def setUp(self):
# Load data
Y = np.loadtxt(os.path.join(base_path, self.outputfile))
X = np.loadtxt(os.path.join(base_path, self.inputfile))
m = deepgp.DeepGP([Y.shape[1], 5, X.shape[1]],
Y,
X=X,
kernels=[
GPy.kern.RBF(5, ARD=True),
GPy.kern.RBF(X.shape[1], ARD=True)
],
num_inducing=2,
back_constraint=False)
if not os.path.exists(os.path.join(base_path, self.modelfile)):
# Create the model file
m.randomize()
m._trigger_params_changed()
m.save(os.path.join(base_path, self.modelfile))
with h5py.File(os.path.join(base_path, self.modelfile), 'r+') as f:
L = f.create_dataset("L", (1, ), dtype=np.float)
L[:] = m._log_marginal_likelihood
f.close()
# Load model parameters
with tables.open_file(os.path.join(base_path, self.modelfile),
'r') as f:
m.param_array[:] = f.root.param_array[:]
L = float(f.root.L[:])
m._trigger_params_changed()
f.close()
self.model = m
self.L = L
def deepGaussianProcessClassifier(self, testIndex):
print("Deep Gaussian Process Classification")
trainIndex = self.posMusic + self.negMusic
features = self.newFactory.musicFiles.mtFeatures
likes = self.newFactory.musicFiles.like
X1 = numpy.zeros((len(trainIndex), len(features[0][:33])))
y1 = numpy.zeros((len(trainIndex), 1))
for i, mem in enumerate(trainIndex):
X1[i, :] = squeeze(features[mem][:33])
if likes[mem] == True:
y1[i] = 0
else:
y1[i] = 1
Q = 2 ## Binary classification
kern1 = GPy.kern.RBF(Q, ARD=True) + GPy.kern.Bias(Q)
kern2 = GPy.kern.RBF(Q, ARD=False) + GPy.kern.Bias(X1.shape[1])
m = deepgp.DeepGP([2, 2, X1.shape[1]],
y1,
X_tr=X1,
kernels=[kern1, kern2],
num_inducing=10,
back_constraint=False)
m.optimize(max_iters=1500, messages=True)
X2 = numpy.zeros((len(testIndex), len(features[0][:33])))
for i, mem in enumerate(testIndex):
X2[i, :] = squeeze(features[mem][:33])
result = m.predict(X2)
print(result[0], result[0].shape)
for i in range(len(testIndex)):
if likes[testIndex[i]] == True:
print(i, "0")
else:
print(i, "1")
#--------- Model Construction ----------#
# Number of latent dimensions per layer
Q1, Q2 = 5, 4
# Type of kernel per layer
kern1 = GPy.kern.RBF(Q1,ARD=True) + GPy.kern.Bias(Q1)
kern2 = GPy.kern.RBF(Q2,ARD=True) + GPy.kern.Bias(Q2)
# Number of inducing points per layer (can be set to different if given as list).
num_inducing = 40
# Whether to use back-constraint for variational posterior
back_constraint = False
# Dimensions of the MLP back-constraint if set to true
encoder_dims=[[300],[150]]
m = deepgp.DeepGP([Y.shape[1],Q1,Q2],Y,kernels=[kern1,kern2], num_inducing=num_inducing, back_constraint=back_constraint, encoder_dims = encoder_dims)
#--------- Optimization ----------#
# Make sure initial noise variance gives a reasonable signal to noise ratio
for i in range(len(m.layers)):
output_var = m.layers[i].Y.var() if i==0 else m.layers[i].Y.mean.var()
m.layers[i].Gaussian_noise.variance = output_var*0.01
m.optimize(max_iters=5000, messages=True)
#--------- Inspection ----------#
# Plot ARD scales per layer
m.obslayer.kern.plot_ARD()
m.layer_1.kern.plot_ARD()
# Number of latent dimensions (single hidden layer, since the top layer is observed)
Q = 5
# Define what kernels to use per layer
kern1 = GPy.kern.RBF(Q, ARD=True) + GPy.kern.Bias(Q)
kern2 = GPy.kern.RBF(Q, ARD=False) + GPy.kern.Bias(X_tr.shape[1])
# Number of inducing points to use
num_inducing = 40
# Whether to use back-constraint for variational posterior
back_constraint = True
# Dimensions of the MLP back-constraint if set to true
encoder_dims = [[300], [150]]
m = deepgp.DeepGP([Y_tr.shape[1], Q, X_tr.shape[1]],
Y_tr,
X_tr=X_tr,
kernels=[kern1, kern2],
num_inducing=num_inducing,
back_constraint=back_constraint)
#--------- Optimization ----------#
# Make sure initial noise variance gives a reasonable signal to noise ratio.
# Fix to that value for a few iterations to avoid early local minima
for i in range(len(m.layers)):
output_var = m.layers[i].Y.var() if i == 0 else m.layers[i].Y.mean.var()
m.layers[i].Gaussian_noise.variance = output_var * 0.01
m.layers[i].Gaussian_noise.variance.fix()
m.optimize(max_iters=800, messages=True)
# Unfix noise variance now that we have initialized the model
for i in range(len(m.layers)):
m.layers[i].Gaussian_noise.variance.unfix()
# Number of latent dimensions (single hidden layer, since the top layer is observed)
Q = 1
# Define what kernels to use per layer
# 初始超参数选择
kern1 = GPy.kern.RBF(Q, ARD=True) + GPy.kern.Bias(Q) # Q表示隐结点个数
kern2 = GPy.kern.RBF(train_x.shape[1], ARD=True) + GPy.kern.Bias(train_x.shape[1])
# Number of inducing points to use
num_inducing = 100
# Whether to use back-constraint for variational posterior
back_constraint = False
# Dimensions of the MLP back-constraint if set to true
# encoder_dims = [[300], [150]]
m = deepgp.DeepGP([norm_train_y.shape[1], Q, train_x.shape[1]], norm_train_y, X_tr=train_x, kernels=[kern1, kern2], num_inducing=num_inducing,
back_constraint=back_constraint)
# print(np.mean(m.layers[0].Y))
# exit()
# --------- Optimization ----------#
# Make sure initial noise variance gives a reasonable signal to noise ratio.
# Fix to that value for a few iterations to avoid early local minima
for i in range(len(m.layers)):
output_var = m.layers[i].Y.var() if i == 0 else m.layers[i].Y.mean.var()
m.layers[i].Gaussian_noise.variance = output_var * 0.01
m.layers[i].Gaussian_noise.variance.fix()
m.optimize(max_iters=1000, messages=True)
# # Unfix noise variance now that we have initialized the model
# for i in range(len(m.layers)):
kern3 = GPy.kern.RBF(QX, ARD=False) + GPy.kern.Bias(QX) # Layer X -> H2
# Number of inducing points to use
num_inducing = 40
# Whether to use back-constraint for variational posterior
back_constraint = False
# Dimensions of the MLP back-constraint if set to true
encoder_dims = [[300], [150], [150]]
# Collect the dimensions of [Y, H1, H2, X]
dimensions = [Y_tr.shape[1], Q1, Q2, QX]
# Create model
m = deepgp.DeepGP(dimensions,
Y_tr_scaled,
X=X_tr_scaled,
kernels=[kern1, kern2, kern3],
num_inducing=num_inducing,
back_constraint=back_constraint)
# print(m) # This will print the model
#--------- Optimization ----------#
# Make sure initial noise variance gives a reasonable signal to noise ratio.
# Fix to that value for a few iterations to avoid early local minima
for i in range(len(m.layers)):
output_var = m.layers[i].Y.var() if i == 0 else m.layers[i].Y.mean.var()
m.layers[i].Gaussian_noise.variance = output_var * 0.01
m.layers[i].Gaussian_noise.variance.fix()
m.optimize(max_iters=800, messages=True)
# Unfix noise variance now that we have initialized the model
