dict:
|
|||
|
|
p = _to_series_like(p, a.index).astype(float)
|
|||
|
|
a = a.astype(float)
|
|||
|
|
mae = mean_absolute_error(a, p)
|
|||
|
|
try:
|
|||
|
|
rmse = mean_squared_error(a, p, squared=False)
|
|||
|
|
except TypeError:
|
|||
|
|
rmse = mean_squared_error(a, p) ** 0.5
|
|||
|
|
mape = np.nanmean(np.abs((a - p) / np.where(a == 0, np.nan, a))) * 100
|
|||
|
|
return {"MAE": mae, "RMSE": rmse, "MAPE": mape}
|
|||
|
|
|
|||
|
|
results = {}
|
|||
|
|
|
|||
|
|
best_aic, best_order = float('inf'), (1, 0, 1)
|
|||
|
|
for p in p_values:
|
|||
|
|
for d in d_values:
|
|||
|
|
for q in q_values:
|
|||
|
|
try:
|
|||
|
|
aic = ARIMA(train, order=(p, d, q)).fit().aic
|
|||
|
|
if aic < best_aic:
|
|||
|
|
best_aic, best_order = aic, (p, d, q)
|
|||
|
|
except Exception:
|
|||
|
|
continue
|
|||
|
|
arima_train = train.asfreq('D').fillna(0)
|
|||
|
|
arima_pred = ARIMA(arima_train, order=best_order).fit().forecast(steps=horizon)
|
|||
|
|
results['ARIMA'] = _metrics(test, arima_pred)
|
|||
|
|
|
|||
|
|
# Import local utilities to avoid circular dependencies
|
|||
|
|
from services.forecast import knn_forecast_counterfactual, fit_and_extrapolate
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
knn_pred, _ = knn_forecast_counterfactual(series,
|
|||
|
|
train.index[-1] + pd.Timedelta(days=1),
|
|||
|
|
lookback=lookback,
|
|||
|
|
horizon=horizon)
|
|||
|
|
if knn_pred is not None:
|
|||
|
|
results['KNN'] = _metrics(test, knn_pred)
|
|||
|
|
except Exception:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
glm_pred, svr_pred, _ = fit_and_extrapolate(series,
|
|||
|
|
train.index[-1] + pd.Timedelta(days=1),
|
|||
|
|
days=horizon)
|
|||
|
|
if glm_pred is not None:
|
|||
|
|
results['GLM'] = _metrics(test, glm_pred)
|
|||
|
|
if svr_pred is not None:
|
|||
|
|
results['SVR'] = _metrics(test, svr_pred)
|
|||
|
|
except Exception:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
return (pd.DataFrame(results)
|
|||
|
|
.T.sort_values('RMSE')
|
|||
|
|
.round(3))
|
|||
|
|
|