Probabilistic Models

Variational CCA

class cca_zoo.probabilisticmodels.vcca.VariationalCCA(latent_dims=1, copy_data=True, random_state=0, num_samples=100, num_warmup=100)[source]

Bases: cca_zoo.models._cca_base._CCA_Base

A class used to fit a variational bayesian CCA. Not quite the same due to using VI methods rather than EM

Citation:

Wang, Chong. “Variational Bayesian approach to canonical correlation analysis.” IEEE Transactions on Neural Networks 18.3 (2007): 905-910.

fit(views, y=None, **kwargs)[source]

Infer the parameters (mu: mean, psi: within view variance) and latent variables (z) of the generative CCA model

Parameters:views (Iterable[ndarray]) – list/tuple of numpy arrays or array likes with the same number of rows (samples)
transform(views, y=None, **kwargs)[source]

Predict the latent variables that generate the data in views using the sampled model parameters

Parameters:views (Iterable[ndarray]) – list/tuple of numpy arrays or array likes with the same number of rows (samples)
correlations(views, y=None, **kwargs)

Predicts the correlation for the given data using the fit model

Parameters:
  • views (Iterable[ndarray]) – 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:

all_corrs: an array of the pairwise correlations (k,k,self.latent_dims) where k is the number of views
Return type:

np.ndarray
fit_transform(views, y=None, **kwargs)

Fits and then transforms the training data

Parameters:
  • views (Iterable[ndarray]) – 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
get_loadings(views, y=None, **kwargs)

Returns the model loadings for each view for the given data

Parameters:
  • views (Iterable[ndarray]) – 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
score(views, y=None, **kwargs)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:
  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
Returns:

score – \(R^2\) of self.predict(X) wrt. y.
Return type:

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).