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https://github.com/SheffieldML/GPy.git
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Added optimize and plot for classification, non_gaussian and stochastic examples
This commit is contained in:
Alan Saul
parent
68ece19211
commit
3cd808cccc
3 changed files with 132 additions and 121 deletions
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@ -6,12 +6,11 @@
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Gaussian Processes classification
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"""
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import pylab as pb
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import numpy as np
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import GPy
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default_seed = 10000
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def oil(num_inducing=50, max_iters=100, kernel=None):
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def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
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"""
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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.
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@ -25,7 +24,7 @@ def oil(num_inducing=50, max_iters=100, kernel=None):
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Ytest[Ytest.flatten()==-1] = 0
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# Create GP model
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m = GPy.models.SparseGPClassification(X, Y,kernel=kernel,num_inducing=num_inducing)
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m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, num_inducing=num_inducing)
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# Contrain all parameters to be positive
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m.tie_params('.*len')
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@ -33,15 +32,16 @@ def oil(num_inducing=50, max_iters=100, kernel=None):
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m.update_likelihood_approximation()
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# Optimize
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m.optimize(max_iters=max_iters)
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if optimize:
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m.optimize(max_iters=max_iters)
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print(m)
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#Test
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probs = m.predict(Xtest)[0]
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GPy.util.classification.conf_matrix(probs,Ytest)
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GPy.util.classification.conf_matrix(probs, Ytest)
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return m
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def toy_linear_1d_classification(seed=default_seed):
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def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
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"""
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Simple 1D classification example using EP approximation
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@ -58,21 +58,23 @@ def toy_linear_1d_classification(seed=default_seed):
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m = GPy.models.GPClassification(data['X'], Y)
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# Optimize
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#m.update_likelihood_approximation()
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# Parameters optimization:
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#m.optimize()
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#m.update_likelihood_approximation()
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m.pseudo_EM()
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if optimize:
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#m.update_likelihood_approximation()
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# Parameters optimization:
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#m.optimize()
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#m.update_likelihood_approximation()
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m.pseudo_EM()
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# Plot
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fig, axes = pb.subplots(2,1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print(m)
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if plot:
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fig, axes = pb.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print m
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return m
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def toy_linear_1d_classification_laplace(seed=default_seed):
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def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=True):
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"""
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Simple 1D classification example using Laplace approximation
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@ -90,24 +92,25 @@ def toy_linear_1d_classification_laplace(seed=default_seed):
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# Model definition
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m = GPy.models.GPClassification(data['X'], Y, likelihood=laplace_likelihood)
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print m
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# Optimize
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#m.update_likelihood_approximation()
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# Parameters optimization:
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m.optimize('bfgs', messages=1)
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#m.pseudo_EM()
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if optimize:
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#m.update_likelihood_approximation()
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# Parameters optimization:
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m.optimize('bfgs', messages=1)
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#m.pseudo_EM()
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# Plot
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fig, axes = pb.subplots(2,1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print(m)
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if plot:
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fig, axes = pb.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print m
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return m
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def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
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def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, optimize=True, plot=True):
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"""
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Sparse 1D classification example
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@ -121,24 +124,26 @@ def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
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Y[Y.flatten() == -1] = 0
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# Model definition
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m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
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m['.*len']= 4.
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m = GPy.models.SparseGPClassification(data['X'], Y, num_inducing=num_inducing)
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m['.*len'] = 4.
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# Optimize
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#m.update_likelihood_approximation()
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# Parameters optimization:
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#m.optimize()
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m.pseudo_EM()
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if optimize:
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#m.update_likelihood_approximation()
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# Parameters optimization:
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#m.optimize()
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m.pseudo_EM()
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# Plot
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fig, axes = pb.subplots(2,1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print(m)
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if plot:
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fig, axes = pb.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print m
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return m
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def toy_heaviside(seed=default_seed):
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def toy_heaviside(seed=default_seed, optimize=True, plot=True):
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"""
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Simple 1D classification example using a heavy side gp transformation
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@ -153,24 +158,26 @@ def toy_heaviside(seed=default_seed):
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# Model definition
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noise_model = GPy.likelihoods.bernoulli(GPy.likelihoods.noise_models.gp_transformations.Heaviside())
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likelihood = GPy.likelihoods.EP(Y,noise_model)
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likelihood = GPy.likelihoods.EP(Y, noise_model)
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m = GPy.models.GPClassification(data['X'], likelihood=likelihood)
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# Optimize
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m.update_likelihood_approximation()
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# Parameters optimization:
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m.optimize()
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#m.pseudo_EM()
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if optimize:
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m.update_likelihood_approximation()
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# Parameters optimization:
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m.optimize()
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#m.pseudo_EM()
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# Plot
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fig, axes = pb.subplots(2,1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print(m)
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if plot:
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fig, axes = pb.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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print m
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return m
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def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None):
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def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None, optimize=True, plot=True):
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"""
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Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
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@ -187,7 +194,7 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
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Y[Y.flatten()==-1] = 0
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if model_type == 'Full':
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m = GPy.models.GPClassification(data['X'], Y,kernel=kernel)
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m = GPy.models.GPClassification(data['X'], Y, kernel=kernel)
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elif model_type == 'DTC':
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m = GPy.models.SparseGPClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)
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@ -197,8 +204,11 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
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m = GPy.models.FITCClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)
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m['.*len'] = 3.
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m.pseudo_EM()
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print(m)
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m.plot()
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if optimize:
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m.pseudo_EM()
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if plot:
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m.plot()
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print m
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return m
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@ -114,7 +114,7 @@ def student_t_approx(optimize=True, plot=True):
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return m1, m2, m3, m4
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def boston_example():
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def boston_example(optimize=True, plot=True):
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import sklearn
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from sklearn.cross_validation import KFold
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optimizer='bfgs'
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@ -143,7 +143,6 @@ def boston_example():
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noise = 1e-1 #np.exp(-2)
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rbf_len = 0.5
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data_axis_plot = 4
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plot = False
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1]) + GPy.kern.bias(X.shape[1])
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kernelgp = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1]) + GPy.kern.bias(X.shape[1])
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@ -158,17 +157,13 @@ def boston_example():
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mgp['rbf_len'] = rbf_len
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mgp['noise'] = noise
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print mgp
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mgp.optimize(optimizer=optimizer, messages=messages)
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if optimize:
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mgp.optimize(optimizer=optimizer, messages=messages)
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Y_test_pred = mgp.predict(X_test)
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score_folds[1, n] = rmse(Y_test, Y_test_pred[0])
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pred_density[1, n] = np.mean(mgp.log_predictive_density(X_test, Y_test))
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print mgp
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print pred_density
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if plot:
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plt.figure()
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plt.scatter(X_test[:, data_axis_plot], Y_test_pred[0])
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plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x')
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plt.title('GP gauss')
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print "Gaussian Laplace GP"
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N, D = Y_train.shape
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@ -181,20 +176,13 @@ def boston_example():
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mg['rbf_len'] = rbf_len
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mg['noise'] = noise
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print mg
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try:
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if optimize:
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mg.optimize(optimizer=optimizer, messages=messages)
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except Exception:
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print "Blew up"
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Y_test_pred = mg.predict(X_test)
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score_folds[2, n] = rmse(Y_test, Y_test_pred[0])
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pred_density[2, n] = np.mean(mg.log_predictive_density(X_test, Y_test))
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print pred_density
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print mg
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if plot:
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plt.figure()
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plt.scatter(X_test[:, data_axis_plot], Y_test_pred[0])
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plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x')
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plt.title('Lap gauss')
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for stu_num, df in enumerate(degrees_freedoms):
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#Student T
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@ -208,61 +196,71 @@ def boston_example():
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mstu_t['rbf_len'] = rbf_len
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mstu_t['t_noise'] = noise
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print mstu_t
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try:
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if optimize:
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mstu_t.optimize(optimizer=optimizer, messages=messages)
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except Exception:
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print "Blew up"
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Y_test_pred = mstu_t.predict(X_test)
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score_folds[3+stu_num, n] = rmse(Y_test, Y_test_pred[0])
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pred_density[3+stu_num, n] = np.mean(mstu_t.log_predictive_density(X_test, Y_test))
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print pred_density
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print mstu_t
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if plot:
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plt.figure()
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plt.scatter(X_test[:, data_axis_plot], Y_test_pred[0])
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plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x')
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plt.title('Stu t {}df'.format(df))
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if plot:
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plt.figure()
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plt.scatter(X_test[:, data_axis_plot], Y_test_pred[0])
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plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x')
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plt.title('GP gauss')
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plt.figure()
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plt.scatter(X_test[:, data_axis_plot], Y_test_pred[0])
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plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x')
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plt.title('Lap gauss')
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plt.figure()
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plt.scatter(X_test[:, data_axis_plot], Y_test_pred[0])
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plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x')
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plt.title('Stu t {}df'.format(df))
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print "Average scores: {}".format(np.mean(score_folds, 1))
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print "Average pred density: {}".format(np.mean(pred_density, 1))
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#Plotting
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stu_t_legends = ['Student T, df={}'.format(df) for df in degrees_freedoms]
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legends = ['Baseline', 'Gaussian', 'Laplace Approx Gaussian'] + stu_t_legends
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if plot:
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#Plotting
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stu_t_legends = ['Student T, df={}'.format(df) for df in degrees_freedoms]
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legends = ['Baseline', 'Gaussian', 'Laplace Approx Gaussian'] + stu_t_legends
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#Plot boxplots for RMSE density
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fig = plt.figure()
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ax=fig.add_subplot(111)
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plt.title('RMSE')
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bp = ax.boxplot(score_folds.T, notch=0, sym='+', vert=1, whis=1.5)
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plt.setp(bp['boxes'], color='black')
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plt.setp(bp['whiskers'], color='black')
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plt.setp(bp['fliers'], color='red', marker='+')
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xtickNames = plt.setp(ax, xticklabels=legends)
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plt.setp(xtickNames, rotation=45, fontsize=8)
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ax.set_ylabel('RMSE')
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ax.set_xlabel('Distribution')
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#Make grid and put it below boxes
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ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
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alpha=0.5)
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ax.set_axisbelow(True)
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#Plot boxplots for RMSE density
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fig = plt.figure()
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ax=fig.add_subplot(111)
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plt.title('RMSE')
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bp = ax.boxplot(score_folds.T, notch=0, sym='+', vert=1, whis=1.5)
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plt.setp(bp['boxes'], color='black')
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plt.setp(bp['whiskers'], color='black')
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plt.setp(bp['fliers'], color='red', marker='+')
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xtickNames = plt.setp(ax, xticklabels=legends)
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plt.setp(xtickNames, rotation=45, fontsize=8)
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ax.set_ylabel('RMSE')
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ax.set_xlabel('Distribution')
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#Make grid and put it below boxes
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ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
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alpha=0.5)
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ax.set_axisbelow(True)
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#Plot boxplots for predictive density
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fig = plt.figure()
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ax=fig.add_subplot(111)
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plt.title('Predictive density')
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bp = ax.boxplot(pred_density[1:,:].T, notch=0, sym='+', vert=1, whis=1.5)
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plt.setp(bp['boxes'], color='black')
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plt.setp(bp['whiskers'], color='black')
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plt.setp(bp['fliers'], color='red', marker='+')
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xtickNames = plt.setp(ax, xticklabels=legends[1:])
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plt.setp(xtickNames, rotation=45, fontsize=8)
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ax.set_ylabel('Mean Log probability P(Y*|Y)')
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ax.set_xlabel('Distribution')
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#Make grid and put it below boxes
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ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
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alpha=0.5)
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ax.set_axisbelow(True)
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#Plot boxplots for predictive density
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fig = plt.figure()
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ax=fig.add_subplot(111)
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plt.title('Predictive density')
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bp = ax.boxplot(pred_density[1:,:].T, notch=0, sym='+', vert=1, whis=1.5)
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plt.setp(bp['boxes'], color='black')
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plt.setp(bp['whiskers'], color='black')
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plt.setp(bp['fliers'], color='red', marker='+')
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xtickNames = plt.setp(ax, xticklabels=legends[1:])
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plt.setp(xtickNames, rotation=45, fontsize=8)
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ax.set_ylabel('Mean Log probability P(Y*|Y)')
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ax.set_xlabel('Distribution')
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#Make grid and put it below boxes
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ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
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alpha=0.5)
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ax.set_axisbelow(True)
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return mstu_t
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def precipitation_example():
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@ -5,7 +5,7 @@ import pylab as pb
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import numpy as np
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import GPy
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def toy_1d():
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def toy_1d(optimize=True, plot=True):
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N = 2000
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M = 20
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@ -20,15 +20,18 @@ def toy_1d():
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m.param_steplength = 1e-4
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fig = pb.figure()
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ax = fig.add_subplot(111)
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def cb():
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ax.cla()
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m.plot(ax=ax,Z_height=-3)
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ax.set_ylim(-3,3)
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fig.canvas.draw()
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if plot:
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fig = pb.figure()
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ax = fig.add_subplot(111)
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def cb(foo):
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ax.cla()
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m.plot(ax=ax,Z_height=-3)
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ax.set_ylim(-3,3)
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fig.canvas.draw()
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m.optimize(500, callback=cb, callback_interval=1)
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if optimize:
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m.optimize(500, callback=cb, callback_interval=1)
|
||||
|
||||
m.plot_traces()
|
||||
if plot:
|
||||
m.plot_traces()
|
||||
return m
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue