Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
License:
File size: 2,708 Bytes
0832232 | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
def main() -> int:
parser = argparse.ArgumentParser(description="Train a simple UFO-MNIST baseline model.")
parser.add_argument("--dataset", type=Path, default=Path("data/ufo_mnist_v1/ufo_mnist_28x28.npz"))
parser.add_argument("--output", type=Path, default=Path("data/ufo_mnist_v1/baseline_metrics.json"))
parser.add_argument("--seed", type=int, default=1337)
args = parser.parse_args()
data = np.load(args.dataset)
class_names = [str(name) for name in data["class_names"]]
train_x = data["train_images"].reshape(data["train_images"].shape[0], -1).astype(np.float32) / 255.0
test_x = data["test_images"].reshape(data["test_images"].shape[0], -1).astype(np.float32) / 255.0
train_y = data["train_labels"].astype(int)
test_y = data["test_labels"].astype(int)
model = make_pipeline(
StandardScaler(),
LogisticRegression(
C=1.0,
max_iter=2000,
random_state=args.seed,
solver="lbfgs",
),
)
model.fit(train_x, train_y)
pred = model.predict(test_x)
report = classification_report(test_y, pred, target_names=class_names, output_dict=True, zero_division=0)
metrics = {
"model": "StandardScaler + multinomial LogisticRegression",
"dataset": str(args.dataset),
"seed": args.seed,
"train_examples": int(train_x.shape[0]),
"test_examples": int(test_x.shape[0]),
"accuracy": float(accuracy_score(test_y, pred)),
"macro_f1": float(f1_score(test_y, pred, average="macro")),
"weighted_f1": float(f1_score(test_y, pred, average="weighted")),
"classification_report": report,
"confusion_matrix": confusion_matrix(test_y, pred).tolist(),
"class_names": class_names,
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(metrics, indent=2, sort_keys=True) + "\n", encoding="utf-8")
print(f"model: {metrics['model']}")
print(f"train_examples: {metrics['train_examples']}")
print(f"test_examples: {metrics['test_examples']}")
print(f"accuracy: {metrics['accuracy']:.4f}")
print(f"macro_f1: {metrics['macro_f1']:.4f}")
print(f"weighted_f1: {metrics['weighted_f1']:.4f}")
print(f"wrote: {args.output}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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