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Added optimize and plot for classification, non_gaussian and stochastic examples

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Alan Saul 2013-11-29 14:20:59 +00:00
parent 68ece19211
commit 3cd808cccc
3 changed files with 132 additions and 121 deletions
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114
GPy/examples/classification.py
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@ -6,12 +6,11 @@
Gaussian Processes classification
"""
import pylab as pb
import numpy as np
import GPy
default_seed = 10000
def oil(num_inducing=50, max_iters=100, kernel=None):
def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
"""
Run a Gaussian process classification on the three phase oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
@ -25,7 +24,7 @@ def oil(num_inducing=50, max_iters=100, kernel=None):
Ytest[Ytest.flatten()==-1] = 0
# Create GP model
m = GPy.models.SparseGPClassification(X, Y,kernel=kernel,num_inducing=num_inducing)
m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, num_inducing=num_inducing)
# Contrain all parameters to be positive
m.tie_params('.*len')
@ -33,15 +32,16 @@ def oil(num_inducing=50, max_iters=100, kernel=None):
m.update_likelihood_approximation()
# Optimize
m.optimize(max_iters=max_iters)
if optimize:
m.optimize(max_iters=max_iters)
print(m)
#Test
probs = m.predict(Xtest)[0]
GPy.util.classification.conf_matrix(probs,Ytest)
GPy.util.classification.conf_matrix(probs, Ytest)
return m
def toy_linear_1d_classification(seed=default_seed):
def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
"""
Simple 1D classification example using EP approximation
@ -58,21 +58,23 @@ def toy_linear_1d_classification(seed=default_seed):
m = GPy.models.GPClassification(data['X'], Y)
# Optimize
#m.update_likelihood_approximation()
# Parameters optimization:
#m.optimize()
#m.update_likelihood_approximation()
m.pseudo_EM()
if optimize:
#m.update_likelihood_approximation()
# Parameters optimization:
#m.optimize()
#m.update_likelihood_approximation()
m.pseudo_EM()
# Plot
fig, axes = pb.subplots(2,1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print(m)
if plot:
fig, axes = pb.subplots(2, 1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print m
return m
def toy_linear_1d_classification_laplace(seed=default_seed):
def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=True):
"""
Simple 1D classification example using Laplace approximation
@ -90,24 +92,25 @@ def toy_linear_1d_classification_laplace(seed=default_seed):
# Model definition
m = GPy.models.GPClassification(data['X'], Y, likelihood=laplace_likelihood)
print m
# Optimize
#m.update_likelihood_approximation()
# Parameters optimization:
m.optimize('bfgs', messages=1)
#m.pseudo_EM()
if optimize:
#m.update_likelihood_approximation()
# Parameters optimization:
m.optimize('bfgs', messages=1)
#m.pseudo_EM()
# Plot
fig, axes = pb.subplots(2,1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print(m)
if plot:
fig, axes = pb.subplots(2, 1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print m
return m
def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, optimize=True, plot=True):
"""
Sparse 1D classification example
@ -121,24 +124,26 @@ def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
Y[Y.flatten() == -1] = 0
# Model definition
m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
m['.*len']= 4.
m = GPy.models.SparseGPClassification(data['X'], Y, num_inducing=num_inducing)
m['.*len'] = 4.
# Optimize
#m.update_likelihood_approximation()
# Parameters optimization:
#m.optimize()
m.pseudo_EM()
if optimize:
#m.update_likelihood_approximation()
# Parameters optimization:
#m.optimize()
m.pseudo_EM()
# Plot
fig, axes = pb.subplots(2,1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print(m)
if plot:
fig, axes = pb.subplots(2, 1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print m
return m
def toy_heaviside(seed=default_seed):
def toy_heaviside(seed=default_seed, optimize=True, plot=True):
"""
Simple 1D classification example using a heavy side gp transformation
@ -153,24 +158,26 @@ def toy_heaviside(seed=default_seed):
# Model definition
noise_model = GPy.likelihoods.bernoulli(GPy.likelihoods.noise_models.gp_transformations.Heaviside())
likelihood = GPy.likelihoods.EP(Y,noise_model)
likelihood = GPy.likelihoods.EP(Y, noise_model)
m = GPy.models.GPClassification(data['X'], likelihood=likelihood)
# Optimize
m.update_likelihood_approximation()
# Parameters optimization:
m.optimize()
#m.pseudo_EM()
if optimize:
m.update_likelihood_approximation()
# Parameters optimization:
m.optimize()
#m.pseudo_EM()
# Plot
fig, axes = pb.subplots(2,1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print(m)
if plot:
fig, axes = pb.subplots(2, 1)
m.plot_f(ax=axes[0])
m.plot(ax=axes[1])
print m
return m
def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None):
def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None, optimize=True, plot=True):
"""
Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
@ -187,7 +194,7 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
Y[Y.flatten()==-1] = 0
if model_type == 'Full':
m = GPy.models.GPClassification(data['X'], Y,kernel=kernel)
m = GPy.models.GPClassification(data['X'], Y, kernel=kernel)
elif model_type == 'DTC':
m = GPy.models.SparseGPClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)
@ -197,8 +204,11 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
m = GPy.models.FITCClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)
m['.*len'] = 3.
m.pseudo_EM()
print(m)
m.plot()
if optimize:
m.pseudo_EM()
if plot:
m.plot()
print m
return m