Source code for

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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 - API and implementation and/or documentation
#        :author: Francois Gallard
#        :author: Damien Guenot
"""A scatter plot matrix to display optimization history."""

from __future__ import annotations

import logging
from typing import TYPE_CHECKING

from matplotlib import pyplot
from numpy import any
from pandas.core.frame import DataFrame
from pandas.plotting import scatter_matrix

from import OptPostProcessor

    from import Sequence

LOGGER = logging.getLogger(__name__)

[docs] class ScatterPlotMatrix(OptPostProcessor): """Scatter plot matrix among design variables, output functions and constraints. The list of variable names has to be passed as arguments of the plot method. """ DEFAULT_FIG_SIZE = (10.0, 10.0) def _plot( self, variable_names: Sequence[str], filter_non_feasible: bool = False, ) -> None: """ Args: variable_names: The functions names or design variables to plot. If the list is empty, plot all design variables. filter_non_feasible: If ``True``, remove the non-feasible points from the data. Raises: ValueError: If `filter_non_feasible` is set to True and no feasible points exist. If an element from variable_names is not either a function or a design variable. """ # noqa: D205, D212, D415 problem = self.opt_problem add_design_variables = False all_function_names = problem.get_all_function_name() all_design_names = problem.design_space.variable_names if not problem.minimize_objective and self._obj_name in variable_names: obj_index = variable_names.index(self._obj_name) variable_names[obj_index] = self._standardized_obj_name variable_names.sort() if not variable_names: # In this case, plot all design variables, no functions. variable_values = problem.get_data_by_names( names=all_design_names, as_dict=False, filter_non_feasible=filter_non_feasible, ) variable_labels = self._get_design_variable_names( variables=all_design_names ) else: design_names = [] function_names = [] for variable_name in list(variable_names): if ( variable_name not in all_function_names and variable_name not in all_design_names and variable_name not in problem.constraint_names ): raise ValueError( "Cannot build scatter plot matrix: " f"function {variable_name} is neither among " f"optimization problem functions: {all_function_names} " f"nor design variables: {all_design_names}" ) if variable_name in problem.design_space.variable_names: add_design_variables = True design_names.append(variable_name) elif variable_name in problem.constraint_names: function_names.extend(problem.constraint_names[variable_name]) else: function_names.append(variable_name) if not design_names: design_names = None if add_design_variables: # Sort the design variables to be consistent with GEMSEO. design_names = sorted( set(all_design_names) & set(design_names), key=all_design_names.index, ) design_labels = self._get_design_variable_names(variables=design_names) if function_names: _, function_labels, _ = self.database.get_history_array( function_names=function_names, with_x_vect=False ) else: function_labels = [] variable_names = function_names + design_names variable_labels = function_labels + design_labels else: variable_names = function_names _, variable_labels, _ = self.database.get_history_array( function_names=variable_names, with_x_vect=False ) variable_labels.sort() variable_values = problem.get_data_by_names( names=variable_names, as_dict=False, filter_non_feasible=filter_non_feasible, ) if ( self._standardized_obj_name in variable_labels and not problem.minimize_objective and not problem.use_standardized_objective ): index = variable_labels.index(self._standardized_obj_name) variable_labels[index] = self._obj_name variable_values[:, index] *= -1 if filter_non_feasible and not any(variable_values): raise ValueError("No feasible points were found.") # Next line is a trick for a bug workaround in numpy/matplotlib # # pandas-dataframe-valueerror-num-must-be-1-num-0-not-1 scatter_matrix( DataFrame((list(x.real) for x in variable_values), columns=variable_labels), alpha=1.0, figsize=self.DEFAULT_FIG_SIZE, diagonal="kde", ) fig = pyplot.gcf() fig.tight_layout() self._add_figure(fig)