Distances and divergences between distributions implemented in python.
Project description
Distances and divergences between discrete distributions described as dictionaries implemented in python.
These are meant as fast solutions to compute distances and divergences between discrete distributions, expecially when the two distributions contains a significant amount of events with nill probability which are not described in the dictionaries.
How do I install this package?
As usual, just download it using pip:
pip install dictancesAvailable metrics
A number of distances and divergences are available:
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Usage example with points
Suppose you have a point described by my_first_dictionary and another one described by my_second_dictionary:
from dictances import cosine
my_first_dictionary = {
"a": 56,
"b": 34,
"c": 89
}
my_second_dictionary = {
"a": 21,
"d": 51,
"e": 74
}
cosine(my_first_dictionary, my_second_dictionary)
#>>> 0.8847005261889619Usage example with distributions
Suppose you have a point described by my_first_dictionary and another one described by my_second_dictionary:
from dictances import bhattacharyya, bhattacharyya_coefficient
a = {
"event_1": 0.4,
"event_2": 0.1,
"event_3": 0.2,
"event_4": 0.3,
}
b = {
"event_1": 0.1,
"event_2": 0.2,
"event_5": 0.2,
"event_9": 0.5,
}
bhattacharyya_coefficient(a, b)
#>>> 0.3414213562373095
bhattacharyya(a, b)
#>>> 1.07463791569453Handling nested dictionaries
If you need to compute the distance between two nested dictionaries you can use deflate_dict as follows:
from dictances import cosine
from deflate_dict import deflate
my_first_dictionary = {
"a": 8,
"b": {
"c": 3,
"d": 6
}
}
my_second_dictionary = {
"b": {
"c": 8,
"d": 1
},
"y": 3,
}
cosine(deflate(my_first_dictionary), deflate(my_second_dictionary))Release history Release notifications | RSS feed
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