CODEWITHSUNDEEP
pythonoptimizationmachine-learning
Simd
Simd

Reputation: 21353

How to get hyperopt tutorial example to run

I am trying to use hyperopt for the first time. I copied and pasted the tutorial example:

from hyperopt import fmin, tpe, hp
best = fmin(fn=lambda x: x ** 2,
    space=hp.uniform('x', -10, 10),
    algo=tpe.suggest,
    max_evals=100)
print best

But this gives me:

TypeError                                 Traceback (most recent call last)
<ipython-input-1-c6ddf657bb46> in <module>()
      3     space=hp.uniform('x', -10, 10),
      4     algo=tpe.suggest,
----> 5     max_evals=100)
      6 print best

/home/user/.local/lib/python2.7/site-packages/hyperopt/fmin.pyc in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin)
    312 
    313     domain = base.Domain(fn, space,
--> 314                          pass_expr_memo_ctrl=pass_expr_memo_ctrl)
    315 
    316     rval = FMinIter(algo, domain, trials, max_evals=max_evals,

/home/user/.local/lib/python2.7/site-packages/hyperopt/base.pyc in __init__(self, fn, expr, workdir, pass_expr_memo_ctrl, name, loss_target)
    784         before = pyll.dfs(self.expr)
    785         # -- raises exception if expr contains cycles
--> 786         pyll.toposort(self.expr)
    787         vh = self.vh = VectorizeHelper(self.expr, self.s_new_ids)
    788         # -- raises exception if v_expr contains cycles

/home/user/.local/lib/python2.7/site-packages/hyperopt/pyll/base.pyc in toposort(expr)
    713         G.add_edges_from([(n_in, node) for n_in in node.inputs()])
    714     order = nx.topological_sort(G)
--> 715     assert order[-1] == expr
    716     return order
    717 

TypeError: 'generator' object has no attribute '__getitem__'

I am not really sure what this error message means.

What am I doing wrong?

Upvotes: 0

Views: 561

Answers (1)

user2357112
user2357112

Reputation: 281948

hyperopt isn't currently compatible with NetworkX 2.x. The master branch should have the fix, but there hasn't been a fixed hyperopt release.

For now, you'll either have to install hyperopt from the master branch, or downgrade to a 1.x version of NetworkX.

Upvotes: 1

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