[MRG] Changed code example for FAQ #7820
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The code example needs the parameter 'algorithm=brute' for the the custom distance metric with data indexing to work correctly. Otherwise, for larger datasets the default 'algorithm=auto' will choose an incompatible algorithm which breaks the custom data indexing. notebook with code demonstrating the issue: https://github.com/sillystring13/cuddly-barnacle/blob/master/Example%20-%20Sklearn%20FAQ%20code%20test.ipynb
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Please add an explanation that without 'brute', the algorithm may assume that the distance is a function of a real-valued (continuous) feature space. |
jnothman
requested changes
Nov 4, 2016
| @@ -214,7 +214,7 @@ DBSCAN with Levenshtein distances:: | |||
| array([[0], | |||
| [1], | |||
| [2]]) | |||
| >>> dbscan(X, metric=lev_metric, eps=5, min_samples=2) # doctest: +SKIP | |||
| >>> dbscan(X, metric=lev_metric, eps=5, min_samples=2, algorithm='brute') # doctest: +SKIP | |||
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jnothman
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Aug 22, 2018
|
fixed in #11884 |
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Reference Issue
Related to: #7669
What does this implement/fix? Explain your changes.
Problem:
The doc/faq example "How do I deal with string data (or trees, graphs...)?" has a chunk of code that doesn't work if you increase the number of samples.
Fix:
The code example needs the parameter 'algorithm=brute' for the the custom distance metric with data indexing to work correctly. Otherwise, for larger datasets the default 'algorithm=auto' will choose an incompatible algorithm which breaks the custom data indexing.
Any other comments?
Here's a notebook with code demonstrating the issue:
https://github.com/sillystring13/cuddly-barnacle/blob/master/Example%20-%20Sklearn%20FAQ%20code%20test.ipynb