textualheatmap
Create interactive textual heatmaps for Jupiter notebooks.
I originally published this visualization method in my distill paper https://distill.pub/2019/memorization-in-rnns/. In this context, it is used as a saliency map for showing which parts of a sentence are used to predict the next word. However, the visualization method is more general-purpose than that and can be used for any kind of textual heatmap purposes.
textualheatmap works with python 3.6 or newer and is distributed under the
MIT license.
Install
pip install -U textualheatmapAPI
Example
from textualheatmap import TextualHeatmap
data = [[
# GRU data
{"token":" ",
"meta":["the","one","of"],
"heat":[1,0,0,0,0,0,0,0,0]},
{"token":"c",
"meta":["can","called","century"],
"heat":[1,0.22,0,0,0,0,0,0,0]},
{"token":"o",
"meta":["country","could","company"],
"heat":[0.57,0.059,1,0,0,0,0,0,0]},
{"token":"n",
"meta":["control","considered","construction"],
"heat":[1,0.20,0.11,0.84,0,0,0,0,0]},
{"token":"t",
"meta":["control","continued","continental"],
"heat":[0.27,0.17,0.052,0.44,1,0,0,0,0]},
{"token":"e",
"meta":["context","content","contested"],
"heat":[0.17,0.039,0.034,0.22,1,0.53,0,0,0]},
{"token":"x",
"meta":["context","contexts","contemporary"],
"heat":[0.17,0.0044,0.021,0.17,1,0.90,0.48,0,0]},
{"token":"t",
"meta":["context","contexts","contentious"],
"heat":[0.14,0.011,0.034,0.14,0.68,1,0.80,0.86,0]},
{"token":" ",
"meta":["of","and","the"],
"heat":[0.014,0.0063,0.0044,0.011,0.034,0.10,0.32,0.28,1]},
# ...
],[
# LSTM data
# ...
]]
heatmap = TextualHeatmap(
width = 600,
show_meta = True,
facet_titles = ['GRU', 'LSTM']
)
# Set data and render plot, this can be called again to replace
# the data.
heatmap.set_data(data)
# Focus on the token with the given index. Especially useful when
# `interactive=False` is used in `TextualHeatmap`.
heatmap.highlight(159)
heatmap = TextualHeatmap(
show_meta = False,
facet_titles = ['LSTM', 'GRU'],
rotate_facet_titles = True
)
heatmap.set_data(data)
heatmap.highlight(159)
Citation
If you use this in a publication, please cite my Distill publication where I first demonstrated this visualization method.
@article{madsen2019visualizing,
author = {Madsen, Andreas},
title = {Visualizing memorization in RNNs},
journal = {Distill},
year = {2019},
note = {https://distill.pub/2019/memorization-in-rnns},
doi = {10.23915/distill.00016}
}Sponsor
Sponsored by NearForm Research.
