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import os
from collections import Counter
import math
def calculate_topk_upper_bound(file_path, k=5):
"""
Calculates the upper bound for top-k accuracy based on the tokenized text file.
Args:
file_path (str): Path to the input text file.
k (int): Top-k accuracy value to compute.
Returns:
float: The upper bound for top-k accuracy.
"""
try:
# Read the file and tokenize by spaces
with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
text = f.read()
tokens = text.split() # Tokenize by spaces
# Calculate token frequencies
token_counts = Counter(tokens)
total_tokens = len(tokens)
if total_tokens == 0:
return 0
# Convert frequencies to probabilities
token_probabilities = {token: count / total_tokens for token, count in token_counts.items()}
# Calculate entropy
entropy = -sum(p * math.log2(p) for p in token_probabilities.values())
# Calculate top-k accuracy upper bound
sorted_tokens = sorted(token_probabilities.items(), key=lambda x: x[1], reverse=True)
top_k_prob = sum(prob for _, prob in sorted_tokens[:k])
# Print entropy and top-k accuracy upper bound
print(f"Entropy: {entropy:.4f} bits")
print(f"Top-{k} Accuracy Upper Bound: {top_k_prob:.4f}")
return top_k_prob
except Exception as e:
print(f"Error: {e}")
return None
# Example usage
file_path = os.path.expanduser(
"~/torch_datasets/github-python/corpus/data/corpus_processed.txt"
)
top_k_accuracy = calculate_topk_upper_bound(file_path, k=5)
if top_k_accuracy is not None:
print(f"Upper Bound for Top-5 Accuracy: {top_k_accuracy:.4f}")
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