我有一个 AWS lambda 函数连接到安装在 Ubunutu 18 的 EC2 实例上的 AWS EFS。我相信在导入 LightGBM 模型时出现以下错误。

    {\n  "errorMessage": "libgomp.so.1: cannot open shared object file: No such file or directory",\n  "errorType": "OSError",\n  "stackTrace": [\n    "  File \\"/var/lang/lib/python3.8/imp.py\\", line 234, in load_module\\n    return load_source(name, filename, file)\\n",\n    "  File \\"/var/lang/lib/python3.8/imp.py\\", line 171, in load_source\\n    module = _load(spec)\\n",\n    "  File \\"<frozen importlib._bootstrap>\\", line 702, in _load\\n",\n    "  File \\"<frozen importlib._bootstrap>\\", line 671, in _load_unlocked\\n",\n    "  File \\"<frozen importlib._bootstrap_external>\\", line 848, in exec_module\\n",\n    "  File \\"<frozen importlib._bootstrap>\\", …

我已经完成了一个机器学习算法，可以对文本中的类别进行分类。我已经完成了 99%，但是我现在知道将我的预测结果合并回原始数据帧，以查看我开始的内容和预测内容的打印视图。

下面是我的代码。

#imports data from excel file and shows first 5 rows of data
file_name = r'C:\Users\aac1928\Documents\Machine Learning\Training        Data\RFP Training Data.xlsx'
sheet = 'Sheet1'

import pandas as pd
import numpy
import xlsxwriter
import sklearn

df = pd.read_excel(io=file_name,sheet_name=sheet)

#extracts specifics rows from data 
data = df.iloc[: , [0,2]]
print(data)

#Gets data ready for model
newdata = df.iloc[:,[1,2]]
newdata = newdata.rename(columns={'Label':'label'})
newdata = newdata.rename(columns={'RFP Question':'question'})
print(newdata)

# how to define X and yfor use with COUNTVECTORIZER
X = newdata.question
y = …