ENH: NPV: Support calculation for vectors of rates and cashflows #96
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This was taking too long and needs to be reduced
This commit introduces a native hot path using numba. This leads to an ~50x speed up on the `time_broadcast` benchmark with 100x100x100 dimension
This leads to the following improvements on the `time_broadcast` benchmark with dimension 100x100x100: * raw Python: 197 ms * numba only: 4.08 ms * numba + prange 0.3 ms
The newest released numba version is 0.58.1. This version does not support Python 3.12. Since we are using it we also cannot support Python 3.12. This should be added once numba version 0.59 is released.
Kai-Striega
added
enhancement
These were originally added when the code was written as one function without numba. As numba is now being used, and the functions are therefore seperated. This is no longer required and makes the code more complex.
This fixes a bug where we weren't creating decimal arrays in the benchmark. Instead of creating an array and setting the dtype we manually make a list of Decimals and create an array from that list.
Kai-Striega
commented
Dec 9, 2023
| @@ -8,7 +8,7 @@ jobs: | |||
| strategy: | |||
| matrix: | |||
| os: [ubuntu-latest, macos-latest, windows-latest] | |||
| python-version: ["3.9", "3.10", "3.11", "3.12"] | |||
| python-version: ["3.9", "3.10", "3.11"] | |||
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We are required to drop Python 3.12 as Numba 0.58 doesn't support it yet. Python 3.12 should be supported in the next release. This should be released in early 2024.
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NumPy-Financial now supports calculation of NPV for vectors of rates and cashflows e.g.
This was previously not supported. This behaviour was first implemented by naively looping through rates and values. As Python for loops are slow, numba is applied to accelerate the process. This has lead to a significant speedup, especially for native types.
There are some potential drawbacks to using numba:
Decimalsupport