def main():
dataset = 'koelstra-approach'
class_id = 0
ground_truth_variable_count = 3
attr_count = attribute_counts[dataset][class_id]
# attr_count = None
# Load our dataset into memory
Xs, ys = libcv._load_full_dataset(dataset, class_id, ground_truth_variable_count)
# Perform cross-validation on the dataset, using RFE to achieve the target attr_count
actual, predicted = cross_validate_combined_dataset(Xs, ys, attr_count, threaded=False)
print_report(actual, attr_count, class_id, dataset, predicted)
def main():
parser = argparse.ArgumentParser(description='Perform cross-validation on the dataset, cross-validating the behavior of one specific subject.')
parser.add_argument('dataset', help='name of the dataset folder')
parser.add_argument('class_id', type=int, help='target class id, 0-2')
parser.add_argument('ground_truth_count', type=int, help='number of ground truth values, 1-3')
parser.add_argument('rfe', type=int, default=1, help='perform RFE?')
args = parser.parse_args()
print args
attr_count = None if args.rfe == 0 else attribute_counts[args.dataset][args.class_id]
# Load our dataset into memory
Xs, ys = libcv._load_full_dataset(args.dataset, args.class_id, args.ground_truth_count)
# Perform cross-validation on the dataset, using RFE to achieve the target attr_count
actual, predicted = cross_validate_combined_dataset(Xs, ys, attr_count, threaded=False)
print_report(actual, attr_count, args.class_id, args.dataset, predicted)
from sklearn.grid_search import GridSearchCV
parser = argparse.ArgumentParser(description='Perform cross-validation on the dataset, cross-validating the behavior of one specific subject.')
parser.add_argument('dataset', help='name of the dataset folder')
parser.add_argument('class_id', type=int, help='target class id, 0-2')
parser.add_argument('ground_truth_count', type=int, help='number of ground truth values, 1-3')
args = parser.parse_args()
print args
##############################################################################
# Load and prepare data set
#
# dataset for grid search
Xs, ys = libcv._load_full_dataset(args.dataset, args.class_id, args.ground_truth_count)
X = to_float(flatten(Xs))
y = flatten(ys)
# It is usually a good idea to scale the data for SVM training.
# We are cheating a bit in this example in scaling all of the data,
# instead of fitting the transformation on the training set and
# just applying it on the test set.
scaler = StandardScaler()
X = scaler.fit_transform(X)
##############################################################################
# Train classifier
#
# For an initial search, a logarithmic grid with basis
