# 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 ``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 collections.abc import Iterable
from collections.abc import Sequence
from typing import TYPE_CHECKING
from typing import Any
from typing import Union
if TYPE_CHECKING:
from gemseo.datasets.dataset import Dataset
from gemseo.post.dataset._trend import Trend as _Trend
from gemseo.post.dataset._trend import TrendFunctionCreator
from gemseo.post.dataset.dataset_plot import DatasetPlot
ScatterMatrixOption = Union[bool, int, str, None, Sequence[str]]
[docs]
class ScatterMatrix(DatasetPlot):
"""Scatter plot matrix."""
Trend = _Trend
"""The type of trend."""
def __init__(
self,
dataset: Dataset,
variable_names: Iterable[str] | None = None,
classifier: str | None = None,
kde: bool = False,
size: int = 25,
marker: str = "o",
plot_lower: bool = True,
plot_upper: bool = True,
trend: Trend | TrendFunctionCreator = Trend.NONE,
**options: Any,
) -> None:
"""
Args:
variable_names: The names of the variables to consider.
If ``None``, consider all the variables of the dataset.
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.
trend: The trend function to be added on the scatter plots
or a function creating a trend function from a set of *xy*-points.
**options: The options of the underlying pandas scatter matrix.
""" # noqa: D205, D212, D415
super().__init__(
dataset,
variable_names=variable_names,
classifier=classifier,
kde=kde,
size=size,
marker=marker,
plot_lower=plot_lower,
plot_upper=plot_upper,
trend=trend,
options=options,
)
def _create_specific_data_from_dataset(self) -> tuple[tuple[str, str, int] | None]:
"""
Returns:
The column of the dataset associated with the classifier
if the classifier is not ``None``.
Raises:
ValueError: When the classifier does not exist.
""" # noqa: D205, D212, D415
classifier = self._specific_settings.classifier
if classifier is not None and classifier not in self.dataset.variable_names:
msg = (
f"{classifier} cannot be used as a classifier "
f"because it is not a variable name; "
f"available ones are: {self.dataset.variable_names}."
)
raise ValueError(msg)
if self._specific_settings.classifier is not None:
return (self._get_label(self._specific_settings.classifier)[1],)
return (None,)
