def main():
data_folder = './data/' # where the datasets are
source_name = 'dvd' # source domain: books, dvd, kitchen, or electronics
target_name = 'electronics' # traget domain: books, dvd, kitchen, or electronics
adversarial = False # set to False to learn a standard NN
hidden_layer_size = 50
lambda_adapt = 0.1 if adversarial else 0.
learning_rate = 0.001
maxiter = 200
print("Loading data...")
xs, ys, xt, _, xtest, ytest = load_amazon(source_name,
target_name,
data_folder,
verbose=True)
nb_valid = int(0.1 * len(ys))
xv, yv = xs[-nb_valid:, :], ys[-nb_valid:]
xs, ys = xs[0:-nb_valid, :], ys[0:-nb_valid]
print("Fit...")
algo = DANN(lambda_adapt=lambda_adapt,
hidden_layer_size=hidden_layer_size,
learning_rate=learning_rate,
maxiter=maxiter,
epsilon_init=None,
seed=12342,
adversarial_representation=adversarial,
verbose=True)
for i in range(100000):
for j in range(100000):
algo.fit(xs, ys, xt, xv, yv)
print("Predict...")
prediction_train = algo.predict(xs)
prediction_valid = algo.predict(xv)
prediction_test = algo.predict(xtest)
print('Training Risk = %f' % np.mean(prediction_train != ys))
print('Validation Risk = %f' % np.mean(prediction_valid != yv))
print('Test Risk = %f' % np.mean(prediction_test != ytest))
print('==================================================================')
print('Computing PAD on DANN representation...')
pad_dann = compute_proxy_distance(algo.hidden_representation(xs),
algo.hidden_representation(xt),
verbose=True)
print('PAD on DANN representation = %f' % pad_dann)
print('==================================================================')
print('Computing PAD on original data...')
pad_original = compute_proxy_distance(xs, xt, verbose=True)
print('PAD on original data = %f' % pad_original)
def main():
data_folder = './data/' # where the datasets are
source_name = 'dvd' # source domain: books, dvd, kitchen, or electronics
target_name = 'electronics' # traget domain: books, dvd, kitchen, or electronics
adversarial = False # set to False to learn a standard NN
hidden_layer_size = 50
lambda_adapt = 0.1 if adversarial else 0.
learning_rate = 0.001
maxiter = 200
print("Loading data...")
xs, ys, xt, _, xtest, ytest = load_amazon(source_name, target_name, data_folder, verbose=True)
nb_valid = int(0.1 * len(ys))
xv, yv = xs[-nb_valid:, :], ys[-nb_valid:]
xs, ys = xs[0:-nb_valid, :], ys[0:-nb_valid]
print("Fit...")
algo = DANN(lambda_adapt=lambda_adapt, hidden_layer_size=hidden_layer_size, learning_rate=learning_rate,
maxiter=maxiter, epsilon_init=None, seed=12342, adversarial_representation=adversarial, verbose=True)
algo.fit(xs, ys, xt, xv, yv)
print("Predict...")
prediction_train = algo.predict(xs)
prediction_valid = algo.predict(xv)
prediction_test = algo.predict(xtest)
print('Training Risk = %f' % np.mean(prediction_train != ys))
print('Validation Risk = %f' % np.mean(prediction_valid != yv))
print('Test Risk = %f' % np.mean(prediction_test != ytest))
print('==================================================================')
print('Computing PAD on DANN representation...')
pad_dann = compute_proxy_distance(algo.hidden_representation(xs), algo.hidden_representation(xt), verbose=True)
print('PAD on DANN representation = %f' % pad_dann)
print('==================================================================')
print('Computing PAD on original data...')
pad_original = compute_proxy_distance(xs, xt, verbose=True)
print('PAD on original data = %f' % pad_original)
def main():
data_folder = './data/' # where the datasets are
source_name = 'dvd' # source domain: books, dvd, kitchen, or electronics
target_name = 'electronics' # traget domain: books, dvd, kitchen, or electronics
adversarial = False # set to False to learn a standard NN
msda = True
hidden_layer_size = 50
lambda_adapt = 0.1 if adversarial else 0.
learning_rate = 0.001 if not msda else 0.0001
maxiter = 200
print("Loading data...")
xs, ys, xt, _, xtest, ytest = load_amazon(source_name, target_name, data_folder, verbose=True)
if msda:
xs_path, xt_path, xtest_path = ['%s/%s.%s_%s_msda.npy' % (data_folder, source_name, target_name, E)
for E in ('source', 'target', 'test')]
try:
xs_msda = np.load(xs_path)
xt_msda = np.load(xt_path)
xtest_msda = np.load(xtest_path)
print('mSDA representations loaded from disk')
except:
print('Computing mSDA representations...')
xs_msda, xt_msda, xtest_msda = compute_msda_representation(xs, xt, xtest)
np.save(xs_path, xs_msda)
np.save(xt_path, xt_msda)
np.save(xtest_path, xtest_msda)
xs, xt, xtest = xs_msda, xt_msda, xtest_msda
nb_valid = int(0.1 * len(ys))
xv, yv = xs[-nb_valid:, :], ys[-nb_valid:]
xs, ys = xs[0:-nb_valid, :], ys[0:-nb_valid]
print("Fit...")
algo = DANN(lambda_adapt=lambda_adapt, hidden_layer_size=hidden_layer_size, learning_rate=learning_rate,
maxiter=maxiter, epsilon_init=None, seed=12342, adversarial_representation=adversarial, verbose=True)
algo.fit(xs, ys, xt, xv, yv)
print("Predict...")
prediction_train = algo.predict(xs)
prediction_valid = algo.predict(xv)
prediction_test = algo.predict(xtest)
print('Training Risk = %f' % np.mean(prediction_train != ys))
print('Validation Risk = %f' % np.mean(prediction_valid != yv))
print('Test Risk = %f' % np.mean(prediction_test != ytest))
print('==================================================================')
print('Computing PAD on DANN representation...')
pad_dann = compute_proxy_distance(algo.hidden_representation(xs), algo.hidden_representation(xt), verbose=True)
print('PAD on DANN representation = %f' % pad_dann)
print('==================================================================')
print('Computing PAD on original data...')
pad_original = compute_proxy_distance(xs, xt, verbose=True)
print('PAD on original data = %f' % pad_original)
