ITMO_FS.filters.univariate.VDM

class ITMO_FS.filters.univariate.VDM(weighted=True, q=1)

Creates Value Difference Metric builder. For continious features discretesation requered.

Parameters:
  • weighted (bool) – If weighted = False, modified version of metric which omits the weights is used
  • q (int) – Power in VDM usually 1 or 2

Notes

For more details see papers about Improved Heterogeneous Distance Functions and Implicit Future Selection with the VDM.

Examples

>>> x = np.array([[0, 0, 0, 0],
...               [1, 0, 1, 1],
...               [1, 0, 0, 2]])
>>> y = np.array([0,
...               1,
...               1])
>>> vdm = VDM()
>>> vdm.fit(x, y)
array([[0.         4.35355339 4.        ]
       [4.5        0.         0.5       ]
       [4.         0.35355339 0.        ]])
__init__(weighted=True, q=1)

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None, **fit_params)

Fit the algorithm.

Parameters:
  • X (array-like, shape (n_samples, n_features)) – The training input samples.
  • y (array-like, shape (n_samples,), optional) – The class labels.
  • fit_params (dict, optional) – Additional parameters to pass to underlying _fit function.
Returns:
Return type:

Self, i.e. the transformer 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, sparse matrix, dataframe} of shape (n_samples, n_features)) –
  • y (ndarray of shape (n_samples,), default=None) – Target values.
  • **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:mapping of string to any
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 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:object
transform(X)

Transform given data by slicing it with selected features.

Parameters:X (array-like, shape (n_samples, n_features)) – The training input samples.
Returns:
Return type:Transformed 2D numpy array