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Minor fixes to classification to allow kernel choice, change of oil example to use full test set and full training set.

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This commit is contained in:
Neil Lawrence 2013-08-19 07:37:09 +02:00
parent 2004cf3ea9
commit 0380f52702
3 changed files with 83 additions and 66 deletions
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28
GPy/examples/classification.py
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@ -10,7 +10,7 @@ import numpy as np
import GPy
default_seed = 10000
def crescent_data(seed=default_seed): # FIXME
def crescent_data(seed=default_seed, kernel=None): # FIXME
"""Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
@ -32,33 +32,33 @@ def crescent_data(seed=default_seed): # FIXME
m.plot()
return m
def oil(num_inducing=50):
def oil(num_inducing=50, max_iters=100, kernel=None):
"""
Run a Gaussian process classification on the oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
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.
"""
data = GPy.util.datasets.oil()
X = data['X'][:600,:]
X_test = data['X'][600:,:]
Y = data['Y'][:600, 0:1]
X = data['X']
Xtest = data['Xtest']
Y = data['Y'][:, 0:1]
Ytest = data['Ytest'][:, 0:1]
Y[Y.flatten()==-1] = 0
Y_test = data['Y'][600:, 0:1]
Ytest[Ytest.flatten()==-1] = 0
# Create GP model
m = GPy.models.SparseGPClassification(X, Y,num_inducing=num_inducing)
m = GPy.models.SparseGPClassification(X, Y,kernel=kernel,num_inducing=num_inducing)
# Contrain all parameters to be positive
m.constrain_positive('')
m.tie_params('.*len')
m['.*len'] = 10.
m.update_likelihood_approximation()
# Optimize
m.optimize()
m.optimize(max_iters=max_iters)
print(m)
#Test
probs = m.predict(X_test)[0]
GPy.util.classification.conf_matrix(probs,Y_test)
probs = m.predict(Xtest)[0]
GPy.util.classification.conf_matrix(probs,Ytest)
return m
def toy_linear_1d_classification(seed=default_seed):
@ -118,7 +118,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
return m
def sparse_crescent_data(num_inducing=10, seed=default_seed):
def sparse_crescent_data(num_inducing=10, seed=default_seed, kernel=kernel):
"""
Run a Gaussian process classification with DTC approxiamtion on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
@ -133,7 +133,7 @@ def sparse_crescent_data(num_inducing=10, seed=default_seed):
Y = data['Y']
Y[Y.flatten()==-1]=0
m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
m = GPy.models.SparseGPClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)
m['.*len'] = 10.
#m.update_likelihood_approximation()
#m.optimize()