Package: asreview-visualization
ASReview-visualizationThis is a plotting/visualization supplemental package for theASReviewsoftware. It is a fast way to
create a visual impression of the ASReview with different datasets, models and
model parameters.InstallationThe easiest way to install the visualization package is to install from PyPI:pipinstallasreview-visualizationAfter installation of the visualization package,asreviewshould automatically
detect it. Test this by:asreview--helpIt should list the 'plot' modus.Basic usageState 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
datasets, calledaceandptsd, 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.h5Then we can plot the results by:asreviewplotaceptsdBy default, the values shown are expressed as percentages of the total number of papers. Use the-aor--show-absolute-valuesflags to have them expressed in absolute numbers:asreviewplotaceptsd--show-absolute-valuesPlot typesThere are currently four plot types implemented:inclusion,discovery,limit,progression. They can be individually selected with the-tor--typeswitch. Multiple plots can be made by using,as a separator:asreviewplotaceptsd--type'inclusion,discovery'InclusionThis 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.Use--show-metric-labelsto display the labels and values of WSS and RFF
lines.DiscoveryThis 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.LimitThis figure shows how many papers need to be read with a given criterion. A
criterion is expressed as "after readingy% of the papers, at most an
average ofzincluded papers have been not been seen by the reviewer, if he
is using max sampling.". Here,yis shown on the y-axis, while three values
ofzare plotted as three different lines with the same color. The three
values forzare 0.1, 0.5 and 2.0.The quicker the lines touch the black (y=x) line, the better.ProgressionThis 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.APITo 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 placeyouwant it. Examples can be found in
moduleasreviewcontrib.visualization.quick. Those are the scripts that are
used for the command line interface.fromasreviewcontrib.visualization.plotimportPlotwithPlot.from_paths(["PATH_1","PATH_2"])asplot: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 inPATH_1andPATH_2as the files you would like to plot.If the customization is not sufficient, you can also directly manipulate theself.axandself.figattributes of the plotting class.