Spaces:

SolarWine
/

api

Sleeping

Eli Safra

Deploy SolarWine API (FastAPI + Docker, port 7860)

938949f 8 months ago

14.2 kB

"""
TimeSeriesPredictor: lag-based direct multi-horizon forecasting for
photosynthesis rate A.  Uses daytime-session indexing to handle 12h+
nighttime gaps, with per-horizon models (XGBoost / GradientBoosting).
Each growing season (May-Sep) is handled independently — sessions, lags,
and targets never cross the off-season gap (Oct-Apr).
"""

from __future__ import annotations

from typing import Optional

import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

try:
    from xgboost import XGBRegressor
    _HAS_XGB = True
except ImportError:
    _HAS_XGB = False

# Horizons: name -> (steps within session | None, calendar days | None)
HORIZONS = {
    "15min": {"steps": 1, "days": None},
    "1hour": {"steps": 4, "days": None},
    "1day": {"steps": None, "days": 1},
    "1week": {"steps": None, "days": 7},
    "1month": {"steps": None, "days": 30},
}

LAG_COLS = ["A", "ghi_w_m2", "air_temperature_c"]
LAG_STEPS = [1, 2, 3, 4, 8, 12]
ROLLING_WINDOWS = [4, 12]
MAX_GAP_MINUTES = 30


class TimeSeriesPredictor:
    """Train one model per forecast horizon using lag features."""

    def __init__(self):
        self.models: dict[str, object] = {}
        self.feature_cols: Optional[list[str]] = None
        self.results: dict[str, dict] = {}

    # ------------------------------------------------------------------
    # Season splitting
    # ------------------------------------------------------------------

    @staticmethod
    def assign_season(df: pd.DataFrame, ts_col: str = "timestamp_utc") -> pd.DataFrame:
        """Add a 'season' column (year of each row's growing season)."""
        out = df.copy()
        ts = pd.to_datetime(out[ts_col], utc=True)
        out["season"] = ts.dt.year
        return out

    # ------------------------------------------------------------------
    # Session identification
    # ------------------------------------------------------------------

    @staticmethod
    def identify_sessions(df: pd.DataFrame, ts_col: str = "timestamp_utc") -> pd.DataFrame:
        """Assign session_id to contiguous daytime blocks (gap <= MAX_GAP_MINUTES).
        Sessions are identified within each season independently."""
        out = df.copy()
        out = out.sort_values(ts_col).reset_index(drop=True)
        ts = pd.to_datetime(out[ts_col], utc=True)
        gap = ts.diff().dt.total_seconds() / 60
        out["session_id"] = (gap > MAX_GAP_MINUTES).cumsum()
        return out

    # ------------------------------------------------------------------
    # Lag / rolling features
    # ------------------------------------------------------------------

    @staticmethod
    def create_lag_features(df: pd.DataFrame) -> pd.DataFrame:
        """Create within-session lags, rolling stats, and previous-session summary.
        Previous-session features only link sessions within the same season."""
        out = df.copy()

        # Per-session lags and rolling stats
        for col in LAG_COLS:
            if col not in out.columns:
                continue
            for lag in LAG_STEPS:
                col_name = f"{col}_lag{lag}"
                out[col_name] = out.groupby("session_id")[col].shift(lag)
            for w in ROLLING_WINDOWS:
                out[f"{col}_rmean{w}"] = out.groupby("session_id")[col].transform(
                    lambda s: s.shift(1).rolling(w, min_periods=1).mean()
                )
                if col == "A":
                    out[f"{col}_rstd{w}"] = out.groupby("session_id")[col].transform(
                        lambda s: s.shift(1).rolling(w, min_periods=1).std()
                    )

        # Previous-session summary for A (within same season)
        if "A" in out.columns and "season" in out.columns:
            sess_stats = out.groupby("session_id").agg(
                season=("season", "first"),
                mean_A=("A", "mean"),
                max_A=("A", "max"),
            )
            # Shift within season so first session of each season gets NaN
            sess_stats["prev_sess_mean_A"] = sess_stats.groupby("season")["mean_A"].shift(1)
            sess_stats["prev_sess_max_A"] = sess_stats.groupby("season")["max_A"].shift(1)
            out = out.merge(
                sess_stats[["prev_sess_mean_A", "prev_sess_max_A"]],
                left_on="session_id", right_index=True, how="left",
            )

        # Fill NaN lags at session start with prev-session end values (within season)
        for col in LAG_COLS:
            if col not in df.columns:
                continue
            sess_end = df.groupby("session_id").agg(
                last_val=(col, "last"),
            )
            if "season" in df.columns:
                sess_season = df.groupby("session_id")["season"].first()
                sess_end["season"] = sess_season
                sess_end["prev_end"] = sess_end.groupby("season")["last_val"].shift(1)
            else:
                sess_end["prev_end"] = sess_end["last_val"].shift(1)
            out = out.merge(
                sess_end[["prev_end"]].rename(columns={"prev_end": f"_prev_end_{col}"}),
                left_on="session_id", right_index=True, how="left",
            )
            for lag in LAG_STEPS:
                lag_col = f"{col}_lag{lag}"
                if lag_col in out.columns:
                    out[lag_col] = out[lag_col].fillna(out[f"_prev_end_{col}"])
            out.drop(columns=[f"_prev_end_{col}"], inplace=True)

        return out

    # ------------------------------------------------------------------
    # Horizon targets
    # ------------------------------------------------------------------

    @staticmethod
    def create_horizon_target(df: pd.DataFrame, horizon_name: str,
                              ts_col: str = "timestamp_utc") -> pd.Series:
        """Create target column for a given horizon.
        Calendar-day targets only match within the same season."""
        h = HORIZONS[horizon_name]
        ts = pd.to_datetime(df[ts_col], utc=True)

        if h["steps"] is not None:
            # Within-session shift
            target = df.groupby("session_id")["A"].shift(-h["steps"])
        else:
            # Calendar-day match within same season
            days = h["days"]
            target_ts = ts + pd.Timedelta(days=days)

            if "season" in df.columns:
                # Build per-season lookup so targets don't cross seasons
                target = pd.Series(np.nan, index=df.index)
                for season, grp in df.groupby("season"):
                    grp_ts = pd.to_datetime(grp[ts_col], utc=True)
                    lookup = pd.Series(grp["A"].values, index=grp_ts)
                    lookup = lookup[~lookup.index.duplicated(keep="first")]
                    grp_target_ts = (grp_ts + pd.Timedelta(days=days)).dt.floor("15min")
                    matched = grp_target_ts.map(lookup)
                    target.loc[grp.index] = matched.values
            else:
                target_ts_rounded = target_ts.dt.floor("15min")
                lookup = pd.Series(df["A"].values, index=ts)
                lookup = lookup[~lookup.index.duplicated(keep="first")]
                target = target_ts_rounded.map(lookup)
                target = target.reset_index(drop=True)

        return target

    # ------------------------------------------------------------------
    # Feature columns
    # ------------------------------------------------------------------

    def _get_feature_cols(self, df: pd.DataFrame) -> list[str]:
        """Return numeric feature columns, excluding targets / metadata."""
        exclude = {"A", "timestamp_utc", "time", "source", "session_id",
                    "target", "season"}
        cols = [c for c in df.select_dtypes(include=[np.number]).columns if c not in exclude]
        return cols

    # ------------------------------------------------------------------
    # Train / evaluate
    # ------------------------------------------------------------------

    def train_all_horizons(self, df: pd.DataFrame, train_ratio: float = 0.75) -> pd.DataFrame:
        """Train one model per horizon, treating each season independently.
        Within each season the first ``train_ratio`` rows are used for
        training and the remainder for testing.  Training data from all
        seasons is pooled to fit a single model per horizon, and test
        data from all seasons is pooled for evaluation.  Per-season
        metrics are also reported.
        """
        self.feature_cols = self._get_feature_cols(df)
        seasons = sorted(df["season"].unique())
        rows = []

        for horizon_name in HORIZONS:
            # Collect train/test splits per season
            all_X_train, all_y_train = [], []
            all_X_test, all_y_test = [], []
            season_train_n: dict[int, int] = {}
            season_test: dict[int, tuple] = {}

            for season in seasons:
                sdf = df[df["season"] == season].copy()
                target = self.create_horizon_target(sdf, horizon_name)
                sdf = sdf.copy()
                sdf["target"] = target.values
                sdf = sdf.dropna(subset=self.feature_cols + ["target"])

                if len(sdf) < 30:
                    continue

                n = int(len(sdf) * train_ratio)
                if n < 10 or len(sdf) - n < 5:
                    continue

                X_tr = sdf[self.feature_cols].iloc[:n]
                y_tr = sdf["target"].iloc[:n]
                X_te = sdf[self.feature_cols].iloc[n:]
                y_te = sdf["target"].iloc[n:]

                all_X_train.append(X_tr)
                all_y_train.append(y_tr)
                all_X_test.append(X_te)
                all_y_test.append(y_te)
                season_train_n[season] = len(X_tr)
                season_test[season] = (X_te, y_te)

            if not all_X_train or not all_X_test:
                print(f"  {horizon_name}: insufficient data across seasons, skipping")
                continue

            X_train = pd.concat(all_X_train)
            y_train = pd.concat(all_y_train)
            X_test = pd.concat(all_X_test)
            y_test = pd.concat(all_y_test)

            model = self._make_model()
            model.fit(X_train, y_train)
            self.models[horizon_name] = model

            # Overall metrics
            pred = model.predict(X_test)
            rmse = float(np.sqrt(mean_squared_error(y_test, pred)))
            mae = float(mean_absolute_error(y_test, pred))
            r2 = float(r2_score(y_test, pred))
            self.results[horizon_name] = {
                "predictions": pred, "y_test": y_test.values,
                "rmse": rmse, "mae": mae, "r2": r2,
                "n_train": len(X_train), "n_test": len(X_test),
            }
            rows.append({
                "horizon": horizon_name, "season": "all",
                "approach": "time_series",
                "RMSE": round(rmse, 4), "MAE": round(mae, 4), "R2": round(r2, 4),
                "n_train": len(X_train), "n_test": len(X_test),
            })
            print(f"  {horizon_name} [all]: RMSE={rmse:.4f}  MAE={mae:.4f}  "
                  f"R²={r2:.4f}  (train={len(X_train)}, test={len(X_test)})")

            # Per-season metrics
            for season, (X_te_s, y_te_s) in season_test.items():
                pred_s = model.predict(X_te_s)
                rmse_s = float(np.sqrt(mean_squared_error(y_te_s, pred_s)))
                mae_s = float(mean_absolute_error(y_te_s, pred_s))
                r2_s = float(r2_score(y_te_s, pred_s))
                rows.append({
                    "horizon": horizon_name, "season": str(season),
                    "approach": "time_series",
                    "RMSE": round(rmse_s, 4), "MAE": round(mae_s, 4),
                    "R2": round(r2_s, 4),
                    "n_train": season_train_n[season],
                    "n_test": len(X_te_s),
                })
                print(f"  {horizon_name} [{season}]: RMSE={rmse_s:.4f}  "
                      f"MAE={mae_s:.4f}  R²={r2_s:.4f}  (test={len(X_te_s)})")

        return pd.DataFrame(rows)

    def get_comparison_with_baseline(
        self, baseline_metrics: pd.DataFrame
    ) -> pd.DataFrame:
        """Combine TS horizon results with cross-sectional baseline into one table."""
        ts_rows = []
        for horizon_name, res in self.results.items():
            ts_rows.append({
                "horizon": horizon_name, "season": "all",
                "approach": "time_series",
                "RMSE": round(res["rmse"], 4),
                "MAE": round(res["mae"], 4),
                "R2": round(res["r2"], 4),
            })
        ts_df = pd.DataFrame(ts_rows)

        # Best cross-sectional model
        if not baseline_metrics.empty:
            best_idx = baseline_metrics["RMSE"].idxmin()
            best = baseline_metrics.loc[best_idx]
            bl_rows = []
            for h in HORIZONS:
                bl_rows.append({
                    "horizon": h, "season": "all",
                    "approach": f"cross_sectional ({best['model']})",
                    "RMSE": round(float(best["RMSE"]), 4),
                    "MAE": round(float(best["MAE"]), 4),
                    "R2": round(float(best["R2"]), 4),
                })
            bl_df = pd.DataFrame(bl_rows)
            return pd.concat([ts_df, bl_df], ignore_index=True)
        return ts_df

    # ------------------------------------------------------------------
    # Internals
    # ------------------------------------------------------------------

    @staticmethod
    def _make_model():
        if _HAS_XGB:
            return XGBRegressor(
                n_estimators=300, max_depth=4, learning_rate=0.05,
                min_child_weight=10, reg_alpha=0.1, reg_lambda=1.0,
                n_jobs=-1, random_state=42,
            )
        return GradientBoostingRegressor(
            n_estimators=300, max_depth=4, learning_rate=0.05,
            min_samples_leaf=10, random_state=42,
        )

	"""
	TimeSeriesPredictor: lag-based direct multi-horizon forecasting for
	photosynthesis rate A. Uses daytime-session indexing to handle 12h+
	nighttime gaps, with per-horizon models (XGBoost / GradientBoosting).

	Each growing season (May-Sep) is handled independently — sessions, lags,
	and targets never cross the off-season gap (Oct-Apr).
	"""

	from __future__ import annotations

	from typing import Optional

	import numpy as np
	import pandas as pd
	from sklearn.ensemble import GradientBoostingRegressor
	from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

	try:
	from xgboost import XGBRegressor
	_HAS_XGB = True
	except ImportError:
	_HAS_XGB = False

	# Horizons: name -> (steps within session \| None, calendar days \| None)
	HORIZONS = {
	"15min": {"steps": 1, "days": None},
	"1hour": {"steps": 4, "days": None},
	"1day": {"steps": None, "days": 1},
	"1week": {"steps": None, "days": 7},
	"1month": {"steps": None, "days": 30},
	}

	LAG_COLS = ["A", "ghi_w_m2", "air_temperature_c"]
	LAG_STEPS = [1, 2, 3, 4, 8, 12]
	ROLLING_WINDOWS = [4, 12]
	MAX_GAP_MINUTES = 30


	class TimeSeriesPredictor:
	"""Train one model per forecast horizon using lag features."""

	def __init__(self):
	self.models: dict[str, object] = {}
	self.feature_cols: Optional[list[str]] = None
	self.results: dict[str, dict] = {}

	# ------------------------------------------------------------------
	# Season splitting
	# ------------------------------------------------------------------

	@staticmethod
	def assign_season(df: pd.DataFrame, ts_col: str = "timestamp_utc") -> pd.DataFrame:
	"""Add a 'season' column (year of each row's growing season)."""
	out = df.copy()
	ts = pd.to_datetime(out[ts_col], utc=True)
	out["season"] = ts.dt.year
	return out

	# ------------------------------------------------------------------
	# Session identification
	# ------------------------------------------------------------------

	@staticmethod
	def identify_sessions(df: pd.DataFrame, ts_col: str = "timestamp_utc") -> pd.DataFrame:
	"""Assign session_id to contiguous daytime blocks (gap <= MAX_GAP_MINUTES).
	Sessions are identified within each season independently."""
	out = df.copy()
	out = out.sort_values(ts_col).reset_index(drop=True)
	ts = pd.to_datetime(out[ts_col], utc=True)
	gap = ts.diff().dt.total_seconds() / 60
	out["session_id"] = (gap > MAX_GAP_MINUTES).cumsum()
	return out

	# ------------------------------------------------------------------
	# Lag / rolling features
	# ------------------------------------------------------------------

	@staticmethod
	def create_lag_features(df: pd.DataFrame) -> pd.DataFrame:
	"""Create within-session lags, rolling stats, and previous-session summary.
	Previous-session features only link sessions within the same season."""
	out = df.copy()

	# Per-session lags and rolling stats
	for col in LAG_COLS:
	if col not in out.columns:
	continue
	for lag in LAG_STEPS:
	col_name = f"{col}_lag{lag}"
	out[col_name] = out.groupby("session_id")[col].shift(lag)
	for w in ROLLING_WINDOWS:
	out[f"{col}_rmean{w}"] = out.groupby("session_id")[col].transform(
	lambda s: s.shift(1).rolling(w, min_periods=1).mean()
	)
	if col == "A":
	out[f"{col}_rstd{w}"] = out.groupby("session_id")[col].transform(
	lambda s: s.shift(1).rolling(w, min_periods=1).std()
	)

	# Previous-session summary for A (within same season)
	if "A" in out.columns and "season" in out.columns:
	sess_stats = out.groupby("session_id").agg(
	season=("season", "first"),
	mean_A=("A", "mean"),
	max_A=("A", "max"),
	)
	# Shift within season so first session of each season gets NaN
	sess_stats["prev_sess_mean_A"] = sess_stats.groupby("season")["mean_A"].shift(1)
	sess_stats["prev_sess_max_A"] = sess_stats.groupby("season")["max_A"].shift(1)
	out = out.merge(
	sess_stats[["prev_sess_mean_A", "prev_sess_max_A"]],
	left_on="session_id", right_index=True, how="left",
	)

	# Fill NaN lags at session start with prev-session end values (within season)
	for col in LAG_COLS:
	if col not in df.columns:
	continue
	sess_end = df.groupby("session_id").agg(
	last_val=(col, "last"),
	)
	if "season" in df.columns:
	sess_season = df.groupby("session_id")["season"].first()
	sess_end["season"] = sess_season
	sess_end["prev_end"] = sess_end.groupby("season")["last_val"].shift(1)
	else:
	sess_end["prev_end"] = sess_end["last_val"].shift(1)
	out = out.merge(
	sess_end[["prev_end"]].rename(columns={"prev_end": f"_prev_end_{col}"}),
	left_on="session_id", right_index=True, how="left",
	)
	for lag in LAG_STEPS:
	lag_col = f"{col}_lag{lag}"
	if lag_col in out.columns:
	out[lag_col] = out[lag_col].fillna(out[f"_prev_end_{col}"])
	out.drop(columns=[f"_prev_end_{col}"], inplace=True)

	return out

	# ------------------------------------------------------------------
	# Horizon targets
	# ------------------------------------------------------------------

	@staticmethod
	def create_horizon_target(df: pd.DataFrame, horizon_name: str,
	ts_col: str = "timestamp_utc") -> pd.Series:
	"""Create target column for a given horizon.
	Calendar-day targets only match within the same season."""
	h = HORIZONS[horizon_name]
	ts = pd.to_datetime(df[ts_col], utc=True)

	if h["steps"] is not None:
	# Within-session shift
	target = df.groupby("session_id")["A"].shift(-h["steps"])
	else:
	# Calendar-day match within same season
	days = h["days"]
	target_ts = ts + pd.Timedelta(days=days)

	if "season" in df.columns:
	# Build per-season lookup so targets don't cross seasons
	target = pd.Series(np.nan, index=df.index)
	for season, grp in df.groupby("season"):
	grp_ts = pd.to_datetime(grp[ts_col], utc=True)
	lookup = pd.Series(grp["A"].values, index=grp_ts)
	lookup = lookup[~lookup.index.duplicated(keep="first")]
	grp_target_ts = (grp_ts + pd.Timedelta(days=days)).dt.floor("15min")
	matched = grp_target_ts.map(lookup)
	target.loc[grp.index] = matched.values
	else:
	target_ts_rounded = target_ts.dt.floor("15min")
	lookup = pd.Series(df["A"].values, index=ts)
	lookup = lookup[~lookup.index.duplicated(keep="first")]
	target = target_ts_rounded.map(lookup)
	target = target.reset_index(drop=True)

	return target

	# ------------------------------------------------------------------
	# Feature columns
	# ------------------------------------------------------------------

	def _get_feature_cols(self, df: pd.DataFrame) -> list[str]:
	"""Return numeric feature columns, excluding targets / metadata."""
	exclude = {"A", "timestamp_utc", "time", "source", "session_id",
	"target", "season"}
	cols = [c for c in df.select_dtypes(include=[np.number]).columns if c not in exclude]
	return cols

	# ------------------------------------------------------------------
	# Train / evaluate
	# ------------------------------------------------------------------

	def train_all_horizons(self, df: pd.DataFrame, train_ratio: float = 0.75) -> pd.DataFrame:
	"""Train one model per horizon, treating each season independently.

	Within each season the first ``train_ratio`` rows are used for
	training and the remainder for testing. Training data from all
	seasons is pooled to fit a single model per horizon, and test
	data from all seasons is pooled for evaluation. Per-season
	metrics are also reported.
	"""
	self.feature_cols = self._get_feature_cols(df)
	seasons = sorted(df["season"].unique())
	rows = []

	for horizon_name in HORIZONS:
	# Collect train/test splits per season
	all_X_train, all_y_train = [], []
	all_X_test, all_y_test = [], []
	season_train_n: dict[int, int] = {}
	season_test: dict[int, tuple] = {}

	for season in seasons:
	sdf = df[df["season"] == season].copy()
	target = self.create_horizon_target(sdf, horizon_name)
	sdf = sdf.copy()
	sdf["target"] = target.values
	sdf = sdf.dropna(subset=self.feature_cols + ["target"])

	if len(sdf) < 30:
	continue

	n = int(len(sdf) * train_ratio)
	if n < 10 or len(sdf) - n < 5:
	continue

	X_tr = sdf[self.feature_cols].iloc[:n]
	y_tr = sdf["target"].iloc[:n]
	X_te = sdf[self.feature_cols].iloc[n:]
	y_te = sdf["target"].iloc[n:]

	all_X_train.append(X_tr)
	all_y_train.append(y_tr)
	all_X_test.append(X_te)
	all_y_test.append(y_te)
	season_train_n[season] = len(X_tr)
	season_test[season] = (X_te, y_te)

	if not all_X_train or not all_X_test:
	print(f" {horizon_name}: insufficient data across seasons, skipping")
	continue

	X_train = pd.concat(all_X_train)
	y_train = pd.concat(all_y_train)
	X_test = pd.concat(all_X_test)
	y_test = pd.concat(all_y_test)

	model = self._make_model()
	model.fit(X_train, y_train)
	self.models[horizon_name] = model

	# Overall metrics
	pred = model.predict(X_test)
	rmse = float(np.sqrt(mean_squared_error(y_test, pred)))
	mae = float(mean_absolute_error(y_test, pred))
	r2 = float(r2_score(y_test, pred))
	self.results[horizon_name] = {
	"predictions": pred, "y_test": y_test.values,
	"rmse": rmse, "mae": mae, "r2": r2,
	"n_train": len(X_train), "n_test": len(X_test),
	}
	rows.append({
	"horizon": horizon_name, "season": "all",
	"approach": "time_series",
	"RMSE": round(rmse, 4), "MAE": round(mae, 4), "R2": round(r2, 4),
	"n_train": len(X_train), "n_test": len(X_test),
	})
	print(f" {horizon_name} [all]: RMSE={rmse:.4f} MAE={mae:.4f} "
	f"R²={r2:.4f} (train={len(X_train)}, test={len(X_test)})")

	# Per-season metrics
	for season, (X_te_s, y_te_s) in season_test.items():
	pred_s = model.predict(X_te_s)
	rmse_s = float(np.sqrt(mean_squared_error(y_te_s, pred_s)))
	mae_s = float(mean_absolute_error(y_te_s, pred_s))
	r2_s = float(r2_score(y_te_s, pred_s))
	rows.append({
	"horizon": horizon_name, "season": str(season),
	"approach": "time_series",
	"RMSE": round(rmse_s, 4), "MAE": round(mae_s, 4),
	"R2": round(r2_s, 4),
	"n_train": season_train_n[season],
	"n_test": len(X_te_s),
	})
	print(f" {horizon_name} [{season}]: RMSE={rmse_s:.4f} "
	f"MAE={mae_s:.4f} R²={r2_s:.4f} (test={len(X_te_s)})")

	return pd.DataFrame(rows)

	def get_comparison_with_baseline(
	self, baseline_metrics: pd.DataFrame
	) -> pd.DataFrame:
	"""Combine TS horizon results with cross-sectional baseline into one table."""
	ts_rows = []
	for horizon_name, res in self.results.items():
	ts_rows.append({
	"horizon": horizon_name, "season": "all",
	"approach": "time_series",
	"RMSE": round(res["rmse"], 4),
	"MAE": round(res["mae"], 4),
	"R2": round(res["r2"], 4),
	})
	ts_df = pd.DataFrame(ts_rows)

	# Best cross-sectional model
	if not baseline_metrics.empty:
	best_idx = baseline_metrics["RMSE"].idxmin()
	best = baseline_metrics.loc[best_idx]
	bl_rows = []
	for h in HORIZONS:
	bl_rows.append({
	"horizon": h, "season": "all",
	"approach": f"cross_sectional ({best['model']})",
	"RMSE": round(float(best["RMSE"]), 4),
	"MAE": round(float(best["MAE"]), 4),
	"R2": round(float(best["R2"]), 4),
	})
	bl_df = pd.DataFrame(bl_rows)
	return pd.concat([ts_df, bl_df], ignore_index=True)
	return ts_df

	# ------------------------------------------------------------------
	# Internals
	# ------------------------------------------------------------------

	@staticmethod
	def _make_model():
	if _HAS_XGB:
	return XGBRegressor(
	n_estimators=300, max_depth=4, learning_rate=0.05,
	min_child_weight=10, reg_alpha=0.1, reg_lambda=1.0,
	n_jobs=-1, random_state=42,
	)
	return GradientBoostingRegressor(
	n_estimators=300, max_depth=4, learning_rate=0.05,
	min_samples_leaf=10, random_state=42,
	)