sksurv.kernels.ClinicalKernelTransform#
- 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().
- n_features_in_#
Number of features seen during
fit.- Type
int
- feature_names_in_#
Names of features seen during
fit. Defined only when X has feature names that are all strings.- Type
ndarray of shape (n_features_in_,)
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]#
Methods
__init__([fit_once, _numeric_ranges, ...])fit(X[, y])Determine transformation parameters from data in X.
fit_transform(X[, y])Fit to data, then transform it.
get_params([deep])Get parameters for this estimator.
pairwise_kernel(X, Y)Function to use with
sklearn.metrics.pairwise.pairwise_kernels()prepare(X)Determine transformation parameters from data in X.
set_params(**params)Set the parameters of this estimator.
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
- fit_transform(X, y=None, **fit_params)#
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_params(deep=True)#
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- 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
- prepare(X)[source]#
Determine transformation parameters from data in X.
Use if fit_once is True, in which case fit() does not set the parameters of the clinical kernel.
- Parameters
X (pandas.DataFrame, shape = (n_samples, n_features)) – Data to estimate parameters from.
- set_params(**params)#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance