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hyperopt-gpsmbo's Issues

Does this work?

It would be nice to see real bayesian optimization and GP in hyperpot. Does this package work? Should we have hyperopt installed? It would be also nice to have some examples in the readme. Thanks!

AttributeError: 'DomainGP_EI' object has no attribute '_cost_deriv'

Hi @jaberg,
Thanks for the package! This extension on hyperopt can be very handy.

I was trying to run fmin with algo=ei.suggest and got the error message above. The full error message is as follows:

AttributeError                            Traceback (most recent call last)
~/anaconda/envs/ldsa/lib/python3.6/site-packages/hp_gpsmbo/hpsuggest_ei.py in init_fns(self)
     18         try:
---> 19             self._cost_deriv
     20         except AttributeError:

AttributeError: 'DomainGP_EI' object has no attribute '_cost_deriv'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-146-b79d81f8328e> in <module>()
      5                 #algo=ucb.suggest,
      6                 max_evals = 30,
----> 7                 trials=trials)
      8 rf_best

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin)
    305             verbose=verbose,
    306             catch_eval_exceptions=catch_eval_exceptions,
--> 307             return_argmin=return_argmin,
    308         )
    309 

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hyperopt/base.py in fmin(self, fn, space, algo, max_evals, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin)
    633             pass_expr_memo_ctrl=pass_expr_memo_ctrl,
    634             catch_eval_exceptions=catch_eval_exceptions,
--> 635             return_argmin=return_argmin)
    636 
    637 

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin)
    318                     verbose=verbose)
    319     rval.catch_eval_exceptions = catch_eval_exceptions
--> 320     rval.exhaust()
    321     if return_argmin:
    322         return trials.argmin

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hyperopt/fmin.py in exhaust(self)
    197     def exhaust(self):
    198         n_done = len(self.trials)
--> 199         self.run(self.max_evals - n_done, block_until_done=self.async)
    200         self.trials.refresh()
    201         return self

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hyperopt/fmin.py in run(self, N, block_until_done)
    155                                                   d['result'].get('status')))
    156                 new_trials = algo(new_ids, self.domain, trials,
--> 157                                   self.rstate.randint(2 ** 31 - 1))
    158                 assert len(new_ids) >= len(new_trials)
    159                 if len(new_trials):

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hp_gpsmbo/hpsuggest_ei.py in suggest(new_ids, domain, trials, seed, warmup_cutoff, n_buckshots, n_finetunes, stop_at, plot_contours, gp_fit_method, failure_loss, max_ei_thresh)
    200         n_buckshots=n_buckshots,
    201         n_finetunes=n_finetunes,
--> 202         rng=rng,
    203         )
    204     t1 = time.time()

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hp_gpsmbo/hpsuggest_ei.py in optimize_over_X(self, n_buckshots, n_finetunes, rng)
    137                                                 n_finetunes,
    138                                                 rng,
--> 139                                                 ret_raw=True)
    140             if len(self.gpr._params_list) == 1:
    141                 Ks = self._K_new(np.atleast_2d(rval_raw),

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hp_gpsmbo/hpsuggest.py in optimize_over_X(self, n_buckshots, n_finetunes, rng, ret_raw, ret_results)
    360         # -- sample a bunch of points
    361         buckshot = self.draw_n_feature_vecs(n_buckshots, rng)
--> 362         buckshot_crit = self.crit(buckshot)
    363         best_first = np.argsort(buckshot_crit)
    364         #print 'buckshot stats', buckshot_crit.min(), buckshot_crit.max()

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hp_gpsmbo/hpsuggest_ei.py in crit(self, X)
     95 
     96     def crit(self, X):
---> 97         self.init_fns()
     98         #return -self.gpr.logEI(X,
     99                                #self._EI_thresh,

~/anaconda/envs/ldsa/lib/python3.6/site-packages/hp_gpsmbo/hpsuggest_ei.py in init_fns(self)
     53                 on_unused_input='ignore',
     54                 allow_input_downcast=True,
---> 55                 profile=0)
     56             op_Kcond.use_lazy_cholesky = None
     57             op_Kcond.use_lazy_cholesky_idx = None

~/anaconda/envs/ldsa/lib/python3.6/site-packages/theano/compile/function.py in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
    315                    on_unused_input=on_unused_input,
    316                    profile=profile,
--> 317                    output_keys=output_keys)
    318     return fn

~/anaconda/envs/ldsa/lib/python3.6/site-packages/theano/compile/pfunc.py in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)
    484                          accept_inplace=accept_inplace, name=name,
    485                          profile=profile, on_unused_input=on_unused_input,
--> 486                          output_keys=output_keys)
    487 
    488 

~/anaconda/envs/ldsa/lib/python3.6/site-packages/theano/compile/function_module.py in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)
   1839                   name=name)
   1840         with theano.change_flags(compute_test_value="off"):
-> 1841             fn = m.create(defaults)
   1842     finally:
   1843         t2 = time.time()

~/anaconda/envs/ldsa/lib/python3.6/site-packages/theano/compile/function_module.py in create(self, input_storage, trustme, storage_map)
   1713             theano.config.traceback.limit = theano.config.traceback.compile_limit
   1714             _fn, _i, _o = self.linker.make_thunk(
-> 1715                 input_storage=input_storage_lists, storage_map=storage_map)
   1716         finally:
   1717             theano.config.traceback.limit = limit_orig

~/anaconda/envs/ldsa/lib/python3.6/site-packages/theano/gof/link.py in make_thunk(self, input_storage, output_storage, storage_map)
    697         return self.make_all(input_storage=input_storage,
    698                              output_storage=output_storage,
--> 699                              storage_map=storage_map)[:3]
    700 
    701     def make_all(self, input_storage, output_storage):

~/anaconda/envs/ldsa/lib/python3.6/site-packages/theano/gof/vm.py in make_all(self, profiler, input_storage, output_storage, storage_map)
   1089                                                  compute_map,
   1090                                                  [],
-> 1091                                                  impl=impl))
   1092                 linker_make_thunk_time[node] = time.time() - thunk_start
   1093                 if not hasattr(thunks[-1], 'lazy'):

TypeError: ('The following error happened while compiling the node', <hp_gpsmbo.op_Kcond.LazyCholesky object at 0x1364166d8>(Elemwise{Composite{((i0 * exp((((-maximum(((i1 + i2) - i3), i4)) / i5) + (i6 * maximum(((i7 + i8) - i9), i4)) + log(i10)))) + (i11 * i12))}}[(0, 3)].0, reuse_cholesky, reuse_cholesky_idx), '\n', "make_thunk() got an unexpected keyword argument 'impl'") 

Any suggestions on what could be going wrong?

Much appreciated!
Miguel

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