"""Plot functions for PCA results."""
# Copyright 2025 Koji Noshita
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def explained_variance_ratio_plot(pca, n_components=None, ax=None, verbose=False):
"""Plot explained variance ratio of PCA components.
Parameters
----------
pca : sklearn.decomposition.PCA
Fitted PCA object.
n_components : int, optional
Number of principal components to plot. If None, plot all components.
ax : matplotlib.axes.Axes, optional
Axes object to plot on. If None, a new figure and axes are created.
verbose : bool, optional
If True, print explained variance ratios and their cumulative sums.
Returns
-------
ax : matplotlib.axes.Axes
Axes object with the plot.
"""
if ax is None:
fig, ax = plt.subplots(figsize=(6, 4))
if n_components is None:
n_components = pca.n_components_
pc_evr = pca.explained_variance_ratio_[0:n_components]
pc_cum = np.cumsum(pc_evr)
if verbose:
print("Explained variance ratio:")
print(["PC" + str(i + 1) + " " + str(val) for i, val in enumerate(pc_evr)])
print("Cumsum of Explained variance ratio:")
print(["PC" + str(i + 1) + " " + str(val) for i, val in enumerate(pc_cum)])
sns.barplot(
x=["PC" + str(i + 1) for i in range(n_components)],
y=pc_evr,
color="gray",
ax=ax,
)
sns.lineplot(
x=["PC" + str(i + 1) for i in range(n_components)],
y=pc_cum,
color="gray",
ax=ax,
)
sns.scatterplot(
x=["PC" + str(i + 1) for i in range(n_components)],
y=pc_cum,
color="gray",
ax=ax,
)
return ax
def plot_shapes_along_pcs(
descriptor_inverse_transform,
pca,
n_dim=2,
n_PCs=(0, 1, 2),
sd_values=(-2, -1, 0, 1, 2),
morph_color="gray",
morph_alpha=1.0,
fig=None,
dpi=150,
figscale=3.0,
):
"""Plot reconstructed shapes along principal components.
This function is a placeholder for the actual implementation.
"""
pass
