sparse_dot_topn | Python package to accelerate the sparse matrix multiplication and top-n similarity selection | Machine Learning library
kandi X-RAY | sparse_dot_topn Summary
kandi X-RAY | sparse_dot_topn Summary
sparse_dot_topn provides a fast way to performing a sparse matrix multiplication followed by top-n multiplication result selection. Comparing very large feature vectors and picking the best matches, in practice often results in performing a sparse matrix multiplication followed by selecting the top-n multiplication results. In this package, we implement a customized Cython function for this purpose. When comparing our Cythonic approach to doing the same use with SciPy and NumPy functions, our approach improves the speed by about 40% and reduces memory consumption. This package is made by ING Wholesale Banking Advanced Analytics team. This blog or this blog explains how we implement it.
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Top functions reviewed by kandi - BETA
- Compute the scipy sossim op
- Get the indices of a csr row
- Generate the top n threadsim
- Runs aossim implementation of theossim implementation
- Computes theossim implementation of theossim method
- Wrap the Tossim_topn
- Wrap the top n threadsim_topn
- Generate a supersim_topn
- Rewrite top - n threadsim
- Overrides Tossim_topn
- Return the top n threadsim_topn
- Wrap theossim_topn
sparse_dot_topn Key Features
sparse_dot_topn Examples and Code Snippets
Community Discussions
Trending Discussions on sparse_dot_topn
QUESTION
I'm using the sparse_dot_topn library created by the Data Scientists at ING to search for near duplicates in a large set of company names (nearly 1.5M records). A recent update of this library now makes it possible to use multiple threads to compute the cross-product (i.e., the cosine similarity) between the two matrices. I ran a quick benchmark and the performance improvement is significant (depending on how many cores one can use on his machine/remote server):
...ANSWER
Answered 2020-Oct-01 at 22:05Without some examples I can't be sure this is what you're looking for, but I think this is what you want. I'm confused about the top in your example because it just takes the first results and not the results with the largest values.
QUESTION
I am working on my first major data science project. I am attempting to match names between a large list of data from one source, to a cleansed dictionary in another. I am using this string matching blog as a guide.
I am attempting to use two different data sets. Unfortunately, I can't seem to get good results and I think I am not applying this appropriately.
Code:
...ANSWER
Answered 2018-Dec-25 at 22:13You can import awesome_cossim_top function directly from the sparse_dot_topn lib.
Change the function get_matches_df with this:
QUESTION
I'm working on a fuzzy matching project and I have found a very interesting method : awesome_cossim_top
I globally understood the definition but do not understand what is happening when we do fit_transform
...ANSWER
Answered 2020-Mar-12 at 10:53TfidfVectorizer.fit_transform is used to create vocabulary from the training dataset and TfidfVectorizer.transform is used to map that vocabulary to test dataset so that the number of features in test data remain same as train data. Below example might help:
QUESTION
I believe the package required Cython, so I ran the following command.
...ANSWER
Answered 2020-Feb-27 at 19:47This will create a new environment with the required python version. Its a problem with your python version, i tried this by making a new environment and it installed fine.
QUESTION
I can't figure out how to map the top (#1) most similar document in my list back to each document item in my original list.
I go through some preprocessing, ngrams, lemmatization, and TF IDF. Then I use Scikit's linear kernal. I tried using extract features, but am not sure how to work with it in the csr matrix...
Tried various things (Using csr_matrix of items similarities to get most similar items to item X without having to transform csr_matrix to dense matrix)
...ANSWER
Answered 2019-Feb-14 at 02:44import pandas as pd
df = pd.DataFrame(columns=["original df col", "most similar doc", "similarity%"])
for i in range(len(documents)):
cosine_similarities = linear_kernel(tfidf_matrix[i:i+1], tfidf_matrix).flatten()
# make pairs of (index, similarity)
cosine_similarities = list(enumerate(cosine_similarities))
# delete the cosine similarity with itself
cosine_similarities.pop(i)
# get the tuple with max similarity
most_similar, similarity = max(cosine_similarities, key=lambda t:t[1])
df.loc[len(df)] = [documents[i], documents[most_similar], similarity]
QUESTION
I am trying to install the "sparse_dot_topn" package in Alibaba Cloud ECS instance. Firstly I tried to install it through the Anaconda installer.
conda install sparse_dot_topn
It throws like there is no package available
So I tried to install via pip
Pip install spare_dot_topn
But it throws me the following error
What am I missing? Please leave your suggestions
...ANSWER
Answered 2018-Nov-25 at 16:19sparse_dot_topn requires Cython, try installing it this way:
QUESTION
I want to install sparse_dot_topn in python from github. But I don't know how to do it. I did: pip3 install sparse_dot_topn but it failed. I saw sparse_dot_topn in github and tried to run the code in jupyter notebook but I couldn't succeed. Maybe I am doing something wrong. Can you please help me with the steps to install sparse_dot_topn from github? Many thanks in advance!
...ANSWER
Answered 2018-Jun-25 at 05:19To install from GitHub with pip you can: pip3 install git+url
example:
pip3 install git+https://github.com/ing-bank/sparse_dot_topn.git
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