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from gemma2b import Gemma2BDependencies
from collections import Counter
class RandomForestDependencies:
def __init__(self, question: str, answer: str):
self.question = question
self.answer = answer
self.gemma2bdependencies = Gemma2BDependencies(
self.question, self.answer)
self.random_forest_features = []
def calculate_features(self, probability: float, backspace_count: int, typing_duration: int, letter_click_counts: dict[str, int]):
cosine_similarity = self.gemma2bdependencies.calculate_cosine_similarity(
self.question, self.answer)
backspace_count_normalized = backspace_count / len(self.answer)
typing_duration_normalized = typing_duration / len(self.answer)
letter_discrepancy = self.calculate_letter_discrepancy(
self.answer, letter_click_counts)
self.random_forest_features = [
cosine_similarity, probability, backspace_count_normalized,
typing_duration_normalized, letter_discrepancy
]
def calculate_letter_discrepancy(self, letter_click_counts: dict[str, int]):
# Calculate letter frequencies in the text
text_letter_counts = Counter(self.answer.lower())
# Calculate the ratio of click counts to text counts for each letter, adjusting for letters not in text
ratios = [letter_click_counts.get(letter, 0) / (text_letter_counts.get(letter, 0) + 1)
for letter in "abcdefghijklmnopqrstuvwxyz"]
# Average the ratios and normalize by the length of the text
average_ratio = sum(ratios) / len(ratios)
discrepancy_ratio_normalized = average_ratio / \
(len(self.answer) if len(self.answer) > 0 else 1)
return discrepancy_ratio_normalized
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