def test_check_architecture2(arq="iris.arff"):
pipe = Workflow(
File(arq),
Partition(),
Map(PCA(), SVMC(), Metric(enhance=False)),
Summ(field="Y", function="mean", enhance=False),
Report("mean ... S: $S", enhance=False),
)
# tenho file na frente
train_ = pipe.enhancer.transform(sd.NoData)
test_ = pipe.model(sd.NoData).transform(sd.NoData)
test_ = pipe.model(sd.NoData).transform((sd.NoData, sd.NoData))
train_, test_ = pipe.dual_transform(sd.NoData, sd.NoData)
train_, test_ = pipe.dual_transform(sd.NoData, (sd.NoData, sd.NoData))
def test_check_architecture(arq="iris.arff"):
pipe = Workflow(
File(arq),
Partition(partitions=2),
Map(PCA(), SVMC(), Metric(enhance=False)),
Summ(field="Y", function="mean", enhance=False),
)
# tenho file na frente
train_01 = pipe.enhancer.transform(sd.NoData)
test_01 = pipe.model(sd.NoData).transform(sd.NoData)
train_02, test_02 = pipe.dual_transform(sd.NoData, sd.NoData)
# Collection uuid depends on data, which depends on consumption.
for t, *_ in train_01, train_02, test_01, test_02:
# print(111111111, t.y)
pass
assert train_01.uuid == train_02.uuid
assert test_01.uuid == test_02.uuid
def test_sequence_of_classifiers(arq="abalone.arff"):
pipe = Workflow(
File(arq),
Binarize(),
Report('1 {X.shape} {history^name}'),
PCA(n=5),
SVMC(),
Metric(),
Report('2 {X.shape} {history^name}'),
DT(),
Metric(),
Report('3 {X.shape} {history^name}'),
)
print('Enh')
train = pipe.enhancer.transform(sd.NoData)
print('Mod')
test = pipe.model(sd.NoData).transform(
sd.NoData) # TODO: pq report não aparece no test?
print()
print("[test_sequence_of_classifiers] Train.........\n",
train.history ^ "longname")
print("[test_sequence_of_classifiers] Test..........\n",
test.history ^ "longname")