automl/auto-sklearn

FastICA: `n_components is too large: it will be set to _`

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#240 opened on Feb 24, 2017

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Description

FastICA currently looks for n_components between 10 and 2000, with a default of 100. However, assuming there are more rows N than columns M, a data set can only support up to M independent components. The suggested range is therefore between 2 and M, with a default of round(M / 2).

Looking at the implementation, it appears information about the data set is not available in get_hyperparameter_search_space. As a workaround, the n_components property could be replaced by a n_components_rel property that indicates the amount of independent components relative to the total number of features M. When calling sklearn.decomposition.FastICA, you could then simply pass n_components=X.shape[1] * self.n_components_rel.

Contributor guide

Research direction
Locate the FastICA hyperparameter search space definition in the auto sklearn codebase. Understand how n components is currently used and replace it with a relative version n components rel that adapts to the number of features. Ensure the change is backward compatible and test across different datasets.
Tech stack
python
Domain
machine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
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Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Pythonscikit learn
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
65