Python文本分类【NB、LR、SVM、CNN、RNN、TF-IDF、Word2Vec、FastText】

关注

原创最新推荐文章于 2026-03-03 00:07:59 发布 5.6k 阅读 AI 写同款· GEO 优化 ›

文本分类的python实现-基于Xgboost算法描述训练集为评论文本，标签为 pos,neu,neg三种分类，train.csv的第一列为文本content，第二列为label。 python的xgboost包安装方法，网上有很多详细的介绍参数XGBoost的作者把所有的参数分成了三类：1、通用参数：宏观函数控制。2、Booster参数：控制每一步的booster。3、学习目标参数：控制训练目标的表现。阅读详情

评估结果

模型	时间	空间	精度
贝叶斯	`1`	高	0.83
逻辑回归	100	高	`0.85`
决策树	27	高	0.76
随机森林	16	高	0.78
--------------------	--------------------	--------------------	--------------------
贝叶斯+TfIdf	`1`	高	0.83
逻辑回归+TfIdf	13	高	`0.85`
决策树+TfIdf	30	高	0.76
随机森林+TfIdf	16	高	0.80
--------------------	--------------------	--------------------	--------------------
贝叶斯+FastText	`1`	低	0.77
逻辑回归+FastText	21	低	0.82
决策树+FastText	6	低	0.71
随机森林+FastText	23	低	0.80
SVM+FastText	22	低	0.83
--------------------	--------------------	--------------------	--------------------
贝叶斯+Word2Vec	`1`	低	0.78
逻辑回归+Word2Vec	20	低	0.82
决策树+Word2Vec	6	低	0.75
随机森林+Word2Vec	23	低	0.81
SVM+Word2Vec	23	低	0.82
--------------------	--------------------	--------------------	--------------------
贝叶斯+Word2Vec+TfIdf	`1`	低	0.78
逻辑回归+Word2Vec+TfIdf	60	低	0.83
决策树+Word2Vec+TfIdf	7	低	0.75
随机森林+Word2Vec+TfIdf	23	低	0.82
SVM+Word2Vec+TfIdf	51	低	`0.85`
--------------------	--------------------	--------------------	--------------------
CNN	317（5 epoch）	中	0.83
GRU	976（6 epoch）	中	0.78
--------------------	--------------------	--------------------	--------------------
CNN+FastText	181（4 epoch）	中	0.81
GRU+FastText	1218（8 epoch）	中	`0.84`
CNN+Word2Vec	134（3 epoch）	中	0.81
GRU+Word2Vec	1159（8 epoch）	中	`0.84`
CNN+Word2Vec+TfIdf	188（4 epoch）	中	0.80
GRU+Word2Vec+TfIdf	1696（9 epoch）	中	0.82

1、贝叶斯

from a_Data_preprocessing import load_xy, vocabs
from numpy import zeros
from b_metrics import metric, Timer
from sklearn.naive_bayes import MultinomialNB

"""数据加载、预处理"""
X_train, X_test, y_train, y_test = load_xy(1)

def ls_of_w2id(ls_of_wids):
    length = len(ls_of_wids)
    ls_of_wid = zeros((length, vocabs))
    for i in range(length):
        for wid in ls_of_wids[i]:
            ls_of_wid[i, wid - 1] += 1
    return ls_of_wid  # 句向量

X_train = ls_of_w2id(X_train)
X_test = ls_of_w2id(X_test)

"""建模"""
t = Timer()  # 计时器
clf = MultinomialNB()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

"""预测结果"""
metric(y_test, y_pred)

2、贝叶斯+TF-IDF

from a_Data_preprocessing import load_xy, vocabs
from gensim.corpora import Dictionary
from gensim.models import TfidfModel
from numpy import zeros
from sklearn.naive_bayes import MultinomialNB
from b_metrics import metric, Timer

"""数据加载、预处理"""
X_train, X_test, y_train, y_test = load_xy()
dt = Dictionary(X_train)
X_train = [dt.doc2bow(x) for x in X_train]
X_test = [dt.doc2bow(x) for x in X_test]

tfidf = TfidfModel(X_train).idfs  # 字典：ID→TF-IDF向量

def ls_of_w2id(ls_of_wid):
    length = len(ls_of_wid)
    ls_of_idf = zeros((length, vocabs), dtype='float')
    for i in range(length):
        for (wid, cnt) in ls_of_wid[i]:
            ls_of_idf[i, wid] += cnt * tfidf[wid]
    return ls_of_idf  # TF-IDF句向量

X_train = ls_of_w2id(X_train)
X_test = ls_of_w2id(X_test)

"""建模"""
t = Timer()  # 计时器
clf = MultinomialNB()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

"""预测结果"""
metric(y_test, y_pred)

3、逻辑回归+词向量（FastText）

X = [[str(y)] + x for x, y in zip(X_train, y_train)]
model = FastText(X, size=size, window=window)
w2i = {w: i for i, w in enumerate(model.wv.index2word)}
vectors = model.wv.vectors
w2v = lambda w: vectors[w2i[w]]
X_train_v = [[w2v(w) for w in x] for x in X_train]
X_test_v = [[w2v(w) for w in x if w in w2i] for x in X_test]
with open(PATH_XY_VEC, 'wb') as f:
    pickle.dump((X_train_v, X_test_v, y_train, y_test), f)

from a_Data_preprocessing import load_xy
from numpy import mean
from sklearn.linear_model import LogisticRegression
from b_metrics import metric, Timer

"""数据加载、预处理"""
X_train, X_test, y_train, y_test = load_xy(2)
X_train = [mean(x, axis=0) for x in X_train]
X_test = [mean(x, axis=0) for x in X_test]

"""建模"""
t = Timer()  # 计时器
clf = LogisticRegression()
clf.fit(X_train, y_train)

"""预测结果"""
y_pred = clf.predict(X_test)
metric(y_test, y_pred)

4、神经网络（CNN或RNN）

from tensorflow.python.keras.preprocessing.sequence import pad_sequences
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Embedding, Dense
from tensorflow.python.keras.callbacks import EarlyStopping
from tensorflow.python.keras.utils import to_categorical
from numpy import argmax
from a_Data_preprocessing import load_xy, vocabs
from b_metrics import metric, Timer

"""配置"""
maxlen = 200  # 序列长度
input_dim = vocabs + 1
output_dim = 100  # 词向量维度
batch_size = 256
epochs = 99
verbose = 2
patience = 1  # 没有进步的训练轮数
callbacks = [EarlyStopping('val_acc', patience=patience)]
validation_split = .05

"""数据读取与处理"""
X_train, X_test, y_train, y_test = load_xy(1)
X_train = pad_sequences(X_train, maxlen)
X_test = pad_sequences(X_test, maxlen)
y_train = to_categorical(y_train, 9)
y_test = to_categorical(y_test, 9)

"""建模"""
from tensorflow.python.keras.layers import Conv1D, MaxPool1D, GlobalMaxPool1D
filters = 50  # 卷积滤波器数量
kernel_size = 10  # 卷积层滤波器大小
model = Sequential(name='CNN')
model.add(Embedding(input_dim, output_dim, input_length=maxlen, input_shape=(maxlen,)))
model.add(Conv1D(filters, kernel_size * 2, padding='same', activation='relu'))
model.add(MaxPool1D(pool_size=2))  # strides默认等于pool_size
model.add(Conv1D(filters * 2, kernel_size, padding='same', activation='relu'))
model.add(GlobalMaxPool1D())  # 对于时序数据的全局最大池化
model.add(Dense(9, activation='softmax'))
# from tensorflow.python.keras.layers import LSTM
# units = 100  # RNN神经元数量
# model = Sequential(name='RNN')
# model.add(Embedding(input_dim, output_dim, input_length=maxlen, input_shape=(maxlen,)))
# model.add(LSTM(units))  # 返回序列最尾结果
# model.add(Dense(9, activation='softmax'))
model.summary()

"""编译、训练"""
timer = Timer()
model.compile('adam', 'categorical_crossentropy', ['acc'])
model.fit(X_train, y_train, batch_size, epochs, verbose, callbacks, validation_split)

"""结果打印"""
y_pred = model.predict(X_test)
metric(argmax(y_test, axis=1), argmax(y_pred, axis=1))

5、神经网络+词向量（FastText）

from tensorflow.python.keras.preprocessing.sequence import pad_sequences
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.callbacks import EarlyStopping
from tensorflow.python.keras.utils import to_categorical
from numpy import argmax
from a_Data_preprocessing import load_xy, size
from b_metrics import metric, Timer

"""配置"""
maxlen = 200  # 序列长度
output_dim = size  # 词向量维度
batch_size = 512
epochs = 99
verbose = 2
patience = 1  # 没有进步的训练轮数
callbacks = [EarlyStopping('val_acc', patience=patience)]
validation_split = .05

"""数据读取与处理"""
X_train, X_test, y_train, y_test = load_xy(2)
X_train = pad_sequences(X_train, maxlen, dtype='float')
X_test = pad_sequences(X_test, maxlen, dtype='float')
y_train = to_categorical(y_train, 9)
y_test = to_categorical(y_test, 9)

"""建模"""
from tensorflow.python.keras.layers import Conv1D, MaxPool1D, GlobalMaxPool1D
filters = 50  # 卷积滤波器数量
kernel_size = 10  # 卷积层滤波器大小
model = Sequential(name='CNN')
model.add(Conv1D(filters, kernel_size * 2, padding='same', activation='relu', input_shape=(maxlen, size)))
model.add(MaxPool1D(pool_size=2))  # strides默认等于pool_size
model.add(Conv1D(filters * 2, kernel_size, padding='same', activation='relu'))
model.add(GlobalMaxPool1D())  # 对于时序数据的全局最大池化
model.add(Dense(9, activation='softmax'))
# from tensorflow.python.keras.layers import GRU
# units = 100
# model = Sequential(name='CNN')
# model.add(GRU(units, input_shape=(maxlen, size)))
# model.add(Dense(9, activation='softmax'))
model.summary()

"""编译、训练"""
timer = Timer()
model.compile('adam', 'categorical_crossentropy', ['acc'])
model.fit(X_train, y_train, batch_size, epochs, verbose, callbacks, validation_split)

"""结果"""
y_pred = model.predict(X_test)
metric(argmax(y_test, axis=1), argmax(y_pred, axis=1))

附录

模型评估模块

from time import time
from sklearn.metrics import classification_report, confusion_matrix
from a_Data_preprocessing import id2label
from seaborn import heatmap
from matplotlib import pyplot
from pandas import DataFrame

class Timer:
    def __init__(self):
        self.t = time()

    def __del__(self):
        print('\033[033m%.2f分钟\033[0m' % ((time() - self.t) / 60))

def metric(y_test, y_pred):
    i2l = id2label()
    y_test = [i2l[i] for i in y_test]
    y_pred = [i2l[i] for i in y_pred]
    report = classification_report(y_test, y_pred)
    print(report)
    labels = [i2l[i] for i in range(9)]
    matrix = confusion_matrix(y_test, y_pred)
    matrix = DataFrame(matrix, labels, labels)
    heatmap(matrix, center=400, fmt='d', annot=True)
    pyplot.show()