[ENH] Manifold Learning #1624
[ENH] Manifold Learning #1624
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Current coverage is 88.71% (diff: 100%)@@ master #1624 diff @@
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| self.max_iter_spin = self._create_spin_parameter( | ||
| "max_iter", 10, 10 ** 4, "Max iterations:") | ||
| self.random_state_check = self._create_check_parameter( | ||
| "random_state", "Replicable (deterministic) learning") |
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"Replicable (deterministic) learning" is a bit awkward and too long - does not fit into fixed widget size for me.
Suggestion: invert the option and use "Random initialization" checkbox.
Bonus question: does replicable learning use PCA initialization? because the results look different than in the MDS widget...
| self.data = None | ||
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| # GUI | ||
| info_box = gui.vBox(self.controlArea, "Info") |
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While the Info box at the top isn't inappropriate it is also not necessary IMHO.
And comparing this widget with similar ones: PCA, MDS, CA, k-means, hierarchical, or even any classification/regression widget - none of them have an Info box.
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For t-SNE, some metric are redundant and some are hard to understand from default names given in sklearn. My proposal is: Euclidean - remove (sklearn uses Euclidean squared) With t-SNE and cosine metric, I get a complaint "All distances should be positive." The metric is 1-cos(fi), so by definition this should always yield positive. But due to numerical error, cos(fi) on iris indeed returns a negative number (-1e-16). Quick fix for this pull request: do not include cosine (@lanzagar will report an issue to sklearn). |
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Fix for TSNE with cosine distances was merged in scikit-learn 2 days ago so it should work in the next version. |
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