旧代码学习上传记录-天池-零基础入门NLP_-_新闻文本分类

搜寻到以前做过的旧代码操作记录，零基础入门nlp，用了机器学习的算法进行预测，本次给出的数据集格式是csv格式，读取文件的代码是

df = pd.read_csv(file_path, sep='\t')

包含了label和text，其中官网给出的数据为了避免被人为标记已经做了处理，其中数据提取出来如下所示

然后本次算法是选取了随机森林算法等多种算法进行训练预测，由于数据集涉及到20w条，本电脑内存不足，先把训练集分割成10个，取其中一个进行训练，

以下是拆分的代码

import pandas as pd
def split_csv_file(filename, num_files):
    df = pd.read_csv(filename)
    rows_per_file = len(df) 
    if len(df) % num_files != 0:
        rows_per_file += 1
    for i in range(num_files):
        start_row = i * rows_per_file
        end_row = min(start_row + rows_per_file, len(df))
        df_subset = df.iloc[start_row:end_row]
        output_filename = f'split_{i + 1}.csv'
        df_subset.to_csv(output_filename, index=False)
        print(f'Saved {output_filename}')
split_csv_file('./train_set.csv', 10)

随机森林算法

import pandas as pd
import torch
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
df = pd.read_csv('./split_1.csv', sep='\t')
print(df.head(3))
df = pd.DataFrame(df)
X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.3, random_state=42)
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)
classifier = RandomForestClassifier()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')