def mnist_binary_inducing_experiment(method,
components,
sparsity_factor,
run_id,
image=None,
n_threads=1,
partition_size=3000,
optimize_stochastic=False):
"""
Run the binary mnist experiment with inducing point learning.
Parameters
----------
method : str
The method under which to run the experiment (mix1, mix2, or full).
sparsity_factor : float
The sparsity of inducing points.
run_id : int
The id of the configuration.
"""
name = 'mnist_binary'
data = data_source.mnist_binary_data()[run_id - 1]
kernel = [
ExtRBF(data['train_inputs'].shape[1],
variance=11,
lengthscale=np.array((9., )),
ARD=False)
]
cond_ll = likelihood.LogisticLL()
transform = data_transformation.IdentityTransformation(
data['train_inputs'], data['train_outputs'])
return run_model.run_model(data['train_inputs'],
data['train_outputs'],
data['test_inputs'],
data['test_outputs'],
cond_ll,
kernel,
method,
components,
name,
data['id'],
sparsity_factor,
transform,
False,
False,
optimization_config={
'mog': 60,
'hyp': 15,
'inducing': 6
},
max_iter=9,
n_threads=n_threads,
ftol=10,
model_image_dir=image,
partition_size=partition_size,
optimize_stochastic=optimize_stochastic)
def wisconsin_experiment(method,
components,
sparsity_factor,
run_id,
optimize_stochastic=False):
"""
Run the wisconsin experiment.
Parameters
----------
method : str
The method under which to run the experiment (mix1, mix2, or full).
sparsity_factor : float
The sparsity of inducing points.
run_id : int
The id of the configuration.
"""
name = 'breast_cancer'
data = data_source.wisconsin_breast_cancer_data()[run_id - 1]
kernel = get_kernels(data['train_inputs'].shape[1], 1, False)
cond_ll = likelihood.LogisticLL()
transform = data_transformation.IdentityTransformation(
data['train_inputs'], data['train_outputs'])
return run_model.run_model(data['train_inputs'],
data['train_outputs'],
data['test_inputs'],
data['test_outputs'],
cond_ll,
kernel,
method,
components,
name,
data['id'],
sparsity_factor,
transform,
True,
False,
optimization_config={
'mog': 25,
'hyp': 25
},
max_iter=200)