Cosine Similarity Algorithm | Machine Learning

machine_learning/cosine_similarity.py

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+import logging
+import numpy as np
+import spacy
+
+
+class CosineSimilarity:
+    """
+    Cosine Similarity Algorithm
+
+    Use Case:
+    - The Cosine Similarity Algorithm measures the Cosine of the Angle between two Non-Zero Vectors in a Multi-Dimensional Space.
+    - It is used to determine how similar two texts are based on their Vector representations.
+    - The similarity score ranges from -1 (Completely Dissimilar) to 1 (Completely Similar), with 0 indicating no Similarity.
+
+    Dependencies:
+    - spacy: A Natural Language Processing library for Python, used here for Tokenization and Vectorization.
+    - numpy: A Library for Numerical Operations in Python, used for Mathematical Computations.
+    """
+
+    def __init__(self) -> None:
+        """
+        Initializes the Cosine Similarity class by loading the SpaCy model.
+        """
+        self.nlp = spacy.load("en_core_web_md")
+
+    def tokenize(self, text: str) -> list:
+        """
+        Tokenizes the input text into a list of lowercased tokens.
+
+        Parameters:
+        - text (str): The input text to be tokenized.
+
+        Returns:
+        - list: A list of lowercased tokens.
+        """
+        try:
+            doc = self.nlp(text)
+            tokens = [token.text.lower() for token in doc if not token.is_punct]
+            return tokens
+        except Exception as e:
+            logging.error("An error occurred during Tokenization: ", exc_info=e)
+            raise e
+
+    def vectorize(self, tokens: list) -> list:
+        """
+        Converts tokens into their corresponding vector representations.
+
+        Parameters:
+        - tokens (list): A list of tokens to be vectorized.
+
+        Returns:
+        - list: A list of vectors corresponding to the tokens.
+        """
+        try:
+            vectors = [
+                self.nlp(token).vector
+                for token in tokens
+                if self.nlp(token).vector.any()
+            ]
+            return vectors
+        except Exception as e:
+            logging.error("An error occurred during Vectorization: ", exc_info=e)
+            raise e
+
+    def mean_vector(self, vectors: list) -> np.ndarray:
+        """
+        Computes the mean vector of a list of vectors.
+
+        Parameters:
+        - vectors (list): A list of vectors to be averaged.
+
+        Returns:
+        - np.ndarray: The mean vector.
+        """
+        try:
+            if not vectors:
+                return np.zeros(self.nlp.vocab.vectors_length)
+            return np.mean(vectors, axis=0)
+        except Exception as e:
+            logging.error(
+                "An error occurred while computing the Mean Vector: ", exc_info=e
+            )
+            raise e
+
+    def dot_product(self, vector1: np.ndarray, vector2: np.ndarray) -> float:
+        """
+        Computes the dot product between two vectors.
+
+        Parameters:
+        - vector1 (np.ndarray): The first vector.
+        - vector2 (np.ndarray): The second vector.
+
+        Returns:
+        - float: The dot product of the two vectors.
+        """
+        try:
+            return np.dot(vector1, vector2)
+        except Exception as e:
+            logging.error(
+                "An error occurred during the dot Product Calculation: ", exc_info=e
+            )
+            raise e
+
+    def magnitude(self, vector: np.ndarray) -> float:
+        """
+        Computes the magnitude (norm) of a vector.
+
+        Parameters:
+        - vector (np.ndarray): The vector whose magnitude is to be calculated.
+
+        Returns:
+        - float: The magnitude of the vector.
+        """
+        try:
+            return np.sqrt(np.sum(vector**2))
+        except Exception as e:
+            logging.error(
+                "An error occurred while computing the Magnitude: ", exc_info=e
+            )
+            raise e
+
+    def cosine_similarity(self, vector1: np.ndarray, vector2: np.ndarray) -> float:
+        """
+        Computes the cosine similarity between two vectors.
+
+        Parameters:
+        - vector1 (np.ndarray): The first vector.
+        - vector2 (np.ndarray): The second vector.
+
+        Returns:
+        - float: The cosine similarity between the two vectors.
+        """
+        try:
+            dot = self.dot_product(vector1, vector2)
+            magnitude1, magnitude2 = self.magnitude(vector1), self.magnitude(vector2)
+            if magnitude1 == 0 or magnitude2 == 0:
+                return 0.0
+            return dot / (magnitude1 * magnitude2)
+        except Exception as e:
+            logging.error(
+                "An error occurred during Cosine Similarity Calculation: ", exc_info=e
+            )
+            raise e
+
+    def cosine_similarity_percentage(self, text1: str, text2: str) -> float:
+        """
+        Computes the cosine similarity percentage between two texts.
+
+        Parameters:
+        - text1 (str): The first text.
+        - text2 (str): The second text.
+
+        Returns:
+        - float: The cosine similarity percentage between the two texts.
+        """
+        try:
+            tokens1 = self.tokenize(text1)
+            tokens2 = self.tokenize(text2)
+
+            vectors1 = self.vectorize(tokens1)
+            vectors2 = self.vectorize(tokens2)
+
+            mean_vec1 = self.mean_vector(vectors1)
+            mean_vec2 = self.mean_vector(vectors2)
+
+            similarity = self.cosine_similarity(mean_vec1, mean_vec2)
+            return similarity * 100
+        except Exception as e:
+            logging.error(
+                "An error occurred while computing the Cosine Similarity Percentage: ",
+                exc_info=e,
+            )
+            raise e
+
+
+if __name__ == "__main__":
+    """
+    Main function to Test the Cosine Similarity between two Texts.
+    """
+    text1 = "The biggest Infrastructure in the World is Burj Khalifa"
+    text2 = "The name of the talllest Tower in the world is Burj Khalifa"
+
+    similarity_percentage = CosineSimilarity().cosine_similarity_percentage(
+        text1, text2
+    )
+    print(f"Cosine Similarity: {similarity_percentage:.2f}%")