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app.py
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import argparse
def tf_nn_parser(subparsers):
tf_nn = subparsers.add_parser(
'tf_nn',
help='Train a TensorFlow MLP model')
tf_nn.add_argument(
'--nrows',
metavar='int',
type=int,
default=10000,
help='Fraction of data for testing models')
tf_nn.add_argument(
'--test_size',
metavar='float',
type=float,
default=0.2,
help='Select fraction of test data')
tf_nn.add_argument(
'--n_components',
metavar='float',
type=float,
default=0.95,
help='Select number of components for PCA')
tf_nn.add_argument(
'--hidden_layer_sizes',
metavar='tuple',
type=int,
nargs='+',
default=[50, 40, 3],
help='Neurons in hidden layers')
tf_nn.add_argument(
'--activation',
metavar='string',
type=str,
default='relu',
help='Activation function for hidden layers')
tf_nn.add_argument(
'--stddev',
metavar='float',
type=float,
default=0.01,
help='Gaussian noise for not overfitting')
tf_nn.add_argument(
'--epochs',
metavar='int',
type=int,
default=200,
help='Number of epochs')
tf_nn.add_argument(
'--batch_size',
metavar='int',
type=int,
default=1024,
help='Batch_size for fitting the model')
def sk_nn_parser(subparsers):
sk_nn = subparsers.add_parser(
'sk_nn',
help='Train a SKlearn MLPClassifier model')
sk_nn.add_argument(
'--nrows',
metavar='int',
type=int,
default=10000,
help='Fraction of data for testing models')
sk_nn.add_argument(
'--test_size',
metavar='float',
type=float,
default=0.2,
help='Select fraction of test data')
sk_nn.add_argument(
'--n_components',
metavar='float',
type=float,
default=0.95,
help='Select number of components for PCA')
sk_nn.add_argument(
'--hidden_layer_sizes',
metavar='tuple',
type=int,
nargs='+',
default=(50, 40, 3),
help='Neurons in hidden layers')
sk_nn.add_argument(
'--activation',
metavar='string',
type=str,
default='logistic',
help='Activation function for hidden layers')
def sk_tree_parser(subparsers):
sk_tree = subparsers.add_parser(
'sk_tree',
help='Train a SKlearn DecisionTreeClassifier model')
sk_tree.add_argument(
'--nrows',
metavar='int',
type=int,
default=10000,
help='Fraction of data for testing models')
sk_tree.add_argument(
'--test_size',
metavar='float',
type=float,
default=0.2,
help='Select fraction of test data')
sk_tree.add_argument(
'--criterion',
metavar='string',
type=str,
default='gini',
help='Criterion for measure the quality split')
sk_tree.add_argument(
'--max_depth',
metavar='int',
type=int,
default=None,
help='Maximum depth of the tree')
def main():
# main parser and subparsers
parser = argparse.ArgumentParser(description='Select a model to train')
subparsers = parser.add_subparsers(dest='command')
# add subparsers
tf_nn_parser(subparsers)
sk_nn_parser(subparsers)
sk_tree_parser(subparsers)
args = parser.parse_args()
if not args.command:
parser.print_help()
elif args.command == 'tf_nn':
from models.tf_nn import train_tf_nn
train_tf_nn(args)
elif args.command == 'sk_nn':
from models.sk_nn import train_sk_nn
train_sk_nn(args)
elif args.command == 'sk_tree':
from models.sk_tree import train_sk_tree
train_sk_tree(args)
if __name__ == '__main__':
main()