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={})[source]

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.

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 : figure object

Only returned if show=False.

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.

Quick Visualization of Multi-view Data

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={})[source]

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) : tuple of the figure and its axes.

Only returned if show=False.

Notes

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

Quick Visualization of Multi-view Data