Python Filter.options方法代碼示例

# 需要導入模塊: from weka.filters import Filter [as 別名]
# 或者: from weka.filters.Filter import options [as 別名]
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")
#.........這裏部分代碼省略.........

# 需要導入模塊: from weka.filters import Filter [as 別名]
# 或者: from weka.filters.Filter import options [as 別名]
    def runner(self, cdat, heap_size = 16384, seed = None, verbose = True):
        self.set_status(Pipeline.RUNNING)

        self.logs.append('Initializing Pipeline')

        para = self.config

        self.logs.append('Reading Pipeline Configuration')

        head = ''
        name = get_rand_uuid_str()

        self.logs.append('Reading Input File')

        for i, stage in enumerate(self.stages):
            if stage.code in ('dat.fle', 'prp.bgc', 'prp.nrm', 'prp.pmc', 'prp.sum'):
                self.stages[i].status = Pipeline.RUNNING
            if stage.code ==  'dat.fle':
                head    = os.path.abspath(stage.value.path)
                name, _ = os.path.splitext(stage.value.name)

        self.logs.append('Parsing to ARFF')

        path = os.path.join(head, '{name}.arff'.format(name = name))
        # This bug, I don't know why, using Config.schema instead.
        # cdat.toARFF(path, express_config = para.Preprocess.schema, verbose = verbose)

        for i, stage in enumerate(self.stages):
            if stage.code in ('dat.fle', 'prp.bgc', 'prp.nrm', 'prp.pmc', 'prp.sum'):
                self.stages[i].status = Pipeline.COMPLETE

        self.logs.append('Saved ARFF at {path}'.format(path = path))
        self.logs.append('Splitting to Training and Testing Sets')

        JVM.start(max_heap_size = '{size}m'.format(size = heap_size))

        load = Loader(classname = 'weka.core.converters.ArffLoader')
        # data = load.load_file(path)
        # save =  Saver(classname = 'weka.core.converters.ArffSaver')
        data = load.load_file(os.path.join(head, 'iris.arff')) # For Debugging Purposes Only
        data.class_is_last() # For Debugging Purposes Only
        # data.class_index = cdat.iclss

        for i, stage in enumerate(self.stages):
            if stage.code == 'prp.kcv':
                self.stages[i].status = Pipeline.RUNNING

        self.logs.append('Splitting Training Set')

        # TODO - Check if this seed is worth it.
        seed = assign_if_none(seed, random.randint(0, 1000))
        opts = ['-S', str(seed), '-N', str(para.Preprocess.FOLDS)]
        wobj = Filter(classname = 'weka.filters.supervised.instance.StratifiedRemoveFolds', options = opts + ['-V'])
        wobj.inputformat(data)

        tran = wobj.filter(data)

        self.logs.append('Splitting Testing Set')

        wobj.options = opts
        test = wobj.filter(data)

        for i, stage in enumerate(self.stages):
            if stage.code == 'prp.kcv':
                self.stages[i].status = Pipeline.COMPLETE

        self.logs.append('Performing Feature Selection')

        feat = [ ]
        for comb in para.FEATURE_SELECTION:
            if comb.USE:
                for i, stage in enumerate(self.stages):
                    if stage.code == 'ats':
                        search    = stage.value.search.name
                        evaluator = stage.value.evaluator.name

                        if search == comb.Search.NAME and evaluator == comb.Evaluator.NAME:
                            self.stages[i].status = Pipeline.RUNNING

                srch = ASSearch(classname = 'weka.attributeSelection.{classname}'.format(
                    classname = comb.Search.NAME,
                    options   = assign_if_none(comb.Search.OPTIONS, [ ])
                ))
                ewal = ASEvaluation(classname = 'weka.attributeSelection.{classname}'.format(
                    classname = comb.Evaluator.NAME,
                    options   = assign_if_none(comb.Evaluator.OPTIONS, [ ])
                ))

                attr = AttributeSelection()
                attr.search(srch)
                attr.evaluator(ewal)
                attr.select_attributes(tran)

                meta = addict.Dict()
                meta.search    = comb.Search.NAME
                meta.evaluator = comb.Evaluator.NAME
                meta.features  = [tran.attribute(index).name for index in attr.selected_attributes]

                feat.append(meta)

#.........這裏部分代碼省略.........