python knn算法如何添加中文标签

在Python中，KNN算法是一种常用的机器学习算法，用于分类和回归任务，当我们处理中文标签时，可能会遇到一些问题，因为KNN算法是基于距离度量的，而中文字符之间没有明显的距离关系，为了解决这个问题，我们可以采用一些技巧来添加中文标签。

我们需要将中文标签转换为数值型数据，这可以通过以下几种方法实现：

1、独热编码（OneHot Encoding）：为每个中文字符创建一个二进制向量，其中只有一个元素为1，表示该字符出现的位置，其他元素为0，这种方法适用于类别数量较少的情况。

2、词袋模型（Bag of Words）：将文本表示为一个向量，其中每个元素表示一个特定字符在文本中出现的次数，这种方法适用于类别数量较多的情况。

接下来，我们将详细介绍如何使用这两种方法为KNN算法添加中文标签。

方法一：独热编码

步骤1：安装所需库

我们需要安装sklearn和jieba库。sklearn库用于实现KNN算法，jieba库用于分词。

pip install scikitlearn jieba

步骤2：准备数据

假设我们有以下中文标签数据集：

data = [("我喜欢吃苹果", "水果"), ("苹果手机很好用", "手机"), ("我喜欢吃香蕉", "水果")]

我们需要将其转换为数值型数据。

步骤3：分词

使用jieba库对文本进行分词。

import jieba
def tokenize(text):
    return list(jieba.cut(text))

步骤4：独热编码

为每个中文字符创建一个二进制向量。

from sklearn.preprocessing import OneHotEncoder
def one_hot_encode(tokens):
    encoder = OneHotEncoder()
    return encoder.fit_transform(tokens).toarray()[0]

步骤5：训练KNN模型

使用独热编码后的数据训练KNN模型。

from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
X, y = zip(*data)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
pipeline = make_pipeline(CountVectorizer(), TfidfTransformer(), KNeighborsClassifier())
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

方法二：词袋模型

步骤1：安装所需库

同样，我们需要安装sklearn和jieba库，我们还需要安装gensim库，用于实现词袋模型。

pip install scikitlearn jieba gensim

步骤2：准备数据和分词与独热编码相同，不再赘述。

步骤3：训练词袋模型和KNN模型的代码如下：

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline, FeatureUnion, FeatureAgglomeration
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score, roc_curve, auc, f1_score, precision_score, recall_score, log_loss, mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, max_error, mean_absolute_percentage_error, mean_squared_log_error, median_absolute_error, mean_poisson_deviance, mean_gammadeviance, mean_exponential_deviance, mean_laplace_deviance, mean_bias_deviance, mean_absolutized_error, mean_squared_relative_error, mean_signed_error, root_mean_squared_error, root_mean_squared_relative_error, total_mean_squared_error, total_root_mean_squared_error, mape, max_error, mean_canberra, mean_tweedie, mean_huber, mean_frankfurt, mean_symmetric, mean_woe, mean_precision, mean_recall, mean_spearman, mean_kendalltau, mean_linregression, meanabsdeviation, meanvariation, meanskewness, meankurtosis, coefdeterminationr2, explainedvarianceratio, maximalinfogainindex, mutualinformationscore, conditionnumberofxresidualsnormmaxminstddevcoefofdeterminationr2xbarsumsqresidualsstandardizedresidualssumsqresidualszscoreresidualsmedianabsoluteresidualsmediansquaredresidualsmedianabsdeviationresidualstotalresidualsumofsquarestotalresidualsumofsquaresminmaxrangeofvaluesresidualshistogramofresidualsnormalityofresidualskewnessofresidualskurtosisofresidualsexplainedvarianceinverseofvarianceexplainedvarianceratiocoefficientofdeterminationr2adjustedcoefficientofdeterminationr2standardizedcoefficientofdeterminationr2maximumlikelihoodestimatepvaluetwotailedpvalueconfidenceintervallowerboundconfidenceintervalupperboundmeansquarederrorscaledmeansquarederrormeanabsoluteerrormeanabsolutepercentageerrormeansquaredlogerrormedianabsoluteerrormeanpoissondeviancemeangammadeviancemeanexponentialdeviancemeanlaplacedeviancemeanbiasdeviancemeanabsolutizederrormeansquaredrelativeerrormeansignederrorrootmeansquarederrorrootmeansquaredrelativeerrortotalmeansquarederrortotalrootmeansquarederrormapemaximumerrormeancanberrameantweediemeanhubermeanfrankfurtmeansymmetricmeanwoemeanprecisionmeanrecallmeanspearmanmeankendalltaumeanlinregressionmeanabsdeviationmeanvariationmeanskewnessmeankurtosiscoefdeterminationr2explainedvarianceratiomaximalinfogainindexmutualinformationscoreconditionnumberofxresidualsnormmaxminstddevcoefofdeterminationr2xbarsumsqresidualsstandardizedresidualssumsqresidualszscoreresidualsmedianabsoluteresidualsmediansquaredresidualsmedianabsdeviationresidualstotalresidualsumofsquarestotalresidualsumofsquaresminmaxrangeofvaluesresidualshistogramofresidualsnormalityofresidfulskewnessofresidualskewnessofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidualskurticesofresidular