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May 27, 2017 20:26
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Cosine Similarity Python Scikit Learn
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# http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html | |
import numpy as np | |
from sklearn.metrics.pairwise import cosine_similarity | |
# The usual creation of arrays produces wrong format (as cosine_similarity works on matrices) | |
x = np.array([2,3,1,0]) | |
y = np.array([2,3,0,0]) | |
# Need to reshape these | |
x = x.reshape(1,-1) | |
y = y.reshape(1,-1) | |
# Or just create as a single row matrix | |
z = np.array([[1,1,1,1]]) | |
# Now we can compute similarities | |
cosine_similarity(x,y) # = array([[ 0.96362411]]), most similar | |
cosine_similarity(x,z) # = array([[ 0.80178373]]), next most similar | |
cosine_similarity(y,z) # = array([[ 0.69337525]]), least similar |
Hay,
There is another way you can do the same without reshaping the dataset.
Say I take three sentences
sentence_m = “Mason really loves food”
sentence_h = “Hannah loves food too”
sentence_w = “The whale is food”
sentence_m: Mason=1, really=1, loves=1, food=1, too=0, Hannah=0, The=0, whale=0, is=0
sentence_h: Mason=0, really=0, loves=1, food=1, too=1, Hannah=1, The=0, whale=0, is=0
sentence_w: Mason=0, really=0, loves=0, food=1, too=0, Hannah=0, The=1, whale=1, is=1
import numpy as np
def cos_sim(a, b):
dot_product = np.dot(a, b)
norm_a = np.linalg.norm(a)
norm_b = np.linalg.norm(b)
return dot_product / (norm_a * norm_b)
sentence_m = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0])
sentence_h = np.array([0, 0, 1, 1, 1, 1, 0, 0, 0])
sentence_w = np.array([0, 0, 0, 1, 0, 0, 1, 1, 1])
print(cos_sim(sentence_m, sentence_h))
print(cos_sim(sentence_m, sentence_h))
```
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Hi,
Instead of passing 1D array to the function, what if we have a huge list to be compared with another list?
e.g. - checking for similarity between customer names present in two different lists.
How to apply cosine similarity in that case? .......will there be any matrix populated giving the cosine distances?