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_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 = HoldoutEvaluator(D_, configuration)
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_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 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.")
