mirror of
https://github.com/Doctorado-ML/bayesclass.git
synced 2025-08-16 08:05:57 +00:00
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
import sys
|
|
import time
|
|
from sklearn.model_selection import cross_val_score, StratifiedKFold
|
|
from benchmark import Discretizer
|
|
from bayesclass import TAN
|
|
import warnings
|
|
|
|
|
|
if len(sys.argv) < 2:
|
|
print("Usage: python3 example.py <dataset> [n_folds]")
|
|
exit(1)
|
|
# Warnings are not errors
|
|
warnings.simplefilter("ignore")
|
|
start = time.time()
|
|
random_state = 17
|
|
name = sys.argv[1]
|
|
n_folds = int(sys.argv[2]) if len(sys.argv) == 3 else 5
|
|
dt = Discretizer()
|
|
name_list = list(dt) if name == "all" else [name]
|
|
print(f"Accuracy in {n_folds} folds stratified crossvalidation")
|
|
for name in name_list:
|
|
dataset_start = time.time()
|
|
X, y = dt.load(name)
|
|
clf = TAN(random_state=random_state)
|
|
fit_params = dict(
|
|
features=dt.get_features(), class_name=dt.get_class_name(), head=0
|
|
)
|
|
kfold = StratifiedKFold(
|
|
n_splits=n_folds, shuffle=True, random_state=random_state
|
|
)
|
|
score = cross_val_score(clf, X, y, cv=kfold, fit_params=fit_params)
|
|
print(
|
|
f"{name:20s}{'.' * 10}{score.mean():9.7f} "
|
|
f"({time.time()-dataset_start:7.2f} seconds)"
|
|
)
|
|
print(f"Took {time.time()-start:.2f} seconds")
|