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https://github.com/SheffieldML/GPy.git
synced 2026-05-14 22:42:37 +02:00
some minor example modifications and cgd adjustments
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Max Zwiessele
parent
9229100af7
commit
5321bfc8c9
4 changed files with 33 additions and 31 deletions
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@ -176,13 +176,12 @@ def bgplvm_simulation_matlab_compare():
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Y = sim_data['Y']
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S = sim_data['S']
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mu = sim_data['mu']
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M, [_, Q] = 30, mu.shape
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Q = 2
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M, [_, Q] = 20, mu.shape
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from GPy.models import mrd
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from GPy import kern
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reload(mrd); reload(kern)
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#k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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# k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
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# X=mu,
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@ -191,14 +190,15 @@ def bgplvm_simulation_matlab_compare():
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m.ensure_default_constraints()
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m.auto_scale_factor = True
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m['noise'] = Y.var() / 100.
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m['linear_variance'] = .01
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lscstr = '{}'.format(k.parts[0].name)
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# lscstr = '{}'.format(k.parts[0].name)
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# m[lscstr] = .01
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m.unconstrain(lscstr); m.constrain_fixed(lscstr, 10)
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# m.unconstrain(lscstr); m.constrain_fixed(lscstr, 10)
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lscstr = 'X_variance'
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# lscstr = 'X_variance'
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# m[lscstr] = .01
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m.unconstrain(lscstr); m.constrain_fixed(lscstr, .1)
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# m.unconstrain(lscstr); m.constrain_fixed(lscstr, .1)
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# cstr = 'white'
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# m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)
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@ -166,25 +166,26 @@ class Async_Optimize(object):
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except Empty:
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pass
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def fmin_async(self, f, df, x0, callback, update_rule=FletcherReeves,
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def opt_async(self, f, df, x0, callback, update_rule=FletcherReeves,
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messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
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report_every=10, *args, **kwargs):
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self.runsignal.set()
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outqueue = Queue()
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c = None
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if callback:
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self.callback = callback
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c = Thread(target=self.async_callback_collect, args=(outqueue,))
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c.start()
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c = Thread(target=self.async_callback_collect, args=(outqueue,))
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c.start()
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p = _CGDAsync(f, df, x0, update_rule, self.runsignal, self.SENTINEL,
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report_every=report_every, messages=messages, maxiter=maxiter,
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max_f_eval=max_f_eval, gtol=gtol, outqueue=outqueue, *args, **kwargs)
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p.run()
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return p, c
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def fmin(self, f, df, x0, callback=None, update_rule=FletcherReeves,
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def opt(self, f, df, x0, callback=None, update_rule=FletcherReeves,
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messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
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report_every=10, *args, **kwargs):
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p, c = self.fmin_async(f, df, x0, callback, update_rule, messages,
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p, c = self.opt_async(f, df, x0, callback, update_rule, messages,
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maxiter, max_f_eval, gtol,
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report_every, *args, **kwargs)
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while self.runsignal.is_set():
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@ -195,7 +196,8 @@ class Async_Optimize(object):
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# print "^C"
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self.runsignal.clear()
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p.join()
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if c.is_alive():
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c.join()
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if c and c.is_alive():
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print "WARNING: callback still running, optimisation done!"
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return p.result
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@ -208,11 +210,11 @@ class CGD(Async_Optimize):
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if df returns tuple (grad, natgrad) it will optimize according
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to natural gradient rules
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'''
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name = "Conjugate Gradient Descent"
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opt_name = "Conjugate Gradient Descent"
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def fmin_async(self, *a, **kw):
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def opt_async(self, *a, **kw):
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"""
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fmin_async(self, f, df, x0, callback, update_rule=FletcherReeves,
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opt_async(self, f, df, x0, callback, update_rule=FletcherReeves,
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messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
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report_every=10, *args, **kwargs)
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@ -240,11 +242,11 @@ class CGD(Async_Optimize):
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at end of optimization!
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"""
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return super(CGD, self).fmin_async(*a, **kw)
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return super(CGD, self).opt_async(*a, **kw)
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def fmin(self, *a, **kw):
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def opt(self, *a, **kw):
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"""
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fmin(self, f, df, x0, callback=None, update_rule=FletcherReeves,
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opt(self, f, df, x0, callback=None, update_rule=FletcherReeves,
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messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
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report_every=10, *args, **kwargs)
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@ -267,5 +269,5 @@ class CGD(Async_Optimize):
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at end of optimization
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"""
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return super(CGD, self).fmin(*a, **kw)
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return super(CGD, self).opt(*a, **kw)
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@ -259,28 +259,28 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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ax2.text(.5, .5, r"$\mathbf{X}$", alpha=.5, transform=ax2.transAxes,
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ha='center', va='center')
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figs[-1].canvas.draw()
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figs[-1].tight_layout(rect=(0, 0, 1, .9))
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figs[-1].tight_layout(rect=(0, 0, 1, .86))
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# ax3 = pylab.subplot2grid(splotshape, (3, 0), 2, 4, sharex=ax2)
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figs.append(pylab.figure("BGPLVM DEBUG S", figsize=(12, 4)))
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ax3 = self._debug_get_axis(figs)
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ax3.text(.5, .5, r"$\mathbf{S}$", alpha=.5, transform=ax3.transAxes,
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ha='center', va='center')
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figs[-1].canvas.draw()
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figs[-1].tight_layout(rect=(0, 0, 1, .9))
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figs[-1].tight_layout(rect=(0, 0, 1, .86))
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# ax4 = pylab.subplot2grid(splotshape, (5, 0), 2, 2)
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figs.append(pylab.figure("BGPLVM DEBUG Z", figsize=(6, 4)))
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ax4 = self._debug_get_axis(figs)
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ax4.text(.5, .5, r"$\mathbf{Z}$", alpha=.5, transform=ax4.transAxes,
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ha='center', va='center')
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figs[-1].canvas.draw()
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figs[-1].tight_layout(rect=(0, 0, 1, .9))
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figs[-1].tight_layout(rect=(0, 0, 1, .86))
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# ax5 = pylab.subplot2grid(splotshape, (5, 2), 2, 2)
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figs.append(pylab.figure("BGPLVM DEBUG theta", figsize=(6, 4)))
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ax5 = self._debug_get_axis(figs)
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ax5.text(.5, .5, r"${\theta}$", alpha=.5, transform=ax5.transAxes,
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ha='center', va='center')
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figs[-1].canvas.draw()
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figs[-1].tight_layout(rect=(0, 0, 1, .9))
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figs[-1].tight_layout(rect=(.15, 0, 1, .86))
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figs.append(pylab.figure("BGPLVM DEBUG Kmm", figsize=(12, 6)))
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fig = figs[-1]
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ax6 = fig.add_subplot(121)
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@ -345,16 +345,16 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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# loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.15, 1, 1.15),
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# borderaxespad=0, mode="expand")
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ax2.legend(Xlatentplts, [r"$Q_{}$".format(i + 1) for i in range(self.Q)],
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.01, 1, 1.01),
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.1, 1, 1.1),
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borderaxespad=0, mode="expand")
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ax3.legend(Xlatentplts, [r"$Q_{}$".format(i + 1) for i in range(self.Q)],
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.01, 1, 1.01),
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.1, 1, 1.1),
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borderaxespad=0, mode="expand")
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ax4.legend(Xlatentplts, [r"$Q_{}$".format(i + 1) for i in range(self.Q)],
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.01, 1, 1.01),
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.1, 1, 1.1),
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borderaxespad=0, mode="expand")
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ax5.legend(Xlatentplts, [r"$Q_{}$".format(i + 1) for i in range(self.Q)],
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.01, 1, 1.01),
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loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.1, 1, 1.1),
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borderaxespad=0, mode="expand")
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Lleg = ax1.legend()
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Lleg.draggable()
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@ -26,7 +26,7 @@ class Test(unittest.TestCase):
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for _ in range(restarts):
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try:
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x0 = numpy.random.randn(N) * .5
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res = opt.fmin(f, df, x0, messages=0,
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res = opt.opt(f, df, x0, messages=0,
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maxiter=1000, gtol=1e-10)
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assert numpy.allclose(res[0], 0, atol=1e-3)
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break
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@ -48,7 +48,7 @@ class Test(unittest.TestCase):
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for _ in range(restarts):
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try:
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x0 = numpy.random.randn(N) * .5
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res = opt.fmin(f, df, x0, messages=0,
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res = opt.opt(f, df, x0, messages=0,
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maxiter=1000, gtol=1e-2)
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assert numpy.allclose(res[0], 1, atol=.01)
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break
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@ -103,7 +103,7 @@ if __name__ == "__main__":
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if r[-1] != RUNNING:
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res[0] = r
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p, c = opt.fmin_async(f, df, x0.copy(), callback, messages=True, maxiter=1000,
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p, c = opt.opt_async(f, df, x0.copy(), callback, messages=True, maxiter=1000,
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report_every=20, gtol=1e-12)
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