ddl.independent.IndependentDestructor

class ddl.independent.IndependentDestructor(independent_density=None)[source]

Bases: ddl.base.BaseDensityDestructor

Coordinate-wise destructor based on underlying independent density.

This destructor assumes that the underlying density is independent (i.e. IndependentDensity) and thus the transformation merely applys a univariate CDF to each feature independently of other features. The user can specify the univariate densities for each feature using the random variables defined in scipy.stats. The fit method merely fits an independent density. For transform and inverse transform, this destrcutor mereley applies the corresponding CDFs and inverse CDFs to transform each feature independently.

Parameters:
independent_density : IndependentDensity

The independent density estimator for this destructor.
Attributes:
density_ : IndependentDensity

Fitted underlying independent density.

See also

IndependentDensity

Methods

create_fitted(fitted_density, \*\*kwargs) Create fitted density destructor.
fit(self, X[, y, density_fit_params]) [Placeholder].
fit_from_density(self, density) [Placeholder].
fit_transform(self, X[, y]) Fit to data, then transform it.
get_domain(self) Get the domain of this destructor.
get_params(self[, deep]) Get parameters for this estimator.
inverse_transform(self, X[, y]) Apply inverse destructive transformation to X.
sample(self[, n_samples, random_state]) Generate random samples from this density/destructor.
score(self, X[, y]) Return the mean log likelihood (or log(det(Jacobian))).
score_samples(self, X[, y]) Compute log-likelihood (or log(det(Jacobian))) for each sample.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, X[, y]) Apply destructive transformation to X.
__init__(self, independent_density=None)[source]

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

classmethod create_fitted(fitted_density, **kwargs)[source]

Create fitted density destructor.

Parameters:
fitted_density : Density

Fitted density.

**kwargs

Other parameters to pass to Destructor constructor.
Returns:
fitted_transformer : Transformer

Fitted transformer.
fit(self, X, y=None, density_fit_params=None)

[Placeholder].

Parameters:
X :
y :
density_fit_params :
Returns:
obj : object
fit_from_density(self, density)

[Placeholder].

Parameters:
density :
Returns:
obj : object
fit_transform(self, 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_domain(self)

Get the domain of this destructor.

Returns:
domain : array-like, shape (2,) or shape (n_features, 2)

If shape is (2, ), then domain[0] is the minimum and domain[1] is the maximum for all features. If shape is (n_features, 2), then each feature’s domain (which could be different for each feature) is given similar to the first case.
get_params(self, 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.
inverse_transform(self, X, y=None)[source]

Apply inverse destructive transformation to X.

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used in the transformation but kept for compatibility.
Returns:
X_new : array-like, shape (n_samples, n_features)

Transformed data.
sample(self, n_samples=1, random_state=None)

Generate random samples from this density/destructor.

Parameters:
n_samples : int, default=1

Number of samples to generate. Defaults to 1.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by numpy.random.
Returns:
X : array, shape (n_samples, n_features)

Randomly generated sample.
score(self, X, y=None)

Return the mean log likelihood (or log(det(Jacobian))).

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used but kept for compatibility.
Returns:
log_likelihood : float

Mean log likelihood data points in X.
score_samples(self, X, y=None)

Compute log-likelihood (or log(det(Jacobian))) for each sample.

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used but kept for compatibility.
Returns:
log_likelihood : array, shape (n_samples,)

Log likelihood of each data point in X.
set_params(self, **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.

Returns:
self
transform(self, X, y=None)[source]

Apply destructive transformation to X.

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used in the transformation but kept for compatibility.
Returns:
X_new : array-like, shape (n_samples, n_features)

Transformed data.