mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-10 04:22:38 +02:00
Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel
This commit is contained in:
Neil Lawrence
commit
c05540dc31
170 changed files with 30768 additions and 2183 deletions
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@ -1,7 +1,7 @@
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# Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import classification
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import regression
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import dimensionality_reduction
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import non_gaussian
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from . import classification
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from . import regression
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from . import dimensionality_reduction
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from . import non_gaussian
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@ -15,7 +15,7 @@ def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
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data = pods.datasets.oil()
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X = data['X']
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Xtest = data['Xtest']
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@ -52,7 +52,7 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -75,7 +75,7 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
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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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print(m)
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return m
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def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=True):
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@ -88,7 +88,7 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -114,7 +114,7 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
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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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print(m)
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return m
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def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, optimize=True, plot=True):
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@ -127,7 +127,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -147,7 +147,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
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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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print(m)
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return m
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def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
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@ -160,7 +160,7 @@ def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -177,7 +177,7 @@ def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
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# Parameters optimization:
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for _ in range(5):
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m.optimize(max_iters=int(max_iters/5))
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print m
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print(m)
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# Plot
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if plot:
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@ -186,7 +186,7 @@ def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
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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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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, optimize=True, plot=True):
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@ -202,7 +202,7 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
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:type kernel: a GPy kernel
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
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data = pods.datasets.crescent_data(seed=seed)
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Y = data['Y']
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Y[Y.flatten()==-1] = 0
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@ -224,5 +224,5 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
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if plot:
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m.plot()
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print m
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print(m)
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return m
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@ -335,7 +335,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
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m.likelihood.variance = .1
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if optimize:
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print "Optimizing model:"
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print("Optimizing model:")
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m.optimize('bfgs', messages=verbose, max_iters=max_iters,
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gtol=.05)
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if plot:
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@ -360,7 +360,7 @@ def ssgplvm_simulation(optimize=True, verbose=1,
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m.likelihood.variance = .1
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if optimize:
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print "Optimizing model:"
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print("Optimizing model:")
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m.optimize('scg', messages=verbose, max_iters=max_iters,
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gtol=.05)
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if plot:
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@ -390,7 +390,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
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m.Yreal = Y
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if optimize:
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print "Optimizing model:"
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print("Optimizing model:")
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m.optimize('bfgs', messages=verbose, max_iters=max_iters,
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gtol=.05)
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if plot:
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@ -414,7 +414,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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m['.*noise'] = [Y.var() / 40. for Y in Ylist]
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if optimize:
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print "Optimizing Model:"
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print("Optimizing Model:")
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m.optimize(messages=verbose, max_iters=8e3)
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if plot:
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m.X.plot("MRD Latent Space 1D")
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@ -442,7 +442,7 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
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initx="random", initz='permute', **kw)
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if optimize:
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print "Optimizing Model:"
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print("Optimizing Model:")
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m.optimize('bfgs', messages=verbose, max_iters=8e3, gtol=.1)
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if plot:
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m.X.plot("MRD Latent Space 1D")
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@ -607,7 +607,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
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try:
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if optimize: m.optimize('bfgs', messages=verbose, max_iters=5e3, bfgs_factor=10)
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except KeyboardInterrupt:
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print "Keyboard interrupt, continuing to plot and return"
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print("Keyboard interrupt, continuing to plot and return")
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if plot:
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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@ -658,7 +658,7 @@ def ssgplvm_simulation_linear():
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def sample_X(Q, pi):
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x = np.empty(Q)
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dies = np.random.rand(Q)
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for q in xrange(Q):
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for q in range(Q):
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if dies[q] < pi:
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x[q] = np.random.randn()
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else:
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@ -668,7 +668,7 @@ def ssgplvm_simulation_linear():
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Y = np.empty((N, D))
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X = np.empty((N, Q))
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# Generate data from random sampled weight matrices
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for n in xrange(N):
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for n in range(N):
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X[n] = sample_X(Q, pi)
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w = np.random.randn(D, Q)
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Y[n] = np.dot(w, X[n])
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@ -37,7 +37,7 @@ def student_t_approx(optimize=True, plot=True):
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#Add student t random noise to datapoints
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deg_free = 1
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print "Real noise: ", real_std
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print("Real noise: ", real_std)
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initial_var_guess = 0.5
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edited_real_sd = initial_var_guess
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@ -73,7 +73,7 @@ def student_t_approx(optimize=True, plot=True):
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m4['.*t_scale2'].constrain_bounded(1e-6, 10.)
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m4['.*white'].constrain_fixed(1e-5)
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m4.randomize()
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print m4
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print(m4)
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debug=True
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if debug:
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m4.optimize(messages=1)
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@ -81,18 +81,18 @@ def student_t_approx(optimize=True, plot=True):
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pb.plot(m4.X, m4.inference_method.f_hat)
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pb.plot(m4.X, m4.Y, 'rx')
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m4.plot()
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print m4
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print(m4)
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return m4
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if optimize:
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optimizer='scg'
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print "Clean Gaussian"
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print("Clean Gaussian")
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m1.optimize(optimizer, messages=1)
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print "Corrupt Gaussian"
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print("Corrupt Gaussian")
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m2.optimize(optimizer, messages=1)
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print "Clean student t"
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print("Clean student t")
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m3.optimize(optimizer, messages=1)
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print "Corrupt student t"
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print("Corrupt student t")
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m4.optimize(optimizer, messages=1)
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if plot:
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@ -151,7 +151,7 @@ def boston_example(optimize=True, plot=True):
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for n, (train, test) in enumerate(kf):
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X_train, X_test, Y_train, Y_test = X[train], X[test], Y[train], Y[test]
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print "Fold {}".format(n)
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print("Fold {}".format(n))
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noise = 1e-1 #np.exp(-2)
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rbf_len = 0.5
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@ -163,21 +163,21 @@ def boston_example(optimize=True, plot=True):
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score_folds[0, n] = rmse(Y_test, np.mean(Y_train))
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#Gaussian GP
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print "Gauss GP"
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print("Gauss GP")
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mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp.copy())
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mgp.constrain_fixed('.*white', 1e-5)
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mgp['.*len'] = rbf_len
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mgp['.*noise'] = noise
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print mgp
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print(mgp)
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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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print(mgp)
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print(pred_density)
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print "Gaussian Laplace GP"
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print("Gaussian Laplace GP")
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N, D = Y_train.shape
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g_distribution = GPy.likelihoods.noise_model_constructors.gaussian(variance=noise, N=N, D=D)
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g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution)
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@ -186,18 +186,18 @@ def boston_example(optimize=True, plot=True):
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mg.constrain_fixed('.*white', 1e-5)
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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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print(mg)
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if optimize:
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mg.optimize(optimizer=optimizer, messages=messages)
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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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print(pred_density)
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print(mg)
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for stu_num, df in enumerate(degrees_freedoms):
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#Student T
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print "Student-T GP {}df".format(df)
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print("Student-T GP {}df".format(df))
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t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=df, sigma2=noise)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution)
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mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=stu_t_likelihood)
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@ -205,14 +205,14 @@ def boston_example(optimize=True, plot=True):
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mstu_t.constrain_bounded('.*t_scale2', 0.0001, 1000)
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mstu_t['rbf_len'] = rbf_len
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mstu_t['.*t_scale2'] = noise
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print mstu_t
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print(mstu_t)
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if optimize:
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mstu_t.optimize(optimizer=optimizer, messages=messages)
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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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print(pred_density)
|
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print(mstu_t)
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|
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if plot:
|
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plt.figure()
|
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|
|
@ -230,8 +230,8 @@ def boston_example(optimize=True, plot=True):
|
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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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|
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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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print("Average scores: {}".format(np.mean(score_folds, 1)))
|
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print("Average pred density: {}".format(np.mean(pred_density, 1)))
|
||||
|
||||
if plot:
|
||||
#Plotting
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ def olympic_marathon_men(optimize=True, plot=True):
|
|||
"""Run a standard Gaussian process regression on the Olympic marathon data."""
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.olympic_marathon_men()
|
||||
|
||||
|
|
@ -88,7 +88,7 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
|
|||
"""
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.epomeo_gpx()
|
||||
num_data_list = []
|
||||
|
|
@ -135,7 +135,7 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
|
|||
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)
|
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# data['Y'] = data['Y'][0::2, :]
|
||||
|
|
@ -219,7 +219,7 @@ def olympic_100m_men(optimize=True, plot=True):
|
|||
"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.olympic_100m_men()
|
||||
|
||||
|
|
@ -240,7 +240,7 @@ def toy_rbf_1d(optimize=True, plot=True):
|
|||
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.toy_rbf_1d()
|
||||
|
||||
|
|
@ -258,7 +258,7 @@ def toy_rbf_1d_50(optimize=True, plot=True):
|
|||
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.toy_rbf_1d_50()
|
||||
|
||||
|
|
@ -377,7 +377,7 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
|
|||
"""Predict the location of a robot given wirelss signal strength readings."""
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.robot_wireless()
|
||||
|
||||
|
|
@ -398,14 +398,14 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
|
|||
|
||||
sse = ((data['Xtest'] - Xpredict)**2).sum()
|
||||
|
||||
print('Sum of squares error on test data: ' + str(sse))
|
||||
print(('Sum of squares error on test data: ' + str(sse)))
|
||||
return m
|
||||
|
||||
def silhouette(max_iters=100, optimize=True, plot=True):
|
||||
"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
|
||||
try:import pods
|
||||
except ImportError:
|
||||
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
print('pods unavailable, see https://github.com/sods/ods for example datasets')
|
||||
return
|
||||
data = pods.datasets.silhouette()
|
||||
|
||||
|
|
@ -416,7 +416,7 @@ def silhouette(max_iters=100, optimize=True, plot=True):
|
|||
if optimize:
|
||||
m.optimize(messages=True, max_iters=max_iters)
|
||||
|
||||
print m
|
||||
print(m)
|
||||
return m
|
||||
|
||||
def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, optimize=True, plot=True, checkgrad=False):
|
||||
|
|
@ -468,7 +468,7 @@ def sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, opt
|
|||
if plot:
|
||||
m.plot()
|
||||
|
||||
print m
|
||||
print(m)
|
||||
return m
|
||||
|
||||
def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
|
||||
|
|
@ -492,7 +492,7 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
|
|||
if plot:
|
||||
m.plot(ax=axes[0])
|
||||
axes[0].set_title('no input uncertainty')
|
||||
print m
|
||||
print(m)
|
||||
|
||||
# the same Model with uncertainty
|
||||
m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.RBF(1), Z=Z, X_variance=S)
|
||||
|
|
@ -503,5 +503,50 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
|
|||
axes[1].set_title('with input uncertainty')
|
||||
fig.canvas.draw()
|
||||
|
||||
print m
|
||||
print(m)
|
||||
return m
|
||||
|
||||
def simple_mean_function(max_iters=100, optimize=True, plot=True):
|
||||
"""
|
||||
The simplest possible mean function. No parameters, just a simple Sinusoid.
|
||||
"""
|
||||
#create simple mean function
|
||||
mf = GPy.core.Mapping(1,1)
|
||||
mf.f = np.sin
|
||||
mf.update_gradients = lambda a,b: None
|
||||
|
||||
X = np.linspace(0,10,50).reshape(-1,1)
|
||||
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape)
|
||||
|
||||
k =GPy.kern.RBF(1)
|
||||
lik = GPy.likelihoods.Gaussian()
|
||||
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
|
||||
if optimize:
|
||||
m.optimize(max_iters=max_iters)
|
||||
if plot:
|
||||
m.plot(plot_limits=(-10,15))
|
||||
return m
|
||||
|
||||
def parametric_mean_function(max_iters=100, optimize=True, plot=True):
|
||||
"""
|
||||
A linear mean function with parameters that we'll learn alongside the kernel
|
||||
"""
|
||||
#create simple mean function
|
||||
mf = GPy.core.Mapping(1,1)
|
||||
mf.f = np.sin
|
||||
|
||||
X = np.linspace(0,10,50).reshape(-1,1)
|
||||
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape) + 3*X
|
||||
|
||||
mf = GPy.mappings.Linear(1,1)
|
||||
|
||||
k =GPy.kern.RBF(1)
|
||||
lik = GPy.likelihoods.Gaussian()
|
||||
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
|
||||
if optimize:
|
||||
m.optimize(max_iters=max_iters)
|
||||
if plot:
|
||||
m.plot()
|
||||
return m
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue