Comments (1)
The quick answer is: no, I don't think so.
Could Polars potentially improve the speed and efficiency of this package?
How exactly do you envision using Polars to increase the speed?
The polars data structures are optimized for dataframe operations. The apriori algorithm cannot (to my knowledge) easily be expressed as dataframe operations. Just like many other algorithms.
Same with numpy arrays. They are optimized for array operations, but again the apriori algorithm cannot be expressed as array operations.
Open to suggestions and being proven wrong about this, but then please provide some concrete examples as to how you envision polars/numpy data structures using less memory or providing speedup within the context of the apriori algorithm.
from efficient-apriori.
Related Issues (20)
- Input dataset format HOT 4
- Misprint in instructional comment HOT 2
- Clarifications on rules direction meaning HOT 3
- Why not add Rule Power Factor (RPF) as one more property to Rule? HOT 1
- Is there any way to export the rules with data to Excel? HOT 4
- ModuleNotFoundError: No module named 'dataclasses' HOT 2
- run time HOT 7
- Seeing #30, tuning confidence and min support for max itemsets? HOT 3
- lhs and rhs mixed up in the comments? HOT 1
- KeyError while generating rules HOT 6
- the min_support value HOT 1
- H_1 is not reduced, unnecessary rule candidate generation/checking HOT 3
- Using iterator or generator to provide transactions HOT 2
- A doubt HOT 2
- GPL-3.0 license classifier? HOT 2
- Reducing H_1 update broken HOT 3
- weighted Association rule mining HOT 1
- sort step not needed after combinations for join_step HOT 2
- Code for count_all HOT 5
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from efficient-apriori.