In [1]:
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage
from sklearn.gaussian_process import GaussianProcess

In [2]:
def pdense(x, y, sigma, M=1000):
""" Plot probability density of y with known stddev sigma
"""
assert len(x) == len(y) and len(x) == len(sigma)
N = len(x)
# TODO: better y ranging
ymin, ymax = min(y - 2 * sigma), max(y + 2 * sigma)
yy = np.linspace(ymin, ymax, M)
a = [np.exp(-((Y - yy) / s) ** 2) / s for Y, s in zip(y, sigma)]
A = np.array(a)
A = A.reshape(N, M)
plt.imshow(-A.T, cmap='gray', aspect='auto',
origin='lower', extent=(min(x)[0], max(x)[0], ymin, ymax))
plt.title('Density plot')

In [6]:
def gpr(seed=0, N=20, M=1000, sigma=1.0):
""" from scikits.learn demo
"""
np.random.seed(seed)

def f(x):
"""The function to predict."""
return x * np.sin(x)

X = np.linspace(0.1, 9.9, 20)
X = np.atleast_2d(X).T
y = f(X).ravel()
y = np.random.normal(y, sigma)
x = np.atleast_2d(np.linspace(0, 10, M)).T
nugget = (sigma / y) ** 2
gp = GaussianProcess(corr='squared_exponential', theta0=1e-1,
thetaL=1e-1, thetaU=1.0,
nugget=nugget,
random_start=100)
gp.fit(X, y)
y2, MSE = gp.predict(x, eval_MSE=True)
s2 = np.sqrt(MSE)
return X, y, x, y2, s2

In [4]:
X, y, x, y2, s2 = gpr(seed=0)
plt.figure(1)
pdense(x, y2, s2, M=1000)
plt.plot(X, y, 'r.')
plt.plot(x, y2, 'b:')
a = plt.gca()
a.set_ylim(-10, 15)
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.show()

1.0

/Users/kjordahl/dev/github/scikit-learn/sklearn/utils/__init__.py:80: DeprecationWarning: Function theta is deprecated; theta is deprecated and will be removed in 0.14, please use theta_ instead.
warnings.warn(msg, category=DeprecationWarning)

In [9]:
# Run the experiment many times
N = 200
Y = np.nan * np.ones((N, len(x)))
s = np.nan * np.ones((N, len(x)))
print 'Running trial',
for i in xrange(N):
X, y, x, y2, s2 = gpr(seed=i)
Y[i,:] = y2
s[i,:] = s2
print i,
print '\nDone!'
plt.plot(x, Y.T, 'b', alpha=0.15)
a = plt.gca()
a.set_ylim(-10, 15)
plt.title('Bootstrap spaghetti plot')
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.show()

Running trial 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199
Done!

In [10]:
ymin, ymax = -10, 15
bin_width = 0.15
y_bins = np.arange(ymin, ymax, bin_width)
H = np.zeros((len(y_bins)-1, len(x)))
m = np.zeros(x.shape)
for i in xrange(len(x)):
h, e = np.histogram(Y[:,i], bins=np.arange(ymin, ymax, bin_width), density=True)
H[:,i] = h
m[i] = np.median(Y[:,i])
hb = ndimage.gaussian_filter(H, sigma=1)
plt.imshow(-hb, cmap='gray', aspect='auto', origin='lower',
extent=(min(x)[0], max(x)[0], ymin, ymax))
plt.plot(x, m, 'b:')
a = plt.gca()
a.set_ylim(-10, 15)
plt.title('Bootstrap density plot')
plt.xlabel('$x$')
plt.ylabel('$f(x)$')

Out[10]:
<matplotlib.text.Text at 0x109f59450>
In [ ]: