Cosine Similarity Algorithm | Machine Learning #11536
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| import numpy as np | ||
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| class Cosine_Similarity: |
There was a problem hiding this comment.
Class names should follow the CamelCase naming convention. Please update the following name accordingly: Cosine_Similarity
| """ | ||
| self.nlp = spacy.load("en_core_web_md") | ||
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| def Tokenize(self, text: str) -> list: |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Tokenize
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function Tokenize
| logging.error("An error occurred during Tokenization: ", exc_info=e) | ||
| raise e | ||
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| def Vectorize(self, tokens: list) -> list: |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Vectorize
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function Vectorize
| logging.error("An error occurred during Vectorization: ", exc_info=e) | ||
| raise e | ||
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| def Mean_Vector(self, vectors: list) -> np.ndarray: |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Mean_Vector
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function Mean_Vector
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| raise e | ||
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| def Dot_Product(self, vector1: np.ndarray, vector2: np.ndarray) -> float: |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Dot_Product
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function Dot_Product
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| raise e | ||
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| def Magnitude(self, vector: np.ndarray) -> float: |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Magnitude
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function Magnitude
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| raise e | ||
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| def Cosine_Similarity(self, vector1: np.ndarray, vector2: np.ndarray) -> float: |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Cosine_Similarity
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function Cosine_Similarity
| ) | ||
| raise e | ||
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| def Cosine_Similarity_Percentage(self, text1: str, text2: str) -> float: |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Cosine_Similarity_Percentage
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function Cosine_Similarity_Percentage
for more information, see https://pre-commit.ci
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Repository:
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algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| import numpy as np | ||
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| class cosine_similarity: |
There was a problem hiding this comment.
Class names should follow the CamelCase naming convention. Please update the following name accordingly: cosine_similarity
| """ | ||
| self.nlp = spacy.load("en_core_web_md") | ||
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| def tokenize(self, text: str) -> list: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function tokenize
| logging.error("An error occurred during Tokenization: ", exc_info=e) | ||
| raise e | ||
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| def vectorize(self, tokens: list) -> list: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function vectorize
| logging.error("An error occurred during Vectorization: ", exc_info=e) | ||
| raise e | ||
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| def mean_vector(self, vectors: list) -> np.ndarray: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function mean_vector
| ) | ||
| raise e | ||
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| def dot_product(self, vector1: np.ndarray, vector2: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function dot_product
| ) | ||
| raise e | ||
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| def magnitude(self, vector: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function magnitude
| ) | ||
| raise e | ||
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| def cosine_similarity(self, vector1: np.ndarray, vector2: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function cosine_similarity
| ) | ||
| raise e | ||
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| def cosine_similarity_percentage(self, text1: str, text2: str) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function cosine_similarity_percentage
for more information, see https://pre-commit.ci
There was a problem hiding this comment.
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Repository:
Python:
Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| """ | ||
| self.nlp = spacy.load("en_core_web_md") | ||
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||
| def tokenize(self, text: str) -> list: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function tokenize
| logging.error("An error occurred during Tokenization: ", exc_info=e) | ||
| raise e | ||
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| def vectorize(self, tokens: list) -> list: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function vectorize
| logging.error("An error occurred during Vectorization: ", exc_info=e) | ||
| raise e | ||
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| def mean_vector(self, vectors: list) -> np.ndarray: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function mean_vector
| ) | ||
| raise e | ||
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| def dot_product(self, vector1: np.ndarray, vector2: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function dot_product
| ) | ||
| raise e | ||
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| def magnitude(self, vector: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function magnitude
| ) | ||
| raise e | ||
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| def cosine_similarity(self, vector1: np.ndarray, vector2: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function cosine_similarity
| ) | ||
| raise e | ||
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| def cosine_similarity_percentage(self, text1: str, text2: str) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/cosine_similarity.py, please provide doctest for the function cosine_similarity_percentage
for more information, see https://pre-commit.ci
Cosine Similarity Algorithm
Overview
Introduces a New Implementation of the Cosine Similarity Algorithm in the
Cosine_Similarityclass. Cosine Similarity is a widely used metric inNatural Language Processingand Information retrieval to measure the similarity between two texts based on their Vector Representations.Key Features
SpaCy'spre-trainedWord Embeddingsto convert text into Vectors.SpaCy'sembeddings.Mean Vectorfor a set of Word Vectors to represent the overall text.Similarity Scoreranging from -1 to 1.Mathematical Foundation
Dot Product: Measures the Degree of Alignment between two Vectors.
Magnitude (Norm): Computes the length of a Vector.
Cosine Similarity Formula:
where the result is normalized to lie between -1 and 1, with 1 indicating identical vectors, 0 indicating orthogonal vectors, and -1 indicating completely dissimilar vectors.
Usage
The
Cosine_Similarityclass provides methods toTokenize,Vectorize, and calculate theCosine Similaritybetween two pieces of text. It includes:Tokenize(text): Tokenizes the input text into lowercase tokens.Vectorize(tokens): Converts tokens into vector representations.Mean_Vector(vectors): Computes the average vector of a list of vectors.Dot_Product(vector1, vector2): Calculates the dot product of two vectors.Magnitude(vector): Computes the magnitude of a vector.Cosine_Similarity(vector1, vector2): Computes the cosine similarity between two vectors.Cosine_Similarity_Percentage(text1, text2): Calculates the similarity percentage between two texts.Error Handling
Robust Error Handling is implemented for all operations to ensure reliability. Any issues encountered during tokenization, vectorization, or similarity calculations are logged and raised appropriately.
Benefits