from hyperopt import hp, fmin, rand
space = hp.choice('a', [-1, hp.uniform('b', 0, 1)])
best = fmin(fn=lambda x: x, space=space, algo=rand.suggest, max_evals=100)
print best

# put the configuration space in a local var
# so that we can work on it.
print space

from hyperopt import pyll

# The "navigation" approach to finding an internal
# search space node:
randint_node_nav = space.pos_args[0].pos_args[1]
print "by navigation:"
print randint_node_nav

# The "search" approach to finding an internal
# search space node:
randint_nodes = [node for node in pyll.dfs(space) if node.name == 'randint']
randint_node_srch, = randint_nodes
print "by search:"
print randint_node_srch

assert randint_node_nav == randint_node_srch

space_with_fixed_a = pyll.clone(space, memo={randint_node_nav: pyll.as_apply(1)})
print space_with_fixed_a

best = fmin(fn=lambda x: x, space=space_with_fixed_a, algo=rand.suggest, max_evals=100)
print best

from hyperopt import tpe
best = fmin(fn=lambda x: x, space=space_with_fixed_a, algo=tpe.suggest, max_evals=100)
print best

space_with_no_a = pyll.clone(space, memo={space.pos_args[0]: pyll.as_apply(1)})
best = fmin(fn=lambda x: x, space=space_with_no_a, algo=tpe.suggest, max_evals=100)
print best