python实现使用朴素贝叶斯、LOGIT、XGBOOST算法对搜狗文本进行文档分类，并评价模型的准确率、micro F1， macro F1

好的，下面是使用Python实现朴素贝叶斯、LOGIT、XGBOOST算法对搜狗文本进行文档分类，并评价模型的准确率、micro F1， macro F1的代码示例：

# 导入所需的库
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score
import xgboost as xgb

# 读取数据
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

# 数据预处理
def preprocess_data(df):
    # 分词
    df['text'] = df['text'].apply(lambda x: ' '.join(jieba.cut(x)))
    # 去除停用词
    stopwords = set(pd.read_csv('stopwords.txt', sep='\n', header=None, encoding='utf-8-sig')[0])
    df['text'] = df['text'].apply(lambda x: ' '.join([word for word in x.split() if word not in stopwords]))
    # 特征提取
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(df['text'])
    transformer = TfidfTransformer()
    X = transformer.fit_transform(X)
    return X, df['label']

# 朴素贝叶斯分类器
def nb_classifier(X_train, y_train, X_test, y_test):
    clf = MultinomialNB()
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    micro_f1 = f1_score(y_test, y_pred, average='micro')
    macro_f1 = f1_score(y_test, y_pred, average='macro')
    return accuracy, micro_f1, macro_f1

# LOGIT分类器
def logit_classifier(X_train, y_train, X_test, y_test):
    clf = LogisticRegression()
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    micro_f1 = f1_score(y_test, y_pred, average='micro')
    macro_f1 = f1_score(y_test, y_pred, average='macro')
    return accuracy, micro_f1, macro_f1

# XGBoost分类器
def xgb_classifier(X_train, y_train, X_test, y_test):
    dtrain = xgb.DMatrix(X_train, label=y_train)
    dtest = xgb.DMatrix(X_test, label=y_test)
    param = {'max_depth': 3, 'eta': 0.1, 'silent': 1, 'objective': 'multi:softmax', 'num_class': 10}
    num_round = 100
    bst = xgb.train(param, dtrain, num_round)
    y_pred = bst.predict(dtest)
    accuracy = accuracy_score(y_test, y_pred)
    micro_f1 = f1_score(y_test, y_pred, average='micro')
    macro_f1 = f1_score(y_test, y_pred, average='macro')
    return accuracy, micro_f1, macro_f1

# 数据预处理
X_train, y_train = preprocess_data(train_df)
X_test, y_test = preprocess_data(test_df)

# 朴素贝叶斯分类器
nb_accuracy, nb_micro_f1, nb_macro_f1 = nb_classifier(X_train, y_train, X_test, y_test)
print("Naive Bayes Classifier:")
print("Accuracy: ", nb_accuracy)
print("Micro F1: ", nb_micro_f1)
print("Macro F1: ", nb_macro_f1)

# LOGIT分类器
logit_accuracy, logit_micro_f1, logit_macro_f1 = logit_classifier(X_train, y_train, X_test, y_test)
print("LOGIT Classifier:")
print("Accuracy: ", logit_accuracy)
print("Micro F1: ", logit_micro_f1)
print("Macro F1: ", logit_macro_f1)

# XGBoost分类器
xgb_accuracy, xgb_micro_f1, xgb_macro_f1 = xgb_classifier(X_train, y_train, X_test, y_test)
print("XGBoost Classifier:")
print("Accuracy: ", xgb_accuracy)
print("Micro F1: ", xgb_micro_f1)
print("Macro F1: ", xgb_macro_f1)

需要注意的是，上述代码仅为示例代码，具体实现要根据实际情况进行调整。另外，特征提取、模型训练和评估等步骤可以使用交叉验证等方法来进行更加准确的评估。