modify: cleanup project structure and docs

services/metrics.py

@@ -0,0 +1,86 @@
+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))
+