skbold.pipelines package¶
The pipelines module contains some standard MVPA pipelines using the scikit-learn style Pipeline objects.
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create_ftest_kbest_svm
(kernel='linear', k=100, **kwargs)[source]¶ Creates an svm-pipeline with f-test feature selection.
Uses SelectKBest from scikit-learn.feature_selection.
Parameters: - kernel (str) – Kernel for SVM (default: ‘linear’)
- k (int) – How many voxels to select (from the k best)
- **kwargs – Arbitrary keyword arguments for SVC() initialization.
Returns: ftest_svm – Pipeline with f-test feature selection and svm.
Return type: scikit-learn Pipeline object
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create_ftest_percentile_svm
(kernel='linear', perc=10, **kwargs)[source]¶ Creates an svm-pipeline with f-test feature selection.
Uses SelectPercentile from scikit-learn.feature_selection.
Parameters: - kernel (str) – Kernel for SVM (default: ‘linear’)
- perc (int or float) – Percentage of voxels to select
- **kwargs – Arbitrary keyword arguments for SVC() initialization.
Returns: ftest_svm – Pipeline with f-test feature selection and svm.
Return type: scikit-learn Pipeline object
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create_pca_svm
(kernel='linear', n_comp=10, whiten=False, **kwargs)[source]¶ Creates an svm-pipeline with f-test feature selection.
Parameters: - kernel (str) – Kernel for SVM (default: ‘linear’)
- n_comp (int) – How many PCA-components to select
- whiten (bool) – Whether to use whitening in PCA
- **kwargs – Arbitrary keyword arguments for SVC() initialization.
Returns: pca_svm – Pipeline with PCA feature extraction and svm.
Return type: scikit-learn Pipeline object