automl/auto-sklearn

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

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#240 创建于 2017年2月24日

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 (4 评论) (0 反应) (0 负责人)Python (7,270 star) (1,265 fork)batch import
Good first issueenhancement

描述

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.

贡献者指南

技术栈
pythonscikit learn
领域
machine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
half day
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
Pythonscikit learnauto sklearnFastICA concepts
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
70
研究方向
Examine the FastICA component implementation in auto sklearn, focusing on the `get hyperparameter search space` method. The issue suggests replacing `n components` with a relative property `n components rel` that derives from the number of features. Look for the existing hyperparameter definitions and modify them to accept a relative value, then adjust the call to `sklearn.decomposition.FastICA` accordingly. The discussion in the issue might provide further insights.