from lasagne.layers import DenseLayer
from lasagne.layers import InputLayer
from lasagne.layers import DropoutLayer
from lasagne.nonlinearities import softmax
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
if __name__ == '__main__':
encoder = LabelEncoder()
# Identical to StandardScaler using all train and test data.
scaler = OttoScaler()
# Training data
X, y = OttoCompetition.load_data(train=True)
y = encoder.fit_transform(y).astype('int32')
X = scaler.transform(X).astype('float32')
n_classes = np.unique(y).shape[0]
n_features = X.shape[1]
# Split a holdout set
data_idx, hold_idx = next(iter(StratifiedShuffleSplit(y, 1, test_size = 0.2, random_state=0)))
X_data, X_hold = X[data_idx], X[hold_idx]
y_data, y_hold = y[data_idx], y[hold_idx]
# Test data
X_test, _ = OttoCompetition.load_data(train=False)
X_test = scaler.transform(X_test).astype('float32')
