# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
r"""Draw a scatter matrix from a :class:`.Dataset`.
The :class:`.ScatterMatrix` class implements the scatter plot matrix,
which is a way to visualize :math:`n` samples of a
multi-dimensional vector
.. math::
x=(x_1,x_2,\ldots,x_d)\in\mathbb{R}^d
in several 2D subplots where the (i,j) subplot represents the cloud
of points
.. math::
\left(x_i^{(k)},x_j^{(k)}\right)_{1\leq k \leq n}
while the (i,i) subplot represents the empirical distribution of the samples
.. math::
x_i^{(1)},\ldots,x_i^{(n)}
by means of an histogram or a kernel density estimator.
A variable name can be passed to the :meth:`.DatasetPlot.execute` method
by means of the :code:`classifier` keyword in order to color the curves
according to the value of the variable name. This is useful when the data is
labeled.
"""
from __future__ import annotations
from typing import Sequence
from matplotlib import pyplot as plt
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from pandas.plotting import scatter_matrix
from gemseo.core.dataset import Dataset
from gemseo.post.dataset.dataset_plot import DatasetPlot
[docs]class ScatterMatrix(DatasetPlot):
"""Scatter plot matrix."""
def __init__(
self,
dataset: Dataset,
variable_names: Sequence[str] | None = None,
classifier: str | None = None,
kde: bool = False,
size: int = 25,
marker: str = "o",
plot_lower: bool = True,
plot_upper: bool = True,
) -> None:
"""
Args:
classifier: The name of the variable to build the cluster.
kde: The type of the distribution representation.
If True, plot kernel-density estimator on the diagonal.
Otherwise, use histograms.
size: The size of the points.
marker: The marker for the points.
plot_lower: Whether to plot the lower part.
plot_upper: Whether to plot the upper part.
"""
super().__init__(
dataset,
variable_names=variable_names,
classifier=classifier,
kde=kde,
size=size,
marker=marker,
plot_lower=plot_lower,
plot_upper=plot_upper,
)
def _plot(
self,
fig: None | Figure = None,
axes: None | Axes = None,
) -> list[Figure]:
variable_names = self._param.variable_names
classifier = self._param.classifier
kde = self._param.kde
size = self._param.size
marker = self._param.marker
if variable_names is None:
variable_names = self.dataset.variables
if classifier is not None and classifier not in self.dataset.variables:
raise ValueError(
f"{classifier} cannot be used as a classifier "
f"because it is not a variable name; "
f"available ones are: {self.dataset.variables}."
)
if kde:
diagonal = "kde"
else:
diagonal = "hist"
dataframe = self.dataset.export_to_dataframe(variable_names=variable_names)
kwargs = {}
if classifier is not None:
palette = dict(enumerate("bgrcmyk"))
groups = self.dataset.get_data_by_names([classifier], False)[:, 0:1]
kwargs["color"] = [palette[group[0] % len(palette)] for group in groups]
_, variable_name = self._get_label(classifier)
dataframe = dataframe.drop(labels=variable_name, axis=1)
dataframe.columns = self._get_variables_names(dataframe)
fig, axes = self._get_figure_and_axes(fig, axes, self.fig_size)
sub_axes = scatter_matrix(
dataframe,
diagonal=diagonal,
s=size,
marker=marker,
figsize=self.fig_size,
ax=axes,
**kwargs,
)
n_cols = sub_axes.shape[0]
if not (self._param.plot_lower and self._param.plot_upper):
for i in range(n_cols):
for j in range(n_cols):
sub_axes[i, j].get_xaxis().set_visible(False)
sub_axes[i, j].get_yaxis().set_visible(False)
if not self._param.plot_lower:
for i in range(n_cols):
for j in range(i):
sub_axes[i, j].set_visible(False)
for i in range(n_cols):
sub_axes[i, i].get_xaxis().set_visible(True)
sub_axes[i, i].get_yaxis().set_visible(True)
if not self._param.plot_upper:
for i in range(n_cols):
for j in range(i + 1, n_cols):
sub_axes[i, j].set_visible(False)
for i in range(n_cols):
sub_axes[-1, i].get_xaxis().set_visible(True)
sub_axes[i, 0].get_yaxis().set_visible(True)
plt.suptitle(self.title)
return [fig]
