[FIXED] How to verify if two text datasets are from different distribution?

Issue

I have two text datasets. Each dataset consists of multiple sequences and each sequence can have more than one sentence.

How do I measure if both datasets are from same distribution?

The purpose is to verify transfer learning from one distribution to another only if the difference between the distributions is statistically significant.

I am panning to use chi-square test but not sure if it will help for text data considering the high degrees of freedom.

update: Example: Supppose I want to train a sentiment classification model. I train a model on IMDb dataset and evaluate on IMDb and Yelp datasets. I found that my model trained on IMDb still does well on Yelp. But the question is how different these datasets are?

Train Dataset : https://www.kaggle.com/columbine/imdb-dataset-sentiment-analysis-in-csv-format?select=Train.csv

Eval 1: https://www.kaggle.com/columbine/imdb-dataset-sentiment-analysis-in-csv-format?select=Valid.csv

Eval 2: https://www.kaggle.com/omkarsabnis/sentiment-analysis-on-the-yelp-reviews-dataset

Now,

1. How different are train and eval 1?
2. How different are train and eval 2?
3. Is the dissimilarity between train and eval 2 by chance ? What is the statistical significance and p value?

Solution

The question "are text A and text B coming from the same distribution?" is somehow poorly defined. For example, these two questions (1,2) can be viewed as generated from the same distribution (distribution of all questions on StackExchange) or from different distributions (distribution of two different subdomains of StackExchange). So it's not clear what is the property that you want to test.

Anyway, you can come up with any test statistic of your choice, approximate its distribution in case of "single source" by simulation, and calculate the p-value of your test.

As a toy example, let's take two small corpora: two random articles from English Wikipedia. I'll do it in Python

import requests
from bs4 import BeautifulSoup
urls = [
    'https://en.wikipedia.org/wiki/Nanjing_(Liao_dynasty)', 
    'https://en.wikipedia.org/wiki/United_States_Passport_Card'
]
texts = [BeautifulSoup(requests.get(u).text).find('div', {'class': 'mw-parser-output'}).text for u in urls]

Now I use a primitive tokenizer to count individual words in texts, and use root mean squared difference in word relative frequencies as my test statistic. You can use any other statistic, as long as you calculate it consistently.

import re
from collections import Counter
from copy import deepcopy
TOKEN = re.compile(r'([^\W\d]+|\d+|[^\w\s])')
counters = [Counter(re.findall(TOKEN, t)) for t in texts]
print([sum(c.values()) for c in counters])  
# [5068, 4053]: texts are of approximately the same size

def word_freq_rmse(c1, c2):
    result = 0
    vocab = set(c1.keys()).union(set(c2.keys()))
    n1, n2 = sum(c1.values()), sum(c2.values())
    n = len(vocab)
    for word in vocab:
        result += (c1[word]/n1 - c2[word]/n2)**2 / n
    return result**0.5

print(word_freq_rmse(*counters))
# rmse is 0.001178, but is this a small or large difference?

I get a value of 0.001178, but I don't know whether it's a large difference. So I need to simulate the distribution of this test statistic under the null hypothesis: when both texts are from the same distribution. To simulate it, I merge two texts into one, and then split them randomly, and calculate my statistic when comparing these two random parts.

import random
tokens = [tok for t in texts for tok in re.findall(TOKEN, t)]
split = sum(counters[0].values())
distribution = []
for i in range(1000):
    random.shuffle(tokens)
    c1 = Counter(tokens[:split])
    c2 = Counter(tokens[split:])
    distribution.append(word_freq_rmse(c1, c2))

Now I can see how unusual is the value of my observed test statistic under the null hypothesis:

observed = word_freq_rmse(*counters)
p_value = sum(x >= observed for x in distribution) / len(distribution)
print(p_value)  # it is 0.0
print(observed, max(distribution), sum(distribution) / len(distribution)) # 0.0011  0.0006 0.0004

We see that when texts are from the same distribution, my test statistic is on average 0.0004 and almost never exceeds 0.0006, so the value of 0.0011 is very unusual, and the null hypothesis that two my texts originate from the same distribution should be rejected.

Answered By - David Dale

Answer Checked By - Mary Flores (PHPFixing Volunteer)