Plotting

Quick Visualize

mvlearn.plotting.quick_visualize(Xs, labels=None, figsize=(5, 5), title=None, cmap=None, show=True, context='notebook', ax_ticks=True, ax_labels=True, scatter_kwargs={}, fig_kwargs={})

Computes common principal components using MVMDS for dimensionality reduction and plots the multi-view data on a single 2D plot for easy visualization. This can be thought of as the multi-view analog of using PCA to decompose data and plot on principal components.

See also

mvlearn.embed.MVMDS

Parameters

• Xs (list of array-likes or numpy.ndarray) --

  • Xs length: n_views

  • Xs[i] shape: (n_samples, n_features_i)

  The multi-view data to reduce to a single plot.

• labels (boolean, default=None) -- Sets the labels of the samples.

• figsize (tuple, default=(5,5)) -- Sets the figure size.

• title (string, default=None) -- Sets the title of the figure.

• cmap (String, default=None) -- Colormap argument for matplotlib.pyplot.scatter.

• show (boolean, default=True) -- Shows the plots if true. Returns the objects otherwise.

• context (one of {'paper', 'notebook', 'talk', 'poster, None},) -- default='notebook' Sets the seaborn plotting context.

• ax_ticks (boolean, default=True) -- Whether to have tick marks on the axes.

• ax_labels (boolean, default=True) -- Whether to label the axes with the view and dimension numbers.

• scatter_kwargs (dict, default={}) -- Additional matplotlib.pyplot.scatter arguments.

• fig_kwargs (dict, default={}) -- Additional matplotlib.pyplot.figure arguments.

Returns

fig -- Only returned if show=False.

Return type

figure object

Notes

This function simply uses MVMDS with n_components=2 to reduce arbitrarily many views of input data to 2-dimensions, then makes a scatter plot.

Crossviews Plot

mvlearn.plotting.crossviews_plot(Xs, labels=None, dimensions=None, figsize=(10, 10), title=None, cmap=None, show=True, context='notebook', equal_axes=False, ax_ticks=True, ax_labels=True, scatter_kwargs={}, fig_kwargs={})

Plots each dimension fron one view against each dimension from a second view. If both views are the same, this reduces to a pairplot.

Parameters

• Xs (list of array-likes or numpy.ndarray) --

  • Xs length: n_views

  • Xs[i] shape: (n_samples, n_features_i)

  The two views to plot against one another. If one view has fewer dimensions than the other, only that many will be plotted.

• labels (boolean, default=None) -- Sets the labels of the samples.

• dimensions (array-like of ints, default=None) -- The dimensions of the views to plot. If None, all dimensions up to the minimum between the views will be plotted.

• figsize (tuple, default=(10,10)) -- Sets the grid figure size.

• title (string, default=None) -- Sets the title of the grid.

• cmap (String, default=None) -- Colormap argument for matplotlib.pyplot.scatter.

• show (boolean, default=False) -- Shows the plots if true. Returns the objects otherwise.

• context (one of {'paper', 'notebook', 'talk', 'poster, None},) -- default='notebook' Sets the seaborn plotting context.

• equal_axes (boolean, default=False) -- Equalizes the axes of the plots on the diagonals if true.

• ax_ticks (boolean, default=True) -- Whether to have tick marks on the axes.

• ax_labels (boolean, default=True) -- Whether to label the axes with the view and dimension numbers.

• scatter_kwargs (dict, default={}) -- Additional matplotlib.pyplot.scatter arguments.

• fig_kwargs (dict, default={}) -- Additional matplotlib.pyplot.subplots arguments.

Returns

(fig, axes) -- Only returned if show=False.

Return type

tuple of the figure and its axes.

Notes

Below is an example figure generated from 2 views with 2 features each.