Stochastic Models
- class cca_zoo.models._stochastic.IncrementalPLS(latent_dims: int = 1, scale: bool = True, centre=True, copy_data=True, random_state=None, accept_sparse=None, batch_size=1, shuffle=True, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, drop_last=True, timeout=0, worker_init_fn=None, epochs=1, simple=False, val_interval=10)[source]
Bases:
_BaseStochastic
A class used to fit Incremental PLS
References
Arora, Raman, et al. “Stochastic optimization for PCA and PLS.” 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012.
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- class cca_zoo.models._stochastic.PLSStochasticPower(latent_dims: int = 1, scale: bool = True, centre=True, copy_data=True, random_state=None, accept_sparse=None, batch_size=1, shuffle=True, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, drop_last=True, timeout=0, worker_init_fn=None, epochs=1, learning_rate=0.01)[source]
Bases:
_BaseStochastic
A class used to fit Stochastic PLS
References
Arora, Raman, et al. “Stochastic optimization for PCA and PLS.” 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012.
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- class cca_zoo.models._stochastic.PLSGHAGEP(*args, **kwargs)[source]
Bases:
RCCAGHAGEP
References
Chapman, James, Ana Lawry Aguila, and Lennie Wells. “A Generalized EigenGame with Extensions to Multiview Representation Learning.” arXiv preprint arXiv:2211.11323 (2022).
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- class cca_zoo.models._stochastic.CCAGHAGEP(*args, **kwargs)[source]
Bases:
RCCAGHAGEP
References
Chapman, James, Ana Lawry Aguila, and Lennie Wells. “A Generalized EigenGame with Extensions to Multiview Representation Learning.” arXiv preprint arXiv:2211.11323 (2022).
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- class cca_zoo.models._stochastic.RCCAGHAGEP(latent_dims: int = 1, scale: bool = True, centre=True, copy_data=True, random_state=None, accept_sparse=None, batch_size=1, shuffle=True, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, drop_last=True, timeout=0, worker_init_fn=None, epochs=1, learning_rate=0.01, c=0)[source]
Bases:
_BaseStochastic
References
Chapman, James, Ana Lawry Aguila, and Lennie Wells. “A Generalized EigenGame with Extensions to Multiview Representation Learning.” arXiv preprint arXiv:2211.11323 (2022).
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- class cca_zoo.models._stochastic.PLSEigenGame(*args, **kwargs)[source]
Bases:
RCCAEigenGame
References
Chapman, James, Ana Lawry Aguila, and Lennie Wells. “A Generalized EigenGame with Extensions to Multiview Representation Learning.” arXiv preprint arXiv:2211.11323 (2022).
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- class cca_zoo.models._stochastic.CCAEigenGame(*args, **kwargs)[source]
Bases:
RCCAEigenGame
References
Chapman, James, Ana Lawry Aguila, and Lennie Wells. “A Generalized EigenGame with Extensions to Multiview Representation Learning.” arXiv preprint arXiv:2211.11323 (2022).
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- class cca_zoo.models._stochastic.RCCAEigenGame(latent_dims: int = 1, scale: bool = True, centre=True, copy_data=True, random_state=None, accept_sparse=None, batch_size=1, shuffle=True, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, drop_last=True, timeout=0, worker_init_fn=None, epochs=1, learning_rate=0.01, c=0)[source]
Bases:
_BaseStochastic
References
Chapman, James, Ana Lawry Aguila, and Lennie Wells. “A Generalized EigenGame with Extensions to Multiview Representation Learning.” arXiv preprint arXiv:2211.11323 (2022).
- fit(views: Iterable[ndarray], y=None, **kwargs)
Fits the model to the given data
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
self
- Return type
- fit_transform(views: Iterable[ndarray], **kwargs)
Fits the model to the given data and returns the transformed views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
transformed_views
- Return type
list of numpy arrays
- get_factor_loadings(views: Iterable[ndarray], normalize=True, **kwargs)
Returns the factor loadings for each view
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
normalize (bool, optional) – Whether to normalize the factor loadings. Default is True.
kwargs (any additional keyword arguments required by the given model) –
- Returns
factor_loadings
- Return type
list of numpy arrays
- get_params(deep=True)
Get parameters for this estimator.
- pairwise_correlations(views: Iterable[ndarray], **kwargs)
Returns the pairwise correlations between the views in each dimension
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
pairwise_correlations
- Return type
numpy array of shape (n_views, n_views, latent_dims)
- score(views: Iterable[ndarray], y=None, **kwargs)
Returns the average pairwise correlation between the views
- Parameters
views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –
- Returns
score
- Return type
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance