cca_zoo.visualisation.ExplainedCovarianceDisplay#

class cca_zoo.visualisation.ExplainedCovarianceDisplay(explained_covariance_train, explained_covariance_test=None, ratio=True, **kwargs)[source]#

Bases: object

Display the explained covariance of the latent variables of the representations.

Parameters:

  • explained_covariance_train (np.ndarray) – The explained covariance of the train data.

  • explained_covariance_test (np.ndarray) – The explained covariance of the test data.

  • ratio (bool) – Whether to plot the ratio of explained covariance or not.

  • **kwargs (dict) – Keyword arguments to be passed to the seaborn lineplot.

figure_#

The figure of the plot.

Type:

matplotlib.pyplot.figure

Examples

>>> from cca_zoo.visualisation import ExplainedCovarianceDisplay
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from cca_zoo.linear import MCCA
>>>
>>> # Generate Sample Data
>>> # --------------------
>>> X = np.random.rand(100, 10)
>>> Y = np.random.rand(100, 10)
>>>
>>> # Splitting the data into training and testing sets
>>> X_train, X_test = X[:50], X[50:]
>>> Y_train, Y_test = Y[:50], Y[50:]
>>>
>>> representations = [X_train, Y_train]
>>> test_views = [X_test, Y_test]
>>>
>>> # Train an MCCA Model
>>> # -------------------
>>> mcca = MCCA(latent_dimensions=2)
>>> mcca.fit(representations)
>>>
>>> # %%
>>> # Plotting the Explained Covariance
>>> # ---------------------------------
>>> ExplainedCovarianceDisplay.from_estimator(mcca, representations, test_views=test_views).plot()
>>> plt.show()