# -*- coding: utf-8 -*-
# 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"""
Scatter matrix
==============
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 absolute_import, division, unicode_literals
import matplotlib.pyplot as plt
from future import standard_library
try:
from pandas.plotting import scatter_matrix
except ImportError:
from pandas import scatter_matrix
from gemseo.post.dataset.dataset_plot import DatasetPlot
standard_library.install_aliases()
[docs]class ScatterMatrix(DatasetPlot):
""" Scatter plot matrix. """
def _plot(
self,
classifier=None,
kde=False,
size=25,
marker="o",
figsize_x=10,
figsize_y=10,
):
"""Scatter matrix plot.
:param classifier: variable name to build the cluster. Default: None
:type classifier: str
:param kde: if True, plot kernel-density estimator on the diagonal.
Otherwise, use histograms. Default: False.
:type kde: bool
:param figsize_x: size of figure in horizontal direction (inches)
:type figsize_x: int
:param figsize_y: size of figure in vertical direction (inches)
:type figsize_y: int
"""
if classifier is not None and classifier not in self.dataset.variables:
raise ValueError(
"Classifier must be one of these names: "
+ ", ".join(self.dataset.variables)
)
if kde:
diagonal = "kde"
else:
diagonal = "hist"
dataframe = self.dataset.export_to_dataframe()
if classifier is None:
self._scatter_matrix(
dataframe, diagonal, size, marker, figsize_x, figsize_y
)
else:
self._scatter_matrix_for_group(
classifier, dataframe, diagonal, size, marker, figsize_x, figsize_y
)
fig = plt.gcf()
return fig
def _scatter_matrix_for_group(
self, classifier, dataframe, diagonal, size, marker, figsize_x, figsize_y
):
"""Scatter matrix plot for group.
:param classifier: variable name to build the cluster. If None,
use the first group name if the dataset is a GroupDataset,
the first output name if the dataset is an IODataset,
otherwise the last parameter name.
:type classifier: str
:param dataframe: pandas dataframe
:param str diagonal: if 'kde', plot kernel-density estimator
on the diagonal. If 'hist', use histograms.
:param int figsize_x: size of figure in horizontal direction (inches)
:param int figsize_y: size of figure in vertical direction (inches)
"""
palette = dict(enumerate("bgrcmyk"))
groups = self.dataset.get_data_by_names([classifier], False)[:, 0:1]
colors = [palette[group[0] % len(palette)] for group in groups]
_, varname = self._get_label(classifier)
dataframe = dataframe.drop(varname, 1)
dataframe.columns = self._get_varnames(dataframe)
scatter_matrix(
dataframe,
diagonal=diagonal,
color=colors,
s=size,
marker=marker,
figsize=(figsize_x, figsize_y),
)
def _scatter_matrix(self, dataframe, diagonal, size, marker, figsize_x, figsize_y):
"""Scatter matrix plot for group.
:param dataframe: pandas dataframe
:param str diagonal: if 'kde', plot kernel-density estimator
on the diagonal. If 'hist', use histograms.
:param int figsize_x: size of figure in horizontal direction (inches)
:param int figsize_y: size of figure in vertical direction (inches)
"""
dataframe.columns = self._get_varnames(dataframe)
scatter_matrix(
dataframe,
diagonal=diagonal,
figsize=(figsize_x, figsize_y),
s=size,
marker=marker,
)
