ValueError: X has 597 features, but SVC is expecting 605 features as input.

使用训练好的sklearn svm.SVC的模型，去对测试数据预测的时候报错了
2022/04/11

代码：

tfidfTrain = TfidfVectorizer(stop_words=stopWordList,min_df= 0.02).fit_transform(dataTrain_list)tfidfTest = TfidfVectorizer(stop_words=stopWordList,min_df= 0.02,max_features = 605 #之前没加也是同样的报错#觉得是训练集和测试集的特征数量不一样才报错，#打算控制测试集的特征和训练集一样，#结果还是报错).fit_transform(dataTest_list)#用训练集训练模型
model = OneVsRestClassifier(svm.SVC(kernel='linear'))
clf = model.fit(tfidfTrain, labelTrain_list)ytest_pred = clf.predict(tfidfTest)

报错：
ValueError: X has 597 features, but SVC is expecting 605 features as input.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-14-8d3a4697f978> in <module>()30 31 print("对测试集进行模型评估：")
---> 32 ytest_pred = clf.predict(tfidfTest)33 print("classification_report: ")34 print(classification_report(labelTest_list, ytest_pred))~\AppData\Roaming\Python\Python37\site-packages\sklearn\multiclass.py in predict(self, X)441             argmaxima = np.zeros(n_samples, dtype=int)442             for i, e in enumerate(self.estimators_):
--> 443                 pred = _predict_binary(e, X)444                 np.maximum(maxima, pred, out=maxima)445                 argmaxima[maxima == pred] = i~\AppData\Roaming\Python\Python37\site-packages\sklearn\multiclass.py in _predict_binary(estimator, X)98         return estimator.predict(X)99     try:
--> 100         score = np.ravel(estimator.decision_function(X))101     except (AttributeError, NotImplementedError):102         # probabilities of the positive class~\AppData\Roaming\Python\Python37\site-packages\sklearn\svm\_base.py in decision_function(self, X)754         transformation of ovo decision function.755         """
--> 756         dec = self._decision_function(X)757         if self.decision_function_shape == "ovr" and len(self.classes_) > 2:758             return _ovr_decision_function(dec < 0, -dec, len(self.classes_))~\AppData\Roaming\Python\Python37\site-packages\sklearn\svm\_base.py in _decision_function(self, X)512         # NOTE: _validate_for_predict contains check for is_fitted513         # hence must be placed before any other attributes are used.
--> 514         X = self._validate_for_predict(X)515         X = self._compute_kernel(X)516 ~\AppData\Roaming\Python\Python37\site-packages\sklearn\svm\_base.py in _validate_for_predict(self, X)596                 order="C",597                 accept_large_sparse=False,
--> 598                 reset=False,599             )600 ~\AppData\Roaming\Python\Python37\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)583 584         if not no_val_X and check_params.get("ensure_2d", True):
--> 585             self._check_n_features(X, reset=reset)586 587         return out~\AppData\Roaming\Python\Python37\site-packages\sklearn\base.py in _check_n_features(self, X, reset)399         if n_features != self.n_features_in_:400             raise ValueError(
--> 401                 f"X has {n_features} features, but {self.__class__.__name__} "402                 f"is expecting {self.n_features_in_} features as input."403             )ValueError: X has 597 features, but SVC is expecting 605 features as input.

还没好好看源码，先记录一下

2022/04/12:

解决：

把测试数据的fit_transform()改为transform()就可以了

查找解决方法过程：

Python scikit svm “ValueError: X has 62 features p
TfidfVectorizer.fit_transfrom和tfidf.transform有什么区别？

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