K-fold交叉验证实现python

我试图在 python中实现k-fold交叉验证算法.
我知道SKLearn提供了一个实现,但仍然……
这是我现在的代码.

from sklearn import metrics
import numpy as np

class Cross_Validation:

@staticmethod
def 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

@staticmethod
def 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

学习者参数是来自SKlearn库的分类器,k是折叠的数量,示例是由CountVectorizer(再次是SKlearn)生成的稀疏矩阵,其是词袋的表示.
例如:

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from Cross_Validation import Cross_Validation as cv

vectorizer = 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])

我假设某个地方存在一些逻辑错误,因为在训练集上得分为95%(如预期的那样),但在测试测试中几乎为0,但我找不到它.

我希望我很清楚.
提前致谢.

________________________________编辑___________________________________

这是将文本加载到可以传递给矢量化器的向量中的代码.它还返回标签向量.

from nltk.tokenize import word_tokenize
from Categories_Data import categories
import numpy as np
import codecs
import glob
import os
import re

class Data_Preprocessor:

def tokenize(self, text):
    tokens = word_tokenize(text)
    alpha = [t for t in tokens if unicode(t).isalpha()]
    return alpha

def header_not_fully_removed(self, text):
    if ":" in text.splitlines()[0]:
        return len(text.splitlines()[0].split(":")[0].split()) == 1
    else:
        return False

def 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 after

def 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 text

def 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)

这就是“来自Categories_Data导入类别”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)
 ]
验证分数低的原因很微妙.

问题是您如何对数据集进行分区.请记住,在进行交叉验证时,您应该随机拆分数据集.这是你缺少的随机性.

您的数据按类别加载,这意味着在您的输入数据集中,类标签和示例将一个接一个地跟随.通过不进行随机拆分,您已经完全删除了模型在训练阶段从未看到的类,因此您在测试/验证阶段会得到错误的结果.

你可以通过随机随机解决这个问题.所以,这样做:

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 labels
data, 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]))
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