12/29/2023 0 Comments Python cosine similarity![]() The output of df.rolling(window_size).corr() is roughly: (len(df) – window_size + 1, N, N ). The following figure demonstrate how the sliding window works over axis=0.Īs a result, each window will be passed to the corr() function, and the result of corr() is a dataframe of size (N x N) where N is the number of columns in the main dataframe. More information about the rolling correlation can be found here. In other words, I would like to achieve a similar behavior to the pandas native function: df.rolling(window_size).corr() which return the corrlation coefficient for each sliding window over a given dataframe. The main objective of this problem is to calculate the rolling cosine_similarity over a given matrix. Finally, if no pandas native function is available for the cosine_similarity and if there doesn’t exist a solution similar to this: df.rolling(window=3, method="table").apply(lambda table: cosine_similarity(table.T)) I would appreciate having a solution using a for loop and numba for faster computations.īelow is my solution, using numpy and numba for faster processing. I did personally made some comparison on the corr function using pandas native functions and using a for loop, and the former was more than 100 times faster. Else, any solution avoiding a for loop is great, given how slow the code can get using for loop. I would expect the answer to include a solution using pandas native api. import pandas as pdįrom import cosine_similarityĪrray_2D = np.array().T ![]() Kindly note below the entire code to regenerate the same problem. For instance, I would like to have something like: from import cosine_similarityĭf.rolling(window=3, method="table").apply(lambda table: cosine_similarity(table.T)) However, I am wondering how to compute the rolling cosine_similarity. Going through the documentation, I found that, technically we can compute the rolling correlation function using the following syntax: My main objective of this question is to calculate the rolling dot_product or cosine_similarity over a pandas dataframe.
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