| """Feature engineering for train_xgboost.""" |
|
|
| from __future__ import annotations |
|
|
| import logging |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| LOGGER = logging.getLogger("static_xgboost") |
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|
|
|
| def build_feature_frame(df: pd.DataFrame) -> pd.DataFrame: |
| """Build a model-ready frame with engineered features and both targets.""" |
| work = df.copy() |
| if "timestamp" not in work.columns: |
| raise KeyError("Expected canonical 'timestamp' column after loading data.") |
|
|
| work["timestamp"] = pd.to_datetime(work["timestamp"], errors="coerce", utc=True) |
| work = work.dropna(subset=["timestamp"]).sort_values("timestamp") |
|
|
| numeric_cols = [ |
| "temperature", |
| "humidity", |
| "wind_direction", |
| "wind_speed", |
| "wind_gust", |
| "pressure", |
| "ghi", |
| "dni", |
| "dhi", |
| ] |
| for col in numeric_cols: |
| if col in work.columns: |
| work[col] = pd.to_numeric(work[col], errors="coerce") |
|
|
| present_numeric = [col for col in numeric_cols if col in work.columns] |
| work = work.dropna(subset=["ghi"]) |
| work[present_numeric] = work[present_numeric].interpolate(limit_direction="both") |
| work[present_numeric] = work[present_numeric].ffill().bfill() |
|
|
| |
| grouped = work.groupby("timestamp", as_index=False)[present_numeric].mean() |
| grouped = grouped.sort_values("timestamp").reset_index(drop=True) |
|
|
| ts = grouped["timestamp"] |
| grouped["hour"] = ts.dt.hour |
| grouped["day_of_year"] = ts.dt.dayofyear |
| grouped["month"] = ts.dt.month |
|
|
| grouped["hour_sin"] = np.sin(2 * np.pi * grouped["hour"] / 24) |
| grouped["hour_cos"] = np.cos(2 * np.pi * grouped["hour"] / 24) |
| grouped["doy_sin"] = np.sin(2 * np.pi * grouped["day_of_year"] / 365.25) |
| grouped["doy_cos"] = np.cos(2 * np.pi * grouped["day_of_year"] / 365.25) |
|
|
| for lag in (1, 2, 3, 24): |
| grouped[f"ghi_lag_{lag}"] = grouped["ghi"].shift(lag) |
|
|
| if "temperature" in grouped.columns: |
| grouped["temp_lag_1"] = grouped["temperature"].shift(1) |
|
|
| grouped["ghi_rolling_mean_3h"] = grouped["ghi"].shift(1).rolling(3, min_periods=1).mean() |
| grouped["ghi_rolling_std_3h"] = grouped["ghi"].shift(1).rolling(3, min_periods=2).std() |
| grouped["target_ghi_next_1h"] = grouped["ghi"].shift(-1) |
| grouped["target_temperature_next_1h"] = grouped["temperature"].shift(-1) |
|
|
| grouped = grouped.dropna(subset=["target_ghi_next_1h", "target_temperature_next_1h"]).copy() |
| return grouped.fillna(0.0) |
|
|
|
|
| def prepare_features(df: pd.DataFrame, target_mode: str) -> tuple[pd.DataFrame, pd.Series, pd.Series, str]: |
| """Build feature matrix and selected target vector.""" |
| grouped = build_feature_frame(df) |
|
|
| target_column = { |
| "ghi": "target_ghi_next_1h", |
| "temperature": "target_temperature_next_1h", |
| }[target_mode] |
|
|
| feature_cols = [ |
| col |
| for col in grouped.columns |
| if col |
| not in { |
| "timestamp", |
| "target_ghi_next_1h", |
| "target_temperature_next_1h", |
| } |
| ] |
| x = grouped[feature_cols] |
| y = grouped[target_column] |
| ts = grouped["timestamp"] |
|
|
| LOGGER.info("Prepared features for %s: rows=%d cols=%d", target_mode, x.shape[0], x.shape[1]) |
| return x, y, ts, target_column |
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|
