def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="list files")
# flow.print_help()
listfiles = ListFiles()
listfiles.config["dir"] = str(tempfile.gettempdir())
listfiles.config["list_files"] = True
listfiles.config["list_dirs"] = False
listfiles.config["recursive"] = False
listfiles.config["regexp"] = ".*r.*"
# listfiles.print_help()
flow.actors.append(listfiles)
console = Console()
console.config["prefix"] = "Match: "
# console.print_help()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="list files")
# flow.print_help()
listfiles = ListFiles()
listfiles.config["dir"] = str(tempfile.gettempdir())
listfiles.config["list_files"] = True
listfiles.config["list_dirs"] = False
listfiles.config["recursive"] = False
listfiles.config["regexp"] = ".*r.*"
# listfiles.print_help()
flow.actors.append(listfiles)
console = Console()
console.config["prefix"] = "Match: "
# console.print_help()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="list files")
listfiles = ListFiles()
listfiles.config["dir"] = str(helper.get_data_dir())
listfiles.config["list_files"] = True
listfiles.config["list_dirs"] = False
listfiles.config["recursive"] = False
listfiles.config["regexp"] = ".*.arff"
flow.actors.append(listfiles)
tee = Tee()
flow.actors.append(tee)
convert = Convert()
convert.config["setup"] = conversion.PassThrough()
tee.actors.append(convert)
console = Console()
console.config["prefix"] = "Match: "
tee.actors.append(console)
load = LoadDataset()
load.config["use_custom_loader"] = True
flow.actors.append(load)
cross = CrossValidate()
cross.config["setup"] = Classifier(classname="weka.classifiers.trees.J48",
options=["-C", "0.3"])
flow.actors.append(cross)
summary = EvaluationSummary()
summary.config["matrix"] = True
flow.actors.append(summary)
# print flow
flow.setup()
print("\n" + flow.tree + "\n")
# save the flow
fname = tempfile.gettempdir() + os.sep + "simpleflow.json"
Flow.save(flow, fname)
# load flow
fl2 = Flow.load(fname)
# output flow
fl2.setup()
print("\n" + fl2.tree + "\n")
def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="list files")
listfiles = ListFiles()
listfiles.config["dir"] = str(helper.get_data_dir())
listfiles.config["list_files"] = True
listfiles.config["list_dirs"] = False
listfiles.config["recursive"] = False
listfiles.config["regexp"] = ".*.arff"
flow.actors.append(listfiles)
tee = Tee()
flow.actors.append(tee)
convert = Convert()
convert.config["setup"] = conversion.PassThrough()
tee.actors.append(convert)
console = Console()
console.config["prefix"] = "Match: "
tee.actors.append(console)
load = LoadDataset()
load.config["use_custom_loader"] = True
flow.actors.append(load)
cross = CrossValidate()
cross.config["setup"] = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.3"])
flow.actors.append(cross)
summary = EvaluationSummary()
summary.config["matrix"] = True
flow.actors.append(summary)
# print flow
flow.setup()
print("\n" + flow.tree + "\n")
# save the flow
fname = tempfile.gettempdir() + os.sep + "simpleflow.json"
Flow.save(flow, fname)
# load flow
fl2 = Flow.load(fname)
# output flow
fl2.setup()
print("\n" + fl2.tree + "\n")
def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="math expression")
outer = ForLoop()
outer.config["max"] = 100
flow.actors.append(outer)
expr = MathExpression()
expr.config["expression"] = "math.sqrt({X})"
flow.actors.append(expr)
console = Console()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def load_custom_loader():
"""
Loads a dataset using a custom loader.
"""
# setup the flow
helper.print_title("Load dataset (custom loader)")
iris = helper.get_data_dir() + os.sep + "iris.csv"
flow = Flow(name="load dataset")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = False
loaddataset.config["use_custom_loader"] = True
loaddataset.config["custom_loader"] = Loader(
classname="weka.core.converters.CSVLoader")
flow.actors.append(loaddataset)
console = Console()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def load_incremental():
"""
Loads a dataset incrementally.
"""
# setup the flow
helper.print_title("Load dataset (incremental)")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="load dataset")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
flow.actors.append(loaddataset)
console = Console()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("Generate dataset")
flow = Flow(name="generate dataset")
generator = DataGenerator()
generator.config["setup"] = datagen.DataGenerator(
classname="weka.datagenerators.classifiers.classification.Agrawal")
flow.actors.append(generator)
console = Console()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
"""
Loads data from a database.
"""
# setup the flow
helper.print_title("Load from database")
flow = Flow(name="load from database")
loaddatabase = LoadDatabase()
loaddatabase.config["db_url"] = "jdbc:mysql://HOSTNAME:3306/DBNAME"
loaddatabase.config["user"] = "******"
loaddatabase.config["password"] = "******"
loaddatabase.config["query"] = "select * from TABLE"
flow.actors.append(loaddatabase)
console = Console()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
"""
Tests some conversions.
"""
# setup the flow
helper.print_title("conversions")
flow = Flow(name="conversions")
strings = StringConstants()
strings.config["strings"] = [
"weka.classifiers.trees.J48", "weka.classifiers.functions.SMO"
]
flow.actors.append(strings)
c2a = CommandlineToAny()
c2a.config["wrapper"] = "weka.classifiers.Classifier"
convert1 = Convert()
convert1.config["setup"] = c2a
flow.actors.append(convert1)
convert2 = Convert()
convert2.config["setup"] = AnyToCommandline()
flow.actors.append(convert2)
console = Console()
console.config["prefix"] = "setup: "
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("Cross-validate clusterer")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="cross-validate clusterer")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
flow.actors.append(loaddataset)
flter = Filter()
flter.name = "Remove class"
flter.config["filter"] = filters.Filter(
classname="weka.filters.unsupervised.attribute.Remove",
options=["-R", "last"])
flow.actors.append(flter)
cv = CrossValidate()
cv.config["setup"] = Clusterer(classname="weka.clusterers.EM")
flow.actors.append(cv)
console = Console()
console.config["prefix"] = "Loglikelihood: "
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("Cross-validate clusterer")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="cross-validate clusterer")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
flow.actors.append(loaddataset)
flter = Filter()
flter.name = "Remove class"
flter.config["filter"] = filters.Filter(
classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
flow.actors.append(flter)
cv = CrossValidate()
cv.config["setup"] = Clusterer(classname="weka.clusterers.EM")
flow.actors.append(cv)
console = Console()
console.config["prefix"] = "Loglikelihood: "
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
"""
Tests some conversions.
"""
# setup the flow
helper.print_title("conversions")
flow = Flow(name="conversions")
strings = StringConstants()
strings.config["strings"] = ["weka.classifiers.trees.J48", "weka.classifiers.functions.SMO"]
flow.actors.append(strings)
c2a = CommandlineToAny()
c2a.config["wrapper"] = "weka.classifiers.Classifier"
convert1 = Convert()
convert1.config["setup"] = c2a
flow.actors.append(convert1)
convert2 = Convert()
convert2.config["setup"] = AnyToCommandline()
flow.actors.append(convert2)
console = Console()
console.config["prefix"] = "setup: "
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="update storage value")
start = Start()
flow.actors.append(start)
init = InitStorageValue()
init.config["storage_name"] = "max"
init.config["value"] = "int(1)"
flow.actors.append(init)
trigger = Trigger()
flow.actors.append(trigger)
outer = ForLoop()
outer.name = "outer"
outer.config["max"] = 3
trigger.actors.append(outer)
trigger2 = Trigger()
trigger.actors.append(trigger2)
inner = ForLoop()
inner.name = "inner"
inner.config["max"] = "@{max}"
trigger2.actors.append(inner)
console = Console()
trigger2.actors.append(console)
update = UpdateStorageValue()
update.config["storage_name"] = "max"
update.config["expression"] = "{X} + 2"
trigger.actors.append(update)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="combine storage")
outer = ForLoop()
outer.name = "outer"
outer.config["max"] = 3
flow.actors.append(outer)
ssv = SetStorageValue()
ssv.config["storage_name"] = "max"
flow.actors.append(ssv)
trigger = Trigger()
flow.actors.append(trigger)
inner = ForLoop()
inner.name = "inner"
inner.config["max"] = "@{max}"
trigger.actors.append(inner)
ssv2 = SetStorageValue()
ssv2.config["storage_name"] = "inner"
trigger.actors.append(ssv2)
trigger2 = Trigger()
trigger.actors.append(trigger2)
combine = CombineStorage()
combine.config["format"] = "@{max} / @{inner}"
trigger2.actors.append(combine)
console = Console()
trigger2.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("build and save clusterer")
iris = helper.get_data_dir() + os.sep + "iris_no_class.arff"
flow = Flow(name="build and save clusterer")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
flow.actors.append(loaddataset)
train = Train()
train.config["setup"] = Clusterer(classname="weka.clusterers.SimpleKMeans")
flow.actors.append(train)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
flow.actors.append(pick)
console = Console()
pick.actors.append(console)
writer = ModelWriter()
writer.config["output"] = str(
tempfile.gettempdir()) + os.sep + "simplekmeans.model"
flow.actors.append(writer)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def incremental():
"""
Just runs some example code.
"""
"""
Loads/filters a dataset incrementally.
"""
# setup the flow
helper.print_title("Filter datasets (incrementally)")
iris = helper.get_data_dir() + os.sep + "iris.arff"
anneal = helper.get_data_dir() + os.sep + "anneal.arff"
flow = Flow(name="filter datasets (incrementally)")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris, anneal]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
flow.actors.append(loaddataset)
flter = Filter()
flter.config["setup"] = filters.Filter(
classname="weka.filters.unsupervised.attribute.Remove",
options=["-R", "1"])
flter.config["keep_relationname"] = True
flow.actors.append(flter)
console = Console()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
flow = Flow(name="stopping the flow")
outer = ForLoop()
outer.config["max"] = 10
flow.actors.append(outer)
ssv = SetStorageValue()
ssv.config["storage_name"] = "current"
flow.actors.append(ssv)
tee = Tee()
tee.config["condition"] = "@{current} == 7"
flow.actors.append(tee)
stop = Stop()
tee.actors.append(stop)
console = Console()
flow.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("Cross-validate classifier")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="cross-validate classifier")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
flow.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
flow.actors.append(select)
cv = CrossValidate()
cv.config["setup"] = Classifier(classname="weka.classifiers.trees.J48")
flow.actors.append(cv)
branch = Branch()
flow.actors.append(branch)
seqsum = Sequence()
seqsum.name = "summary"
branch.actors.append(seqsum)
summary = EvaluationSummary()
summary.config["title"] = "=== J48/iris ==="
summary.config["complexity"] = False
summary.config["matrix"] = True
seqsum.actors.append(summary)
console = Console()
seqsum.actors.append(console)
seqerr = Sequence()
seqerr.name = "errors"
branch.actors.append(seqerr)
errors = ClassifierErrors()
errors.config["wait"] = False
seqerr.actors.append(errors)
seqroc = Sequence()
seqroc.name = "roc"
branch.actors.append(seqroc)
roc = ROC()
roc.config["wait"] = False
roc.config["class_index"] = [0, 1, 2]
seqroc.actors.append(roc)
seqprc = Sequence()
seqprc.name = "prc"
branch.actors.append(seqprc)
prc = PRC()
prc.config["wait"] = True
prc.config["class_index"] = [0, 1, 2]
seqprc.actors.append(prc)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("cluster data")
iris = helper.get_data_dir() + os.sep + "iris_no_class.arff"
clsfile = str(tempfile.gettempdir()) + os.sep + "simplekmeans.model"
flow = Flow(name="cluster data")
start = Start()
flow.actors.append(start)
build_save = Trigger()
build_save.name = "build and save clusterer"
flow.actors.append(build_save)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
build_save.actors.append(filesupplier)
loaddataset = LoadDataset()
build_save.actors.append(loaddataset)
ssv = SetStorageValue()
ssv.config["storage_name"] = "data"
build_save.actors.append(ssv)
train = Train()
train.config["setup"] = Clusterer(classname="weka.clusterers.SimpleKMeans")
build_save.actors.append(train)
ssv = SetStorageValue()
ssv.config["storage_name"] = "model"
build_save.actors.append(ssv)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
build_save.actors.append(pick)
console = Console()
console.config["prefix"] = "built: "
pick.actors.append(console)
writer = ModelWriter()
writer.config["output"] = clsfile
build_save.actors.append(writer)
pred_serialized = Trigger()
pred_serialized.name = "make predictions (serialized model)"
flow.actors.append(pred_serialized)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
pred_serialized.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
pred_serialized.actors.append(loaddataset)
predict = Predict()
predict.config["model"] = clsfile
pred_serialized.actors.append(predict)
console = Console()
console.config["prefix"] = "serialized: "
pred_serialized.actors.append(console)
pred_storage = Trigger()
pred_storage.name = "make predictions (model from storage)"
flow.actors.append(pred_storage)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
pred_storage.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
pred_storage.actors.append(loaddataset)
predict = Predict()
predict.config["storage_name"] = "model"
pred_storage.actors.append(predict)
console = Console()
console.config["prefix"] = "storage: "
pred_storage.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("Attribute selection")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="attribute selection")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = False
flow.actors.append(loaddataset)
attsel = AttributeSelection()
attsel.config["search"] = ASSearch(
classname="weka.attributeSelection.BestFirst")
attsel.config["eval"] = ASEvaluation(
classname="weka.attributeSelection.CfsSubsetEval")
flow.actors.append(attsel)
results = Tee()
results.name = "output results"
flow.actors.append(results)
picker = ContainerValuePicker()
picker.config["value"] = "Results"
picker.config["switch"] = True
results.actors.append(picker)
console = Console()
console.config["prefix"] = "Attribute selection results:"
results.actors.append(console)
reduced = Tee()
reduced.name = "reduced dataset"
flow.actors.append(reduced)
picker = ContainerValuePicker()
picker.config["value"] = "Reduced"
picker.config["switch"] = True
reduced.actors.append(picker)
console = Console()
console.config["prefix"] = "Reduced dataset:\n\n"
reduced.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("build and evaluate classifier")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="build and evaluate classifier")
start = Start()
flow.actors.append(start)
build_save = Trigger()
build_save.name = "build and store classifier"
flow.actors.append(build_save)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
build_save.actors.append(filesupplier)
loaddataset = LoadDataset()
build_save.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
build_save.actors.append(select)
ssv = SetStorageValue()
ssv.config["storage_name"] = "data"
build_save.actors.append(ssv)
train = Train()
train.config["setup"] = Classifier(classname="weka.classifiers.trees.J48")
build_save.actors.append(train)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
build_save.actors.append(pick)
ssv = SetStorageValue()
ssv.config["storage_name"] = "model"
pick.actors.append(ssv)
evaluate = Trigger()
evaluate.name = "evaluate classifier"
flow.actors.append(evaluate)
gsv = GetStorageValue()
gsv.config["storage_name"] = "data"
evaluate.actors.append(gsv)
evl = Evaluate()
evl.config["storage_name"] = "model"
evaluate.actors.append(evl)
summary = EvaluationSummary()
summary.config["matrix"] = True
evaluate.actors.append(summary)
console = Console()
evaluate.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("Attribute selection")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="attribute selection")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = False
flow.actors.append(loaddataset)
attsel = AttributeSelection()
attsel.config["search"] = ASSearch(classname="weka.attributeSelection.BestFirst")
attsel.config["eval"] = ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval")
flow.actors.append(attsel)
results = Tee()
results.name = "output results"
flow.actors.append(results)
picker = ContainerValuePicker()
picker.config["value"] = "Results"
picker.config["switch"] = True
results.actors.append(picker)
console = Console()
console.config["prefix"] = "Attribute selection results:"
results.actors.append(console)
reduced = Tee()
reduced.name = "reduced dataset"
flow.actors.append(reduced)
picker = ContainerValuePicker()
picker.config["value"] = "Reduced"
picker.config["switch"] = True
reduced.actors.append(picker)
console = Console()
console.config["prefix"] = "Reduced dataset:\n\n"
reduced.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("build, save and load classifier")
iris = helper.get_data_dir() + os.sep + "iris.arff"
clsfile = str(tempfile.gettempdir()) + os.sep + "j48.model"
flow = Flow(name="build, save and load classifier")
start = Start()
flow.actors.append(start)
build_save = Trigger()
build_save.name = "build and save classifier"
flow.actors.append(build_save)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
build_save.actors.append(filesupplier)
loaddataset = LoadDataset()
build_save.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
build_save.actors.append(select)
train = Train()
train.config["setup"] = Classifier(classname="weka.classifiers.trees.J48")
build_save.actors.append(train)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
build_save.actors.append(pick)
console = Console()
console.config["prefix"] = "built: "
pick.actors.append(console)
writer = ModelWriter()
writer.config["output"] = clsfile
build_save.actors.append(writer)
load = Trigger()
load.name = "load classifier"
flow.actors.append(load)
filesupplier = FileSupplier()
filesupplier.config["files"] = [clsfile]
load.actors.append(filesupplier)
reader = ModelReader()
load.actors.append(reader)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
load.actors.append(pick)
console = Console()
console.config["prefix"] = "loaded: "
pick.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
count = 50
helper.print_title("build clusterer incrementally")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="build clusterer incrementally")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
initcounter = InitStorageValue()
initcounter.config["storage_name"] = "counter"
initcounter.config["value"] = 0
flow.actors.append(initcounter)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
flow.actors.append(loaddataset)
remove = Filter(name="remove class attribute")
remove.config["setup"] = filters.Filter(
classname="weka.filters.unsupervised.attribute.Remove",
options=["-R", "last"])
flow.actors.append(remove)
inccounter = UpdateStorageValue()
inccounter.config["storage_name"] = "counter"
inccounter.config["expression"] = "{X} + 1"
flow.actors.append(inccounter)
train = Train()
train.config["setup"] = Clusterer(classname="weka.clusterers.Cobweb")
flow.actors.append(train)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
pick.config["switch"] = True
flow.actors.append(pick)
tee = Tee(name="output model every " + str(count) + " instances")
tee.config["condition"] = "@{counter} % " + str(count) + " == 0"
flow.actors.append(tee)
trigger = Trigger(name="output # of instances")
tee.actors.append(trigger)
getcounter = GetStorageValue()
getcounter.config["storage_name"] = "counter"
trigger.actors.append(getcounter)
console = Console()
console.config["prefix"] = "# of instances: "
trigger.actors.append(console)
console = Console(name="output model")
tee.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
count = 50
helper.print_title("build classifier incrementally")
iris = helper.get_data_dir() + os.sep + "iris.arff"
flow = Flow(name="build classifier incrementally")
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
flow.actors.append(filesupplier)
initcounter = InitStorageValue()
initcounter.config["storage_name"] = "counter"
initcounter.config["value"] = 0
flow.actors.append(initcounter)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
flow.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
flow.actors.append(select)
inccounter = UpdateStorageValue()
inccounter.config["storage_name"] = "counter"
inccounter.config["expression"] = "{X} + 1"
flow.actors.append(inccounter)
train = Train()
train.config["setup"] = Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")
flow.actors.append(train)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
pick.config["switch"] = True
flow.actors.append(pick)
tee = Tee(name="output model every " + str(count) + " instances")
tee.config["condition"] = "@{counter} % " + str(count) + " == 0"
flow.actors.append(tee)
trigger = Trigger(name="output # of instances")
tee.actors.append(trigger)
getcounter = GetStorageValue()
getcounter.config["storage_name"] = "counter"
trigger.actors.append(getcounter)
console = Console()
console.config["prefix"] = "# of instances: "
trigger.actors.append(console)
console = Console(name="output model")
tee.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("build, save and load classifier")
iris = helper.get_data_dir() + os.sep + "iris.arff"
clsfile = str(tempfile.gettempdir()) + os.sep + "j48.model"
flow = Flow(name="build, save and load classifier")
start = Start()
flow.actors.append(start)
build_save = Trigger()
build_save.name = "build and save classifier"
flow.actors.append(build_save)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
build_save.actors.append(filesupplier)
loaddataset = LoadDataset()
build_save.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
build_save.actors.append(select)
train = Train()
train.config["setup"] = Classifier(classname="weka.classifiers.trees.J48")
build_save.actors.append(train)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
build_save.actors.append(pick)
console = Console()
console.config["prefix"] = "built: "
pick.actors.append(console)
writer = ModelWriter()
writer.config["output"] = clsfile
build_save.actors.append(writer)
load = Trigger()
load.name = "load classifier"
flow.actors.append(load)
filesupplier = FileSupplier()
filesupplier.config["files"] = [clsfile]
load.actors.append(filesupplier)
reader = ModelReader()
load.actors.append(reader)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
load.actors.append(pick)
console = Console()
console.config["prefix"] = "loaded: "
pick.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
def main():
"""
Just runs some example code.
"""
# setup the flow
helper.print_title("classify data")
iris = helper.get_data_dir() + os.sep + "iris.arff"
clsfile = str(tempfile.gettempdir()) + os.sep + "j48.model"
flow = Flow(name="classify data")
start = Start()
flow.actors.append(start)
build_save = Trigger()
build_save.name = "build and save classifier"
flow.actors.append(build_save)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
build_save.actors.append(filesupplier)
loaddataset = LoadDataset()
build_save.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
build_save.actors.append(select)
ssv = SetStorageValue()
ssv.config["storage_name"] = "data"
build_save.actors.append(ssv)
train = Train()
train.config["setup"] = Classifier(classname="weka.classifiers.trees.J48")
build_save.actors.append(train)
ssv = SetStorageValue()
ssv.config["storage_name"] = "model"
build_save.actors.append(ssv)
pick = ContainerValuePicker()
pick.config["value"] = "Model"
build_save.actors.append(pick)
console = Console()
console.config["prefix"] = "built: "
pick.actors.append(console)
writer = ModelWriter()
writer.config["output"] = clsfile
build_save.actors.append(writer)
pred_serialized = Trigger()
pred_serialized.name = "make predictions (serialized model)"
flow.actors.append(pred_serialized)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
pred_serialized.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
pred_serialized.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
pred_serialized.actors.append(select)
predict = Predict()
predict.config["model"] = clsfile
pred_serialized.actors.append(predict)
console = Console()
console.config["prefix"] = "serialized: "
pred_serialized.actors.append(console)
pred_storage = Trigger()
pred_storage.name = "make predictions (model from storage)"
flow.actors.append(pred_storage)
filesupplier = FileSupplier()
filesupplier.config["files"] = [iris]
pred_storage.actors.append(filesupplier)
loaddataset = LoadDataset()
loaddataset.config["incremental"] = True
pred_storage.actors.append(loaddataset)
select = ClassSelector()
select.config["index"] = "last"
pred_storage.actors.append(select)
predict = Predict()
predict.config["storage_name"] = "model"
pred_storage.actors.append(predict)
console = Console()
console.config["prefix"] = "storage: "
pred_storage.actors.append(console)
# run the flow
msg = flow.setup()
if msg is None:
print("\n" + flow.tree + "\n")
msg = flow.execute()
if msg is not None:
print("Error executing flow:\n" + msg)
else:
print("Error setting up flow:\n" + msg)
flow.wrapup()
flow.cleanup()
