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from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Sequence, Tuple
import numpy as np
TARGETS = ("Nd", "reff", "LWP", "CF")
PRESSURE_VARIABLES = ("temperature", "specific_humidity", "relative_humidity", "u_wind", "v_wind", "omega", "geopotential", "cloud_liquid", "cloud_fraction")
PRESSURE_LEVELS = (1000, 950, 900, 850, 800, 750, 700, 650, 600, 550)
SINGLE_FEATURES = (
"sst", "surface_pressure", "mslp", "skin_temperature", "t2m", "d2m",
"u10", "v10", "surface_solar_radiation", "surface_thermal_radiation",
"latent_heat_flux", "sensible_heat_flux", "boundary_layer_height",
"total_column_water_vapour", "total_column_cloud_liquid", "cape", "cin",
"low_cloud_cover", "sea_ice_fraction", "precipitation", "cos_sza",
"latitude", "longitude", "platform_hour",
)
def feature_names() -> List[str]:
names = [f"{variable}_{level}hPa" for variable in PRESSURE_VARIABLES for level in PRESSURE_LEVELS]
names.extend(SINGLE_FEATURES)
if len(names) != 114:
raise RuntimeError("The ERA5 predictor ledger must contain exactly 114 features")
return names
def regression_metrics(y_true: np.ndarray, y_pred: np.ndarray) -> Dict[str, float]:
mask = np.isfinite(y_true) & np.isfinite(y_pred)
if mask.sum() < 2:
return {"n": int(mask.sum()), "mse": float("nan"), "r2": float("nan"), "pearson": float("nan")}
y = np.asarray(y_true[mask], dtype=np.float64)
p = np.asarray(y_pred[mask], dtype=np.float64)
mse = float(np.mean((y - p) ** 2))
variance = float(np.sum((y - y.mean()) ** 2))
r2 = float(1.0 - np.sum((y - p) ** 2) / variance) if variance > 0 else float("nan")
pearson = float(np.corrcoef(y, p)[0, 1]) if np.std(y) > 0 and np.std(p) > 0 else float("nan")
return {"n": int(mask.sum()), "mse": mse, "r2": r2, "pearson": pearson}
@dataclass
class TreeConfig:
min_leaf: int = 7
max_features: int = 38
max_depth: Optional[int] = None
split_candidates: int = 12
class RandomRegressionTree:
"""CART regressor with random feature subsets and compact array state."""
def __init__(self, config: TreeConfig, seed: int):
self.config = config
self.seed = int(seed)
self.feature: List[int] = []
self.threshold: List[float] = []
self.left: List[int] = []
self.right: List[int] = []
self.value: List[float] = []
def fit(self, x: np.ndarray, y: np.ndarray) -> "RandomRegressionTree":
x = np.asarray(x, dtype=np.float32)
y = np.asarray(y, dtype=np.float64)
rng = np.random.default_rng(self.seed)
def build(indices: np.ndarray, depth: int) -> int:
node = len(self.value)
self.feature.append(-1)
self.threshold.append(np.nan)
self.left.append(-1)
self.right.append(-1)
self.value.append(float(y[indices].mean()))
if indices.size < 2 * self.config.min_leaf:
return node
if self.config.max_depth is not None and depth >= self.config.max_depth:
return node
parent_sse = float(np.sum((y[indices] - y[indices].mean()) ** 2))
if parent_sse <= 1e-12:
return node
n_features = min(self.config.max_features, x.shape[1])
candidates = rng.choice(x.shape[1], size=n_features, replace=False)
best: Optional[Tuple[float, int, float, np.ndarray]] = None
quantiles = np.linspace(0.05, 0.95, self.config.split_candidates)
for feature in candidates:
values = x[indices, feature]
thresholds = np.unique(np.quantile(values, quantiles))
for threshold in thresholds:
is_left = values <= threshold
nl = int(is_left.sum())
nr = indices.size - nl
if nl < self.config.min_leaf or nr < self.config.min_leaf:
continue
yl, yr = y[indices[is_left]], y[indices[~is_left]]
score = float(np.sum((yl - yl.mean()) ** 2) + np.sum((yr - yr.mean()) ** 2))
if best is None or score < best[0]:
best = (score, int(feature), float(threshold), is_left.copy())
if best is None or best[0] >= parent_sse - 1e-12:
return node
_, split_feature, split_threshold, is_left = best
self.feature[node] = split_feature
self.threshold[node] = split_threshold
self.left[node] = build(indices[is_left], depth + 1)
self.right[node] = build(indices[~is_left], depth + 1)
return node
build(np.arange(y.size, dtype=np.int64), 0)
return self
def predict(self, x: np.ndarray) -> np.ndarray:
x = np.asarray(x, dtype=np.float32)
output = np.empty(x.shape[0], dtype=np.float32)
for row in range(x.shape[0]):
node = 0
while self.feature[node] >= 0:
node = self.left[node] if x[row, self.feature[node]] <= self.threshold[node] else self.right[node]
output[row] = self.value[node]
return output
def state_dict(self) -> Dict[str, Any]:
return {
"seed": self.seed,
"config": self.config.__dict__.copy(),
"feature": np.asarray(self.feature, dtype=np.int32),
"threshold": np.asarray(self.threshold, dtype=np.float32),
"left": np.asarray(self.left, dtype=np.int32),
"right": np.asarray(self.right, dtype=np.int32),
"value": np.asarray(self.value, dtype=np.float32),
}
@classmethod
def from_state_dict(cls, state: Dict[str, Any]) -> "RandomRegressionTree":
tree = cls(TreeConfig(**state["config"]), int(state["seed"]))
for name in ("feature", "threshold", "left", "right", "value"):
setattr(tree, name, np.asarray(state[name]).tolist())
return tree
class BootstrapRandomForestRegressor:
"""Regression forest with explicit approximately 60% bootstrap and OOB state."""
def __init__(self, n_trees: int = 100, min_leaf: int = 7, max_features: int = 38,
bootstrap_fraction: float = 0.6, max_depth: Optional[int] = None,
split_candidates: int = 12, seed: int = 0):
if n_trees < 1 or min_leaf < 1 or not 0 < bootstrap_fraction <= 1:
raise ValueError("Invalid forest configuration")
self.n_trees = int(n_trees)
self.bootstrap_fraction = float(bootstrap_fraction)
self.seed = int(seed)
self.tree_config = TreeConfig(int(min_leaf), int(max_features), max_depth, int(split_candidates))
self.trees: List[RandomRegressionTree] = []
self.oob_indices: List[np.ndarray] = []
def fit(self, x: np.ndarray, y: np.ndarray) -> "BootstrapRandomForestRegressor":
x = np.asarray(x, dtype=np.float32)
y = np.asarray(y, dtype=np.float32)
if x.ndim != 2 or x.shape[1] != 114 or y.shape != (x.shape[0],):
raise ValueError(f"Expected X [N,114] and y [N], got {x.shape} and {y.shape}")
rng = np.random.default_rng(self.seed)
draw_size = max(2 * self.tree_config.min_leaf, int(round(self.bootstrap_fraction * x.shape[0])))
self.trees, self.oob_indices = [], []
for _ in range(self.n_trees):
bootstrap = rng.integers(0, x.shape[0], size=draw_size)
used = np.zeros(x.shape[0], dtype=bool)
used[np.unique(bootstrap)] = True
oob = np.flatnonzero(~used)
tree_seed = int(rng.integers(0, 2**31 - 1))
self.trees.append(RandomRegressionTree(self.tree_config, tree_seed).fit(x[bootstrap], y[bootstrap]))
self.oob_indices.append(oob.astype(np.int32))
return self
def predict_trees(self, x: np.ndarray) -> np.ndarray:
if not self.trees:
raise RuntimeError("Forest is not fitted")
return np.stack([tree.predict(x) for tree in self.trees], axis=1)
def predict(self, x: np.ndarray) -> np.ndarray:
return self.predict_trees(x).mean(axis=1)
def oob_predict(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
sums = np.zeros(x.shape[0], dtype=np.float64)
counts = np.zeros(x.shape[0], dtype=np.int32)
for tree, indices in zip(self.trees, self.oob_indices):
if indices.size:
sums[indices] += tree.predict(x[indices])
counts[indices] += 1
prediction = np.full(x.shape[0], np.nan, dtype=np.float32)
valid = counts > 0
prediction[valid] = (sums[valid] / counts[valid]).astype(np.float32)
return prediction, counts
def permutation_importance(self, x: np.ndarray, y: np.ndarray, seed: int = 0) -> np.ndarray:
"""Breiman OOB permuted-predictor delta MSE, averaged over eligible trees."""
rng = np.random.default_rng(seed)
deltas = np.zeros(x.shape[1], dtype=np.float64)
counts = np.zeros(x.shape[1], dtype=np.int32)
for tree, indices in zip(self.trees, self.oob_indices):
if indices.size < 2:
continue
xo = np.asarray(x[indices], dtype=np.float32)
yo = np.asarray(y[indices], dtype=np.float32)
baseline = float(np.mean((yo - tree.predict(xo)) ** 2))
for feature in range(x.shape[1]):
changed = xo.copy()
changed[:, feature] = changed[rng.permutation(indices.size), feature]
deltas[feature] += float(np.mean((yo - tree.predict(changed)) ** 2)) - baseline
counts[feature] += 1
return np.divide(deltas, counts, out=np.zeros_like(deltas), where=counts > 0).astype(np.float32)
def state_dict(self) -> Dict[str, Any]:
return {
"n_trees": self.n_trees,
"bootstrap_fraction": self.bootstrap_fraction,
"seed": self.seed,
"tree_config": self.tree_config.__dict__.copy(),
"trees": [tree.state_dict() for tree in self.trees],
"oob_indices": self.oob_indices,
}
@classmethod
def from_state_dict(cls, state: Dict[str, Any]) -> "BootstrapRandomForestRegressor":
config = state["tree_config"]
forest = cls(state["n_trees"], config["min_leaf"], config["max_features"],
state["bootstrap_fraction"], config["max_depth"],
config["split_candidates"], state["seed"])
forest.trees = [RandomRegressionTree.from_state_dict(item) for item in state["trees"]]
forest.oob_indices = [np.asarray(item, dtype=np.int32) for item in state["oob_indices"]]
return forest
def validate_multimodal_keys(data: Dict[str, np.ndarray]) -> None:
required = ("year", "month", "platform", "latitude", "longitude", "X", "Y")
missing = [key for key in required if key not in data]
if missing:
raise ValueError(f"Missing aligned arrays: {missing}")
n = data["X"].shape[0]
if data["X"].shape[1] != 114 or data["Y"].shape != (n, 4):
raise ValueError("Predictors must be [N,114] and targets [N,4]")
if any(np.asarray(data[key]).shape[0] != n for key in required[:-2]):
raise ValueError("Year/month/platform/coordinates are not row-aligned")
keys = list(zip(data["year"].tolist(), data["month"].tolist(), data["platform"].tolist(),
np.round(data["latitude"], 4).tolist(), np.round(data["longitude"], 4).tolist()))
if len(set(keys)) != n:
raise ValueError("Multimodal year-month-platform-latitude-longitude keys are not unique")
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