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https://github.com/SheffieldML/GPy.git
synced 2026-05-08 11:32:39 +02:00
added debug plot
This commit is contained in:
Max Zwiessele
parent
5f76e7cd12
commit
04261f045b
3 changed files with 104 additions and 21 deletions
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@ -100,7 +100,7 @@ def oil_100():
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# m.plot_latent(labels=data['Y'].argmax(axis=1))
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return m
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def mrd_simulation():
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def mrd_simulation(plot_sim=False):
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# num = 2
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ard1 = np.array([1., 1, 0, 0], dtype=float)
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ard2 = np.array([0., 1, 1, 0], dtype=float)
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@ -119,8 +119,8 @@ def mrd_simulation():
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# Y2 -= Y2.mean(0)
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# make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
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D1, D2, N, M, Q = 50, 100, 150, 15, 4
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x = np.linspace(0, 2 * np.pi, N)[:, None]
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D1, D2, N, M, Q = 5, 10, 150, 15, 3
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x = np.linspace(0, 4 * np.pi, N)[:, None]
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s1 = np.vectorize(lambda x: np.sin(x))
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s2 = np.vectorize(lambda x: np.cos(x))
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@ -132,9 +132,30 @@ def mrd_simulation():
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Y1 = S1.dot(np.random.randn(S1.shape[1], D1))
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Y2 = S2.dot(np.random.randn(S2.shape[1], D2))
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Y1 -= Y1.mean(0)
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Y2 -= Y2.mean(0)
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if plot_sim:
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import pylab
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fig = pylab.figure("MRD Simulation")
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ax = fig.add_subplot(2, 2, 1)
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ax.imshow(S1)
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ax.set_title("S1")
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ax = fig.add_subplot(2, 2, 2)
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ax.imshow(S2)
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ax.set_title("S2")
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ax = fig.add_subplot(2, 2, 3)
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ax.imshow(Y1)
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ax.set_title("Y1")
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ax = fig.add_subplot(2, 2, 4)
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ax.imshow(Y2)
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ax.set_title("Y2")
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pylab.draw()
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pylab.tight_layout()
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k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
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m = MRD(Y1, Y2, Q=Q, M=M, kernel=k, init="PCA", _debug=False)
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m = MRD(Y1, Y2, Q=Q, M=M, kernel=k, init="concat", _debug=True)
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m.ensure_default_constraints()
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# fig = pyplot.figure("expected", figsize=(8, 3))
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@ -145,6 +166,10 @@ def mrd_simulation():
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return m
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def mrd_silhouette():
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pass
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def brendan_faces():
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data = GPy.util.datasets.brendan_faces()
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Y = data['Y'][0:-1:10, :]
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@ -104,7 +104,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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iteration += 1
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if display:
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print 'Iteration: {0:<5g} Objective:{1:< 12g} Scale:{2:< 12g}\r'.format(iteration, fnow, beta),
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print '\r',
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print 'Iteration: {0:<5g} Objective:{1:< 12g} Scale:{2:< 12g}'.format(iteration, fnow, beta),
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# print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
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sys.stdout.flush()
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@ -10,7 +10,7 @@ from GPy.models.sparse_GP import sparse_GP
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import itertools
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from matplotlib import pyplot
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import pylab
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from GPy.util.linalg import PCA
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class MRD(model):
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"""
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@ -22,7 +22,7 @@ class MRD(model):
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:type Ylist: [np.ndarray]
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:param names: names for different gplvm models
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:type names: [str]
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:param Q: latent dimensionality
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:param Q: latent dimensionality (will raise
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:type Q: int
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:param init: initialisation method for the latent space
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:type init: 'PCA'|'random'
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@ -40,10 +40,11 @@ class MRD(model):
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kernel to use
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"""
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def __init__(self, *Ylist, **kwargs):
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self._debug = False
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if kwargs.has_key("_debug"):
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self._debug = kwargs['_debug']
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del kwargs['_debug']
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else:
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self._debug = False
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if kwargs.has_key("names"):
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self.names = kwargs['names']
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del kwargs['names']
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@ -55,14 +56,30 @@ class MRD(model):
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del kwargs['kernel']
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else:
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k = lambda: None
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self.bgplvms = [Bayesian_GPLVM(Y, kernel=k(), **kwargs) for Y in Ylist]
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if kwargs.has_key('init'):
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init = kwargs['init']
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del kwargs['init']
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try:
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self.Q = kwargs["Q"]
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except KeyError:
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raise ValueError("Need Q for MRD")
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try:
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self.M = kwargs["M"]
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except KeyError:
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self.M = 10
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X = self._init_X(Ylist, init)
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Z = numpy.random.permutation(X.copy())[:self.M]
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self.bgplvms = [Bayesian_GPLVM(Y, kernel=k(), X=X, Z=Z, **kwargs) for Y in Ylist]
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self.gref = self.bgplvms[0]
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nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
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self.nparams = nparams.cumsum()
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self.Q = self.gref.Q
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self.N = self.gref.N
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self.NQ = self.N * self.Q
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self.M = self.gref.M
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self.MQ = self.M * self.Q
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model.__init__(self) # @UndefinedVariable
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@ -87,9 +104,16 @@ class MRD(model):
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| mu | S | Z || theta1 | theta2 | .. | thetaN |
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=================================================================
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"""
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X = self.gref.X.flatten()
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X_var = self.gref.X_variance.flatten()
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Z = self.gref.Z.flatten()
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X = self.gref.X.ravel()
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X_var = self.gref.X_variance.ravel()
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Z = self.gref.Z.ravel()
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if self._debug:
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for g in self.bgplvms:
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assert numpy.allclose(g.X.ravel(), X)
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assert numpy.allclose(g.X_variance.ravel(), X_var)
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assert numpy.allclose(g.Z.ravel(), Z)
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thetas = [sparse_GP._get_params(g)[g.Z.size:] for g in self.bgplvms]
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params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
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return params
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@ -105,14 +129,20 @@ class MRD(model):
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def _set_params(self, x):
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start = 0; end = self.NQ
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X = x[start:end].reshape(self.N, self.Q).copy()
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X = x[start:end].reshape(self.N, self.Q)
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start = end; end += start
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X_var = x[start:end].reshape(self.N, self.Q).copy()
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X_var = x[start:end].reshape(self.N, self.Q)
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start = end; end += self.MQ
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Z = x[start:end].reshape(self.M, self.Q).copy()
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Z = x[start:end].reshape(self.M, self.Q)
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thetas = x[end:]
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# set params for all others:
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if self._debug:
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for g in self.bgplvms:
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assert numpy.allclose(g.X, self.gref.X)
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assert numpy.allclose(g.X_variance, self.gref.X_variance)
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assert numpy.allclose(g.Z, self.gref.Z)
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# set params for all:
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for g, s, e in itertools.izip(self.bgplvms, self.nparams, self.nparams[1:]):
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self._set_var_params(g, X, X_var, Z)
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self._set_kern_params(g, thetas[s:e].copy())
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@ -121,10 +151,10 @@ class MRD(model):
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def log_likelihood(self):
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ll = +self.gref.KL_divergence()
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ll = -self.gref.KL_divergence()
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for g in self.bgplvms:
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ll -= sparse_GP.log_likelihood(g)
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return -ll
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ll += sparse_GP.log_likelihood(g)
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return ll
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def _log_likelihood_gradients(self):
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dLdmu, dLdS = reduce(lambda a, b: [a[0] + b[0], a[1] + b[1]], (g.dL_dmuS() for g in self.bgplvms))
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@ -141,6 +171,17 @@ class MRD(model):
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partial=g.partial_for_likelihood)]) \
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for g in self.bgplvms])))
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def _init_X(self, Ylist, init='PCA_concat'):
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if init in "PCA_concat":
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X = PCA(numpy.hstack(Ylist), self.Q)[0]
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elif init in "PCA_single":
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X = numpy.zeros((Ylist[0].shape[0], self.Q))
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for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.Q), len(Ylist)), Ylist):
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X[:, qs] = PCA(Y, len(qs))[0]
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else: # init == 'random':
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X = numpy.random.randn(Ylist[0].shape[0], self.Q)
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return X
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def plot_X(self):
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fig = pylab.figure("MRD X", figsize=(4 * len(self.bgplvms), 3))
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fig.clf()
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@ -180,3 +221,19 @@ class MRD(model):
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pylab.draw()
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fig.tight_layout()
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return fig
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def _debug_plot(self):
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self.plot_X()
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self.plot_latent()
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self.plot_scales()
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def _debug_optimize(self, opt='scg', maxiters=500, itersteps=10):
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iters = 0
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optstep = lambda: self.optimize(opt, messages=1, max_iters=itersteps)
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self._debug_plot()
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raw_input("enter to start debug")
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while iters < maxiters:
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optstep()
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self._debug_plot()
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iters += itersteps
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