survival.kernels.ClinicalKernelTransform

class survival.kernels.ClinicalKernelTransform(fit_once=False, _numeric_ranges=None, _numeric_columns=None, _nominal_columns=None)

Transform data using a clinical Kernel

The clinical kernel distinguishes between continuous ordinal,and nominal variables.

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 to False, it behaves like a regular estimator, i.e., you need to call fit() before transform().

References

[R12]

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)

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)

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.

Returns:

self : object

Returns the instance itself.

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 : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

pairwise_kernel(X, Y)

Function to use with sklearn.metrics.pairwise.pairwise_kernels()

Parameters:

X : array, shape = [n_features]

Y : array, shape = [n_features]

Returns:

similarity : float

Similarities are normalized to be within [0, 1]

prepare(X)

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 pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self

transform(Y)

Compute all pairwise distances between self.X_fit_ and Y.

Parameters:

y : array-like, shape = [n_samples_y, n_features]

Returns:

kernel : array, shape = [n_samples_y, n_samples_X_fit_]

Kernel matrix. Values are normalized to lie within [0, 1].