论文分析Task4——论文种类分类(待补充)
任务说明
学习主题:论文分类(数据建模任务),利用已有数据建模,对新论文进行类别分类;
学习内容:使用论文标题完成类别分类;
文本分类涉及知识点及思路
思路1:TF-IDF+机器学习分类器
直接使用TF-IDF对文本提取特征,使用分类器进行分类,分类器的选择上可以使用SVM、LR、XGboost等
思路2:FastText
FastText是入门款的词向量,利用Facebook提供的FastText工具,可以快速构建分类器
思路3:WordVec+深度学习分类器
WordVec是进阶款的词向量,并通过构建深度学习分类完成分类。深度学习分类的网络结构可以选择TextCNN、TextRnn或者BiLSTM。
思路4:Bert词向量
Bert是高配款的词向量,具有强大的建模学习能力
数据预处理通过标题和摘要完成论文分类
导入包
# 导入所需的package
import json #读取数据,我们的数据为json格式的
import pandas as pd #数据处理,数据分析
import matplotlib.pyplot as plt #画图工具
导入数据
data = []
with open("E:/datawhale数据分析/arxiv-metadata-oai-2019.json",'r') as f:
for idx, line in enumerate(f):
d = json.loads(line)
d = {
'title': d['title'], 'categories': d['categories'], 'abstract': d['abstract']}
data.append(d)
# 选择部分数据
if idx > 200000:
break
data = pd.DataFrame(data)#将list变为dataframe格式,方便用pandas进行分析
data.shape
#通过标题和摘要内容进行论文分类,因此进行合并
data['text'] = data['title'] + data['abstract']
data['text'][0]
data['text'] = data['text'].apply(lambda x:x.replace('\n',' '))
data['text'] = data['text'].apply(lambda x:x.lower())
data['text'][0]
data = data.drop(['title','abstract'],axis = 1)
#因为类别有多个需要进行分割
data['categories'][7]
#out:'math.DG'
# 多个类别,包含子分类
data['categories'] = data['categories'].apply(lambda x : x.split(' '))
data['categories'][7]
#out:'math.DG'
# 单个类别,不包含子分类
data['categories_big'] = data['categories'].apply(lambda x : [xx.split('.')[0] for xx in x])
data['categories_big'][7]
#out:math
将类别进行编码
用于编码的sklearn.preprocessing.MultiLabelBinarizer
#将类别进行编码,这里类别是多个,所以需要多编码
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
data_label = mlb.fit_transform(data['categories_big'].iloc[:])
data_label[0]
思路1使用TFIDF
TF-IDF介绍及代码
TfidfVectorizer文档
#思路1使用TFIDF提取特征,限制最多4000个单词,文本向量化:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(max_features=4000)
data_tfidf = vectorizer.fit_transform(data['text'].iloc[:])
#多标签分类,可以使用sklearn的多标签分类进行封装:
# 划分训练集和验证集
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_tfidf, data_label,
test_size = 0.2,random_state = 1)
# 构建多标签分类模型
from sklearn.multioutput import MultiOutputClassifier
from sklearn.naive_bayes import MultinomialNB
clf = MultiOutputClassifier(MultinomialNB()).fit(x_train, y_train)
#验证模型精度
from sklearn.metrics import classification_report
print(classification_report(y_test, clf.predict(x_test)))
思路2使用深度学习模型,单词进行词嵌入然后训练。
首先按照文本划分数据集,将数据集处理进行编码,并进行截断
定义模型并完成训练
Tokenizer使用方法
tokenizer的制作和embedding的使用
#首先划分训练集和测试集
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data['text'].iloc[:], data_label,
test_size = 0.2,random_state = 1)
#将数据集进行编码并截断
# parameter
max_features= 500
max_len= 150
embed_size=100
batch_size = 128
epochs = 5
from keras.preprocessing.text import Tokenizer
from keras.preprocessing import sequence
tokens = Tokenizer(num_words = max_features)
tokens.fit_on_texts(list(x_train)+list(x_test))
x_sub_train = tokens.texts_to_sequences(x_train)
x_sub_test = tokens.texts_to_sequences(x_test)
x_sub_train=sequence.pad_sequences(x_sub_train, maxlen=max_len)
x_sub_test=sequence.pad_sequences(x_sub_test, maxlen=max_len)
#定义模型并进行训练
# LSTM model
# Keras Layers:
from keras.layers import Dense,Input,LSTM,Bidirectional,Activation,Conv1D,GRU
from keras.layers import Dropout,Embedding,GlobalMaxPooling1D, MaxPooling1D, Add, Flatten
from keras.layers import GlobalAveragePooling1D, GlobalMaxPooling1D, concatenate, SpatialDropout1D# Keras Callback Functions:
from keras.callbacks import Callback
from keras.callbacks import EarlyStopping,ModelCheckpoint
from keras import initializers, regularizers, constraints, optimizers, layers, callbacks
from keras.models import Model
from keras.optimizers import Adam
sequence_input = Input(shape=(max_len, ))
x = Embedding(max_features, embed_size,trainable = False)(sequence_input)
x = SpatialDropout1D(0.2)(x)
x = Bidirectional(GRU(128, return_sequences=True,dropout=0.1,recurrent_dropout=0.1))(x)
x = Conv1D(64, kernel_size = 3, padding = "valid", kernel_initializer = "glorot_uniform")(x)
avg_pool = GlobalAveragePooling1D()(x)
max_pool = GlobalMaxPooling1D()(x)
x = concatenate([avg_pool, max_pool])
preds = Dense(34, activation="sigmoid")(x)
model = Model(sequence_input, preds)
model.compile(loss='binary_crossentropy',optimizer=Adam(lr=1e-3),metrics=['accuracy'])
model.fit(x_sub_train, y_train, batch_size=batch_size, epochs=epochs)