How to plot parallel coordinae plot ftrom Hyperparameter Tuning with the HParams Dashboard?

Question

I am trying to replicate the parallel coordinate plot form Hyperparameter Tuning tutorial in this Tensorflow tutorial and I have writen my own csv file where I store my results. My output reading the csv file is like this:

    conv_layers  filters  dropout  accuracy
0             4       16      0.5  0.447917
1             4       16      0.6  0.458333
2             4       32      0.5  0.635417
3             4       32      0.6  0.447917
4             4       64      0.5  0.604167
5             4       64      0.6  0.645833
6             8       16      0.5  0.437500
7             8       16      0.6  0.437500
8             8       32      0.5  0.437500
9             8       32      0.6  0.562500
10            8       64      0.5  0.562500
11            8       64      0.6  0.437500

How can I create the same plot like in the tutorial in python?

Answer 1

so I found the answer using plotly

import os
import sys
import pandas as pd
from plotly.offline import init_notebook_mode, iplot
import plotly.graph_objects as go

init_notebook_mode(connected=True)

df = pd.read_csv('path/to/csv')

fig = go.Figure(data=
    go.Parcoords(
        line = dict(color = df['accuracy'],
                  colorbar = [],
                   colorscale = [[0, '#6C9E12'], ## 
                                [0.25,'#0D5F67'], ##
                                [0.5,'#AA1B13'], ## 
                                [0.75, '#69178C'], ## 
                                [1, '#DE9733']]),
        dimensions = list([
            dict(range = [0,12],
                label = 'Conv_layers', values = df['conv_layers']),
            dict(range = [8,64],
                label = 'filter_number', values = df['filters']),
            dict(range = [0.2,0.8],
                label = 'dropout_rate', values = df['dropout']),
            dict(range = [0.2,0.8],
                label = 'dense_num', values = df['dense']),
             dict(range = [0.1,1.0],
                label = 'accuracy', values = df['accuracy'])
        ])
    )
)


fig.update_layout(
    plot_bgcolor = '#E5E5E5',
    paper_bgcolor = '#E5E5E5',    
    title="Parallel Coordinates Plot"
)

# print the plot
fig.show()