def test_evaluate_multiclass_classification(self):
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']
configuration_space = get_configuration_space(D.info,
include_estimators = ['ridge'],
include_preprocessors = ['select_rates'])
err = np.zeros([N_TEST_RUNS])
for i in range(N_TEST_RUNS):
print "Evaluate configuration: %d; result:" % i,
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration)
if not self._fit(evaluator):
print
continue
err[i] = evaluator.predict()
print err[i]
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
print "Number of times it was worse than random guessing:" + str(np.sum(err > 1))
def test_5000_classes(self):
weights = ([0.0002] * 4750) + ([0.0001] * 250)
X, Y = sklearn.datasets.make_classification(n_samples=10000,
n_features=20,
n_classes=5000,
n_clusters_per_class=1,
n_informative=15,
n_redundant=5,
n_repeated=0,
weights=weights,
flip_y=0,
class_sep=1.0,
hypercube=True,
shift=None,
scale=1.0,
shuffle=True,
random_state=1)
self.assertEqual(250, np.sum(np.bincount(Y) == 1))
D = Dummy()
D.info = {'metric': 'r2_metric', 'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False, 'target_num': 1}
D.data = {'X_train': X, 'Y_train': Y,
'X_valid': X, 'X_test': X}
D.feat_type = ['numerical'] * 5000
configuration_space = get_configuration_space(D.info,
include_estimators=['extra_trees'],
include_preprocessors=['no_preprocessing'])
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration)
evaluator.fit()
def test_with_abalone(self):
dataset = "abalone"
dataset_dir = os.path.join(os.path.dirname(__file__), ".datasets")
D = CompetitionDataManager(dataset, dataset_dir)
configuration_space = get_configuration_space(
D.info,
include_estimators=['extra_trees'],
include_preprocessors=['no_preprocessing'])
errors = []
for i in range(N_TEST_RUNS):
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = NestedCVEvaluator(D_,
configuration,
inner_cv_folds=2,
outer_cv_folds=2)
if not self._fit(evaluator):
print
continue
err = evaluator.predict()
self.assertLess(err, 0.99)
self.assertTrue(np.isfinite(err))
errors.append(err)
# This is a reasonable bound
self.assertEqual(10, len(errors))
self.assertLess(min(errors), 0.77)
def test_evaluate_multiclass_classification(self):
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']
configuration_space = get_configuration_space(D.info,
include_estimators=['ridge'],
include_preprocessors=['select_rates'])
err = np.zeros([N_TEST_RUNS])
num_models_better_than_random = 0
for i in range(N_TEST_RUNS):
print "Evaluate configuration: %d; result:" % i,
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = CVEvaluator(D_, configuration,
with_predictions=True)
if not self._fit(evaluator):
print
continue
e_, Y_optimization_pred, Y_valid_pred, Y_test_pred = \
evaluator.predict()
err[i] = e_
print err[i], configuration['classifier']
num_targets = len(np.unique(Y_train))
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
# Test that ten models were trained
self.assertEqual(len(evaluator.models), 10)
self.assertEqual(Y_optimization_pred.shape[0], Y_train.shape[0])
self.assertEqual(Y_optimization_pred.shape[1], num_targets)
self.assertEqual(Y_valid_pred.shape[0], Y_valid.shape[0])
self.assertEqual(Y_valid_pred.shape[1], num_targets)
self.assertEqual(Y_test_pred.shape[0], Y_test.shape[0])
self.assertEqual(Y_test_pred.shape[1], num_targets)
# Test some basic statistics of the dataset
if err[i] < 0.5:
self.assertTrue(0.3 < Y_valid_pred.mean() < 0.36666)
self.assertGreaterEqual(Y_valid_pred.std(), 0.01)
self.assertTrue(0.3 < Y_test_pred.mean() < 0.36666)
self.assertGreaterEqual(Y_test_pred.std(), 0.01)
num_models_better_than_random += 1
self.assertGreater(num_models_better_than_random, 5)
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
def test_predict_proba_binary_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
eliminate_class_two = Y_train != 2
X_train = X_train[eliminate_class_two]
Y_train = Y_train[eliminate_class_two]
eliminate_class_two = Y_test != 2
X_test = X_test[eliminate_class_two]
Y_test = Y_test[eliminate_class_two]
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
class Dummy2(object):
def predict_proba(self, y, batch_size=200):
return np.array([[0.1, 0.9], [0.7, 0.3]])
model = Dummy2()
task_type = BINARY_CLASSIFICATION
D = Dummy()
D.info = {'metric': 'bac_metric', 'task': task_type,
'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']
configuration_space = get_configuration_space(
D.info, include_estimators=['ridge'],
include_preprocessors=['select_rates'])
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration)
pred = evaluator.predict_proba(None, model, task_type)
expected = [[0.9], [0.3]]
for i in range(len(expected)):
self.assertEqual(expected[i], pred[i])
def test_with_abalone(self):
dataset = "abalone"
dataset_dir = os.path.join(os.path.dirname(__file__), ".datasets")
D = CompetitionDataManager(dataset, dataset_dir)
configuration_space = get_configuration_space(D.info,
include_estimators=['extra_trees'],
include_preprocessors=['no_preprocessing'])
errors = []
for i in range(N_TEST_RUNS):
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = CVEvaluator(D_, configuration, cv_folds=5)
if not self._fit(evaluator):
print
continue
err = evaluator.predict()
self.assertLess(err, 0.99)
self.assertTrue(np.isfinite(err))
errors.append(err)
# This is a reasonable bound
self.assertEqual(10, len(errors))
self.assertLess(min(errors), 0.77)
def _fit(self, D):
# TODO: check that data and task definition fit together!
self.metric_ = D.info['metric']
self.task_ = D.info['task']
self.target_num_ = D.info['target_num']
# Set environment variable:
seed = os.environ.get("AUTOSKLEARN_SEED")
if seed is not None and int(seed) != self.seed:
raise ValueError("It seems you have already started an instance "
"of AutoSklearn in this thread.")
else:
os.environ["AUTOSKLEARN_SEED"] = str(self.seed)
# == Split dataset and store Data for the ensemble script
X_train, X_ensemble, Y_train, Y_ensemble = split_data.split_data(
D.data['X_train'], D.data['Y_train'])
true_labels_ensemble_filename = os.path.join(self.tmp_dir,
"true_labels_ensemble.npy")
true_labels_ensemble_lock = true_labels_ensemble_filename + ".lock"
with lockfile.LockFile(true_labels_ensemble_lock):
if not os.path.exists(true_labels_ensemble_filename):
np.save(true_labels_ensemble_filename, Y_ensemble)
del X_train, X_ensemble, Y_train, Y_ensemble
time_needed_to_load_data = self.stopwatch_.wall_elapsed(self.basename_)
time_left_after_reading = max(0, self.time_left_for_this_task -
time_needed_to_load_data)
self.logger.info("Remaining time after reading %s %5.2f sec" %
(self.basename_, time_left_after_reading))
self.stopwatch_.stop_task("LoadData")
# == Calculate metafeatures
self.stopwatch_.start_task("CalculateMetafeatures")
categorical = [True if feat_type.lower() in ["categorical"] else False
for feat_type in D.feat_type]
if self.initial_configurations_via_metalearning <= 0:
ml = None
elif D.info["task"] in \
[MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION]:
ml = metalearning.MetaLearning()
self.logger.debug("Start calculating metafeatures for %s" %
self.basename_)
ml.calculate_metafeatures_with_labels(D.data["X_train"],
D.data["Y_train"],
categorical=categorical,
dataset_name=self.basename_)
else:
ml = None
self.logger.critical("Metafeatures not calculated")
self.stopwatch_.stop_task("CalculateMetafeatures")
self.logger.debug("Calculating Metafeatures (categorical attributes) took %5.2f" % self.stopwatch_.wall_elapsed("CalculateMetafeatures"))
self.stopwatch_.start_task("OneHot")
D.perform1HotEncoding()
self.ohe_ = D.encoder_
self.stopwatch_.stop_task("OneHot")
# == Pickle the data manager
self.stopwatch_.start_task("StoreDatamanager")
data_manager_path = os.path.join(self.tmp_dir,
self.basename_ + "_Manager.pkl")
data_manager_lockfile = data_manager_path + ".lock"
with lockfile.LockFile(data_manager_lockfile):
if not os.path.exists(data_manager_path):
pickle.dump(D,
open(data_manager_path, 'w'), protocol=-1)
self.logger.debug("Pickled Datamanager at %s" %
data_manager_path)
else:
self.logger.debug("Data manager already presend at %s" %
data_manager_path)
self.stopwatch_.stop_task("StoreDatamanager")
# = Create a searchspace
self.stopwatch_.start_task("CreateConfigSpace")
configspace_path = os.path.join(self.tmp_dir, "space.pcs")
self.configuration_space = paramsklearn.get_configuration_space(
D.info)
self.configuration_space_created_hook()
sp_string = pcs_parser.write(self.configuration_space)
configuration_space_lockfile = configspace_path + ".lock"
with lockfile.LockFile(configuration_space_lockfile):
if not os.path.exists(configspace_path):
with open(configspace_path, "w") as fh:
fh.write(sp_string)
self.logger.debug("Configuration space written to %s" %
configspace_path)
else:
self.logger.debug("Configuration space already present at %s" %
configspace_path)
self.stopwatch_.stop_task("CreateConfigSpace")
if ml is None:
initial_configurations = []
elif D.info["task"]in \
[MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION]:
self.stopwatch_.start_task("CalculateMetafeaturesEncoded")
ml.calculate_metafeatures_encoded_labels(X_train=D.data["X_train"],
Y_train=D.data["Y_train"],
categorical=[False] * D.data["X_train"].shape[0],
dataset_name=self.basename_)
self.stopwatch_.stop_task("CalculateMetafeaturesEncoded")
self.logger.debug(
"Calculating Metafeatures (encoded attributes) took %5.2fsec" %
self.stopwatch_.wall_elapsed("CalculateMetafeaturesEncoded"))
self.logger.debug(ml._metafeatures_labels.__repr__(verbosity=2))
self.logger.debug(ml._metafeatures_encoded_labels.__repr__(verbosity=2))
self.stopwatch_.start_task("InitialConfigurations")
try:
initial_configurations = ml.create_metalearning_string_for_smac_call(
self.configuration_space, self.basename_, self.metric_,
self.task_, True if D.info['is_sparse'] == 1 else False,
self.initial_configurations_via_metalearning, self.metadata_directory)
except Exception as e:
import traceback
self.logger.error(str(e))
self.logger.error(traceback.format_exc())
initial_configurations = []
self.stopwatch_.stop_task("InitialConfigurations")
self.logger.debug("Initial Configurations: (%d)", len(initial_configurations))
for initial_configuration in initial_configurations:
self.logger.debug(initial_configuration)
self.logger.debug("Looking for initial configurations took %5.2fsec" %
self.stopwatch_.wall_elapsed("InitialConfigurations"))
self.logger.info(
"Time left for %s after finding initial configurations: %5.2fsec" %
(self.basename_, self.time_left_for_this_task -
self.stopwatch_.wall_elapsed(self.basename_)))
else:
initial_configurations = []
self.logger.critical("Metafeatures encoded not calculated")
# == Set up a directory where all the trained models will be pickled to
if self.keep_models:
self.model_directory_ = os.path.join(self.tmp_dir,
"models_%d" % self.seed)
os.mkdir(self.model_directory_)
self.ensemble_indices_directory_ = os.path.join(self.tmp_dir,
"ensemble_indices_%d" % self.seed)
os.mkdir(self.ensemble_indices_directory_)
# == RUN SMAC
self.stopwatch_.start_task("runSmac")
# = Create an empty instance file
instance_file = os.path.join(self.tmp_dir, "instances.txt")
instance_file_lock = instance_file + ".lock"
with lockfile.LockFile(instance_file_lock):
if not os.path.exists(instance_file_lock):
with open(instance_file, "w") as fh:
fh.write("holdout")
self.logger.debug("Created instance file %s" % instance_file)
else:
self.logger.debug("Instance file already present at %s" % instance_file)
# = Start SMAC
time_left_for_smac = max(0, self.time_left_for_this_task - (
self.stopwatch_.wall_elapsed(self.basename_)))
self.logger.debug("Start SMAC with %5.2fsec time left" % time_left_for_smac)
proc_smac, smac_call = \
submit_process.run_smac(dataset_name=self.basename_,
dataset=data_manager_path,
tmp_dir=self.tmp_dir,
searchspace=configspace_path,
instance_file=instance_file,
limit=time_left_for_smac,
cutoff_time=self.per_run_time_limit,
initial_challengers=initial_configurations,
memory_limit=self.ml_memory_limit,
seed=self.seed)
self.logger.debug(smac_call)
self.stopwatch_.stop_task("runSmac")
# == RUN ensemble builder
self.stopwatch_.start_task("runEnsemble")
time_left_for_ensembles = max(0, self.time_left_for_this_task - (
self.stopwatch_.wall_elapsed(self.basename_)))
self.logger.debug("Start Ensemble with %5.2fsec time left" % time_left_for_ensembles)
proc_ensembles = \
submit_process.run_ensemble_builder(tmp_dir=self.tmp_dir,
dataset_name=self.basename_,
task_type=self.task_,
metric=self.metric_,
limit=time_left_for_ensembles,
output_dir=self.output_dir,
ensemble_size=self.ensemble_size,
ensemble_nbest=self.ensemble_nbest,
seed=self.seed,
ensemble_indices_output_dir=self.ensemble_indices_directory_)
self.stopwatch_.stop_task("runEnsemble")
del D
if self.queue is not None:
self.queue.put([time_needed_to_load_data, data_manager_path,
proc_smac, proc_ensembles])
else:
proc_smac.wait()
proc_ensembles.wait()
# Delete AutoSklearn environment variable
del os.environ["AUTOSKLEARN_SEED"]
return self
def test_metalearning(self):
dataset_name = 'digits'
initial_challengers = {
'acc_metric': [
"--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.400363929326' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '319' "
"-balancing:strategy 'none' "
"-classifier 'adaboost' "
"-imputation:strategy 'most_frequent' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'auc_metric': [
"--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.966883114819' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '412' "
"-balancing:strategy 'weighting' "
"-classifier 'adaboost' "
"-imputation:strategy 'median' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'bac_metric': [
"--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.400363929326' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '319' "
"-balancing:strategy 'none' "
"-classifier 'adaboost' "
"-imputation:strategy 'most_frequent' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'f1_metric': [
"--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.966883114819' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '412' "
"-balancing:strategy 'weighting' "
"-classifier 'adaboost' "
"-imputation:strategy 'median' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'pac_metric': [
"--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.400363929326' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '319' "
"-balancing:strategy 'none' "
"-classifier 'adaboost' "
"-imputation:strategy 'most_frequent' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
]
}
for metric in initial_challengers:
configuration_space = get_configuration_space(
{
'metric': metric,
'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False
},
include_preprocessors=['no_preprocessing'])
X_train, Y_train, X_test, Y_test = get_dataset(dataset_name)
categorical = [False] * X_train.shape[1]
ml = MetaLearning()
ml.calculate_metafeatures_with_labels(X_train, Y_train,
categorical, dataset_name)
ml.calculate_metafeatures_encoded_labels(X_train, Y_train,
categorical, dataset_name)
initial_configuration_strings_for_smac = \
ml.create_metalearning_string_for_smac_call(
configuration_space, dataset_name, metric,
MULTICLASS_CLASSIFICATION, False, 1, None)
print metric
self.assertEqual(initial_challengers[metric],
initial_configuration_strings_for_smac)
def test_metalearning(self):
dataset_name = 'digits'
initial_challengers = {'acc_metric':
["--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.400363929326' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '319' "
"-balancing:strategy 'none' "
"-classifier 'adaboost' "
"-imputation:strategy 'most_frequent' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'auc_metric':
["--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.966883114819' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '412' "
"-balancing:strategy 'weighting' "
"-classifier 'adaboost' "
"-imputation:strategy 'median' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'bac_metric':
["--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.400363929326' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '319' "
"-balancing:strategy 'none' "
"-classifier 'adaboost' "
"-imputation:strategy 'most_frequent' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'f1_metric':
["--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.966883114819' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '412' "
"-balancing:strategy 'weighting' "
"-classifier 'adaboost' "
"-imputation:strategy 'median' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
],
'pac_metric':
["--initial-challengers \" "
"-adaboost:algorithm 'SAMME.R' "
"-adaboost:learning_rate '0.400363929326' "
"-adaboost:max_depth '5' "
"-adaboost:n_estimators '319' "
"-balancing:strategy 'none' "
"-classifier 'adaboost' "
"-imputation:strategy 'most_frequent' "
"-preprocessor 'no_preprocessing' "
"-rescaling:strategy 'min/max'\""
]}
for metric in initial_challengers:
configuration_space = get_configuration_space(
{'metric': metric,
'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False}, include_preprocessors=['no_preprocessing'])
X_train, Y_train, X_test, Y_test = get_dataset(dataset_name)
categorical = [False] * X_train.shape[1]
ml = MetaLearning()
ml.calculate_metafeatures_with_labels(
X_train, Y_train, categorical, dataset_name)
ml.calculate_metafeatures_encoded_labels(
X_train, Y_train, categorical, dataset_name)
initial_configuration_strings_for_smac = \
ml.create_metalearning_string_for_smac_call(
configuration_space, dataset_name, metric,
MULTICLASS_CLASSIFICATION, False, 1, None)
print metric
self.assertEqual(initial_challengers[metric],
initial_configuration_strings_for_smac)
def main(dataset_info, mode, seed, params, mode_args=None):
"""This command line interface has three different operation modes:
* CV: useful for the Tweakathon
* 1/3 test split: useful to evaluate a configuration
* cv on 2/3 train split: useful to optimize hyperparameters in a training
mode before testing a configuration on the 1/3 test split.
It must by no means be used for the Auto part of the competition!
"""
if mode != "test":
num_run = get_new_run_num()
for key in params:
try:
params[key] = int(params[key])
except:
try:
params[key] = float(params[key])
except:
pass
if seed is not None:
seed = int(float(seed))
else:
seed = 1
output_dir = os.getcwd()
D = store_and_or_load_data(dataset_info=dataset_info, outputdir=output_dir)
cs = get_configuration_space(D.info)
configuration = configuration_space.Configuration(cs, params)
metric = D.info['metric']
global evaluator
# Train/test split
if mode == 'holdout':
evaluator = HoldoutEvaluator(D,
configuration,
with_predictions=True,
all_scoring_functions=True,
output_y_test=True,
seed=seed,
num_run=num_run)
evaluator.fit()
signal.signal(15, empty_signal_handler)
evaluator.finish_up()
model_directory = os.path.join(os.getcwd(), "models_%d" % seed)
if os.path.exists(model_directory):
model_filename = os.path.join(model_directory,
"%s.model" % num_run)
with open(model_filename, "w") as fh:
pickle.dump(evaluator.model, fh, -1)
elif mode == 'test':
evaluator = TestEvaluator(D,
configuration,
all_scoring_functions=True,
seed=seed)
evaluator.fit()
scores = evaluator.predict()
duration = time.time() - evaluator.starttime
score = scores[metric]
additional_run_info = ";".join(
["%s: %s" % (m_, value) for m_, value in scores.items()])
additional_run_info += ";" + "duration: " + str(duration)
print "Result for ParamILS: %s, %f, 1, %f, %d, %s" % (
"SAT", abs(duration), score, evaluator.seed, additional_run_info)
# CV on the whole training set
elif mode == 'cv':
evaluator = CVEvaluator(D,
configuration,
with_predictions=True,
all_scoring_functions=True,
output_y_test=True,
cv_folds=mode_args['folds'],
seed=seed,
num_run=num_run)
evaluator.fit()
signal.signal(15, empty_signal_handler)
evaluator.finish_up()
elif mode == 'partial_cv':
evaluator = CVEvaluator(D,
configuration,
all_scoring_functions=True,
cv_folds=mode_args['folds'],
seed=seed,
num_run=num_run)
evaluator.partial_fit(mode_args['fold'])
scores = evaluator.predict()
duration = time.time() - evaluator.starttime
score = scores[metric]
additional_run_info = ";".join(
["%s: %s" % (m_, value) for m_, value in scores.items()])
additional_run_info += ";" + "duration: " + str(duration)
print "Result for ParamILS: %s, %f, 1, %f, %d, %s" % (
"SAT", abs(duration), score, evaluator.seed, additional_run_info)
elif mode == 'nested-cv':
evaluator = NestedCVEvaluator(D,
configuration,
with_predictions=True,
inner_cv_folds=mode_args['inner_folds'],
outer_cv_folds=mode_args['outer_folds'],
all_scoring_functions=True,
output_y_test=True,
seed=seed,
num_run=num_run)
evaluator.fit()
signal.signal(15, empty_signal_handler)
evaluator.finish_up()
else:
raise ValueError("Must choose a legal mode.")
