mirror of
https://github.com/SheffieldML/GPy.git
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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Zhenwen Dai
commit
171ec1a563
22 changed files with 363 additions and 389 deletions
11
AUTHORS.txt
11
AUTHORS.txt
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@ -1,8 +1,3 @@
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James Hensman
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Nicolo Fusi
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Ricardo Andrade
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Nicolas Durrande
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Alan Saul
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Max Zwiessele
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Neil D. Lawrence
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Zhenwen Dai
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# This list was out of date, I've put "see contributors" for the moment. Not sure how to fix long term but don't want to be
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# embarrassed by some off the great contributors of late being omitted from a list.
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See contributors.
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@ -21,6 +21,8 @@ from . import plotting
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from .core import Model
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from .core.parameterization import Param, Parameterized, ObsAr
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from .__version__ import __version__
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#@nottest
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try:
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#Get rid of nose dependency by only ignoring if you have nose installed
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1
GPy/__version__.py
Normal file
1
GPy/__version__.py
Normal file
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@ -0,0 +1 @@
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__version__ = "0.8.3"
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@ -535,7 +535,7 @@ class GP(Model):
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which_data_ycols='all', fixed_inputs=[],
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levels=20, samples=0, fignum=None, ax=None, resolution=None,
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plot_raw=False, linecol=None,fillcol=None, Y_metadata=None,
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data_symbol='kx', predict_kw=None, plot_training_data=True):
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data_symbol='kx', predict_kw=None, plot_training_data=True, samples_y=0, apply_link=False):
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"""
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Plot the posterior of the GP.
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- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
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@ -558,7 +558,7 @@ class GP(Model):
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:param levels: number of levels to plot in a contour plot.
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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:param samples: the number of a posteriori samples to plot
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:param samples: the number of a posteriori samples to plot, p(f*|y)
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:type samples: int
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:param fignum: figure to plot on.
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:type fignum: figure number
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@ -574,6 +574,10 @@ class GP(Model):
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:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
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:param plot_training_data: whether or not to plot the training points
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:type plot_training_data: boolean
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:param samples_y: the number of a posteriori samples to plot, p(y*|y)
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:type samples_y: int
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:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*, when plotting posterior samples f
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:type apply_link: boolean
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import models_plots
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@ -587,7 +591,7 @@ class GP(Model):
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levels, samples, fignum, ax, resolution,
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plot_raw=plot_raw, Y_metadata=Y_metadata,
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data_symbol=data_symbol, predict_kw=predict_kw,
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plot_training_data=plot_training_data, **kw)
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plot_training_data=plot_training_data, samples_y=samples_y, apply_link=apply_link, **kw)
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def plot_data(self, which_data_rows='all',
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@ -613,7 +617,7 @@ class GP(Model):
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:param levels: number of levels to plot in a contour plot.
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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:param samples: the number of a posteriori samples to plot
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:param samples: the number of a posteriori samples to plot, p(f*|y)
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:type samples: int
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:param fignum: figure to plot on.
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:type fignum: figure number
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@ -366,6 +366,7 @@ class InverseGamma(Gamma):
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def rvs(self, n):
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return 1. / np.random.gamma(scale=1. / self.b, shape=self.a, size=n)
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class DGPLVM_KFDA(Prior):
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"""
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Implementation of the Discriminative Gaussian Process Latent Variable function using
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@ -512,6 +513,7 @@ class DGPLVM_KFDA(Prior):
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self.A = self.compute_A(lst_ni)
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self.x_shape = x_shape
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class DGPLVM(Prior):
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"""
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Implementation of the Discriminative Gaussian Process Latent Variable model paper, by Raquel.
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@ -669,7 +671,7 @@ class DGPLVM(Prior):
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M_i = self.compute_Mi(cls)
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Sb = self.compute_Sb(cls, M_i, M_0)
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Sw = self.compute_Sw(cls, M_i)
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# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
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# sb_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
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#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
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#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
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Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
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@ -1198,6 +1200,7 @@ class DGPLVM_T(Prior):
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class HalfT(Prior):
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"""
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Implementation of the half student t probability function, coupled with random variables.
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@ -1208,15 +1211,17 @@ class HalfT(Prior):
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"""
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domain = _POSITIVE
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_instances = []
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def __new__(cls, A, nu): # Singleton:
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def __new__(cls, A, nu): # Singleton:
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if cls._instances:
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cls._instances[:] = [instance for instance in cls._instances if instance()]
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for instance in cls._instances:
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if instance().A == A and instance().nu == nu:
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return instance()
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return instance()
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o = super(Prior, cls).__new__(cls, A, nu)
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cls._instances.append(weakref.ref(o))
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return cls._instances[-1]()
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def __init__(self, A, nu):
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self.A = float(A)
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self.nu = float(nu)
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@ -1225,37 +1230,81 @@ class HalfT(Prior):
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def __str__(self):
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return "hT({:.2g}, {:.2g})".format(self.A, self.nu)
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def lnpdf(self,theta):
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return (theta>0) * ( self.constant -.5*(self.nu+1) * np.log( 1.+ (1./self.nu) * (theta/self.A)**2 ) )
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def lnpdf(self, theta):
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return (theta > 0) * (self.constant - .5*(self.nu + 1) * np.log(1. + (1./self.nu) * (theta/self.A)**2))
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#theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
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#lnpdfs = np.zeros_like(theta)
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#theta = np.array([theta])
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#above_zero = theta.flatten()>1e-6
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#v = self.nu
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#sigma2=self.A
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#stop
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#lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5)
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# - gammaln(v * 0.5)
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# - 0.5*np.log(sigma2 * v * np.pi)
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# - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2))
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#)
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#return lnpdfs
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# theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
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# lnpdfs = np.zeros_like(theta)
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# theta = np.array([theta])
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# above_zero = theta.flatten()>1e-6
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# v = self.nu
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# sigma2=self.A
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# stop
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# lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5)
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# - gammaln(v * 0.5)
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# - 0.5*np.log(sigma2 * v * np.pi)
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# - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2))
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# )
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# return lnpdfs
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def lnpdf_grad(self,theta):
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theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
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def lnpdf_grad(self, theta):
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theta = theta if isinstance(theta, np.ndarray) else np.array([theta])
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grad = np.zeros_like(theta)
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above_zero = theta>1e-6
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above_zero = theta > 1e-6
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v = self.nu
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sigma2=self.A
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sigma2 = self.A
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grad[above_zero] = -0.5*(v+1)*(2*theta[above_zero])/(v*sigma2 + theta[above_zero][0]**2)
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return grad
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def rvs(self, n):
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#return np.random.randn(n) * self.sigma + self.mu
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from scipy.stats import t
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#[np.abs(x) for x in t.rvs(df=4,loc=0,scale=50, size=10000)])
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ret = t.rvs(self.nu,loc=0,scale=self.A, size=n)
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ret[ret<0] = 0
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return ret
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# return np.random.randn(n) * self.sigma + self.mu
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from scipy.stats import t
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# [np.abs(x) for x in t.rvs(df=4,loc=0,scale=50, size=10000)])
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ret = t.rvs(self.nu, loc=0, scale=self.A, size=n)
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ret[ret < 0] = 0
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return ret
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class Exponential(Prior):
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"""
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Implementation of the Exponential probability function,
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coupled with random variables.
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:param l: shape parameter
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"""
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domain = _POSITIVE
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_instances = []
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def __new__(cls, l): # Singleton:
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if cls._instances:
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cls._instances[:] = [instance for instance in cls._instances if instance()]
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for instance in cls._instances:
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if instance().l == l:
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return instance()
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o = super(Exponential, cls).__new__(cls, l)
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cls._instances.append(weakref.ref(o))
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return cls._instances[-1]()
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def __init__(self, l):
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self.l = l
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def __str__(self):
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return "Exp({:.2g})".format(self.l)
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def summary(self):
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ret = {"E[x]": 1. / self.l,
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"E[ln x]": np.nan,
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"var[x]": 1. / self.l**2,
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"Entropy": 1. - np.log(self.l),
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"Mode": 0.}
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return ret
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def lnpdf(self, x):
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return np.log(self.l) - self.l * x
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def lnpdf_grad(self, x):
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return - self.l
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def rvs(self, n):
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return np.random.exponential(scale=self.l, size=n)
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@ -62,7 +62,7 @@ class Transformation(object):
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import matplotlib.pyplot as plt
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from ...plotting.matplot_dep import base_plots
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x = np.linspace(-8,8)
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base_plots.meanplot(x, self.f(x),axes=axes*args,**kw)
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base_plots.meanplot(x, self.f(x), *args, ax=axes, **kw)
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axes = plt.gca()
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axes.set_xlabel(xlabel)
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axes.set_ylabel(ylabel)
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@ -488,7 +488,7 @@ class Logistic(Transformation):
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return instance()
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newfunc = super(Transformation, cls).__new__
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if newfunc is object.__new__:
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o = newfunc(cls)
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o = newfunc(cls)
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else:
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o = newfunc(cls, lower, upper, *args, **kwargs)
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cls._instances.append(weakref.ref(o))
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|
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@ -1 +1,2 @@
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from .hmc import HMC
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from .samplers import *
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@ -18,11 +18,11 @@ class Metropolis_Hastings:
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def __init__(self,model,cov=None):
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"""Metropolis Hastings, with tunings according to Gelman et al. """
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self.model = model
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current = self.model._get_params_transformed()
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current = self.model.optimizer_array
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self.D = current.size
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self.chains = []
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if cov is None:
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self.cov = model.Laplace_covariance()
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self.cov = np.eye(self.D)
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else:
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self.cov = cov
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self.scale = 2.4/np.sqrt(self.D)
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@ -33,20 +33,20 @@ class Metropolis_Hastings:
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if start is None:
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self.model.randomize()
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else:
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self.model._set_params_transformed(start)
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self.model.optimizer_array = start
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def sample(self, Ntotal, Nburn, Nthin, tune=True, tune_throughout=False, tune_interval=400):
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current = self.model._get_params_transformed()
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fcurrent = self.model.log_likelihood() + self.model.log_prior()
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def sample(self, Ntotal=10000, Nburn=1000, Nthin=10, tune=True, tune_throughout=False, tune_interval=400):
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current = self.model.optimizer_array
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fcurrent = self.model.log_likelihood() + self.model.log_prior() + \
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self.model._log_det_jacobian()
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accepted = np.zeros(Ntotal,dtype=np.bool)
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for it in range(Ntotal):
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print("sample %d of %d\r"%(it,Ntotal), end=' ')
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print("sample %d of %d\r"%(it,Ntotal),end="\t")
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sys.stdout.flush()
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prop = np.random.multivariate_normal(current, self.cov*self.scale*self.scale)
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self.model._set_params_transformed(prop)
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fprop = self.model.log_likelihood() + self.model.log_prior()
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self.model.optimizer_array = prop
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fprop = self.model.log_likelihood() + self.model.log_prior() + \
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self.model._log_det_jacobian()
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if fprop>fcurrent:#sample accepted, going 'uphill'
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accepted[it] = True
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@ -74,10 +74,11 @@ class Metropolis_Hastings:
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def predict(self,function,args):
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"""Make a prediction for the function, to which we will pass the additional arguments"""
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param = self.model._get_params()
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param = self.model.param_array
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fs = []
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for p in self.chain:
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self.model._set_params(p)
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self.model.param_array = p
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fs.append(function(*args))
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self.model._set_params(param)# reset model to starting state
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# reset model to starting state
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self.model.param_array = param
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return fs
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|
|
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|
|
@ -256,8 +256,6 @@ class Kern(Parameterized):
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:param other: the other kernel to be added
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:type other: GPy.kern
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:param tensor: whether or not to use the tensor space (default is false).
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:type tensor: bool
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"""
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assert isinstance(other, Kern), "only kernels can be multiplied to kernels..."
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|
|
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|
@ -27,8 +27,6 @@ class Prod(CombinationKernel):
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|||
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:param k1, k2: the kernels to multiply
|
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:type k1, k2: Kern
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:param tensor: The kernels are either multiply as functions defined on the same input space (default) or on the product of the input spaces
|
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:type tensor: Boolean
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:rtype: kernel object
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||||
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"""
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|
|
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|||
|
|
@ -124,7 +124,7 @@ class Exponential(Likelihood):
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|||
#d3lik_dlink3 = 6*y/(link_f**4) - 2./(link_f**3)
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return d3lik_dlink3
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||||
def samples(self, gp):
|
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def samples(self, gp, Y_metadata=None):
|
||||
"""
|
||||
Returns a set of samples of observations based on a given value of the latent variable.
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||||
|
||||
|
|
|
|||
|
|
@ -622,7 +622,7 @@ class Likelihood(Parameterized):
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Nf_samp = 300
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Ny_samp = 1
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s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu
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ss_y = self.samples(s, Y_metadata, samples=Ny_samp)
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ss_y = self.samples(s, Y_metadata)#, samples=Ny_samp)
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#ss_y = ss_y.reshape(mu.shape[0], mu.shape[1], Nf_samp*Ny_samp)
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pred_quantiles = [np.percentile(ss_y, q, axis=1)[:,None] for q in quantiles]
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||||
|
|
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|||
|
|
@ -137,7 +137,7 @@ class Poisson(Likelihood):
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|||
"""
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return self.gp_link.transf(gp)
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||||
def samples(self, gp, Y_metadata=None, samples=1):
|
||||
def samples(self, gp, Y_metadata=None):
|
||||
"""
|
||||
Returns a set of samples of observations based on a given value of the latent variable.
|
||||
|
||||
|
|
@ -145,5 +145,7 @@ class Poisson(Likelihood):
|
|||
"""
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||||
orig_shape = gp.shape
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||||
gp = gp.flatten()
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Ysim = np.random.poisson(self.gp_link.transf(gp), [samples, gp.size]).T
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return Ysim.reshape(orig_shape+(samples,))
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# Ysim = np.random.poisson(self.gp_link.transf(gp), [samples, gp.size]).T
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# return Ysim.reshape(orig_shape+(samples,))
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Ysim = np.random.poisson(self.gp_link.transf(gp))
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return Ysim.reshape(orig_shape)
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||||
|
|
|
|||
|
|
@ -75,7 +75,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
levels=20, samples=0, fignum=None, ax=None, resolution=None,
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plot_raw=False,
|
||||
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx',
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||||
apply_link=False, samples_f=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
|
||||
apply_link=False, samples_y=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
|
||||
"""
|
||||
Plot the posterior of the GP.
|
||||
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
|
||||
|
|
@ -93,24 +93,30 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
:type which_data_rows: 'all' or a list of integers
|
||||
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
|
||||
:type fixed_inputs: a list of tuples
|
||||
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
|
||||
:type resolution: int
|
||||
:param levels: number of levels to plot in a contour plot.
|
||||
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
|
||||
:type levels: int
|
||||
:param samples: the number of a posteriori samples to plot p(y*|y)
|
||||
:param samples: the number of a posteriori samples to plot p(f*|y)
|
||||
:type samples: int
|
||||
:param fignum: figure to plot on.
|
||||
:type fignum: figure number
|
||||
:param ax: axes to plot on.
|
||||
:type ax: axes handle
|
||||
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
|
||||
:type resolution: int
|
||||
:param plot_raw: Whether to plot the raw function p(f|y)
|
||||
:type plot_raw: boolean
|
||||
:param linecol: color of line to plot.
|
||||
:type linecol:
|
||||
:type linecol: hex or color
|
||||
:param fillcol: color of fill
|
||||
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
|
||||
:param apply_link: apply the link function if plotting f (default false)
|
||||
:type fillcol: hex or color
|
||||
:param apply_link: apply the link function if plotting f (default false), as well as posterior samples if requested
|
||||
:type apply_link: boolean
|
||||
:param samples_f: the number of posteriori f samples to plot p(f*|y)
|
||||
:type samples_f: int
|
||||
:param samples_y: the number of posteriori f samples to plot p(y*|y)
|
||||
:type samples_y: int
|
||||
:param plot_uncertain_inputs: plot the uncertainty of the inputs as error bars if they have uncertainty (BGPLVM etc.)
|
||||
:type plot_uncertain_inputs: boolean
|
||||
:param predict_kw: keyword args for _raw_predict and predict functions if required
|
||||
:type predict_kw: dict
|
||||
:param plot_training_data: whether or not to plot the training points
|
||||
:type plot_training_data: boolean
|
||||
"""
|
||||
|
|
@ -185,17 +191,17 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
|
||||
#optionally plot some samples
|
||||
if samples: #NOTE not tested with fixed_inputs
|
||||
Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata)
|
||||
print(Ysim.shape)
|
||||
print(Xnew.shape)
|
||||
for yi in Ysim.T:
|
||||
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], '#3300FF', linewidth=0.25)
|
||||
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
|
||||
|
||||
if samples_f: #NOTE not tested with fixed_inputs
|
||||
Fsim = model.posterior_samples_f(Xgrid, samples_f)
|
||||
Fsim = model.posterior_samples_f(Xgrid, samples)
|
||||
if apply_link:
|
||||
Fsim = model.likelihood.gp_link.transf(Fsim)
|
||||
for fi in Fsim.T:
|
||||
plots['posterior_samples_f'] = ax.plot(Xnew, fi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
|
||||
plots['posterior_samples'] = ax.plot(Xnew, fi[:,None], '#3300FF', linewidth=0.25)
|
||||
#ax.plot(Xnew, fi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
|
||||
|
||||
if samples_y: #NOTE not tested with fixed_inputs
|
||||
Ysim = model.posterior_samples(Xgrid, samples_y, Y_metadata=Y_metadata)
|
||||
for yi in Ysim.T:
|
||||
plots['posterior_samples_y'] = ax.scatter(Xnew, yi[:,None], s=5, c=Tango.colorsHex['darkBlue'], marker='o', alpha=0.5)
|
||||
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import numpy as np
|
||||
import scipy as sp
|
||||
from GPy.util.linalg import jitchol
|
||||
import GPy
|
||||
from ..util.linalg import jitchol,trace_dot, ijk_jlk_to_il, ijk_ljk_to_ilk
|
||||
|
||||
class LinalgTests(np.testing.TestCase):
|
||||
def setUp(self):
|
||||
|
|
@ -37,18 +36,19 @@ class LinalgTests(np.testing.TestCase):
|
|||
except sp.linalg.LinAlgError:
|
||||
return True
|
||||
|
||||
def test_einsum_ijk_jlk_to_il(self):
|
||||
A = np.random.randn(50, 150, 5)
|
||||
B = np.random.randn(150, 100, 5)
|
||||
pure = np.einsum('ijk,jlk->il', A, B)
|
||||
quick = GPy.util.linalg.ijk_jlk_to_il(A, B)
|
||||
np.testing.assert_allclose(pure, quick)
|
||||
def test_trace_dot(self):
|
||||
N = 5
|
||||
A = np.random.rand(N,N)
|
||||
B = np.random.rand(N,N)
|
||||
trace = np.trace(A.dot(B))
|
||||
test_trace = trace_dot(A,B)
|
||||
np.testing.assert_allclose(trace,test_trace,atol=1e-13)
|
||||
|
||||
def test_einsum_ij_jlk_to_ilk(self):
|
||||
A = np.random.randn(15, 150, 5)
|
||||
B = np.random.randn(150, 50, 5)
|
||||
pure = np.einsum('ijk,jlk->il', A, B)
|
||||
quick = GPy.util.linalg.ijk_jlk_to_il(A,B)
|
||||
quick = ijk_jlk_to_il(A,B)
|
||||
np.testing.assert_allclose(pure, quick)
|
||||
|
||||
def test_einsum_ijk_ljk_to_ilk(self):
|
||||
|
|
@ -56,5 +56,5 @@ class LinalgTests(np.testing.TestCase):
|
|||
B = np.random.randn(150, 20, 5)
|
||||
#B = A.copy()
|
||||
pure = np.einsum('ijk,ljk->ilk', A, B)
|
||||
quick = GPy.util.linalg.ijk_ljk_to_ilk(A,B)
|
||||
quick = ijk_ljk_to_ilk(A,B)
|
||||
np.testing.assert_allclose(pure, quick)
|
||||
|
|
|
|||
101
GPy/testing/rv_transformation_tests.py
Normal file
101
GPy/testing/rv_transformation_tests.py
Normal file
|
|
@ -0,0 +1,101 @@
|
|||
# Written by Ilias Bilionis
|
||||
"""
|
||||
Test if hyperparameters in models are properly transformed.
|
||||
"""
|
||||
|
||||
|
||||
import unittest
|
||||
import numpy as np
|
||||
import scipy.stats as st
|
||||
import GPy
|
||||
|
||||
|
||||
class TestModel(GPy.core.Model):
|
||||
"""
|
||||
A simple GPy model with one parameter.
|
||||
"""
|
||||
def __init__(self):
|
||||
GPy.core.Model.__init__(self, 'test_model')
|
||||
theta = GPy.core.Param('theta', 1.)
|
||||
self.link_parameter(theta)
|
||||
|
||||
def log_likelihood(self):
|
||||
return 0.
|
||||
|
||||
|
||||
class RVTransformationTestCase(unittest.TestCase):
|
||||
|
||||
def _test_trans(self, trans):
|
||||
m = TestModel()
|
||||
prior = GPy.priors.LogGaussian(.5, 0.1)
|
||||
m.theta.set_prior(prior)
|
||||
m.theta.unconstrain()
|
||||
m.theta.constrain(trans)
|
||||
# The PDF of the transformed variables
|
||||
p_phi = lambda phi : np.exp(-m._objective_grads(phi)[0])
|
||||
# To the empirical PDF of:
|
||||
theta_s = prior.rvs(100000)
|
||||
phi_s = trans.finv(theta_s)
|
||||
# which is essentially a kernel density estimation
|
||||
kde = st.gaussian_kde(phi_s)
|
||||
# We will compare the PDF here:
|
||||
phi = np.linspace(phi_s.min(), phi_s.max(), 100)
|
||||
# The transformed PDF of phi should be this:
|
||||
pdf_phi = np.array([p_phi(p) for p in phi])
|
||||
# UNCOMMENT TO SEE GRAPHICAL COMPARISON
|
||||
#import matplotlib.pyplot as plt
|
||||
#fig, ax = plt.subplots()
|
||||
#ax.hist(phi_s, normed=True, bins=100, alpha=0.25, label='Histogram')
|
||||
#ax.plot(phi, kde(phi), '--', linewidth=2, label='Kernel Density Estimation')
|
||||
#ax.plot(phi, pdf_phi, ':', linewidth=2, label='Transformed PDF')
|
||||
#ax.set_xlabel(r'transformed $\theta$', fontsize=16)
|
||||
#ax.set_ylabel('PDF', fontsize=16)
|
||||
#plt.legend(loc='best')
|
||||
#plt.show(block=True)
|
||||
# END OF PLOT
|
||||
# The following test cannot be very accurate
|
||||
self.assertTrue(np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1)
|
||||
# Check the gradients at a few random points
|
||||
for i in range(10):
|
||||
m.theta = theta_s[i]
|
||||
self.assertTrue(m.checkgrad(verbose=True))
|
||||
|
||||
def test_Logexp(self):
|
||||
self._test_trans(GPy.constraints.Logexp())
|
||||
self._test_trans(GPy.constraints.Exponent())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
quit()
|
||||
m = TestModel()
|
||||
prior = GPy.priors.LogGaussian(0., .9)
|
||||
m.theta.set_prior(prior)
|
||||
|
||||
# The following should return the PDF in terms of the transformed quantities
|
||||
p_phi = lambda phi : np.exp(-m._objective_grads(phi)[0])
|
||||
|
||||
# Let's look at the transformation phi = log(exp(theta - 1))
|
||||
trans = GPy.constraints.Exponent()
|
||||
m.theta.constrain(trans)
|
||||
# Plot the transformed probability density
|
||||
phi = np.linspace(-8, 8, 100)
|
||||
fig, ax = plt.subplots()
|
||||
# Let's draw some samples of theta and transform them so that we see
|
||||
# which one is right
|
||||
theta_s = prior.rvs(10000)
|
||||
# Transform it to the new variables
|
||||
phi_s = trans.finv(theta_s)
|
||||
# And draw their histogram
|
||||
ax.hist(phi_s, normed=True, bins=100, alpha=0.25, label='Empirical')
|
||||
# This is to be compared to the PDF of the model expressed in terms of these new
|
||||
# variables
|
||||
ax.plot(phi, [p_phi(p) for p in phi], label='Transformed PDF', linewidth=2)
|
||||
ax.set_xlim(-3, 10)
|
||||
ax.set_xlabel(r'transformed $\theta$', fontsize=16)
|
||||
ax.set_ylabel('PDF', fontsize=16)
|
||||
plt.legend(loc='best')
|
||||
# Now let's test the gradients
|
||||
m.checkgrad(verbose=True)
|
||||
# And show the plot
|
||||
plt.show(block=True)
|
||||
|
|
@ -157,7 +157,7 @@ def trace_dot(a, b):
|
|||
"""
|
||||
Efficiently compute the trace of the matrix product of a and b
|
||||
"""
|
||||
return np.sum(a * b)
|
||||
return np.einsum('ij,ji->', a, b)
|
||||
|
||||
def mdot(*args):
|
||||
"""
|
||||
|
|
|
|||
79
README.md
79
README.md
|
|
@ -1,5 +1,6 @@
|
|||
# GPy
|
||||
|
||||
|
||||
A Gaussian processes framework in Python.
|
||||
|
||||
* [GPy homepage](http://sheffieldml.github.io/GPy/)
|
||||
|
|
@ -10,23 +11,31 @@ A Gaussian processes framework in Python.
|
|||
|
||||
Continuous integration status: 
|
||||
|
||||
### Citation
|
||||
|
||||
@Misc{gpy2014,
|
||||
author = {{The GPy authors}},
|
||||
title = {{GPy}: A Gaussian process framework in python},
|
||||
howpublished = {\url{http://github.com/SheffieldML/GPy}},
|
||||
year = {2012--2015}
|
||||
}
|
||||
|
||||
### Pronounciation
|
||||
|
||||
We like to pronounce it 'Gee-pie'.
|
||||
|
||||
### Getting started: installing with pip
|
||||
|
||||
We are now requiring the newest version of  and thus, we strongly recommend using
|
||||
the .
|
||||
We are now requiring the newest version (0.16) of
|
||||
[scipy](http://www.scipy.org/) and thus, we strongly recommend using
|
||||
the [anaconda python distribution](http://continuum.io/downloads).
|
||||
With anaconda you can install GPy by the following:
|
||||
|
||||
conda update scipy
|
||||
pip install gpy
|
||||
|
||||
. We've also had luck with , although enthought currently (as of 8th Sep. 2015) does not support scipy 0.16.
|
||||
|
||||
On a fresh install of windows 8.1, we downloaded the Anaconda python distribution, started the anaconda command prompt and typed
|
||||
|
||||
conda update scipy
|
||||
pip install GPy
|
||||
|
||||
Everything seems to work: from here you can type `ipython` and then `import GPy; GPy.tests()`. Working as of 21/11/14
|
||||
We've also had luck with [enthought](http://www.enthought.com),
|
||||
although enthought currently (as of 8th Sep. 2015) does not support scipy 0.16.
|
||||
|
||||
If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on.
|
||||
|
||||
|
|
@ -55,21 +64,13 @@ The command `python3 setup.py build_ext --inplace` builds the Cython extensions.
|
|||
* All weave functions not covered by the test suite are *simply commented out*. Can add equivalents later as test functions become available
|
||||
* A set of benchmarks would be useful!
|
||||
|
||||
### Citation
|
||||
|
||||
@Misc{gpy2014,
|
||||
author = {The GPy authors},
|
||||
title = {{GPy}: A Gaussian process framework in python},
|
||||
howpublished = {\url{http://github.com/SheffieldML/GPy}},
|
||||
year = {2012--2014}
|
||||
}
|
||||
|
||||
### Pronounciation
|
||||
|
||||
We like to pronounce it 'Gee-pie'.
|
||||
|
||||
### Ubuntu hackers
|
||||
|
||||
:Note: Right now the Ubuntu package index does not include scipy 0.16.0, and thus, cannot
|
||||
be used for GPy. We hope this gets fixed soon.
|
||||
|
||||
For the most part, the developers are using ubuntu. To install the required packages:
|
||||
|
||||
sudo apt-get install python-numpy python-scipy python-matplotlib
|
||||
|
|
@ -83,10 +84,29 @@ clone this git repository and add it to your path:
|
|||
|
||||
### OSX
|
||||
|
||||
Everything appears to work out-of-the box using  on osx Mavericks. Download/clone GPy, and then add GPy to your PYTHONPATH
|
||||
|
||||
git clone git@github.com:SheffieldML/GPy.git ~/SheffieldML
|
||||
echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.profile
|
||||
We were working hard to make pre-built distributions ready.
|
||||
You can now install GPy via pip on MacOSX using
|
||||
[anaconda python distribution](http://continuum.io/downloads):
|
||||
|
||||
conda update scipy
|
||||
pip install gpy
|
||||
|
||||
If this does not work, then you need to build GPy yourself,
|
||||
using the [development toolkits](https://developer.apple.com/xcode/).
|
||||
Download/clone GPy and run the build process:
|
||||
|
||||
conda update scipy
|
||||
git clone git@github.com:SheffieldML/GPy.git ~/GPy
|
||||
cd ~/GPy
|
||||
python setup.py install
|
||||
|
||||
If you do not wish to build the C extensions (10 times speedup),
|
||||
you can run the pure python installations, by just adding GPy
|
||||
to your python path.
|
||||
|
||||
echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.profile
|
||||
|
||||
|
||||
|
||||
### Compiling documentation:
|
||||
|
|
@ -137,7 +157,9 @@ Run nosetests from the root directory of the repository:
|
|||
or from within IPython
|
||||
|
||||
import GPy; GPy.tests()
|
||||
|
||||
|
||||
|
||||
|
||||
## Funding Acknowledgements
|
||||
|
||||
|
||||
|
|
@ -145,15 +167,12 @@ Current support for the GPy software is coming through the following projects.
|
|||
|
||||
* [EU FP7-PEOPLE Project Ref 316861](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/mlpm/) "MLPM2012: Machine Learning for Personalized Medicine"
|
||||
|
||||
* [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
|
||||
|
||||
* MRC Special Training Fellowship "Bayesian models of expression in the transcriptome for clinical RNA-seq"
|
||||
|
||||
* [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
|
||||
|
||||
* [EU FP7-ICT Project Ref 612139](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/wysiwyd/) "WYSIWYD: What You Say is What You Did"
|
||||
|
||||
Previous support for the GPy software came from the following projects:
|
||||
|
||||
* [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
|
||||
* [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
|
||||
* [BBSRC Project No BB/H018123/2](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/iterative/) "An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates"
|
||||
* [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"
|
||||
|
|
|
|||
222
doc/log.txt
222
doc/log.txt
|
|
@ -1,222 +0,0 @@
|
|||
/home/maxz/Documents/gpy/GPy/__init__.py:docstring of GPy.load:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
|
||||
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.optimize:8: ERROR: Unknown interpreted text role "module".
|
||||
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predict_wishard_embedding:6: WARNING: Field list ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation.
|
||||
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:10: WARNING: Block quote ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/core/model.py:docstring of GPy.core.model.Model.optimize_restarts:29: WARNING: Explicit markup ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/doc/GPy.core.rst:57: WARNING: autodoc: failed to import module u'GPy.core.svigp'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
ImportError: No module named svigp
|
||||
/home/maxz/Documents/gpy/doc/GPy.core.rst:65: WARNING: autodoc: failed to import module u'GPy.core.symbolic'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
File "/home/maxz/Documents/gpy/GPy/core/symbolic.py", line 10, in <module>
|
||||
from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
|
||||
ImportError: No module named lambdify
|
||||
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Indexable.unset_priors:1: WARNING: Inline emphasis start-string without end-string.
|
||||
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Nameable.hierarchy_name:4: WARNING: Field list ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline emphasis start-string without end-string.
|
||||
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline strong start-string without end-string.
|
||||
/home/maxz/Documents/gpy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:18: ERROR: Unexpected indentation.
|
||||
/home/maxz/Documents/gpy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:20: WARNING: Block quote ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/core/parameterization/ties_and_remappings.py:docstring of GPy.core.parameterization.ties_and_remappings.Tie:18: SEVERE: Unexpected section title or transition.
|
||||
|
||||
================================
|
||||
/home/maxz/Documents/gpy/doc/GPy.examples.rst:50: WARNING: autodoc: failed to import module u'GPy.examples.stochastic'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
ImportError: No module named stochastic
|
||||
/home/maxz/Documents/gpy/doc/GPy.examples.rst:58: WARNING: autodoc: failed to import module u'GPy.examples.tutorials'; the following exception was raised:
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Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
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__import__(self.modname)
|
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ImportError: No module named tutorials
|
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/home/maxz/Documents/gpy/doc/GPy.inference.latent_function_inference.rst:82: WARNING: autodoc: failed to import module u'GPy.inference.latent_function_inference.var_dtc_gpu'; the following exception was raised:
|
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Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
ImportError: No module named var_dtc_gpu
|
||||
/home/maxz/Documents/gpy/doc/GPy.inference.optimization.rst:42: WARNING: autodoc: failed to import module u'GPy.inference.optimization.sgd'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
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ImportError: No module named sgd
|
||||
/home/maxz/Documents/gpy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation.
|
||||
/home/maxz/Documents/gpy/doc/GPy.kern._src.rst:73: WARNING: autodoc: failed to import module u'GPy.kern._src.hierarchical'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
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ImportError: No module named hierarchical
|
||||
/home/maxz/Documents/gpy/GPy/kern/_src/independent_outputs.py:docstring of GPy.kern._src.independent_outputs.IndependentOutputs:9: WARNING: Field list ends without a blank line; unexpected unindent.
|
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/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:24: ERROR: Unexpected indentation.
|
||||
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:22: WARNING: Inline literal start-string without end-string.
|
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/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:25: WARNING: Block quote ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Definition list ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Inline literal start-string without end-string.
|
||||
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Inline interpreted text or phrase reference start-string without end-string.
|
||||
/home/maxz/Documents/gpy/doc/GPy.kern._src.rst:177: WARNING: autodoc: failed to import module u'GPy.kern._src.symbolic'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
File "/home/maxz/Documents/gpy/GPy/kern/_src/symbolic.py", line 5, in <module>
|
||||
from ...core.symbolic import Symbolic_core
|
||||
File "/home/maxz/Documents/gpy/GPy/core/symbolic.py", line 10, in <module>
|
||||
from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
|
||||
ImportError: No module named lambdify
|
||||
/home/maxz/Documents/gpy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:13: ERROR: Unexpected indentation.
|
||||
/home/maxz/Documents/gpy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:18: WARNING: Block quote ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/doc/GPy.models.rst:66: WARNING: autodoc: failed to import module u'GPy.models.gp_multioutput_regression'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
ImportError: No module named gp_multioutput_regression
|
||||
/home/maxz/Documents/gpy/GPy/models/gp_var_gauss.py:docstring of GPy.models.gp_var_gauss.GPVariationalGaussianApproximation:9: WARNING: Definition list ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Field list ends without a blank line; unexpected unindent.
|
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/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Inline interpreted text or phrase reference start-string without end-string.
|
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/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:34: WARNING: Definition list ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/doc/GPy.models.rst:138: WARNING: autodoc: failed to import module u'GPy.models.sparse_gp_multioutput_regression'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
ImportError: No module named sparse_gp_multioutput_regression
|
||||
/home/maxz/Documents/gpy/doc/GPy.models.rst:178: WARNING: autodoc: failed to import module u'GPy.models.svigp_regression'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
ImportError: No module named svigp_regression
|
||||
/home/maxz/Documents/gpy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imread:6: SEVERE: Unexpected section title.
|
||||
|
||||
Examples
|
||||
--------
|
||||
/home/maxz/Documents/gpy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imsave:4: SEVERE: Unexpected section title.
|
||||
|
||||
Examples
|
||||
--------
|
||||
/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:6: ERROR: Unexpected indentation.
|
||||
/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:7: WARNING: Block quote ends without a blank line; unexpected unindent.
|
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/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:7: WARNING: Definition list ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:9: ERROR: Unexpected indentation.
|
||||
docstring of GPy.util.datasets.hapmap3:7: WARNING: Block quote ends without a blank line; unexpected unindent.
|
||||
/home/maxz/Documents/gpy/doc/GPy.util.rst:74: WARNING: autodoc: failed to import module u'GPy.util.erfcx'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
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ImportError: No module named erfcx
|
||||
/home/maxz/Documents/gpy/doc/GPy.util.rst:146: WARNING: autodoc: failed to import module u'GPy.util.mpi'; the following exception was raised:
|
||||
Traceback (most recent call last):
|
||||
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
|
||||
__import__(self.modname)
|
||||
ImportError: No module named mpi
|
||||
/home/maxz/Documents/gpy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imread:6: SEVERE: Unexpected section title.
|
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|
||||
Examples
|
||||
--------
|
||||
/home/maxz/Documents/gpy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imsave:4: SEVERE: Unexpected section title.
|
||||
|
||||
Examples
|
||||
--------
|
||||
/home/maxz/Documents/gpy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.subarray_and_sorting".
|
||||
/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:8: ERROR: Unexpected indentation.
|
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/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:11: SEVERE: Unexpected section title.
|
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|
||||
Examples:
|
||||
=========
|
||||
/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:19: ERROR: Unexpected indentation.
|
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/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:21: WARNING: Block quote ends without a blank line; unexpected unindent.
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/home/maxz/Documents/gpy/GPy/util/block_matrices.py:docstring of GPy.util.block_matrices.block_dot:3: ERROR: Undefined substitution referenced: "A11.B11|B12.B12".
|
||||
/home/maxz/Documents/gpy/GPy/util/block_matrices.py:docstring of GPy.util.block_matrices.block_dot:3: ERROR: Undefined substitution referenced: "A21.B21|A22.B22".
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/home/maxz/Documents/gpy/doc/installation.rst:22: ERROR: Unexpected indentation.
|
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/home/maxz/Documents/gpy/doc/installation.rst:27: ERROR: Unexpected indentation.
|
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/home/maxz/Documents/gpy/doc/tuto_creating_new_kernels.rst:58: WARNING: Inline literal start-string without end-string.
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/home/maxz/Documents/gpy/doc/tuto_creating_new_models.rst:24: ERROR: Unknown target name: "parameterized".
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/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
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Interacting with Parameters:
|
||||
=======================
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
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Interacting with Parameters:
|
||||
=======================
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:109: WARNING: Title underline too short.
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Regular expressions
|
||||
----------------
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short.
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Setting and fetching parameters `parameter_array`
|
||||
------------------------------------------
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/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short.
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Setting and fetching parameters `parameter_array`
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||||
------------------------------------------
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/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short.
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Getting the model parameter's gradients
|
||||
============================
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short.
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Getting the model parameter's gradients
|
||||
============================
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short.
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Adjusting the model's constraints
|
||||
================================
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short.
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Adjusting the model's constraints
|
||||
================================
|
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/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short.
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Available Constraints
|
||||
==============
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short.
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Available Constraints
|
||||
==============
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short.
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Tying Parameters
|
||||
============
|
||||
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short.
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Tying Parameters
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||||
============
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||||
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:3: WARNING: Title overline too short.
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||||
*******************
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||||
Parameterization handling
|
||||
*******************
|
||||
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:10: WARNING: Title underline too short.
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Parameter handles
|
||||
==============
|
||||
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short.
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:py:class:`~GPy.core.parameterization.parameterized.Parameterized`
|
||||
==========
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||||
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short.
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:py:class:`~GPy.core.parameterization.parameterized.Parameterized`
|
||||
==========
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||||
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short.
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:py:class:`~GPy.core.parameterization.param.Param`
|
||||
===========
|
||||
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short.
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:py:class:`~GPy.core.parameterization.param.Param`
|
||||
===========
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||||
/home/maxz/Documents/gpy/doc/installation.rst:: WARNING: document isn't included in any toctree
|
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/home/maxz/Documents/gpy/doc/kernel_implementation.rst:: WARNING: document isn't included in any toctree
|
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/home/maxz/Documents/gpy/doc/modules.rst:: WARNING: document isn't included in any toctree
|
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/home/maxz/Documents/gpy/doc/tuto_GP_regression.rst:: WARNING: document isn't included in any toctree
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/home/maxz/Documents/gpy/doc/tuto_creating_new_kernels.rst:: WARNING: document isn't included in any toctree
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/home/maxz/Documents/gpy/doc/tuto_creating_new_models.rst:: WARNING: document isn't included in any toctree
|
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/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:: WARNING: document isn't included in any toctree
|
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/home/maxz/Documents/gpy/doc/tuto_kernel_overview.rst:: WARNING: document isn't included in any toctree
|
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/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:: WARNING: document isn't included in any toctree
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/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:336: WARNING: undefined label: creating_new_kernels (if the link has no caption the label must precede a section header)
|
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|
@ -13,9 +13,9 @@ First we import the libraries we will need ::
|
|||
|
||||
For most kernels, the dimension is the only mandatory parameter to define a kernel object. However, it is also possible to specify the values of the parameters. For example, the three following commands are valid for defining a squared exponential kernel (ie rbf or Gaussian) ::
|
||||
|
||||
ker1 = GPy.kern.rbf(1) # Equivalent to ker1 = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
|
||||
ker2 = GPy.kern.rbf(input_dim=1, variance = .75, lengthscale=2.)
|
||||
ker3 = GPy.kern.rbf(1, .5, .5)
|
||||
ker1 = GPy.kern.RBF(1) # Equivalent to ker1 = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=1.)
|
||||
ker2 = GPy.kern.RBF(input_dim=1, variance = .75, lengthscale=2.)
|
||||
ker3 = GPy.kern.RBF(1, .5, .5)
|
||||
|
||||
A ``print`` and a ``plot`` functions are implemented to represent kernel objects. The commands ::
|
||||
|
||||
|
|
@ -52,21 +52,18 @@ Operations to combine kernels
|
|||
|
||||
In ``GPy``, kernel objects can be added or multiplied. In both cases, two kinds of operations are possible since one can assume that the kernels to add/multiply are defined on the same space or on different subspaces. In other words, it is possible to use two kernels :math:`k_1,\ k_2` over :math:`\mathbb{R} \times \mathbb{R}` to create
|
||||
|
||||
* a kernel over :math:`\mathbb{R} \times \mathbb{R}`: :math:`k(x,y) = k_1(x,y) \times k_2(x,y)`
|
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* a kernel over :math:`\mathbb{R}^2 \times \mathbb{R}^2`: :math:`k(\mathbf{x},\mathbf{y}) = k_1(x_1,y_1) \times k_2(x_2,y_2)`
|
||||
* a kernel over :math:`\mathbb{R} \times \mathbb{R}`: :math:`k(x,y) = k_1(x,y) \times k_2(x,y)`.
|
||||
|
||||
These two options are available in GPy using the flag ``tensor`` in the ``add`` and ``prod`` functions. Here is a quick example ::
|
||||
This is available in GPy via the ``add`` and ``prod`` functions. Here is a quick example ::
|
||||
|
||||
k1 = GPy.kern.rbf(1,1.,2.)
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k1 = GPy.kern.RBF(1,1.,2.)
|
||||
k2 = GPy.kern.Matern32(1, 0.5, 0.2)
|
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|
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# Product of kernels
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k_prod = k1.prod(k2) # By default, tensor=False
|
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k_prodtens = k1.prod(k2,tensor=True)
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k_prod = k1.prod(k2)
|
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|
||||
# Sum of kernels
|
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k_add = k1.add(k2) # By default, tensor=False
|
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k_addtens = k1.add(k2,tensor=True)
|
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k_add = k1.add(k2)
|
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|
||||
.. # plots
|
||||
pb.figure(figsize=(8,8))
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|
@ -75,23 +72,23 @@ These two options are available in GPy using the flag ``tensor`` in the ``add``
|
|||
pb.title('prod')
|
||||
pb.subplot(2,2,2)
|
||||
k_prodtens.plot()
|
||||
pb.title('tensor prod')
|
||||
pb.title('prod')
|
||||
pb.subplot(2,2,3)
|
||||
k_add.plot()
|
||||
pb.title('sum')
|
||||
pb.subplot(2,2,4)
|
||||
k_addtens.plot()
|
||||
pb.title('tensor sum')
|
||||
pb.title('sum')
|
||||
pb.subplots_adjust(wspace=0.3, hspace=0.3)
|
||||
|
||||
.. figure:: Figures/tuto_kern_overview_multadd.png
|
||||
:align: center
|
||||
:height: 500px
|
||||
|
||||
A shortcut for ``add`` and ``prod`` (with default flag ``tensor=False``) is provided by the usual ``+`` and ``*`` operators. Here is another example where we create a periodic kernel with some decay ::
|
||||
A shortcut for ``add`` and ``prod`` is provided by the usual ``+`` and ``*`` operators. Here is another example where we create a periodic kernel with some decay ::
|
||||
|
||||
k1 = GPy.kern.rbf(1,1.,2)
|
||||
k2 = GPy.kern.periodic_Matern52(1,variance=1e3, lengthscale=1, period = 1.5, lower=-5., upper = 5)
|
||||
k1 = GPy.kern.RBF(1,1.,2)
|
||||
k2 = GPy.kern.PeriodicMatern52(1,variance=1e3, lengthscale=1, period = 1.5, lower=-5., upper = 5)
|
||||
|
||||
k = k1 * k2 # equivalent to k = k1.prod(k2)
|
||||
print k
|
||||
|
|
@ -116,14 +113,14 @@ A shortcut for ``add`` and ``prod`` (with default flag ``tensor=False``) is prov
|
|||
In general, ``kern`` objects can be seen as a sum of ``kernparts`` objects, where the later are covariance functions defined on the same space. For example, the following code ::
|
||||
|
||||
k = (k1+k2)*(k1+k2)
|
||||
print k.parts[0].name, '\n', k.parts[1].name, '\n', k.parts[2].name, '\n', k.parts[3].name
|
||||
print k.parts[0].name, '\n', k.parts[1].name, '\n', k.parts[1].parts[0].name, '\n', k.parts[1].parts[1].name, '\n'
|
||||
|
||||
returns ::
|
||||
add_1
|
||||
add_2
|
||||
rbf
|
||||
periodic_Matern52
|
||||
|
||||
rbf<times>rbf
|
||||
rbf<times>periodic_Mat52
|
||||
periodic_Mat52<times>rbf
|
||||
periodic_Mat52<times>periodic_Mat52
|
||||
|
||||
Constraining the parameters
|
||||
===========================
|
||||
|
|
@ -137,9 +134,9 @@ Various constrains can be applied to the parameters of a kernel
|
|||
|
||||
When calling one of these functions, the parameters to constrain can either by specified by a regular expression that matches its name or by a number that corresponds to the rank of the parameter. Here is an example ::
|
||||
|
||||
k1 = GPy.kern.rbf(1)
|
||||
k1 = GPy.kern.RBF(1)
|
||||
k2 = GPy.kern.Matern32(1)
|
||||
k3 = GPy.kern.white(1)
|
||||
k3 = GPy.kern.White(1)
|
||||
|
||||
k = k1 + k2 + k3
|
||||
print k
|
||||
|
|
@ -182,9 +179,9 @@ In two dimensions ANOVA kernels have the following form:
|
|||
|
||||
Let us assume that we want to define an ANOVA kernel with a Matern 3/2 kernel for :math:`k_i`. As seen previously, we can define this kernel as follows ::
|
||||
|
||||
k_cst = GPy.kern.bias(1,variance=1.)
|
||||
k_mat = GPy.kern.Matern52(1,variance=1., lengthscale=3)
|
||||
Kanova = (k_cst + k_mat).prod(k_cst + k_mat,tensor=True)
|
||||
k_cst = GPy.kern.Bias(1,variance=1.)
|
||||
k_mat = GPy.kern.Matern52(1,variance=1.,lengthscale=3)
|
||||
Kanova = (k_cst + k_mat).prod(k_cst + k_mat)
|
||||
print Kanova
|
||||
|
||||
Printing the resulting kernel outputs the following ::
|
||||
|
|
@ -257,17 +254,17 @@ The submodels can be represented with the option ``which_function`` of ``plot``:
|
|||
import GPy
|
||||
pb.ion()
|
||||
|
||||
ker1 = GPy.kern.rbf(D=1) # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)
|
||||
ker2 = GPy.kern.rbf(D=1, variance = .75, lengthscale=3.)
|
||||
ker3 = GPy.kern.rbf(1, .5, .25)
|
||||
ker1 = GPy.kern.RBF(D=1) # Equivalent to ker1 = GPy.kern.RBF(D=1, variance=1., lengthscale=1.)
|
||||
ker2 = GPy.kern.RBF(D=1, variance = .75, lengthscale=3.)
|
||||
ker3 = GPy.kern.RBF(1, .5, .25)
|
||||
|
||||
ker1.plot()
|
||||
ker2.plot()
|
||||
ker3.plot()
|
||||
#pb.savefig("Figures/tuto_kern_overview_basicdef.png")
|
||||
|
||||
kernels = [GPy.kern.rbf(1), GPy.kern.exponential(1), GPy.kern.Matern32(1), GPy.kern.Matern52(1), GPy.kern.Brownian(1), GPy.kern.bias(1), GPy.kern.linear(1), GPy.kern.spline(1), GPy.kern.periodic_exponential(1), GPy.kern.periodic_Matern32(1), GPy.kern.periodic_Matern52(1), GPy.kern.white(1)]
|
||||
kernel_names = ["GPy.kern.rbf", "GPy.kern.exponential", "GPy.kern.Matern32", "GPy.kern.Matern52", "GPy.kern.Brownian", "GPy.kern.bias", "GPy.kern.linear", "GPy.kern.spline", "GPy.kern.periodic_exponential", "GPy.kern.periodic_Matern32", "GPy.kern.periodic_Matern52", "GPy.kern.white"]
|
||||
kernels = [GPy.kern.RBF(1), GPy.kern.Exponential(1), GPy.kern.Matern32(1), GPy.kern.Matern52(1), GPy.kern.Brownian(1), GPy.kern.Bias(1), GPy.kern.Linear(1), GPy.kern.PeriodicExponential(1), GPy.kern.PeriodicMatern32(1), GPy.kern.PeriodicMatern52(1), GPy.kern.White(1)]
|
||||
kernel_names = ["GPy.kern.RBF", "GPy.kern.Exponential", "GPy.kern.Matern32", "GPy.kern.Matern52", "GPy.kern.Brownian", "GPy.kern.Bias", "GPy.kern.Linear", "GPy.kern.PeriodicExponential", "GPy.kern.PeriodicMatern32", "GPy.kern.PeriodicMatern52", "GPy.kern.White"]
|
||||
|
||||
pb.figure(figsize=(16,12))
|
||||
pb.subplots_adjust(wspace=.5, hspace=.5)
|
||||
|
|
|
|||
10
setup.cfg
Normal file
10
setup.cfg
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
[bumpversion]
|
||||
current_version = 0.8.3
|
||||
tag = True
|
||||
commit = True
|
||||
|
||||
[bumpversion:file:GPy/__version__.py]
|
||||
|
||||
[bdist_wheel]
|
||||
universal = 1
|
||||
|
||||
32
setup.py
32
setup.py
|
|
@ -1,18 +1,29 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from __future__ import print_function
|
||||
import os
|
||||
import sys
|
||||
from setuptools import setup, Extension
|
||||
import numpy as np
|
||||
|
||||
# Version number
|
||||
version = '0.6.1'
|
||||
|
||||
def read(fname):
|
||||
return open(os.path.join(os.path.dirname(__file__), fname)).read()
|
||||
|
||||
#Mac OS X Clang doesn't support OpenMP th the current time.
|
||||
def read_to_rst(fname):
|
||||
try:
|
||||
import pypandoc
|
||||
#print 'Warning in installation: For rst formatting in pypi, consider installing pypandoc for conversion'
|
||||
return pypandoc.convert('README.md', 'rst')
|
||||
except:
|
||||
return read(fname)
|
||||
|
||||
version_dummy = {}
|
||||
exec(read('GPy/__version__.py'), version_dummy)
|
||||
__version__ = version_dummy['__version__']
|
||||
del version_dummy
|
||||
|
||||
#Mac OS X Clang doesn't support OpenMP at the current time.
|
||||
#This detects if we are building on a Mac
|
||||
def ismac():
|
||||
platform = sys.platform
|
||||
|
|
@ -23,7 +34,7 @@ def ismac():
|
|||
|
||||
if ismac():
|
||||
compile_flags = [ '-O3', ]
|
||||
link_args = ['']
|
||||
link_args = []
|
||||
else:
|
||||
compile_flags = [ '-fopenmp', '-O3', ]
|
||||
link_args = ['-lgomp']
|
||||
|
|
@ -48,9 +59,9 @@ ext_mods = [Extension(name='GPy.kern._src.stationary_cython',
|
|||
extra_compile_args=compile_flags)]
|
||||
|
||||
setup(name = 'GPy',
|
||||
version = version,
|
||||
version = __version__,
|
||||
author = read('AUTHORS.txt'),
|
||||
author_email = "james.hensman@gmail.com",
|
||||
author_email = "gpy.authors@gmail.com",
|
||||
description = ("The Gaussian Process Toolbox"),
|
||||
license = "BSD 3-clause",
|
||||
keywords = "machine-learning gaussian-processes kernels",
|
||||
|
|
@ -72,12 +83,13 @@ setup(name = 'GPy',
|
|||
package_dir={'GPy': 'GPy'},
|
||||
package_data = {'GPy': ['defaults.cfg', 'installation.cfg',
|
||||
'util/data_resources.json',
|
||||
'util/football_teams.json']},
|
||||
'util/football_teams.json',
|
||||
]},
|
||||
include_package_data = True,
|
||||
py_modules = ['GPy.__init__'],
|
||||
test_suite = 'GPy.testing',
|
||||
long_description=read('README.md'),
|
||||
install_requires=['numpy>=1.7', 'scipy>=0.16'],
|
||||
long_description=read_to_rst('README.md'),
|
||||
install_requires=['numpy>=1.7', 'scipy>=0.16', 'six'],
|
||||
extras_require = {'docs':['matplotlib >=1.3','Sphinx','IPython']},
|
||||
classifiers=['License :: OSI Approved :: BSD License',
|
||||
'Natural Language :: English',
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue