def test_pdf_eval():
tf_sess = tf.Session()
backends = [
numpy_backend(poisson_from_normal=True),
pytorch_backend(),
tensorflow_backend(session=tf_sess),
mxnet_backend()
]
values = []
for b in backends:
pyhf.set_backend(b)
source = {
"binning": [2, -0.5, 1.5],
"bindata": {
"data": [120.0, 180.0],
"bkg": [100.0, 150.0],
"bkgsys_up": [102, 190],
"bkgsys_dn": [98, 100],
"sig": [30.0, 95.0]
}
}
spec = {
'channels': [{
'name':
'singlechannel',
'samples': [{
'name':
'signal',
'data':
source['bindata']['sig'],
'modifiers': [{
'name': 'mu',
'type': 'normfactor',
'data': None
}]
}, {
'name':
'background',
'data':
source['bindata']['bkg'],
'modifiers': [{
'name': 'bkg_norm',
'type': 'histosys',
'data': {
'lo_data': source['bindata']['bkgsys_dn'],
'hi_data': source['bindata']['bkgsys_up']
}
}]
}]
}]
}
pdf = pyhf.hfpdf(spec)
data = source['bindata']['data'] + pdf.config.auxdata
v1 = pdf.logpdf(pdf.config.suggested_init(), data)
values.append(pyhf.tensorlib.tolist(v1)[0])
assert np.std(values) < 1e-6
def test_pdf_eval_2():
tf_sess = tf.Session()
backends = [
numpy_backend(poisson_from_normal=True),
pytorch_backend(),
tensorflow_backend(session=tf_sess),
mxnet_backend()
]
values = []
for b in backends:
pyhf.set_backend(b)
source = {
"binning": [2, -0.5, 1.5],
"bindata": {
"data": [120.0, 180.0],
"bkg": [100.0, 150.0],
"bkgerr": [10.0, 10.0],
"sig": [30.0, 95.0]
}
}
pdf = hepdata_like(source['bindata']['sig'], source['bindata']['bkg'],
source['bindata']['bkgerr'])
data = source['bindata']['data'] + pdf.config.auxdata
v1 = pdf.logpdf(pdf.config.suggested_init(), data)
values.append(pyhf.tensorlib.tolist(v1)[0])
assert np.std(values) < 1e-6
def test_common_tensor_backends():
tf_sess = tf.Session()
for tb in [
numpy_backend(),
pytorch_backend(),
tensorflow_backend(session=tf_sess),
mxnet_backend()
]:
assert tb.tolist(tb.astensor([1, 2, 3])) == [1, 2, 3]
assert tb.tolist(tb.ones((2, 3))) == [[1, 1, 1], [1, 1, 1]]
assert tb.tolist(tb.sum([[1, 2, 3], [4, 5, 6]], axis=0)) == [5, 7, 9]
assert tb.tolist(tb.product([[1, 2, 3], [4, 5, 6]],
axis=0)) == [4, 10, 18]
assert tb.tolist(tb.power([1, 2, 3], [1, 2, 3])) == [1, 4, 27]
assert tb.tolist(tb.divide([4, 9, 16], [2, 3, 4])) == [2, 3, 4]
assert tb.tolist(tb.outer([1, 2, 3], [4, 5, 6])) == [[4, 5, 6],
[8, 10, 12],
[12, 15, 18]]
assert tb.tolist(tb.sqrt([4, 9, 16])) == [2, 3, 4]
assert tb.tolist(
tb.stack([tb.astensor([1, 2, 3]),
tb.astensor([4, 5, 6])])) == [[1, 2, 3], [4, 5, 6]]
assert tb.tolist(
tb.concatenate([tb.astensor([1, 2, 3]),
tb.astensor([4, 5, 6])])) == [1, 2, 3, 4, 5, 6]
assert tb.tolist(tb.log(tb.exp([2, 3, 4]))) == [2, 3, 4]
assert tb.tolist(
tb.where(tb.astensor([1, 0, 1]), tb.astensor([1, 1, 1]),
tb.astensor([2, 2, 2]))) == [1, 2, 1]
assert tb.tolist(tb.clip(tb.astensor([-2, -1, 0, 1, 2]), -1,
1)) == [-1, -1, 0, 1, 1]
assert tb.tolist(tb.normal_cdf(tb.astensor([0.8]))) == pytest.approx(
[0.7881446014166034], 1e-07)
assert list(
map(
tb.tolist,
tb.simple_broadcast(tb.astensor([1, 1, 1]), tb.astensor([2]),
tb.astensor([3, 3, 3])))) == [[1, 1, 1],
[2, 2, 2],
[3, 3, 3]]
assert list(map(tb.tolist, tb.simple_broadcast(1, [2, 3, 4], [5, 6, 7]))) \
== [[1, 1, 1], [2, 3, 4], [5, 6, 7]]
assert list(map(tb.tolist, tb.simple_broadcast([1], [2, 3, 4], [5, 6, 7]))) \
== [[1, 1, 1], [2, 3, 4], [5, 6, 7]]
with pytest.raises(Exception):
tb.simple_broadcast([1], [2, 3], [5, 6, 7])
def test_einsum():
tf_sess = tf.Session()
backends = [numpy_backend(poisson_from_normal=True),
pytorch_backend(),
tensorflow_backend(session=tf_sess),
mxnet_backend() #no einsum in mxnet
]
for b in backends[:-1]:
pyhf.set_backend(b)
x = np.arange(20).reshape(5,4).tolist()
assert np.all(b.tolist(b.einsum('ij->ji',x)) == np.asarray(x).T.tolist())
assert b.tolist(b.einsum('i,j->ij',b.astensor([1,1,1]),b.astensor([1,2,3]))) == [[1,2,3]]*3
for b in backends[-1:]:
pyhf.set_backend(b)
x = np.arange(20).reshape(5,4).tolist()
with pytest.raises(NotImplementedError):
assert b.einsum('ij->ji',[1,2,3])
def test_common_tensor_backends():
tf_sess = tf.Session()
for tb in [
numpy_backend(),
pytorch_backend(),
tensorflow_backend(session=tf_sess),
mxnet_backend()
]:
assert tb.tolist(tb.astensor([1, 2, 3])) == [1, 2, 3]
assert tb.tolist(tb.ones((2, 3))) == [[1, 1, 1], [1, 1, 1]]
assert tb.tolist(tb.sum([[1, 2, 3], [4, 5, 6]], axis=0)) == [5, 7, 9]
assert tb.tolist(tb.product([[1, 2, 3], [4, 5, 6]],
axis=0)) == [4, 10, 18]
assert tb.tolist(tb.power([1, 2, 3], [1, 2, 3])) == [1, 4, 27]
assert tb.tolist(tb.divide([4, 9, 16], [2, 3, 4])) == [2, 3, 4]
assert tb.tolist(tb.outer([1, 2, 3], [4, 5, 6])) == [[4, 5, 6],
[8, 10, 12],
[12, 15, 18]]
assert tb.tolist(tb.sqrt([4, 9, 16])) == [2, 3, 4]
assert tb.tolist(
tb.stack([tb.astensor([1, 2, 3]),
tb.astensor([4, 5, 6])])) == [[1, 2, 3], [4, 5, 6]]
assert tb.tolist(
tb.concatenate([tb.astensor([1, 2, 3]),
tb.astensor([4, 5, 6])])) == [1, 2, 3, 4, 5, 6]
assert tb.tolist(tb.log(tb.exp([2, 3, 4]))) == [2, 3, 4]
assert tb.tolist(
tb.where(tb.astensor([1, 0, 1]), tb.astensor([1, 1, 1]),
tb.astensor([2, 2, 2]))) == [1, 2, 1]
assert tb.tolist(tb.clip(tb.astensor([-2, -1, 0, 1, 2]), -1,
1)) == [-1, -1, 0, 1, 1]
assert list(
map(
tb.tolist,
tb.simple_broadcast(tb.astensor([1, 1, 1]), tb.astensor([2]),
tb.astensor([3, 3, 3])))) == [[1, 1, 1],
[2, 2, 2],
[3, 3, 3]]
