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| """ | |
| Similarity metrics for text comparison | |
| """ | |
| import numpy as np | |
| from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| def calculate_cosine_similarity(text1, text2): | |
| """ | |
| Calculate cosine similarity between two texts using TF-IDF vectorization. | |
| Args: | |
| text1 (str): First text | |
| text2 (str): Second text | |
| Returns: | |
| float: Cosine similarity score between 0 and 1 | |
| """ | |
| # Create TF-IDF vectorizer | |
| vectorizer = TfidfVectorizer() | |
| try: | |
| # Transform texts into TF-IDF vectors | |
| tfidf_matrix = vectorizer.fit_transform([text1, text2]) | |
| # Calculate cosine similarity | |
| similarity = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0] | |
| return float(similarity) | |
| except Exception as e: | |
| print(f"Error calculating cosine similarity: {e}") | |
| return 0.0 | |
| def calculate_jaccard_similarity(text1, text2): | |
| """ | |
| Calculate Jaccard similarity between two texts (word-level). | |
| Args: | |
| text1 (str): First text | |
| text2 (str): Second text | |
| Returns: | |
| float: Jaccard similarity score between 0 and 1 | |
| """ | |
| # Convert to sets of words | |
| words1 = set(text1.lower().split()) | |
| words2 = set(text2.lower().split()) | |
| # Calculate Jaccard similarity | |
| if not words1 and not words2: | |
| return 1.0 # If both are empty, they're identical | |
| try: | |
| intersection = len(words1.intersection(words2)) | |
| union = len(words1.union(words2)) | |
| return intersection / union | |
| except Exception as e: | |
| print(f"Error calculating Jaccard similarity: {e}") | |
| return 0.0 | |
| def calculate_semantic_similarity(text1, text2): | |
| """ | |
| Calculate pseudo-semantic similarity by comparing word overlap patterns. | |
| This is a simplified approach that doesn't use embedding models like Word2Vec or BERT. | |
| Args: | |
| text1 (str): First text | |
| text2 (str): Second text | |
| Returns: | |
| float: Semantic similarity score between 0 and 1 | |
| """ | |
| # For now, this is a weighted combination of cosine and Jaccard similarity | |
| # In a real app, you'd use a proper semantic model | |
| cosine = calculate_cosine_similarity(text1, text2) | |
| jaccard = calculate_jaccard_similarity(text1, text2) | |
| # Weight more towards cosine similarity | |
| return 0.7 * cosine + 0.3 * jaccard | |
| def calculate_similarity(text1, text2, metrics=None): | |
| """ | |
| Calculate various similarity metrics between two texts. | |
| Args: | |
| text1 (str): First text | |
| text2 (str): Second text | |
| metrics (list): List of metrics to calculate | |
| Returns: | |
| dict: Dictionary of similarity scores | |
| """ | |
| if metrics is None: | |
| metrics = ["Cosine Similarity", "Jaccard Similarity", "Semantic Similarity"] | |
| results = {} | |
| if "Cosine Similarity" in metrics: | |
| results["cosine_similarity"] = calculate_cosine_similarity(text1, text2) | |
| if "Jaccard Similarity" in metrics: | |
| results["jaccard_similarity"] = calculate_jaccard_similarity(text1, text2) | |
| if "Semantic Similarity" in metrics: | |
| results["semantic_similarity"] = calculate_semantic_similarity(text1, text2) | |
| return results | |