Backend Development
Python Tutorial
A relatively memory-saving sparse matrix Python storage solution
A relatively memory-saving sparse matrix Python storage solution
Recommendation systems often need to process data like user_id, item_id, rating, which are actually sparse matrices in mathematics. Scipy provides the sparse module to solve this problem, but scipy.sparse has many problems that are not suitable for use: 1. It cannot It supports fast slicing of data[i, ...], data[..., j], and data[i, j] at the same time; 2. Because the data is stored in memory, it cannot well support massive data processing.
To support fast slicing of data[i, ...], data[..., j], the data of i or j needs to be stored centrally; at the same time, in order to save massive data, part of the data also needs to be placed in On the hard disk, use memory as a buffer. The solution here is relatively simple. Use a Dict-like thing to store data. For a certain i (such as 9527), its data is stored in dict['i9527']. Similarly, for a certain j (such as 3306) , all its data is stored in dict['j3306']. When you need to take out data[9527, ...], just take out dict['i9527']. dict['i9527'] is originally a dict object , stores the value corresponding to a certain j. In order to save memory space, we store this dict in the form of a binary string and directly enter the code:
'''
Sparse Matrix
'''
import struct
import numpy as np
import bsddb
from cStringIO import StringIO
class DictMatrix():
def __init__(self, container = {}, dft = 0.0):
self._data = container
self._dft = dft
self._nums = 0
def __setitem__(self, index, value):
try:
i, j = index
except:
raise IndexError('invalid index')
ik = ('i%d' % i)
# 为了节省内存，我们把j, value打包成字二进制字符串
ib = struct.pack('if', j, value)
jk = ('j%d' % j)
jb = struct.pack('if', i, value)
try:
self._data[ik] += ib
except:
self._data[ik] = ib
try:
self._data[jk] += jb
except:
self._data[jk] = jb
self._nums += 1
def __getitem__(self, index):
try:
i, j = index
except:
raise IndexError('invalid index')
if (isinstance(i, int)):
ik = ('i%d' % i)
if not self._data.has_key(ik): return self._dft
ret = dict(np.fromstring(self._data[ik], dtype = 'i4,f4'))
if (isinstance(j, int)): return ret.get(j, self._dft)
if (isinstance(j, int)):
jk = ('j%d' % j)
if not self._data.has_key(jk): return self._dft
ret = dict(np.fromstring(self._data[jk], dtype = 'i4,f4'))
return ret
def __len__(self):
return self._nums
def __iter__(self):
pass
'''
从文件中生成matrix
考虑到dbm读写的性能不如内存，我们做了一些缓存，每1000W次批量写入一次
考虑到字符串拼接性能不太好，我们直接用StringIO来做拼接
'''
def from_file(self, fp, sep = 't'):
cnt = 0
cache = {}
for l in fp:
if 10000000 == cnt:
self._flush(cache)
cnt = 0
cache = {}
i, j, v = [float(i) for i in l.split(sep)]
ik = ('i%d' % i)
ib = struct.pack('if', j, v)
jk = ('j%d' % j)
jb = struct.pack('if', i, v)
try:
cache[ik].write(ib)
except:
cache[ik] = StringIO()
cache[ik].write(ib)
try:
cache[jk].write(jb)
except:
cache[jk] = StringIO()
cache[jk].write(jb)
cnt += 1
self._nums += 1
self._flush(cache)
return self._nums
def _flush(self, cache):
for k,v in cache.items():
v.seek(0)
s = v.read()
try:
self._data[k] += s
except:
self._data[k] = s
if __name__ == '__main__':
db = bsddb.btopen(None, cachesize = 268435456)
data = DictMatrix(db)
data.from_file(open('/path/to/log.txt', 'r'), ',')Test 4500W rating data (integer, integer, floating point format ), a 922MB text file is imported. If the memory dict is used to store it, the construction is completed in 12 minutes, consuming 1.2G of memory. Using the bdb storage in the sample code, the construction is completed in 20 minutes, occupying about 300~400MB of memory, which is not much larger than cachesize. Data reading Take the test:
import timeit timeit.Timer('foo = __main__.data[9527, ...]', 'import __main__').timeit(number = 1000)
consumes 1.4788 seconds, and it takes about 1.5ms to read a piece of data.
Another benefit of using Dict class to store data is that you can use memory Dict or any other form of DBM, or even the legendary Tokyo Cabinet…
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
PHP and Python: Different Paradigms Explained
Apr 18, 2025 am 12:26 AM
PHP is mainly procedural programming, but also supports object-oriented programming (OOP); Python supports a variety of paradigms, including OOP, functional and procedural programming. PHP is suitable for web development, and Python is suitable for a variety of applications such as data analysis and machine learning.
Choosing Between PHP and Python: A Guide
Apr 18, 2025 am 12:24 AM
PHP is suitable for web development and rapid prototyping, and Python is suitable for data science and machine learning. 1.PHP is used for dynamic web development, with simple syntax and suitable for rapid development. 2. Python has concise syntax, is suitable for multiple fields, and has a strong library ecosystem.
Python vs. JavaScript: The Learning Curve and Ease of Use
Apr 16, 2025 am 12:12 AM
Python is more suitable for beginners, with a smooth learning curve and concise syntax; JavaScript is suitable for front-end development, with a steep learning curve and flexible syntax. 1. Python syntax is intuitive and suitable for data science and back-end development. 2. JavaScript is flexible and widely used in front-end and server-side programming.
PHP and Python: A Deep Dive into Their History
Apr 18, 2025 am 12:25 AM
PHP originated in 1994 and was developed by RasmusLerdorf. It was originally used to track website visitors and gradually evolved into a server-side scripting language and was widely used in web development. Python was developed by Guidovan Rossum in the late 1980s and was first released in 1991. It emphasizes code readability and simplicity, and is suitable for scientific computing, data analysis and other fields.
Can vs code run in Windows 8
Apr 15, 2025 pm 07:24 PM
VS Code can run on Windows 8, but the experience may not be great. First make sure the system has been updated to the latest patch, then download the VS Code installation package that matches the system architecture and install it as prompted. After installation, be aware that some extensions may be incompatible with Windows 8 and need to look for alternative extensions or use newer Windows systems in a virtual machine. Install the necessary extensions to check whether they work properly. Although VS Code is feasible on Windows 8, it is recommended to upgrade to a newer Windows system for a better development experience and security.
Can visual studio code be used in python
Apr 15, 2025 pm 08:18 PM
VS Code can be used to write Python and provides many features that make it an ideal tool for developing Python applications. It allows users to: install Python extensions to get functions such as code completion, syntax highlighting, and debugging. Use the debugger to track code step by step, find and fix errors. Integrate Git for version control. Use code formatting tools to maintain code consistency. Use the Linting tool to spot potential problems ahead of time.
How to run python with notepad
Apr 16, 2025 pm 07:33 PM
Running Python code in Notepad requires the Python executable and NppExec plug-in to be installed. After installing Python and adding PATH to it, configure the command "python" and the parameter "{CURRENT_DIRECTORY}{FILE_NAME}" in the NppExec plug-in to run Python code in Notepad through the shortcut key "F6".
How to run programs in terminal vscode
Apr 15, 2025 pm 06:42 PM
In VS Code, you can run the program in the terminal through the following steps: Prepare the code and open the integrated terminal to ensure that the code directory is consistent with the terminal working directory. Select the run command according to the programming language (such as Python's python your_file_name.py) to check whether it runs successfully and resolve errors. Use the debugger to improve debugging efficiency.
