import matplotlib
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from ..impute import get_observed_mod_indicator
[docs]
def plot_missing_modality(Xs, ax: matplotlib.axes.Axes = None, figsize: tuple = None, sort: bool = True):
r"""
Plot modality missing. Missing modalities appear as white, while black indicates available modalities.
Parameters
----------
Xs : list of array-likes objects, default=None
- Xs length: n_mods
- Xs[i] shape: (n_samples, n_features_i)
A list of different modalities. If rus is provided, it will not be used.
ax : matplotlib.axes.Axes, default=None
Axes where to draw the figure.
figsize : tuple, default=None
Figure size (tuple) in inches.
sort : bool, default=True
If True, samples will be sort based on their available modalities.
Returns
-------
fig : `matplotlib.figure.Figure`
Figure object.
ax : `matplotlib.axes.Axes`
Axes object.
"""
if not isinstance(Xs, list):
raise ValueError(f"Invalid Xs. It must be a list. A {type(Xs)} was passed.")
if any(len(X) == 0 for X in Xs):
raise ValueError("Invalid Xs. All elements must have at least one sample.")
if len(set(len(X) for X in Xs)) > 1:
raise ValueError("Invalid Xs. All elements must have the same number of samples.")
if (ax is not None) and (not isinstance(ax, matplotlib.axes.Axes)):
raise ValueError(f"Invalid ax. It must be a matplotlib.axes.Axes. A {type(ax)} was passed.")
if (figsize is not None) and (not isinstance(figsize, tuple)):
raise ValueError(f"Invalid figsize. It must be a tuple. A {type(figsize)} was passed.")
if not isinstance(sort, bool):
raise ValueError(f"Invalid sort. It must be a bool. A {type(sort)} was passed.")
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=figsize)
else:
fig = None
xlabel, ylabel = "Modality", "Samples"
observed_view_indicator = get_observed_mod_indicator(Xs)
observed_view_indicator = pd.DataFrame(observed_view_indicator)
if sort:
observed_view_indicator = observed_view_indicator.sort_values(list(range(len(Xs))))
observed_view_indicator.columns = observed_view_indicator.columns + 1
ax.pcolor(observed_view_indicator, cmap="binary", edgecolors="black", vmin=0., vmax=2.)
ax.set_xticks(np.arange(0.5, len(observed_view_indicator.columns), 1), observed_view_indicator.columns)
_ = ax.set_xlabel(xlabel), ax.set_ylabel(ylabel)
return fig, ax