我正在尝试在Python中实现k折交叉验证算法。我知道SKLearn提供了实现，但还是…这是我目前的代码。

from sklearn import metricsimport numpy as npclass Cross_Validation:@staticmethoddef partition(vector, fold, k):    size = vector.shape[0]    start = (size/k)*fold    end = (size/k)*(fold+1)    validation = vector[start:end]    if str(type(vector)) == "<class 'scipy.sparse.csr.csr_matrix'>":        indices = range(start, end)        mask = np.ones(vector.shape[0], dtype=bool)        mask[indices] = False        training = vector[mask]    elif str(type(vector)) == "<type 'numpy.ndarray'>":        training = np.concatenate((vector[:start], vector[end:]))    return training, validation@staticmethoddef Cross_Validation(learner, k, examples, labels):    train_folds_score = []    validation_folds_score = []    for fold in range(0, k):        training_set, validation_set = Cross_Validation.partition(examples, fold, k)        training_labels, validation_labels = Cross_Validation.partition(labels, fold, k)        learner.fit(training_set, training_labels)        training_predicted = learner.predict(training_set)        validation_predicted = learner.predict(validation_set)        train_folds_score.append(metrics.accuracy_score(training_labels, training_predicted))        validation_folds_score.append(metrics.accuracy_score(validation_labels, validation_predicted))    return train_folds_score, validation_folds_score

learner参数是来自SKlearn库的分类器，k是折数，examples是CountVectorizer（也是SKlearn）生成的稀疏矩阵，表示词袋模型。例如：

from sklearn.feature_extraction.text import CountVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom Cross_Validation import Cross_Validation as cvvectorizer = CountVectorizer(stop_words='english', lowercase=True, min_df=2, analyzer="word")data = vectorizer.fit_transform("""textual data""")clfMNB = MultinomialNB(alpha=.0001)score = cv.Cross_Validation(clfMNB, 10, data, labels)print "Train score" + str(score[0])print "Test score" + str(score[1])

我认为在某个地方存在逻辑错误，因为训练集上的得分是95％（如预期），但在测试集上的得分实际上为0，但我找不到它。

希望我已经解释清楚了。提前感谢。

________________________________EDIT___________________________________

这是将文本加载到可以传递给向量化的向量中的代码。它还返回标签向量。

from nltk.tokenize import word_tokenizefrom Categories_Data import categoriesimport numpy as npimport codecsimport globimport osimport reclass Data_Preprocessor:def tokenize(self, text):    tokens = word_tokenize(text)    alpha = [t for t in tokens if unicode(t).isalpha()]    return alphadef header_not_fully_removed(self, text):    if ":" in text.splitlines()[0]:        return len(text.splitlines()[0].split(":")[0].split()) == 1    else:        return Falsedef strip_newsgroup_header(self, text):    _before, _blankline, after = text.partition('\n\n')    if len(after) > 0 and self.header_not_fully_removed(after):        after = self.strip_newsgroup_header(after)    return afterdef strip_newsgroup_quoting(self, text):    _QUOTE_RE = re.compile(r'(writes in|writes:|wrote:|says:|said:'r'|^In article|^Quoted from|^\||^>)')    good_lines = [line for line in text.split('\n')        if not _QUOTE_RE.search(line)]    return '\n'.join(good_lines)def strip_newsgroup_footer(self, text):    lines = text.strip().split('\n')    for line_num in range(len(lines) - 1, -1, -1):        line = lines[line_num]        if line.strip().strip('-') == '':            break    if line_num > 0:        return '\n'.join(lines[:line_num])    else:        return textdef raw_to_vector(self, path, to_be_stripped=["header", "footer", "quoting"], noise_threshold=-1):    base_dir = os.getcwd()    train_data = []    label_data = []    for category in categories:        os.chdir(base_dir)        os.chdir(path+"/"+category[0])        for filename in glob.glob("*"):            with codecs.open(filename, 'r', encoding='utf-8', errors='replace') as target:                data = target.read()                if "quoting" in to_be_stripped:                    data = self.strip_newsgroup_quoting(data)                if "header" in to_be_stripped:                    data = self.strip_newsgroup_header(data)                if "footer" in to_be_stripped:                    data = self.strip_newsgroup_footer(data)                if len(data) > noise_threshold:                    train_data.append(data)                    label_data.append(category[1])    os.chdir(base_dir)    return np.array(train_data), np.array(label_data)

这是“from Categories_Data import categories”导入的内容…

categories = [    ('alt.atheism',0),    ('comp.graphics',1),    ('comp.os.ms-windows.misc',2),    ('comp.sys.ibm.pc.hardware',3),    ('comp.sys.mac.hardware',4),    ('comp.windows.x',5),    ('misc.forsale',6),    ('rec.autos',7),    ('rec.motorcycles',8),    ('rec.sport.baseball',9),    ('rec.sport.hockey',10),    ('sci.crypt',11),    ('sci.electronics',12),    ('sci.med',13),    ('sci.space',14),    ('soc.religion.christian',15),    ('talk.politics.guns',16),    ('talk.politics.mideast',17),    ('talk.politics.misc',18),    ('talk.religion.misc',19) ]

回答：

你的验证分数低的原因很微妙。

问题在于你如何划分数据集。记住，在进行交叉验证时，你应该随机分割数据集。你缺少的就是这种随机性。

你的数据是按类别加载的，这意味着在你的输入数据集中，类标签和示例是依次排列的。由于没有进行随机分割，你完全移除了一个类，使得你的模型在训练阶段从未见过这个类，因此在测试/验证阶段得到很差的结果。

你可以通过进行随机洗牌来解决这个问题。所以，执行以下操作：

from sklearn.utils import shuffle    processor = Data_Preprocessor()td, tl = processor.raw_to_vector(path="C:/Users/Pankaj/Downloads/ng/")vectorizer = CountVectorizer(stop_words='english', lowercase=True, min_df=2, analyzer="word")data = vectorizer.fit_transform(td)# Shuffle the data and labelsdata, tl = shuffle(data, tl, random_state=0)clfMNB = MultinomialNB(alpha=.0001)score = Cross_Validation.Cross_Validation(clfMNB, 10, data, tl)print("Train score" + str(score[0]))print("Test score" + str(score[1]))