Test metric #466
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Test metric #466
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Codecov Report
@@ Coverage Diff @@
## development #466 +/- ##
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- Coverage 78.58% 78.57% -0.01%
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Files 130 130
Lines 10073 10074 +1
===============================================
Hits 7916 7916
- Misses 2157 2158 +1
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mfeurer
requested changes
Apr 19, 2018
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mfeurer
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mfeurer
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Looks mostly good, only minor change requests.
test/test_metric/test_metrics.py
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| y_pred = y_true.copy() | ||
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| # the best possible score of r2 loss is 1. | ||
| if metric == 'r2': |
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Could you use the optimum attribute here?
test/test_metric/test_metrics.py
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| for metric, scorer in autosklearn.metrics.CLASSIFICATION_METRICS.items(): | ||
| # Skip functions not applicable for binary classification. | ||
| # TODO: Average precision should work for binary classification, | ||
| # TODO: but its behavior is not right. |
test/test_metric/test_metrics.py
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| y_true = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) | ||
| y_pred = np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], | ||
| [1.0, 0.0], [1.0, 0.0]]) | ||
| if metric is 'log_loss': |
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Could this be done via the optimum attribute of the scorer?
test/test_metric/test_metrics.py
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| y_pred = np.array([[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], | ||
| [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) | ||
| if metric is 'log_loss': # the best possible score for log_loss is 0. | ||
| previous_score = 0 |
Contributor
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Could this be done via the optimum attribute of the scorer?
test/test_metric/test_metrics.py
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| continue | ||
| y_true = np.array([[1, 0, 0], [1, 1, 0], [0, 1, 1], [1, 1, 1]]) | ||
| y_pred = y_true.copy() | ||
| previous_score = 1 |
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Could this be done via the optimum attribute of the scorer?
mfeurer
approved these changes
Apr 20, 2018
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