ddl.autoregressive.AutoregressiveDestructor

class ddl.autoregressive.AutoregressiveDestructor(density_estimator=None, order=None, random_state=None)[source]

Bases: ddl.base.BaseDensityDestructor

Autoregressive destructor using densities that can compute conditionals.

The density estimator should implement the method conditional_densities that will return conditional densities, marginal_cdf that will return the marginal cdf for a particular feature index, and marginal_inverse_cdf that will return the marginal inverse cdf for a particular feature index. For an example of this type of density, see ddl.gaussian.GaussianDensity or ddl.mixture.GaussianMixtureDensity.

Note that this interface has not been fully standardized yet and is likely to change in the future.

Parameters:
density_estimator : estimator

Density estimator to be used for the autoregressive destructor. Note that this estimator must implement conditional_densities, marginal_cdf, and marginal_inverse_cdf.

order : {None, ‘random’, array-like with shape (n_features,)}, default=None

If None, then simply choose the original index order. If ‘random’, then use the random number generator defined by the random_state parameter to generate a random permutation of feature indices. If an array-like is given, then use this as the order of features to regress.

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

Used to determine random feature order if order is not given.

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.
Attributes:
density_ : estimator

Fitted underlying density.

Methods

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, density_estimator=None, order=None, random_state=None)[source]

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

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.