如何在pyweka上使用attributeselectedclassifier？
How to use attributeselectedclassifier on pyweka?
我正在将在 weka 上完成的模型翻译成 python-weka-wrapper3，我不知道如何在 attributeselectedclassifier 上进行评估和搜索选项。
这是weka上的模型：
weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1" -W weka.classifiers.meta.MultiSearch -- -E FM -search "weka.core.setupgenerator.MathParameter -property classifier.classifier.classifier.numOfBoostingIterations -min 5.0 -max 50.0 -step 1.0 -base 10.0 -expression I" -class-label 1 -algorithm "weka.classifiers.meta.multisearch.DefaultSearch -sample-size 100.0 -initial-folds 2 -subsequent-folds 10 -initial-test-set . -subsequent-test-set . -num-slots 1" -log-file /Applications/weka-3-8-3 -S 1 -W weka.classifiers.meta.Bagging -- -P 100 -S 1 -num-slots 1 -I 100 -W weka.classifiers.meta.FilteredClassifier -- -F "weka.filters.supervised.instance.SMOTE -C 0 -K 3 -P 250.0 -S 1" -S 1 -W weka.classifiers.meta.CostSensitiveClassifier -- -cost-matrix "[0.0 1.0; 1.0 0.0]" -S 1 -W weka.classifiers.trees.ADTree -- -B 10 -E -3 -S 1
我现在有这个：
base = Classifier(classname="weka.classifiers.trees.ADTree", options=["-B", "10", "-E", "-3", "-S", "1"])
cls = SingleClassifierEnhancer(classname="weka.classifiers.meta.CostSensitiveClassifier",
options =["-cost-matrix", "[0.0 1.0; 1.0 0.0]", "-S", "1"])
cls.classifier = base
smote = Filter(classname="weka.filters.supervised.instance.SMOTE", options=["-C", "0", "-K", "3", "-P", "250.0", "-S", "1"])
fc = FilteredClassifier()
fc.filter = smote
fc.classifier = cls
bagging_cls = Classifier(classname="weka.classifiers.meta.Bagging",
options=["-P", "100", "-S", "1", "-num-slots", "1", "-I", "100"])
bagging_cls.classifier = fc
multisearch_cls = MultiSearch(
options = ["-S", "1"])
multisearch_cls.evaluation = "FM"
multisearch_cls.log_file = "/home/pablo/Escritorio/TFG/OUTPUT.txt"
multisearch_cls.search = ["-sample-size", "100", "-initial-folds", "2", "-subsequent-folds", "10",
"-initial-test-set", ".", "-subsequent-test-set", ".", "-num-slots", "1"]
mparam = MathParameter()
mparam.prop = "numOfBoostingIterations"
mparam.minimum = 5.0
mparam.maximum = 50.0
mparam.step = 1.0
mparam.base = 10.0
mparam.expression = "I"
multisearch_cls.parameters = [mparam]
multisearch_cls.classifier = bagging_cls
AttS_cls = AttributeSelectedClassifier()
AttS_cls.evaluator = "weka.attributeSelection.CfsSubsetEval -P 1 -E 1"
AttS_cls.search = "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1"
AttS_cls.classifier = multisearch_cls
train, test = data_modelos_1_2.train_test_split(70.0, Random(1))
AttS_cls.build_classifier(train)
evl = Evaluation(train)
evl.crossvalidate_model(AttS_cls, test, 10, Random(1))
print(AttS_cls)
#graph.plot_dot_graph(AttS_cls.graph)
print("")
print("=== Setup ===")
print("Classifier: " + AttS_cls.to_commandline())
print("Dataset: ")
print(test.relationname)
print("")
print(evl.summary("=== " + str(10) + " -fold Cross-Validation ==="))
print(evl.class_details())
plcls.plot_roc(evl, class_index=[0, 1], wait=True)
但当我这样做时
AttS_cls.evaluator = "weka.attributeSelection.CfsSubsetEval -P 1 -E 1"
AttS_cls.search = "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1"
它给我这个错误：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_40724/2750622902.py in <module>
30
31 AttS_cls = AttributeSelectedClassifier()
---> 32 AttS_cls.search = "weka.attributeSelection.GreedyStepwise"
33 AttS_cls.classifier = multisearch_cls
34
/usr/local/lib/python3.8/dist-packages/weka/classifiers.py in search(self, search)
435 :type search: ASSearch
436 """
--> 437 javabridge.call(self.jobject, "setSearch", "(Lweka/attributeSelection/ASSearch;)V", search.jobject)
438
439
AttributeError: 'str' object has no attribute 'jobject'
我知道我必须将它们设置为对象，因为它会引发此错误，因为我尝试将它们设置为字符串，但我不知道如何设置。
您需要实例化 ASSearch 和 ASEvaluation 对象。如果你有 command-lines，你可以像这样使用 from_commandline 辅助方法：
from weka.core.classes import from_commandline, get_classname
from weka.attribute_selection import ASSearch
from weka.attribute_selection import ASEvaluation
search = from_commandline('weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1', classname=get_classname(ASSearch))
evaluation = from_commandline('weka.attributeSelection.CfsSubsetEval -P 1 -E 1', classname=get_classname(ASEvaluation))
from_commandline 方法的第二个参数是要使用的包装器的 classname 而不是 OptionHandler。为简单起见，我导入了正确的包装器，然后使用 get_classname 方法来 return 包装器 class 的点符号。这样我就可以避免 classname 字符串中的意外拼写错误。
此外，通过使用单引号，您不必担心 command-lines 中的 Weka 引号，您可以逐字使用 Weka command-line 字符串。
您也可以使用相同的方法来实例化 AttributeSelectedClassifier 包装器本身，而不必分别进行实例化搜索和评估：
from weka.core.classes import from_commandline, get_classname
from weka.classifiers import AttributeSelectedClassifier
cls = from_commandline('weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1" -W weka.classifiers.meta.MultiSearch -- -E FM -search "weka.core.setupgenerator.MathParameter -property classifier.classifier.classifier.numOfBoostingIterations -min 5.0 -max 50.0 -step 1.0 -base 10.0 -expression I" -class-label 1 -algorithm "weka.classifiers.meta.multisearch.DefaultSearch -sample-size 100.0 -initial-folds 2 -subsequent-folds 10 -initial-test-set . -subsequent-test-set . -num-slots 1" -log-file /Applications/weka-3-8-3 -S 1 -W weka.classifiers.meta.Bagging -- -P 100 -S 1 -num-slots 1 -I 100 -W weka.classifiers.meta.FilteredClassifier -- -F "weka.filters.supervised.instance.SMOTE -C 0 -K 3 -P 250.0 -S 1" -S 1 -W weka.classifiers.meta.CostSensitiveClassifier -- -cost-matrix "[0.0 1.0; 1.0 0.0]" -S 1 -W weka.classifiers.trees.ADTree -- -B 10 -E -3 -S 1', get_classname(AttributeSelectedClassifier))
我正在将在 weka 上完成的模型翻译成 python-weka-wrapper3，我不知道如何在 attributeselectedclassifier 上进行评估和搜索选项。
这是weka上的模型：
weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1" -W weka.classifiers.meta.MultiSearch -- -E FM -search "weka.core.setupgenerator.MathParameter -property classifier.classifier.classifier.numOfBoostingIterations -min 5.0 -max 50.0 -step 1.0 -base 10.0 -expression I" -class-label 1 -algorithm "weka.classifiers.meta.multisearch.DefaultSearch -sample-size 100.0 -initial-folds 2 -subsequent-folds 10 -initial-test-set . -subsequent-test-set . -num-slots 1" -log-file /Applications/weka-3-8-3 -S 1 -W weka.classifiers.meta.Bagging -- -P 100 -S 1 -num-slots 1 -I 100 -W weka.classifiers.meta.FilteredClassifier -- -F "weka.filters.supervised.instance.SMOTE -C 0 -K 3 -P 250.0 -S 1" -S 1 -W weka.classifiers.meta.CostSensitiveClassifier -- -cost-matrix "[0.0 1.0; 1.0 0.0]" -S 1 -W weka.classifiers.trees.ADTree -- -B 10 -E -3 -S 1
我现在有这个：
base = Classifier(classname="weka.classifiers.trees.ADTree", options=["-B", "10", "-E", "-3", "-S", "1"])
cls = SingleClassifierEnhancer(classname="weka.classifiers.meta.CostSensitiveClassifier",
options =["-cost-matrix", "[0.0 1.0; 1.0 0.0]", "-S", "1"])
cls.classifier = base
smote = Filter(classname="weka.filters.supervised.instance.SMOTE", options=["-C", "0", "-K", "3", "-P", "250.0", "-S", "1"])
fc = FilteredClassifier()
fc.filter = smote
fc.classifier = cls
bagging_cls = Classifier(classname="weka.classifiers.meta.Bagging",
options=["-P", "100", "-S", "1", "-num-slots", "1", "-I", "100"])
bagging_cls.classifier = fc
multisearch_cls = MultiSearch(
options = ["-S", "1"])
multisearch_cls.evaluation = "FM"
multisearch_cls.log_file = "/home/pablo/Escritorio/TFG/OUTPUT.txt"
multisearch_cls.search = ["-sample-size", "100", "-initial-folds", "2", "-subsequent-folds", "10",
"-initial-test-set", ".", "-subsequent-test-set", ".", "-num-slots", "1"]
mparam = MathParameter()
mparam.prop = "numOfBoostingIterations"
mparam.minimum = 5.0
mparam.maximum = 50.0
mparam.step = 1.0
mparam.base = 10.0
mparam.expression = "I"
multisearch_cls.parameters = [mparam]
multisearch_cls.classifier = bagging_cls
AttS_cls = AttributeSelectedClassifier()
AttS_cls.evaluator = "weka.attributeSelection.CfsSubsetEval -P 1 -E 1"
AttS_cls.search = "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1"
AttS_cls.classifier = multisearch_cls
train, test = data_modelos_1_2.train_test_split(70.0, Random(1))
AttS_cls.build_classifier(train)
evl = Evaluation(train)
evl.crossvalidate_model(AttS_cls, test, 10, Random(1))
print(AttS_cls)
#graph.plot_dot_graph(AttS_cls.graph)
print("")
print("=== Setup ===")
print("Classifier: " + AttS_cls.to_commandline())
print("Dataset: ")
print(test.relationname)
print("")
print(evl.summary("=== " + str(10) + " -fold Cross-Validation ==="))
print(evl.class_details())
plcls.plot_roc(evl, class_index=[0, 1], wait=True)
但当我这样做时
AttS_cls.evaluator = "weka.attributeSelection.CfsSubsetEval -P 1 -E 1"
AttS_cls.search = "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1"
它给我这个错误：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_40724/2750622902.py in <module>
30
31 AttS_cls = AttributeSelectedClassifier()
---> 32 AttS_cls.search = "weka.attributeSelection.GreedyStepwise"
33 AttS_cls.classifier = multisearch_cls
34
/usr/local/lib/python3.8/dist-packages/weka/classifiers.py in search(self, search)
435 :type search: ASSearch
436 """
--> 437 javabridge.call(self.jobject, "setSearch", "(Lweka/attributeSelection/ASSearch;)V", search.jobject)
438
439
AttributeError: 'str' object has no attribute 'jobject'
我知道我必须将它们设置为对象，因为它会引发此错误，因为我尝试将它们设置为字符串，但我不知道如何设置。
您需要实例化 ASSearch 和 ASEvaluation 对象。如果你有 command-lines，你可以像这样使用 from_commandline 辅助方法：
from weka.core.classes import from_commandline, get_classname
from weka.attribute_selection import ASSearch
from weka.attribute_selection import ASEvaluation
search = from_commandline('weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1', classname=get_classname(ASSearch))
evaluation = from_commandline('weka.attributeSelection.CfsSubsetEval -P 1 -E 1', classname=get_classname(ASEvaluation))
from_commandline 方法的第二个参数是要使用的包装器的 classname 而不是 OptionHandler。为简单起见，我导入了正确的包装器，然后使用 get_classname 方法来 return 包装器 class 的点符号。这样我就可以避免 classname 字符串中的意外拼写错误。
此外，通过使用单引号，您不必担心 command-lines 中的 Weka 引号，您可以逐字使用 Weka command-line 字符串。
您也可以使用相同的方法来实例化 AttributeSelectedClassifier 包装器本身，而不必分别进行实例化搜索和评估：
from weka.core.classes import from_commandline, get_classname
from weka.classifiers import AttributeSelectedClassifier
cls = from_commandline('weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.GreedyStepwise -B -T -1.7976931348623157E308 -N -1 -num-slots 1" -W weka.classifiers.meta.MultiSearch -- -E FM -search "weka.core.setupgenerator.MathParameter -property classifier.classifier.classifier.numOfBoostingIterations -min 5.0 -max 50.0 -step 1.0 -base 10.0 -expression I" -class-label 1 -algorithm "weka.classifiers.meta.multisearch.DefaultSearch -sample-size 100.0 -initial-folds 2 -subsequent-folds 10 -initial-test-set . -subsequent-test-set . -num-slots 1" -log-file /Applications/weka-3-8-3 -S 1 -W weka.classifiers.meta.Bagging -- -P 100 -S 1 -num-slots 1 -I 100 -W weka.classifiers.meta.FilteredClassifier -- -F "weka.filters.supervised.instance.SMOTE -C 0 -K 3 -P 250.0 -S 1" -S 1 -W weka.classifiers.meta.CostSensitiveClassifier -- -cost-matrix "[0.0 1.0; 1.0 0.0]" -S 1 -W weka.classifiers.trees.ADTree -- -B 10 -E -3 -S 1', get_classname(AttributeSelectedClassifier))