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 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 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, 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()
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()
