我目前正在处理ODP文档的大规模分层文本分类。提供给我的数据集是以libSVM格式呈现的。我正尝试使用Python中的scikit-learn库运行线性核的SVM来开发模型。以下是训练样本的示例数据:
29 9454:1 11742:1 18884:14 26840:1 35147:1 52782:1 72083:1 73244:1 78945:1 79913:1 79986:1 86710:3 117286:1 139820:1 142458:1 146315:1 151005:2 161454:3 172237:1 1091130:1 1113562:1 1133451:1 1139046:1 1157534:1 1180618:2 1182024:1 1187711:1 1194345:3
33 2474:1 8152:1 19529:2 35038:1 48104:1 59738:1 61854:3 67943:1 74093:1 78945:1 88558:1 90848:1 97087:1 113284:16 118917:1 122375:1 124939:1
以下是我用来构建线性SVM模型的代码:
from sklearn.datasets import load_svmlight_file
from sklearn import svm
X_train, y_train = load_svmlight_file("/path-to-file/train.txt")
X_test, y_test = load_svmlight_file("/path-to-file/test.txt")
clf = svm.SVC(kernel='linear')
clf.fit(X_train, y_train)
print clf.score(X_test,y_test)
运行clf.score()时,我遇到以下错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-b285fbfb3efe> in <module>()
1 start_time = time.time()
----> 2 print clf.score(X_test,y_test)
3 print time.time() - start_time, "seconds"
/Users/abc/anaconda/lib/python2.7/site-packages/sklearn/base.pyc in score(self, X, y)
292 """
293 from .metrics import accuracy_score
--> 294 return accuracy_score(y, self.predict(X))
295
296
/Users/abc/anaconda/lib/python2.7/site-packages/sklearn/svm/base.pyc in predict(self, X)
464 Class labels for samples in X.
465 """
--> 466 y = super(BaseSVC, self).predict(X)
467 return self.classes_.take(y.astype(np.int))
468
/Users/abc/anaconda/lib/python2.7/site-packages/sklearn/svm/base.pyc in predict(self, X)
280 y_pred : array, shape (n_samples,)
281 """
--> 282 X = self._validate_for_predict(X)
283 predict = self._sparse_predict if self._sparse else self._dense_predict
284 return predict(X)
/Users/abc/anaconda/lib/python2.7/site-packages/sklearn/svm/base.pyc in _validate_for_predict(self, X)
402 raise ValueError("X.shape[1] = %d should be equal to %d, "
403 "the number of features at training time" %
--> 404 (n_features, self.shape_fit_[1]))
405 return X
406
ValueError: X.shape[1] = 1199847 should be equal to 1199830, the number of features at training time
请问这段代码或者数据有什么问题?谢谢。以下是X_train,y_train,X_test和y_test的值:
X_train:
(0, 9453) 1.0
(0, 11741) 1.0
(0, 18883) 14.0
(0, 26839) 1.0
(0, 35146) 1.0
(0, 52781) 1.0
(0, 72082) 1.0
(0, 73243) 1.0
(0, 78944) 1.0
(0, 79912) 1.0
(0, 79985) 1.0
(0, 86709) 3.0
(0, 117285) 1.0
(0, 139819) 1.0
(0, 142457) 1.0
(0, 146314) 1.0
(0, 151004) 2.0
(0, 161453) 3.0
(0, 172236) 1.0
(0, 187531) 2.0
(0, 202462) 1.0
(0, 210417) 1.0
(0, 250581) 1.0
(0, 251689) 1.0
(0, 296384) 2.0
: :
(4462, 735469) 1.0
(4462, 737059) 15.0
(4462, 740127) 1.0
(4462, 743798) 1.0
(4462, 766063) 1.0
(4462, 778958) 2.0
(4462, 784004) 4.0
(4462, 837264) 2.0
(4462, 839095) 22.0
(4462, 844735) 6.0
(4462, 859721) 2.0
(4462, 875267) 1.0
(4462, 910761) 1.0
(4462, 931244) 1.0
(4462, 945069) 6.0
(4462, 948728) 1.0
(4462, 948850) 2.0
(4462, 957682) 1.0
(4462, 975170) 1.0
(4462, 989192) 1.0
(4462, 1014294) 1.0
(4462, 1042424) 1.0
(4462, 1049027) 1.0
(4462, 1072931) 1.0
(4462, 1145790) 1.0
y_train:
[ 2.90000000e+01 3.30000000e+01 3.30000000e+01 ..., 1.65475000e+05
1.65518000e+05 1.65518000e+05]
X_test:
(0, 18573) 1.0
(0, 23501) 1.0
(0, 29954) 1.0
(0, 42112) 1.0
(0, 46402) 1.0
(0, 63041) 2.0
(0, 67942) 2.0
(0, 83522) 1.0
(0, 88413) 2.0
(0, 99454) 1.0
(0, 126041) 1.0
(0, 139819) 1.0
(0, 142678) 1.0
(0, 151004) 1.0
(0, 166351) 2.0
(0, 173794) 1.0
(0, 192162) 3.0
(0, 210417) 2.0
(0, 254468) 1.0
(0, 263895) 2.0
(0, 277567) 1.0
(0, 278419) 2.0
(0, 279181) 2.0
(0, 281319) 2.0
(0, 298898) 1.0
: :
(1857, 1100504) 3.0
(1857, 1103247) 1.0
(1857, 1105578) 1.0
(1857, 1108986) 2.0
(1857, 1118486) 1.0
(1857, 1120807) 9.0
(1857, 1129243) 2.0
(1857, 1131786) 1.0
(1857, 1134029) 2.0
(1857, 1134410) 5.0
(1857, 1134494) 1.0
(1857, 1139045) 25.0
(1857, 1142239) 3.0
(1857, 1142651) 1.0
(1857, 1144787) 1.0
(1857, 1151891) 1.0
(1857, 1152094) 1.0
(1857, 1157533) 1.0
(1857, 1159376) 1.0
(1857, 1178944) 1.0
(1857, 1181310) 2.0
(1857, 1182023) 1.0
(1857, 1187098) 1.0
(1857, 1194344) 2.0
(1857, 1195819) 9.0
y_test:
[ 2.90000000e+01 3.30000000e+01 1.56000000e+02 ..., 1.65434000e+05
1.65475000e+05 1.65518000e+05]