Refactor of metrics.roc_curve method #350
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The new implementation, even if it looks very naive, reduces the
computation time of fpr/tpr vectors.
roc_curve computation time depends now only on the length of y_score.
For comparison, here are the results between the old and the new
implementation for the following vectors:
- 10^6 length vector (y_score has 1000 unique values):
- old impl.: 28.29 seconds
- new impl.: 3.14 seconds
- 10^6 length vector (y_score has 10000 unique values):
- old impl.: 267.61 seconds
- new impl.: 3.64 seconds
| tpr = np.empty(thresholds.size) # True positive rate | ||
| fpr = np.empty(thresholds.size) # False positive rate | ||
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| # Buid tpr/fpr vector |
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Also please merge the upstream master into your branch as your as still using the old package names and this branch is not mergeable as it is. |
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ping @agramfort (this PR review is for you :) |
| @@ -121,16 +121,39 @@ def roc_curve(y_true, y_score): | |||
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| y_score = y_score.ravel() | |||
| thresholds = np.sort(np.unique(y_score))[::-1] | |||
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this "thresholds“ is not used later.
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ooops .... reminiscence of the old implementation :-/.
Fixed in 52cc103
Adds dtype on tpr/fpr array creation.
agramfort
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Sep 14, 2011
Refactor and speed up of metrics.roc_curve method
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The new implementation, even if it looks very naive, reduces the
computation time of fpr/tpr vectors.
roc_curve computation time depends now only on the length of y_score.
For comparison, here are the results between the old and the new
implementation for the following vectors: