askcarl package

Submodules

askcarl.gaussian module

Multivariate Gaussians with support for upper limits and missing data.

askcarl.gaussian.pdfcdf(x, mask, mean, cov)[source]

Compute the mixed PDF and CDF for a multivariate Gaussian distribution.

Parameters:

x (array) – The point (vector) at which to evaluate the probability.
mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).
mean (array) – mean vector of the multivariate normal distribution.
cov (array) – covariance matrix of the multivariate normal distribution.

Returns:

pdf – Probability density

Return type:

float

class askcarl.gaussian.Gaussian(mean, cov)[source]

Bases: object

Multivariate Gaussians with support for upper limits and missing data.

Parameters:

mean (array) – mean vector of the multivariate normal distribution.
cov (array) – covariance matrix of the multivariate normal distribution.

get_conditional_rv(mask)[source]

Build conditional distribution.

Parameters:

mask (array) – A boolean mask, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

cov_cross (array) – Covariance matrix part of upper bound and value dimensions.
cov_exact (array) – Covariance matrix part of value dimensions.
inv_cov_exact (array) – Inverse covariance matrix part of value dimensions.
rv (scipy.stats.multivariate_normal) – Multivariate Normal Distribution of the upper bound dimensions, conditioned with mask.

conditional_pdf(x, mask=Ellipsis)[source]

Compute conditional PDF.

Parameters:

x (array) – The points (vector) at which to evaluate the probability.
mask (array) – A boolean mask of the same shape as x.shape[1], indicating whether the entry is a value (True) or a upper bound (False).

Returns:

pdf – Probability density. One value for each x.

Return type:

array

conditional_logpdf(x, mask=Ellipsis)[source]

Compute conditional log-PDF.

Parameters:

x (array) – The points (vector) at which to evaluate the probability.
mask (array) – A boolean mask of the same shape as x.shape[1], indicating whether the entry is a value (True) or a upper bound (False).

Returns:

logpdf – logarithm of the probability density. One value for each x.

Return type:

array

pdf(x, mask)[source]

Compute conditional PDF.

Parameters:

x (array) – The points (vector) at which to evaluate the probability.
mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

pdf – probability density. One value for each x.

Return type:

array

logpdf(x, mask)[source]

Compute conditional log-PDF.

Parameters:

x (array) – The points (vector) at which to evaluate the probability.
mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

logpdf – logarithm of the probability density. One value for each x.

Return type:

array

askcarl.lightgmm module

A extremely fast-to-train GMM.

class askcarl.lightgmm.LightGMM(n_components, refine_weights=False, init_kwargs={'init': 'random', 'max_iter': 1, 'n_init': 1}, warm_start=False, covariance_type='full')[source]

Bases: object

Wrapper which transforms KMeans results into a GMM.

Initialise.

Parameters:

n_components (int) – number of Gaussian components.
refine_weights (bool) – whether to include a E step at the end.
init_kwargs (dict) – arguments passed to KMeans
warm_start (bool) – not supported, has to be False
covariance_type (str) – only “full” is supported

fit(X, sample_weight=None)[source]

Fit.

Parameters:

X (array) – data, of shape (N, D)
sample_weight (array) – weights of observations. shape (N,)

to_sklearn()[source]

Convert to a scikit-learn GaussianMixture object.

Returns:: gmm – scikit-learn GaussianMixture
Return type:: object

score_samples(X)[source]

Compute score of samples.

Parameters:: X (array) – data, of shape (N, D)
Returns:: logprob – log-probabilities, one entry for each entry in X, of shape (N)
Return type:: array

score(X, sample_weight=None)[source]

Compute score of samples.

Parameters:

X (array) – data, of shape (N, D)
sample_weight (array) – weights of observations. shape (N,)

Returns:

logprob – average log-probabilities, one entry for each entry in X, of shape (N)

Return type:

float

sample(N)[source]

Generate samples from model.

Parameters:: N (int) – number of samples
Returns:: X – data, of shape (N, D)
Return type:: array

askcarl.mixture module

Mixture of Gaussians.

class askcarl.mixture.GaussianMixture(weights, means, covs)[source]

Bases: object

Mixture of Gaussians.

Parameters:

weights (list) – weight for each Gaussian component
means (list) – mean vector for each Gaussian component.
covs (list) – covariance matrix for each Gaussian component.

weights

weight for each Gaussian component

Type:: list

components

list of Gaussian components.

Type:: list

static from_pypmc(mix)[source]

Initialize from a pypmc Gaussian mixture model (GMM).

Parameters:: mix (pypmc.density.mixture.GaussianMixture) – Gaussian mixture.
Returns:: mix – Generalized Gaussian mixture.
Return type:: GaussianMixture

static from_sklearn(skgmm)[source]

Initialize from a scikit-learn Gaussian mixture model (GMM).

Parameters:: mix (sklearn.mixture.GaussianMixture) – Gaussian mixture.
Returns:: mix – Generalized Gaussian mixture.
Return type:: GaussianMixture

pdf(x, mask)[source]

Compute probability density at x.

Parameters:

x (array) – The points (vector) at which to evaluate the probability.
mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

pdf – probability density. One value for each x.

Return type:

array

logpdf(x, mask)[source]

Compute logarithm of probability density.

Parameters:

x (array) – The points (vector) at which to evaluate the probability.
mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

logpdf – logarithm of the probability density. One value for each x.

Return type:

array

Module contents

Multivariate Gaussians with support for upper limits and missing data.