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

[lsorber]

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.

[duskybomb]

I want to work on this issue, let me know if it's still up for grabs.

PR idea:

• I was thinking we can have a new key x_shape in the info property of datamanager object. x_shape will be a tuple of array dimensions for X_train, similar to shape for NumPy arrays.
• Add x_shape to dataset_properties.
• In the function get_hyperparameter_search_space(), we use data_properties to change n_components to n_components_rel.

[BradKML]

Is there even a possibility that an optimal N exists for dimensionality reduction?

[eddiebergman]

There is no optimal, that's why it's a hyperparameter to be searched over :)

[BradKML]

@eddiebergman is there such a thing as optimal N for dimensionality reduction, then optimal K for clustering with the least amount of error? https://www.kaggle.com/datasets/ramontanoeiro/big-five-personality-test-removed-nan-and-0 https://www.nature.com/articles/s41562-018-0419-z