MESMER-X: Test distrib_cov
#540
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #540 +/- ##
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+ Coverage 62.00% 62.71% +0.71%
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Files 50 50
Lines 3561 3554 -7
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+ Hits 2208 2229 +21
+ Misses 1353 1325 -28
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| "SLSQP", | ||
| "CG", | ||
| "Newton-CG", | ||
| if self.method_fit not in [ |
| " 'threshold_stopping_rule' and 'ind_year_thres', threshold_stopping_rule", | ||
| "and 'ind_year_thres' must be used together, and only for 'fcNLL'", |
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| " 'threshold_stopping_rule' and 'ind_year_thres', threshold_stopping_rule", | |
| "and 'ind_year_thres' must be used together, and only for 'fcNLL'", | |
| " 'threshold_stopping_rule' and 'ind_year_thres', threshold_stopping_rule," | |
| " and 'ind_year_thres' must be used together, and only for 'fcNLL'", |
| @@ -649,7 +651,7 @@ def _get_weights_nll(self, n_bins_density=40): | |||
| # interpolating over whole region | |||
| gmt_hist, edges = np.histogramdd(sample=tmp, bins=bins.T) | |||
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| gmt_bins_center = [0.5 * (edge[1:] + edges[:-1]) for edge in edges] | |||
| gmt_bins_center = [0.5 * (edge[1:] + edge[:-1]) for edge in edges] | |||
| @@ -38,6 +38,7 @@ def wrapper(*args, **kwargs): | |||
| return wrapper | |||
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| # TODO: would want to switch this, have a distrib class that takes xarrays and have a training func that potentially works on xarrays | |||
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| # TODO: would want to switch this, have a distrib class that takes xarrays and have a training func that potentially works on xarrays | |
| # TODO: enable distrib class and training func for xarray objs |
| np.testing.assert_equal(dist.data_pred, {"tas": pred}) | ||
| np.testing.assert_equal(dist.weights_driver, np.ones(n) / n) | ||
| assert dist.n_sample == n | ||
| assert dist.expr_fit == expression |
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Does that actually work? Ah it does but I think it checks that it's the same objects ant thus better to use is to be more explicit.
| assert dist.expr_fit == expression | |
| assert dist.expr_fit is expression |
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Ah thanks!
| assert dist.boundaries_coeffs == {} | ||
| assert dist.first_guess is None | ||
| assert dist.func_first_guess is None | ||
| assert dist.n_coeffs == 2 |
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I think that should be a property of expr_fit (no need to do that here, though)
| data_targ_addtest = rng.normal(loc=2 * pred, scale=0.1, size=n) | ||
| data_preds_addtest = {"tas": np.linspace(0, 0.9, n)} | ||
| threshold_min_proba = 0.1 | ||
| boundaries_params = {"loc": [-10, 10], "scale": [0, 1]} |
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| boundaries_params = {"loc": [-10, 10], "scale": [0, 1]} | |
| boundaries_params = {"loc": [-10, 10], "scale": [-1, 1]} |
Could try -1 here to check we end up with 0 again
| np.testing.assert_equal(dist.data_targ_addtest, data_targ_addtest) | ||
| np.testing.assert_equal(dist.data_preds_addtest, data_preds_addtest) | ||
| assert dist.n_sample == n | ||
| assert dist.expr_fit == expression |
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| assert dist.expr_fit == expression | |
| assert dist.expr_fit is expression |
| np.array([1, 2, 3]), | ||
| {"tas": np.array([1, 2, 3])}, | ||
| expression, | ||
| options_solver="this is not a dictionary", |
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Ahh sorry, this is not at all done yet, I just pushed it to get the coverage report 😅 |
Ahh well looks good already 😉 |
CHANGELOG.rst