def printing_test(arq="iris.arff"):
print(Chain(Map(select(File(arq)))))
exp = Workflow(
File(arq),
Partition(),
Map(PCA(), SVMC(), Metric(enhance=False)),
Map(Report("<---------------------- fold"), enhance=False),
Summ(function="mean", enhance=False),
Reduce(),
Report("mean ... S: $S", enhance=False),
)
print(exp)
print(select(DT(), SVMC()))
sel = select(DT(), SVMC())
print(sel)
print(Map(DT()))
exp = ChainCS(
File(arq),
Partition(),
Map(PCA(), select(SVMC(), DT(criterion="gini")),
Metric(enhance=False)),
Report("teste"),
Map(Report("<---------------------- fold")),
)
print(exp)
def test_with_summ_reduce(arq="iris.arff"):
pipe = Workflow(
File(arq),
Partition(),
Map(PCA(), SVMC(), Metric()),
Map(Report("<---------------------- etapa")),
Summ(),
Reduce(),
Report("mean ... S: $S"),
)
train, test = pipe.dual_transform()
print("Train..............\n", train.history ^ "longname")
print("Test..........\n", test.history ^ "longname")
def test_partition(arq="iris.arff"):
pipe = Workflow(
File(arq),
Partition(),
Map(PCA(), SVMC(), Metric(enhance=False)),
Summ(function="mean", enhance=False),
Reduce(),
Report("mean ... S: $S", enhance=False),
Report("$X"),
Report("$y"),
)
train, test = pipe.dual_transform()
print("Train..............\n", train)
print("Test..........\n", test)
def test_cache(arq="iris.arff"):
pipe = Workflow(Cache(File(arq), storage_alias="default_sqlite"),
Report("{history}"))
train, test = pipe.dual_transform()
print("Train..............\n", train.history ^ "name")
print("Test..........\n", test.history ^ "name")
def ger_workflow(seed=0, arq="iris.arff"):
np.random.seed(seed)
workflow = Workflow(File(arq),
Partition(),
Map(PCA(), select(SVMC(), DT(criterion="gini")),
Metric(enhance=False)),
Summ(function="mean", enhance=False),
Reduce(),
Report("Mean S: $S", enhance=False),
seed=seed)
return workflow
def test_split_train_test(arq="iris.arff"):
pipe = Cache(
File(arq),
TsSplit(
), # TsSplit should come before TrSplit to ensure the same original data is used as input for both.
TrSplit(),
PCA(),
SVMC(),
Metric(enhance=False),
Report("metric ... R: $R", enhance=False),
storage_alias="oka")
train, test = pipe.dual_transform()
print("Train..............\n", train)
print("Test..........\n", test)
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")
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 random_search(arq="iris.arff"):
np.random.seed(0)
exp = Workflow(
File(arq),
Partition(),
Map(PCA(), select(SVMC(), DT(criterion="gini")), Metric()),
# Map(Report("<---------------------- fold"), enhance=False),
Summ(function="mean"),
Reduce(),
Report("Mean S: $S"),
)
expr = sample(exp, n=10)
result = optimize(expr, n=5)
result.disable_pretty_printing()
print(result)
# s = cs.sample()
# print(s)
# exit()
expr = Workflow(
OnlyApply(File("abalone3.arff"), Binarize()),
Partition(),
Map(
Wrap(
select(SelectBest),
ApplyUsing(select(DT, RF, NB)),
OnlyApply(Metric(functions=['length'])),
OnlyUse(Metric(functions=['accuracy', 'error'])),
# AfterUse(Metric(function=['diversity']))
), ),
Report('HISTORY ... S: {history}'),
Summ(function='mean_std'),
Report('mean and std ... S: $S'),
OnlyApply(Copy(from_field="S", to_field="B")),
OnlyApply(Report('copy S to B ... B: $B')),
OnlyUse(
MConcat(input_field1="B",
input_field2="S",
output_field="S",
direction='vertical')),
OnlyUse(Report('comcat B with S (vertical) ... S: $S')),
OnlyUse(Calc(functions=['flatten'])),
OnlyUse(Report('flatten S ... S: $S')),
OnlyUse(Calc(functions=['mean'])),
OnlyUse(Report('mean S ... S: $S')),
Report('End ...\n'),
from pjml.tool.data.modeling.supervised.classifier.svmc import SVMC
from pjml.tool.data.processing.feature.binarize import Binarize
from pjml.tool.data.processing.instance.sampler.over.random import OverS
from pjml.tool.meta.mfe import MFE
# ML 1 ========================================================================
# # Armazenar dataset, sem depender do pacote pjml.
# from cururu.pickleserver import PickleServer
#
# try:
# PickleServer().store(read_arff('iris.arff'))
# except DuplicateEntryException:
# pass
pipe = Pipeline(
Cache(File('bank.arff'), Binarize(), NB(), Metric(), Report('$X')))
print('aaaaaaaa')
m = pipe.apply()
print(m.data)
print('uuuuuuuuuuuuuuu')
d = m.use()
print(d)
exit()
# # Source('messedup-dataset'),
# Keep(evaluator(
# Cache(
# ApplyUsing(
# NB()
# ),
# Metric(function='accuracy')
Metric(functions=['accuracy'])
)
)
)
# {history.last.config['function']}
print(expr)
print('sample .................')
pipe = full(rnd(expr, n=10), field='S').sample()
pipe.enable_pretty_printing()
print(f'Pipe:\n{pipe}')
print(f'Wrapped:\n{pipe.unwrap}')
pipe = Chain(File('abalone3.arff'), Binarize(), Split(), pipe.unwrap,
Metric(), Report())
print('apply .................')
model = pipe.apply()
# print(222222222222222, dataout.history)
# data morre no apply() do predictor
print('use .................')
# print(3333333333333333, dataout.history)
# RUS desaparece no use()
exit(0)
Wrap(
shuffle(Std, MinMax),
# shuffle(Std, select(UnderS, OverS), MinMax),
ApplyUsing(select(DT, NB)),
),
Metric(functions=['accuracy']))))
# {history.last.config['function']}
print(expr)
print('sample .................')
pipe = full(rnd(expr, n=10), field='S').sample()
pipe.enable_pretty_printing()
print(f'Pipe:\n{pipe}')
print(f'Wrapped:\n{pipe.unwrap}')
pipe = Chain(File('abalone3.arff'), Binarize(), Split(), pipe.unwrap, Metric(),
Report())
print('apply .................')
model = pipe.apply()
# print(222222222222222, dataout.history)
# data morre no apply() do predictor
print('use .................')
# print(3333333333333333, dataout.history)
# RUS desaparece no use()
exit(0)
#
# ),
# Summ(function='mean_std'),
# Report('$S'),
# )
pipe = Pipeline(
File("abalone3.arff"),
Binarize(),
Partition(),
Map(
UnderS(sampling_strategy='not minority'),
# RF(),
Cache(RF()),
Metric()),
Summ(function='mean_std'),
Report('mean S --> \n$S'),
Report('mean S --> $S'),
OnlyApply(Copy(from_field="S", to_field="B")),
OnlyUse(MConcat(fields=["S", "S"], output_field="S",
direction='vertical')),
Calc(functions=['flatten']),
Report('mean S --> $S'))
print('Applying...')
model = pipe.apply()
if model.data:
for i, t in enumerate(model.data.history):
print(f'hist {i}', t)
# exit()
print('Using...')