]
# Initialization
if os.path.isfile('./output/result.csv'):
os.remove('./output/result.csv')
# Ok...
warnings.filterwarnings('ignore')
# Loop over datasets, then over encoders, and finally, over the models
for dataset_name in datasets:
X, y, fold_count = arff_loader.load(dataset_name)
non_numeric = list(X.select_dtypes(exclude=[np.number]).columns.values)
for encoder in encoders:
print("Encoding:", dataset_name, y.name, encoder.__class__.__name__)
folds, fit_encoder_time, score_encoder_time = train_encoder(
X, y, fold_count, encoder)
for model in models:
print('Evaluating:', dataset_name, encoder.__class__.__name__,
model.__class__.__name__)
scores, fit_model_time, score_model_time = train_model(
folds, model)
# Log into csv
result = pd.DataFrame([
dataset_name, y.name, encoder.__class__.__name__,
model.__class__.__name__, X.shape[1], folds[0][0].shape[1],
fit_encoder_time, score_encoder_time, fit_model_time,
score_model_time
] + list(scores)).T
if not os.path.isfile('./output/result.csv'):
result.to_csv('./output/result.csv',
# category_encoders.PolynomialEncoder(),
# category_encoders.SumEncoder(),
category_encoders.TargetEncoder(),
category_encoders.WOEEncoder()]
# Initialization
if os.path.isfile('./output/result.csv'):
os.remove('./output/result.csv')
# Loop over datasets, then over encoders, and finally, over the models
for dataset_name in datasets:
X, y, fold_count = arff_loader.load(dataset_name)
non_numeric = list(X.select_dtypes(exclude=[np.number]).columns.values)
for encoder in encoders:
print("Encoding:", dataset_name, y.name, encoder.__class__.__name__)
folds, fit_encoder_time, score_encoder_time = train_encoder(X, y, fold_count, encoder)
for model in models:
print('Evaluating:', dataset_name, encoder.__class__.__name__, model.__class__.__name__)
scores, fit_model_time, score_model_time = train_model(folds, model)
# Log into csv
result = pd.DataFrame([dataset_name, y.name, encoder.__class__.__name__, model.__class__.__name__, X.shape[1],
folds[0][0].shape[1], fit_encoder_time, score_encoder_time, fit_model_time, score_model_time]
+ list(scores)).T
if not os.path.isfile('./output/result.csv'):
result.to_csv('./output/result.csv',
header=['dataset', 'target', 'encoder', 'model', 'input_features', 'output_features', 'fit_encoder_time',
'score_encoder_time', 'fit_model_time', 'score_model_time', 'test_matthews', 'train_matthews',
'test_auc', 'train_auc', 'test_brier', 'train_brier'], index=False)
else:
result.to_csv('./output/result.csv', mode='a', header=False, index=False)