我正在学习Mallet文本分类命令行。估计不同类别的输出值都是相同的1.0。我不知道我在哪里不正确。你能帮我吗？
槌版本：E：\ Mallet \ mallet-2.0.8RC3
//there is a txt file about cat breed (catmaterial.txt) in cat dir.
//command 1
C:\Users\toshiba>mallet import-dir --input E:\Mallet\testmaterial\cat --output E
:\Mallet\testmaterial\cat.mallet --remove-stopwords
//command 1 output
Labels =
E:\Mallet\testmaterial\cat
//command 2, save classifier as catClass.classifier
C:\Users\toshiba>mallet train-classifier --input E:\Mallet\testmaterial\cat.mall
et --trainer NaiveBayes --output-classifier E:\Mallet\testmaterial\catClass.clas
sifier
//command 2 output
Training portion = 1.0
Unlabeled training sub-portion = 0.0
Validation portion = 0.0
Testing portion = 0.0
-------------------- Trial 0 --------------------
Trial 0 Training NaiveBayesTrainer with 1 instances
Trial 0 Training NaiveBayesTrainer finished
No examples with predicted label !
No examples with true label !
No examples with predicted label !
No examples with true label !
Trial 0 Trainer NaiveBayesTrainer training data accuracy = 1.0
Trial 0 Trainer NaiveBayesTrainer Test Data Confusion Matrix
No examples with predicted label !
Trial 0 Trainer NaiveBayesTrainer test data precision() = 1.0
No examples with true label !
Trial 0 Trainer NaiveBayesTrainer test data recall() = 1.0
No examples with predicted label !
No examples with true label !
Trial 0 Trainer NaiveBayesTrainer test data F1() = 1.0
Trial 0 Trainer NaiveBayesTrainer test data accuracy = NaN
NaiveBayesTrainer
Summary. train accuracy mean = 1.0 stddev = 0.0 stderr = 0.0
Summary. test accuracy mean = NaN stddev = NaN stderr = NaN
Summary. test precision() mean = 1.0 stddev = 0.0 stderr = 0.0
Summary. test recall() mean = 1.0 stddev = 0.0 stderr = 0.0
Summary. test f1() mean = 1.0 stddev = 0.0 stderr = 0.0
//command 3, estimate classes of the three files about cat, deer and dog. The cat file is the same as the one for cat.mallet
C:\Users\toshiba>mallet classify-dir --input E:\Mallet\testmaterial\test_cat_dir
--output - --classifier E:\Mallet\testmaterial\catClass.classifier
//command 3 output
file:/E:/Mallet/testmaterial/test_cat_dir/catmaterial.txt 1.0
file:/E:/Mallet/testmaterial/test_cat_dir/deertext.txt 1.0
file:/E:/Mallet/testmaterial/test_cat_dir/dogmaterial.txt 1.0
// why the three classes are all 1.0 ?
C:\Users\toshiba>
你能帮我吗？
谢谢。
++++++++++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++
更新：
感谢您的回答，但对于所有文件仍输出1.0。
我的想法是，我将一些狗文件放在狗目录中，并将这些狗文件视为实例，经过训练的模型，然后在test_dir中测试了一些文件以查看结果。
我根据对您建议的理解进行了尝试，但仍输出相同的1.0。
您可以在下面的命令行帮助我吗？
在E：\ Mallet \ train_dir \ dog中，有4个dog txt文件（dog 2.txt，dog4.txt，dog5.txt，dogmaterial.txt）。
在E：\ Mallet \ test_dir中，有9个txt文件（cat2.txt，catmaterial.txt，deermaterial.txt，dog3.txt，dog6.txt，dog 2.txt，dog4.txt，dog5.txt，dogmaterial.txt ）。
C:\Users\toshiba>mallet import-dir --input E:\Mallet\train_dir\dog --output E:\M
allet\classifier_dir\3animal.mallet --remove-stopwords
Labels =
E:\Mallet\train_dir\dog
C:\Users\toshiba>mallet train-classifier --input E:\Mallet\classifier_dir\3anima
l.mallet --trainer NaiveBayes --output-classifier E:\Mallet\classifier_dir\3anim
alClass.classifier
Training portion = 1.0
Unlabeled training sub-portion = 0.0
Validation portion = 0.0
Testing portion = 0.0
-------------------- Trial 0 --------------------
Trial 0 Training NaiveBayesTrainer with 4 instances
Trial 0 Training NaiveBayesTrainer finished
No examples with predicted label !
No examples with true label !
No examples with predicted label !
No examples with true label !
Trial 0 Trainer NaiveBayesTrainer training data accuracy = 1.0
Trial 0 Trainer NaiveBayesTrainer Test Data Confusion Matrix
No examples with predicted label !
Trial 0 Trainer NaiveBayesTrainer test data precision() = 1.0
No examples with true label !
Trial 0 Trainer NaiveBayesTrainer test data recall() = 1.0
No examples with predicted label !
No examples with true label !
Trial 0 Trainer NaiveBayesTrainer test data F1() = 1.0
Trial 0 Trainer NaiveBayesTrainer test data accuracy = NaN
NaiveBayesTrainer
Summary. train accuracy mean = 1.0 stddev = 0.0 stderr = 0.0
Summary. test accuracy mean = NaN stddev = NaN stderr = NaN
Summary. test precision() mean = 1.0 stddev = 0.0 stderr = 0.0
Summary. test recall() mean = 1.0 stddev = 0.0 stderr = 0.0
Summary. test f1() mean = 1.0 stddev = 0.0 stderr = 0.0
C:\Users\toshiba>mallet classify-dir --input E:\Mallet\test_dir --output - --cla
ssifier E:\Mallet\classifier_dir\3animalClass.classifier
file:/E:/Mallet/test_dir/cat2.txt 1.0
file:/E:/Mallet/test_dir/catmaterial.txt 1.0
file:/E:/Mallet/test_dir/deertext.txt 1.0
file:/E:/Mallet/test_dir/dog%202.txt 1.0
file:/E:/Mallet/test_dir/dog3.txt 1.0
file:/E:/Mallet/test_dir/dog4.txt 1.0
file:/E:/Mallet/test_dir/dog5.txt 1.0
file:/E:/Mallet/test_dir/dog6.txt 1.0
file:/E:/Mallet/test_dir/dogmaterial.txt 1.0
C:\Users\toshiba>
谢谢。
最佳答案
有两个输入选项。 input-dir将目录视为类，并将每个目录中的每个文件视为输入实例。 input-file逐行读取输入文件，并将行中的各个字段视为标签和实例数据。您正在使用“目录中的文件”输入类型，因此要创建一个具有一个类和一个实例的分类器。我猜你想要文件中的行类型。
关于machine-learning - 为什么Mallet文本分类为所有测试文件输出相同的值1.0？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/49649946/