def do_transform(train_path, test_path, new_train_path, new_test_path,
pickle_path, model_path, pecent_encode, my_training_epochs,
mysample_mathod, encode_function):
# left is the left part besides the feature matrix
matrix_train, train_left, train_right = file2matrix(train_path)
matrix_test, test_left, test_right = file2matrix(test_path)
dim_in = max(matrix_train.shape[1], matrix_test.shape[1])
extra_col = 0
# the dimension of two sparse matrix may be different
if dim_in > matrix_train.shape[1]:
extra_col = dim_in - matrix_train.shape[1]
matrix_train = np.hstack((matrix_train,
np.zeros((matrix_train.shape[0], extra_col),
dtype=int)))
if dim_in > matrix_test.shape[1]:
extra_col = dim_in - matrix_test.shape[1]
matrix_test = np.hstack(
(matrix_test, np.zeros((matrix_test.shape[0], extra_col),
dtype=int)))
#[matrix_train_sampled, matrix_test_sampled] = get_sampled(matrix_train, matrix_test, 0.01)
# save the trainset and test set (which will be used by dA module)
f = gzip.open(pickle_path, 'w')
cPickle.dump([matrix_train, matrix_test], f)
f.close()
dim_out = int(dim_in * pecent_encode)
print 'before sampling', matrix_train.shape, matrix_test.shape, 'mapped to dim_out = ', dim_out
matrix_train_new, matrix_test_new = dA.test_dA(
0,
dim_in,
dim_out,
learning_rate=0.1,
training_epochs=my_training_epochs,
dataset=pickle_path,
batch_size=10,
output_path=model_path,
sample_method=mysample_mathod,
encode_function=encode_function)
matrix2file(matrix_train_new, train_left, train_right, new_train_path)
matrix2file(matrix_test_new, test_left, test_right, new_test_path)
print 'encoded file written'
def do_transform(train_path, test_path, new_train_path, new_test_path, pickle_path, model_path, pecent_encode,my_training_epochs, mysample_mathod, encode_function):
# left is the left part besides the feature matrix
matrix_train, train_left, train_right= file2matrix(train_path)
matrix_test, test_left, test_right = file2matrix(test_path)
dim_in = max(matrix_train.shape[1], matrix_test.shape[1])
extra_col = 0
# the dimension of two sparse matrix may be different
if dim_in > matrix_train.shape[1]:
extra_col = dim_in - matrix_train.shape[1]
matrix_train = np.hstack((matrix_train, np.zeros((matrix_train.shape[0], extra_col), dtype=int)))
if dim_in > matrix_test.shape[1]:
extra_col = dim_in - matrix_test.shape[1]
matrix_test = np.hstack((matrix_test, np.zeros((matrix_test.shape[0], extra_col), dtype= int)))
#[matrix_train_sampled, matrix_test_sampled] = get_sampled(matrix_train, matrix_test, 0.01)
# save the trainset and test set (which will be used by dA module)
f = gzip.open(pickle_path, 'w')
cPickle.dump([matrix_train, matrix_test], f)
f.close()
dim_out = int(dim_in*pecent_encode)
print 'before sampling', matrix_train.shape, matrix_test.shape, 'mapped to dim_out = ', dim_out
matrix_train_new, matrix_test_new = dA.test_dA(0, dim_in, dim_out, learning_rate=0.1, training_epochs=my_training_epochs,dataset=pickle_path, batch_size=10, output_path=model_path, sample_method=mysample_mathod, encode_function=encode_function)
matrix2file(matrix_train_new, train_left, train_right, new_train_path)
matrix2file(matrix_test_new, test_left, test_right, new_test_path)
print 'encoded file written'
def test_dA():
dA.test_dA(training_epochs=1, output_folder='tmp_dA_plots')
def test_dA():
dA.test_dA(training_epochs=1, output_folder='tmp_dA_plots')
def test_dA():
dA.test_dA(training_epochs=3, output_folder="tmp_dA_plots")
def test_dA():
t0 = time.time()
dA.test_dA(training_epochs=3, output_folder='tmp_dA_plots')
print >> sys.stderr, "test_dA took %.3fs expected Xs in our buildbot" % (
time.time() - t0)
def test_dA():
t0=time.time()
dA.test_dA(training_epochs = 3, output_folder = 'tmp_dA_plots')
print >> sys.stderr, "test_dA took %.3fs expected Xs in our buildbot"%(time.time()-t0)
# -*- coding: utf-8 -*- """ Created on Sat Feb 27 01:04:40 2016 @author: bo run and save a dA model, to initialize my deep_clus model """ import dA dA.test_dA()
