"""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.
from __future__ import annotations
from collections.abc import Sequence
from typing import Any
import numpy as np
from ._base import require_dependencies
[docs]
def explained_variance_ratio_plot(
pca: Any,
n_components: int | None = None,
ax: object | None = None,
verbose: bool = False,
) -> object:
"""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.
Raises
------
ImportError
If matplotlib or seaborn are not installed.
"""
require_dependencies("matplotlib", "seaborn")
import matplotlib.pyplot as plt
import seaborn as sns
if ax is None:
fig, ax = plt.subplots(figsize=(6, 4))
if n_components is None:
n_components = pca.n_components_
max_components = len(pca.explained_variance_ratio_)
if n_components > max_components:
raise ValueError(
f"n_components ({n_components}) exceeds the number of fitted "
f"components ({max_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: Any,
pca: Any,
n_dim: int = 2,
n_pcs: Sequence[int] = (0, 1, 2),
sd_values: Sequence[float] = (-2, -1, 0, 1, 2),
morph_color: str = "gray",
morph_alpha: float = 1.0,
fig: object | None = None,
dpi: int = 150,
figscale: float = 3.0,
) -> None:
"""Plot reconstructed shapes along principal components.
This function is a placeholder for the actual implementation.
Raises
------
NotImplementedError
This function is not yet implemented.
"""
raise NotImplementedError("plot_shapes_along_pcs is not yet implemented.")
