ScipyDist

ScipyDist

class ScipyDist(metric='euclidean', p=2, colalign='intersect', var_weights=None, metric_kwargs=None)[source]

Interface to scipy distances.

computes pairwise distances using scipy.spatial.distance.cdist

includes Euclidean distance and p-norm (Minkowski) distance

note: weighted distances are not supported

Parameters:

metricstring or function, as in cdist; default = euclidean

if string, one of: braycurtis, canberra, chebyshev, cityblock,

correlation, cosine, dice, euclidean, hamming, jaccard, jensenshannon, kulsinski (< scipy 1.11) or kulczynski1 (scipy >=1.11, <1.17), mahalanobis, matching, minkowski, rogerstanimoto, russellrao, seuclidean, sokalmichener, sokalsneath, sqeuclidean, yule

if function, should have signature 1D-np.array x 1D-np.array -> float

p: if metric=``minkowski``, the ``p`` in ``p-norm``, otherwise irrelevant

colalignstring, one of intersect (default), force-align, none

controls column alignment if X, X2 passed in fit are pd.DataFrame columns between X and X2 are aligned via column names.

if intersect, distance is computed on columns occurring both in X and X2,

other columns are discarded; column ordering in X2 is copied from X

if force-align, raises an error if the set of columns in X, X2 differs;

column ordering in X2 is copied from X

if none, X and X2 are passed through unmodified (no columns are aligned)

note: this will potentially align “non-matching” columns

var_weights1D np.array of float or None, default=None

weight/scaling vector applied to variables in X/X2 before being passed to cdist, i-th col of X/X2 is multiplied by var_weights[i] if None, equivalent to all-ones vector

metric_kwargsdict, optional, default=None

any kwargs passed to the metric in addition, i.e., to the function cdist common kwargs: w : array-like, same length as X.columns, weights for metric refer to scipy.spatial.distance.dist for a documentation of other extra kwargs

Attributes:

is_fitted

Whether fit has been called.

Methods

__call__(X[, X2])	Compute distance/kernel matrix, call shorthand.
check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit([X, X2])	Fit method for interface compatibility (no logic inside).
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, X2])	Compute distance/kernel matrix.