compcor 1.0.5

Corpus level similarity measures.

comparing-corpora

A python library of similarity measures which allow measuring the perceptual similarity between set embeddings corpora.

Calculating distances between two text corpora

import compcor.corpus_metrics as corpus_metrics
from compcor.text_tokenizer_embedder import TextTokenizerEmbedder

setA = ['can you tell me how i would normally say thank you as a french person',
        'can you translate hi into spanish for me',
        'can you translate milk into spanish for me',
        'how can i say thank you very much in chinese',
        'how can i thank somebody in italian',
        'how could i say twin in chinese',
        'how do germans say goodnight',
        'how do i ask about the weather in chinese',
        'how do i say hotel in finnish',
        'how do i say bathroom in italian']

	setB = ['how can i say thank you very much in chinese',
			'how can i thank somebody in italian',
			'how could i say twin in chinese',
			'how do they say tacos in mexico',
			'how do they say yes in brazil',
			'how do vietnameses people say hello',
			'how do you say cat in spanish',
			'how do you say dog in spanish',
			'how do you say fast in spanish',
			'how do you say good bye in french',
			'how do you say goodbye in spanish',
			'how do you say hello in french',
			'how do you say hello in japanese',
			'how do you say hello in mexico']

Given two corpora of strings, we want to calculate the distance between them. comparing-corpora provides the following distance metrics. The first metrics operate on a sentence-level embedding, while the last two operate on the token frequencies.

Name	function	level	description
T-test	ttest_distance	sentence	T-test p-value on difference in elementwise means
Medoid	medoid_distance	sentence	Cosine distance between elementwise means
Classifier	classifier_distance	sentence	Classifiability between reference and target
PR (recision and recall)	PR_distance	sentence	Assessing distributional precision and recall
IRPR (information-retrieval precision and recall)	IRPR_distance	sentence	Average distance between closest samples pairs
DC (density and coverage)	dc_distance	sentence	Estimating manifolds density and coverage
MAUVE	mauve_distance	sentence	Quality and diversity via divergence frontiers
FID (Frechet Inception Distance)	fid_distance	sentence	Wasserstein distance between densities
Chi-squared ($\chi^2$)	chi_square_distance	token	Word/Token count comparison
Zipf	zipf_distance	token	Unigram rank-frequency statistics

To calculate inter-corpora distance, the raw corpora can be provided, in which case they will be embedded inside the function; this is determined by the 'model' argument (by default, 'all-MiniLM-L6-v2'), if the distance operates on the sentence level, otherwise by the 'tokenizer' argument.

distance = corpus_metrics.fid_distance(corpus1=setA, corpus2=setB)
print("fid_distance={}".format(distance))
#fid_distance=0.7498146370755459

The metrics also accept already-embedded corpora (possibly using a custom embedding):

embedder = TextTokenizerEmbedder(embedding_model_name = "all-MiniLM-L12-v2")

# due to change of the embedding model the results should differ
embeddingA = embedder.embed_sentences(setA)
embeddingB = embedder.embed_sentences(setB)

distance = corpus_metrics.fid_distance(corpus1=embeddingA, corpus2=embeddingB)
print("fid_distance={}".format(distance))
#fid_distance=0.7671713341732544

tokensA = embedder.tokenize_sentences(setA)
tokensB = embedder.tokenize_sentences(setB)

distance = corpus_metrics.chi_square_distance(corpus1=tokensA, corpus2=tokensB)
print("chi_square_distance={}".format(distance))
#chi_square_distance=0.9987177546738071