本文整理汇总了Python中weka.classifiers.Evaluation.to_matrix方法的典型用法代码示例。如果您正苦于以下问题：Python Evaluation.to_matrix方法的具体用法？Python Evaluation.to_matrix怎么用？Python Evaluation.to_matrix使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.classifiers.Evaluation的用法示例。
在下文中一共展示了Evaluation.to_matrix方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: print
# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import to_matrix [as 别名]
fname = data_dir + os.sep + "ReutersGrain-test.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
test = loader.load_file(fname)
test.set_class_index(test.num_attributes() - 1)
setups = (
("weka.classifiers.trees.J48", []),
("weka.classifiers.bayes.NaiveBayes", []),
("weka.classifiers.bayes.NaiveBayesMultinomial", []),
("weka.classifiers.bayes.NaiveBayesMultinomial", ["-C"]),
("weka.classifiers.bayes.NaiveBayesMultinomial", ["-C", "-L", "-S"])
)
# cross-validate classifiers
for setup in setups:
classifier, opt = setup
print("\n--> %s (filter options: %s)\n" % (classifier, " ".join(opt)))
cls = FilteredClassifier()
cls.set_classifier(Classifier(classname=classifier))
cls.set_filter(Filter(classname="weka.filters.unsupervised.attribute.StringToWordVector", options=opt))
cls.build_classifier(data)
evl = Evaluation(test)
evl.test_model(cls, test)
print("Accuracy: %0.0f%%" % evl.percent_correct())
tcdata = plc.generate_thresholdcurve_data(evl, 0)
print("AUC: %0.3f" % plc.get_auc(tcdata))
print(evl.to_matrix("Matrix:"))
jvm.stop()
示例2: print
# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import to_matrix [as 别名]
jvm.start()
# load glass
fname = data_dir + os.sep + "glass.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.set_class_index(data.num_attributes() - 1)
# cross-validate default J48
print("\nDefault J48")
cls = Classifier(classname="weka.classifiers.trees.J48")
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
print(evl.to_summary())
print(evl.to_matrix())
# build and plot model
cls.build_classifier(data)
plg.plot_dot_graph(cls.graph())
# cross-validate unpruned J48 with larger leaf size
print("\nUnpruned J48 (minNumObj=15)")
cls = Classifier(classname="weka.classifiers.trees.J48", options=["-U", "-M", "15"])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
print(evl.to_summary())
print(evl.to_matrix())
# build and plot model
cls.build_classifier(data)