textualheatmap

Create interactive textual heat maps for Jupiter notebooks


Keywords
saliency heatmap text textual jupyter colab interactive
License
MIT
Install
pip install textualheatmap==1.1.0

Documentation

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.

Gif of textualheatmap

Install

pip install -U textualheatmap

API

Example

Open In Colab

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)

Gif of learning-curve for keras example

heatmap = TextualHeatmap(
    show_meta = False,
    facet_titles = ['LSTM', 'GRU'],
    rotate_facet_titles = True
)
heatmap.set_data(data)
heatmap.highlight(159)

Gif of learning-curve for keras example

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.