[ENH] Nomogram: Support for sparse data#2197
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@@ Coverage Diff @@
## master #2197 +/- ##
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+ Coverage 67.64% 67.67% +0.03%
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Files 319 319
Lines 54871 54926 +55
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+ Hits 37119 37173 +54
- Misses 17752 17753 +1Continue to review full report at Codecov.
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Orange/statistics/util.py
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| return np.prod(x.shape) != x.data.size | ||
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| def _nan_min_max(x, axis=0, func=None): |
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None is not callable (L245)...
The default could be one of min/max or this could be a required parameter.
Orange/statistics/util.py
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| if axis == 0: | ||
| x = x.T | ||
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| # TODO check & transform to correct format |
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In what (incorrect) format is it now?
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X is usually in csr and hence when one calls this with axis=0 x becomes csc (due to transposing), which is isn't efficient for row slicing.
Orange/statistics/util.py
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| if n_nans: | ||
| return float('nan') | ||
| else: | ||
| n_values = np.prod(x.shape) - n_nans |
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Why - n_nans ? Isn't it 0 in this else part.
Orange/statistics/util.py
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| return np.unique(x, return_counts=return_counts) | ||
| else: | ||
| n_zeros = np.prod(x.shape) - x.data.size | ||
| r = np.unique(x.data, return_counts=return_counts) |
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x.data can contain explicit zeros right? E.g. make a csr matrix and set a non-zero element to 0.
In this case you need to be careful about inserting another 0 below...
| """ Equivalent of np.unique that supports sparse or dense matrices. """ | ||
| if not sp.issparse(x): | ||
| return np.unique(x, return_counts=return_counts) | ||
| else: |
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else is unnecessary here and in other functions, which first check if x is not sparse and return something.
It just adds an extra indentation to all of the actual function body.
Orange/statistics/util.py
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| def _sparse_has_zeros(x): | ||
| """ Check if sparse matrix contains any implicit zeros. """ | ||
| return np.prod(x.shape) != x.data.size |
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It is probably better to use x.nnz instead of x.data.size everywhere.
Looks like the spmatrix base class has nnz so every type should have it, while e.g. dok_matrix does not have .data
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Corrected. Though, methods still won't work for for dox_matrix since we rely on x.data elsewhere.
Compute values usually have a reference to the original variable so SharedComputeValue should have it too.
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@lanzagar I think all issues are addressed now. Please, check again. |
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lanzagar
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Issue
Fixes #2165.
Description of changes
nanmin,nanmax,average,uniqueequivalents of numpy's that support sparse or dense matrices.reconstruct_domainmethod: 3.0s -> 0.03 scalculate_log_reg_coefficientsmethod: TLDW (minutes+) -> 1.5 sIncludes