**autonet_config)
# Test logging cont.
autonet.pipeline[LogFunctionsSelector.get_name()].add_log_function(
name=test_predictions_for_ensemble.__name__,
log_function=test_predictions_for_ensemble(autonet, X_test, y_test),
loss_transform=False)
autonet.pipeline[LogFunctionsSelector.get_name()].add_log_function(
name=test_result_ens.__name__,
log_function=test_result_ens(autonet, X_test, y_test))
autonet.pipeline[BaselineTrainer.get_name()].add_test_data(X_test)
print(autonet.get_current_autonet_config())
fit_results = autonet.fit(X_train, y_train,
**autonet.get_current_autonet_config())
score = autonet.score(X_test, y_test) if y_test is not None else None
print("Test score:", score)
# Write to json
results = dict()
results["run_id"] = int(args.run_id)
results["test_score"] = score
results["seed"] = int(seed)
with open(logdir + "/results_dump.json", "w") as f:
json.dump(results, f)
__author__ = "Max Dippel, Michael Burkart and Matthias Urban"
__version__ = "0.0.1"
__license__ = "BSD"
import os, sys
sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
from autoPyTorch import AutoNetClassification, AutoNetEnsemble
from autoPyTorch.data_management.data_manager import DataManager
# Note: You can write your own datamanager! Call fit with respective train, valid data (numpy matrices)
dm = DataManager()
dm.generate_classification(num_classes=3, num_features=21, num_samples=1500)
# Note: every parameter has a default value, you do not have to specify anything. The given parameter allow a fast test.
autonet = AutoNetEnsemble(AutoNetClassification,
budget_type='epochs',
min_budget=1,
max_budget=9,
num_iterations=1,
log_level='debug')
res = autonet.fit(X_train=dm.X,
Y_train=dm.Y,
cross_validator="k_fold",
cross_validator_args={"n_splits": 3},
validation_split=0.2,
ensemble_only_consider_n_best=3)
print(res)
print("Score:", autonet.score(X_test=dm.X_train, Y_test=dm.Y_train))