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)
dataset_name,
noise=0.5,
suffix='t')
xtest, ytest = load_representations(context_folder,
dataset_name,
noise=0.5,
suffix='test')
ys = (ys + 1) / 2
ytest = (ytest + 1) / 2
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)
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))
def train_atk_classifier(key, size=1900):
pca = None
X_train, Y_train = [], []
for i in [0, 1]:
f = open(PATH.format(key, i), 'r')
sents = [x[:-1] for x in f if x[:-1] != '']
embs = embedding(sents, EMB_PATH.format(key, i), ARCH)
if args.prefix != 'part':
embs = embs[np.random.choice(len(embs), size, replace=False), :]
X_train.append(embs)
Y_train.extend([i] * embs.shape[0])
X_train = np.concatenate(X_train, axis=0)
Y_train = np.array(Y_train)
train_embs = np.load(TRAIN_EMB_PATH)
# BottleNeck
# X_train = np.load(TRAIN_EMB_PATH)
# raw_train = list(open(TRAIN_PATH, 'r'))
# if IS_BALANCED:
# raw_train, X_train = balance(key, raw_train, X_train)
# Y_train = np.array([(key in x) for x in raw_train])
# load validation set
raw_valid, X_valid = list(open(TARGET_PATH, 'r')), np.load(TARGET_EMB_PATH)
if (key != 'potato' and IS_BALANCED):
raw_valid, X_valid = balance(key, raw_valid, X_valid)
print(len(raw_valid))
Y_valid = np.array([(key in x) for x in raw_valid])
acc = -1
# learn a transfer
# clf = linear_model.SGDClassifier(max_iter = 1000, verbose = 0)
# clf = SVC(kernel = 'rbf', gamma = 'scale', verbose = False)
# clf = KNeighborsClassifier(n_neighbors=1, p = 1)
if (NONLINEAR):
# clf = DANN(input_size = EMB_DIM, maxiter = 2000, verbose = False, name = key, batch_size = 128)
clf = DANN(input_size=EMB_DIM,
maxiter=4000,
verbose=True,
name=key,
batch_size=64,
lambda_adapt=1.0,
hidden_layer_size=25)
acc = clf.fit(X_train,
Y_train,
X_adapt=train_embs,
X_valid=X_valid,
Y_valid=Y_valid)
print("DANN Acc.: {:.4f}".format(acc))
# train_embs = train_embs[np.random.choice(len(train_embs), 2000), :]
# # apply pca first
# if(DO_PCA):
# train_embs = train_embs[np.random.choice(len(train_embs), size = 6 * int(len(X_train)), replace = False)]
# package = np.concatenate([X_train, train_embs], axis = 0)
# pca = PCA(n_components=INPUT_DIM)
# pca.fit(package)
# X_train, train_embs = pca.transform(X_train), pca.transform(train_embs)
# if NONLINEAR:
# clf = NonLinearClassifier(key, ARCH, cls_num = 2, pca = pca, use_pca = DO_PCA)
# clf.fit(X_train, Y_train)
if NONLINEAR:
clf.to(torch.device('cpu'))
# on current set
# correct = 0
if (VERBOSE):
print("TRAIN INFERENCE MODEL FROM EXTERNAL SOURCES (# = {})".format(
len(X_train)))
correct = np.sum((clf.predict(X_train) == Y_train))
print("Source Domain Acc.: {:.4f}".format(correct / len(Y_train)))
return clf, pca, acc
def main():
data_folder = './data/' # where the datasets are
source_name = 'books' # source domain: books, dvd, kitchen, or electronics
target_name = 'data' # traget domain: books, dvd, kitchen, or electronics
adversarial = True # 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 = 100
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)
ds, ns = np.shape(xs_msda)
print 'shape(xs_msda)'
print ds, ns
# print xs_msda
dt, nt = np.shape(xt_msda)
print 'shape(xt_msda)'
print dt, nt
# print xt_msda
dxtest, nxtest = np.shape(xtest_msda)
print 'shape(xtest_msda)'
print dxtest, nxtest
# 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)
va = count(prediction_valid)
print '验证集正向的预测标签比率'
print va[0], "%.2f%%" % ((float(va[0]) / len(prediction_valid)) * 100)
print '验证集负向的预测标签比率'
print va[1], "%.2f%%" % ((float(va[1]) / len(prediction_valid)) * 100)
te = count(prediction_test)
print '测试集正向的预测标签比率'
print te[0], "%.2f%%" % ((float(te[0]) / len(prediction_test)) * 100)
print '测试集负向的预测标签比率'
print te[1], "%.2f%%" % ((float(te[1]) / len(prediction_test)) * 100)
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 '验证集正向和负向的预测准确率'
print compare(yv, prediction_valid)
print '测试集正向和负向的预测准确率'
print compare(ytest, prediction_test)
print('==================================================================')
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)
