针对SST和QNLI两个数据集的概率分布通过电影评价分别计算在SST和QNLI概率分布下的熵率用拉普拉斯平滑考虑零概率情况并分析所计算的熵率结果PROBLEM 5 – Entropy Rate 20 pts Write a python function that computes the per-word entropy rate of a message relative to a speci

import numpy as np
import requests
from bs4 import BeautifulSoup
from collections import Counter

# Function to compute the per-word entropy rate
def compute_entropy_rate(message, distribution):
    # Convert the message to lowercase
    message = message.lower()
    
    # Calculate the probability of each word in the message
    word_count = len(message.split())
    word_probs = Counter(message.split())
    for word in word_probs:
        word_probs[word] = (word_probs[word] + 1) / (word_count + len(distribution))
    
    # Calculate the entropy rate
    entropy_rate = 0
    for word in word_probs:
        prob = distribution[word] if word in distribution else 1 / (word_count + len(distribution))
        entropy_rate += word_probs[word] * np.log2(prob)
    
    return -entropy_rate

# Get the movie review from a website
def get_movie_review(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    review = soup.find('div', class_='review').text.strip()
    return review

# SST distribution
sst_distribution = {'good': 0.2, 'great': 0.3, 'bad': 0.1, 'terrible': 0.05, 'average': 0.15, 'excellent': 0.2}

# QNLI distribution
qnli_distribution = {'positive': 0.25, 'negative': 0.25, 'neutral': 0.5}

# Get the movie review
review_url = 'https://www.example.com/movie-review'
review = get_movie_review(review_url)

# Compute the entropy rates
sst_entropy_rate = compute_entropy_rate(review, sst_distribution)
qnli_entropy_rate = compute_entropy_rate(review, qnli_distribution)

# Print the results
print("SST Entropy Rate:", sst_entropy_rate)
print("QNLI Entropy Rate:", qnli_entropy_rate)

In this code, we first define a function compute_entropy_rate that takes a message and a probability distribution as input and calculates the per-word entropy rate of the message relative to the distribution. The function uses laplace smoothing to handle zero probabilities.

Then, we define a function get_movie_review that takes a URL of a movie review website and returns the text of the review.

Next, we define the probability distributions for the SST and QNLI datasets. These distributions can be estimated from the training data of the respective datasets.

We then use the get_movie_review function to get a movie review from a website. You need to replace the review_url with the actual URL of a movie review website.

Finally, we compute the entropy rates of the movie review using the SST and QNLI distributions, and print the results.

The entropy rate is a measure of the average uncertainty or randomness of a sequence of words. A higher entropy rate indicates more randomness or unpredictability in the sequence. In the context of movie reviews, a higher entropy rate would suggest that the review contains a more diverse range of opinions or sentiments. On the other hand, a lower entropy rate would suggest that the review is more focused or consistent in its sentiment.