Python sklearn LinearSVC - X 每个样本有 1 个特征；期待 5
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sklearn LinearSVC - X has 1 features per sample; expecting 5
提问by Radu Gheorghiu
I'm trying to predict the class of a test array, but I'm getting the below error, along with the stack trace:
我正在尝试预测测试数组的类，但出现以下错误以及堆栈跟踪：
Traceback (most recent call last):
File "/home/radu/PycharmProjects/Recommender/Temporary/classify_dict_test.py", line 24, in <module>
print classifier.predict(test)
File "/home/radu/.local/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 215, in predict
scores = self.decision_function(X)
File "/home/radu/.local/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 196, in decision_function
% (X.shape[1], n_features))
ValueError: X has 1 features per sample; expecting 5
The code which is generating this is:
生成这个的代码是：
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus = [
"I am super good with Java and JEE",
"I am super good with .NET and C#",
"I am really good with Python and R",
"I am really good with C++ and pointers"
]
classes = ["java developer", ".net developer", "data scientist", "C++ developer"]
test = ["I think I'm a good developer with really good understanding of .NET"]
tvect = TfidfVectorizer(min_df=1, max_df=1)
X = tvect.fit_transform(corpus)
classifier = LinearSVC()
classifier.fit(X, classes)
print classifier.predict(test)
I've tried looking into the LinearSVC documentationfor guidelines or hints as to what might throw this error, but I can't figure it out.
我曾尝试查看LinearSVC 文档以获取有关可能引发此错误的指南或提示，但我无法弄清楚。
Any help is greatly appreciated!
任何帮助是极大的赞赏！
采纳答案by Alexander Bauer
The variable test is a string - the SVC needs a feature vector with the same number of dimensions as X. You have to transform the test string to a feature vector using the same vectorizer instance, before you feed it to the SVC:
变量 test 是一个字符串——SVC 需要一个与 X 维数相同的特征向量。在将它提供给 SVC 之前，您必须使用相同的向量化器实例将测试字符串转换为特征向量：
X_test=tvect.transform(test)
classifier.predict(X_test)
