Question

我正在学习Mallet文本分类命令行。估计不同类的输出值都是相同的1.0。我不知道我在哪里不正确。你能帮忙吗？

槌版：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>

你能帮忙吗？感谢。

+++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++

更新：

感谢您的回答，但仍为所有文件输出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作为实例处理，训练模型，然后在test_dir中测试一些文件以查看结果。

我根据我对你的建议的理解尝试但仍然输出所有相同的1.0。

你能帮我解决下面的命令吗？

在E：\ Mallet \ train_dir \ dog中，有4个狗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。

Answer 1

有两个输入选项。 input-dir将目录视为类，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files-in-directories输入类型，因此您要创建一个具有一个类和一个实例的分类器。我猜你想要行文件类型。

为什么Mallet文本分类为所有测试文件输出相同的值1.0？

1 个答案: