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| from gemma2b_dependencies import Gemma2BDependencies | |
| from collections import Counter | |
| class RandomForestDependencies: | |
| def __init__(self): | |
| self.gemma2bdependencies = Gemma2BDependencies() | |
| self.random_forest_features = [] | |
| def calculate_features(self, question: str, answer: str, probability: float, backspace_count: int, typing_duration: int, letter_click_counts: dict[str, int]): | |
| cosine_similarity = self.gemma2bdependencies.calculate_cosine_similarity( | |
| question, answer) | |
| backspace_count_normalized = backspace_count / len(answer) | |
| typing_duration_normalized = typing_duration / len(answer) | |
| letter_discrepancy = self.calculate_letter_discrepancy( | |
| answer, letter_click_counts) | |
| self.random_forest_features = [ | |
| cosine_similarity, probability, backspace_count_normalized, | |
| typing_duration_normalized, letter_discrepancy | |
| ] | |
| def calculate_letter_discrepancy(self, text: str, letter_click_counts: dict[str, int]): | |
| # Calculate letter frequencies in the text | |
| text_letter_counts = Counter(text.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(text) if len(text) > 0 else 1) | |
| return discrepancy_ratio_normalized | |