keep_configurations = tuple(
[tuple(kc.split('=')) for kc in keep_configurations])
else:
keep_configurations = None
meta_base = MetaBase(task_files_list, experiments_list,
keep_configurations)
metafeatures = meta_base.get_all_train_metafeatures_as_pandas()
runs = meta_base.get_all_runs()
# This can print the best hyperparameters of every dataset
# for dataset in runs:
# print dataset, sorted(runs[dataset], key=lambda t: t.result)[0]
rf = LearnedDistanceRF(**params)
X, Y = rf._create_dataset(metafeatures, runs)
import cPickle
with open("test.pkl", "w") as fh:
cPickle.dump((X, Y, metafeatures), fh, -1)
print "Metafeatures", metafeatures.shape
print "X", X.shape, np.isfinite(X).all().all()
print "Y", Y.shape, np.isfinite(Y).all()
metafeature_sets = Queue.Queue()
if 'forward_selection' in args:
used_metafeatures = []
metafeature_performance = []
print "Starting forward selection ",
i = 0
keep_configurations = keep_configurations.split(',')
keep_configurations = tuple(
[tuple(kc.split('=')) for kc in keep_configurations])
else:
keep_configurations = None
meta_base = MetaBase(task_files_list, experiments_list, keep_configurations)
metafeatures = meta_base.get_all_train_metafeatures_as_pandas()
runs = meta_base.get_all_runs()
# This can print the best hyperparameters of every dataset
# for dataset in runs:
# print dataset, sorted(runs[dataset], key=lambda t: t.result)[0]
rf = LearnedDistanceRF(**params)
X, Y = rf._create_dataset(metafeatures, runs)
import cPickle
with open("test.pkl", "w") as fh:
cPickle.dump((X, Y, metafeatures), fh, -1)
print "Metafeatures", metafeatures.shape
print "X", X.shape, np.isfinite(X).all().all()
print "Y", Y.shape, np.isfinite(Y).all()
metafeature_sets = Queue.Queue()
if 'forward_selection' in args:
used_metafeatures = []
metafeature_performance = []
print "Starting forward selection ",
i = 0
