sksurv.kernels.ClinicalKernelTransform¶
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class
sksurv.kernels.
ClinicalKernelTransform
(fit_once=False, _numeric_ranges=None, _numeric_columns=None, _nominal_columns=None)[source]¶ Transform data using a clinical Kernel
The clinical kernel distinguishes between continuous ordinal,and nominal variables.
See [1] for further description.
Parameters: fit_once (bool, optional) – If set to True
, fit() does only transform the training data, but not update its internal state. You should call prepare() once before calling transform(). If set toFalse
, it behaves like a regular estimator, i.e., you need to call fit() before transform().References
[1] Daemen, A., De Moor, B., “Development of a kernel function for clinical data”. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5913-7, 2009 -
__init__
(fit_once=False, _numeric_ranges=None, _numeric_columns=None, _nominal_columns=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([fit_once, _numeric_ranges, …])Initialize self. fit
(X[, y])Determine transformation parameters from data in X. pairwise_kernel
(X, Y)Function to use with sklearn.metrics.pairwise.pairwise_kernels()
prepare
(X)Determine transformation parameters from data in X. transform
(Y)Compute all pairwise distances between self.X_fit_ and Y. -
fit
(X, y=None, **kwargs)[source]¶ Determine transformation parameters from data in X.
Subsequent calls to transform(Y) compute the pairwise distance to X. Parameters of the clinical kernel are only updated if fit_once is False, otherwise you have to explicitly call prepare() once.
Parameters: - X (pandas.DataFrame, shape = (n_samples, n_features)) – Data to estimate parameters from.
- y (None) – Argument is ignored (included for compatibility reasons).
- kwargs (dict) – Argument is ignored (included for compatibility reasons).
Returns: self – Returns the instance itself.
Return type: object
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pairwise_kernel
(X, Y)[source]¶ Function to use with
sklearn.metrics.pairwise.pairwise_kernels()
Parameters: - X (array, shape = (n_features,)) –
- Y (array, shape = (n_features,)) –
Returns: similarity – Similarities are normalized to be within [0, 1]
Return type: float
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