Visual dataset and results representation

Datasets

A truth discovery dataset can be represented visually as directed graph, where the nodes are the sources, claims and objects, and there is an edge from each source to each claim it makes, and an edge between each claim and the variable it relates to.

Such graphs can be created as a PNG image with the GraphRenderer class.

from truthdiscovery import Dataset, GraphRenderer
tuples = [
    ("source 1", "x", 4),
    ("source 1", "y", 7),
    ("source 2", "y", 7),
    ("source 2", "z", 5),
    ("source 3", "x", 3),
    ("source 3", "z", 5),
    ("source 4", "x", 3),
    ("source 4", "y", 6),
    ("source 4", "z", 8)
]
mydata = Dataset(tuples)
renderer = GraphRenderer()
with open("/tmp/my-dataset-as-a-graph.png", "wb") as imgfile:
    renderer.render(mydata, imgfile)

Note that the output file must be opened in binary mode. See the GraphRenderer documentation for the available options (such as image size, node size, line widths etc…).

Results

The results of a truth discovery algorithm can be visualised in the graph representation by colouring the sources/claims according to their trust and belief scores. This is done with the ResultsGradientColourScheme colour scheme:

from truthdiscovery import Sums, GraphRenderer, ResultsGradientColourScheme
# create `mydata` as above...
results = Sums().run(mydata)
colour_scheme = ResultsGradientColourScheme(results)
renderer = GraphRenderer(colours=colour_scheme)
with open("/tmp/my-dataset-with-results.png", "wb") as imgfile:
    renderer.render(mydata, imgfile)

Lighter and darker colours represent low and high trust/belief scores Respectively. Note that there are 9 fixed colours in this scale – it is not a continuous gradient. This means that nodes with the same colour do not necessarily have identical trust/belief scores.

Animations

For iterative algorithms, one may wish to visualise not just the final results, but the partial results at each iteration as the algorithm progresses. The GifAnimator class implements creating animated GIF files for this purpose:

from truthdiscovery import (
    ConvergenceIterator, Dataset, DistanceMeasures, GifAnimator, Investment
)

tuples = [
    ("source 1", "x", 4), ("source 1", "y", 7), ("source 2", "y", 7),
    ("source 2", "z", 5), ("source 3", "x", 3), ("source 3", "z", 5),
    ("source 4", "x", 3), ("source 4", "y", 6), ("source 4", "z", 8)
]
mydata = Dataset(tuples)

alg = Investment(iterator=ConvergenceIterator(DistanceMeasures.L2, 0.01))
animator = GifAnimator(frame_duration=0.2)  # 0.2 seconds per iteration

with open("/tmp/animation.gif", "wb") as imgfile:
    animator.animate(imgfile, alg, mydata)