def test_tensors():
m = Message(tensors, vectors)
t = m.tensors()
assert t == TensorMessage(tensors)
t = m.tensors(keys=['a'])
assert t == TensorMessage({'a': tensors['a']})
t = m.tensors(keys=['a','c'])
assert t == TensorMessage({'a': tensors['a'], 'c': torch.Tensor(vectors['c'])})
def test_TensorMessage_eq():
a = [1,2,3]
b = [4, 5, 6]
# Test init
email = TensorMessage({'a': a, 'b':b})
gmail = TensorMessage(email)
def test_append():
t = tensors
v = vectors
m1 = Message(t, v)
m2 = Message(t, v)
m3 = Message(t)
m4 = TensorMessage(t)
m5 = Message(pd.DataFrame(v))
m6 = pd.DataFrame(v)
m0 = Message()
assert(len(m0) == 0)
m = m0.append(Message(t))
assert m == Message(t)
m = m0.append(Message(v))
assert m == Message(v)
m = m0.append(Message(t,v))
assert m == Message(t,v)
m = m1.append(m2)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])}, {'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
m = m3.append(t)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m3.append(m3)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m3.append(m4)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m4.append(t)
assert len(m) == 6
assert m == TensorMessage({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m4.append(m3)
assert len(m) == 6
assert m == TensorMessage({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m4.append(m4)
assert len(m) == 6
assert m == TensorMessage({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m5.append(v)
assert len(m) == 6
assert m == Message({'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
m = m5.append(m5)
assert len(m) == 6
assert m == Message({'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
m = m5.append(m6)
assert len(m) == 6
assert m == Message({'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
# Test type conversions on appending to TensorMessage
m = m4.append({'a': np.array([42]), 'b': np.array([24])})
assert len(m) == 4
assert m == TensorMessage({'a': torch.Tensor([1,2,3,42]), 'b': torch.Tensor([4,5,6,24])})
def test_join():
t = tensors
v = vectors
t2 = {'d': torch.Tensor([13,14,15])}
v2 = {'e': np.array([16,17,18])}
m1 = Message(t,v)
m2 = Message(t)
m2_t = TensorMessage(t)
m3 = Message(v)
m4 = Message(t2,v2)
m5 = Message(t2)
m5_t = TensorMessage(t2)
m6 = Message(v2)
m7 = Message(t,v2)
m8 = Message(t2, v)
# Test if a tensor message can be merged into a message and vice versa
assert m2.merge(m3) == m1
assert m3.merge(m2) == m1
assert m3.merge(m2_t) == m1
assert m3.merge(t) == m1
# Test if the tensors in messages can be merged
assert m2.merge(t2) == Message({**t, **t2})
assert m2.merge(m5) == Message({**t, **t2})
assert m2.merge(m5_t) == Message({**t, **t2})
assert m2_t.merge(t2) == TensorMessage({**t, **t2})
assert m2_t.merge(m5) == TensorMessage({**t, **t2})
assert m2_t.merge(m5_t) == TensorMessage({**t, **t2})
# Test if the dataframes in messages can be merged
assert m3.merge(m6) == Message({**v, **v2})
assert m6.merge(m3) == Message({**v, **v2})
assert m3.merge(v2) == Message({**v, **v2})
def test_TensorMessage_set_get_del():
a = [1, 2, 3]
b = [4, 5, 6]
email = TensorMessage({'a': a, 'b': b})
replacement = {'a': torch.Tensor([42]), 'b': torch.Tensor([43])}
gmail = TensorMessage({
'a': torch.Tensor([42, 2, 3]),
'b': torch.Tensor([43, 5, 6])
})
yahoomail = TensorMessage({
'a': torch.Tensor([2, 3]),
'b': torch.Tensor([5, 6])
})
assert email != gmail
email[0] = replacement
assert email == gmail
assert len(email) == 3
email['a'] = torch.Tensor([9, 9, 9])
assert torch.equal(email['a'], torch.Tensor([9, 9, 9]))
assert gmail != yahoomail
del gmail[0]
assert len(gmail) == 2
assert gmail == yahoomail
# Test column deletions
email = TensorMessage({'a': a, 'b': b})
assert set(email.columns) == set(['a', 'b'])
del email['a']
assert set(email.columns) == set(['b'])
# Test that out of bounds requests raise errors
try:
email[3]
assert False
except IndexError:
assert True
try:
email[3:5]
assert False
except IndexError:
assert True
# Test length adjustment if all columns are deleted
zohomail = TensorMessage({'a': a, 'b': b})
assert len(zohomail) == 3
del zohomail['a']
assert len(zohomail) == 3
del zohomail['b']
assert len(zohomail) == 0
def test_TensorMessage_permute():
a = [1,2,3]
b = [4, 5, 6]
email = TensorMessage({'a': a, 'b':b})
gmail = email.permute([2,1,0])
assert gmail == TensorMessage({'a':[3,2,1], 'b':[6,5,4]})
gmail = email.permute([0,0,0])
assert gmail == TensorMessage({'a':[1,1,1], 'b':[4,4,4]})
def test_Message_iter():
m = Message(tensors, vectors)
l = len(m)
for x,i in zip(m, count()):
assert type(x) is Message
if i > l:
assert False
assert i == l - 1
t = TensorMessage(tensors)
l = len(t)
for x,i in zip(t, count()):
assert type(x) is TensorMessage
if i > l:
assert False
assert i == l - 1
def test_TensorMessage():
a = [1,2,3]
b = [4, 5, 6]
# Test init
empty = TensorMessage()
assert len(empty) == 0
assert empty.keys() == {}.keys()
email = TensorMessage({'a': a, 'b':b})
# Test error cases
# TODO: test error cases
# Test length
assert len(email) == 3
# Test getitem
x = email[2]
assert set(x.keys()) == set(['a','b'])
assert (x['a'] == torch.Tensor([3])).all()
assert (x['b'] == torch.Tensor([6])).all()
x = email[0:2]
assert set(x.keys()) == set(['a','b'])
assert (x['a'] == torch.Tensor([1,2])).all()
assert (x['b'] == torch.Tensor([4,5])).all()
# Test for length 1 init
gmail = TensorMessage({'a':1, 'b': 80})
assert len(gmail) == 1
y = gmail[0]
assert set(y.keys()) == set(['a','b'])
assert (y['a'] == torch.Tensor([1])).all()
assert (y['b'] == torch.Tensor([80])).all()
# Test extend
yahoomail = email.append(gmail)
assert len(yahoomail) == 4
z = yahoomail[0:4]
assert set(z.keys()) == set(['a','b'])
assert (z['a'] == torch.Tensor([1,2,3,1])).all()
assert (z['b'] == torch.Tensor([4, 5, 6, 80])).all()
def test_Message():
""" Test init, getitem, and len methopl. """
def attribute_test(message, length = 3):
assert len(message) == length
assert message[0].tensors() == {
'a': torch.Tensor([1]),
'b': torch.Tensor([4]),
}
assert message[0].dataframe().equals(pd.DataFrame({
'c': np.array([7]),
'd': np.array([10]),
}))
assert message[0] == Message({'a': torch.Tensor([1]),'b': torch.Tensor([4])}, pd.DataFrame({'c': np.array([7]),'d': np.array([10]),}))
assert message[1:3].tensors() == {
'a': torch.Tensor([2,3]),
'b': torch.Tensor([5,6]),
}
assert message[1:3].dataframe().equals(pd.DataFrame({
'c': np.array([8,9]),
'd': np.array([11,12]),
}))
assert (message['a'] == torch.Tensor([1,2,3])).all()
assert message[['a','c']] == Message({'a': torch.Tensor([1,2,3]), 'c': np.array([7,8,9])})
assert message[1:3] == Message({'a': torch.Tensor([2,3]),'b': torch.Tensor([5,6])}, pd.DataFrame({'c': np.array([8,9]),'d': np.array([11,12])}))
# Test length
assert len(message) == length
# Test __getitem__
# Init empty message
m = Message()
assert len(m) == 0
# Init message from tensor_dict / TensorMessage and dict of arrays / dataframe using positional arguments.
tensor_message = TensorMessage(tensors)
tensor_as_message = Message(tensors = tensors)
df = pd.DataFrame(vectors)
df_as_message = Message(df = vectors)
# Try every combination
tensor_options = [tensors, tensor_message, tensor_as_message]
vector_options = [vectors, df, df_as_message]
for t, v in product(tensor_options, vector_options):
m = Message(t, v)
attribute_test(m)
m = Message(tensors = t, df = v)
attribute_test(m)
# Test one sided Messages
for t in tensor_options:
m = Message(t, None)
assert len(m) == 3
assert m == Message(tensors)
for v in vector_options:
m = Message(None, v)
assert len(m) == 3
assert m == Message(vectors)
# Init message from a single dict
everything = {**tensors, **vectors}
m = Message(everything)
attribute_test(m)
