Пример #1
0
    def __init__(self,
                 model,
                 opt,
                 maxiters,
                 verbose=False,
                 current_iteration=0,
                 ipython_notebook=True,
                 clear_after_finish=False):
        self.verbose = verbose
        if self.verbose:
            self.model = model
            self.iteration = current_iteration
            self.p_iter = self.iteration
            self.maxiters = maxiters
            self.len_maxiters = len(str(maxiters))
            self.opt_name = opt.opt_name
            self.model.add_observer(self, self.print_status)
            self.status = 'running'
            self.clear = clear_after_finish

            self.update()

            try:
                from IPython.display import display
                from IPython.html.widgets import IntProgress, HTML, Box, VBox, HBox, FlexBox
                self.text = HTML(width='100%')
                self.progress = IntProgress(min=0, max=maxiters)
                #self.progresstext = Text(width='100%', disabled=True, value='0/{}'.format(maxiters))
                self.model_show = HTML()
                self.ipython_notebook = ipython_notebook
            except:
                # Not in Ipython notebook
                self.ipython_notebook = False

            if self.ipython_notebook:
                left_col = VBox(children=[self.progress, self.text],
                                padding=2,
                                width='40%')
                right_col = Box(children=[self.model_show],
                                padding=2,
                                width='60%')
                self.hor_align = FlexBox(children=[left_col, right_col],
                                         width='100%',
                                         orientation='horizontal')

                display(self.hor_align)

                try:
                    self.text.set_css('width', '100%')
                    left_col.set_css({
                        'padding': '2px',
                        'width': "100%",
                    })

                    right_col.set_css({
                        'padding': '2px',
                    })

                    self.hor_align.set_css({
                        'width': "100%",
                    })

                    self.hor_align.remove_class('vbox')
                    self.hor_align.add_class('hbox')

                    left_col.add_class("box-flex1")
                    right_col.add_class('box-flex0')

                except:
                    pass

                #self.text.add_class('box-flex2')
                #self.progress.add_class('box-flex1')
            else:
                self.exps = exponents(self.fnow, self.current_gradient)
                print('Running {} Code:'.format(self.opt_name))
                print('  {3:7s}   {0:{mi}s}   {1:11s}    {2:11s}'.format(
                    "i", "f", "|g|", "runtime", mi=self.len_maxiters))
Пример #2
0
class VerboseOptimization(object):
    def __init__(self,
                 model,
                 opt,
                 maxiters,
                 verbose=False,
                 current_iteration=0,
                 ipython_notebook=True,
                 clear_after_finish=False):
        self.verbose = verbose
        if self.verbose:
            self.model = model
            self.iteration = current_iteration
            self.p_iter = self.iteration
            self.maxiters = maxiters
            self.len_maxiters = len(str(maxiters))
            self.opt_name = opt.opt_name
            self.model.add_observer(self, self.print_status)
            self.status = 'running'
            self.clear = clear_after_finish

            self.update()

            try:
                from IPython.display import display
                from IPython.html.widgets import IntProgress, HTML, Box, VBox, HBox, FlexBox
                self.text = HTML(width='100%')
                self.progress = IntProgress(min=0, max=maxiters)
                #self.progresstext = Text(width='100%', disabled=True, value='0/{}'.format(maxiters))
                self.model_show = HTML()
                self.ipython_notebook = ipython_notebook
            except:
                # Not in Ipython notebook
                self.ipython_notebook = False

            if self.ipython_notebook:
                left_col = VBox(children=[self.progress, self.text],
                                padding=2,
                                width='40%')
                right_col = Box(children=[self.model_show],
                                padding=2,
                                width='60%')
                self.hor_align = FlexBox(children=[left_col, right_col],
                                         width='100%',
                                         orientation='horizontal')

                display(self.hor_align)

                try:
                    self.text.set_css('width', '100%')
                    left_col.set_css({
                        'padding': '2px',
                        'width': "100%",
                    })

                    right_col.set_css({
                        'padding': '2px',
                    })

                    self.hor_align.set_css({
                        'width': "100%",
                    })

                    self.hor_align.remove_class('vbox')
                    self.hor_align.add_class('hbox')

                    left_col.add_class("box-flex1")
                    right_col.add_class('box-flex0')

                except:
                    pass

                #self.text.add_class('box-flex2')
                #self.progress.add_class('box-flex1')
            else:
                self.exps = exponents(self.fnow, self.current_gradient)
                print('Running {} Code:'.format(self.opt_name))
                print('  {3:7s}   {0:{mi}s}   {1:11s}    {2:11s}'.format(
                    "i", "f", "|g|", "runtime", mi=self.len_maxiters))

    def __enter__(self):
        self.start = time.time()
        self._time = self.start
        return self

    def print_out(self, seconds):
        if seconds < 60:
            ms = (seconds % 1) * 100
            self.timestring = "{s:0>2d}s{ms:0>2d}".format(s=int(seconds),
                                                          ms=int(ms))
        else:
            m, s = divmod(seconds, 60)
            if m > 59:
                h, m = divmod(m, 60)
                if h > 23:
                    d, h = divmod(h, 24)
                    self.timestring = '{d:0>2d}d{h:0>2d}h{m:0>2d}'.format(
                        m=int(m), h=int(h), d=int(d))
                else:
                    self.timestring = '{h:0>2d}h{m:0>2d}m{s:0>2d}'.format(
                        m=int(m), s=int(s), h=int(h))
            else:
                ms = (seconds % 1) * 100
                self.timestring = '{m:0>2d}m{s:0>2d}s{ms:0>2d}'.format(
                    m=int(m), s=int(s), ms=int(ms))
        if self.ipython_notebook:
            names_vals = [
                ['optimizer', "{:s}".format(self.opt_name)],
                ['runtime', "{:>s}".format(self.timestring)],
                [
                    'evaluation', "{:>0{l}}".format(self.iteration,
                                                    l=self.len_maxiters)
                ],
                ['objective', "{: > 12.3E}".format(self.fnow)],
                [
                    '||gradient||',
                    "{: >+12.3E}".format(float(self.current_gradient))
                ],
                ['status', "{:s}".format(self.status)],
            ]
            #message = "Lik:{:5.3E} Grad:{:5.3E} Lik:{:5.3E} Len:{!s}".format(float(m.log_likelihood()), np.einsum('i,i->', grads, grads), float(m.likelihood.variance), " ".join(["{:3.2E}".format(l) for l in m.kern.lengthscale.values]))
            html_begin = """<style type="text/css">
    .tg-opt  {font-family:"Courier New", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}
    .tg-opt td{font-family:"Courier New", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
    .tg-opt th{font-family:"Courier New", Courier, monospace !important;font-weight:normal;color:#fff;background-color:#26ADE4;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
    .tg-opt .tg-left{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:left;}
    .tg-opt .tg-right{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:right;}
    </style>
    <table class="tg-opt">"""
            html_end = "</table>"
            html_body = ""
            for name, val in names_vals:
                html_body += "<tr>"
                html_body += "<td class='tg-left'>{}</td>".format(name)
                html_body += "<td class='tg-right'>{}</td>".format(val)
                html_body += "</tr>"
            self.text.value = html_begin + html_body + html_end
            self.progress.value = (self.iteration + 1)
            #self.progresstext.value = '0/{}'.format((self.iteration+1))
            self.model_show.value = self.model._repr_html_()
        else:
            n_exps = exponents(self.fnow, self.current_gradient)
            if self.iteration - self.p_iter >= 20 * np.random.rand():
                a = self.iteration >= self.p_iter * 2.78
                b = np.any(n_exps < self.exps)
                if a or b:
                    self.p_iter = self.iteration
                    print('')
                if b:
                    self.exps = n_exps
            print('\r', end=' ')
            print(
                '{3:}  {0:>0{mi}g}  {1:> 12e}  {2:> 12e}'.format(
                    self.iteration,
                    float(self.fnow),
                    float(self.current_gradient),
                    "{:>8s}".format(self.timestring),
                    mi=self.len_maxiters),
                end=' '
            )  # print 'Iteration:', iteration, ' Objective:', fnow, '  Scale:', beta, '\r',
            sys.stdout.flush()

    def print_status(self, me, which=None):
        self.update()

        t = time.time()
        seconds = t - self.start
        #sys.stdout.write(" "*len(self.message))
        if t - self._time > .3 or seconds < .3:
            self.print_out(seconds)
            self._time = t

        self.iteration += 1

    def update(self):
        self.fnow = self.model.objective_function()
        if self.model.obj_grads is not None:
            grad = self.model.obj_grads
            self.current_gradient = np.dot(grad, grad)
        else:
            self.current_gradient = np.nan

    def finish(self, opt):
        self.status = opt.status
        if self.verbose and self.ipython_notebook:
            if 'conv' in self.status.lower():
                self.progress.bar_style = 'success'
            elif self.iteration >= self.maxiters:
                self.progress.bar_style = 'warning'
            else:
                self.progress.bar_style = 'danger'

    def __exit__(self, type, value, traceback):
        if self.verbose:
            self.stop = time.time()
            self.model.remove_observer(self)
            self.print_out(self.stop - self.start)

            if not self.ipython_notebook:
                print()
                print('Runtime: {}'.format("{:>9s}".format(self.timestring)))
                print('Optimization status: {0}'.format(self.status))
                print()
            elif self.clear:
                self.hor_align.close()
Пример #3
0
    def __init__(self, model, opt, maxiters, verbose=False, current_iteration=0, ipython_notebook=True, clear_after_finish=False):
        self.verbose = verbose
        if self.verbose:
            self.model = model
            self.iteration = current_iteration
            self.p_iter = self.iteration
            self.maxiters = maxiters
            self.len_maxiters = len(str(maxiters))
            self.opt_name = opt.opt_name
            self.model.add_observer(self, self.print_status)
            self.status = 'running'
            self.clear = clear_after_finish

            self.update()

            try:
                from IPython.display import display
                from IPython.html.widgets import IntProgress, HTML, Box, VBox, HBox, FlexBox
                self.text = HTML(width='100%')
                self.progress = IntProgress(min=0, max=maxiters)
                #self.progresstext = Text(width='100%', disabled=True, value='0/{}'.format(maxiters))
                self.model_show = HTML()
                self.ipython_notebook = ipython_notebook
            except:
                # Not in Ipython notebook
                self.ipython_notebook = False

            if self.ipython_notebook:
                left_col = VBox(children=[self.progress, self.text], padding=2, width='40%')
                right_col = Box(children=[self.model_show], padding=2, width='60%')
                self.hor_align = FlexBox(children = [left_col, right_col], width='100%', orientation='horizontal')

                display(self.hor_align)

                try:
                    self.text.set_css('width', '100%')
                    left_col.set_css({
                             'padding': '2px',
                             'width': "100%",
                             })

                    right_col.set_css({
                             'padding': '2px',
                             })

                    self.hor_align.set_css({
                             'width': "100%",
                             })

                    self.hor_align.remove_class('vbox')
                    self.hor_align.add_class('hbox')

                    left_col.add_class("box-flex1")
                    right_col.add_class('box-flex0')

                except:
                    pass

                #self.text.add_class('box-flex2')
                #self.progress.add_class('box-flex1')
            else:
                self.exps = exponents(self.fnow, self.current_gradient)
                print('Running {} Code:'.format(self.opt_name))
                print('  {3:7s}   {0:{mi}s}   {1:11s}    {2:11s}'.format("i", "f", "|g|", "runtime", mi=self.len_maxiters))
Пример #4
0
class VerboseOptimization(object):
    def __init__(self, model, opt, maxiters, verbose=False, current_iteration=0, ipython_notebook=True, clear_after_finish=False):
        self.verbose = verbose
        if self.verbose:
            self.model = model
            self.iteration = current_iteration
            self.p_iter = self.iteration
            self.maxiters = maxiters
            self.len_maxiters = len(str(maxiters))
            self.opt_name = opt.opt_name
            self.model.add_observer(self, self.print_status)
            self.status = 'running'
            self.clear = clear_after_finish

            self.update()

            try:
                from IPython.display import display
                from IPython.html.widgets import IntProgress, HTML, Box, VBox, HBox, FlexBox
                self.text = HTML(width='100%')
                self.progress = IntProgress(min=0, max=maxiters)
                #self.progresstext = Text(width='100%', disabled=True, value='0/{}'.format(maxiters))
                self.model_show = HTML()
                self.ipython_notebook = ipython_notebook
            except:
                # Not in Ipython notebook
                self.ipython_notebook = False

            if self.ipython_notebook:
                left_col = VBox(children=[self.progress, self.text], padding=2, width='40%')
                right_col = Box(children=[self.model_show], padding=2, width='60%')
                self.hor_align = FlexBox(children = [left_col, right_col], width='100%', orientation='horizontal')

                display(self.hor_align)

                try:
                    self.text.set_css('width', '100%')
                    left_col.set_css({
                             'padding': '2px',
                             'width': "100%",
                             })

                    right_col.set_css({
                             'padding': '2px',
                             })

                    self.hor_align.set_css({
                             'width': "100%",
                             })

                    self.hor_align.remove_class('vbox')
                    self.hor_align.add_class('hbox')

                    left_col.add_class("box-flex1")
                    right_col.add_class('box-flex0')

                except:
                    pass

                #self.text.add_class('box-flex2')
                #self.progress.add_class('box-flex1')
            else:
                self.exps = exponents(self.fnow, self.current_gradient)
                print('Running {} Code:'.format(self.opt_name))
                print('  {3:7s}   {0:{mi}s}   {1:11s}    {2:11s}'.format("i", "f", "|g|", "runtime", mi=self.len_maxiters))

    def __enter__(self):
        self.start = time.time()
        self._time = self.start
        return self

    def print_out(self, seconds):
        if seconds<60:
            ms = (seconds%1)*100
            self.timestring = "{s:0>2d}s{ms:0>2d}".format(s=int(seconds), ms=int(ms))
        else:
            m, s = divmod(seconds, 60)
            if m>59:
                h, m = divmod(m, 60)
                if h>23:
                    d, h = divmod(h, 24)
                    self.timestring = '{d:0>2d}d{h:0>2d}h{m:0>2d}'.format(m=int(m), h=int(h), d=int(d))
                else:
                    self.timestring = '{h:0>2d}h{m:0>2d}m{s:0>2d}'.format(m=int(m), s=int(s), h=int(h))
            else:
                ms = (seconds%1)*100
                self.timestring = '{m:0>2d}m{s:0>2d}s{ms:0>2d}'.format(m=int(m), s=int(s), ms=int(ms))
        if self.ipython_notebook:
            names_vals = [['optimizer', "{:s}".format(self.opt_name)],
                          ['runtime', "{:>s}".format(self.timestring)],
                          ['evaluation', "{:>0{l}}".format(self.iteration, l=self.len_maxiters)],
                          ['objective', "{: > 12.3E}".format(self.fnow)],
                          ['||gradient||', "{: >+12.3E}".format(float(self.current_gradient))],
                          ['status', "{:s}".format(self.status)],
                      ]
            #message = "Lik:{:5.3E} Grad:{:5.3E} Lik:{:5.3E} Len:{!s}".format(float(m.log_likelihood()), np.einsum('i,i->', grads, grads), float(m.likelihood.variance), " ".join(["{:3.2E}".format(l) for l in m.kern.lengthscale.values]))
            html_begin = """<style type="text/css">
    .tg-opt  {font-family:"Courier New", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}
    .tg-opt td{font-family:"Courier New", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
    .tg-opt th{font-family:"Courier New", Courier, monospace !important;font-weight:normal;color:#fff;background-color:#26ADE4;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
    .tg-opt .tg-left{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:left;}
    .tg-opt .tg-right{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:right;}
    </style>
    <table class="tg-opt">"""
            html_end = "</table>"
            html_body = ""
            for name, val in names_vals:
                html_body += "<tr>"
                html_body += "<td class='tg-left'>{}</td>".format(name)
                html_body += "<td class='tg-right'>{}</td>".format(val)
                html_body += "</tr>"
            self.text.value = html_begin + html_body + html_end
            self.progress.value = (self.iteration+1)
            #self.progresstext.value = '0/{}'.format((self.iteration+1))
            self.model_show.value = self.model._repr_html_()
        else:
            n_exps = exponents(self.fnow, self.current_gradient)
            if self.iteration - self.p_iter >= 20 * np.random.rand():
                a = self.iteration >= self.p_iter * 2.78
                b = np.any(n_exps < self.exps)
                if a or b:
                    self.p_iter = self.iteration
                    print('')
                if b:
                    self.exps = n_exps
            print('\r', end=' ')
            print('{3:}  {0:>0{mi}g}  {1:> 12e}  {2:> 12e}'.format(self.iteration, float(self.fnow), float(self.current_gradient), "{:>8s}".format(self.timestring), mi=self.len_maxiters), end=' ') # print 'Iteration:', iteration, ' Objective:', fnow, '  Scale:', beta, '\r',
            sys.stdout.flush()

    def print_status(self, me, which=None):
        self.update()

        t = time.time()
        seconds = t-self.start
        #sys.stdout.write(" "*len(self.message))
        if t-self._time > .3 or seconds < .3:
            self.print_out(seconds)
            self._time = t

        self.iteration += 1

    def update(self):
        self.fnow = self.model.objective_function()
        if self.model.obj_grads is not None:
            grad = self.model.obj_grads
            self.current_gradient = np.dot(grad, grad)
        else:
            self.current_gradient = np.nan

    def finish(self, opt):
        self.status = opt.status
        if self.verbose and self.ipython_notebook:
            if 'conv' in self.status.lower():
                self.progress.bar_style = 'success'
            elif self.iteration >= self.maxiters:
                self.progress.bar_style = 'warning'
            else:
                self.progress.bar_style = 'danger'

    def __exit__(self, type, value, traceback):
        if self.verbose:
            self.stop = time.time()
            self.model.remove_observer(self)
            self.print_out(self.stop - self.start)

            if not self.ipython_notebook:
                print()
                print('Runtime: {}'.format("{:>9s}".format(self.timestring)))
                print('Optimization status: {0}'.format(self.status))
                print()
            elif self.clear:
                self.hor_align.close()
Пример #5
0
    def optimize(self, method='HS', maxiter=500, ftol=1e-6, gtol=1e-6, step_length=1., callback=None, verbose=True):
        """
        Optimize the model.

        The strategy is to run conjugate natural gradients on the variational
        parameters, interleaved with gradient based optimization of any
        non-variational parameters. self.hyperparam_interval dictates how
        often this happens.

        Arguments
        ---------
        :method: ['FR', 'PR','HS','steepest'] -- conjugate gradient method
        :maxiter: int
        :ftol: float
        :gtol: float
        :step_length: float

        """

        assert method in ['FR', 'PR','HS','steepest'], 'invalid conjugate gradient method specified.'

        ## For GPy style notebook verbosity

        if verbose:
            try:
                from IPython.display import display
                from IPython.html.widgets import IntProgress, HTML, Box, VBox, HBox, FlexBox
                self.text = HTML(width='100%')
                self.progress = IntProgress(min=0, max=maxiter)
                self.progress.bar_style = 'info'
                self.status = 'Running'

                html_begin = """<style type="text/css">
                .tg-opt  {font-family:"Courier New", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}
                .tg-opt td{font-family:"Courier New", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
                .tg-opt th{font-family:"Courier New", Courier, monospace !important;font-weight:normal;color:#fff;background-color:#26ADE4;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
                .tg-opt .tg-left{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:left;}
                .tg-opt .tg-right{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:right;}
                </style>
                <table class="tg-opt">"""

                html_end = "</table>"

                self.ipython_notebook = True
            except:
                # Not in Ipython notebook
                self.ipython_notebook = False
        else:
            self.ipython_notebook = False

        if self.ipython_notebook:
            left_col = VBox(children=[self.progress, self.text], padding=2, width='100%')
            self.hor_align = FlexBox(children = [left_col], width='100%', orientation='horizontal')

            display(self.hor_align)

            try:
                self.text.set_css('width', '100%')
                left_col.set_css({
                         'padding': '2px',
                         'width': "100%",
                         })

                self.hor_align.set_css({
                         'width': "100%",
                         })

                self.hor_align.remove_class('vbox')
                self.hor_align.add_class('hbox')

                left_col.add_class("box-flex1")

            except:
                pass

        self.start = time.time()
        self._time = self.start

        ## ---

        iteration = 0
        bound_old = self.bound()
        searchDir_old = 0.
        iteration_failed = False
        while True:

            if callback is not None:
                callback()

            grad,natgrad = self.vb_grad_natgrad()
            grad,natgrad = -grad,-natgrad
            squareNorm = np.dot(natgrad,grad) # used to monitor convergence

            #find search direction
            if (method=='steepest') or not iteration:
                beta = 0
            elif (method=='PR'):
                beta = np.dot((natgrad-natgrad_old),grad)/squareNorm_old
            elif (method=='FR'):
                beta = squareNorm/squareNorm_old
            elif (method=='HS'):
                beta = np.dot((natgrad-natgrad_old),grad)/np.dot((natgrad-natgrad_old),grad_old)
            if np.isnan(beta) or (beta < 0.):
                beta = 0.
            searchDir = -natgrad + beta*searchDir_old

            # Try a conjugate step
            phi_old = self.get_vb_param().copy()
            try:
                self.set_vb_param(phi_old + step_length*searchDir)
                bound = self.bound()
            except LinAlgError:
                self.set_vb_param(phi_old)
                bound = bound_old-1

            iteration += 1

            # Make sure there's an increase in the bound, else revert to steepest, which is guaranteed to increase the bound.
            # (It's the same as VBEM.)
            if bound < bound_old:
                searchDir = -natgrad
                try:
                    self.set_vb_param(phi_old + step_length*searchDir)
                    bound = self.bound()
                except LinAlgError:
                    import warnings
                    warnings.warn("Caught LinalgError in setting variational parameters, trying to continue with old parameter settings", LinAlgWarning)
                    self.set_vb_param(phi_old)
                    bound = self.bound()
                    iteration_failed = False
                iteration += 1


            if verbose:
                if self.ipython_notebook:

                    t = time.time()
                    seconds = t-self.start

                    self.status = 'Running'
                    self.progress.bar_style = 'info'

                    names_vals = [['conjugate gradient method', "{:s}".format(method)],
                                  ['runtime', "{:.1f}s".format(seconds)],
                                  ['evaluation', "{}".format(iteration)],
                                  ['objective', "{:12.5f}".format(-bound)],
                                  ['||gradient||', "{:12.5f}".format(float(squareNorm))],
                                  ['beta', "{:12.5f}".format(beta)],
                                  ['status', "{:s}".format(self.status)],
                                ]

                    html_body = ""
                    for name, val in names_vals:
                        html_body += "<tr>"
                        html_body += "<td class='tg-left'>{}</td>".format(name)
                        html_body += "<td class='tg-right'>{}</td>".format(val)
                        html_body += "</tr>"

                    self.progress.value = iteration
                    self.text.value = html_begin + html_body + html_end

                else:
                    print('\riteration '+str(iteration)+' bound='+str(bound) + ' grad='+str(squareNorm) + ', beta='+str(beta))
                    sys.stdout.flush()

            # Converged yet? try the parameters if so
            if np.abs(bound-bound_old) <= ftol:
                if verbose:
                    if self.ipython_notebook:
                        self.status = 'vb converged (ftol)'
                        names_vals[-1] = ['status', "{:s}".format(self.status)]

                        html_body = ""
                        for name, val in names_vals:
                            html_body += "<tr>"
                            html_body += "<td class='tg-left'>{}</td>".format(name)
                            html_body += "<td class='tg-right'>{}</td>".format(val)
                            html_body += "</tr>"

                        self.text.value = html_begin + html_body + html_end
                        self.progress.bar_style = 'success'

                    else:
                        print('vb converged (ftol)')

                if self.optimize_parameters() < 1e-1:
                    break

            if squareNorm <= gtol:
                if verbose:
                    if self.ipython_notebook:
                        self.status = 'vb converged (gtol)'
                        names_vals[-1] = ['status', "{:s}".format(self.status)]

                        html_body = ""
                        for name, val in names_vals:
                            html_body += "<tr>"
                            html_body += "<td class='tg-left'>{}</td>".format(name)
                            html_body += "<td class='tg-right'>{}</td>".format(val)
                            html_body += "</tr>"

                        self.text.value = html_begin + html_body + html_end
                        self.progress.bar_style = 'success'

                    else:
                        print('vb converged (gtol)')

                if self.optimize_parameters() < 1e-1:
                    break

            if iteration >= maxiter:
                if verbose:
                    if self.ipython_notebook:
                        self.status = 'maxiter exceeded'
                        names_vals[-1] = ['status', "{:s}".format(self.status)]

                        html_body = ""
                        for name, val in names_vals:
                            html_body += "<tr>"
                            html_body += "<td class='tg-left'>{}</td>".format(name)
                            html_body += "<td class='tg-right'>{}</td>".format(val)
                            html_body += "</tr>"

                        self.text.value = html_begin + html_body + html_end
                        self.progress.bar_style = 'warning'

                    else:
                        print('maxiter exceeded')
                break

            #store essentials of previous iteration
            natgrad_old = natgrad.copy() # copy: better safe than sorry.
            grad_old = grad.copy()
            searchDir_old = searchDir.copy()
            squareNorm_old = squareNorm

            # hyper param_optimisation
            if ((iteration >1) and not (iteration%self.hyperparam_interval)) or iteration_failed:
                self.optimize_parameters()

            bound_old = bound

        # Clean up temporary fields after optimization
        if self.ipython_notebook:
            del self.text
            del self.progress
            del self.hor_align
Пример #6
0
    def optimize(self,
                 method='HS',
                 maxiter=500,
                 ftol=1e-6,
                 gtol=1e-6,
                 step_length=1.,
                 callback=None,
                 verbose=True):
        """
        Optimize the model.

        The strategy is to run conjugate natural gradients on the variational
        parameters, interleaved with gradient based optimization of any
        non-variational parameters. self.hyperparam_interval dictates how
        often this happens.

        Arguments
        ---------
        :method: ['FR', 'PR','HS','steepest'] -- conjugate gradient method
        :maxiter: int
        :ftol: float
        :gtol: float
        :step_length: float

        """

        assert method in ['FR', 'PR', 'HS', 'steepest'
                          ], 'invalid conjugate gradient method specified.'

        ## For GPy style notebook verbosity

        if verbose:
            try:
                from IPython.display import display
                from IPython.html.widgets import IntProgress, HTML, Box, VBox, HBox, FlexBox
                self.text = HTML(width='100%')
                self.progress = IntProgress(min=0, max=maxiter)
                self.progress.bar_style = 'info'
                self.status = 'Running'

                html_begin = """<style type="text/css">
                .tg-opt  {font-family:"Courier New", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}
                .tg-opt td{font-family:"Courier New", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
                .tg-opt th{font-family:"Courier New", Courier, monospace !important;font-weight:normal;color:#fff;background-color:#26ADE4;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}
                .tg-opt .tg-left{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:left;}
                .tg-opt .tg-right{font-family:"Courier New", Courier, monospace !important;font-weight:normal;text-align:right;}
                </style>
                <table class="tg-opt">"""

                html_end = "</table>"

                self.ipython_notebook = True
            except:
                # Not in Ipython notebook
                self.ipython_notebook = False
        else:
            self.ipython_notebook = False

        if self.ipython_notebook:
            left_col = VBox(children=[self.progress, self.text],
                            padding=2,
                            width='100%')
            self.hor_align = FlexBox(children=[left_col],
                                     width='100%',
                                     orientation='horizontal')

            display(self.hor_align)

            try:
                self.text.set_css('width', '100%')
                left_col.set_css({
                    'padding': '2px',
                    'width': "100%",
                })

                self.hor_align.set_css({
                    'width': "100%",
                })

                self.hor_align.remove_class('vbox')
                self.hor_align.add_class('hbox')

                left_col.add_class("box-flex1")

            except:
                pass

        self.start = time.time()
        self._time = self.start

        ## ---

        iteration = 0
        bound_old = self.bound()
        searchDir_old = 0.
        iteration_failed = False
        while True:

            if callback is not None:
                callback()

            grad, natgrad = self.vb_grad_natgrad()
            grad, natgrad = -grad, -natgrad
            squareNorm = np.dot(natgrad, grad)  # used to monitor convergence

            #find search direction
            if (method == 'steepest') or not iteration:
                beta = 0
            elif (method == 'PR'):
                beta = np.dot((natgrad - natgrad_old), grad) / squareNorm_old
            elif (method == 'FR'):
                beta = squareNorm / squareNorm_old
            elif (method == 'HS'):
                beta = np.dot((natgrad - natgrad_old), grad) / np.dot(
                    (natgrad - natgrad_old), grad_old)
            if np.isnan(beta) or (beta < 0.):
                beta = 0.
            searchDir = -natgrad + beta * searchDir_old

            # Try a conjugate step
            phi_old = self.get_vb_param().copy()
            try:
                self.set_vb_param(phi_old + step_length * searchDir)
                bound = self.bound()
            except LinAlgError:
                self.set_vb_param(phi_old)
                bound = bound_old - 1

            iteration += 1

            # Make sure there's an increase in the bound, else revert to steepest, which is guaranteed to increase the bound.
            # (It's the same as VBEM.)
            if bound < bound_old:
                searchDir = -natgrad
                try:
                    self.set_vb_param(phi_old + step_length * searchDir)
                    bound = self.bound()
                except LinAlgError:
                    import warnings
                    warnings.warn(
                        "Caught LinalgError in setting variational parameters, trying to continue with old parameter settings",
                        LinAlgWarning)
                    self.set_vb_param(phi_old)
                    bound = self.bound()
                    iteration_failed = False
                iteration += 1

            if verbose:
                if self.ipython_notebook:

                    t = time.time()
                    seconds = t - self.start

                    self.status = 'Running'
                    self.progress.bar_style = 'info'

                    names_vals = [
                        ['conjugate gradient method', "{:s}".format(method)],
                        ['runtime', "{:.1f}s".format(seconds)],
                        ['evaluation', "{}".format(iteration)],
                        ['objective', "{:12.5f}".format(-bound)],
                        ['||gradient||', "{:12.5f}".format(float(squareNorm))],
                        ['beta', "{:12.5f}".format(beta)],
                        ['status', "{:s}".format(self.status)],
                    ]

                    html_body = ""
                    for name, val in names_vals:
                        html_body += "<tr>"
                        html_body += "<td class='tg-left'>{}</td>".format(name)
                        html_body += "<td class='tg-right'>{}</td>".format(val)
                        html_body += "</tr>"

                    self.progress.value = iteration
                    self.text.value = html_begin + html_body + html_end

                else:
                    print('\riteration ' + str(iteration) + ' bound=' +
                          str(bound) + ' grad=' + str(squareNorm) + ', beta=' +
                          str(beta))
                    sys.stdout.flush()

            # Converged yet? try the parameters if so
            if np.abs(bound - bound_old) <= ftol:
                if verbose:
                    if self.ipython_notebook:
                        self.status = 'vb converged (ftol)'
                        names_vals[-1] = ['status', "{:s}".format(self.status)]

                        html_body = ""
                        for name, val in names_vals:
                            html_body += "<tr>"
                            html_body += "<td class='tg-left'>{}</td>".format(
                                name)
                            html_body += "<td class='tg-right'>{}</td>".format(
                                val)
                            html_body += "</tr>"

                        self.text.value = html_begin + html_body + html_end
                        self.progress.bar_style = 'success'

                    else:
                        print('vb converged (ftol)')

                if self.optimize_parameters() < 1e-1:
                    break

            if squareNorm <= gtol:
                if verbose:
                    if self.ipython_notebook:
                        self.status = 'vb converged (gtol)'
                        names_vals[-1] = ['status', "{:s}".format(self.status)]

                        html_body = ""
                        for name, val in names_vals:
                            html_body += "<tr>"
                            html_body += "<td class='tg-left'>{}</td>".format(
                                name)
                            html_body += "<td class='tg-right'>{}</td>".format(
                                val)
                            html_body += "</tr>"

                        self.text.value = html_begin + html_body + html_end
                        self.progress.bar_style = 'success'

                    else:
                        print('vb converged (gtol)')

                if self.optimize_parameters() < 1e-1:
                    break

            if iteration >= maxiter:
                if verbose:
                    if self.ipython_notebook:
                        self.status = 'maxiter exceeded'
                        names_vals[-1] = ['status', "{:s}".format(self.status)]

                        html_body = ""
                        for name, val in names_vals:
                            html_body += "<tr>"
                            html_body += "<td class='tg-left'>{}</td>".format(
                                name)
                            html_body += "<td class='tg-right'>{}</td>".format(
                                val)
                            html_body += "</tr>"

                        self.text.value = html_begin + html_body + html_end
                        self.progress.bar_style = 'warning'

                    else:
                        print('maxiter exceeded')
                break

            #store essentials of previous iteration
            natgrad_old = natgrad.copy()  # copy: better safe than sorry.
            grad_old = grad.copy()
            searchDir_old = searchDir.copy()
            squareNorm_old = squareNorm

            # hyper param_optimisation
            if ((iteration > 1) and not (iteration % self.hyperparam_interval)
                ) or iteration_failed:
                self.optimize_parameters()

            bound_old = bound

        # Clean up temporary fields after optimization
        if self.ipython_notebook:
            del self.text
            del self.progress
            del self.hor_align