Finding Cosine Similarity of values of keys present in 2 Dictionaries-CodePudding

I have two dictionaries:

dict1 = {'A':'3','B':'6','E':'9'}   

dict2 = {'A':'4','B':'8','C':'12','D':'16','E':'20'}

I need to extract key-value pairs from dict2 such that those keys are present in dict1 as well and then find the cosign similarity.

I need to make a new dictionary:

dict3 = {'A':'4','B':'8','E':'20'}

from dict2, how do I do that? I have tried looping through both dictionaries but I'm not able to append.

How do I find the cosine similarity between dict1 & dict3? Should the values be converted to vectors first or is there a way to find it by keeping them as dictionaries?

CodePudding user response：

You can simply use dictionary comprehension

def cosine_similarity(dict1, dict2):
    return { _k:_v for _k, _v in dict2.items() if _k in dict1} 


dict3 = cosine_similarity(dict1, dict2)

will give you that

{'A': '4', 'B': '8', 'E': '20'}

CodePudding user response：

First part of your question you can achieve doing a dict-comprehension. Second part of your question is based on this question.

from scipy import spatial

dict1 = {"A": "3", "B": "6", "E": "9"}

dict2 = {"A": "4", "B": "8", "C": "12", "D": "16", "E": "20"}

dict3 = {key: int(value) for key, value in dict2.items() if key in dict1}

dict1_values = list(map(int, dict1.values()))
# [3,6,9]
dict3_values = list(dict3.values())
# [4,8,20]
cos_sim = 1 - spatial.distance.cosine(dict1_values, dict3_values)
print(cos_sim)

0.9759000729485332