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b11ef36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | """Pure NumPy multi-output random forest for Beijing air-quality normalization."""
from dataclasses import dataclass
import pickle
import numpy as np
FORMAT_VERSION = "meteonorm_rf_v1"
MODEL_NAME = "MeteoNorm-RF"
POLLUTANTS = ("PM2.5", "PM10", "NO2", "SO2", "O3", "CO")
STATIONS = ("dongsi", "tiantan", "guanyuan", "wanshouxigong", "aotizhongxin",
"nongzhanguan", "wanliu", "beibuxinqu", "zhiwuyuan", "fengtaihuayuan",
"yungang", "gucheng")
BASE_FEATURES = ("ttrend", "day_of_year", "weekend", "hour", "wind_speed",
"wind_direction", "pressure", "temperature", "relative_humidity")
FEATURE_NAMES = BASE_FEATURES + tuple(f"station={name}" for name in STATIONS)
def encode_features(data):
"""Encode station as an explicit 12-column one-hot categorical feature."""
station = np.asarray(data["station_id"], dtype=np.int64)
if np.any((station < 0) | (station >= len(STATIONS))):
raise ValueError("station_id must be in [0, 11]")
numeric = np.column_stack([np.asarray(data[name], dtype=np.float32)
for name in BASE_FEATURES])
one_hot = np.eye(len(STATIONS), dtype=np.float32)[station]
result = np.concatenate([numeric, one_hot], axis=1)
if result.shape[1] != len(FEATURE_NAMES) or not np.isfinite(result).all():
raise ValueError("features must be finite and follow the 21-column protocol")
return result.astype(np.float32)
@dataclass
class TreeConfig:
max_depth: int = 8
min_samples_leaf: int = 8
max_features: object = "sqrt"
split_candidates: int = 12
class RegressionTree:
def __init__(self, config, seed=0):
self.config = config
self.rng = np.random.default_rng(seed)
self.nodes = []
def _feature_count(self, total):
value = self.config.max_features
if value == "sqrt":
return max(1, int(np.sqrt(total)))
if isinstance(value, float):
return max(1, min(total, int(np.ceil(total * value))))
return max(1, min(total, int(value)))
def fit(self, x, y):
self.nodes = []
self._grow(np.asarray(x, np.float32), np.asarray(y, np.float32),
np.arange(len(x)), 0)
return self
def _grow(self, x, y, indices, depth):
node_id = len(self.nodes)
self.nodes.append(None)
value = y[indices].mean(0).astype(np.float32)
minimum = int(self.config.min_samples_leaf)
if depth >= self.config.max_depth or len(indices) < 2 * minimum:
self.nodes[node_id] = {"value": value}
return node_id
best = None
features = self.rng.choice(x.shape[1], self._feature_count(x.shape[1]), replace=False)
for feature in features:
values = x[indices, feature]
low, high = float(values.min()), float(values.max())
if low >= high:
continue
for threshold in self.rng.uniform(low, high, self.config.split_candidates):
mask = values <= threshold
left, right = indices[mask], indices[~mask]
if len(left) < minimum or len(right) < minimum:
continue
loss = (np.square(y[left] - y[left].mean(0)).sum() +
np.square(y[right] - y[right].mean(0)).sum())
if best is None or loss < best[0]:
best = (float(loss), int(feature), float(threshold), left, right)
if best is None:
self.nodes[node_id] = {"value": value}
return node_id
_, feature, threshold, left, right = best
self.nodes[node_id] = {"feature": feature, "threshold": threshold,
"left": self._grow(x, y, left, depth + 1),
"right": self._grow(x, y, right, depth + 1)}
return node_id
def predict(self, x):
output = []
for row in np.asarray(x, np.float32):
node = self.nodes[0]
while "value" not in node:
branch = "left" if row[node["feature"]] <= node["threshold"] else "right"
node = self.nodes[node[branch]]
output.append(node["value"])
return np.asarray(output, dtype=np.float32)
class MultiOutputRandomForest:
def __init__(self, n_trees=12, seed=2019, **tree_options):
self.n_trees, self.seed = int(n_trees), int(seed)
self.tree_config = TreeConfig(**tree_options)
self.trees = []
def fit(self, x, y, tree_indices=None):
x, y = np.asarray(x, np.float32), np.asarray(y, np.float32)
indices = range(self.n_trees) if tree_indices is None else tree_indices
self.trees = []
for index in indices:
rng = np.random.default_rng(self.seed + 7919 * (index + 1))
sample = rng.integers(0, len(x), len(x))
tree = RegressionTree(self.tree_config, self.seed + 104729 * (index + 1))
self.trees.append((int(index), tree.fit(x[sample], y[sample])))
return self
def predict(self, x):
if not self.trees:
raise RuntimeError("forest is not fitted")
return np.mean([tree.predict(x) for _, tree in self.trees], axis=0, dtype=np.float32)
def state_dict(self):
return {"n_trees": self.n_trees, "seed": self.seed,
"tree_config": vars(self.tree_config),
"trees": [(index, tree.nodes) for index, tree in self.trees]}
@classmethod
def from_state_dict(cls, state):
model = cls(state["n_trees"], state["seed"], **state["tree_config"])
for index, nodes in state["trees"]:
tree = RegressionTree(model.tree_config)
tree.nodes = nodes
model.trees.append((index, tree))
return model
class MeteoNormRF:
def __init__(self, forest):
self.forest = forest
self.statistics = {}
def fit(self, x, y, tree_indices=None):
x, y = np.asarray(x, np.float32), np.asarray(y, np.float32)
self.statistics = {"x_mean": x.mean(0), "x_std": np.maximum(x.std(0), 1e-6),
"y_mean": y.mean(0), "y_std": np.maximum(y.std(0), 1e-6)}
self.forest.fit((x - self.statistics["x_mean"]) / self.statistics["x_std"],
(y - self.statistics["y_mean"]) / self.statistics["y_std"], tree_indices)
return self
def predict(self, x):
x = np.asarray(x, np.float32)
value = self.forest.predict((x - self.statistics["x_mean"]) / self.statistics["x_std"])
return np.maximum(value * self.statistics["y_std"] + self.statistics["y_mean"], 0.0)
def state_dict(self):
return {"forest": self.forest.state_dict(), "statistics": self.statistics}
@classmethod
def from_state_dict(cls, state):
model = cls(MultiOutputRandomForest.from_state_dict(state["forest"]))
model.statistics = state["statistics"]
return model
def merge_states(states):
base = states[0]
trees = [item for state in states for item in state["forest"]["trees"]]
base["forest"]["trees"] = sorted(trees, key=lambda item: item[0])
return base
def save_checkpoint(path, model, metadata):
with open(path, "wb") as stream:
pickle.dump({"format_version": FORMAT_VERSION, "model_name": MODEL_NAME,
"model": model.state_dict(), "metadata": metadata}, stream)
def load_checkpoint(path):
with open(path, "rb") as stream:
state = pickle.load(stream)
if state.get("format_version") != FORMAT_VERSION or state.get("model_name") != MODEL_NAME:
raise ValueError("incompatible checkpoint")
return MeteoNormRF.from_state_dict(state["model"]), state["metadata"]
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