A simple library to handle time gaps in data , especially in AI 人工智能中时间数据预处理
Project description
process serial data for AI training
人工智能训练中,对于时间断层数据的处理工具包
- 根据断点,分配数据集,保证训练数据的连续性
- allocate data set according to breakpoints, to ensure the continuity of training data
code demo
# Attention:
# 传入的数据必须指定时间列(或其他顺序列)
# You must specify the time column (or other sequential column) of the incoming data
# 传入的数据必须已按顺序排列好
# The incoming data must be sorted in order
from datetime import timedelta
from serial_data_handler_zxw import 生成训练数据_避开时间断点, 时间列_三角函数化
import pandas as pd
csv_path = "/Volumes/time_serial_data.csv"
data = pd.read_csv(csv_path)
# specific the time column named '收盘时间' ,
# and set the gap is 2 minutes ,
# it means that if the gap between two adjacent data > 2 minutes, it will be considered as a breakpoint
# 生成训练数据
x = 生成训练数据_避开时间断点(data, column_timestamp='收盘时间', gap=timedelta(minutes=2))
print(x.断点)
训练数据index = x.数据划分_避开断点(input长度=100, output长度=100, step=1)
print(len(训练数据index))
# If your data interval < 1s, please do the corresponding multiplication conversion
# for example: 1ms data, you should multiply by 1000, convert to second-level data
# time column trigonometric function
# 如果您的数据间隔小于1秒,请做相应的乘法转换, 例如: 1毫秒的数据,请乘以1000,转换为秒级数据
# 时间列_三角函数化
data['收盘时间'] = pd.to_datetime(data['收盘时间'])
data['收盘时间'] = 时间列_三角函数化(data['收盘时间'], 周期=timedelta(days=1))
print(data['收盘时间'])
python setup.py sdist bdist_wheel twine upload dist/*
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for serial_data_handler_zxw-0.3.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | b350618700cfbfdec5367ee901f52b0dfeef4a5fa789ba20e6fca473ecdb77be |
|
MD5 | 6980bad149e509445a7124b465f494bf |
|
BLAKE2b-256 | 63cb2e6190d80bce247eceec4af517bbe00d0b473dcf46556b7f0dbaac9da3bc |
Close
Hashes for serial_data_handler_zxw-0.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 84ae19eb55a39820e75e5af990c03a28763d3697424e3cc5e5c376a1cee16314 |
|
MD5 | 97ac2738182e8b6988528b318dd408a4 |
|
BLAKE2b-256 | c204ba9d2810b155400d19a3deca9711bac1e6c8010483af92c4b6c51d837e55 |