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("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("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("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
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("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()
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("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()
