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examples corrected

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Ricardo 2013-06-05 18:01:05 +01:00
parent c774432fee
commit fa035ac39e
1 changed files with 40 additions and 31 deletions
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71
GPy/examples/classification.py
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@ -26,32 +26,40 @@ def crescent_data(seed=default_seed): # FIXME
m = GPy.models.GPClassification(data['X'], Y) m = GPy.models.GPClassification(data['X'], Y)
m.ensure_default_constraints() m.ensure_default_constraints()
m.update_likelihood_approximation() #m.update_likelihood_approximation()
m.optimize() #m.optimize()
m.pseudo_EM()
print(m) print(m)
m.plot() m.plot()
return m return m
def oil(): def oil(num_inducing=50):
""" """
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 oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
""" """
data = GPy.util.datasets.oil() data = GPy.util.datasets.oil()
Y = data['Y'][:, 0:1] X = data['X'][:600,:]
X_test = data['X'][600:,:]
Y = data['Y'][:600, 0:1]
Y[Y.flatten()==-1] = 0 Y[Y.flatten()==-1] = 0
Y_test = data['Y'][600:, 0:1]
# Create GP model # Create GP model
m = GPy.models.GPClassification(data['X'], Y) m = GPy.models.SparseGPClassification(X, Y,num_inducing=num_inducing)
# Contrain all parameters to be positive # Contrain all parameters to be positive
m.constrain_positive('') m.constrain_positive('')
m.tie_params('.*len') m.tie_params('.*len')
m['.*len'] = 10.
m.update_likelihood_approximation() m.update_likelihood_approximation()
# Optimize # Optimize
m.optimize() m.optimize()
print(m) print(m)
#Test
probs = m.predict(X_test)[0]
GPy.util.classification.conf_matrix(probs,Y_test)
return m return m
def toy_linear_1d_classification(seed=default_seed): def toy_linear_1d_classification(seed=default_seed):
@ -70,20 +78,20 @@ def toy_linear_1d_classification(seed=default_seed):
m.ensure_default_constraints() m.ensure_default_constraints()
# Optimize # Optimize
m.update_likelihood_approximation() #m.update_likelihood_approximation()
# Parameters optimization: # Parameters optimization:
m.optimize() #m.optimize()
m.pseudo_EM()
# Plot # Plot
pb.subplot(211) fig, axes = pb.subplots(2,1)
m.plot_f() m.plot_f(ax=axes[0])
pb.subplot(212) m.plot(ax=axes[1])
m.plot()
print(m) print(m)
return m return m
def sparse_toy_linear_1d_classification(seed=default_seed): def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
""" """
Sparse 1D classification example Sparse 1D classification example
:param seed : seed value for data generation (default is 4). :param seed : seed value for data generation (default is 4).
@ -95,25 +103,25 @@ def sparse_toy_linear_1d_classification(seed=default_seed):
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
# Model definition # Model definition
m = GPy.models.SparseGPClassification(data['X'], Y) m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
m['.*len']= 2. m['.*len']= 4.
m.ensure_default_constraints() m.ensure_default_constraints()
# Optimize # Optimize
m.update_likelihood_approximation() #m.update_likelihood_approximation()
# Parameters optimization: # Parameters optimization:
m.optimize() #m.optimize()
m.pseudo_EM()
# Plot # Plot
pb.subplot(211) fig, axes = pb.subplots(2,1)
m.plot_f() m.plot_f(ax=axes[0])
pb.subplot(212) m.plot(ax=axes[1])
m.plot()
print(m) print(m)
return m return m
def sparse_crescent_data(inducing=10, seed=default_seed): def sparse_crescent_data(num_inducing=10, seed=default_seed):
""" """
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. 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.
@ -128,16 +136,17 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
Y = data['Y'] Y = data['Y']
Y[Y.flatten()==-1]=0 Y[Y.flatten()==-1]=0
m = GPy.models.SparseGPClassification(data['X'], Y) m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
m.ensure_default_constraints() m.ensure_default_constraints()
m['.*len'] = 10. m['.*len'] = 10.
m.update_likelihood_approximation() #m.update_likelihood_approximation()
m.optimize() #m.optimize()
m.pseudo_EM()
print(m) print(m)
m.plot() m.plot()
return m return m
def FITC_crescent_data(inducing=10, seed=default_seed): def FITC_crescent_data(num_inducing=10, seed=default_seed):
""" """
Run a Gaussian process classification with FITC approximation on the crescent data. The demonstration uses EP to approximate the likelihood. Run a Gaussian process classification with FITC approximation on the crescent data. The demonstration uses EP to approximate the likelihood.
@ -145,7 +154,7 @@ def FITC_crescent_data(inducing=10, seed=default_seed):
:param seed : seed value for data generation. :param seed : seed value for data generation.
:type seed: int :type seed: int
:param inducing : number of inducing variables (only used for 'FITC' or 'DTC'). :param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
:type inducing: int :type num_inducing: int
""" """
data = GPy.util.datasets.crescent_data(seed=seed) data = GPy.util.datasets.crescent_data(seed=seed)
@ -157,12 +166,12 @@ def FITC_crescent_data(inducing=10, seed=default_seed):
Y = data['Y'] Y = data['Y']
Y[Y.flatten()==-1]=0 Y[Y.flatten()==-1]=0
m = GPy.models.FITCClassification(data['X'], Y) m = GPy.models.FITCClassification(data['X'], Y,num_inducing=num_inducing)
m.ensure_default_constraints() m.ensure_default_constraints()
m['.*len'] = 3. m['.*len'] = 3.
m.update_likelihood_approximation() #m.update_likelihood_approximation()
m.optimize() #m.optimize()
m.pseudo_EM()
print(m) print(m)
m.plot() m.plot()
return m return m