def test_file_output(self):
output_dir = os.path.join(os.getcwd(), ".test")
try:
shutil.rmtree(output_dir)
except:
pass
X_train, Y_train, X_test, Y_test = get_dataset('iris')
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {'metric': 'bac_metric', 'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False, 'target_num': 3}
D.data = {'X_train': X_train, 'Y_train': Y_train,
'X_valid': X_valid, 'X_test': X_test}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
D.basename = "test"
configuration_space = get_configuration_space(D.info)
while True:
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration,
with_predictions=True,
all_scoring_functions=True,
output_dir=output_dir,
output_y_test=True)
if not self._fit(evaluator):
print
continue
evaluator.predict()
evaluator.file_output()
self.assertTrue(os.path.exists(os.path.join(output_dir,
"y_optimization.npy")))
break
