import logging
from typing import TypeVar, List
import pandas as pd
from plotly import graph_objs as go
from plotly import io as pio
from plotly import offline
from jmetal.lab.visualization.plotting import Plot
LOGGER = logging.getLogger('jmetal')
S = TypeVar('S')
[docs]class InteractivePlot(Plot):
def __init__(self,
title: str = 'Pareto front approximation',
reference_front: List[S] = None,
reference_point: list = None,
axis_labels: list = None):
super(InteractivePlot, self).__init__(title, reference_front, reference_point, axis_labels)
self.figure = None
self.layout = None
self.data = []
[docs] def plot(self, front, label=None, normalize: bool = False, filename: str = None, format: str = 'HTML'):
""" Plot a front of solutions (2D, 3D or parallel coordinates).
:param front: List of solutions.
:param label: Front name.
:param normalize: Normalize the input front between 0 and 1 (for problems with more than 3 objectives).
:param filename: Output filename.
"""
if not isinstance(label, list):
label = [label]
self.layout = go.Layout(
margin=dict(l=80, r=80, b=80, t=150),
height=800,
title='{}<br>{}'.format(self.plot_title, label[0]),
scene=dict(
xaxis=dict(title=self.axis_labels[0:1][0] if self.axis_labels[0:1] else None),
yaxis=dict(title=self.axis_labels[1:2][0] if self.axis_labels[1:2] else None),
zaxis=dict(title=self.axis_labels[2:3][0] if self.axis_labels[2:3] else None)
),
hovermode='closest'
)
# If any reference front, plot
if self.reference_front:
points, _ = self.get_points(self.reference_front)
trace = self.__generate_trace(points=points, legend='Reference front', normalize=normalize,
color='black', size=2)
self.data.append(trace)
# If any reference point, plot
if self.reference_point:
points = pd.DataFrame(self.reference_point)
trace = self.__generate_trace(points=points, legend='Reference point', color='red', size=8)
self.data.append(trace)
# Get points and metadata
points, _ = self.get_points(front)
metadata = list(solution.__str__() for solution in front)
trace = self.__generate_trace(points=points, metadata=metadata, legend='Front approximation',
normalize=normalize)
self.data.append(trace)
self.figure = go.Figure(data=self.data, layout=self.layout)
# Plot the figure
if filename:
if format == 'HTML':
self.export_to_html(filename)
else:
pio.write_image(self.figure, filename + '.' + format)
[docs] def export_to_html(self, filename: str) -> str:
""" Export the graph to an interactive HTML (solutions can be selected to show some metadata).
:param filename: Output file name.
:return: Script as string. """
html_string = '''
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8"/>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<script src="https://unpkg.com/sweetalert2@7.7.0/dist/sweetalert2.all.js"></script>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css">
</head>
<body>
''' + self.export_to_div(filename=None, include_plotlyjs=False) + '''
<script>
var myPlot = document.querySelectorAll('div')[0];
myPlot.on('plotly_click', function(data){
var pts = '';
for(var i=0; i < data.points.length; i++){
pts = '(x, y) = ('+data.points[i].x +', '+ data.points[i].y.toPrecision(4)+')';
cs = data.points[i].customdata
}
if(typeof cs !== "undefined"){
swal({
title: 'Closest solution clicked:',
text: cs,
type: 'info',
position: 'bottom-end'
})
}
});
window.onresize = function() {
Plotly.Plots.resize(myPlot);
};
</script>
</body>
</html>'''
with open(filename + '.html', 'w') as outf:
outf.write(html_string)
return html_string
[docs] def export_to_div(self, filename=None, include_plotlyjs: bool = False) -> str:
""" Export as a `div` for embedding the graph in an HTML file.
:param filename: Output file name (if desired, default to None).
:param include_plotlyjs: If True, include plot.ly JS script (default to False).
:return: Script as string.
"""
script = offline.plot(self.figure, output_type='div', include_plotlyjs=include_plotlyjs, show_link=False)
if filename:
with open(filename + '.html', 'w') as outf:
outf.write(script)
return script
def __generate_trace(self, points: pd.DataFrame, legend: str, metadata: list = None, normalize: bool = False,
**kwargs):
dimension = points.shape[1]
# tweak points size for 3D plots
marker_size = 8
if dimension == 3:
marker_size = 4
# if indicated, perform normalization
if normalize:
points = (points - points.min()) / (points.max() - points.min())
marker = dict(
color='#236FA4',
size=marker_size,
symbol='circle',
line=dict(
color='#236FA4',
width=1
),
opacity=0.8
)
marker.update(**kwargs)
if dimension == 2:
trace = go.Scattergl(
x=points[0],
y=points[1],
mode='markers',
marker=marker,
name=legend,
customdata=metadata
)
elif dimension == 3:
trace = go.Scatter3d(
x=points[0],
y=points[1],
z=points[2],
mode='markers',
marker=marker,
name=legend,
customdata=metadata
)
else:
dimensions = list()
for column in points:
dimensions.append(
dict(range=[0, 1],
label=self.axis_labels[column:column + 1][0] if self.axis_labels[column:column + 1] else None,
values=points[column])
)
trace = go.Parcoords(
line=dict(
color='#236FA4'
),
dimensions=dimensions,
name=legend,
)
return trace
