compairer 0.1.3

A versatile comparison library for Python

Compairer Module

Overview

The compairer module is a powerful, flexible Python library for comparing various types of data. It provides a suite of comparison methods for strings, vectors, and custom objects, along with utilities for normalization, statistical analysis, and detailed explanations of comparison results.

Features

• Multiple comparison methods (Levenshtein, Jaccard, Cosine, etc.)
• Support for string and vector compairer
• Customizable comparison methods
• Normalization and statistical utilities
• Detailed explanations for comparison results
• Batch comparison capabilities
• Incremental comparison for streaming data

Installation

pip install compairer

Quick Start

from compairer import compare

ref = "hello"
targets = ["hello", "hola", "bonjour"]

results = compare(ref, targets)
print(f"Most similar word to '{ref}' is {results.top()}. All scores: {results.scores}")

Core Components

Comparison Methods

The module includes several built-in comparison methods:

• String compairer: Levenshtein, Jaccard, Cosine, Fuzzy, Regex
• Vector compairer: Euclidean, Manhattan, Cosine, Jaccard

Example:

from compairer import compare

ref, target = "hello", "hola"
levenshteinScore = compare(ref, target, method="levenshtein")
jaccardScore = compare(ref, target, method="jaccard")

print(f"Levenshtein similarity: {levenshteinScore}")
print(f"Jaccard similarity: {jaccardScore}")

Custom Comparison Methods

You can create custom comparison methods:

from compairer.methods import CustomComparison

def myCompareFunc(ref, target):
    return len(set(ref) & set(target)) / len(set(ref) | set(target))

customMethod = CustomComparison(myCompareFunc)
score = customMethod("hello", "hola")
print(f"Custom similarity: {score}")

Batch Compairer

Compare multiple targets against a reference:

from compairer import compare

ref = "hello"
targets = ["hello", "hola", "bonjour", "ciao"]

results = compare(ref, targets)
print(f"Similarities: {results.scores}")
print(f"Most similar: {results.top()}")
print(f"Top 2 similar: {results.top(2)}")

Chained Operations

Perform multiple operations in a chain:

from compairer import compare

ref = "hello"
targets = ["hello", "hola", "bonjour", "ciao", "hi"]

results = (compare(ref, targets)
           .normalize()
           .filter(threshold=0.5)
           .top(3))

print(f"Top 3 similar words (similarity > 0.5): {results}")

Incremental Compairer

For streaming data or updating compairer:

from compairer.models import IncrementalComparison
from compairer.methods import LevenshteinComparison

incComp = IncrementalComparison(LevenshteinComparison(), initialRef="hello")

newData = ["hola", "bonjour", "ciao"]
for data in newData:
    score = incComp.update(data)
    print(f"Updated score after comparing with '{data}': {score}")

print(f"Final score: {incComp.getCurrentScore()}")
print(f"Comparison history: {incComp.getHistory()}")

Explanation Generation

Get detailed explanations for comparison results:

from compairer import compare

ref, target = "hello", "hola"
result = compare(ref, target, method="levenshtein")
explanation = result.explain()

print(explanation)

Advanced Usage

Using Type Hints

The module supports type hinting for better code clarity:

from compairer import compare
from typing import List

def findBestMatch(reference: str, candidates: List[str]) -> str:
    result = compare(reference, candidates)
    return result.top()

bestMatch = findBestMatch("hello", ["hola", "bonjour", "ciao"])
print(f"Best match: {bestMatch}")

Context Managers for Batch Compairer

Use context managers for efficient batch compairer:

from compairer import compare
from contextlib import contextmanager

@contextmanager
def batchCompare(ref, method="levenshtein"):
    comparer = compare(ref, method=method)
    try:
        yield comparer
    finally:
        print("Batch comparison completed")

ref = "hello"
with batchCompare(ref) as comparer:
    result1 = comparer("hola")
    result2 = comparer("bonjour")
    result3 = comparer("ciao")

print(f"Results: {result1}, {result2}, {result3}")

Decorators for Comparison Caching

Implement caching for expensive compairer:

from functools import lru_cache
from compairer import compare

@lru_cache(maxsize=100)
def cachedCompare(ref, target, method="levenshtein"):
    return compare(ref, target, method=method)

# First call will compute the result
result1 = cachedCompare("hello", "hola")
# Second call will retrieve from cache
result2 = cachedCompare("hello", "hola")

print(f"Results: {result1}, {result2}")

Contributing

Contributions are welcome! Please check out our Contribution Guidelines for details on how to get started.

License

This project is licensed under the MIT License - see the LICENSE file for details.