def test_file_output(self):
self.output_dir = os.path.join(os.getcwd(), '.test_file_output')
D = get_regression_datamanager()
D.name = 'test'
configuration_space = get_configuration_space(D.info)
configuration = configuration_space.sample_configuration()
backend_api = backend.create(self.output_dir, self.output_dir)
evaluator = HoldoutEvaluator(D,
backend_api,
configuration,
with_predictions=True,
all_scoring_functions=True,
output_y_test=True)
loss, Y_optimization_pred, Y_valid_pred, Y_test_pred = \
evaluator.fit_predict_and_loss()
evaluator.file_output(loss, Y_optimization_pred, Y_valid_pred,
Y_test_pred)
self.assertTrue(
os.path.exists(
os.path.join(self.output_dir, '.auto-sklearn',
'true_targets_ensemble.npy')))
def test_file_output(self):
output_dir = os.path.join(os.getcwd(), '.test')
try:
shutil.rmtree(output_dir)
except Exception:
pass
X_train, Y_train, X_test, Y_test = get_dataset('boston')
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': R2_METRIC,
'task': REGRESSION,
'is_sparse': False,
'label_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.name = '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):
continue
evaluator.predict()
evaluator.file_output()
self.assertTrue(
os.path.exists(
os.path.join(output_dir, '.auto-sklearn',
'true_targets_ensemble.npy')))
break
def test_file_output(self):
output_dir = os.path.join(os.getcwd(), '.test')
try:
shutil.rmtree(output_dir)
except Exception:
pass
X_train, Y_train, X_test, Y_test = get_dataset('boston')
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': R2_METRIC,
'task': REGRESSION,
'is_sparse': False,
'label_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.name = '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):
continue
evaluator.predict()
evaluator.file_output()
self.assertTrue(os.path.exists(os.path.join(
output_dir, '.auto-sklearn', 'true_targets_ensemble.npy')))
break
def test_file_output(self):
self.output_dir = os.path.join(os.getcwd(), '.test')
D = get_regression_datamanager()
D.name = 'test'
configuration_space = get_configuration_space(D.info)
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, self.output_dir, configuration,
with_predictions=True,
all_scoring_functions=True,
output_y_test=True)
loss, Y_optimization_pred, Y_valid_pred, Y_test_pred = \
evaluator.fit_predict_and_loss()
evaluator.file_output(loss, Y_optimization_pred, Y_valid_pred,
Y_test_pred)
self.assertTrue(os.path.exists(os.path.join(
self.output_dir, '.auto-sklearn', 'true_targets_ensemble.npy')))
def test_file_output(self):
output_dir = os.path.join(os.getcwd(), ".test")
try:
shutil.rmtree(output_dir)
except Exception:
pass
X_train, Y_train, X_test, Y_test = get_dataset("boston")
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": R2_METRIC, "task": REGRESSION, "is_sparse": False, "label_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.name = "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):
continue
evaluator.predict()
evaluator.file_output()
self.assertTrue(os.path.exists(os.path.join(output_dir, ".auto-sklearn", "true_targets_ensemble.npy")))
break
