File size: 6,320 Bytes
426b48a | 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 | """Train and score the Bleu.js forest-cover XGBoost classifier.
Protocol, fixed before the test set is scored:
- Data: sklearn.datasets.fetch_covtype (UCI Covertype, full table)
- Labels shifted from 1..7 to 0..6
- Outer split: stratified 80/20, random_state=42. The 20% is scored once.
- Inner split: stratified 90/10 of the training rows, random_state=42,
used only to pick n_estimators with early stopping.
- Final fit: all training rows, n_estimators = best_iteration + 1,
sample_weight = balanced class weights from the training rows.
- Baselines, same test rows, no retuning: majority class; a depth-3
XGBoost with 50 trees and no class weights.
"""
import json
from pathlib import Path
import numpy as np
import xgboost as xgb
from sklearn.datasets import fetch_covtype
from sklearn.metrics import (
accuracy_score,
classification_report,
f1_score,
log_loss,
)
from sklearn.model_selection import train_test_split
from sklearn.utils.class_weight import compute_sample_weight
OUT = Path(__file__).resolve().parent
RANDOM_STATE = 42
CLASS_NAMES = [
"Spruce/Fir",
"Lodgepole Pine",
"Ponderosa Pine",
"Cottonwood/Willow",
"Aspen",
"Douglas-fir",
"Krummholz",
]
FEATURE_NAMES = [
"Elevation",
"Aspect",
"Slope",
"Horizontal_Distance_To_Hydrology",
"Vertical_Distance_To_Hydrology",
"Horizontal_Distance_To_Roadways",
"Hillshade_9am",
"Hillshade_Noon",
"Hillshade_3pm",
"Horizontal_Distance_To_Fire_Points",
*[f"Wilderness_Area_{i}" for i in range(4)],
*[f"Soil_Type_{i}" for i in range(40)],
]
def scores(y_true, pred, proba):
return {
"accuracy": float(accuracy_score(y_true, pred)),
"macro_f1": float(f1_score(y_true, pred, average="macro")),
"weighted_f1": float(f1_score(y_true, pred, average="weighted")),
"log_loss": float(log_loss(y_true, proba, labels=list(range(7)))),
}
def main():
bunch = fetch_covtype(as_frame=False)
X = np.asarray(bunch.data, dtype=np.float32)
y = np.asarray(bunch.target, dtype=np.int32) - 1
if X.shape[1] != len(FEATURE_NAMES) or y.min() != 0 or y.max() != 6:
raise SystemExit(f"Unexpected covertype shape {X.shape} labels {y.min()}..{y.max()}")
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=RANDOM_STATE, stratify=y
)
X_fit, X_val, y_fit, y_val = train_test_split(
X_train, y_train, test_size=0.1, random_state=RANDOM_STATE, stratify=y_train
)
common = dict(
max_depth=8,
learning_rate=0.1,
subsample=0.8,
colsample_bytree=0.8,
min_child_weight=5,
reg_lambda=1.0,
tree_method="hist",
objective="multi:softprob",
num_class=7,
random_state=RANDOM_STATE,
n_jobs=-1,
)
search = xgb.XGBClassifier(n_estimators=400, early_stopping_rounds=20, **common)
search.fit(
X_fit,
y_fit,
sample_weight=compute_sample_weight("balanced", y_fit),
eval_set=[(X_val, y_val)],
verbose=False,
)
n_trees = int(search.best_iteration) + 1
model = xgb.XGBClassifier(n_estimators=n_trees, **common)
model.fit(
X_train,
y_train,
sample_weight=compute_sample_weight("balanced", y_train),
verbose=False,
)
simple = xgb.XGBClassifier(
n_estimators=50,
max_depth=3,
learning_rate=0.1,
tree_method="hist",
objective="multi:softprob",
num_class=7,
random_state=RANDOM_STATE,
n_jobs=-1,
)
simple.fit(X_train, y_train, verbose=False)
proba = model.predict_proba(X_test)
pred = proba.argmax(axis=1)
simple_proba = simple.predict_proba(X_test)
simple_pred = simple_proba.argmax(axis=1)
majority = int(np.bincount(y_train).argmax())
majority_pred = np.full_like(y_test, majority)
majority_proba = np.zeros_like(proba)
majority_proba[:, majority] = 1.0
report = classification_report(
y_test, pred, target_names=CLASS_NAMES, output_dict=True, digits=4
)
gain = model.get_booster().get_score(importance_type="gain")
importance = sorted(
(
{"feature": FEATURE_NAMES[int(k[1:])], "gain": float(v)}
for k, v in gain.items()
),
key=lambda row: row["gain"],
reverse=True,
)
payload = {
"dataset": "UCI Covertype",
"source": "sklearn.datasets.fetch_covtype",
"citation": "Blackard, Jock A. Covertype. UCI Machine Learning Repository.",
"n_rows": int(X.shape[0]),
"n_features": int(X.shape[1]),
"class_names": CLASS_NAMES,
"label_encoding": "model class i is UCI cover type i+1",
"split": {
"test_size": 0.2,
"inner_val_size": 0.1,
"random_state": RANDOM_STATE,
"stratify": True,
},
"n_train": int(len(y_train)),
"n_test": int(len(y_test)),
"n_estimators_selected_on_inner_val": n_trees,
"hyperparameters": {**common, "n_estimators": n_trees, "sample_weight": "balanced"},
"xgboost_version": xgb.__version__,
"trainer_note": (
"XGBClassifier. bleujs.ml.HybridTrainer targets binary classification "
"with binary:logistic, so this multiclass model is trained with the "
"same library and a documented multiclass objective."
),
"test_metrics": scores(y_test, pred, proba),
"simple_xgboost_baseline": scores(y_test, simple_pred, simple_proba),
"majority_baseline": scores(y_test, majority_pred, majority_proba),
"per_class_f1": {name: float(report[name]["f1-score"]) for name in CLASS_NAMES},
"per_class_support": {name: int(report[name]["support"]) for name in CLASS_NAMES},
"feature_importance_gain": importance,
}
model.save_model(OUT / "model.json")
(OUT / "metrics.json").write_text(json.dumps(payload, indent=2))
print(json.dumps({
"n_trees": n_trees,
"test": payload["test_metrics"],
"simple": payload["simple_xgboost_baseline"],
"majority": payload["majority_baseline"],
"per_class_f1": payload["per_class_f1"],
}, indent=2))
if __name__ == "__main__":
main()
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