pmlearn.mixture package

Submodules

pmlearn.mixture.dirichlet_process module

Dirichlet Process Mixture Model.

class pmlearn.mixture.dirichlet_process.DirichletProcessMixture[source]

Bases: pmlearn.base.BayesianModel, pmlearn.base.BayesianDensityMixin

Custom Dirichlet Process Mixture Model built using PyMC3.

create_model()[source]

Creates and returns the PyMC3 model.

Note: The size of the shared variables must match the size of the training data. Otherwise, setting the shared variables later will raise an error. See http://docs.pymc.io/advanced_theano.html

The DensityDist class is used as the likelihood term. The second argument, logp_gmix(mus, pi, np.eye(D)), is a python function which recieves observations (denoted by ‘value’) and returns the tensor representation of the log-likelihood.

Returns:
Return type:the PyMC3 model
load(file_prefix)[source]

Loads a saved version of the trace, and custom param files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to load the) –
  • trace for this model. (saved) – Ex: given file_prefix = “path/to/file/” This will attempt to load “path/to/file/trace.pickle”
  • load_custom_params (Boolean flag to indicate whether custom parameters) –
  • be loaded. Defaults to False. (should) –
Returns:

custom_params
Return type:

Dictionary of custom parameters
predict_proba(X, return_std=False)[source]

Predicts probabilities of new data with a trained Dirichlet Process Mixture Model

Parameters:
  • X (numpy array, shape [n_samples, n_features]) –
  • cats (numpy array, shape [n_samples, ]) –
  • return_std (Boolean flag) – Boolean flag of whether to return standard deviations with mean probabilities. Defaults to False.
save(file_prefix)[source]

Saves the trace and custom params to files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to save the) –
  • for this model. (trace) – Ex: given file_prefix = “path/to/file/” This will attempt to save to “path/to/file/trace.pickle”
  • custom_params (Dictionary of custom parameters to save.) – Defaults to None

pmlearn.mixture.gaussian_mixture module

Gaussian Mixture Model.

class pmlearn.mixture.gaussian_mixture.GaussianMixture[source]

Bases: pmlearn.base.BayesianModel, pmlearn.base.BayesianDensityMixin

Custom Gaussian Mixture Model built using PyMC3.

create_model()[source]

Creates and returns the PyMC3 model.

Note: The size of the shared variables must match the size of the training data. Otherwise, setting the shared variables later will raise an error. See http://docs.pymc.io/advanced_theano.html

Returns:
Return type:the PyMC3 model
load(file_prefix)[source]

Loads a saved version of the trace, and custom param files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to load the) –
  • trace for this model. (saved) – Ex: given file_prefix = “path/to/file/” This will attempt to load “path/to/file/trace.pickle”
  • load_custom_params (Boolean flag to indicate whether custom parameters) –
  • be loaded. Defaults to False. (should) –
Returns:

custom_params
Return type:

Dictionary of custom parameters
save(file_prefix)[source]

Saves the trace and custom params to files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to save the) –
  • for this model. (trace) – Ex: given file_prefix = “path/to/file/” This will attempt to save to “path/to/file/trace.pickle”
  • custom_params (Dictionary of custom parameters to save.) – Defaults to None

pmlearn.mixture.util module

class pmlearn.mixture.util.logp_gmix(mus, pi, tau, num_training_samples)[source]

Bases: object

Module contents

The pmlearn.mixture module implements mixture models.

class pmlearn.mixture.GaussianMixture[source]

Bases: pmlearn.base.BayesianModel, pmlearn.base.BayesianDensityMixin

Custom Gaussian Mixture Model built using PyMC3.

create_model()[source]

Creates and returns the PyMC3 model.

Note: The size of the shared variables must match the size of the training data. Otherwise, setting the shared variables later will raise an error. See http://docs.pymc.io/advanced_theano.html

Returns:
Return type:the PyMC3 model
load(file_prefix)[source]

Loads a saved version of the trace, and custom param files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to load the) –
  • trace for this model. (saved) – Ex: given file_prefix = “path/to/file/” This will attempt to load “path/to/file/trace.pickle”
  • load_custom_params (Boolean flag to indicate whether custom parameters) –
  • be loaded. Defaults to False. (should) –
Returns:

custom_params
Return type:

Dictionary of custom parameters
save(file_prefix)[source]

Saves the trace and custom params to files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to save the) –
  • for this model. (trace) – Ex: given file_prefix = “path/to/file/” This will attempt to save to “path/to/file/trace.pickle”
  • custom_params (Dictionary of custom parameters to save.) – Defaults to None
class pmlearn.mixture.DirichletProcessMixture[source]

Bases: pmlearn.base.BayesianModel, pmlearn.base.BayesianDensityMixin

Custom Dirichlet Process Mixture Model built using PyMC3.

create_model()[source]

Creates and returns the PyMC3 model.

Note: The size of the shared variables must match the size of the training data. Otherwise, setting the shared variables later will raise an error. See http://docs.pymc.io/advanced_theano.html

The DensityDist class is used as the likelihood term. The second argument, logp_gmix(mus, pi, np.eye(D)), is a python function which recieves observations (denoted by ‘value’) and returns the tensor representation of the log-likelihood.

Returns:
Return type:the PyMC3 model
load(file_prefix)[source]

Loads a saved version of the trace, and custom param files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to load the) –
  • trace for this model. (saved) – Ex: given file_prefix = “path/to/file/” This will attempt to load “path/to/file/trace.pickle”
  • load_custom_params (Boolean flag to indicate whether custom parameters) –
  • be loaded. Defaults to False. (should) –
Returns:

custom_params
Return type:

Dictionary of custom parameters
predict_proba(X, return_std=False)[source]

Predicts probabilities of new data with a trained Dirichlet Process Mixture Model

Parameters:
  • X (numpy array, shape [n_samples, n_features]) –
  • cats (numpy array, shape [n_samples, ]) –
  • return_std (Boolean flag) – Boolean flag of whether to return standard deviations with mean probabilities. Defaults to False.
save(file_prefix)[source]

Saves the trace and custom params to files with the given file_prefix.

Parameters:
  • file_prefix (str, path and prefix used to identify where to save the) –
  • for this model. (trace) – Ex: given file_prefix = “path/to/file/” This will attempt to save to “path/to/file/trace.pickle”
  • custom_params (Dictionary of custom parameters to save.) – Defaults to None