Python Evaluation.class_details方法代码示例

# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import class_details [as 别名]
    def crossValidate(self, arrfFile = None, classname="weka.classifiers.trees.J48", options=["-C", "0.3"]):
        
        if arrfFile is not None:
            self.initData( arrfFile )
            
        if self.data is None:
            return 

        print 'Classificador ' + str(classname) + ' ' + ' '.join(options)
        cls = Classifier(classname=classname, options=options)
        
        evl = Evaluation(self.data)
        evl.crossvalidate_model(cls, self.data, 10, Random(1))

        print(evl.percent_correct)
        print(evl.summary())
        print(evl.class_details())

# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import class_details [as 别名]

#.........这里部分代码省略.........
    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")
    meta = MultipleClassifiersCombiner(classname="weka.classifiers.meta.Vote")
    classifiers = [
        Classifier(classname="weka.classifiers.functions.SMO"),
        Classifier(classname="weka.classifiers.trees.J48")
    ]
    meta.classifiers = classifiers
    print(meta.to_commandline())

    # cross-validate nominal classifier
    helper.print_title("Cross-validating NaiveBayes on diabetes")
    diabetes_file = helper.get_data_dir() + os.sep + "diabetes.arff"
    helper.print_info("Loading dataset: " + diabetes_file)
    loader = Loader("weka.core.converters.ArffLoader")
    diabetes_data = loader.load_file(diabetes_file)
    diabetes_data.class_is_last()
    classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
    pred_output = PredictionOutput(
        classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
    evaluation = Evaluation(diabetes_data)
    evaluation.crossvalidate_model(classifier, diabetes_data, 10, Random(42), output=pred_output)
    print(evaluation.summary())
    print(evaluation.class_details())
    print(evaluation.matrix())
    print("areaUnderPRC/0: " + str(evaluation.area_under_prc(0)))
    print("weightedAreaUnderPRC: " + str(evaluation.weighted_area_under_prc))
    print("areaUnderROC/1: " + str(evaluation.area_under_roc(1)))
    print("weightedAreaUnderROC: " + str(evaluation.weighted_area_under_roc))
    print("avgCost: " + str(evaluation.avg_cost))
    print("totalCost: " + str(evaluation.total_cost))
    print("confusionMatrix: " + str(evaluation.confusion_matrix))
    print("correct: " + str(evaluation.correct))
    print("pctCorrect: " + str(evaluation.percent_correct))
    print("incorrect: " + str(evaluation.incorrect))
    print("pctIncorrect: " + str(evaluation.percent_incorrect))
    print("unclassified: " + str(evaluation.unclassified))
    print("pctUnclassified: " + str(evaluation.percent_unclassified))
    print("coverageOfTestCasesByPredictedRegions: " + str(evaluation.coverage_of_test_cases_by_predicted_regions))
    print("sizeOfPredictedRegions: " + str(evaluation.size_of_predicted_regions))
    print("falseNegativeRate: " + str(evaluation.false_negative_rate(1)))
    print("weightedFalseNegativeRate: " + str(evaluation.weighted_false_negative_rate))
    print("numFalseNegatives: " + str(evaluation.num_false_negatives(1)))
    print("trueNegativeRate: " + str(evaluation.true_negative_rate(1)))
    print("weightedTrueNegativeRate: " + str(evaluation.weighted_true_negative_rate))
    print("numTrueNegatives: " + str(evaluation.num_true_negatives(1)))
    print("falsePositiveRate: " + str(evaluation.false_positive_rate(1)))
    print("weightedFalsePositiveRate: " + str(evaluation.weighted_false_positive_rate))
    print("numFalsePositives: " + str(evaluation.num_false_positives(1)))
    print("truePositiveRate: " + str(evaluation.true_positive_rate(1)))
    print("weightedTruePositiveRate: " + str(evaluation.weighted_true_positive_rate))
    print("numTruePositives: " + str(evaluation.num_true_positives(1)))
    print("fMeasure: " + str(evaluation.f_measure(1)))
    print("weightedFMeasure: " + str(evaluation.weighted_f_measure))
    print("unweightedMacroFmeasure: " + str(evaluation.unweighted_macro_f_measure))
    print("unweightedMicroFmeasure: " + str(evaluation.unweighted_micro_f_measure))

# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import class_details [as 别名]
from weka.filters import Filter
# convert csv into arff format (weka compatable)
# use convertcsvtoarff.py file

# load arff file

loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file("reviewsinformation_task2.arff")
iris_data.class_is_last()
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()

# 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))

#print("model:\n" + str(classifier))


evaluation = Evaluation('test_data.arff')
evaluation.crossvalidate_model(classifier, diabetes_data, 10, Random(42), output=pred_output)
print(evaluation.summary())
print(evaluation.class_details())
print(evaluation.matrix())