def test_vectorize_simple():
N = as_apply(15)
p0 = hp_uniform('p0', 0, 1)
loss = p0**2
print loss
expr_idxs = scope.range(N)
vh = VectorizeHelper(loss, expr_idxs, build=True)
vloss = vh.v_expr
full_output = as_apply([vloss, vh.idxs_by_label(), vh.vals_by_label()])
fo2 = replace_repeat_stochastic(full_output)
new_vc = recursive_set_rng_kwarg(
fo2,
as_apply(np.random.RandomState(1)),
)
#print new_vc
losses, idxs, vals = rec_eval(new_vc)
print 'losses', losses
print 'idxs p0', idxs['p0']
print 'vals p0', vals['p0']
p0dct = dict(zip(idxs['p0'], vals['p0']))
for ii, li in enumerate(losses):
assert p0dct[ii]**2 == li
def test_vectorize_simple():
N = as_apply(15)
p0 = hp_uniform('p0', 0, 1)
loss = p0 ** 2
print(loss)
expr_idxs = scope.range(N)
vh = VectorizeHelper(loss, expr_idxs, build=True)
vloss = vh.v_expr
full_output = as_apply([vloss,
vh.idxs_by_label(),
vh.vals_by_label()])
fo2 = replace_repeat_stochastic(full_output)
new_vc = recursive_set_rng_kwarg(
fo2,
as_apply(np.random.RandomState(1)),
)
#print new_vc
losses, idxs, vals = rec_eval(new_vc)
print('losses', losses)
print('idxs p0', idxs['p0'])
print('vals p0', vals['p0'])
p0dct = dict(list(zip(idxs['p0'], vals['p0'])))
for ii, li in enumerate(losses):
assert p0dct[ii] ** 2 == li
def test_vectorize_multipath():
N = as_apply(15)
p0 = hp_uniform('p0', 0, 1)
loss = hp_choice('p1', [1, p0, -p0]) ** 2
expr_idxs = scope.range(N)
vh = VectorizeHelper(loss, expr_idxs, build=True)
vloss = vh.v_expr
print(vloss)
full_output = as_apply([vloss,
vh.idxs_by_label(),
vh.vals_by_label()])
new_vc = recursive_set_rng_kwarg(
full_output,
as_apply(np.random.RandomState(1)),
)
losses, idxs, vals = rec_eval(new_vc)
print('losses', losses)
print('idxs p0', idxs['p0'])
print('vals p0', vals['p0'])
print('idxs p1', idxs['p1'])
print('vals p1', vals['p1'])
p0dct = dict(list(zip(idxs['p0'], vals['p0'])))
p1dct = dict(list(zip(idxs['p1'], vals['p1'])))
for ii, li in enumerate(losses):
print(ii, li)
if p1dct[ii] != 0:
assert li == p0dct[ii] ** 2
else:
assert li == 1
def test_vectorize_multipath():
N = as_apply(15)
p0 = hp_uniform('p0', 0, 1)
loss = hp_choice('p1', [1, p0, -p0])**2
expr_idxs = scope.range(N)
vh = VectorizeHelper(loss, expr_idxs, build=True)
vloss = vh.v_expr
print vloss
full_output = as_apply([vloss, vh.idxs_by_label(), vh.vals_by_label()])
new_vc = recursive_set_rng_kwarg(
full_output,
as_apply(np.random.RandomState(1)),
)
losses, idxs, vals = rec_eval(new_vc)
print 'losses', losses
print 'idxs p0', idxs['p0']
print 'vals p0', vals['p0']
print 'idxs p1', idxs['p1']
print 'vals p1', vals['p1']
p0dct = dict(zip(idxs['p0'], vals['p0']))
p1dct = dict(zip(idxs['p1'], vals['p1']))
for ii, li in enumerate(losses):
print ii, li
if p1dct[ii] != 0:
assert li == p0dct[ii]**2
else:
assert li == 1
def test_vectorize_multipath():
N = as_apply(15)
p0 = hp_uniform("p0", 0, 1)
loss = hp_choice("p1", [1, p0, -p0])**2
expr_idxs = scope.range(N)
vh = VectorizeHelper(loss, expr_idxs, build=True)
vloss = vh.v_expr
print(vloss)
full_output = as_apply([vloss, vh.idxs_by_label(), vh.vals_by_label()])
new_vc = recursive_set_rng_kwarg(full_output,
as_apply(np.random.RandomState(1)))
losses, idxs, vals = rec_eval(new_vc)
print("losses", losses)
print("idxs p0", idxs["p0"])
print("vals p0", vals["p0"])
print("idxs p1", idxs["p1"])
print("vals p1", vals["p1"])
p0dct = dict(list(zip(idxs["p0"], vals["p0"])))
p1dct = dict(list(zip(idxs["p1"], vals["p1"])))
for ii, li in enumerate(losses):
print(ii, li)
if p1dct[ii] != 0:
assert li == p0dct[ii]**2
else:
assert li == 1
def test_vectorize_trivial():
N = as_apply(15)
p0 = hp_uniform("p0", 0, 1)
loss = p0
print(loss)
expr_idxs = scope.range(N)
vh = VectorizeHelper(loss, expr_idxs, build=True)
vloss = vh.v_expr
full_output = as_apply([vloss, vh.idxs_by_label(), vh.vals_by_label()])
fo2 = replace_repeat_stochastic(full_output)
new_vc = recursive_set_rng_kwarg(fo2, as_apply(np.random.RandomState(1)))
# print new_vc
losses, idxs, vals = rec_eval(new_vc)
print("losses", losses)
print("idxs p0", idxs["p0"])
print("vals p0", vals["p0"])
p0dct = dict(list(zip(idxs["p0"], vals["p0"])))
for ii, li in enumerate(losses):
assert p0dct[ii] == li
def test_vectorize_config0():
p0 = hp_uniform('p0', 0, 1)
p1 = hp_loguniform('p1', 2, 3)
p2 = hp_choice('p2', [-1, p0])
p3 = hp_choice('p3', [-2, p1])
p4 = 1
p5 = [3, 4, p0]
p6 = hp_choice('p6', [-3, p1])
d = locals()
d['p1'] = None # -- don't sample p1 all the time, only if p3 says so
config = as_apply(d)
N = as_apply('N:TBA')
expr = config
expr_idxs = scope.range(N)
vh = VectorizeHelper(expr, expr_idxs, build=True)
vconfig = vh.v_expr
full_output = as_apply([vconfig, vh.idxs_by_label(), vh.vals_by_label()])
if 1:
print('=' * 80)
print('VECTORIZED')
print(full_output)
print('\n' * 1)
fo2 = replace_repeat_stochastic(full_output)
if 0:
print('=' * 80)
print('VECTORIZED STOCHASTIC')
print(fo2)
print('\n' * 1)
new_vc = recursive_set_rng_kwarg(
fo2,
as_apply(np.random.RandomState(1))
)
if 0:
print('=' * 80)
print('VECTORIZED STOCHASTIC WITH RNGS')
print(new_vc)
Nval = 10
foo, idxs, vals = rec_eval(new_vc, memo={N: Nval})
print('foo[0]', foo[0])
print('foo[1]', foo[1])
assert len(foo) == Nval
if 0: # XXX refresh these values to lock down sampler
assert foo[0] == {
'p0': 0.39676747423066994,
'p1': None,
'p2': 0.39676747423066994,
'p3': 2.1281244479293568,
'p4': 1,
'p5': (3, 4, 0.39676747423066994) }
assert foo[1] != foo[2]
print(idxs)
print(vals['p3'])
print(vals['p6'])
print(idxs['p1'])
print(vals['p1'])
assert len(vals['p3']) == Nval
assert len(vals['p6']) == Nval
assert len(idxs['p1']) < Nval
p1d = dict(list(zip(idxs['p1'], vals['p1'])))
for ii, (p3v, p6v) in enumerate(zip(vals['p3'], vals['p6'])):
if p3v == p6v == 0:
assert ii not in idxs['p1']
if p3v:
assert foo[ii]['p3'] == p1d[ii]
if p6v:
print('p6', foo[ii]['p6'], p1d[ii])
assert foo[ii]['p6'] == p1d[ii]
def test_vectorize_config0():
p0 = hp_uniform('p0', 0, 1)
p1 = hp_loguniform('p1', 2, 3)
p2 = hp_choice('p2', [-1, p0])
p3 = hp_choice('p3', [-2, p1])
p4 = 1
p5 = [3, 4, p0]
p6 = hp_choice('p6', [-3, p1])
d = locals()
d['p1'] = None # -- don't sample p1 all the time, only if p3 says so
config = as_apply(d)
N = as_apply('N:TBA')
expr = config
expr_idxs = scope.range(N)
vh = VectorizeHelper(expr, expr_idxs, build=True)
vconfig = vh.v_expr
full_output = as_apply([vconfig, vh.idxs_by_label(), vh.vals_by_label()])
if 1:
print '=' * 80
print 'VECTORIZED'
print full_output
print '\n' * 1
fo2 = replace_repeat_stochastic(full_output)
if 0:
print '=' * 80
print 'VECTORIZED STOCHASTIC'
print fo2
print '\n' * 1
new_vc = recursive_set_rng_kwarg(fo2, as_apply(np.random.RandomState(1)))
if 0:
print '=' * 80
print 'VECTORIZED STOCHASTIC WITH RNGS'
print new_vc
Nval = 10
foo, idxs, vals = rec_eval(new_vc, memo={N: Nval})
print 'foo[0]', foo[0]
print 'foo[1]', foo[1]
assert len(foo) == Nval
if 0: # XXX refresh these values to lock down sampler
assert foo[0] == {
'p0': 0.39676747423066994,
'p1': None,
'p2': 0.39676747423066994,
'p3': 2.1281244479293568,
'p4': 1,
'p5': (3, 4, 0.39676747423066994)
}
assert foo[1] != foo[2]
print idxs
print vals['p3']
print vals['p6']
print idxs['p1']
print vals['p1']
assert len(vals['p3']) == Nval
assert len(vals['p6']) == Nval
assert len(idxs['p1']) < Nval
p1d = dict(zip(idxs['p1'], vals['p1']))
for ii, (p3v, p6v) in enumerate(zip(vals['p3'], vals['p6'])):
if p3v == p6v == 0:
assert ii not in idxs['p1']
if p3v:
assert foo[ii]['p3'] == p1d[ii]
if p6v:
print 'p6', foo[ii]['p6'], p1d[ii]
assert foo[ii]['p6'] == p1d[ii]
def test_vectorize_config0():
p0 = hp_uniform("p0", 0, 1)
p1 = hp_loguniform("p1", 2, 3)
p2 = hp_choice("p2", [-1, p0])
p3 = hp_choice("p3", [-2, p1])
p4 = 1
p5 = [3, 4, p0]
p6 = hp_choice("p6", [-3, p1])
d = locals()
d["p1"] = None # -- don't sample p1 all the time, only if p3 says so
config = as_apply(d)
N = as_apply("N:TBA")
expr = config
expr_idxs = scope.range(N)
vh = VectorizeHelper(expr, expr_idxs, build=True)
vconfig = vh.v_expr
full_output = as_apply([vconfig, vh.idxs_by_label(), vh.vals_by_label()])
if 1:
print("=" * 80)
print("VECTORIZED")
print(full_output)
print("\n" * 1)
fo2 = replace_repeat_stochastic(full_output)
if 0:
print("=" * 80)
print("VECTORIZED STOCHASTIC")
print(fo2)
print("\n" * 1)
new_vc = recursive_set_rng_kwarg(fo2, as_apply(np.random.RandomState(1)))
if 0:
print("=" * 80)
print("VECTORIZED STOCHASTIC WITH RNGS")
print(new_vc)
Nval = 10
foo, idxs, vals = rec_eval(new_vc, memo={N: Nval})
print("foo[0]", foo[0])
print("foo[1]", foo[1])
assert len(foo) == Nval
if 0: # XXX refresh these values to lock down sampler
assert foo[0] == {
"p0": 0.39676747423066994,
"p1": None,
"p2": 0.39676747423066994,
"p3": 2.1281244479293568,
"p4": 1,
"p5": (3, 4, 0.39676747423066994),
}
assert (foo[1].keys() != foo[2].keys()) or (foo[1].values() !=
foo[2].values())
print(idxs)
print(vals["p3"])
print(vals["p6"])
print(idxs["p1"])
print(vals["p1"])
assert len(vals["p3"]) == Nval
assert len(vals["p6"]) == Nval
assert len(idxs["p1"]) < Nval
p1d = dict(list(zip(idxs["p1"], vals["p1"])))
for ii, (p3v, p6v) in enumerate(zip(vals["p3"], vals["p6"])):
if p3v == p6v == 0:
assert ii not in idxs["p1"]
if p3v:
assert foo[ii]["p3"] == p1d[ii]
if p6v:
print("p6", foo[ii]["p6"], p1d[ii])
assert foo[ii]["p6"] == p1d[ii]