Visualization tools for the ASReview project
This is a plotting/visualization supplemental package for the ASReview software. It is a fast way to create a visual impression of the ASReview with different datasets, models and model parameters.
The easiest way to install the visualization package is to install from PyPI:
pip install asreview-visualization
After installation of the visualization package,
asreview should automatically
detect it. Test this by:
It should list the 'plot' modus.
State files that were created with the same ASReview settings can be put together/averaged by putting them in the same directory. State files with different settings/datasets should be put in different directories to compare them.
As an example consider the following directory structure, where we have two
ptsd, each of which have 8 runs:
├── ace │ ├── results_0.h5 │ ├── results_1.h5 │ ├── results_2.h5 │ ├── results_3.h5 │ ├── results_4.h5 │ ├── results_5.h5 │ ├── results_6.h5 │ └── results_7.h5 └── ptsd ├── results_0.h5 ├── results_1.h5 ├── results_2.h5 ├── results_3.h5 ├── results_4.h5 ├── results_5.h5 ├── results_6.h5 └── results_7.h5
Then we can plot the results by:
asreview plot ace ptsd
By default, the values shown are expressed as percentages of the total number of papers. Use the
--absolute-values flags to have them expressed in absolute numbers:
asreview plot ace ptsd --absolute-values
There are currently four plot types implemented: inclusion, discovery,
limit, progression. They can be individually selected with the
--type switch. Multiple plots can be made by using
, as a separator:
asreview plot ace ptsd --type 'inclusion,discovery'
This figure shows the number/percentage of included papers found as a function of the number/percentage of papers reviewed. Initial included/excluded papers are subtracted so that the line always starts at (0,0).
The quicker the line goes to a 100%, the better the performance.
--show-metric-labels to display the labels and values of WSS and RFF
This figure shows the distribution of the number of papers that have to be read before discovering each inclusion. Not every paper is equally hard to find.
The closer to the left, the better.
This figure shows how many papers need to be read with a given criterion. A criterion is expressed as "after reading y % of the papers, at most an average of z included papers have been not been seen by the reviewer, if he is using max sampling.". Here, y is shown on the y-axis, while three values of z are plotted as three different lines with the same color. The three values for z are 0.1, 0.5 and 2.0.
The quicker the lines touch the black (
y=x) line, the better.
This figure shows the average inclusion rate as a function of time, number of papers read. The more concentrated on the left, the better. The thick line is the average of individual runs (thin lines). The visualization package will automatically detect which are directories and which are files. The curve is smoothed out by using a Gaussian smoothing algorithm.
To make use of the more advanced features, you can also use the visualization
package as a library. The advantage is that you can make more reproducible
plots where text, etc. is in the place you want it. Examples can be found in
asreviewcontrib.visualization.quick. Those are the scripts that are
used for the command line interface.
from asreviewcontrib.visualization.plot import Plot with Plot.from_paths(["PATH_1", "PATH_2"]) as plot: inc_plot = plot.new("inclusion") inc_plot.set_grid() inc_plot.set_xlim(0, 30) inc_plot.set_ylim(0, 101) inc_plot.set_legend() inc_plot.show() inc_plot.save("SOME_FILE.png")
Of course fill in
PATH_2 as the files you would like to plot.
If the customization is not sufficient, you can also directly manipulate the
self.fig attributes of the plotting class.
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