mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-10 20:42:39 +02:00
Convert print to function for Python 3 compatibility.
This commit is contained in:
Mike Croucher
parent
29da6ff065
commit
906f69e20e
5 changed files with 61 additions and 61 deletions
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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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@ -333,7 +333,7 @@ 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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@ -358,7 +358,7 @@ 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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@ -388,7 +388,7 @@ 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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@ -411,7 +411,7 @@ 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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@ -439,7 +439,7 @@ 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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@ -603,7 +603,7 @@ 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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@ -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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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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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)))
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if plot:
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#Plotting
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@ -15,7 +15,7 @@ def olympic_marathon_men(optimize=True, plot=True):
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"""Run a standard Gaussian process regression on the Olympic marathon data."""
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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data = pods.datasets.olympic_marathon_men()
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@ -88,7 +88,7 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
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"""
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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data = pods.datasets.epomeo_gpx()
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num_data_list = []
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@ -135,7 +135,7 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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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, :]
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@ -219,7 +219,7 @@ def olympic_100m_men(optimize=True, plot=True):
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"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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data = pods.datasets.olympic_100m_men()
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@ -240,7 +240,7 @@ def toy_rbf_1d(optimize=True, plot=True):
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"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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data = pods.datasets.toy_rbf_1d()
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@ -258,7 +258,7 @@ def toy_rbf_1d_50(optimize=True, plot=True):
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"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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data = pods.datasets.toy_rbf_1d_50()
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@ -377,7 +377,7 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
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"""Predict the location of a robot given wirelss signal strength readings."""
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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data = pods.datasets.robot_wireless()
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@ -398,14 +398,14 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
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sse = ((data['Xtest'] - Xpredict)**2).sum()
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print('Sum of squares error on test data: ' + str(sse))
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print(('Sum of squares error on test data: ' + str(sse)))
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return m
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def silhouette(max_iters=100, optimize=True, plot=True):
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"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
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try:import pods
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except ImportError:
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print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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print('pods unavailable, see https://github.com/sods/ods for example datasets')
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return
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data = pods.datasets.silhouette()
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@ -416,7 +416,7 @@ def silhouette(max_iters=100, optimize=True, plot=True):
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if optimize:
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m.optimize(messages=True, max_iters=max_iters)
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print m
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print(m)
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return m
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def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, optimize=True, plot=True, checkgrad=False):
|
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|
|
@ -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)
|
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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 +503,5 @@ 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
|
||||
|
|
|
|||
16
README.md
16
README.md
|
|
@ -1,13 +1,5 @@
|
|||
# GPy
|
||||
|
||||
# Moving to Python 3
|
||||
Work is underway to make GPy run on Python 3. We are not there yet! Changes performed so far have retained compatibility with Python 2.6 and abive.
|
||||
|
||||
Work done so far:
|
||||
|
||||
* Use 2to3 to fix relative imports
|
||||
* use 2to3 to convert print from statement to function. Some advanced uses of print meant that this could not be done in a way that retained compatibility with old versions of Python. The oldest version of Python that is supported by this version is 2.6 due to the required future imports.
|
||||
|
||||
A Gaussian processes framework in Python.
|
||||
|
||||
* [GPy homepage](http://sheffieldml.github.io/GPy/)
|
||||
|
|
@ -18,6 +10,14 @@ A Gaussian processes framework in Python.
|
|||
|
||||
Continuous integration status: 
|
||||
|
||||
### Moving to Python 3
|
||||
Work is underway to make GPy run on Python 3. We are not there yet! Changes performed so far have retained compatibility with Python 2.6 and abive.
|
||||
|
||||
Work done so far:
|
||||
|
||||
* Use 2to3 to fix relative imports
|
||||
* use 2to3 to convert print from statement to function. Some advanced uses of print meant that this could not be done in a way that retained compatibility with old versions of Python. The oldest version of Python that is supported by this version is 2.6 due to the required future imports.
|
||||
|
||||
### Citation
|
||||
|
||||
@Misc{gpy2014,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue