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minhsphuc12 opened this issue Jul 28, 2019 · 11 comments
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wrong input into entropy function #14497

minhsphuc12 opened this issue Jul 28, 2019 · 11 comments
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module:cluster

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@minhsphuc12
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scikit-learn/sklearn/metrics/cluster/supervised.py

Line 751 in 74ae6a0

h_true, h_pred = entropy(labels_true), entropy(labels_pred)

entropy must be inputed with frequency table of labels_true / labels_pred, not those array directly.
Example solution

from collections import Counter
freq_table = [x[1] for x in dict(Counter(labels_true)).items()]
entropy(freq_table)
@vasujogani
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working on it

@jnothman
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jnothman commented Jul 28, 2019 via email

@vasujogani
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I think you might be right. I just saw entropy using np.unique which I believe would take care of the issue

@minhsphuc12
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Please consider example
vec_a = np.asarray([0, 1, 2, 0, 3, 4, 5, 1])
stats.entropy(vec_a) #1.6304325826288886

from collections import Counter
freq_table = [x[1] for x in dict(Counter(vec_a)).items()]
stats.entropy(freq_table) #1.7328679513998633

As you can see, vec_a is cluster vector, does not has same length as freq_table.

The issue here is not normalizing the input (same length), but rather using cluster vector, instead of using frequency table as input.

@jnothman
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jnothman commented Jul 29, 2019 via email

@minhsphuc12
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minhsphuc12 commented Jul 29, 2019 via email

@jnothman
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jnothman commented Jul 29, 2019 via email

@minhsphuc12
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Thanks problem solved. Root cause is I do not aware that averaging method used in my sklearn version is ‘max’, instead of ‘arithmetic’ of 0.22.

@minhsphuc12
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Although we would need to update the documentation example.
image

@thomasjpfan
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How would you want to update the example?

@cmarmo
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cmarmo commented Mar 23, 2022

Closing this one as it is quite old, the main issue has been solved and there was no answer to the last question.
Feel free to reopen if things can be clarified.

@cmarmo cmarmo closed this as completed Mar 23, 2022
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@jnothman @cmarmo @thomasjpfan @minhsphuc12 @vasujogani
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