Beispiel #1
0
    def work(self, **kwargs):
        self.__dict__.update(kwargs)
        bandit = opt_q_uniform(self.target)
        prior_weight = 2.5
        gamma = 0.20
        algo = partial(tpe.suggest,
                prior_weight=prior_weight,
                n_startup_jobs=2,
                n_EI_candidates=128,
                gamma=gamma)
        #print algo.opt_idxs['x']
        #print algo.opt_vals['x']

        trials = Trials()
        fmin(passthrough,
            space=bandit.expr,
            algo=algo,
            trials=trials,
            max_evals=self.LEN)
        if self.show_vars:
            import hyperopt.plotting
            hyperopt.plotting.main_plot_vars(trials, bandit, do_show=1)

        idxs, vals = miscs_to_idxs_vals(trials.miscs)
        idxs = idxs['x']
        vals = vals['x']

        losses = trials.losses()

        from hyperopt.tpe import ap_filter_trials
        from hyperopt.tpe import adaptive_parzen_samplers

        qu = scope.quniform(1.01, 10, 1)
        fn = adaptive_parzen_samplers['quniform']
        fn_kwargs = dict(size=(4,), rng=np.random)
        s_below = pyll.Literal()
        s_above = pyll.Literal()
        b_args = [s_below, prior_weight] + qu.pos_args
        b_post = fn(*b_args, **fn_kwargs)
        a_args = [s_above, prior_weight] + qu.pos_args
        a_post = fn(*a_args, **fn_kwargs)

        #print b_post
        #print a_post
        fn_lpdf = getattr(scope, a_post.name + '_lpdf')
        print fn_lpdf
        # calculate the llik of b_post under both distributions
        a_kwargs = dict([(n, a) for n, a in a_post.named_args
                    if n not in ('rng', 'size')])
        b_kwargs = dict([(n, a) for n, a in b_post.named_args
                    if n not in ('rng', 'size')])
        below_llik = fn_lpdf(*([b_post] + b_post.pos_args), **b_kwargs)
        above_llik = fn_lpdf(*([b_post] + a_post.pos_args), **a_kwargs)
        new_node = scope.broadcast_best(b_post, below_llik, above_llik)

        print '=' * 80

        do_show = self.show_steps

        for ii in range(2, 9):
            if ii > len(idxs):
                break
            print '-' * 80
            print 'ROUND', ii
            print '-' * 80
            all_vals = [2, 3, 4, 5, 6, 7, 8, 9, 10]
            below, above = ap_filter_trials(idxs[:ii],
                    vals[:ii], idxs[:ii], losses[:ii], gamma)
            below = below.astype('int')
            above = above.astype('int')
            print 'BB0', below
            print 'BB1', above
            #print 'BELOW',  zip(range(100), np.bincount(below, minlength=11))
            #print 'ABOVE',  zip(range(100), np.bincount(above, minlength=11))
            memo = {b_post: all_vals, s_below: below, s_above: above}
            bl, al, nv = pyll.rec_eval([below_llik, above_llik, new_node],
                    memo=memo)
            #print bl - al
            print 'BB2', dict(zip(all_vals, bl - al))
            print 'BB3', dict(zip(all_vals, bl))
            print 'BB4', dict(zip(all_vals, al))
            print 'ORIG PICKED', vals[ii]
            print 'PROPER OPT PICKS:', nv

            #assert np.allclose(below, [3, 3, 9])
            #assert len(below) + len(above) == len(vals)

            if do_show:
                plt.subplot(8, 1, ii)
                #plt.scatter(all_vals,
                #    np.bincount(below, minlength=11)[2:], c='b')
                #plt.scatter(all_vals,
                #    np.bincount(above, minlength=11)[2:], c='c')
                plt.scatter(all_vals, bl, c='g')
                plt.scatter(all_vals, al, c='r')
        if do_show:
            plt.show()
    def work(self, **kwargs):
        self.__dict__.update(kwargs)
        bandit = opt_q_uniform(self.target)
        prior_weight = 2.5
        gamma = 0.20
        algo = partial(tpe.suggest,
                       prior_weight=prior_weight,
                       n_startup_jobs=2,
                       n_EI_candidates=128,
                       gamma=gamma)
        #print algo.opt_idxs['x']
        #print algo.opt_vals['x']

        trials = Trials()
        fmin(passthrough,
             space=bandit.expr,
             algo=algo,
             trials=trials,
             max_evals=self.LEN)
        if self.show_vars:
            import hyperopt.plotting
            hyperopt.plotting.main_plot_vars(trials, bandit, do_show=1)

        idxs, vals = miscs_to_idxs_vals(trials.miscs)
        idxs = idxs['x']
        vals = vals['x']

        losses = trials.losses()

        from hyperopt.tpe import ap_filter_trials
        from hyperopt.tpe import adaptive_parzen_samplers

        qu = scope.quniform(1.01, 10, 1)
        fn = adaptive_parzen_samplers['quniform']
        fn_kwargs = dict(size=(4, ), rng=np.random)
        s_below = pyll.Literal()
        s_above = pyll.Literal()
        b_args = [s_below, prior_weight] + qu.pos_args
        b_post = fn(*b_args, **fn_kwargs)
        a_args = [s_above, prior_weight] + qu.pos_args
        a_post = fn(*a_args, **fn_kwargs)

        #print b_post
        #print a_post
        fn_lpdf = getattr(scope, a_post.name + '_lpdf')
        print fn_lpdf
        # calculate the llik of b_post under both distributions
        a_kwargs = dict([(n, a) for n, a in a_post.named_args
                         if n not in ('rng', 'size')])
        b_kwargs = dict([(n, a) for n, a in b_post.named_args
                         if n not in ('rng', 'size')])
        below_llik = fn_lpdf(*([b_post] + b_post.pos_args), **b_kwargs)
        above_llik = fn_lpdf(*([b_post] + a_post.pos_args), **a_kwargs)
        new_node = scope.broadcast_best(b_post, below_llik, above_llik)

        print '=' * 80

        do_show = self.show_steps

        for ii in range(2, 9):
            if ii > len(idxs):
                break
            print '-' * 80
            print 'ROUND', ii
            print '-' * 80
            all_vals = [2, 3, 4, 5, 6, 7, 8, 9, 10]
            below, above = ap_filter_trials(idxs[:ii], vals[:ii], idxs[:ii],
                                            losses[:ii], gamma)
            below = below.astype('int')
            above = above.astype('int')
            print 'BB0', below
            print 'BB1', above
            #print 'BELOW',  zip(range(100), np.bincount(below, minlength=11))
            #print 'ABOVE',  zip(range(100), np.bincount(above, minlength=11))
            memo = {b_post: all_vals, s_below: below, s_above: above}
            bl, al, nv = pyll.rec_eval([below_llik, above_llik, new_node],
                                       memo=memo)
            #print bl - al
            print 'BB2', dict(zip(all_vals, bl - al))
            print 'BB3', dict(zip(all_vals, bl))
            print 'BB4', dict(zip(all_vals, al))
            print 'ORIG PICKED', vals[ii]
            print 'PROPER OPT PICKS:', nv

            #assert np.allclose(below, [3, 3, 9])
            #assert len(below) + len(above) == len(vals)

            if do_show:
                plt.subplot(8, 1, ii)
                #plt.scatter(all_vals,
                #    np.bincount(below, minlength=11)[2:], c='b')
                #plt.scatter(all_vals,
                #    np.bincount(above, minlength=11)[2:], c='c')
                plt.scatter(all_vals, bl, c='g')
                plt.scatter(all_vals, al, c='r')
        if do_show:
            plt.show()