本文整理汇总了Python中weka.classifiers.Classifier.__str__方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.__str__方法的具体用法？Python Classifier.__str__怎么用？Python Classifier.__str__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.Classifier的用法示例。
在下文中一共展示了Classifier.__str__方法的1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: __init__
# 需要导入模块: from weka.classifiers import Classifier [as 别名]
# 或者: from weka.classifiers.Classifier import __str__ [as 别名]
class Experiment:
data = None
class_index = -1
classifier = None
attrs = []
def __init__(self):
# jvm.start(max_heap_size="2500M")
pass
def out(self, x):
print x.__str__().encode('ascii', 'ignore')
def loadCSV(self, filename, path='/home/sbiastoch/Schreibtisch/csv_files/'):
weka_loader = Loader(classname="weka.core.converters.CSVLoader")
self.data = weka_loader.load_file(path+filename)
def setClassIndex(self, index):
if index < 0:
self.data.class_index = self.data.num_attributes + index
else:
self.data.class_index = index
def train_J48(self, min_per_rule=20):
params = [
'-C','0.3',
'-M',str(min_per_rule),
# '-N',str(folds),
# '-R',
]
self.base_classifier = Classifier(classname='weka.classifiers.trees.J48', options=params)
self._train()
def train_JRip(self, min_per_rule=20, optimizations=2, folds=3, seed=42):
params = [
'-F', str(folds), # folds
'-N', str(min_per_rule), # min elements per rule
'-O', str(optimizations), # optimizations
'-S', str(seed) #seed
]
self.base_classifier = Classifier(classname='weka.classifiers.rules.JRip', options=params)
self._train()
def _train(self):
params = [
'-F','weka.filters.unsupervised.attribute.RemoveByName -E ^('+'|'.join(self.attrs)+')$ -V',
'-W', self.base_classifier.classname, '--',
]
params.extend(self.base_classifier.options)
# self.classifier = Classifier(classname='weka.classifiers.meta.FilteredClassifier', options=params)
self.classifier = FilteredClassifier(options=params)
# self.classifier.filter(Filter(classname="weka.filters.unsupervised.attribute.RemoveByName", options=['-E','^('+'|'.join(self.attrs)+')$','-V']))
self.classifier.build_classifier(self.data)
self.out(self.classifier.__str__().encode('ascii', 'ignore').split("\n")[-2])
def test(self, folds = 10):
evaluation = Evaluation(self.data) # initialize with priors
evaluation.crossvalidate_model(self.classifier, self.data, folds, Random(42)) # 10-fold CV
print('Total number of instances: '+str(evaluation.num_instances)+'.')
print(str(round(evaluation.percent_correct,2))+'% / '+str(round(evaluation.correct, 2))+' correct.')
print(str(round(evaluation.percent_incorrect,2))+'% / '+str(round(evaluation.incorrect, 2))+' incorrect.')
def saveCSV(self, filename, path='/home/sbiastoch/Schreibtisch/csv_files/'):
saver = Saver(classname="weka.core.converters.CSVSaver")
saver.save_file(self.data, path+filename)
def loadClassifier(self, filename, path='/home/sbiastoch/Schreibtisch/classifiers/'):
objects = serialization.read_all(path+filename)
self.classifier = Classifier(jobject=objects[0])
#self.data = Instances(jobject=objects[1])
def saveClassifier(self, filename, path='/home/sbiastoch/Schreibtisch/classifiers/'):
serialization.write_all(path+filename, [self.classifier, Instances.template_instances(self.data)])
def remove_correct_classified(self, invert = False):
options=[
'-W', self.classifier.to_commandline(),
'-C', str(self.class_index), #classindex
# '-F','0', # folds
# '-T','0.1', #threshold by numeric classes
'-I','0', # max iterations
'-V' if not invert else ''
] # invert
classname = "weka.filters.unsupervised.instance.RemoveMisclassified"
remove = Filter(classname=classname, options=options)
remove.inputformat(self.data)
self.data = remove.filter(self.data)
def remove_incorrect_classified(self):
self.remove_correct_classified(True)
def set_attributes(self, attrs):
self.attrs = attrs
def select_missclassified(self):
remove = Filter(classname="weka.filters.supervised.attribute.AddClassification", options=['-classification' ,'-error' ,'-W' ,self.base_classifier.to_commandline()])
remove.inputformat(self.data)
#.........这里部分代码省略.........