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("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, 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 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()