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Boston housing works (apart from variance of student
t is not valid below 2)
This commit is contained in:
Alan Saul
parent
9d7b670160
commit
2c419d2f48
1 changed files with 184 additions and 97 deletions
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@ -657,6 +657,190 @@ def boston_example():
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data = datasets.boston_housing()
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data = datasets.boston_housing()
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X = data['X'].copy()
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X = data['X'].copy()
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Y = data['Y'].copy()
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Y = data['Y'].copy()
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X = X-X.mean(axis=0)
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X = X/X.std(axis=0)
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Y = Y-Y.mean()
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Y = Y/Y.std()
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num_folds = 10
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kf = KFold(len(Y), n_folds=num_folds, indices=True)
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score_folds = np.zeros((6, num_folds))
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def rmse(Y, Ystar):
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return np.sqrt(np.mean((Y-Ystar)**2))
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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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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 = True
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#Gaussian GP
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print "Gauss GP"
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kernelgp = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp)
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mgp.ensure_default_constraints()
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mgp.constrain_fixed('white', 1e-5)
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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(messages=1)
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Y_test_pred = mgp.predict(X_test)
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score_folds[0, n] = rmse(Y_test, Y_test_pred[0])
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print mgp
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print score_folds
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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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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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N, D = Y_train.shape
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g_distribution = GPy.likelihoods.functions.Gaussian(variance=noise, N=N, D=D)
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g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution, opt='rasm')
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mg = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=g_likelihood)
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mg.ensure_default_constraints()
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mg.constrain_positive('noise_variance')
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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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try:
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mg.optimize(messages=1)
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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[1, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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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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#Student T
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deg_free = 1
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print "Student-T GP {}df".format(deg_free)
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
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mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
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mstu_t.ensure_default_constraints()
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mstu_t.constrain_fixed('white', 1e-5)
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mstu_t.constrain_bounded('t_noise', 0.0001, 1000)
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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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mstu_t.optimize(messages=1)
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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[2, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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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(deg_free))
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deg_free = 2
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print "Student-T GP {}df".format(deg_free)
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
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mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
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mstu_t.ensure_default_constraints()
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mstu_t.constrain_fixed('white', 1e-5)
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mstu_t.constrain_bounded('t_noise', 0.0001, 1000)
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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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mstu_t.optimize(messages=1)
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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, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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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(deg_free))
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#Student t likelihood
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deg_free = 3
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print "Student-T GP {}df".format(deg_free)
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
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mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
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mstu_t.ensure_default_constraints()
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mstu_t.constrain_fixed('white', 1e-5)
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mstu_t.constrain_bounded('t_noise', 0.0001, 1000)
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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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mstu_t.optimize(messages=1)
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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[4, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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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(deg_free))
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deg_free = 5
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print "Student-T GP {}df".format(deg_free)
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
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mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
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mstu_t.ensure_default_constraints()
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mstu_t.constrain_fixed('white', 1e-5)
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mstu_t.constrain_bounded('t_noise', 0.0001, 1000)
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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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mstu_t.optimize(messages=1)
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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[5, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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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(deg_free))
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import ipdb; ipdb.set_trace() # XXX BREAKPOINT
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return score_folds
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def precipitation_example():
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import sklearn
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from sklearn.cross_validation import KFold
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data = datasets.boston_housing()
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X = data['X'].copy()
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Y = data['Y'].copy()
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X = X-X.mean(axis=0)
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X = X/X.std(axis=0)
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Y = Y-Y.mean()
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Y = Y-Y.mean()
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Y = Y/Y.std()
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Y = Y/Y.std()
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import ipdb; ipdb.set_trace() # XXX BREAKPOINT
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import ipdb; ipdb.set_trace() # XXX BREAKPOINT
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@ -670,103 +854,6 @@ def boston_example():
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X_train, X_test, Y_train, Y_test = X[train], X[test], Y[train], Y[test]
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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 = np.exp(-2)
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#Gaussian GP
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print "Gauss GP"
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kernelgp = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.01)
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mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp)
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mgp.ensure_default_constraints()
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mgp['noise'] = noise
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mgp.constrain_fixed('white', 0.01)
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print mgp
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mgp.optimize(messages=1)
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Y_test_pred = mgp.predict(X_test)
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score_folds[0, n] = rmse(Y_test, Y_test_pred[0])
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print mgp
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print score_folds
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#plt.figure()
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#plt.scatter(X_test[:, 0], Y_test_pred[0])
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#plt.scatter(X_test[:, 0], 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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sigma2_start = 1
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.1)
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N, D = Y_train.shape
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g_distribution = GPy.likelihoods.functions.Gaussian(variance=noise, N=N, D=D)
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g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution, opt='rasm')
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mg = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=g_likelihood)
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mg.ensure_default_constraints()
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mg.constrain_positive('noise_variance')
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mg.constrain_fixed('white', 0.01)
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mg['noise'] = noise
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print mg
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try:
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mg.optimize(messages=1)
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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[1, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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print mg
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#plt.figure()
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#plt.scatter(X_test[:, 0], Y_test_pred[0])
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#plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
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#plt.title('Lap gauss')
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#Student t likelihood
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deg_free = 5
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print "Student-T GP {}df".format(deg_free)
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.1)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
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mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
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mstu_t.ensure_default_constraints()
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mstu_t.constrain_fixed('white', 0.01)
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#mstu_t.constrain_positive('t_noise')
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mstu_t.constrain_bounded('t_noise', 0.001, 1000)
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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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mstu_t.optimize(messages=1)
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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[2, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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print mstu_t
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#plt.figure()
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#plt.scatter(X_test[:, 0], Y_test_pred[0])
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#plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
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#plt.title('Stu t {}df'.format(deg_free))
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deg_free = 3
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print "Student-T GP {}df".format(deg_free)
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kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.1)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
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mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
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mstu_t.ensure_default_constraints()
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mstu_t.constrain_fixed('white', 0.01)
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#mstu_t.constrain_positive('t_noise')
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mstu_t.constrain_bounded('t_noise', 0.001, 1000)
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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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mstu_t.optimize(messages=1)
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except Exception:
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print "Blew up"
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mstu_t.optimize(messages=1)
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Y_test_pred = mstu_t.predict(X_test)
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score_folds[3, n] = rmse(Y_test, Y_test_pred[0])
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print score_folds
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print mstu_t
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#plt.figure()
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#plt.scatter(X_test[:, 0], Y_test_pred[0])
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#plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
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#plt.title('Stu t {}df'.format(deg_free))
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def plot_f_approx(model):
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def plot_f_approx(model):
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plt.figure()
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plt.figure()
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