def main():
parser = argparse.ArgumentParser(
description='Multilayer neural network parser')
parser.add_argument('-d',
'--dataset',
help='The name (without extension) of the dataset',
required=True)
parser.add_argument('-n',
'--network',
help='The filename of the network configuration',
required=False)
args = parser.parse_args()
try:
with open(DATA_PATH + args.dataset + '.json', 'r') as filetypes:
types = json.load(filetypes)
except:
print('Dataset types not found, automatic types will be used.')
types = {}
df = preprocess(
pd.read_csv(DATA_PATH + args.dataset + '.tsv', sep='\t', dtype=types),
types)
network_file = open(args.network, 'r')
cv = CrossValidator(
NeuralNetwork(network_file=network_file,
target_attribute='target',
data_instance=df.iloc[0]))
cv.cross_validate(df, 10, 1)
def main():
parser = argparse.ArgumentParser(description='Random Forest parser')
parser.add_argument('--opt', help='test-benchmark or test-dataset.', required=True)
parser.add_argument('--dataset', help='The dataset filename.', default='', required=False)
parser.add_argument('--target_attribute', help='Target attribute to be predicted.', default='', required=False)
parser.add_argument('--n_trees', help='The number of trees. The default is 5.', default=5, type=int, required=False)
parser.add_argument('--n_attributes', help='The number of attributes. The default is the squared root of otal attributes.', default=-1, type=int, required=False)
parser.add_argument('--k_folds', help='The number of folds for cross validation. The default is 5', default=5, type=int, required=False)
parser.add_argument('--r', help='The number of repetitions for repeated cross validation. The default is 1', default=1, type=int, required=False)
args = parser.parse_args()
if args.opt == 'test-benchmark':
test_benchmark_categorical()
test_benchmark_numerical()
if args.opt == 'test-dataset':
if args.dataset == '' or not os.path.isfile(DATA_PATH + args.dataset):
print('Dataset not found.')
return
try:
with open(DATA_PATH + args.dataset[:-3] + 'json', 'r') as filetypes:
types = json.load(filetypes)
except:
print('Dataset types not found, automatic types will be used.')
types = {}
data = pd.read_csv(
DATA_PATH + args.dataset,
delimiter='\t' if args.dataset[-3:] == 'tsv' else ',',
dtype=types
)
if args.target_attribute not in data.columns:
print("Target attribute doesn't exist on dataset.")
return
n_trees = args.n_trees
n_random_attributes = args.n_attributes
if n_random_attributes == -1:
n_random_attributes = int((len(data.columns) - 1) ** 1/2)
cv = CrossValidator(
RandomForest(n_trees, args.target_attribute, n_random_attributes)
)
cv.cross_validate(data, args.k_folds, args.r)
print('\nGlobal accuracy: %.3f (%.3f)' % (cv.accuracy, cv.accuracy_std))