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Testing Parallel POS Tagging Performance in Jieba Chinese Text Segmentation

This test suite evaluates parallel part-of-speech (POS) tagging functionality in the Jieba Chinese text segmentation library. It measures performance and accuracy of parallel POS processing by analyzing text content from an input file using multiple processing threads.

Test Coverage Overview

The test suite covers parallel processing capabilities of Jieba’s POS tagging system.

Key areas tested include:

File input/output operations
Parallel processing configuration (4 threads)
POS tagging accuracy
Performance metrics calculation
Results logging functionality

Implementation Analysis

The testing approach implements a practical performance evaluation of Jieba’s parallel POS tagging.

Technical implementation features:

Command-line argument handling for file input
Binary file reading for content processing
Time-based performance measurement
Processing speed calculation in bytes/second
Structured output logging

Technical Details

Testing components and configuration:

Python standard libraries: sys, time
Jieba segmentation library
Parallel processing enabled with 4 threads
File-based I/O for input and logging
Performance timing mechanisms

Best Practices Demonstrated

The test demonstrates several testing best practices for performance evaluation.

Notable practices include:

Isolated timing measurements
Structured output logging
Command-line parameter handling
Resource cleanup
Performance metric calculations

fxsjy/jieba

test/parallel/test_pos_file.py

            
from __future__ import print_function
import sys,time
import sys
sys.path.append("../../")
import jieba
import jieba.posseg as pseg

jieba.enable_parallel(4)

url = sys.argv[1]
content = open(url,"rb").read()
t1 = time.time()
words = list(pseg.cut(content))

t2 = time.time()
tm_cost = t2-t1

log_f = open("1.log","w")
log_f.write(' / '.join(map(str, words)))

print('speed' , len(content)/tm_cost, " bytes/second")