def test_recursion():
scope.define(
Lambda(
"Fact",
[("x", p0)],
expr=scope.switch(p0 > 1, 1, p0 * base.apply("Fact", p0 - 1)),
))
print(scope.Fact(3))
assert rec_eval(scope.Fact(3)) == 6
def test_switch_and_Raise():
i = Literal()
ab = scope.switch(i, 'a', 'b', scope.Raise(Exception))
assert rec_eval(ab, memo={i: 0}) == 'a'
assert rec_eval(ab, memo={i: 1}) == 'b'
assert_raises(Exception, rec_eval, ab, memo={i: 2})
def test_switch_and_Raise():
i = Literal()
ab = scope.switch(i, "a", "b", scope.Raise(Exception))
assert rec_eval(ab, memo={i: 0}) == "a"
assert rec_eval(ab, memo={i: 1}) == "b"
assert_raises(Exception, rec_eval, ab, memo={i: 2})
def pchoice(*options):
# Like hp.pchoice, but without a label.
p, options = zip(*p_options)
n_options = len(options)
ch = scope.categorical(p, upper=n_options)
return scope.switch(ch, *options)
def choice(*options):
# Like hp.choice, but without a label.
return scope.switch(scope.randint(len(options)), *options)
def _hyperopt_space(model_name, total_samples):
use_pretrained = hp.choice('use_pretrained', [True, False])
common_param_space = {
'total_samples':
total_samples,
'lr':
hp.choice('lr', [0.1, 0.01, 0.001, 0.0001, 0.00001]),
'l1_lambda':
hp.choice('l1_lambda', np.append(np.logspace(-7, -2, 6), 0)),
'dropout_p':
hp.quniform('dropout_p', 0.0, 0.75, 0.05),
'margin_multiplier':
hp.choice('margin_multiplier', [0.5, 0.75, 1.0, 1.25, 1.5])
}
pre_trained_params = {
True: {
'use_pretrained': True,
'l2_lambda': 0,
'dense_dim': 300,
'enable_fine_tune': hp.choice('enable_fine_tune',
[True, False])
},
False: {
'use_pretrained':
False,
'l2_lambda':
hp.choice('l2_lambda', np.append(np.logspace(-7, -2, 6), 0)),
'dense_dim':
scope.int(hp.quniform('dense_dim', 25, 325, 25)),
'enable_fine_tune':
True # doesn't matter what goes here
}
}
# the search space
# note that the scope.int code is a hack to get integers out of the sampler
if model_name == CITATION_RANKER_MODEL:
ranker_model_params = {
'embedding_type': hp.choice('embedding_type', ['sum']),
'metadata_dim': scope.int(hp.quniform('metadata_dim', 5, 55,
5)),
}
space = scope.switch(
scope.int(use_pretrained), {
**pre_trained_params[False],
**common_param_space,
**ranker_model_params,
}, {
**pre_trained_params[True],
**common_param_space,
**ranker_model_params
})
elif model_name == PAPER_EMBEDDING_MODEL:
space = scope.switch(scope.int(use_pretrained), {
**pre_trained_params[False],
**common_param_space
}, {
**pre_trained_params[True],
**common_param_space
})
else:
# Should not come here. Adding this to make pycharm happy.
assert False
return space
def test_recursion():
scope.define(Lambda('Fact', [('x', p0)],
expr=scope.switch(p0 > 1, 1, p0 * base.apply('Fact', p0 - 1))))
print(scope.Fact(3))
assert rec_eval(scope.Fact(3)) == 6
def test_switch_and_Raise():
i = Literal()
ab = scope.switch(i, 'a', 'b', scope.Raise(Exception))
assert rec_eval(ab, memo={i: 0}) == 'a'
assert rec_eval(ab, memo={i: 1}) == 'b'
assert_raises(Exception, rec_eval, ab, memo={i: 2})
