cca_zoo.visualisation.ExplainedVarianceDisplay#

class cca_zoo.visualisation.ExplainedVarianceDisplay(explained_variance_train, explained_variance_test=None, ratio=True, view_labels=None, **kwargs)[source]#

Bases: object

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

Parameters:

  • explained_variance_train (np.ndarray) – The explained variance of the train data.

  • explained_variance_test (np.ndarray) – The explained variance of the test data.

  • ratio (bool) – Whether to plot the ratio of explained variance 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 ExplainedVarianceDisplay
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from cca_zoo.linear import _MCCALoss
>>>
>>> # 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 _MCCALoss Model
>>> # -------------------
>>> mcca = _MCCALoss(latent_dimensions=2)
>>> mcca.fit(representations)
>>>
>>> # %%
>>> # Plotting the Explained Variance
>>> # ---------------------------------
>>> ExplainedVarianceDisplay.from_estimator(mcca, representations, test_views=test_views).plot()
>>> plt.show()