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
flow = Flow(name="example loop")
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)
console = Console()
trigger.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
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("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("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("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("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()
