ASReview-visualization is a plotting and 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,
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 datasets, called
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
Since version 0.15, you can plot project files (exported from asreview lab) as well. Use the following code:
asreview plot my_project_file.asreview
There are four plot types implemented: inclusion,
discovery, limit, progression. They can be individually selected
--type switch. Multiple plots can be made by
, 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.
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 and/or incorporate plotting into code, you can use the visualization package as a library using the build-in API.
API basic usage¶
To set up a plot for a generated HDF5 file (e.g. myreview.h5), this code can be used:
from asreviewcontrib.visualization.plot import Plot with Plot.from_paths(["myreview.h5"]) as plot: my_plot = plot.new(plot_type="INSERT_PLOT_TYPE") inc_plot.show()
INSERT_PLOT_TYPE must be set to one or more of the available plot type; inclusion, discovery, limit, progression.
Multiple plots can be generated at the same time by adding the state files to a list; [“myreview.h5”, “myreview_2.h5”].
API Advanced usage¶
Add a grid to the plot.
Add limits to the plot.
my_plot.set_xlim('lowerlimit', 'upperlimit') my_plot.set_ylim('lowerlimit', 'upperlimit')
Add a legend to the plot.
Add the Work Saved over Sampling (WSS) or Relevant References Found (RRF) line
to the plot. Only available for inclusion-type plots (
The percentage value used for the WSS and RRF metric can be set to any number from 0 to 100 (currently set to 95 and 10).
all_files = all(plot.is_file.values()) for key in list(plot.analyses): if all_files or not plot.is_file[key]: inc_plot.add_wss( key, 95, add_text=show_metric_labels, add_value=True, add_text=True) inc_plot.add_rrf( key, 10, add_text=show_metric_labels, add_value=True, add_text=True)
Add the random line to the plot. This dashed grey diagonal line corresponds to the expected recall curve when publications are screened in random order.
Save the plot to the disk.
To change the plot from relative to absolute values, an argument can be added
to the plot the following way.
INSERT_RESULT_FORMAT can be set to “number” for
absolute values or “percentage” (default) for percentages.
with Plot.from_paths(["myreview.h5"]) as plot: my_plot = plot.new(plot_type="type", result_format="INSERT_RESULT_FORMAT")
Examples using the API can be found in module