Python Classifier.update_classifier方法代码示例

# 需要导入模块: from weka.classifiers import Classifier [as 别名]
# 或者: from weka.classifiers.Classifier import update_classifier [as 别名]
def main(args):
    """
    Trains a NaiveBayesUpdateable classifier incrementally on a dataset. The dataset can be supplied as parameter.
    :param args: the commandline arguments
    :type args: list
    """

    # load a dataset
    if len(args) <= 1:
        data_file = helper.get_data_dir() + os.sep + "vote.arff"
    else:
        data_file = args[1]
    helper.print_info("Loading dataset: " + data_file)
    loader = Loader(classname="weka.core.converters.ArffLoader")
    data = loader.load_file(data_file, incremental=True)
    data.class_is_last()

    # classifier
    nb = Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")
    nb.build_classifier(data)

    # train incrementally
    for inst in loader:
        nb.update_classifier(inst)

    print(nb)

# 需要导入模块: from weka.classifiers import Classifier [as 别名]
# 或者: from weka.classifiers.Classifier import update_classifier [as 别名]
def main():
    """
    Just runs some example code.
    """

    # load a dataset
    iris_file = helper.get_data_dir() + os.sep + "iris.arff"
    helper.print_info("Loading dataset: " + iris_file)
    loader = Loader("weka.core.converters.ArffLoader")
    iris_data = loader.load_file(iris_file)
    iris_data.class_is_last()

    # classifier help
    helper.print_title("Creating help string")
    classifier = Classifier(classname="weka.classifiers.trees.J48")
    print(classifier.to_help())

    # partial classname
    helper.print_title("Creating classifier from partial classname")
    clsname = ".J48"
    classifier = Classifier(classname=clsname)
    print(clsname + " --> " + classifier.classname)

    # classifier from commandline
    helper.print_title("Creating SMO from command-line string")
    cmdline = 'weka.classifiers.functions.SMO -K "weka.classifiers.functions.supportVector.NormalizedPolyKernel -E 3.0"'
    classifier = from_commandline(cmdline, classname="weka.classifiers.Classifier")
    classifier.build_classifier(iris_data)
    print("input: " + cmdline)
    print("output: " + classifier.to_commandline())
    print("model:\n" + str(classifier))

    # kernel classifier
    helper.print_title("Creating SMO as KernelClassifier")
    kernel = Kernel(classname="weka.classifiers.functions.supportVector.RBFKernel", options=["-G", "0.001"])
    classifier = KernelClassifier(classname="weka.classifiers.functions.SMO", options=["-M"])
    classifier.kernel = kernel
    classifier.build_classifier(iris_data)
    print("classifier: " + classifier.to_commandline())
    print("model:\n" + str(classifier))

    # build a classifier and output model
    helper.print_title("Training J48 classifier on iris")
    classifier = Classifier(classname="weka.classifiers.trees.J48")
    # Instead of using 'options=["-C", "0.3"]' in the constructor, we can also set the "confidenceFactor"
    # property of the J48 classifier itself. However, being of type float rather than double, we need
    # to convert it to the correct type first using the double_to_float function:
    classifier.set_property("confidenceFactor", typeconv.double_to_float(0.3))
    classifier.build_classifier(iris_data)
    print(classifier)
    print(classifier.graph)
    print(classifier.to_source("MyJ48"))
    plot_graph.plot_dot_graph(classifier.graph)

    # evaluate model on test set
    helper.print_title("Evaluating J48 classifier on iris")
    evaluation = Evaluation(iris_data)
    evl = evaluation.test_model(classifier, iris_data)
    print(evl)
    print(evaluation.summary())

    # evaluate model on train/test split
    helper.print_title("Evaluating J48 classifier on iris (random split 66%)")
    classifier = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.3"])
    evaluation = Evaluation(iris_data)
    evaluation.evaluate_train_test_split(classifier, iris_data, 66.0, Random(1))
    print(evaluation.summary())

    # load a dataset incrementally and build classifier incrementally
    helper.print_title("Build classifier incrementally on iris")
    helper.print_info("Loading dataset: " + iris_file)
    loader = Loader("weka.core.converters.ArffLoader")
    iris_inc = loader.load_file(iris_file, incremental=True)
    iris_inc.class_is_last()
    classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")
    classifier.build_classifier(iris_inc)
    for inst in loader:
        classifier.update_classifier(inst)
    print(classifier)

    # construct meta-classifiers
    helper.print_title("Meta classifiers")
    # generic FilteredClassifier instantiation
    print("generic FilteredClassifier instantiation")
    meta = SingleClassifierEnhancer(classname="weka.classifiers.meta.FilteredClassifier")
    meta.classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
    flter = Filter("weka.filters.unsupervised.attribute.Remove")
    flter.options = ["-R", "first"]
    meta.set_property("filter", flter.jobject)
    print(meta.to_commandline())
    # direct FilteredClassifier instantiation
    print("direct FilteredClassifier instantiation")
    meta = FilteredClassifier()
    meta.classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
    flter = Filter("weka.filters.unsupervised.attribute.Remove")
    flter.options = ["-R", "first"]
    meta.filter = flter
    print(meta.to_commandline())
    # generic Vote
    print("generic Vote instantiation")
#.........这里部分代码省略.........