3. [WIP] OASIS algorithm implementation #330
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Trying to run tests with github actions
added 15 commits
October 19, 2021 15:47
…s B weakly learners. Tests refactor.
mvargas33
commented
Nov 3, 2021
metric_learn/oasis.py
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| def _vi_matrix(self, triplet): | ||
| """ | ||
| Computes V_i, the gradient matrix in a triplet | ||
| """ | ||
| # (pi+ - pi-) | ||
| diff = np.subtract(triplet[1], triplet[2]) # Shape (, d) | ||
| result = [] | ||
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| # For each scalar in first triplet, multiply by the diff of pi+ and pi- | ||
| for v in triplet[0]: | ||
| result.append(v * diff) | ||
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| return np.array(result) # Shape (d, d) |
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This part is correct, just checked manually, but I've found a better way to compute it. This is equivalent to:
return np.outer(triplet[0], np.subtract(triplet[1], triplet[2]))For d=100, and executing this function n=1.000.000 times, I get the following timings:
Current solution: 139.7970213389999 [s]
Using numpy outer: 13.34107805200074 [s]
I've changed it for this optimized approach
added 11 commits
November 3, 2021 14:50
…ith partial fit now. Fix BilinearMocks. Moved indices test to test_utils.py
… the correct one. Changed indices var to private. RNG init in fit, not in constructor. Made n_iter a local varl in fit
… to avoid code duplication, trade-off with isinstance(). Added SCML basis that got lost in the list.
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Hi!
I am currently implementing the OASIS algorithm and I open this PR to make the implementation transparent while working on it. Any discussion, question or comments is very welcomed.
This PR is under the WIP (Work In Progress) tag because as of now, I have a draft implementation of the algorithm out-of-the-package itself. It's a file in the root directory, with a test file in root as well.
Over these days I will move the algorithm to metric_learn folder to make it compatible with the current API. Same for testing.
Current testing only checks that nothing is broken, I'll make some test regarding KNN tasks to verify that the algorithm performs better at least for a handmade toy test.
This PR depends on the Bilinear PR #329 acceptance beforehand.