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I have a 300.000 row pd.DataFrame comprised of multiple columns, out of which, one is a 50-dimension numpy array of shape (1,50) like so:

ID                     Array1    
1         [2.4252 ... 5.6363] 
2         [3.1242 ... 9.0091] 
3         [6.6775 ... 12.958]  
...
300000    [0.1260 ... 5.3323]

I then generate a new numpy array (let's call it array2) with the same shape and calculate the cosine similarity between each row of the dataframe and the generated array. For this, I am currently using sklearn.metrics.pairwise.cosine_similarity and save the results in a new column:

from sklearn.metrics.pairwise import cosine_similarity
df['Cosine'] = cosine_similarity(df['Array1].tolist(), array2)

Which works as intended and takes, on average, 2.5 seconds to execute. I am currently trying to lower this time to under 1 second simply for the sake of having less waiting time in the system I am building.

I am beginning to learn about Vaex and Dask as alternatives to pandas but am failing to convert the code I provided to a working equivalent that is also faster.

Preferably with one of the technologies I mentioned, how can I go about making pairwise cosine calculations even faster for large datasets?

Johnny
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  • You may not need Dask here. Take a look at https://docs.python.org/2/library/multiprocessing.html, esp. pool.map(function, iterable) – ndr Jan 06 '23 at 03:21

1 Answers1

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You could use Faiss here and apply a knn operation. To do this, you would put dataframe into a Faiss index and then search it using the array with k=3000000 (or whatever the total number of rows of your dataframe).

import faiss

dimension = 100

array1 = np.random.random((n, dimension)).astype('float32')


index = faiss.IndexFlatIP(d)
#add the rows of the dataframe into Faiss
for index, row in df.iterrows():  
    index.add(row)

k= len(df)
D, I = index.search(array1, k)

Note that you'll need to normalise the vectors to make this work (as the above solution is based on inner product).

greenstreets2
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