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
- __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 metadata routing of this object.
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_output
(*[, transform])Set output container.
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_metadata_routing()#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- 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_output(*, transform=None)#
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
”polars”: Polars output
None: Transform configuration is unchanged
New in version 1.4: “polars” option was added.
- Returns:
self – Estimator instance.
- Return type:
estimator instance
- 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
- transform(Y)#
Compute all pairwise distances between self.X_fit_ and Y.
- Parameters:
Y (array-like, shape = (n_samples_y, n_features)) –
- Returns:
kernel – Kernel matrix. Values are normalized to lie within [0, 1].
- Return type:
ndarray, shape = (n_samples_y, n_samples_X_fit_)