def test_gp(plot=False, method='full'):
"""
Compares model prediction with an exact GP (without optimisation)
"""
# note that this test fails without latent noise in the case of full Gaussian
np.random.seed(111)
num_input_samples = 10
num_samples = 10000
gaussian_sigma = .2
X, Y, kernel = DataSource.normal_generate_samples(num_input_samples, gaussian_sigma, 1)
kernel = [GPy.kern.RBF(1, variance=1., lengthscale=np.array((1.,)))]
if method == 'full':
m = SAVIGP_SingleComponent(X, Y, num_input_samples, UnivariateGaussian(np.array(gaussian_sigma)),
kernel, num_samples, None, 0.001, True, True)
if method == 'diag':
m = SAVIGP_Diag(X, Y, num_input_samples, 1, UnivariateGaussian(np.array(gaussian_sigma)),
kernel, num_samples, None, 0.001, True, True)
# update model using optimal parameters
# gp = SAVIGP_Test.gpy_prediction(X, Y, gaussian_sigma, kernel[0])
# gp_mean, gp_var = gp.predict(X, full_cov=True)
# m.MoG.m[0,0] = gp_mean[:,0]
# m.MoG.update_covariance(0, gp_var - gaussian_sigma * np.eye(10))
try:
folder_name = 'test' + '_' + ModelLearn.get_ID()
logger = ModelLearn.get_logger(folder_name, logging.DEBUG)
Optimizer.optimize_model(m, 10000, logger, ['mog'])
except KeyboardInterrupt:
pass
sa_mean, sa_var = m.predict(X)
gp = SAVIGP_Test.gpy_prediction(X, Y, gaussian_sigma, deepcopy(kernel[0]))
gp_mean, gp_var = gp.predict(X)
mean_error = (np.abs(sa_mean - gp_mean)).sum() / sa_mean.shape[0]
var_error = (np.abs(sa_var - gp_var)).sum() / gp_var.T.shape[0]
if mean_error < 0.1:
print bcolors.OKBLUE, "passed: mean gp prediction ", mean_error
else:
print bcolors.WARNING, "failed: mean gp prediction ", mean_error
print bcolors.ENDC
if var_error < 0.1:
print bcolors.OKBLUE, "passed: var gp prediction ", var_error
else:
print bcolors.WARNING, "failed: var gp prediction ", var_error
print bcolors.ENDC
if plot:
plot_fit(m)
gp.plot()
show(block=True)
def creep_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.creep_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'creep'
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, True)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = WarpLL(np.array([3.8715, 3.8898, 2.8759]),
np.array([1.5925, -1.3360, -2.0289]),
np.array([0.7940, -4.1855, -3.0289]),
np.log(0.01))
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp', 'll'], MinTransformation, True,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 25, 'hyp': 25, 'll': 25},
max_iter=200))
def boston_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.boston_data()
d = data[config['run_id'] - 1]
names = []
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'boston'
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, True)
# gaussian_sigma = np.var(Ytrain)/4 + 1e-4
gaussian_sigma = 1.0
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
cond_ll = UnivariateGaussian(np.array(gaussian_sigma))
num_samples = 2000
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['hyp', 'mog', 'll'], MeanTransformation, True,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 25, 'hyp': 25, 'll': 25},
max_iter=200))
return names
def wisconsin_breast_cancer_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.wisconsin_breast_cancer_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'breast_cancer'
# uncomment these lines to use softmax
# kernel = Experiments.get_kernels(Xtrain.shape[1], 2, False)
# Ytrain = np.array([(Ytrain[:,0] + 1) / 2, (-Ytrain[:,0] + 1) / 2]).T
# Ytest = np.array([(Ytest[:,0] + 1) / 2, (-Ytest[:,0] + 1) / 2]).T
# cond_ll = SoftmaxLL(2)
# uncomment these lines to use logistic
cond_ll = LogisticLL()
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp'], IdentityTransformation, True,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 25, 'hyp': 25, 'll': 25},
max_iter=200))
return names
def mining_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mining_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'mining'
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = LogGaussianCox(math.log(191. / 811))
kernel[0].variance = 1.0
kernel[0].lengthscale = 13516.
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog'], IdentityTransformation, True,
config['log_level'], True, latent_noise=0.001,
opt_per_iter={'mog': 15000},
max_iter=1))
return names
def abalone_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.abalone_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'abalone'
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = WarpLL(np.array([-2.0485, 1.7991, 1.5814]),
np.array([2.7421, 0.9426, 1.7804]),
np.array([0.1856, 0.7024, -0.7421]),
np.log(0.1))
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp', 'll'], MinTransformation, True,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 25, 'hyp': 25, 'll': 25},
max_iter=200))
def wisconsin_breast_cancer_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.wisconsin_breast_cancer_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'breast_cancer'
# uncomment these lines to use softmax
# kernel = Experiments.get_kernels(Xtrain.shape[1], 2, False)
# Ytrain = np.array([(Ytrain[:,0] + 1) / 2, (-Ytrain[:,0] + 1) / 2]).T
# Ytest = np.array([(Ytest[:,0] + 1) / 2, (-Ytest[:,0] + 1) / 2]).T
# cond_ll = SoftmaxLL(2)
# uncomment these lines to use logistic
cond_ll = LogisticLL()
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp'],
IdentityTransformation,
True,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 25,
'hyp': 25,
'll': 25
},
max_iter=200))
return names
def sarcos_all_joints_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.sarcos_all_joints_data()
names = []
d = data[0]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'sarcos_all_joints'
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 8, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = CogLL(0.1, 7, 1)
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'll', 'hyp'], MeanStdYTransformation, True,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 50, 'hyp': 10, 'll': 10},
max_iter=200,
partition_size=partition_size,
ftol=10,
n_threads=n_threads,
model_image_file=image))
def creep_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.creep_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'creep'
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, True)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = WarpLL(np.array([3.8715, 3.8898, 2.8759]),
np.array([1.5925, -1.3360, -2.0289]),
np.array([0.7940, -4.1855, -3.0289]), np.log(0.01))
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp', 'll'],
MinTransformation,
True,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 25,
'hyp': 25,
'll': 25
},
max_iter=200))
def USPS_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.USPS_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'USPS'
kernel = [
ExtRBF(Xtrain.shape[1],
variance=2,
lengthscale=np.array((4., )),
ARD=False) for j in range(3)
]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = SoftmaxLL(3)
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp'],
IdentityTransformation,
True,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 25,
'hyp': 25,
'll': 25
},
max_iter=300))
def abalone_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.abalone_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'abalone'
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = WarpLL(np.array([-2.0485, 1.7991, 1.5814]),
np.array([2.7421, 0.9426, 1.7804]),
np.array([0.1856, 0.7024, -0.7421]), np.log(0.1))
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp', 'll'],
MinTransformation,
True,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 25,
'hyp': 25,
'll': 25
},
max_iter=200))
def mining_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mining_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'mining'
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = LogGaussianCox(math.log(191. / 811))
kernel[0].variance = 1.0
kernel[0].lengthscale = 13516.
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog'],
IdentityTransformation,
True,
config['log_level'],
True,
latent_noise=0.001,
opt_per_iter={
'mog': 15000,
'hyp': 25,
'll': 25
},
max_iter=1))
return names
def boston_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.boston_data()
d = data[config['run_id'] - 1]
names = []
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'boston'
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 1, True)
# gaussian_sigma = np.var(Ytrain)/4 + 1e-4
gaussian_sigma = 1.0
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
cond_ll = UnivariateGaussian(np.array(gaussian_sigma))
num_samples = 2000
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['hyp', 'mog', 'll'],
MeanTransformation,
True,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 25,
'hyp': 25,
'll': 25
},
max_iter=200))
return names
def USPS_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.USPS_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'USPS'
kernel = [ExtRBF(Xtrain.shape[1], variance=2, lengthscale=np.array((4.,)), ARD=False) for j in range(3)]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = SoftmaxLL(3)
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp'], IdentityTransformation, True,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 25, 'hyp': 25, 'll': 25},
max_iter=300))
def test_model_learn(config):
"""
Compares the model output with exact GP
"""
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
names = []
num_input_samples = 20
gaussian_sigma = .2
X, Y, kernel = DataSource.normal_generate_samples(num_input_samples, gaussian_sigma)
train_n = int(0.5 * num_input_samples)
Xtrain = X[:train_n, :]
Ytrain = Y[:train_n, :]
Xtest = X[train_n:, :]
Ytest = Y[train_n:, :]
kernel1 = ModelLearn.get_kernels(Xtrain.shape[1], 1, True)
kernel2 = ModelLearn.get_kernels(Xtrain.shape[1], 1, True)
gaussian_sigma = 1.0
#number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 10000
cond_ll = UnivariateGaussian(np.array(gaussian_sigma))
n1, _ = ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel1, method,
'test_' + ModelLearn.get_ID(), 'test', num_inducing,
num_samples, sparsify_factor, ['mog', 'll', 'hyp'], IdentityTransformation, True,
logging.DEBUG, True)
n2, _ =ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel2, 'gp',
'test_' + ModelLearn.get_ID(), 'test', num_inducing,
num_samples, sparsify_factor, ['mog', 'll', 'hyp'], IdentityTransformation)
PlotOutput.plot_output('test', ModelLearn.get_output_path(), [n1, n2], None, False)
def sarcos_all_joints_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.sarcos_all_joints_data()
names = []
d = data[0]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'sarcos_all_joints'
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
kernel = ExperimentSetup.get_kernels(Xtrain.shape[1], 8, False)
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = CogLL(0.1, 7, 1)
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'll', 'hyp'],
MeanStdYTransformation,
True,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 50,
'hyp': 10,
'll': 10
},
max_iter=200,
partition_size=partition_size,
ftol=10,
n_threads=n_threads,
model_image_file=image))
def MNIST_binary_inducing_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain_full = d['train_Y']
Xtest = d['test_X']
Ytest_full = d['test_Y']
name = 'mnist_binary'
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
Ytrain = np.apply_along_axis(
lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1,
Ytrain_full).astype(int)[:, np.newaxis]
Ytest = np.apply_along_axis(
lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1,
Ytest_full).astype(int)[:, np.newaxis]
kernel = [
ExtRBF(Xtrain.shape[1],
variance=11,
lengthscale=np.array((9., )),
ARD=False) for j in range(1)
]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = LogisticLL()
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp', 'inducing'],
IdentityTransformation,
False,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 60,
'hyp': 15,
'inducing': 6
},
max_iter=9,
n_threads=n_threads,
ftol=10,
model_image_file=image,
partition_size=partition_size))
# plots for an specific experiment
# PlotOutput.plot_output_all('abalone_graph', Experiments.get_output_path(),
# lambda x: x['experiment'] == 'abalone', False)
def run_config(config):
try:
logger.info('started config: ' + str(config))
getattr(ExperimentSetup, config['method_to_run'])(config)
logger.info('finished config: ' + str(config))
except Exception as e:
logger.exception(config)
if __name__ == '__main__':
logger = ModelLearn.get_logger(ModelLearn.get_logger_path(), 'general_' + ModelLearn.get_ID(), logging.DEBUG)
# uncomment to run experiments in parallel
# ExperimentRunner.run_parallel(3)
# runs an individual configuration
# ExperimentRunner.boston_experiment()
# ExperimentRunner.wisconsin_breast_experiment()
# ExperimentRunner.USPS_experiment()
# ExperimentRunner.mining_experiment()
# ExperimentRunner.abalone_experiment()
# ExperimentRunner.mnist_binary_inducing_experiment()
ExperimentRunner.mnist_binary_experiment()
# ExperimentRunner.sarcos_all_joins_experiment()
# ExperimentRunner.sarcos_experiment()
def MNIST_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'mnist'
# uncomment these lines to delete unused features
# features_rm = np.array([])
# for n in range(Xtrain.shape[1]):
# if Xtrain[:, n].sum() ==0:
# features_rm = np.append(features_rm, n)
# Xtrain = np.delete(Xtrain, features_rm.astype(int), 1)
# Xtest = np.delete(Xtest, features_rm.astype(int), 1)
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
kernel = [ExtRBF(Xtrain.shape[1], variance=11, lengthscale=np.array((9.,)), ARD=False) for j in range(10)]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = SoftmaxLL(10)
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp'], IdentityTransformation, False,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 50, 'hyp': 10},
max_iter=300, n_threads=n_threads, ftol=10,
model_image_file=image, partition_size=partition_size))
def MNIST_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'mnist'
# uncomment these lines to delete unused features
# features_rm = np.array([])
# for n in range(Xtrain.shape[1]):
# if Xtrain[:, n].sum() ==0:
# features_rm = np.append(features_rm, n)
# Xtrain = np.delete(Xtrain, features_rm.astype(int), 1)
# Xtest = np.delete(Xtest, features_rm.astype(int), 1)
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
kernel = [
ExtRBF(Xtrain.shape[1],
variance=11,
lengthscale=np.array((9., )),
ARD=False) for j in range(10)
]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = SoftmaxLL(10)
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp'],
IdentityTransformation,
False,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 50,
'hyp': 10
},
max_iter=300,
n_threads=n_threads,
ftol=10,
model_image_file=image,
partition_size=partition_size))
def MNIST_binary_inducing_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain_full = d['train_Y']
Xtest = d['test_X']
Ytest_full = d['test_Y']
name = 'mnist_binary'
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
Ytrain = np.apply_along_axis(lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1, Ytrain_full).astype(int)[:, np.newaxis]
Ytest = np.apply_along_axis(lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1, Ytest_full).astype(int)[:, np.newaxis]
kernel = [ExtRBF(Xtrain.shape[1], variance=11, lengthscale=np.array((9.,)), ARD=False) for j in range(1)]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = LogisticLL()
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp', 'inducing'], IdentityTransformation, False,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 60, 'hyp': 15, 'inducing': 6},
max_iter=9, n_threads=n_threads, ftol=10,
model_image_file=image, partition_size=partition_size))
def plot():
PlotOutput.plot_output_all('boston', ModelLearn.get_output_path(),
lambda x: x['method'] == 'full', False)
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernels,
method,
name,
d['id'],
num_inducing,
num_samples,
num_inducing / Xtrain.shape[0],
# optimise hyper-parameters (hyp), posterior parameters (mog), and likelihood parameters (ll)
['hyp', 'mog', 'll'],
# Transform data before training
MeanTransformation,
# place inducting points on training data. If False, they will be places using clustering
True,
# level of logging
logging.DEBUG,
# do not export training data into csv files
False,
# add a small latent noise to the kernel for stability of numerical computations
latent_noise=0.001,
# for how many iterations each set of parameters will be optimised
opt_per_iter={'mog': 25, 'hyp': 25, 'll': 25},
# total number of global optimisations
max_iter=200,
# number of threads
n_threads=1,
# size of each partition of data
partition_size=3000)
def plot():
PlotOutput.plot_output_all('boston', ModelLearn.get_output_path(),
lambda x: x['method'] == 'full', False)
# PlotOutput.plot_output_all('abalone_graph', Experiments.get_output_path(),
# lambda x: x['experiment'] == 'abalone', False)
def run_config(config):
try:
logger.info('started config: ' + str(config))
getattr(ExperimentSetup, config['method_to_run'])(config)
logger.info('finished config: ' + str(config))
except Exception as e:
logger.exception(config)
if __name__ == '__main__':
logger = ModelLearn.get_logger(ModelLearn.get_logger_path(),
'general_' + ModelLearn.get_ID(),
logging.DEBUG)
# uncomment to run experiments in parallel
# ExperimentRunner.run_parallel(3)
# runs an individual configuration
ExperimentRunner.boston_experiment()
# ExperimentRunner.wisconsin_breast_experiment()
# ExperimentRunner.USPS_experiment()
# ExperimentRunner.mining_experiment()
# ExperimentRunner.abalone_experiment()
# ExperimentRunner.mnist_binary_inducing_experiment()
# ExperimentRunner.mnist_binary_experiment()
# ExperimentRunner.sarcos_all_joins_experiment()
# ExperimentRunner.sarcos_experiment()
